mirror of
https://github.com/zebrajr/pytorch.git
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99203580a9
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/61841 Redo of #60863. Test Plan: Imported from OSS Reviewed By: ngimel Differential Revision: D30408145 Pulled By: mruberry fbshipit-source-id: 0b34ebc7f23ba38ecd89640b61d8aca59b7eab58
87 lines
2.5 KiB
Python
87 lines
2.5 KiB
Python
"""This module exists since the `torch.testing` exposed a lot of stuff that shouldn't have been public. Although this
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was never documented anywhere, some other internal FB projects as well as downstream OSS projects might use this. Thus,
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we don't internalize without warning, but still go through a deprecation cycle.
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"""
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import functools
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import warnings
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from typing import Any, Callable, Optional, Tuple
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import torch
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__all__ = [
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"rand",
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"randn",
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"assert_allclose",
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]
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def warn_deprecated(instructions: str) -> Callable:
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def outer_wrapper(fn: Callable) -> Callable:
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msg = (
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f"torch.testing.{fn.__name__} is deprecated and will be removed in a future release. "
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f"{instructions.strip()}"
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)
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@functools.wraps(fn)
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def inner_wrapper(*args: Any, **kwargs: Any) -> Any:
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warnings.warn(msg, FutureWarning)
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return fn(*args, **kwargs)
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return inner_wrapper
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return outer_wrapper
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rand = warn_deprecated("Use torch.rand instead.")(torch.rand)
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randn = warn_deprecated("Use torch.randn instead.")(torch.randn)
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_DTYPE_PRECISIONS = {
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torch.float16: (1e-3, 1e-3),
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torch.float32: (1e-4, 1e-5),
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torch.float64: (1e-5, 1e-8),
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}
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def _get_default_rtol_and_atol(actual: torch.Tensor, expected: torch.Tensor) -> Tuple[float, float]:
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actual_rtol, actual_atol = _DTYPE_PRECISIONS.get(actual.dtype, (0.0, 0.0))
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expected_rtol, expected_atol = _DTYPE_PRECISIONS.get(expected.dtype, (0.0, 0.0))
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return max(actual_rtol, expected_rtol), max(actual_atol, expected_atol)
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# TODO: include the deprecation as soon as torch.testing.assert_close is stable
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# @warn_deprecated(
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# "Use torch.testing.assert_close instead. "
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# "For detailed upgrade instructions see https://github.com/pytorch/pytorch/issues/61844."
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# )
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def assert_allclose(
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actual: Any,
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expected: Any,
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rtol: Optional[float] = None,
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atol: Optional[float] = None,
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equal_nan: bool = True,
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msg: str = "",
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) -> None:
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if not isinstance(actual, torch.Tensor):
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actual = torch.tensor(actual)
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if not isinstance(expected, torch.Tensor):
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expected = torch.tensor(expected, dtype=actual.dtype)
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if rtol is None and atol is None:
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rtol, atol = _get_default_rtol_and_atol(actual, expected)
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torch.testing.assert_close(
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actual,
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expected,
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rtol=rtol,
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atol=atol,
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equal_nan=equal_nan,
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check_device=True,
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check_dtype=False,
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check_stride=False,
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check_is_coalesced=False,
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msg=msg or None,
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)
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