| |
|
|
| #include <nanobind/nanobind.h> |
| #include <nanobind/stl/optional.h> |
| #include <nanobind/stl/pair.h> |
| #include <nanobind/stl/string.h> |
| #include <nanobind/stl/tuple.h> |
| #include <nanobind/stl/variant.h> |
| #include <nanobind/stl/vector.h> |
|
|
| #include "mlx/fast.h" |
| #include "mlx/ops.h" |
| #include "python/src/small_vector.h" |
| #include "python/src/utils.h" |
|
|
| namespace mx = mlx::core; |
| namespace nb = nanobind; |
| using namespace nb::literals; |
|
|
| namespace { |
|
|
| struct PyCustomKernelFunction { |
| PyCustomKernelFunction(mx::fast::CustomKernelFunction kernel, const char* tag) |
| : kernel_(std::move(kernel)), tag_(tag) {} |
|
|
| std::vector<mx::array> operator()( |
| const std::vector<ScalarOrArray>& inputs_, |
| const std::vector<mx::Shape>& output_shapes, |
| const std::vector<mx::Dtype>& output_dtypes, |
| std::tuple<int, int, int> grid, |
| std::tuple<int, int, int> threadgroup, |
| const std::optional<std::vector<std::pair<std::string, nb::object>>>& |
| template_args_ = std::nullopt, |
| std::optional<float> init_value = std::nullopt, |
| bool verbose = false, |
| mx::StreamOrDevice s = {}) const { |
| std::vector<mx::array> inputs; |
| for (const auto& value : inputs_) { |
| inputs.push_back(to_array(value, std::nullopt)); |
| } |
| std::vector<std::pair<std::string, mx::fast::TemplateArg>> template_args; |
| if (template_args_) { |
| for (const auto& [name, value] : template_args_.value()) { |
| |
| if (nb::isinstance<bool>(value)) { |
| bool bool_val = nb::cast<bool>(value); |
| template_args.emplace_back(name, bool_val); |
| } else if (nb::isinstance<int>(value)) { |
| int int_val = nb::cast<int>(value); |
| template_args.emplace_back(name, int_val); |
| } else if (nb::isinstance<mx::Dtype>(value)) { |
| mx::Dtype dtype = nb::cast<mx::Dtype>(value); |
| template_args.emplace_back(name, dtype); |
| } else { |
| std::ostringstream msg; |
| msg << tag_ |
| << " Invalid template argument. Must be `mlx.core.Dtype`, `int` or `bool`."; |
| throw std::invalid_argument(msg.str()); |
| } |
| } |
| } |
| return kernel_( |
| inputs, |
| output_shapes, |
| output_dtypes, |
| grid, |
| threadgroup, |
| template_args, |
| init_value, |
| verbose, |
| s); |
| } |
|
|
| mx::fast::CustomKernelFunction kernel_; |
| const char* tag_; |
| }; |
|
|
| } |
|
|
| void init_fast(nb::module_& parent_module) { |
| auto m = |
| parent_module.def_submodule("fast", "mlx.core.fast: fast operations"); |
|
|
| m.def( |
| "rms_norm", |
| &mx::fast::rms_norm, |
| "x"_a, |
| "weight"_a.none(), |
| "eps"_a, |
| nb::kw_only(), |
| "stream"_a = nb::none(), |
| nb::sig( |
| "def rms_norm(x: array, weight: Optional[array], eps: float, *, stream: Union[None, Stream, Device] = None) -> array"), |
| R"pbdoc( |
| Root Mean Square normalization (RMS norm). |
| |
| The normalization is with respect to the last axis of the input ``x``. |
| |
| Args: |
| x (array): Input array. |
| weight (array, optional): A multiplicative weight to scale the result by. |
| The ``weight`` should be one-dimensional with the same size |
| as the last axis of ``x``. If set to ``None`` then no scaling happens. |
| eps (float): A small additive constant for numerical stability. |
| |
| Returns: |
| array: The output array. |
| )pbdoc"); |
|
|
| m.def( |
| "layer_norm", |
| &mx::fast::layer_norm, |
| "x"_a, |
| "weight"_a.none(), |
| "bias"_a.none(), |
| "eps"_a, |
| nb::kw_only(), |
| "stream"_a = nb::none(), |
| nb::sig( |
| "def layer_norm(x: array, weight: Optional[array], bias: Optional[array], eps: float, *, stream: Union[None, Stream, Device] = None) -> array"), |
| R"pbdoc( |
| Layer normalization. |
| |
