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Revert "Add error checking for padding modules (#106147)"

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This reverts commit 0547b6279d6f7249c0e588508c2561589514d3aa.

Reverted https://github.com/pytorch/pytorch/pull/106147 on behalf of https://github.com/jeanschmidt due to sadly it is breaking internal builds, and I can't coordinate a FF due to timezone differences ([comment](https://github.com/pytorch/pytorch/pull/106147#issuecomment-1661870970))
This commit is contained in:
PyTorch MergeBot
2023-08-02 09:37:40 +00:00
parent fdd4b3aaa8
commit d83b887f2a
3 changed files with 4 additions and 168 deletions
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66
torch/testing/_internal/common_modules.py
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@ -2416,60 +2416,6 @@ rnn_gru_lstm_module_info_decorators = (
# Start of module error inputs functions.
def module_error_inputs_torch_nn_Pad1d(module_info, device, dtype, requires_grad, training, **kwargs):
make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad)
is_constant = kwargs.get('is_constant', False)
return [
ErrorModuleInput(
ModuleInput(
constructor_input=FunctionInput(1, 3) if is_constant else FunctionInput(3),
forward_input=FunctionInput(make_input((2, 3, 4, 5))),
),
error_on=ModuleErrorEnum.FORWARD_ERROR,
error_type=ValueError,
error_regex=r"expected 2D or 3D input \(got 4D input\)",
),
]
def module_error_inputs_torch_nn_Pad2d(module_info, device, dtype, requires_grad, training, **kwargs):
make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad)
is_constant = kwargs.get('is_constant', False)
return [
ErrorModuleInput(
ModuleInput(
constructor_input=FunctionInput(1, 3) if is_constant else FunctionInput(3),
forward_input=FunctionInput(make_input((2, 3))),
),
error_on=ModuleErrorEnum.FORWARD_ERROR,
error_type=ValueError,
error_regex=r"expected 3D or 4D input \(got 2D input\)",
),
]
def module_error_inputs_torch_nn_Pad3d(module_info, device, dtype, requires_grad, training, **kwargs):
make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad)
is_constant = kwargs.get('is_constant', False)
return [
ErrorModuleInput(
ModuleInput(
constructor_input=FunctionInput(1, 3) if is_constant else FunctionInput(3),
forward_input=FunctionInput(make_input((2, 3))),
),
error_on=ModuleErrorEnum.FORWARD_ERROR,
error_type=ValueError,
error_regex=r"expected 4D or 5D input \(got 2D input\)",
),
]
def module_error_inputs_torch_nn_RNN_GRU_Cell(module_info, device, dtype, requires_grad, training, **kwargs):
make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad)
samples = [
@ -3300,13 +3246,11 @@ module_db: List[ModuleInfo] = [
decorators=rnn_gru_lstm_module_info_decorators),
ModuleInfo(torch.nn.ReflectionPad1d,
module_inputs_func=module_inputs_torch_nn_ReflectionPad1d,
module_error_inputs_func=module_error_inputs_torch_nn_Pad1d,
skips=(
DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),)
),
ModuleInfo(torch.nn.ReflectionPad2d,
module_inputs_func=module_inputs_torch_nn_ReflectionPad2d,
module_error_inputs_func=module_error_inputs_torch_nn_Pad2d,
gradcheck_nondet_tol=GRADCHECK_NONDET_TOL,
skips=(
DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format',
@ -3317,7 +3261,6 @@ module_db: List[ModuleInfo] = [
),
ModuleInfo(torch.nn.ReflectionPad3d,
module_inputs_func=module_inputs_torch_nn_ReflectionPad3d,
module_error_inputs_func=module_error_inputs_torch_nn_Pad3d,
gradcheck_nondet_tol=GRADCHECK_NONDET_TOL,
skips=(
DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format',
@ -3328,13 +3271,11 @@ module_db: List[ModuleInfo] = [
),
ModuleInfo(torch.nn.ReplicationPad1d,
module_inputs_func=module_inputs_torch_nn_ReplicationPad1d,
module_error_inputs_func=module_error_inputs_torch_nn_Pad1d,
skips=(
DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),)
),
ModuleInfo(torch.nn.ReplicationPad2d,
module_inputs_func=module_inputs_torch_nn_ReplicationPad2d,
module_error_inputs_func=module_error_inputs_torch_nn_Pad2d,
gradcheck_nondet_tol=GRADCHECK_NONDET_TOL,
skips=(
DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format',
@ -3345,7 +3286,6 @@ module_db: List[ModuleInfo] = [
),
ModuleInfo(torch.nn.ReplicationPad3d,
module_inputs_func=module_inputs_torch_nn_ReplicationPad3d,
module_error_inputs_func=module_error_inputs_torch_nn_Pad3d,
gradcheck_nondet_tol=GRADCHECK_NONDET_TOL,
skips=(
DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format',
@ -3363,13 +3303,11 @@ module_db: List[ModuleInfo] = [
),
ModuleInfo(torch.nn.ZeroPad1d,
module_inputs_func=module_inputs_torch_nn_ZeroPad1d,
module_error_inputs_func=module_error_inputs_torch_nn_Pad1d,
skips=(
DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),)
),
ModuleInfo(torch.nn.ZeroPad2d,
module_inputs_func=module_inputs_torch_nn_ZeroPad2d,
module_error_inputs_func=module_error_inputs_torch_nn_Pad2d,
skips=(
DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),
# Fails with channels last test on MPS backend
@ -3377,7 +3315,6 @@ module_db: List[ModuleInfo] = [
),
ModuleInfo(torch.nn.ZeroPad3d,
module_inputs_func=module_inputs_torch_nn_ZeroPad3d,
module_error_inputs_func=module_error_inputs_torch_nn_Pad3d,
skips=(
DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),
# Fails with channels last test on MPS backend
@ -3405,13 +3342,11 @@ module_db: List[ModuleInfo] = [
),
ModuleInfo(torch.nn.ConstantPad1d,
module_inputs_func=module_inputs_torch_nn_ConstantPad1d,
module_error_inputs_func=partial(module_error_inputs_torch_nn_Pad1d, is_constant=True),
skips=(
DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),)
),
ModuleInfo(torch.nn.ConstantPad2d,
module_inputs_func=module_inputs_torch_nn_ConstantPad2d,
module_error_inputs_func=partial(module_error_inputs_torch_nn_Pad2d, is_constant=True),
skips=(
DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),
# Fails with channels last test on MPS backend
@ -3419,7 +3354,6 @@ module_db: List[ModuleInfo] = [
),
ModuleInfo(torch.nn.ConstantPad3d,
module_inputs_func=module_inputs_torch_nn_ConstantPad3d,
module_error_inputs_func=partial(module_error_inputs_torch_nn_Pad3d, is_constant=True),
skips=(
DecorateInfo(skipIfMps, 'TestModule', dtypes=[torch.float64]),
# Fails with channels last test on MPS backend