Fix mypy errors attributed to pytorch_lightning.core.mixins.device_dtype_mixin

pyproject.toml

@@ -53,7 +53,6 @@ module = [
     "pytorch_lightning.callbacks.stochastic_weight_avg",
     "pytorch_lightning.core.datamodule",
     "pytorch_lightning.core.decorators",
-    "pytorch_lightning.core.mixins.device_dtype_mixin",
     "pytorch_lightning.core.module",
     "pytorch_lightning.core.saving",
     "pytorch_lightning.demos.boring_classes",

src/pytorch_lightning/core/mixins/device_dtype_mixin.py

@@ -16,16 +16,7 @@
 
 import torch
 from torch.nn import Module
-
-try:
-    from typing_extensions import Self
-except ImportError:
-    # workaround for Python 3.7.
-    # see https://www.python.org/dev/peps/pep-0673/
-    from typing import TypeVar
-
-    Self = TypeVar("TDeviceDtypeModuleMixin", bound="DeviceDtypeModuleMixin")
-
+from typing_extensions import Self
 
 import pytorch_lightning as pl
 
@@ -57,7 +48,7 @@ def device(self) -> Union[str, torch.device]:
 
         return device
 
-    def to(self, *args: Any, **kwargs: Any) -> Self:
+    def to(self, *args: Any, **kwargs: Any) -> Self:  # type: ignore[valid-type]
         """Moves and/or casts the parameters and buffers.
 
         This can be called as
@@ -121,7 +112,7 @@ def to(self, *args: Any, **kwargs: Any) -> Self:
         self.__update_properties(device=out[0], dtype=out[1])
         return super().to(*args, **kwargs)
 
-    def cuda(self, device: Optional[Union[torch.device, int]] = None) -> Self:
+    def cuda(self, device: Optional[Union[torch.device, int]] = None) -> Self:  # type: ignore[valid-type]
         """Moves all model parameters and buffers to the GPU. This also makes associated parameters and buffers
         different objects. So it should be called before constructing optimizer if the module will live on GPU
         while being optimized.
@@ -134,11 +125,12 @@ def cuda(self, device: Optional[Union[torch.device, int]] = None) -> Self:
             Module: self
         """
         if device is None or isinstance(device, int):
+            assert isinstance(device, (int, type(None)))
             device = torch.device("cuda", index=device)
         self.__update_properties(device=device)
         return super().cuda(device=device)
 
-    def cpu(self) -> Self:
+    def cpu(self) -> Self:  # type: ignore[valid-type]
         """Moves all model parameters and buffers to the CPU.
 
         Returns:
@@ -147,7 +139,7 @@ def cpu(self) -> Self:
         self.__update_properties(device=torch.device("cpu"))
         return super().cpu()
 
-    def type(self, dst_type: Union[str, torch.dtype]) -> Self:
+    def type(self, dst_type: Union[str, torch.dtype]) -> Self:  # type: ignore[valid-type]
         """Casts all parameters and buffers to :attr:`dst_type`.
 
         Arguments:
@@ -159,7 +151,7 @@ def type(self, dst_type: Union[str, torch.dtype]) -> Self:
         self.__update_properties(dtype=dst_type)
         return super().type(dst_type=dst_type)
 
-    def float(self) -> Self:
+    def float(self) -> Self:  # type: ignore[valid-type]
         """Casts all floating point parameters and buffers to ``float`` datatype.
 
         Returns:
@@ -168,7 +160,7 @@ def float(self) -> Self:
         self.__update_properties(dtype=torch.float)
         return super().float()
 
-    def double(self) -> Self:
+    def double(self) -> Self:  # type: ignore[valid-type]
         """Casts all floating point parameters and buffers to ``double`` datatype.
 
         Returns:
@@ -177,7 +169,7 @@ def double(self) -> Self:
         self.__update_properties(dtype=torch.double)
         return super().double()
 
-    def half(self) -> Self:
+    def half(self) -> Self:  # type: ignore[valid-type]
         """Casts all floating point parameters and buffers to ``half`` datatype.
 
         Returns: