[pytorch] simplify tensor options logic in pybinding codegen (pytorch…

…#46976)

Summary:
Pull Request resolved: pytorch#46976

Technically, it's not semantic preserving, e.g.: emition of
'requires_grad' is no longer gated by 'has_tensor_return' - there is no
guarantee that is_like_or_new_function should all have tensor return.
But the output is identical so there might be some invariant - could
also add assertion to fail loudly when it's broken.

Test Plan: Imported from OSS

Reviewed By: ezyang

Differential Revision: D24589211

Pulled By: ljk53

fbshipit-source-id: 47c7e43b080e4e67a526fde1a8a53aae99df4432

tools/codegen/api/python.py

@@ -515,42 +515,31 @@ def signature(f: NativeFunction, *, method: bool = False) -> PythonSignature:
     has_tensor_return = any(r.type.is_tensor_like() for r in f.func.returns)
 
     name: str = cpp.name(f.func)
-    has_options_arg = has_tensor_options(f)
-
-    is_like_function = name.endswith('_like') or f.category_override == 'like'
-    is_new_function = name.startswith('new_') or f.category_override == 'new'
-    is_factory_function = has_tensor_return and not has_tensor_input_arg \
-        or f.category_override == 'factory'
-    is_like_or_new_function_with_options = \
-        (is_like_function or is_new_function) and has_options_arg
+    is_factory_function = f.category_override == 'factory' or (has_tensor_return and not has_tensor_input_arg)
+    is_like_or_new_function = f.category_override in ('new', 'like') or name.startswith('new_') or name.endswith('_like')
 
     tensor_options_args: List[PythonArgument] = []
-    if is_factory_function or has_options_arg:
+    if is_factory_function or is_like_or_new_function:
         tensor_options_args.append(PythonArgument(
             name='dtype',
             cpp_type_str='const ScalarType &',
             type=BaseType(BaseTy.ScalarType),
             default=_dtype_default_type_hack(name),
-            default_init='self.scalar_type()'
-            if is_like_or_new_function_with_options else None,
+            default_init='self.scalar_type()' if is_like_or_new_function else None,
         ))
-
-    if is_factory_function or is_like_or_new_function_with_options:
         tensor_options_args.append(PythonArgument(
             name='layout',
             cpp_type_str='c10::optional<Layout>',
             type=BaseType(BaseTy.Layout),
             default='torch.strided',
-            default_init='layout_from_backend(self.options().backend())'
-            if is_like_or_new_function_with_options else None,
+            default_init='layout_from_backend(self.options().backend())' if is_like_or_new_function else None,
         ))
         tensor_options_args.append(PythonArgument(
             name='device',
             cpp_type_str='const Device &',
             type=BaseType(BaseTy.Device),
             default='None',
-            default_init='self.device()'
-            if is_like_or_new_function_with_options else None,
+            default_init='self.device()' if is_like_or_new_function else None,
         ))
         tensor_options_args.append(PythonArgument(
             name='pin_memory',
@@ -559,8 +548,6 @@ def signature(f: NativeFunction, *, method: bool = False) -> PythonSignature:
             default='False',
             default_init=None,
         ))
-
-    if has_tensor_return and (is_factory_function or is_like_function or is_new_function):
         tensor_options_args.append(PythonArgument(
             name='requires_grad',
             cpp_type_str='bool',