| import sys |
| from collections import namedtuple |
| from typing import Any, Optional, Tuple, Type |
| |
| import torch |
| from ._core import _unravel_index |
| |
| __all__ = ["assert_tensors_equal", "assert_tensors_close"] |
| |
| |
| # The UsageError should be raised in case the test function is not used correctly. With this the user is able to |
| # differentiate between a test failure (there is a bug in the tested code) and a test error (there is a bug in the |
| # test). If pytest is the test runner, we use the built-in UsageError instead our custom one. |
| |
| try: |
| # The module 'pytest' will be imported if the 'pytest' runner is used. This will only give false-positives in case |
| # a previously imported module already directly or indirectly imported 'pytest', but the test is run by another |
| # runner such as 'unittest'. |
| # 'mypy' is not able to handle this within a type annotation |
| # (see https://mypy.readthedocs.io/en/latest/common_issues.html#variables-vs-type-aliases for details). In case |
| # 'UsageError' is used in an annotation, add a 'type: ignore[valid-type]' comment. |
| UsageError: Type[Exception] = sys.modules["pytest"].UsageError # type: ignore[attr-defined] |
| except (KeyError, AttributeError): |
| |
| class UsageError(Exception): # type: ignore[no-redef] |
| pass |
| |
| |
| # This is copy-pasted from torch.testing._internal.common_utils.TestCase.dtype_precisions. With this we avoid a |
| # dependency on torch.testing._internal at import. See |
| # https://github.com/pytorch/pytorch/pull/54769#issuecomment-813174256 for details. |
| # {dtype: (rtol, atol)} |
| _DTYPE_PRECISIONS = { |
| torch.float16: (0.001, 1e-5), |
| torch.bfloat16: (0.016, 1e-5), |
| torch.float32: (1.3e-6, 1e-5), |
| torch.float64: (1e-7, 1e-7), |
| torch.complex32: (0.001, 1e-5), |
| torch.complex64: (1.3e-6, 1e-5), |
| torch.complex128: (1e-7, 1e-7), |
| } |
| |
| |
| def _get_default_rtol_and_atol(a: torch.Tensor, b: torch.Tensor) -> Tuple[float, float]: |
| dtype = a.dtype if a.dtype == b.dtype else torch.promote_types(a.dtype, b.dtype) |
| return _DTYPE_PRECISIONS.get(dtype, (0.0, 0.0)) |
| |
| |
| def _check_are_tensors(a: Any, b: Any) -> Optional[AssertionError]: |
| """Checks if both inputs are tensors. |
| |
| Args: |
| a (Any): First input. |
| b (Any): Second input. |
| |
| Returns: |
| (Optional[AssertionError]): If check did not pass. |
| """ |
| if not (isinstance(a, torch.Tensor) and isinstance(b, torch.Tensor)): |
| return AssertionError(f"Both inputs have to be tensors, but got {type(a)} and {type(b)} instead.") |
| |
| return None |
| |
| |
| def _check_supported_tensors(a: torch.Tensor, b: torch.Tensor) -> Optional[UsageError]: # type: ignore[valid-type] |
| """Checks if the tensors are supported by the current infrastructure. |
| |
| All checks are temporary and will be relaxed in the future. |
| |
| Args: |
| a (torch.Tensor): First tensor. |
| b (torch.Tensor): Second tensor. |
| |
| Returns: |
| (Optional[UsageError]): If check did not pass. |
| """ |
| if any(t.dtype in (torch.complex32, torch.complex64, torch.complex128) for t in (a, b)): |
| return UsageError("Comparison for complex tensors is not supported yet.") |
| if any(t.is_quantized for t in (a, b)): |
| return UsageError("Comparison for quantized tensors is not supported yet.") |
| if any(t.is_sparse for t in (a, b)): |
| return UsageError("Comparison for sparse tensors is not supported yet.") |
| |
| return None |
| |
| |
| def _check_attributes_equal( |
| a: torch.Tensor, |
| b: torch.Tensor, |
| *, |
| check_device: bool = True, |
| check_dtype: bool = True, |
| check_stride: bool = True, |
| ) -> Optional[AssertionError]: |
| """Checks if the attributes of two tensors match. |
| |
| Always checks the :attr:`~torch.Tensor.shape`. Checks for :attr:`~torch.Tensor.device`, |
| :attr:`~torch.Tensor.dtype`, and :meth:`~torch.Tensor.stride` are optional and can be disabled. |
| |
| Args: |
| a (torch.Tensor): First tensor. |
| b (torch.Tensor): Second tensor. |
| check_device (bool): If ``True`` (default), asserts that both :attr:`a` and :attr:`b` live in the same |
