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Fix code descriptions in the test package. (#148145)

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The parameter and function description have something wrong and make them correct.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/148145
Approved by: https://github.com/janeyx99
This commit is contained in:
Afanti 2025-03-04 19:14:37 +00:00 committed by PyTorch MergeBot
parent e8900fbe4f
commit c219c5ca38
3 changed files with 5 additions and 5 deletions
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2
torch/testing/_comparison.py
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@ -953,7 +953,7 @@ class TensorLikePair(Pair):
), ),
) )
# Compressed and plain indices in the CSR / CSC / BSR / BSC sparse formates can be `torch.int32` _or_ # Compressed and plain indices in the CSR / CSC / BSR / BSC sparse formats can be `torch.int32` _or_
# `torch.int64`. While the same dtype is enforced for the compressed and plain indices of a single tensor, it # `torch.int64`. While the same dtype is enforced for the compressed and plain indices of a single tensor, it
# can be different between two tensors. Thus, we need to convert them to the same dtype, or the comparison will # can be different between two tensors. Thus, we need to convert them to the same dtype, or the comparison will
# fail. # fail.

6
torch/testing/_internal/common_modules.py
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@ -222,7 +222,7 @@ class ModuleInfo:
# channels last output # channels last output
train_and_eval_differ=False, # whether the module has differing behavior between train and eval train_and_eval_differ=False, # whether the module has differing behavior between train and eval
module_error_inputs_func=None, # Function to generate module inputs that error module_error_inputs_func=None, # Function to generate module inputs that error
gradcheck_fast_mode=None, # Whether to use the fast implmentation for gradcheck/gradgradcheck. gradcheck_fast_mode=None, # Whether to use the fast implementation for gradcheck/gradgradcheck.
# When set to None, defers to the default value provided by the wrapper # When set to None, defers to the default value provided by the wrapper
# function around gradcheck (testing._internal.common_utils.gradcheck) # function around gradcheck (testing._internal.common_utils.gradcheck)
): ):
@ -3575,7 +3575,7 @@ module_db: list[ModuleInfo] = [
DecorateInfo(skipCUDAIfCudnnVersionLessThan(version=7603), 'TestModule', 'test_memory_format'), DecorateInfo(skipCUDAIfCudnnVersionLessThan(version=7603), 'TestModule', 'test_memory_format'),
# Failure on ROCM for float32 issue #70125 # Failure on ROCM for float32 issue #70125
DecorateInfo(skipCUDAIfRocm, 'TestModule', 'test_memory_format', dtypes=[torch.float32]), DecorateInfo(skipCUDAIfRocm, 'TestModule', 'test_memory_format', dtypes=[torch.float32]),
# Not implmented for chalf on CPU # Not implemented for chalf on CPU
DecorateInfo(unittest.expectedFailure, 'TestModule', 'test_cpu_gpu_parity', DecorateInfo(unittest.expectedFailure, 'TestModule', 'test_cpu_gpu_parity',
dtypes=(torch.chalf,), device_type='cuda'), dtypes=(torch.chalf,), device_type='cuda'),
# See #119108: MPSNDArrayConvolutionA14.mm:3976: failed assertion `destination datatype must be fp32' # See #119108: MPSNDArrayConvolutionA14.mm:3976: failed assertion `destination datatype must be fp32'
@ -3640,7 +3640,7 @@ module_db: list[ModuleInfo] = [
# These fail only on ROCm # These fail only on ROCm
DecorateInfo(unittest.expectedFailure, "TestModule", "test_memory_format", device_type='cuda', DecorateInfo(unittest.expectedFailure, "TestModule", "test_memory_format", device_type='cuda',
dtypes=[torch.complex32, torch.complex64], active_if=TEST_WITH_ROCM), dtypes=[torch.complex32, torch.complex64], active_if=TEST_WITH_ROCM),
# Not implmented for chalf on CPU # Not implemented for chalf on CPU
DecorateInfo(unittest.expectedFailure, 'TestModule', 'test_cpu_gpu_parity', DecorateInfo(unittest.expectedFailure, 'TestModule', 'test_cpu_gpu_parity',
dtypes=(torch.chalf,), device_type='cuda'), dtypes=(torch.chalf,), device_type='cuda'),
), ),

2
torch/testing/_utils.py
View File

@ -45,7 +45,7 @@ def freeze_rng_state():
# In the long run torch.cuda.set_rng_state should probably be # In the long run torch.cuda.set_rng_state should probably be
# an operator. # an operator.
# #
# NB: Mode disable is to avoid running cross-ref tests on thes seeding # NB: Mode disable is to avoid running cross-ref tests on this seeding
with torch.utils._mode_utils.no_dispatch(), torch._C._DisableFuncTorch(): with torch.utils._mode_utils.no_dispatch(), torch._C._DisableFuncTorch():
if torch.cuda.is_available(): if torch.cuda.is_available():
torch.cuda.set_rng_state(cuda_rng_state) # type: ignore[possibly-undefined] torch.cuda.set_rng_state(cuda_rng_state) # type: ignore[possibly-undefined]