mirror of
https://github.com/zebrajr/pytorch.git
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[MPS] Move ops modifiers to testing utils so other tests can reuse (#151781)
Test collection check: ``` python -m pytest test/test_mps.py --collect-only ``` Before: ``` 6390 tests collected in 8.34s ``` After: ``` 6390 tests collected in 7.71s ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/151781 Approved by: https://github.com/malfet
This commit is contained in:
Li-Huai (Allan) Lin
PyTorch MergeBot
parent
982062dfc4
commit
fbd29527d8
883
test/test_mps.py
883
test/test_mps.py
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@ -24,6 +24,7 @@ from torch.testing._internal import opinfo
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from torch.testing._internal.common_utils import \
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(gradcheck, gradgradcheck, parametrize, run_tests, TestCase, download_file, MACOS_VERSION, IS_CI,
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NoTest, skipIfSlowGradcheckEnv, suppress_warnings, serialTest, instantiate_parametrized_tests)
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from torch.testing._internal.common_mps import mps_ops_modifier, mps_ops_grad_modifier, mps_ops_error_inputs_modifier
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from torch.testing import make_tensor
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from torch.testing._internal.common_dtype import get_all_dtypes, integral_types
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import torch.backends.mps
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@ -32,7 +33,6 @@ from functools import partial
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from torch.testing._internal.common_methods_invocations import (
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op_db,
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DecorateInfo,
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UnaryUfuncInfo,
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ReductionOpInfo,
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SpectralFuncInfo,
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@ -79,880 +79,6 @@ def xfailIf(condition):
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return func
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return wrapper
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def mps_ops_grad_modifier(ops):
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XFAILLIST_GRAD = {
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# precision issues
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'special.polygammaspecial_polygamma_n_0': [torch.float16],
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'polygammapolygamma_n_0': [torch.float16],
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# Unimplemented ops
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'__getitem__': [torch.float16],
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'_segment_reduce': [torch.float16, torch.float32],
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'_chunk_cat': [torch.float16, torch.float32],
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'_upsample_bilinear2d_aa': None, # `_upsample_bilinear2d_aa_backward_out` not implemented for MPS
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'_upsample_bicubic2d_aa': None, # `_upsample_bilinear2d_aa_backward_out` not implemented for MPS
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'sparse.mmreduce': [torch.float32], # csr not supported
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'unique_consecutive': [torch.float16, torch.float32],
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'scalar_tensor': [torch.float16, torch.float32],
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'cdist': [torch.float32],
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'masked.scatter': [torch.float16, torch.float32],
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'index_fill': [torch.float16, torch.float32], # missing `aten::_unique`.
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'linalg.solve': [torch.float16, torch.float32], # missing `aten::lu_solve`.
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'linalg.solve_ex': [torch.float16, torch.float32], # missing `aten::lu_solve`.
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'linalg.tensorsolve': [torch.float16, torch.float32], # missing `aten::lu_solve`.
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'linalg.det': [torch.float16, torch.float32], # missing aten::lu_solve.out
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'linalg.slogdet': [torch.float16, torch.float32], # missing aten::lu_solve.out
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'logdet': [torch.float16, torch.float32], # missing aten::lu_solve.out
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'aminmax': [torch.float32, torch.float16],
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'special.i1': [torch.float16], # "i1_backward" not implemented for 'Half'
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'special.i1e': [torch.float16], # "i1e_backward" not implemented for 'Half'
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# Correctness issues
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'atanh': [torch.float32],
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# Random output
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'exponential': [torch.float16, torch.float32],
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# CPU errors
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# derivative for zeta is not implemented
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'special.zeta': None,
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# derivative for aten::nextafter is not implemented on CPU
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'nextafter': None,
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# derivative for aten::floor_divide is not implemented on CPU
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'floor_divide': [torch.float16, torch.float32],
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# derivative for aten::narrow_copy is not implemented on CPU
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'narrow_copy': [torch.float16, torch.float32],
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# derivative for aten::_histogramdd_from_bin_cts is not implemented on CPU
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'histogramdd': [torch.float16, torch.float32],
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# derivative for aten::histogram is not implemented
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'histogram': [torch.float16, torch.float32],
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# 'bool' object is not iterable
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'allclose': [torch.float16, torch.float32],
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'equal': [torch.float16, torch.float32],
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# 'float' object is not iterable
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'item': [torch.float16, torch.float32],
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# "mse_backward_cpu_out" not implemented for 'Half'
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'nn.functional.mse_loss': [torch.float16],
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# "smooth_l1_backward_cpu_out" not implemented for 'Half'
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'nn.functional.smooth_l1_loss': [torch.float16],
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# cpu error: grad requires non-empty inputs
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'randn': [torch.float16, torch.float32],
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'signal.windows.bartlett': [torch.float32],
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'signal.windows.blackman': [torch.float32],
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'signal.windows.cosine': [torch.float32],
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'signal.windows.exponential': [torch.float32],
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'signal.windows.gaussian': [torch.float32],
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'signal.windows.general_cosine': [torch.float32],
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'signal.windows.general_hamming': [torch.float32],
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'signal.windows.hamming': [torch.float32],
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'signal.windows.hann': [torch.float32],
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'signal.windows.kaiser': [torch.float32],
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'signal.windows.nuttall': [torch.float32],
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'eye': [torch.float16, torch.float32],
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# trunc_tensor not working properly for float16
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'divtrunc_rounding': [torch.float16],
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'fmod': [torch.float16],
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# round not working properly for float16
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'round': [torch.float16],
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# atomic operation in backward pass
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'_unsafe_masked_index': [torch.float16],
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'_unsafe_masked_index_put_accumulate': [torch.float16],
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}
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MACOS_BEFORE_13_3_XFAILLIST_GRAD = {
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# Failures due to precision issues (due to fast-math). These has been fixed in MacOS 13.3+
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'masked.softmin': [torch.float32, torch.float16],
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'masked.softmax': [torch.float32, torch.float16],
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'masked.log_softmax': [torch.float32, torch.float16],
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# Unsupported Border padding mode, forward pass success as fallback to cpu
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'grid_sampler_2d': [torch.float32, torch.float16, torch.bfloat16],
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# Same issue as `argsort` and `sort` with duplicate elements (undefined behaviour).