| The normalization is with respect to the last axis of the input ``x``. |
| |
| Args: |
| x (array): Input array. |
| weight (array, optional): A multiplicative weight to scale the result by. |
| The ``weight`` should be one-dimensional with the same size |
| as the last axis of ``x``. If set to ``None`` then no scaling happens. |
| bias (array, optional): An additive offset to be added to the result. |
| The ``bias`` should be one-dimensional with the same size |
| as the last axis of ``x``. If set to ``None`` then no translation happens. |
| eps (float): A small additive constant for numerical stability. |
| |
| Returns: |
| array: The output array. |
| )pbdoc"); |
|
|
| m.def( |
| "rope", |
| [](const mx::array& a, |
| int dims, |
| bool traditional, |
| std::optional<float> base, |
| float scale, |
| const ScalarOrArray& offset, |
| const std::optional<mx::array>& freqs , |
| mx::StreamOrDevice s ) { |
| return mx::fast::rope( |
| a, dims, traditional, base, scale, to_array(offset), freqs, s); |
| }, |
| "a"_a, |
| "dims"_a, |
| nb::kw_only(), |
| "traditional"_a, |
| "base"_a.none(), |
| "scale"_a, |
| "offset"_a, |
| "freqs"_a = nb::none(), |
| "stream"_a = nb::none(), |
| nb::sig( |
| "def rope(a: array, dims: int, *, traditional: bool, base: Optional[float], scale: float, offset: Union[int, array], freqs: Optional[array] = None, stream: Union[None, Stream, Device] = None) -> array"), |
| R"pbdoc( |
| Apply rotary positional encoding to the input. |
| |
| The input is expected to be at least 3D with shape ``(B, *, T, D)`` where: |
| * ``B`` is the batch size. |
| * ``T`` is the sequence length. |
| * ``D`` is the feature dimension. |
| |
| Args: |
| a (array): The input array. |
| dims (int): The feature dimensions to be rotated. If the input feature |
| is larger than dims then the rest is left unchanged. |
| traditional (bool): If set to ``True`` choose the traditional |
| implementation which rotates consecutive dimensions. |
| base (float, optional): The base used to compute angular frequency for |
| each dimension in the positional encodings. Exactly one of ``base`` and |
| ``freqs`` must be ``None``. |
| scale (float): The scale used to scale the positions. |
| offset (int or array): The position offset to start at. If an |
| :obj:`array` is given it can be a scalar or vector of ``B`` |
| offsets for each example in the batch. |
| freqs (array, optional): Optional frequencies to use with RoPE. |
| If set, the ``base`` parameter must be ``None``. Default: ``None``. |
| |
| Returns: |
| array: The output array. |
| )pbdoc"); |
|
|
| m.def( |
| "scaled_dot_product_attention", |
| [](const mx::array& queries, |
| const mx::array& keys, |
| const mx::array& values, |
| const float scale, |
| const std::variant<std::monostate, std::string, mx::array>& mask, |
| const std::optional<mx::array>& sinks, |
| mx::StreamOrDevice s) { |
| bool has_mask = !std::holds_alternative<std::monostate>(mask); |
| bool has_str_mask = |
| has_mask && std::holds_alternative<std::string>(mask); |
| bool has_arr_mask = has_mask && std::holds_alternative<mx::array>(mask); |
|
|
| if (has_mask) { |
| if (has_str_mask) { |
| auto mask_str = std::get<std::string>(mask); |
| if (mask_str != "causal") { |
| std::ostringstream msg; |
| msg << "[scaled_dot_product_attention] invalid mask option '" |
| << mask_str << "'. Must be 'causal', or an array."; |
| throw std::invalid_argument(msg.str()); |
| } |
| return mx::fast::scaled_dot_product_attention( |
| queries, keys, values, scale, mask_str, {}, sinks, s); |
| } else { |
| auto mask_arr = std::get<mx::array>(mask); |
| return mx::fast::scaled_dot_product_attention( |
