[BE][CI][Easy] Run lintrunner on generated .pyi stub files (#150732)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/150732
Approved by: https://github.com/malfet, https://github.com/cyyever, https://github.com/aorenste

.github/scripts/lintrunner.sh

@@ -31,6 +31,9 @@ python3 -m tools.pyi.gen_pyi \
     --deprecated-functions-path "tools/autograd/deprecated.yaml"
 python3 torch/utils/data/datapipes/gen_pyi.py
 
+# Also check generated pyi files
+find torch -name '*.pyi' -exec git add --force -- "{}" +
+
 RC=0
 # Run lintrunner on all files
 if ! lintrunner --force-color --tee-json=lint.json ${ADDITIONAL_LINTRUNNER_ARGS} 2> /dev/null; then
@@ -41,6 +44,9 @@ if ! lintrunner --force-color --tee-json=lint.json ${ADDITIONAL_LINTRUNNER_ARGS}
     RC=1
 fi
 
+# Unstage temporally added pyi files
+find torch -name '*.pyi' -exec git restore --staged -- "{}" +
+
 # Use jq to massage the JSON lint output into GitHub Actions workflow commands.
 jq --raw-output \
     '"::\(if .severity == "advice" or .severity == "disabled" then "warning" else .severity end) file=\(.path),line=\(.line),col=\(.char),title=\(.code) \(.name)::" + (.description | gsub("\\n"; "%0A"))' \

pyproject.toml

@@ -212,6 +212,11 @@ select = [
 "__init__.py" = [
     "F401",
 ]
+"*.pyi" = [
+    "PYI011", # typed-argument-default-in-stub
+    "PYI021", # docstring-in-stub
+    "PYI053", # string-or-bytes-too-long
+]
 "functorch/notebooks/**" = [
     "F401",
 ]

test/test_mps.py

@@ -113,9 +113,9 @@ class MpsMemoryLeakCheck:
         self.caching_allocator_before = torch.mps.current_allocated_memory()
         self.driver_before = torch.mps.driver_allocated_memory()
 
-    def __exit__(self, exec_type, exec_value, traceback):
+    def __exit__(self, exc_type, exc_value, traceback):
         # Don't check for leaks if an exception was thrown
-        if exec_type is not None:
+        if exc_type is not None:
             return
         # Compares caching allocator before/after statistics
         # An increase in allocated memory is a discrepancy indicating a possible memory leak

torch/_C/_VariableFunctions.pyi.in

@@ -1,20 +1,11 @@
 # ${generated_comment}
 # mypy: disable-error-code="type-arg"
 # mypy: allow-untyped-defs
+# ruff: noqa: F401,PYI054
 
-import builtins
+from collections.abc import Sequence
 from types import EllipsisType
-from typing import (
-    Any,
-    Callable,
-    ContextManager,
-    Iterator,
-    Literal,
-    NamedTuple,
-    overload,
-    Sequence,
-    TypeVar,
-)
+from typing import Any, Callable, Literal, overload, TypeVar
 
 import torch
 from torch import (

torch/_C/__init__.pyi.in

@@ -1,8 +1,9 @@
 # ${generated_comment}
 # mypy: disable-error-code="type-arg"
 # mypy: allow-untyped-defs
+# ruff: noqa: F401
 
-import builtins
+from collections.abc import Iterable, Iterator, Sequence
 from enum import Enum, IntEnum
 from pathlib import Path
 from types import EllipsisType
@@ -10,17 +11,13 @@ from typing import (
     Any,
     AnyStr,
     Callable,
-    ContextManager,
     Generic,
     IO,
-    Iterable,
-    Iterator,
     Literal,
     NamedTuple,
     overload,
     Protocol,
     runtime_checkable,
-    Sequence,
     SupportsIndex,
     TypeVar,
 )
@@ -71,15 +68,15 @@ from torch.utils._python_dispatch import TorchDispatchMode
 
 # This module is defined in torch/csrc/Module.cpp
 
-K = TypeVar("K")
-T = TypeVar("T")
-S = TypeVar("S", bound=torch.Tensor)
-P = ParamSpec("P")
-ReturnVal = TypeVar("ReturnVal", covariant=True)  # return value (always covariant)
-_T_co = TypeVar("_T_co", covariant=True)
+K = TypeVar("K")  # noqa: PYI001
+T = TypeVar("T")  # noqa: PYI001
+S = TypeVar("S", bound=torch.Tensor)  # noqa: PYI001
+P = ParamSpec("P")  # noqa: PYI001
+R = TypeVar("R", covariant=True)  # return value (always covariant)  # noqa: PYI001
+T_co = TypeVar("T_co", covariant=True)  # noqa: PYI001
 
 @runtime_checkable
-class _NestedSequence(Protocol[_T_co]):
+class _NestedSequence(Protocol[T_co]):
     """A protocol for representing nested sequences.
 