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# Forward pass is passing since `msort` doesn't return the indices, just the values, which match the CPU.
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# On the backward pass for `sort` both are used (values and indices), thus resulting in a issmatch between CPU and MPS.
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# Running `msort` with stable `sort` passes.
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'msort': [torch.float16],
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}
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SKIPLIST_GRAD = {
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'nn.functional.pairwise_distance': [torch.float16],
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# failed assertion `destination datatype must be fp32'
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'nn.functional.conv1d': [torch.float16],
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'nn.functional.conv2d': [torch.float16],
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'nn.functional.conv3d': [torch.float16],
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'nn.functional.conv_transpose1d': [torch.float16],
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'nn.functional.conv_transpose2d': [torch.float16],
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'nn.functional.conv_transpose3d': [torch.float16],
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}
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MACOS_13_3_XFAILLIST_GRAD = {
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# Same issue as `argsort` and `sort` with duplicate elements (undefined behaviour).
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# Forward pass is passing since `msort` doesn't return the indices, just the values, which match the CPU.
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# On the backward pass for `sort` both are used (values and indices), thus resulting in a issmatch between CPU and MPS.
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# Running `msort` with stable `sort` passes.
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'msort': [torch.float16],
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}
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ON_MPS_XFAILLIST = {
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# Failures due to lack of implementation of downstream functions on MPS backend
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# TODO: remove these once downstream function 'aten::_linalg_svd.U' have been implemented
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'linalg.matrix_rank': None,
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# Exception: Caused by sample input at index 3 on MPS
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'nn.functional.conv3d': [torch.float32],
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}
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def addDecorator(op, d) -> None:
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op.decorators = list(op.decorators) if op.decorators is not None else []
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op.decorators.append(d)
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for op in ops:
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key = op.name + op.variant_test_name
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if key in XFAILLIST_GRAD:
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addDecorator(op, DecorateInfo(
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unittest.expectedFailure,
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dtypes=XFAILLIST_GRAD[key]))
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if key in SKIPLIST_GRAD:
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addDecorator(op, DecorateInfo(
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unittest.skip,
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dtypes=SKIPLIST_GRAD[key]))
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if key in ON_MPS_XFAILLIST:
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addDecorator(op, DecorateInfo(
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unittest.expectedFailure,
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dtypes=ON_MPS_XFAILLIST[key]))
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if key in MACOS_BEFORE_13_3_XFAILLIST_GRAD and (torch.backends.mps.is_macos13_or_newer() and MACOS_VERSION < 13.3):
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addDecorator(op, DecorateInfo(
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unittest.expectedFailure,
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dtypes=MACOS_BEFORE_13_3_XFAILLIST_GRAD[key]))
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if key in MACOS_13_3_XFAILLIST_GRAD and (MACOS_VERSION >= 13.3):
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addDecorator(op, DecorateInfo(
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unittest.expectedFailure,
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dtypes=MACOS_13_3_XFAILLIST_GRAD[key]))
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yield op
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def mps_ops_modifier(ops):
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# Supported complex OPS
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SUPPORTED_COMPLEX_OPS = {
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'__radd__',
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'__rmul__',
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'__getitem__',
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'_unsafe_masked_index',
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'abs',
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'add',
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'alias_copy',
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'argwhere',
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'atleast_1d',
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'atleast_2d',
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'atleast_3d',
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'as_strided',
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'as_strided_copy',
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'as_strided_scatter',
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'broadcast_tensors',
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'broadcast_to',
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'chalf',
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'cfloat',
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'chunk',
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'clone',
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'conj',
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'conj_physical',
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'contiguous',
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'diag',
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'diag_embed',
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'diagflat',
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'diagonal',
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'diagonal_copy',
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'diagonal_scatter',
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'dsplit',
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'empty',
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'empty_permuted',
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'empty_strided',
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'exp',
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'expand',
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'expand_as',
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'expand_copy',
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'flatten',
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'fill',
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'full',
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'H',
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'hsplit',
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'imag',
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'index_select',
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'isfinite',
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'isinf',
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'isreal',
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'item',
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'kron',
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'linalg.diagonal',
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'linalg.svd',
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'mH',
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'mT',
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'masked_fill',
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'masked_scatter',
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'masked_select',
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'meshgridlist_of_tensors',
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'meshgridvariadic_tensors',
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'movedim',
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'mul',
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'narrow',
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'narrow_copy',
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'new_full',
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'new_ones',
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'new_zeros',
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'nn.functional.conv1d',
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'nn.functional.conv2d',
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'nn.functional.conv_transpose1d',
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'nn.functional.conv_transpose2d',
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'nn.functional.feature_alpha_dropoutwithout_train',
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'nn.functional.padcircular',
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'nn.functional.tanhshrink',
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'nn.functional.unfold',
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'nonzero',
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'ones',
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'outer',
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'permute',
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'permute_copy',
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'positive',
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'randn',
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'ravel',
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'real',
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'repeat_interleave',
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'reshape_as',
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'reshape',
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'resolve_conj',
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'resolve_neg',
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'scalar_tensor',
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'select',
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'sgn',
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'sinc',
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'slice',
