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Validate input types for torch.nn.Linear and torch.nn.Bilinear (#135596)

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Adding validation checks to check the input types and display better error messages for the same.
Fixes https://github.com/pytorch/pytorch/issues/135463

Pull Request resolved: https://github.com/pytorch/pytorch/pull/135596
Approved by: https://github.com/malfet
This commit is contained in:
Sanskar Modi
2024-09-12 21:28:37 +00:00
committed by PyTorch MergeBot
parent b897ab0540
commit e157ce3ebb
2 changed files with 82 additions and 0 deletions
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20
torch/nn/modules/linear.py
View File

@ -98,6 +98,13 @@ class Linear(Module):
device=None,
dtype=None,
) -> None:
# validation checks for input types
if not isinstance(in_features, int):
raise TypeError(f"Expected int for in_features but got {type(in_features)}")
if not isinstance(out_features, int):
raise TypeError(
f"Expected int for out_features but got {type(out_features)}"
)
factory_kwargs = {"device": device, "dtype": dtype}
super().__init__()
self.in_features = in_features
@ -200,6 +207,19 @@ class Bilinear(Module):
device=None,
dtype=None,
) -> None:
# validation checks for input types
if not isinstance(in1_features, int):
raise TypeError(
f"Expected int for in1_features but got {type(in1_features)}"
)
if not isinstance(in2_features, int):
raise TypeError(
f"Expected int for in2_features but got {type(in2_features)}"
)
if not isinstance(out_features, int):
raise TypeError(
f"Expected int for out_features but got {type(out_features)}"
)
factory_kwargs = {"device": device, "dtype": dtype}
super().__init__()
self.in1_features = in1_features

62
torch/testing/_internal/common_modules.py
View File

@ -3180,6 +3180,66 @@ rnn_gru_lstm_module_info_decorators = (
# Start of module error inputs functions.
def module_error_inputs_torch_nn_Linear(module_info, device, dtype, requires_grad, training, **kwargs):
make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad)
samples = [
ErrorModuleInput(
ModuleInput(
constructor_input=FunctionInput("10", 20),
forward_input=FunctionInput(make_input(3, 10)),
),
error_on=ModuleErrorEnum.CONSTRUCTION_ERROR,
error_type=TypeError,
error_regex=r"Expected int for in_features but got <class 'str'>"
),
ErrorModuleInput(
ModuleInput(
constructor_input=FunctionInput(10, 20.7),
forward_input=FunctionInput(make_input(3, 10)),
),
error_on=ModuleErrorEnum.CONSTRUCTION_ERROR,
error_type=TypeError,
error_regex=r"Expected int for out_features but got <class 'float'>"
),
]
return samples
def module_error_inputs_torch_nn_Bilinear(module_info, device, dtype, requires_grad, training, **kwargs):
make_input = partial(make_tensor, device=device, dtype=dtype, requires_grad=requires_grad)
samples = [
ErrorModuleInput(
ModuleInput(
constructor_input=FunctionInput("10", 20, 30),
forward_input=FunctionInput(make_input(3, 10), make_input(3, 20)),
),
error_on=ModuleErrorEnum.CONSTRUCTION_ERROR,
error_type=TypeError,
error_regex=r"Expected int for in1_features but got <class 'str'>"
),
ErrorModuleInput(
ModuleInput(
constructor_input=FunctionInput(10, 20.7, 30),
forward_input=FunctionInput(make_input(3, 10), make_input(3, 20)),
),
error_on=ModuleErrorEnum.CONSTRUCTION_ERROR,
error_type=TypeError,
error_regex=r"Expected int for in2_features but got <class 'float'>"
),
ErrorModuleInput(
ModuleInput(
constructor_input=FunctionInput(10, 20, "30"),
forward_input=FunctionInput(make_input(3, 10), make_input(3, 20)),
),
error_on=ModuleErrorEnum.CONSTRUCTION_ERROR,
error_type=TypeError,
error_regex=r"Expected int for out_features but got <class 'str'>"
),
]
return samples
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 = [
@ -3832,12 +3892,14 @@ module_db: List[ModuleInfo] = [
)),
ModuleInfo(torch.nn.Linear,
module_inputs_func=module_inputs_torch_nn_Linear,
module_error_inputs_func=module_error_inputs_torch_nn_Linear,
skips=(
# No channels_last support for Linear currently.
DecorateInfo(unittest.skip("Skipped!"), 'TestModule', 'test_memory_format'),)
),
ModuleInfo(torch.nn.Bilinear,
module_inputs_func=module_inputs_torch_nn_Bilinear,
module_error_inputs_func=module_error_inputs_torch_nn_Bilinear,
decorators=[
DecorateInfo(
toleranceOverride({