[BE]: Update Typeguard to TypeIs for better type inference (pytorch#1…

…33814)

Uses TypeIs instead of TypeGuard for better inference. See https://peps.python.org/pep-0742/

Pull Request resolved: pytorch#133814
Approved by: https://github.com/ezyang

setup.py

@@ -1132,7 +1132,7 @@ def main():
         )
     install_requires = [
         "filelock",
-        "typing-extensions>=4.8.0",
+        "typing-extensions>=4.10.0",
         'setuptools ; python_version >= "3.12"',
         'sympy==1.12.1 ; python_version == "3.8"',
         'sympy==1.13.1 ; python_version >= "3.9"',

torch/__init__.py

@@ -34,7 +34,7 @@
     TypeVar as _TypeVar,
     Union as _Union,
 )
-from typing_extensions import ParamSpec as _ParamSpec, TypeGuard as _TypeGuard
+from typing_extensions import ParamSpec as _ParamSpec, TypeIs as _TypeIs
 
 
 if TYPE_CHECKING:
@@ -1002,7 +1002,7 @@ def typename(obj: _Any, /) -> str:
     return f"{module}.{qualname}"
 
 
-def is_tensor(obj: _Any, /) -> _TypeGuard["torch.Tensor"]:
+def is_tensor(obj: _Any, /) -> _TypeIs["torch.Tensor"]:
     r"""Returns True if `obj` is a PyTorch tensor.
 
     Note that this function is simply doing ``isinstance(obj, Tensor)``.
@@ -1022,7 +1022,7 @@ def is_tensor(obj: _Any, /) -> _TypeGuard["torch.Tensor"]:
     return isinstance(obj, torch.Tensor)
 
 
-def is_storage(obj: _Any, /) -> _TypeGuard[_Union["TypedStorage", "UntypedStorage"]]:
+def is_storage(obj: _Any, /) -> _TypeIs[_Union["TypedStorage", "UntypedStorage"]]:
     r"""Returns True if `obj` is a PyTorch storage object.
 
     Args:

torch/_dynamo/utils.py

@@ -53,7 +53,7 @@
     Union,
     ValuesView,
 )
-from typing_extensions import Literal, TypeGuard
+from typing_extensions import Literal, TypeIs
 
 import torch
 import torch._functorch.config
@@ -532,14 +532,14 @@ def clear(self):
 
 
 @overload
-def istype(obj: object, allowed_types: Type[T]) -> TypeGuard[T]:
+def istype(obj: object, allowed_types: Type[T]) -> TypeIs[T]:
     ...
 
 
 @overload
 def istype(
     obj: object, allowed_types: Tuple[Type[List[T]], Type[Tuple[T, ...]]]
-) -> TypeGuard[T]:
+) -> TypeIs[T]:
     ...
 
 

torch/_inductor/pattern_matcher.py

@@ -70,7 +70,7 @@
     TypeVar,
     Union,
 )
-from typing_extensions import Self, TypeGuard
+from typing_extensions import Self, TypeIs
 
 import torch
 import torch._guards
@@ -277,10 +277,10 @@ def __bool__(self) -> bool:
 MatchResult = Union[Match, FailedMatch]
 
 
-def is_match(m: MatchResult) -> TypeGuard[Match]:
+def is_match(m: MatchResult) -> TypeIs[Match]:
     """
-    TypeGuards cannot act on `self`. Thus this function exists to let mypy
-    recognize FailedMatch.__bool__ as a TypeGuard.
+    TypeIs cannot act on `self`. Thus this function exists to let mypy
+    recognize FailedMatch.__bool__ as a TypeIs.
     """
     return bool(m)
 

torch/_subclasses/fake_tensor.py

@@ -31,7 +31,7 @@
     TypeVar,
     Union,
 )
-from typing_extensions import Self, TypeGuard
+from typing_extensions import Self, TypeIs
 from weakref import ReferenceType
 
 import torch
@@ -168,7 +168,7 @@ def get_plain_tensors(subclass: Tensor) -> List[Tensor]:
     return plain_tensors
 
 
-def is_fake(x: object) -> TypeGuard[Tensor]:
+def is_fake(x: object) -> TypeIs[Tensor]:
     if isinstance(x, FakeTensor):
         return True
     if is_traceable_wrapper_subclass(x):
@@ -1213,7 +1213,7 @@ def reset_nt_tensor_id_counter(self) -> None:
     # In this case, it's insufficient to test only one FakeTensor: you need
     # to distinguish between our fake tensor and other fake tensors.  That's
     # what this function does.
-    def is_our_fake(self, t: object) -> TypeGuard[FakeTensor]:
+    def is_our_fake(self, t: object) -> TypeIs[FakeTensor]:
         return isinstance(t, FakeTensor) and t.fake_mode is self
 
     # If we should avoid device init. This changes the behavior of various APIs:

torch/masked/maskedtensor/core.py

@@ -3,7 +3,7 @@
 
 import warnings
 from typing import Any
-from typing_extensions import TypeGuard
+from typing_extensions import TypeIs
 
 import torch
 from torch.overrides import get_default_nowrap_functions
@@ -15,7 +15,7 @@
 ]
 
 
-def is_masked_tensor(obj: Any, /) -> TypeGuard["MaskedTensor"]:
+def is_masked_tensor(obj: Any, /) -> TypeIs["MaskedTensor"]:
     r"""Returns True if the input is a MaskedTensor, else False
 
     Args:

torch/nn/parameter.pyi

@@ -1,5 +1,5 @@
 # mypy: allow-untyped-defs
-from typing_extensions import TypeGuard
+from typing_extensions import TypeIs
 
 from torch import device, dtype, Tensor
 
@@ -8,7 +8,7 @@ class Parameter(Tensor):
 
 def is_lazy(
     param: Tensor,
-) -> TypeGuard[UninitializedParameter | UninitializedBuffer]: ...
+) -> TypeIs[UninitializedParameter | UninitializedBuffer]: ...
 
 class UninitializedParameter(Tensor):
     def __init__(self, data: Tensor = ..., requires_grad: bool = ...) -> None: ...

torch/serialization.py

@@ -27,7 +27,7 @@
     Type,
     Union,
 )
-from typing_extensions import TypeAlias, TypeGuard  # Python 3.10+
+from typing_extensions import TypeAlias, TypeIs
 
 import torch
 import torch._weights_only_unpickler as _weights_only_unpickler
@@ -549,7 +549,7 @@ def storage_to_tensor_type(storage):
     return getattr(module, storage_type.__name__.replace("Storage", "Tensor"))
 
 
-def _is_path(name_or_buffer) -> TypeGuard[Union[str, os.PathLike]]:
+def _is_path(name_or_buffer) -> TypeIs[Union[str, os.PathLike]]:
     return isinstance(name_or_buffer, (str, os.PathLike))
 
 

torch/utils/_python_dispatch.py

@@ -4,7 +4,7 @@
 import warnings
 from dataclasses import dataclass
 from typing import Any, Dict, List, Optional, Set, Union, Protocol, Tuple, Sequence, overload, Deque
-from typing_extensions import TypeGuard
+from typing_extensions import TypeIs
 from collections import deque
 
 import torch
@@ -354,7 +354,7 @@ def to(
 
 
 
-def is_traceable_wrapper_subclass(t: object) -> TypeGuard[TensorWithFlatten]:
+def is_traceable_wrapper_subclass(t: object) -> TypeIs[TensorWithFlatten]:
     """
     Returns whether or not a tensor subclass that implements __torch_dispatch__
     is 'traceable' with torch.compile.