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'special.spherical_bessel_j0',
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'special.entr',
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'special.xlog1py',
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'special.zeta',
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'split',
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'split_with_sizes',
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'split_with_sizes_copy',
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'splitlist_args',
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'sqrt',
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'squeeze',
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'squeeze_copy',
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'squeezemultiple',
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'sub',
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'svd',
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't',
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't_copy',
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'tanh',
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'tensor_split',
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'transpose',
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'transpose_copy',
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'T',
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'unbind',
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'unbind_copy',
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'unflatten',
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'unfold',
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'unfold_copy',
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'unsafe_chunk',
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'unsafe_split',
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'unsqueeze',
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'unsqueeze_copy',
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'view_as',
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'view_as_real',
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'view',
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'view_copy',
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'vsplit',
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'zero_',
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'zeros',
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}
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AFTER_MACOS_14_0_SUPPORTED_COMPLEX_OPS = {
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'__rdiv__',
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'__rmatmul__',
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'_chunk_cat',
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'acos',
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'acosh',
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'all',
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'allclose',
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'angle',
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'any',
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'addcdiv',
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'addcmul',
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'addmmdecomposed',
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'addmv',
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'asin',
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'atan',
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'atanh',
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'bfloat16',
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'bmm',
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'bool',
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'cartesian_prod',
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'cat',
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'char',
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'column_stack',
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'combinations',
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'corrcoef',
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'constant_pad_nd',
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'cos',
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'cosh',
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'cov',
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'count_nonzero',
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'diff',
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'div',
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'divno_rounding_mode',
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'dot',
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'dstack',
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'einsum',
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'eq',
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'equal',
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'exp2',
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'expm1',
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'eye',
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'fft.fft',
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'fft.fft2',
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'fft.fftn',
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'fft.fftshift',
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'fft.ifft',
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'fft.ifft2',
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'fft.ifftn',
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'fft.ifftshift',
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'fft.irfftn',
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'fft.irfft2',
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'fft.irfft',
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'fft.hfftn',
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'fft.hfft2',
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'fft.hfft',
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'flip',
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'fliplr',
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'flipud',
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'float',
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'gradient',
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'half',
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'hstack',
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'inner',
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'int',
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'isclose',
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'isnan',
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'ldexp',
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'linalg.multi_dot',
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'linalg.pinv',
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'linspace',
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'linspacetensor_overload',
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'log10',
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'log1p',
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'log2',
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'log',
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'logical_and',
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'logical_not',
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'logical_or',
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'logical_xor',
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'logsumexp',
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'long',
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'masked.mean',
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'masked.prod',
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'masked.std',
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'masked.sum',
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'masked.var',
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'masked.logsumexp',
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'matmul',
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'mean',
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'mm',
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'mv',
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'ne',
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'neg',
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'nn.functional.padconstant',
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'nn.functional.padreflect',
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'nn.functional.padreplicate',
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'nn.functional.pixel_shuffle',
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'nn.functional.pixel_unshuffle',
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'nn.functional.rms_norm',
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'nn.functional.softsign',
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'pinverse',
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'prod',
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'reciprocal',
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'roll',
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'rot90',
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'rsqrt',
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'short',
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'sigmoid',
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'sin',
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'sinh',
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'sqrt',
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'square',
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'stack',
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'stft',
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'sum',
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'sum_to_size',
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'tan',
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'tensordot',
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'trace',
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'trapz',
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'trapezoid',
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'tril',
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'triu',
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'true_divide',
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'vstack',
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'where',
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'byte',
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}
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# Those ops worked on MacOS12, but broken on MacOS13, see https://github.com/pytorch/pytorch/issues/85758
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MACOS_BEFORE_13_3_XFAILLIST = {
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# Failures due to precision issues (due to fast-math). These has been fixed in MacOS 13.3+
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'tan': [torch.float32],
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'cdist': [torch.float32],
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# CPU Error: cpu not giving nan for x/0.0
|
||||
'atan2': [torch.bool, torch.int16, torch.int32, torch.int64, torch.uint8, torch.int8],
