Fix mypy in utilities.device_dtype_mixin (#8127)

pytorch_lightning/utilities/device_dtype_mixin.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Optional, Union
+from typing import Any, Optional, Union
 
 import torch
 from torch.nn import Module
@@ -21,9 +21,9 @@ from torch.nn import Module
 class DeviceDtypeModuleMixin(Module):
     __jit_unused_properties__ = ['device', 'dtype']
 
-    def __init__(self):
+    def __init__(self) -> None:
         super().__init__()
-        self._dtype = torch.get_default_dtype()
+        self._dtype: Union[str, torch.dtype] = torch.get_default_dtype()
         self._device = torch.device('cpu')
 
     @property
@@ -31,7 +31,7 @@ class DeviceDtypeModuleMixin(Module):
         return self._dtype
 
     @dtype.setter
-    def dtype(self, new_dtype: Union[str, torch.dtype]):
+    def dtype(self, new_dtype: Union[str, torch.dtype]) -> None:
         # necessary to avoid infinite recursion
         raise RuntimeError('Cannot set the dtype explicitly. Please use module.to(new_dtype).')
 
@@ -45,7 +45,7 @@ class DeviceDtypeModuleMixin(Module):
 
         return device
 
-    def to(self, *args, **kwargs) -> Module:
+    def to(self, *args: Any, **kwargs: Any) -> 'DeviceDtypeModuleMixin':
         """Moves and/or casts the parameters and buffers.
 
         This can be called as
@@ -108,7 +108,7 @@ class DeviceDtypeModuleMixin(Module):
         self.__update_properties(device=out[0], dtype=out[1])
         return super().to(*args, **kwargs)
 
-    def cuda(self, device: Optional[Union[torch.device, int]] = None) -> Module:
+    def cuda(self, device: Optional[Union[torch.device, int]] = None) -> 'DeviceDtypeModuleMixin':
         """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
@@ -121,11 +121,13 @@ class DeviceDtypeModuleMixin(Module):
         Returns:
             Module: self
         """
-        property_device = device if isinstance(device, torch.device) else torch.device('cuda', index=device)
+        property_device = (
+            device if isinstance(device, torch.device) else torch.device('cuda', index=device)  # type: ignore
+        )  # mypy expects `device` for `index` to be int, while `Optional[int]` is okay => ignore typing for now
         self.__update_properties(device=property_device)
         return super().cuda(device=device)
 
-    def cpu(self) -> Module:
+    def cpu(self) -> 'DeviceDtypeModuleMixin':
         """Moves all model parameters and buffers to the CPU.
 
         Returns:
@@ -134,7 +136,7 @@ class DeviceDtypeModuleMixin(Module):
         self.__update_properties(device=torch.device('cpu'))
         return super().cpu()
 
-    def type(self, dst_type: Union[str, torch.dtype]) -> Module:
+    def type(self, dst_type: Union[str, torch.dtype]) -> 'DeviceDtypeModuleMixin':
         """Casts all parameters and buffers to :attr:`dst_type`.
 
         Arguments:
@@ -146,8 +148,8 @@ class DeviceDtypeModuleMixin(Module):
         self.__update_properties(dtype=dst_type)
         return super().type(dst_type=dst_type)
 
-    def float(self) -> Module:
-        """Casts all floating point parameters and buffers to float datatype.
+    def float(self) -> 'DeviceDtypeModuleMixin':
+        """Casts all floating point parameters and buffers to ``float`` datatype.
 
         Returns:
             Module: self
@@ -155,7 +157,7 @@ class DeviceDtypeModuleMixin(Module):
         self.__update_properties(dtype=torch.float)
         return super().float()
 
-    def double(self) -> Module:
+    def double(self) -> 'DeviceDtypeModuleMixin':
         """Casts all floating point parameters and buffers to ``double`` datatype.
 
         Returns:
@@ -164,7 +166,7 @@ class DeviceDtypeModuleMixin(Module):
         self.__update_properties(dtype=torch.double)
         return super().double()
 
-    def half(self) -> Module:
+    def half(self) -> 'DeviceDtypeModuleMixin':
         """Casts all floating point parameters and buffers to ``half`` datatype.
 
         Returns:
@@ -173,9 +175,11 @@ class DeviceDtypeModuleMixin(Module):
         self.__update_properties(dtype=torch.half)
         return super().half()
 
-    def __update_properties(self, device: Optional[torch.device] = None, dtype: Optional[torch.dtype] = None):
+    def __update_properties(
+        self, device: Optional[torch.device] = None, dtype: Optional[Union[str, torch.dtype]] = None
+    ) -> None:
 
-        def apply_fn(module):
+        def apply_fn(module: Union['DeviceDtypeModuleMixin', Module]) -> None:
             if not isinstance(module, DeviceDtypeModuleMixin):
                 return
             if device is not None:

setup.cfg

@@ -183,6 +183,8 @@ ignore_errors = True
 ignore_errors = True
 [mypy-pytorch_lightning.utilities.cli]
 ignore_errors = False
+[mypy-pytorch_lightning.utilities.device_dtype_mixin]
+ignore_errors = False
 [mypy-pytorch_lightning.utilities.device_parser]
 ignore_errors = False
 [mypy-pytorch_lightning.utilities.parsing]