     References::
@@ -88,10 +85,10 @@ class _NestedSequence(Protocol[_T_co]):
     """
 
     def __len__(self, /) -> _int: ...
-    def __getitem__(self, index: _int, /) -> _T_co | _NestedSequence[_T_co]: ...
+    def __getitem__(self, index: _int, /) -> T_co | _NestedSequence[T_co]: ...
     def __contains__(self, x: object, /) -> _bool: ...
-    def __iter__(self, /) -> Iterator[_T_co | _NestedSequence[_T_co]]: ...
-    def __reversed__(self, /) -> Iterator[_T_co | _NestedSequence[_T_co]]: ...
+    def __iter__(self, /) -> Iterator[T_co | _NestedSequence[T_co]]: ...
+    def __reversed__(self, /) -> Iterator[T_co | _NestedSequence[T_co]]: ...
     def count(self, value: Any, /) -> _int: ...
     def index(self, value: Any, /) -> _int: ...
 
@@ -146,7 +143,7 @@ class Stream:
     def record_event(self, event: Event | None = None) -> Event: ...
     def __hash__(self) -> _int: ...
     def __eq__(self, other: object) -> _bool: ...
-    def __enter__(self) -> Stream: ...
+    def __enter__(self) -> Self: ...
     def __exit__(self, exc_type, exc_val, exc_tb) -> None: ...
 
 # Defined in torch/csrc/Event.cpp
@@ -321,14 +318,14 @@ def _set_print_stack_traces_on_fatal_signal(print: _bool) -> None: ...
 def unify_type_list(types: list[JitType]) -> JitType: ...
 def _freeze_module(
     module: ScriptModule,
-    preserved_attrs: list[str] = [],
+    preserved_attrs: list[str] = ...,
     freeze_interfaces: _bool = True,
     preserveParameters: _bool = True,
 ) -> ScriptModule: ...
 def _jit_pass_optimize_frozen_graph(Graph, optimize_numerics: _bool = True) -> None: ...
 def _jit_pass_optimize_for_inference(
     module: torch.jit.ScriptModule,
-    other_methods: list[str] = [],
+    other_methods: list[str] = ...,
 ) -> None: ...
 def _jit_pass_fold_frozen_conv_bn(graph: Graph): ...
 def _jit_pass_fold_frozen_conv_add_or_sub(graph: Graph): ...
@@ -759,7 +756,7 @@ class AliasDb: ...
 
 class _InsertPoint:
     def __enter__(self) -> None: ...
-    def __exit__(self, *args: Any) -> None: ...
+    def __exit__(self, *exc_info: object) -> None: ...
 
 # Defined in torch/csrc/jit/ir/ir.h
 class Use:
@@ -1078,8 +1075,8 @@ class LiteScriptModule:
     def run_method(self, method_name: str, *input): ...
 
 # NOTE: switch to collections.abc.Callable in python 3.9
-class ScriptFunction(Generic[P, ReturnVal]):
-    def __call__(self, *args: P.args, **kwargs: P.kwargs) -> ReturnVal: ...
+class ScriptFunction(Generic[P, R]):
+    def __call__(self, *args: P.args, **kwargs: P.kwargs) -> R: ...
     def save(self, filename: str, _extra_files: dict[str, bytes]) -> None: ...
     def save_to_buffer(self, _extra_files: dict[str, bytes]) -> bytes: ...
     @property
@@ -1092,9 +1089,9 @@ class ScriptFunction(Generic[P, ReturnVal]):
     def qualified_name(self) -> str: ...
 