|
||||
|
||||
# test blow pass on macOS 12 as it falls back to cpu
|
||||
# Argsort case using duplicate indices (undefined behaviour):
|
||||
# - CPU output: tensor([2546, 6917, 3181, ..., 7128, 5133, 30], devuce='cpu')
|
||||
# - MPS output: tensor([2546, 6917, 3181, ..., 7128, 30, 5133], device='mps:0')
|
||||
# Elements from index 30 and 5133 are both equal.
|
||||
# Since CPU is not using argsort with stable=True, these cases result in undefined behaviour.
|
||||
'argsort': [torch.float16, torch.int8, torch.uint8, torch.bool],
|
||||
# Same issue as `argsort` with duplicate indices. This test checks both the sorted values and the indices.
|
||||
# The values of the sorted tensor match the CPU, but in case of the returned indices this results in undefined behaviour.
|
||||
'sort': [torch.int8, torch.uint8, torch.bool, torch.float16],
|
||||
# Unsupported dtypes
|
||||
'cumsum': [torch.int64],
|
||||
'cumprod': [torch.int64],
|
||||
'cumulative_trapezoid': [torch.int64],
|
||||
'masked.cumsum': [torch.int64],
|
||||
'masked.cumprod': [torch.int64],
|
||||
'linalg.vander': [torch.int64],
|
||||
}
|
||||
|
||||
MACOS_AFTER_13_1_XFAILLIST = {
|
||||
# before macOS 13.2 it falls back to cpu and pass the forward pass
|
||||
'grid_sampler_2d': [torch.float32, torch.float16, torch.bfloat16], # Unsupported Border padding mode
|
||||
}
|
||||
|
||||
MACOS_13_3_XFAILLIST = {
|
||||
# Failure due to precision issue for fp16
|
||||
# on both cpu and mps there are test cases that might produce inf result
|
||||
# 'nn.functional.pairwise_distance': [torch.float16],
|
||||
|
||||
# test blow pass on macOS 12 as it falls back to cpu
|
||||
# Argsort case using duplicate indices (undefined behaviour):
|
||||
# - CPU output: tensor([2546, 6917, 3181, ..., 7128, 5133, 30], devuce='cpu')
|
||||
# - MPS output: tensor([2546, 6917, 3181, ..., 7128, 30, 5133], device='mps:0')
|
||||
# Elements from index 30 and 5133 are both equal.
|
||||
# Since CPU is not using argsort with stable=True, these cases result in undefined behaviour.
|
||||
'argsort': [torch.float16, torch.int8, torch.uint8, torch.bool, torch.bfloat16],
|
||||
# Same issue as `argsort` with duplicate indices. This test checks both the sorted values and the indices.
|
||||
# The values of the sorted tensor match the CPU, but in case of the returned indices this results in undefined behaviour.
|
||||
'sort': [torch.int8, torch.uint8, torch.bool, torch.float16, torch.bfloat16],
|
||||
}
|
||||
|
||||
MACOS_BEFORE_14_4_XFAILLIST = {
|
||||
# These ops work fine in 14.4 but fail in 14.2 or 13.x
|
||||
'fft.hfft2': [torch.complex64],
|
||||
}
|
||||
|
||||
# Those ops are not expected to work
|
||||
UNIMPLEMENTED_XFAILLIST = {
|
||||
# Failures due to lack of op implementation on MPS backend
|
||||
'login': None,
|
||||
'logspace': None,
|
||||
'logspacetensor_overload': None,
|
||||
'linalg.eig': None,
|
||||
'linalg.eigvals': None,
|
||||
'put': None,
|
||||
'nn.functional.conv_transpose3d': None,
|
||||
'__rsub__': None,
|
||||
'cauchy_': None,
|
||||
'cauchy': None,
|
||||
'cholesky_inverse': None,
|
||||
'cholesky_solve': None,
|
||||
'cummax': None,
|
||||
'cummin': None,
|
||||
'erfc': None,
|
||||
'frexp': None,
|
||||
'gcd': None,
|
||||
'geqrf': None,
|
||||
'nn.functional.grid_sample': None, # Unsupported Border padding mode
|
||||
'heaviside': None,
|
||||
'igamma': None,
|
||||
'igammac': None,
|
||||
'index_copy': None,
|
||||
'index_reduceprod': None,
|
||||
'index_reducemean': None,
|
||||
'index_reduceamax': None,
|
||||
'index_reduceamin': None,