| :attr:`~torch.Tensor.device` memory. |
| check_dtype (bool): If ``True`` (default), asserts that both :attr:`a` and :attr:`b` have the same |
| :attr:`~torch.Tensor.dtype`. |
| check_stride (bool): If ``True`` (default), asserts that both :attr:`a` and :attr:`b` have the same |
| :meth:`~torch.Tensor.stride`. |
| |
| Returns: |
| (Optional[AssertionError]): If check did not pass. |
| """ |
| msg_fmtstr = "The values for attribute '{}' do not match: {} != {}." |
| |
| if a.shape != b.shape: |
| return AssertionError(msg_fmtstr.format("shape", a.shape, b.shape)) |
| |
| if check_device and a.device != b.device: |
| return AssertionError(msg_fmtstr.format("device", a.device, b.device)) |
| |
| if check_dtype and a.dtype != b.dtype: |
| return AssertionError(msg_fmtstr.format("dtype", a.dtype, b.dtype)) |
| |
| if check_stride and a.stride() != b.stride(): |
| return AssertionError(msg_fmtstr.format("stride()", a.stride(), b.stride())) |
| |
| return None |
| |
| |
| def _equalize_attributes(a: torch.Tensor, b: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: |
| """Equalizes some attributes of two tensors for value comparison. |
| |
| If :attr:`a` and :attr:`b` |
| - do not live in the same memory :attr:`~torch.Tensor.device`, they are moved CPU memory, and |
| - do not have the same :attr:`~torch.Tensor.dtype`, they are copied to the :class:`~torch.dtype` returned by |
| :func:`torch.promote_types`. |
| |
| Args: |
| a (torch.Tensor): First tensor. |
| b (torch.Tensor): Second tensor. |
| |
| Returns: |
| Tuple(torch.Tensor, torch.Tensor): Equalized tensors. |
| """ |
| if a.device != b.device: |
| a = a.cpu() |
| b = b.cpu() |
| |
| if a.dtype != b.dtype: |
| dtype = torch.promote_types(a.dtype, b.dtype) |
| a = a.to(dtype) |
| b = b.to(dtype) |
| |
| return a, b |
| |
| |
| _Trace = namedtuple("_Trace", ("total", "abs", "rel", "idx", "diff", "a", "b")) |
| |
| |
| def _trace_mismatches(a: torch.Tensor, b: torch.Tensor, mismatches: torch.Tensor) -> _Trace: |
| """Traces mismatches and returns the found information. |
| |
| The returned named tuple has the following fields: |
| - total (int): Total number of values in :attr:`a` and :attr:`b`. |
| - abs (int): Absolute number of mismatches. |
| - rel (float): Relative number of mismatches. |
| - idx (Union[int, Tuple[int, ...]]): Index of greatest absolute difference. |
| - diff (Union[int, float]): Greatest absolute difference. |
| - a (Union[int, float]): Value of :attr:`a` at the greatest absolute difference. |
| - b (Union[int, float]): Value of :attr:`a` at the greatest absolute difference. |
| |
| For ``diff``, ``a``, and ``b`` the returned type depends on the :attr:`~torch.Tensor.dtype` of :attr:`a` and |
| :attr:`b`. |
| |
| Args: |
| a (torch.Tensor): First tensor. |
| b (torch.Tensor): Second tensor. |
| mismatches (torch.Tensor): Boolean mask of the same shape as :attr:`a` and :attr:`b` that indicates the |
| location of mismatches. |
| """ |
| total = mismatches.numel() |
| abs = torch.sum(mismatches).item() |
| rel = abs / total |
| |
| dtype = torch.float64 if a.dtype.is_floating_point else torch.int64 |
| a_flat = a.flatten().to(dtype) |
| b_flat = b.flatten().to(dtype) |
| |
| abs_diff_flat = torch.abs(a_flat - b_flat) |
| idx_flat = torch.argmax(abs_diff_flat) |
| |
| return _Trace( |
| total=total, |
| abs=abs, |
| rel=rel, |
| idx=_unravel_index(idx_flat, a.shape), |
| diff=abs_diff_flat[idx_flat].item(), |
| a=a_flat[idx_flat].item(), |
| b=b_flat[idx_flat].item(), |
| ) |
| |
| |
| def _check_values_equal(a: torch.Tensor, b: torch.Tensor) -> Optional[AssertionError]: |
| """Checks if the values of two tensors are bitwise equal. |
| |
| Args: |
| a (torch.Tensor): First tensor. |
| b (torch.Tensor): Second tensor. |
| |
| Returns: |
| (Optional[AssertionError]): If check did not pass. |
| """ |
| mismatches = torch.ne(a, b) |
| if not torch.any(mismatches): |
| return None |
| |
| trace = _trace_mismatches(a, b, mismatches) |
| return AssertionError( |
| f"Found {trace.abs} different element(s) out of {trace.total} ({trace.rel:.1%}). " |
| f"The greatest difference of {trace.diff} ({trace.a} vs. {trace.b}) occurred at index {trace.idx}" |