| queries, keys, values, scale, "", {mask_arr}, sinks, s); |
| } |
|
|
| } else { |
| return mx::fast::scaled_dot_product_attention( |
| queries, keys, values, scale, "", {}, sinks, s); |
| } |
| }, |
| "q"_a, |
| "k"_a, |
| "v"_a, |
| nb::kw_only(), |
| "scale"_a, |
| "mask"_a = nb::none(), |
| "sinks"_a = nb::none(), |
| "stream"_a = nb::none(), |
| nb::sig( |
| "def scaled_dot_product_attention(q: array, k: array, v: array, *, scale: float, mask: Union[None, str, array] = None, sinks: Optional[array] = None, stream: Union[None, Stream, Device] = None) -> array"), |
| R"pbdoc( |
| A fast implementation of multi-head attention: ``O = softmax(Q @ K.T, dim=-1) @ V``. |
| |
| Supports: |
| |
| * `Multi-Head Attention <https://arxiv.org/abs/1706.03762>`_ |
| * `Grouped Query Attention <https://arxiv.org/abs/2305.13245>`_ |
| * `Multi-Query Attention <https://arxiv.org/abs/1911.02150>`_ |
| |
| .. note:: |
| |
| * The softmax operation is performed in ``float32`` regardless of |
| the input precision. |
| * For Grouped Query Attention and Multi-Query Attention, the ``k`` |
| and ``v`` inputs should not be pre-tiled to match ``q``. |
| |
| In the following the dimensions are given by: |
| |
| * ``B``: The batch size. |
| * ``N_q``: The number of query heads. |
| * ``N_kv``: The number of key and value heads. |
| * ``T_q``: The number of queries per example. |
| * ``T_kv``: The number of keys and values per example. |
| * ``D``: The per-head dimension. |
| |
| Args: |
| q (array): Queries with shape ``[B, N_q, T_q, D]``. |
| k (array): Keys with shape ``[B, N_kv, T_kv, D]``. |
| v (array): Values with shape ``[B, N_kv, T_kv, D]``. |
| scale (float): Scale for queries (typically ``1.0 / sqrt(q.shape(-1)``). |
| mask (str or array, optional): The mask to apply to the |
| query-key scores. The mask can be an array or a string indicating |
| the mask type. The only supported string type is ``"causal"``. If |
| the mask is an array it can be a boolean or additive mask. The mask |
| can have at most 4 dimensions and must be broadcast-compatible with |
| the shape ``[B, N, T_q, T_kv]``. If an additive mask is given its |
| type must promote to the promoted type of ``q``, ``k``, and ``v``. |
| sinks (array, optional): An optional array of attention sinks. |
| Default: ``None``. |
| |
| Returns: |
| array: The output array. |
| |
| Example: |
| |
| .. code-block:: python |
| |
| B = 2 |
| N_q = N_kv = 32 |
| T_q = T_kv = 1000 |
| D = 128 |
| |
| q = mx.random.normal(shape=(B, N_q, T_q, D)) |
| k = mx.random.normal(shape=(B, N_kv, T_kv, D)) |
| v = mx.random.normal(shape=(B, N_kv, T_kv, D)) |
| scale = D ** -0.5 |
| out = mx.fast.scaled_dot_product_attention(q, k, v, scale=scale, mask="causal") |
| )pbdoc"); |
|
|
| m.def( |
| "metal_kernel", |
| [](const std::string& name, |
| const std::vector<std::string>& input_names, |
| const std::vector<std::string>& output_names, |
| const std::string& source, |
| const std::string& header, |
| bool ensure_row_contiguous, |
| bool atomic_outputs) { |
| auto kernel = mx::fast::metal_kernel( |
| name, |
| input_names, |
| output_names, |
| source, |
| header, |
| ensure_row_contiguous, |
| atomic_outputs); |
| return nb::cpp_function( |
| PyCustomKernelFunction(std::move(kernel), "[metal_kernel]"), |
| nb::kw_only(), |
| "inputs"_a, |
| "output_shapes"_a, |
| "output_dtypes"_a, |
| "grid"_a, |
| "threadgroup"_a, |
| "template"_a = nb::none(), |
| "init_value"_a = nb::none(), |
| "verbose"_a = false, |
| "stream"_a = nb::none(), |
| nb::sig( |