 # NOTE: switch to collections.abc.Callable in python 3.9
-class ScriptMethod(Generic[P, ReturnVal]):
+class ScriptMethod(Generic[P, R]):
     graph: Graph
-    def __call__(self, *args: P.args, **kwargs: P.kwargs) -> ReturnVal: ...
+    def __call__(self, *args: P.args, **kwargs: P.kwargs) -> R: ...
     @property
     def owner(self) -> ScriptModule: ...
     @property
@@ -1481,9 +1478,7 @@ def _get_function_stack_at(idx: _int) -> Any: ...
 def _len_torch_function_stack() -> _int: ...
 def _set_torch_dispatch_mode(cls: Any) -> None: ...
 def _push_on_torch_dispatch_stack(cls: TorchDispatchMode) -> None: ...
-def _pop_torch_dispatch_stack(
-    mode_key: _TorchDispatchModeKey | None = None,
-) -> Any: ...
+def _pop_torch_dispatch_stack(mode_key: _TorchDispatchModeKey | None = None) -> Any: ...
 def _get_dispatch_mode(mode_key: _TorchDispatchModeKey | None) -> Any: ...
 def _unset_dispatch_mode(mode: _TorchDispatchModeKey) -> TorchDispatchMode | None: ...
 def _set_dispatch_mode(mode: TorchDispatchMode) -> None: ...
@@ -1494,42 +1489,42 @@ def _activate_gpu_trace() -> None: ...
 class _DisableTorchDispatch:
     def __init__(self) -> None: ...
     def __enter__(self): ...
-    def __exit__(self, *args: Any) -> None: ...
+    def __exit__(self, *exc_info: object) -> None: ...
 
 class _EnableTorchFunction:
     def __init__(self) -> None: ...
     def __enter__(self): ...
-    def __exit__(self, *args: Any) -> None: ...
+    def __exit__(self, *exc_info: object) -> None: ...
 
 class _EnablePythonDispatcher:
     def __init__(self) -> None: ...
     def __enter__(self): ...
-    def __exit__(self, *args: Any) -> None: ...
+    def __exit__(self, *exc_info: object) -> None: ...
 
 class _DisablePythonDispatcher:
     def __init__(self) -> None: ...
     def __enter__(self): ...
-    def __exit__(self, *args: Any) -> None: ...
+    def __exit__(self, *exc_info: object) -> None: ...
 
 class _EnablePreDispatch:
     def __init__(self) -> None: ...
     def __enter__(self): ...
-    def __exit__(self, *args: Any) -> None: ...
+    def __exit__(self, *exc_info: object) -> None: ...
 
 class _DisableFuncTorch:
     def __init__(self) -> None: ...
     def __enter__(self): ...
-    def __exit__(self, *args: Any) -> None: ...
+    def __exit__(self, *exc_info: object) -> None: ...
 
 class _DisableAutocast:
     def __init__(self) -> None: ...
     def __enter__(self): ...
-    def __exit__(self, *args: Any) -> None: ...
+    def __exit__(self, *exc_info: object) -> None: ...
 
 class _InferenceMode:
     def __init__(self, enabled: _bool) -> None: ...
     def __enter__(self): ...
-    def __exit__(self, *args: Any) -> None: ...
+    def __exit__(self, *exc_info: object) -> None: ...
 
 def _set_autograd_fallback_mode(mode: str) -> None: ...
 def _get_autograd_fallback_mode() -> str: ...
@@ -1783,32 +1778,32 @@ def _commit_update(a: Tensor) -> None: ...
 class _ExcludeDispatchKeyGuard:
     def __init__(self, keyset: DispatchKeySet) -> None: ...
     def __enter__(self): ...
-    def __exit__(self, *args: Any) -> None: ...
+    def __exit__(self, *exc_info: object) -> None: ...
 
 class _IncludeDispatchKeyGuard:
     def __init__(self, k: DispatchKey) -> None: ...
     def __enter__(self): ...
-    def __exit__(self, *args: Any) -> None: ...
+    def __exit__(self, *exc_info: object) -> None: ...
 
 class _ForceDispatchKeyGuard:
     def __init__(self, include: DispatchKeySet, exclude: DispatchKeySet) -> None: ...
     def __enter__(self): ...
-    def __exit__(self, *args: Any) -> None: ...
+    def __exit__(self, *exc_info: object) -> None: ...
 
 class _PreserveDispatchKeyGuard:
     def __init__(self) -> None: ...
     def __enter__(self): ...
-    def __exit__(self, *args: Any) -> None: ...
+    def __exit__(self, *exc_info: object) -> None: ...
 
 class _AutoDispatchBelowAutograd:
     def __init__(self) -> None: ...
     def __enter__(self): ...
-    def __exit__(self, *args: Any) -> None: ...
+    def __exit__(self, *exc_info: object) -> None: ...
 
 class _AutoDispatchBelowADInplaceOrView:
     def __init__(self) -> None: ...
     def __enter__(self): ...
-    def __exit__(self, *args: Any) -> None: ...
+    def __exit__(self, *exc_info: object) -> None: ...
 
 def _dispatch_print_registrations_for_dispatch_key(dispatch_key: str = "") -> None: ...
 def _dispatch_get_registrations_for_dispatch_key(
@@ -1827,18 +1822,16 @@ class _TorchDispatchModeKey(Enum):
 class _SetExcludeDispatchKeyGuard:
     def __init__(self, k: DispatchKey, enabled: _bool) -> None: ...
     def __enter__(self): ...
-    def __exit__(self, *args: Any) -> None: ...
+    def __exit__(self, *exc_info: object) -> None: ...
 