| ) |
| |
| |
| def _check_values_close( |
| a: torch.Tensor, |
| b: torch.Tensor, |
| *, |
| rtol, |
| atol, |
| ) -> Optional[AssertionError]: |
| """Checks if the values of two tensors are close up to a desired tolerance. |
| |
| Args: |
| a (torch.Tensor): First tensor. |
| b (torch.Tensor): Second tensor. |
| rtol (float): Relative tolerance. |
| atol (float): Absolute tolerance. |
| |
| Returns: |
| (Optional[AssertionError]): If check did not pass. |
| """ |
| mismatches = ~torch.isclose(a, b, rtol=rtol, atol=atol) |
| if not torch.any(mismatches): |
| return None |
| |
| trace = _trace_mismatches(a, b, mismatches) |
| return AssertionError( |
| f"With rtol={rtol} and atol={atol}, " |
| f"found {trace.abs} different element(s) out of {trace.total} ({trace.rel:.1%}). " |
| f"The greatest difference of {trace.diff} ({trace.a} vs. {trace.b}) occurred at index {trace.idx}" |
| ) |
| |
| |
| def assert_tensors_equal( |
| a: torch.Tensor, |
| b: torch.Tensor, |
| *, |
| check_device: bool = True, |
| check_dtype: bool = True, |
| check_stride: bool = True, |
| ) -> None: |
| """Asserts that the values of two tensors are bitwise equal. |
| |
| Optionally, checks that some attributes of both tensors are equal. |
| |
| Args: |
| a (torch.Tensor): First tensor. |
| b (torch.Tensor): Second tensor. |
| check_device (bool): If ``True`` (default), asserts that both :attr:`a` and :attr:`b` live in the same |
| :attr:`~torch.Tensor.device` memory. If this check is disabled **and** :attr:`a` and :attr:`b` do not live |
| in the same memory :attr:`~torch.Tensor.device`, they are moved CPU memory before their values are |
| compared. |
| check_dtype (bool): If ``True`` (default), asserts that both :attr:`a` and :attr:`b` have the same |
| :attr:`~torch.Tensor.dtype`. If this check is disabled **and** :attr:`a` and :attr:`b` do not have the same |
| :attr:`~torch.Tensor.dtype`, they are copied to the :class:`~torch.dtype` returned by |
| :func:`torch.promote_types` before their values are compared. |
| check_stride (bool): If ``True`` (default), asserts that both :attr:`a` and :attr:`b` have the same stride. |
| |
| Raises: |
| UsageError: If :attr:`a` or :attr:`b` is complex, quantized, or sparse. This is a temporary restriction and |
| will be relaxed in the future. |
| AssertionError: If :attr:`a` and :attr:`b` do not have the same :attr:`~torch.Tensor.shape`. |
| AssertionError: If :attr:`check_device`, but :attr:`a` and :attr:`b` do not live in the same |
| :attr:`~torch.Tensor.device` memory. |
| AssertionError: If :attr:`check_dtype`, but :attr:`a` and :attr:`b` do not have the same |
| :attr:`~torch.Tensor.dtype`. |
| AssertionError: If :attr:`check_stride`, but :attr:`a` and :attr:`b` do not have the same stride. |
| AssertionError: If the values of :attr:`a` and :attr:`b` are not bitwise equal. |
| |
| .. seealso:: |
| |
| To assert that the values in two tensors are are close but are not required to be bitwise equal, use |
| :func:`assert_tensors_close` instead. |
| """ |
| exc: Optional[Exception] = _check_are_tensors(a, b) |
| if exc: |
| raise exc |
| |
| exc = _check_supported_tensors(a, b) |
| if exc: |
| raise exc |
| |
| exc = _check_attributes_equal(a, b, check_device=check_device, check_dtype=check_dtype, check_stride=check_stride) |
| if exc: |
| raise exc |
| a, b = _equalize_attributes(a, b) |
| |
| exc = _check_values_equal(a, b) |
| if exc: |
| raise exc |
| |
| |
| def assert_tensors_close( |
| a: torch.Tensor, |
| b: torch.Tensor, |
| *, |
| rtol: Optional[float] = None, |
| atol: Optional[float] = None, |
| check_device: bool = True, |
| check_dtype: bool = True, |
| check_stride: bool = True, |
| ) -> None: |
| """Asserts that the values of two tensors are close up to a desired tolerance. |
| |
| If both tolerances, :attr:`rtol` and :attr:`rtol`, are ``0``, asserts that :attr:`a` and :attr:`b` are bitwise |
| equal. Optionally, checks that some attributes of both tensors are equal. |
| |
| Args: |
| a (torch.Tensor): First tensor. |
| b (torch.Tensor): Second tensor. |
| rtol (Optional[float]): Relative tolerance. If specified :attr:`atol` must also be specified. If omitted, |