| "def __call__(self, *, inputs: List[Union[scalar, array]], output_shapes: List[Sequence[int]], output_dtypes: List[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: Optional[List[Tuple[str, Union[bool, int, Dtype]]]] = None, init_value: Optional[float] = None, verbose: bool = false, stream: Union[None, Stream, Device] = None)"), |
| R"pbdoc( |
| Run the kernel. |
| |
| Args: |
| inputs (List[array]): The inputs passed to the Metal kernel. |
| output_shapes (List[Sequence[int]]): The list of shapes for each output in ``output_names``. |
| output_dtypes (List[Dtype]): The list of data types for each output in ``output_names``. |
| grid (tuple[int, int, int]): 3-tuple specifying the grid to launch the kernel with. |
| This will be passed to ``MTLComputeCommandEncoder::dispatchThreads``. |
| threadgroup (tuple[int, int, int]): 3-tuple specifying the threadgroup size to use. |
| This will be passed to ``MTLComputeCommandEncoder::dispatchThreads``. |
| template (List[Tuple[str, Union[bool, int, Dtype]]], optional): Template arguments. |
| These will be added as template arguments to the kernel definition. Default: ``None``. |
| init_value (float, optional): Optional value to use to initialize all of the output arrays. |
| By default, output arrays are uninitialized. Default: ``None``. |
| verbose (bool, optional): Whether to print the full generated source code of the kernel |
| when it is run. Default: ``False``. |
| stream (mx.stream, optional): Stream to run the kernel on. Default: ``None``. |
| |
| Returns: |
| List[array]: The list of output arrays.)pbdoc"); |
| }, |
| "name"_a, |
| "input_names"_a, |
| "output_names"_a, |
| "source"_a, |
| "header"_a = "", |
| "ensure_row_contiguous"_a = true, |
| "atomic_outputs"_a = false, |
| R"pbdoc( |
| A jit-compiled custom Metal kernel defined from a source string. |
| |
| Full documentation: :ref:`custom_metal_kernels`. |
| |
| Args: |
| name (str): Name for the kernel. |
| input_names (List[str]): The parameter names of the inputs in the |
| function signature. |
| output_names (List[str]): The parameter names of the outputs in the |
| function signature. |
| source (str): Source code. This is the body of a function in Metal, |
| the function signature will be automatically generated. |
| header (str): Header source code to include before the main function. |
| Useful for helper functions or includes that should live outside of |
| the main function body. |
| ensure_row_contiguous (bool): Whether to ensure the inputs are row contiguous |
| before the kernel runs. Default: ``True``. |
| atomic_outputs (bool): Whether to use atomic outputs in the function signature |
| e.g. ``device atomic<float>``. Default: ``False``. |
| |
| Returns: |
| Callable ``metal_kernel``. |
| |
| Example: |
| |
| .. code-block:: python |
| |
| def exp_elementwise(a: mx.array): |
| source = ''' |
| uint elem = thread_position_in_grid.x; |
| T tmp = inp[elem]; |
| out[elem] = metal::exp(tmp); |
| ''' |
| |
| kernel = mx.fast.metal_kernel( |
| name="myexp", |
| input_names=["inp"], |
| output_names=["out"], |
| source=source |
| ) |
| outputs = kernel( |
| inputs=[a], |
| template=[("T", mx.float32)], |
| grid=(a.size, 1, 1), |
| threadgroup=(256, 1, 1), |
| output_shapes=[a.shape], |
| output_dtypes=[a.dtype], |
| verbose=True, |
| ) |
| return outputs[0] |
| |
| a = mx.random.normal(shape=(4, 16)).astype(mx.float16) |
| b = exp_elementwise(a) |
| assert mx.allclose(b, mx.exp(a)) |
| )pbdoc"); |
|
|
| m.def( |
| "cuda_kernel", |
| [](const std::string& name, |
| const std::vector<std::string>& input_names, |
| const std::vector<std::string>& output_names, |
| const std::string& source, |
| const std::string& header, |
| bool ensure_row_contiguous, |
| int shared_mem) { |
| auto kernel = mx::fast::cuda_kernel( |
| name, |
| input_names, |