|
||||
'kthvalue': None,
|
||||
'lcm': None,
|
||||
'linalg.cond': None,
|
||||
'linalg.eigh': None,
|
||||
'linalg.eigvalsh': None,
|
||||
'linalg.householder_product': None,
|
||||
'linalg.ldl_factor': None,
|
||||
'linalg.ldl_factor_ex': None,
|
||||
'linalg.ldl_solve': None,
|
||||
'linalg.lstsq': None,
|
||||
'linalg.lstsqgrad_oriented': None,
|
||||
'linalg.lu': None,
|
||||
'linalg.lu_solve': None,
|
||||
'linalg.matrix_norm': [torch.float32],
|
||||
'linalg.norm': [torch.float32],
|
||||
'linalg.normsubgradients_at_zero': [torch.float32],
|
||||
'linalg.qr': None,
|
||||
'linalg.svdvals': None,
|
||||
'linalg.vecdot': None,
|
||||
'logcumsumexp': None,
|
||||
'lu_solve': None,
|
||||
'masked.median': None,
|
||||
'matrix_exp': None,
|
||||
'mode': None,
|
||||
'native_dropout_backward': None,
|
||||
'normnuc': None,
|
||||
'nn.functional.fractional_max_pool2d': None,
|
||||
'nn.functional.fractional_max_pool3d': None,
|
||||
'nn.functional.adaptive_avg_pool3d': None,
|
||||
'nn.functional.adaptive_max_pool3d': None,
|
||||
'nn.functional.interpolatearea': None,
|
||||
'nn.functional.interpolatebicubic': [torch.uint8],
|
||||
'nn.functional.interpolatetrilinear': None,
|
||||
'nn.functional.max_unpool1dgrad': None,
|
||||
'nn.functional.max_unpool2dgrad': None,
|
||||
'nn.functional.max_unpool3dgrad': None,
|
||||
'nn.functional.avg_pool3d': None,
|
||||
'nn.functional.ctc_loss': None,
|
||||
'nn.functional.embedding_bag': None,
|
||||
'nn.functional.hardshrink': None,
|
||||
'nn.functional.max_pool3d': None,
|
||||
'nn.functional.max_unpool1d': None,
|
||||
'nn.functional.max_unpool2d': None,
|
||||
'nn.functional.max_unpool3d': None,
|
||||
'nn.functional.multi_margin_loss': None,
|
||||
'nn.functional.multilabel_margin_loss': None,
|
||||
'nn.functional.pdist': None,
|
||||
'nn.functional.rrelu': None,
|
||||
'nn.functional.norm': None,
|
||||
'ormqr': None,
|
||||
'pca_lowrank': None,
|
||||
'qr': None,
|
||||
'rsub': None,
|
||||
'scatter_reduceamax': [torch.int32, torch.int64] if MACOS_VERSION < 15.0 else [torch.int64],
|
||||
'scatter_reduceamin': [torch.int32, torch.int64] if MACOS_VERSION < 15.0 else [torch.int64],
|
||||
'segment_reduce': None,
|
||||
'_segment.reduce': None,
|
||||
'segment.reduce': None,
|
||||
'segment_reduce_offsets': None,
|
||||
'_segment_reduce_offsets': None,
|
||||
'_segment_reduce_lengths': None,
|
||||
'_segment_reducelengths': None,
|
||||
'_segment_reduceoffsets': None,
|
||||
'sparse.mm': None,
|
||||
'sparse.mmreduce': None,
|
||||
'special.airy_ai': None,
|
||||
'special.erfcx': None,
|
||||
'special.laguerre_polynomial_l': None,
|
||||
'special.log_ndtr': None,
|
||||
'special.ndtri': None,
|
||||
'svd_lowrank': None,
|
||||
'symeig': None,
|
||||
'take': None,
|
||||
'to': None,
|
||||
'to_sparse': None,
|
||||
'unique': None,
|
||||
'vdot': None,
|
||||
'segment_reduce_': None,
|
||||
'_upsample_bilinear2d_aa': [torch.uint8], # uint8 is for CPU only
|
||||
'_upsample_bicubic2d_aa': [torch.uint8], # uint8 is for CPU only
|
||||
'geometric' : None,
|
||||
'geometric_': None,
|
||||
'log_normal_': None,
|
||||
'log_normal': None,
|
||||
'cdouble': None,
|
||||
'double': None,
|
||||
'nn.functional.softminwith_dtype': None,
|
||||
'log_softmaxwith_dtype': None,
|
||||
'softmaxwith_dtype': None,
|
||||
'float_power': None,
|
||||
'full_like': None,
|
||||
'linalg.matrix_rankhermitian': None,
|
||||
'linalg.pinvhermitian': None,
|
||||
'nonzero_static': None,
|
||||
|
||||
# MPS: input sizes must be divisible by output sizes
|
||||