| default values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. |
| atol (Optional[float]): Absolute tolerance. If specified :attr:`rtol` must also be specified. If omitted, |
| default values based on the :attr:`~torch.Tensor.dtype` are selected with the below table. |
| check_device (bool): If ``True`` (default), asserts that both :attr:`a` and :attr:`b` live in the same |
| :attr:`~torch.Tensor.device` memory. If this check is disabled **and** :attr:`a` and :attr:`b` do not live |
| in the same memory :attr:`~torch.Tensor.device`, they are moved CPU memory before their values are |
| compared. |
| check_dtype (bool): If ``True`` (default), asserts that both :attr:`a` and :attr:`b` have the same |
| :attr:`~torch.Tensor.dtype`. If this check is disabled **and** :attr:`a` and :attr:`b` do not have the same |
| :attr:`~torch.Tensor.dtype`, they are copied to the :class:`~torch.dtype` returned by |
| :func:`torch.promote_types` before their values are compared. |
| check_stride (bool): If ``True`` (default), asserts that both :attr:`a` and :attr:`b` have the same stride. |
| |
| Raises: |
| UsageError: If :attr:`a` or :attr:`b` is complex, quantized, or sparse. This is a temporary restriction and |
| will be relaxed in the future. |
| AssertionError: If :attr:`a` and :attr:`b` do not have the same :attr:`~torch.Tensor.shape`. |
| AssertionError: If :attr:`check_device`, but :attr:`a` and :attr:`b` do not live in the same |
| :attr:`~torch.Tensor.device` memory. |
| AssertionError: If :attr:`check_dtype`, but :attr:`a` and :attr:`b` do not have the same |
| :attr:`~torch.Tensor.dtype`. |
| AssertionError: If :attr:`check_stride`, but :attr:`a` and :attr:`b` do not have the same stride. |
| AssertionError: If the values of :attr:`a` and :attr:`b` are close up to a desired tolerance. |
| |
| |
| |
| The following table displays the default ``rtol`` and ``atol`` for floating point :attr:`~torch.Tensor.dtype`'s. |
| For integer :attr:`~torch.Tensor.dtype`'s, ``rtol = atol = 0.0`` is used. |
| |
| +===========================+============+==========+ |
| | :class:`~torch.dtype` | ``rtol`` | ``atol`` | |
| +===========================+============+==========+ |
| | :attr:`~torch.float16` | ``1e-3`` | ``1e-5`` | |
| +---------------------------+------------+----------+ |
| | :attr:`~torch.bfloat16` | ``1.6e-2`` | ``1e-5`` | |
| +---------------------------+------------+----------+ |
| | :attr:`~torch.float32` | ``1.3e-6`` | ``1e-5`` | |
| +---------------------------+------------+----------+ |
| | :attr:`~torch.float64` | ``1e-7`` | ``1e-7`` | |
| +---------------------------+------------+----------+ |
| | :attr:`~torch.complex32` | ``1e-3`` | ``1e-5`` | |
| +---------------------------+------------+----------+ |
| | :attr:`~torch.complex64` | ``1.3e-6`` | ``1e-5`` | |
| +---------------------------+------------+----------+ |
| | :attr:`~torch.complex128` | ``1e-7`` | ``1e-7`` | |
| +---------------------------+------------+----------+ |
| |
| .. seealso:: |
| |
| To assert that the values in two tensors are bitwise equal, use :func:`assert_tensors_equal` instead. |
| """ |
| exc: Optional[Exception] = _check_are_tensors(a, b) |
| if exc: |
| raise exc |
| |
| exc = _check_supported_tensors(a, b) |
| if exc: |
| raise exc |
| |
| if (rtol is None) ^ (atol is None): |
| # We require both tolerance to be omitted or specified, because specifying only one might lead to surprising |
| # results. Imagine setting atol=0.0 and the tensors still match because rtol>0.0. |
| raise UsageError( |
| f"Both 'rtol' and 'atol' must be omitted or specified, " f"but got rtol={rtol} and atol={atol} instead." |
| ) |
| elif rtol is None: |
| rtol, atol = _get_default_rtol_and_atol(a, b) |
| |
| exc = _check_attributes_equal(a, b, check_device=check_device, check_dtype=check_dtype, check_stride=check_stride) |
| if exc: |
| raise exc |
| a, b = _equalize_attributes(a, b) |
| |
| if (rtol == 0.0) and (atol == 0.0): |
| exc = _check_values_equal(a, b) |
| else: |
| exc = _check_values_close(a, b, rtol=rtol, atol=atol) |
| if exc: |
| raise exc |