| output_names, |
| source, |
| header, |
| ensure_row_contiguous, |
| shared_mem); |
| return nb::cpp_function( |
| PyCustomKernelFunction(std::move(kernel), "[cuda_kernel]"), |
| nb::kw_only(), |
| "inputs"_a, |
| "output_shapes"_a, |
| "output_dtypes"_a, |
| "grid"_a, |
| "threadgroup"_a, |
| "template"_a = nb::none(), |
| "init_value"_a = nb::none(), |
| "verbose"_a = false, |
| "stream"_a = nb::none(), |
| nb::sig( |
| "def __call__(self, *, inputs: List[Union[scalar, array]], output_shapes: List[Sequence[int]], output_dtypes: List[Dtype], grid: tuple[int, int, int], threadgroup: tuple[int, int, int], template: Optional[List[Tuple[str, Union[bool, int, Dtype]]]] = None, init_value: Optional[float] = None, verbose: bool = false, stream: Union[None, Stream, Device] = None)"), |
| R"pbdoc( |
| Run the kernel. |
| |
| Args: |
| inputs (List[array]): The inputs passed to the CUDA kernel. |
| output_shapes (List[Sequence[int]]): The list of shapes for each output in ``output_names``. |
| output_dtypes (List[Dtype]): The list of data types for each output in ``output_names``. |
| grid (tuple[int, int, int]): 3-tuple specifying the grid to launch the kernel with. |
| For compatibility with :func:`metal_kernel` the grid is in threads and not in threadgroups. |
| threadgroup (tuple[int, int, int]): 3-tuple specifying the threadgroup size to use. |
| template (List[Tuple[str, Union[bool, int, Dtype]]], optional): Template arguments. |
| These will be added as template arguments to the kernel definition. Default: ``None``. |
| init_value (float, optional): Optional value to use to initialize all of the output arrays. |
| By default, output arrays are uninitialized. Default: ``None``. |
| verbose (bool, optional): Whether to print the full generated source code of the kernel |
| when it is run. Default: ``False``. |
| stream (mx.stream, optional): Stream to run the kernel on. Default: ``None``. |
| |
| Returns: |
| List[array]: The list of output arrays.)pbdoc"); |
| }, |
| "name"_a, |
| "input_names"_a, |
| "output_names"_a, |
| "source"_a, |
| "header"_a = "", |
| "ensure_row_contiguous"_a = true, |
| "shared_memory"_a = 0, |
| R"pbdoc( |
| A jit-compiled custom CUDA kernel defined from a source string. |
| |
| This is the CUDA equivalent of :ref:`custom_metal_kernels`. |
| |
| Args: |
| name (str): Name for the kernel. |
| input_names (List[str]): The parameter names of the inputs in the |
| function signature. |
| output_names (List[str]): The parameter names of the outputs in the |
| function signature. |
| source (str): Source code. This is the body of a function in CUDA, |
| the function signature will be automatically generated. |
| header (str): Header source code to include before the main function. |
| Useful for helper functions or includes that should live outside of |
| the main function body. |
| ensure_row_contiguous (bool): Whether to ensure the inputs are row contiguous |
| before the kernel runs. Default: ``True``. |
| shared_memory (int): The dynamic shared memory to request for the |
| kernel. A value of 0 means no dynamic shared memory. Default: ``0``. |
| |
| Returns: |
| Callable ``cuda_kernel``. |
| |
| Example: |
| |
| .. code-block:: python |
| |
| def exp_elementwise(a: mx.array): |
| source = ''' |
| auto elem = cooperative_groups::this_grid().thread_rank(); |
| T tmp = inp[elem]; |
| out[elem] = exp(tmp); |
| ''' |
| |
| kernel = mx.fast.cuda_kernel( |
| name="myexp", |
| input_names=["inp"], |
| output_names=["out"], |
| source=source |
| ) |
| outputs = kernel( |
| inputs=[a], |
| template=[("T", mx.float32)], |
| grid=(a.size, 1, 1), |
| threadgroup=(256, 1, 1), |