 # Defined in torch/csrc/utils/schema_info.h
 
 class _SchemaInfo:
     def __init__(self, schema: _int) -> None: ...
-
     @overload
     def is_mutable(self) -> _bool: ...
     @overload
     def is_mutable(self, name: str) -> _bool: ...
-
     def has_argument(self, name: str) -> _bool: ...
 
 # Defined in torch/csrc/utils/init.cpp
@@ -2431,7 +2424,7 @@ def _create_graph_by_tracing(
     strict: Any,
     force_outplace: Any,
     self: Any = None,
-    argument_names: list[str] = [],
+    argument_names: list[str] = ...,
 ) -> tuple[Graph, Stack]: ...
 def _tracer_warn_use_python(): ...
 def _get_tracing_state() -> TracingState: ...
@@ -2458,8 +2451,6 @@ class InferredType:
     def success(self) -> _bool: ...
     def reason(self) -> str: ...
 
-R = TypeVar("R", bound=JitType)
-
 class Type(JitType):
     def str(self) -> _str: ...
     def containedTypes(self) -> list[JitType]: ...
@@ -2565,9 +2556,11 @@ class InterfaceType(JitType):
     def getMethod(self, name: str) -> FunctionSchema | None: ...
     def getMethodNames(self) -> list[str]: ...
 
-class OptionalType(JitType, Generic[R]):
-    def __init__(self, a: JitType) -> None: ...
-    def getElementType(self) -> JitType: ...
+JitTypeT = TypeVar("JitTypeT", bound=JitType)  # noqa: PYI001
+
+class OptionalType(JitType, Generic[JitTypeT]):
+    def __init__(self, a: JitTypeT) -> None: ...
+    def getElementType(self) -> JitTypeT: ...
     @staticmethod
     def ofTensor() -> OptionalType: ...
 
@@ -2681,6 +2674,7 @@ def _fuse_to_static_module(
 # Defined in torch/csrc/fx/node.cpp
 def _fx_map_aggregate(a: Any, fn: Callable[[Any], Any]) -> Any: ...
 def _fx_map_arg(a: Any, fn: Callable[[Any], Any]) -> Any: ...
+
 class _NodeBase:
     _erased: _bool
     _prev: FxNode

torch/_C/_monitor.pyi

@@ -3,7 +3,7 @@
 import datetime
 from enum import Enum
 from types import TracebackType
-from typing import Callable, Optional
+from typing import Callable
 
 class Aggregation(Enum):
     VALUE = ...
@@ -48,9 +48,9 @@ class _WaitCounterTracker:
     def __enter__(self) -> None: ...
     def __exit__(
         self,
-        exec_type: Optional[type[BaseException]] = None,
-        exec_value: Optional[BaseException] = None,
-        traceback: Optional[TracebackType] = None,
+        exc_type: type[BaseException] | None = None,
+        exc_value: BaseException | None = None,
+        traceback: TracebackType | None = None,
     ) -> None: ...
 
 class _WaitCounter:

torch/_C/_nn.pyi.in

@@ -1,7 +1,8 @@
 # ${generated_comment}
 # mypy: disable-error-code="type-arg"
 
-from typing import Literal, overload, Sequence
+from collections.abc import Sequence
+from typing import Literal, overload
 
 from torch import memory_format, Tensor
 from torch.types import _bool, _device, _dtype, _int, _size

torch/_C/_profiler.pyi

@@ -1,5 +1,5 @@
 from enum import Enum
-from typing import Any, Literal, Optional
+from typing import Literal
 from typing_extensions import TypeAlias
 
 from torch._C import device, dtype, layout
@@ -73,7 +73,7 @@ class ProfilerConfig:
         with_flops: bool,
         with_modules: bool,
         experimental_config: _ExperimentalConfig,
-        trace_id: Optional[str] = None,
+        trace_id: str | None = None,
     ) -> None: ...
 
 class _ProfilerEvent:
@@ -243,4 +243,4 @@ class _RecordFunctionFast:
         keyword_values: dict | None = None,
     ) -> None: ...
     def __enter__(self) -> None: ...
-    def __exit__(self, *args: Any) -> None: ...
+    def __exit__(self, *exc_info: object) -> None: ...