'nn.functional.adaptive_avg_pool1d': None,
|
||||
'nn.functional.adaptive_avg_pool2d': None,
|
||||
|
||||
# Unsupported dtypes
|
||||
'ones_like': None,
|
||||
'zeros_like': None,
|
||||
|
||||
# Convolution for integral types is not supported on MPS
|
||||
'nn.functional.conv1d': [torch.int64],
|
||||
'nn.functional.conv2d': [torch.int64],
|
||||
'nn.functional.conv3d': [torch.int64],
|
||||
'nn.functional.conv_transpose1d': [torch.int64],
|
||||
'nn.functional.conv_transpose2d': [torch.int64, torch.bfloat16],
|
||||
|
||||
# Unsupported dtypes
|
||||
'dot': [torch.int64] if MACOS_VERSION < 14.0 else [],
|
||||
'histc': [torch.float16, torch.bfloat16],
|
||||
'index_add': [torch.int64],
|
||||
|
||||
# Operations not supported for integral types
|
||||
'special.xlog1py': [torch.bool, torch.int16, torch.int32, torch.int64, torch.uint8, torch.int8],
|
||||
'special.zeta': [torch.bool, torch.int16, torch.int32, torch.int64, torch.uint8, torch.int8],
|
||||
'special.chebyshev_polynomial_t': [torch.bool, torch.int16, torch.int32, torch.int64, torch.uint8, torch.int8],
|
||||
'special.chebyshev_polynomial_u': [torch.bool, torch.int16, torch.int32, torch.int64, torch.uint8, torch.int8],
|
||||
'special.hermite_polynomial_h': [torch.bool, torch.int16, torch.int32, torch.int64, torch.uint8, torch.int8],
|
||||
'special.hermite_polynomial_he': [torch.bool, torch.int16, torch.int32, torch.int64, torch.uint8, torch.int8],
|
||||
|
||||
# entr does not support boolean types
|
||||
'special.entr': [torch.bool],
|
||||
|
||||
# GEMM on MPS is not supported for integral types
|
||||
'nn.functional.linear': [torch.int16, torch.int32, torch.int64, torch.uint8, torch.int8],
|
||||
'addmmdecomposed': [torch.int16, torch.int32, torch.int64, torch.uint8, torch.int8],
|
||||
'addbmm': [torch.int16, torch.int32, torch.int64, torch.uint8, torch.int8],
|
||||
'addmm': [torch.int16, torch.int32, torch.int64, torch.uint8, torch.int8],
|
||||
'baddbmm': [torch.int16, torch.int32, torch.int64, torch.uint8, torch.int8],
|
||||
'mat': [torch.int16, torch.int32, torch.int64, torch.uint8, torch.int8],
|
||||
'matmul': [torch.int64] if MACOS_VERSION < 14.0 else [],
|
||||
'__rmatmul__': [torch.int64] if MACOS_VERSION < 14.0 else [],
|
||||
'unravel_index': [torch.int32, torch.int64],
|
||||
|
||||
# returned output on CPU is float64
|
||||
'bincount': [torch.int16, torch.int32, torch.int64, torch.uint8, torch.int8],
|
||||
|
||||
# trunc_tensor not working properly for float16 and bfloat16
|
||||
'divtrunc_rounding': [torch.float16, torch.bfloat16],
|
||||
'fmod': [torch.float16],
|
||||
|
||||
# round not working properly for float16 and bfloat16
|
||||
'round': [torch.float16, torch.bfloat16],
|
||||
'rounddecimals_0': [torch.bfloat16],
|
||||
|
||||
# bfloat16 have weird issues with rounding
|
||||
'divfloor_rounding': [torch.bfloat16],
|
||||
'floor_divide': [torch.bfloat16],
|
||||
'remainder': [torch.bfloat16],
|
||||
|
||||
# atomic operations not supported
|
||||
'_unsafe_masked_index_put_accumulate': [torch.bool, torch.int8, torch.uint8, torch.float16, torch.int16, torch.int64,
|
||||
torch.bfloat16],
|
||||
}
|
||||
|
||||
if MACOS_VERSION < 14.0:
|
||||
# FFT and BFloat16 support was added in MacOS 14
|
||||
UNIMPLEMENTED_XFAILLIST.update({
|
||||
'bfloat16': None,
|
||||
'fft.fft': None,
|
||||
'fft.fft2': None,
|
||||
'fft.fftn': None,
|
||||
'fft.hfft': None,
|
||||
'fft.hfft2': None,
|
||||
'fft.hfftn': None,
|
||||
'fft.ifft': None,
|
||||
'fft.ifft2': None,
|
||||
'fft.ifftn': None,
|
||||
'fft.ihfft': None,
|
||||
'fft.ihfft2': None,