| output_shapes=[a.shape], |
| output_dtypes=[a.dtype], |
| verbose=True, |
| ) |
| return outputs[0] |
| |
| a = mx.random.normal(shape=(16, 16)).astype(mx.float16) |
| b = exp_elementwise(a) |
| assert mx.allclose(b, mx.exp(a)) |
| )pbdoc"); |
|
|
| m.def( |
| "precompiled_cuda_kernel", |
| [](const std::string& name, |
| const nb::bytes compiled_source, |
| const std::vector<ScalarOrArray>& inputs_, |
| const std::vector<mx::Shape>& output_shapes, |
| const std::vector<mx::Dtype>& output_dtypes, |
| const std::vector<nb::object>& scalars_, |
| std::tuple<int, int, int> grid, |
| std::tuple<int, int, int> threadgroup, |
| int shared_memory, |
| std::optional<float> init_value = std::nullopt, |
| bool ensure_row_contiguous = false, |
| mx::StreamOrDevice s = {}) { |
| |
| std::vector<mx::array> inputs; |
| for (const auto& value : inputs_) { |
| inputs.push_back(to_array(value, std::nullopt)); |
| } |
|
|
| |
| std::vector<mx::fast::ScalarArg> scalars; |
| scalars.reserve(scalars_.size()); |
| for (const auto& v : scalars_) { |
| if (nb::isinstance<bool>(v)) { |
| scalars.push_back(nb::cast<bool>(v)); |
| } else if (nb::isinstance<int>(v)) { |
| scalars.push_back(nb::cast<int>(v)); |
| } else if (nb::isinstance<float>(v)) { |
| scalars.push_back(nb::cast<float>(v)); |
| } else { |
| nb::object vtype = v.attr("__class__"); |
| std::string vtype_name = |
| nb::cast<std::string>(vtype.attr("__name__")); |
| std::ostringstream msg; |
| msg << "[precompiled_cuda_kernel] Invalid scalar argument type. " |
| << "Received " << vtype_name |
| << " but must be one of bool, int or float"; |
| throw std::invalid_argument(msg.str()); |
| } |
| } |
|
|
| return mx::fast::precompiled_cuda_kernel( |
| name, |
| std::string( |
| static_cast<const char*>(compiled_source.data()), |
| compiled_source.size()), |
| inputs, |
| output_shapes, |
| output_dtypes, |
| scalars, |
| grid, |
| threadgroup, |
| shared_memory, |
| init_value, |
| ensure_row_contiguous, |
| s); |
| }, |
| nb::kw_only(), |
| "name"_a, |
| "compiled_source"_a, |
| "inputs"_a, |
| "output_shapes"_a, |
| "output_dtypes"_a, |
| "scalars"_a, |
| "grid"_a, |
| "threadgroup"_a, |
| "shared_memory"_a = 0, |
| "init_value"_a = nb::none(), |
| "ensure_row_contiguous"_a = false, |
| "stream"_a = nb::none(), |
| R"pbdoc( |
| Run a precompiled CUDA kernel defined from PTX or cubin. |
| |
| This op is still experimental and various parts of the API may change. |
| |
| Args: |
| name (str): Name for the kernel |
| compiled_source (bytes): The precompiled kernel in raw bytes. |
| inputs (List[array]): The inputs passed to the CUDA kernel. |
| output_shapes (List[Sequence[int]]): The list of shapes for each output. |
| output_dtypes (List[Dtype]): The list of data types for each output. |
| scalars (List[Union[bool, int, float]]): A list of scalar arguments to |
| pass to the kernel. |
| grid (tuple[int, int, int]): 3-tuple specifying the grid to launch the kernel with. |
| For compatibility with :func:`metal_kernel` the grid is in threads and not in threadblocks. |
| threadgroup (tuple[int, int, int]): 3-tuple specifying the threadgroup size to use. |
| shared_memory (int): The dynamic shared memory to request for the |
| kernel. A value of 0 means no dynamic shared memory. Default: ``0``. |
| init_value (float, optional): Optional value to use to initialize all of the output arrays. |
| By default, output arrays are uninitialized. Default: ``None``. |
| ensure_row_contiguous (bool): Whether to ensure the inputs are row contiguous |
| before the kernel runs. Default: ``False``. |
| stream (mx.stream, optional): Stream to run the kernel on. Default: ``None``. |
| )pbdoc"); |
| } |
|
|
