[BE][Easy] improve submodule discovery for torch.ao type annotations (

pytorch#144680)

Pull Request resolved: pytorch#144680
Approved by: https://github.com/Skylion007

torch/ao/__init__.py

@@ -1,17 +1,29 @@
-# mypy: allow-untyped-defs
 # torch.ao is a package with a lot of interdependencies.
 # We will use lazy import to avoid cyclic dependencies here.
 
+from typing import TYPE_CHECKING as _TYPE_CHECKING
+
+
+if _TYPE_CHECKING:
+    from types import ModuleType
+
+    from torch.ao import (  # noqa: TC004
+        nn as nn,
+        ns as ns,
+        pruning as pruning,
+        quantization as quantization,
+    )
+
 
 __all__ = [
     "nn",
     "ns",
-    "quantization",
     "pruning",
+    "quantization",
 ]
 
 
-def __getattr__(name):
+def __getattr__(name: str) -> "ModuleType":
     if name in __all__:
         import importlib
 

torch/ao/nn/__init__.py

@@ -1,10 +1,21 @@
-# mypy: allow-untyped-defs
 # We are exposing all subpackages to the end-user.
 # Because of possible inter-dependency, we want to avoid
 # the cyclic imports, thus implementing lazy version
 # as per https://peps.python.org/pep-0562/
 
-import importlib
+from typing import TYPE_CHECKING as _TYPE_CHECKING
+
+
+if _TYPE_CHECKING:
+    from types import ModuleType
+
+    from torch.ao.nn import (  # noqa: TC004
+        intrinsic as intrinsic,
+        qat as qat,
+        quantizable as quantizable,
+        quantized as quantized,
+        sparse as sparse,
+    )
 
 
 __all__ = [
@@ -16,7 +27,9 @@
 ]
 
 
-def __getattr__(name):
+def __getattr__(name: str) -> "ModuleType":
     if name in __all__:
+        import importlib
+
         return importlib.import_module("." + name, __name__)
     raise AttributeError(f"module {__name__!r} has no attribute {name!r}")

torch/ao/nn/quantized/modules/rnn.py

@@ -49,7 +49,7 @@ def from_float(cls, *args, **kwargs):
 
     @classmethod
     def from_observed(cls, other):
-        assert isinstance(other, cls._FLOAT_MODULE)
+        assert isinstance(other, cls._FLOAT_MODULE)  # type: ignore[has-type]
         converted = torch.ao.quantization.convert(
             other, inplace=False, remove_qconfig=True
         )