torch/_C/return_types.pyi.in

@@ -1,31 +1,11 @@
 # ${generated_comment}
 # mypy: allow-untyped-defs
 
-from typing import (
-    Any,
-    Callable,
-    ContextManager,
-    Final,
-    Iterator,
-    Literal,
-    NamedTuple,
-    NoReturn,
-    overload,
-    Sequence,
-    TypeVar,
-)
+from typing import Final, NoReturn
 from typing_extensions import Self
 
-from torch import (
-    contiguous_format,
-    Generator,
-    inf,
-    memory_format,
-    strided,
-    SymInt,
-    Tensor,
-)
-from torch.types import (
+from torch import SymInt, Tensor
+from torch.types import (  # noqa: F401
     _bool,
     _device,
     _dtype,

torch/nn/functional.pyi.in

@@ -1,7 +1,8 @@
 # ${generated_comment}
 # mypy: allow-untyped-defs
 
-from typing import Any, Callable, Literal, overload, Sequence
+from collections.abc import Sequence
+from typing import Any, Callable, Literal, overload
 from typing_extensions import TypeAlias
 
 from torch import Tensor

torch/testing/_internal/common_utils.py

@@ -2383,7 +2383,7 @@ class CudaNonDefaultStream:
                                      device_type=deviceStream.device_type)
         torch._C._cuda_setDevice(beforeDevice)
 
-    def __exit__(self, exec_type, exec_value, traceback):
+    def __exit__(self, exc_type, exc_value, traceback):
         # After completing CUDA test load previously active streams on all
         # CUDA devices.
         beforeDevice = torch.cuda.current_device()
@@ -2431,9 +2431,9 @@ class CudaMemoryLeakCheck:
             driver_mem_allocated = bytes_total - bytes_free
             self.driver_befores.append(driver_mem_allocated)
 
-    def __exit__(self, exec_type, exec_value, traceback):
+    def __exit__(self, exc_type, exc_value, traceback):
         # Don't check for leaks if an exception was thrown
-        if exec_type is not None:
+        if exc_type is not None:
             return
 
         # Compares caching allocator before/after statistics

torch/utils/data/datapipes/datapipe.pyi.in

@@ -5,19 +5,8 @@
 # Note that, for mypy, .pyi file takes precedent over .py file, such that we must define the interface for other
 # classes/objects here, even though we are not injecting extra code into them at the moment.
 
-from typing import (
-    Any,
-    Callable,
-    Dict,
-    Iterable,
-    Iterator,
-    List,
-    Literal,
-    Optional,
-    Type,
-    TypeVar,
-    Union,
-)
+from collections.abc import Iterable, Iterator
+from typing import Any, Callable, Literal, Optional, TypeVar, Union
 
 from torch.utils.data import Dataset, default_collate, IterableDataset
 from torch.utils.data.datapipes._hook_iterator import _SnapshotState
@@ -27,19 +16,19 @@ _T = TypeVar("_T")
 _T_co = TypeVar("_T_co", covariant=True)
 UNTRACABLE_DATAFRAME_PIPES: Any
 
-class DataChunk(List[_T]):
-    items: List[_T]
+class DataChunk(list[_T]):
+    items: list[_T]
     def __init__(self, items: Iterable[_T]) -> None: ...
     def as_str(self, indent: str = "") -> str: ...
     def __iter__(self) -> Iterator[_T]: ...
     def raw_iterator(self) -> Iterator[_T]: ...
 
 class MapDataPipe(Dataset[_T_co], metaclass=_DataPipeMeta):
-    functions: Dict[str, Callable] = ...
-    reduce_ex_hook: Optional[Callable] = ...
-    getstate_hook: Optional[Callable] = ...
-    str_hook: Optional[Callable] = ...
-    repr_hook: Optional[Callable] = ...
+    functions: dict[str, Callable] = ...
+    reduce_ex_hook: Callable | None = ...
+    getstate_hook: Callable | None = ...
+    str_hook: Callable | None = ...
+    repr_hook: Callable | None = ...
     def __getattr__(self, attribute_name: Any): ...
     @classmethod
     def register_function(cls, function_name: Any, function: Any) -> None: ...
@@ -58,7 +47,7 @@ class MapDataPipe(Dataset[_T_co], metaclass=_DataPipeMeta):
     ${MapDataPipeMethods}
 
 class IterDataPipe(IterableDataset[_T_co], metaclass=_IterDataPipeMeta):
-    functions: Dict[str, Callable] = ...
+    functions: dict[str, Callable] = ...
     reduce_ex_hook: Optional[Callable] = ...
     getstate_hook: Optional[Callable] = ...
     str_hook: Optional[Callable] = ...