|
||||
'fft.ihfftn': None,
|
||||
'fft.irfft': None,
|
||||
'fft.irfft2': None,
|
||||
'fft.irfftn': None,
|
||||
'fft.rfft': None,
|
||||
'fft.rfft2': None,
|
||||
'fft.rfftn': None,
|
||||
'stft': None,
|
||||
# Error in TestConsistencyCPU.test_output_match_isin_cpu fails for integers,
|
||||
# not reproducible in later OS. Added assert to op if used in < 14.0
|
||||
'isin': [torch.int64, torch.int32, torch.int16, torch.uint8, torch.int8],
|
||||
'nn.functional.max_pool2d': [torch.uint8],
|
||||
})
|
||||
|
||||
if MACOS_VERSION < 15.0:
|
||||
UNIMPLEMENTED_XFAILLIST.update({
|
||||
'quantile': None,
|
||||
'nanquantile': None,
|
||||
})
|
||||
|
||||
UNDEFINED_XFAILLIST = {
|
||||
# Top 60 operators
|
||||
# topk fails with duplicate indices
|
||||
'topk': [torch.int16, torch.int32, torch.int64, torch.uint8, torch.int8, torch.bfloat16],
|
||||
|
||||
# Failures due to random output that they generate using
|
||||
# Philox engine causing mismatch with CPU results
|
||||
'multinomial': [torch.float16, torch.float32, torch.bfloat16], # random results
|
||||
'uniform': [torch.float16, torch.float32, torch.bfloat16],
|
||||
'rand_like': [torch.float16, torch.float32, torch.bfloat16],
|
||||
'randint': None,
|
||||
'randint_like': None,
|
||||
'randn': None,
|
||||
'randn_like': None,
|
||||
'bernoulli': [torch.float16, torch.float32, torch.bfloat16],
|
||||
'exponential': [torch.float16, torch.float32, torch.bfloat16],
|
||||
'nn.functional.feature_alpha_dropoutwith_train': [torch.float16, torch.float32, torch.bfloat16],
|
||||
'normal': [torch.float16, torch.float32, torch.bfloat16],
|
||||
'normalin_place': [torch.float16, torch.float32, torch.bfloat16],
|
||||
'normalnumber_mean': [torch.float16, torch.float32, torch.bfloat16],
|
||||
'nn.functional.alpha_dropout': [torch.float16, torch.float32, torch.bfloat16],
|
||||
'nn.functional.dropout': [torch.float16, torch.float32, torch.bfloat16],
|
||||
'nn.functional.dropout2d': [torch.float16, torch.float32, torch.bfloat16],
|
||||
'nn.functional.dropout3d': [torch.float16, torch.float32, torch.bfloat16],
|
||||
# See https://github.com/pytorch/pytorch/issues/111479
|
||||
'nn.functional.multi_head_attention_forward': [torch.float32, torch.float16, torch.bfloat16],
|
||||
|
||||
'index_put': [torch.bool, torch.uint8, torch.int8, torch.int16, torch.int64, torch.float16, torch.bfloat16],
|
||||
# zero to negative integer powers are undefined
|
||||
'__rpow__': [torch.int8, torch.int16, torch.int32, torch.int64],
|
||||
'resize_': [torch.float16, torch.float32, torch.bfloat16],
|
||||
'resize_as_': [torch.float16, torch.float32, torch.bfloat16],
|
||||
|
||||
# CPU Errors:
|
||||
'addr': [torch.bool, torch.int16, torch.int32,
|
||||
torch.int64, torch.uint8, torch.int8], # "addmv_impl_cpu" not implemented for 'Half'
|
||||
'as_stridedpartial_views': None, # cpu result off, showing random values
|
||||
|
||||
# random results
|
||||
# mps vs cpu:
|
||||
# Mismatched elements: 40 / 96 (41.7%)
|
||||
# Greatest absolute difference: 17.892311096191406 at index (1, 0, 2) (up to 1e-05 allowed)
|
||||
# Greatest relative difference: inf at index (1, 0, 0) (up to 1.3e-06 allowed)
|
||||
# cuda(2.0.0.dev20230301+cu117) vs cpu:
|
||||
# Mismatched elements: 56 / 96 (58.3%)
|
||||
# Greatest absolute difference: 17.892311096191406 at index (1, 0, 2) (up to 1e-05 allowed)
|
||||
# Greatest relative difference: inf at index (1, 0, 0) (up to 1.3e-06 allowed)
|
||||
'nn.functional.scaled_dot_product_attention': [torch.float32, torch.float16, torch.bfloat16],
