Use strict to toggle strict options in MYPYSTRICT (pytorch#118479)

As we force a specific version of mypy, it's OK to use the agglomerated flag.

Signed-off-by: Edward Z. Yang <[email protected]>
Pull Request resolved: pytorch#118479
Approved by: https://github.com/Skylion007, https://github.com/albanD
ghstack dependencies: pytorch#118414, pytorch#118418, pytorch#118432, pytorch#118467, pytorch#118468, pytorch#118469, pytorch#118475

Author: ezyang

benchmarks/instruction_counts/core/expand.py

@@ -79,7 +79,7 @@ def _generate_torchscript_file(model_src: str, name: str) -> Optional[str]:
     assert isinstance(
         jit_model, (torch.jit.ScriptFunction, torch.jit.ScriptModule)
     ), f"Expected ScriptFunction or ScriptModule, got: {type(jit_model)}"
-    jit_model.save(artifact_path)
+    jit_model.save(artifact_path)  # type: ignore[call-arg]
 
     # Cleanup now that we have the actual serialized model.
     os.remove(module_path)

mypy-strict.ini

@@ -17,21 +17,8 @@ show_column_numbers = True
 warn_no_return = True
 disallow_any_unimported = True
 
-# Across versions of mypy, the flags toggled by --strict vary.  To ensure
-# we have reproducible type check, we instead manually specify the flags
-warn_unused_configs = True
-disallow_any_generics = True
-disallow_subclassing_any = True
-disallow_untyped_calls = True
-disallow_untyped_defs = True
-disallow_incomplete_defs = True
-check_untyped_defs = True
-disallow_untyped_decorators = True
-no_implicit_optional = True
-warn_redundant_casts = True
-warn_return_any = True
+strict = True
 implicit_reexport = False
-strict_equality = True
 
 # do not reenable this:
 # https://github.com/pytorch/pytorch/pull/60006#issuecomment-866130657

tools/update_masked_docs.py

@@ -41,7 +41,7 @@ def main() -> None:
 
     for func_name in sorted(torch.masked._ops.__all__):
         func = getattr(torch.masked._ops, func_name)
-        func_doc = torch.masked._generate_docstring(func)
+        func_doc = torch.masked._generate_docstring(func)  # type: ignore[no-untyped-call, attr-defined]
         _new_content.append(f'{func_name}_docstring = """{func_doc}"""\n')
 
     new_content = "\n".join(_new_content)

torch/utils/_cxx_pytree.py

@@ -29,7 +29,7 @@
 
 import torch
 
-if torch._running_with_deploy():
+if torch._running_with_deploy():  # type: ignore[no-untyped-call]
     raise ImportError("C++ pytree utilities do not work with torch::deploy.")
 
 import optree

torch/utils/benchmark/utils/timer.py

@@ -13,7 +13,7 @@
 __all__ = ["Timer", "timer", "Language"]
 
 
-if torch.backends.cuda.is_built() and torch.cuda.is_available():
+if torch.backends.cuda.is_built() and torch.cuda.is_available():  # type: ignore[no-untyped-call]
     def timer() -> float:
         torch.cuda.synchronize()
         return timeit.default_timer()

torch/utils/benchmark/utils/valgrind_wrapper/timer_interface.py

@@ -465,7 +465,7 @@ def construct(self) -> str:
                 path = os.path.join(self._data_dir, f"{name}.pt")
                 load_lines.append(f"{name} = torch.jit.load({repr(path)})")
                 with open(path, "wb") as f:
-                    torch.jit.save(wrapped_value.value, f)
+                    torch.jit.save(wrapped_value.value, f)  # type: ignore[no-untyped-call]
 
             else:
                 raise NotImplementedError(
@@ -502,7 +502,7 @@ def __init__(self) -> None:
                 ).returncode
 
         self._build_type: Optional[str] = None
-        build_search = re.search("BUILD_TYPE=(.+),", torch.__config__.show())
+        build_search = re.search("BUILD_TYPE=(.+),", torch.__config__.show())  # type: ignore[no-untyped-call]
         if build_search is not None:
             self._build_type = build_search.groups()[0].split(",")[0]
 

torchgen/decompositions/gen_jit_decompositions.py

@@ -55,15 +55,15 @@
 
 def gen_serialized_decompisitions() -> str:
     return "\n".join(
-        [scripted_func.code for scripted_func in decomposition_table.values()]
+        [scripted_func.code for scripted_func in decomposition_table.values()]  # type: ignore[misc]
     )
 
 
 def gen_decomposition_mappings() -> str:
     decomposition_mappings = []
     for schema, scripted_func in decomposition_table.items():
         decomposition_mappings.append(
-            '    {"' + schema + '", "' + scripted_func.name + '"},'
+            '    {"' + schema + '", "' + scripted_func.name + '"},'  # type: ignore[operator]
         )
     return "\n".join(decomposition_mappings)
 

torchgen/fuse_attention_patterns/gen_attention_patterns.py

@@ -89,7 +89,7 @@ def serialize_functions() -> None:
     for (
         key,
         kwargs,
-    ) in _get_sfdp_patterns():
+    ) in _get_sfdp_patterns():  # type: ignore[no-untyped-call]
         pattern_name = kwargs["search_fn"].__name__
         gen_kwargs = {
             key: kwargs[key]
@@ -134,5 +134,5 @@ def serialize_functions() -> None:
 
 
 if __name__ == "__main__":
-    with torch._subclasses.FakeTensorMode():
+    with torch._subclasses.FakeTensorMode():  # type: ignore[no-untyped-call]
         serialize_functions()