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Fix error, remove file from pyrefly checking (#165094)

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Reported issue with formatting and parsing.

Removing suppressions and avoiding this file in future type checking until we can get a more complete fix in .

Pull Request resolved: https://github.com/pytorch/pytorch/pull/165094
Approved by: https://github.com/albanD
This commit is contained in:
Maggie Moss 2025-10-10 04:34:47 +00:00 committed by PyTorch MergeBot
parent 7ab00c7c17
commit 48fe858fef
2 changed files with 1 additions and 15 deletions
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1
pyrefly.toml
View File

@ -33,6 +33,7 @@ project-excludes = [
"torch/_export/utils.py", "torch/_export/utils.py",
"torch/fx/experimental/unification/multipledispatch/__init__.py", "torch/fx/experimental/unification/multipledispatch/__init__.py",
"torch/nn/modules/__init__.py", "torch/nn/modules/__init__.py",
"torch/nn/modules/rnn.py", # only remove when parsing errors are fixed
"torch/_inductor/codecache.py", "torch/_inductor/codecache.py",
"torch/distributed/elastic/metrics/__init__.py", "torch/distributed/elastic/metrics/__init__.py",
# ==== # ====

15
torch/nn/modules/rnn.py
View File

@ -111,7 +111,6 @@ class RNNBase(Module):
if ( if (
not isinstance(dropout, numbers.Number) not isinstance(dropout, numbers.Number)
# pyrefly: ignore # unsupported-operation
or not 0 <= dropout <= 1 or not 0 <= dropout <= 1
or isinstance(dropout, bool) or isinstance(dropout, bool)
): ):
@ -120,7 +119,6 @@ class RNNBase(Module):
"representing the probability of an element being " "representing the probability of an element being "
"zeroed" "zeroed"
) )
# pyrefly: ignore # unsupported-operation
if dropout > 0 and num_layers == 1: if dropout > 0 and num_layers == 1:
warnings.warn( warnings.warn(
"dropout option adds dropout after all but last " "dropout option adds dropout after all but last "
@ -641,12 +639,10 @@ class RNN(RNNBase):
@overload @overload
@torch._jit_internal._overload_method # noqa: F811 @torch._jit_internal._overload_method # noqa: F811
# pyrefly: ignore # bad-override
def forward( def forward(
self, self,
input: Tensor, input: Tensor,
hx: Optional[Tensor] = None, hx: Optional[Tensor] = None,
# pyrefly: ignore # bad-return
) -> tuple[Tensor, Tensor]: ) -> tuple[Tensor, Tensor]:
pass pass
@ -656,7 +652,6 @@ class RNN(RNNBase):
self, self,
input: PackedSequence, input: PackedSequence,
hx: Optional[Tensor] = None, hx: Optional[Tensor] = None,
# pyrefly: ignore # bad-return
) -> tuple[PackedSequence, Tensor]: ) -> tuple[PackedSequence, Tensor]:
pass pass
@ -782,7 +777,6 @@ class RNN(RNNBase):
if isinstance(orig_input, PackedSequence): if isinstance(orig_input, PackedSequence):
output_packed = PackedSequence( output_packed = PackedSequence(
output, output,
# pyrefly: ignore # bad-argument-type
batch_sizes, batch_sizes,
sorted_indices, sorted_indices,
unsorted_indices, unsorted_indices,
@ -1009,7 +1003,6 @@ class LSTM(RNNBase):
# In the future, we should prevent mypy from applying contravariance rules here. # In the future, we should prevent mypy from applying contravariance rules here.
# See torch/nn/modules/module.py::_forward_unimplemented # See torch/nn/modules/module.py::_forward_unimplemented
# pyrefly: ignore # bad-override
def check_forward_args( def check_forward_args(
self, self,
input: Tensor, input: Tensor,
@ -1043,12 +1036,10 @@ class LSTM(RNNBase):
# Same as above, see torch/nn/modules/module.py::_forward_unimplemented # Same as above, see torch/nn/modules/module.py::_forward_unimplemented
@overload # type: ignore[override] @overload # type: ignore[override]
@torch._jit_internal._overload_method # noqa: F811 @torch._jit_internal._overload_method # noqa: F811
# pyrefly: ignore # bad-override
def forward( def forward(
self, self,
input: Tensor, input: Tensor,
hx: Optional[tuple[Tensor, Tensor]] = None, hx: Optional[tuple[Tensor, Tensor]] = None,
# pyrefly: ignore # bad-return
) -> tuple[Tensor, tuple[Tensor, Tensor]]: # noqa: F811 ) -> tuple[Tensor, tuple[Tensor, Tensor]]: # noqa: F811
pass pass
@ -1059,7 +1050,6 @@ class LSTM(RNNBase):
self, self,
input: PackedSequence, input: PackedSequence,
hx: Optional[tuple[Tensor, Tensor]] = None, hx: Optional[tuple[Tensor, Tensor]] = None,
# pyrefly: ignore # bad-return
) -> tuple[PackedSequence, tuple[Tensor, Tensor]]: # noqa: F811 ) -> tuple[PackedSequence, tuple[Tensor, Tensor]]: # noqa: F811
pass pass
@ -1174,7 +1164,6 @@ class LSTM(RNNBase):
if isinstance(orig_input, PackedSequence): if isinstance(orig_input, PackedSequence):
output_packed = PackedSequence( output_packed = PackedSequence(
output, output,
# pyrefly: ignore # bad-argument-type
batch_sizes, batch_sizes,
sorted_indices, sorted_indices,
unsorted_indices, unsorted_indices,
@ -1343,12 +1332,10 @@ class GRU(RNNBase):
@overload # type: ignore[override] @overload # type: ignore[override]
@torch._jit_internal._overload_method # noqa: F811 @torch._jit_internal._overload_method # noqa: F811
# pyrefly: ignore # bad-override
def forward( def forward(
self, self,
input: Tensor, input: Tensor,
hx: Optional[Tensor] = None, hx: Optional[Tensor] = None,
# pyrefly: ignore # bad-return
) -> tuple[Tensor, Tensor]: # noqa: F811 ) -> tuple[Tensor, Tensor]: # noqa: F811
pass pass
@ -1358,7 +1345,6 @@ class GRU(RNNBase):
self, self,
input: PackedSequence, input: PackedSequence,
hx: Optional[Tensor] = None, hx: Optional[Tensor] = None,
# pyrefly: ignore # bad-return
) -> tuple[PackedSequence, Tensor]: # noqa: F811 ) -> tuple[PackedSequence, Tensor]: # noqa: F811
pass pass
@ -1453,7 +1439,6 @@ class GRU(RNNBase):
if isinstance(orig_input, PackedSequence): if isinstance(orig_input, PackedSequence):
output_packed = PackedSequence( output_packed = PackedSequence(
output, output,
# pyrefly: ignore # bad-argument-type
batch_sizes, batch_sizes,
sorted_indices, sorted_indices,
unsorted_indices, unsorted_indices,