Instructions to use Motif-Technologies/activation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Kernels
How to use Motif-Technologies/activation with Kernels:
# !pip install kernels from kernels import get_kernel kernel = get_kernel("Motif-Technologies/activation") - Notebooks
- Google Colab
- Kaggle
| """Kernel test utils""" | |
| import unittest | |
| from typing import Any, Dict, Optional, Sequence, Tuple, Union | |
| import torch | |
| from torch._prims_common import TensorLikeType | |
| from .allclose_default import get_default_atol, get_default_rtol | |
| # For now, disable "test_aot_dispatch_dynamic" since there are some | |
| # bugs related to this test in PyTorch 2.4. | |
| DEFAULT_OPCHECK_TEST_UTILS: Tuple[str, ...] = ( | |
| "test_schema", | |
| "test_autograd_registration", | |
| "test_faketensor", | |
| ) | |
| ALL_OPCHECK_TEST_UTILS: Tuple[str, ...] = ( | |
| "test_schema", | |
| "test_autograd_registration", | |
| "test_faketensor", | |
| "test_aot_dispatch_dynamic", | |
| ) | |
| def assert_close( | |
| a: TensorLikeType, | |
| b: TensorLikeType, | |
| atol: float | None = None, | |
| rtol: float | None = None, | |
| ) -> None: | |
| atol = atol if atol is not None else get_default_atol(a) | |
| rtol = rtol if rtol is not None else get_default_rtol(a) | |
| torch.testing.assert_close(a, b, atol=atol, rtol=rtol) | |
| # Copied/modified from torch._refs.__init__.py | |
| def fp8_allclose( | |
| a: TensorLikeType, | |
| b: TensorLikeType, | |
| rtol: float = 1e-05, | |
| atol: float = 1e-08, | |
| equal_nan: bool = False, | |
| ) -> bool: | |
| """ | |
| Reference implementation of torch.allclose | |
| """ | |
| torch._refs._check_close_args(name="torch.allclose", | |
| a=a, | |
| b=b, | |
| rtol=rtol, | |
| atol=atol) | |
| return bool( | |
| torch.all( | |
| torch.isclose(a.double(), | |
| b.double(), | |
| rtol=rtol, | |
| atol=atol, | |
| equal_nan=equal_nan)).item()) | |
| # A special version of op check that has a restricted default set of test_utils | |
| # and a patched version of allclose that supports fp8 types. | |
| def opcheck( | |
| op: Union[ | |
| torch._ops.OpOverload, | |
| torch._ops.OpOverloadPacket, | |
| torch._library.custom_ops.CustomOpDef, | |
| ], | |
| args: Tuple[Any, ...], | |
| kwargs: Optional[Dict[str, Any]] = None, | |
| *, | |
| test_utils: Union[str, Sequence[str]] = ALL_OPCHECK_TEST_UTILS, | |
| raise_exception: bool = True, | |
| cond: bool = True, | |
| ) -> Dict[str, str]: | |
| with unittest.mock.patch("torch.allclose", new=fp8_allclose): | |
| return (torch.library.opcheck(op, | |
| args, | |
| kwargs, | |
| test_utils=test_utils, | |
| raise_exception=raise_exception) | |
| if cond else {}) | |