|
||||
}
|
||||
|
||||
ON_MPS_XFAILLIST = {
|
||||
# Failures due to lack of implementation of downstream functions on MPS backend
|
||||
# TODO: remove these once downstream function 'aten::_linalg_svd.U' have been implemented
|
||||
'linalg.matrix_rank': None,
|
||||
|
||||
# Exception: Caused by `torch.arange(-8.001, -4.0, dtype=torch.uint8, device="mps")`
|
||||
'arange': [torch.uint8],
|
||||
}
|
||||
|
||||
EMPTY_OPS_SKIPLIST = {
|
||||
# Fill tensors with uninitialized data, causing mismatch with CPU.
|
||||
# They occasionally match, thus skipping them.
|
||||
# See https://github.com/pytorch/pytorch/issues/100175
|
||||
'new_empty': None,
|
||||
'new_empty_strided': None,
|
||||
'empty_strided': None,
|
||||
# CPU: empty is returning all 0's and there is a mismatch with MPS
|
||||
# allocation (MacOS 13). According to
|
||||
# https://pytorch.org/docs/2.0/generated/torch.empty.html
|
||||
'empty': None,
|
||||
'empty_like': None,
|
||||
'empty_permuted': None,
|
||||
}
|
||||
|
||||
SKIPLIST = {
|
||||
# Unsupported
|
||||
|
||||
# This doesn't work on M1, but is partially working on M2 with the exception of torch.float16
|
||||
'nn.functional.conv3d': None,
|
||||
}
|
||||
|
||||
def addDecorator(op, d) -> None:
|
||||
op.decorators = list(op.decorators) if op.decorators is not None else []
|
||||
op.decorators.append(d)
|
||||
|
||||
for op in ops:
|
||||
key = op.name + op.variant_test_name
|
||||
if key in EMPTY_OPS_SKIPLIST:
|
||||
addDecorator(op, DecorateInfo(
|
||||
unittest.skip("Skipping empty ops."),
|
||||
dtypes=EMPTY_OPS_SKIPLIST[key]))
|
||||
if key in SKIPLIST:
|
||||
addDecorator(op, DecorateInfo(unittest.skip("Skipped!"), dtypes=SKIPLIST[key]))
|
||||
for xfaillist in [UNIMPLEMENTED_XFAILLIST, UNDEFINED_XFAILLIST, ON_MPS_XFAILLIST]:
|
||||
if key in xfaillist:
|
||||
addDecorator(op, DecorateInfo(
|
||||
unittest.expectedFailure,
|
||||
dtypes=xfaillist[key]))
|
||||
|
||||
if key in MACOS_BEFORE_14_4_XFAILLIST and (MACOS_VERSION < 14.4):
|
||||
addDecorator(op, DecorateInfo(
|
||||
unittest.expectedFailure,
|
||||
dtypes=MACOS_BEFORE_14_4_XFAILLIST[key]))
|
||||
|
||||
if key in MACOS_BEFORE_13_3_XFAILLIST and (torch.backends.mps.is_macos13_or_newer() and MACOS_VERSION < 13.3):
|
||||
addDecorator(op, DecorateInfo(
|
||||
unittest.expectedFailure,
|
||||
dtypes=MACOS_BEFORE_13_3_XFAILLIST[key]))
|
||||
|
||||
if key in MACOS_AFTER_13_1_XFAILLIST and torch.backends.mps.is_macos13_or_newer(2):
|
||||
addDecorator(op, DecorateInfo(
|
||||
unittest.expectedFailure,
|
||||
dtypes=MACOS_AFTER_13_1_XFAILLIST[key]))
|
||||
|
||||
if key in MACOS_13_3_XFAILLIST and (MACOS_VERSION >= 13.3):
|
||||
addDecorator(op, DecorateInfo(
|
||||
unittest.expectedFailure,
|
||||
dtypes=MACOS_13_3_XFAILLIST[key]))
|
||||
|
||||
# If ops is not supported for complex types, expect it to fail
|
||||
if key not in SUPPORTED_COMPLEX_OPS and (key not in AFTER_MACOS_14_0_SUPPORTED_COMPLEX_OPS or MACOS_VERSION < 14.0):
|
||||
addDecorator(op, DecorateInfo(unittest.expectedFailure, dtypes=[torch.complex32, torch.complex64]))
|
||||
|
||||
yield op
|
||||
|
||||
def mps_ops_error_inputs_modifier(ops):
|
||||
# Error input samples do not take a dtype argument.
|
||||
XFAILLIST = {
|
||||
# Exceptions are not raised
|
||||
'__rmod__',
|
||||
'__rsub__',
|
||||
'__rpow__',
|
||||
'bernoulli',
|
||||
'clamp_max',
|
||||
'clamp_min',
|
||||
'masked_scatter',
|
||||
|
||||
# unsupported float64 dtype
|
||||
'cat',
|
||||
'complex',
|
||||
'multinomial',
|
||||
'nn.functional.conv1d',
|
||||
'nn.functional.conv2d',
|
||||
'nn.functional.conv3d',
|
||||
'gather',
|
||||
'scatter',
|
||||
'scatter_add',
|
||||
|
||||
# MPS does not support tensor dimensions > 16
|
||||
'amax',
|
||||
'amin',
|
||||
'aminmax',
|
||||
|
||||
# memory overlapping checks
|
||||
'index_select',
|
||||
|
||||
# unimplemented
|
||||
'logcumsumexp',
|
||||
}
|
||||
|
||||
def addDecorator(op, d) -> None:
|
||||
op.decorators = list(op.decorators) if op.decorators is not None else []
|
||||
op.decorators.append(d)
|
||||
|
||||
for op in ops:
|
||||
if op.error_inputs_func is None:
|
||||
continue
|
||||
key = op.name + op.variant_test_name
|
||||
if key in XFAILLIST:
|
||||
addDecorator(op, DecorateInfo(unittest.expectedFailure))
|
||||
yield op
|
||||
|
||||
# Same logic as test_cuda.py
|
||||
if not torch.backends.mps.is_available():
|
||||
|
|
@ -12863,7 +11989,12 @@ class TestConsistency(TestCaseMPS):
|
|||
class TestErrorInputs(TestCase):
|
||||
_ignore_not_implemented_error = True
|
||||
|
||||
@ops(mps_ops_error_inputs_modifier(test_error_inputs_op_db), dtypes=OpDTypes.none)
|
||||
@ops(
|
||||
mps_ops_error_inputs_modifier(
|
||||
[op for op in test_error_inputs_op_db if op.error_inputs_func is not None]
|
||||
),
|
||||
dtypes=OpDTypes.none
|
||||
)
|
||||
def test_error_inputs(self, device, op):
|
||||
self.assertEqual(device, "mps:0")
|
||||
|
||||
|
|
|
|||
1071
torch/testing/_internal/common_mps.py
Normal file
1071
torch/testing/_internal/common_mps.py
Normal file
File diff suppressed because it is too large
Load Diff
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Reference in New Issue
Block a user