fbcode//executorch/backends/vulkan/test

backends/vulkan/test/test_vulkan_delegate_header.py

@@ -101,4 +101,4 @@ def test_to_bytes_same_as_from_bytes(self) -> None:
         header = VulkanDelegateHeader.from_bytes(EXAMPLE_HEADER_DATA)
 
         to_bytes = header.to_bytes()
-        self.assertEquals(EXAMPLE_HEADER_DATA, to_bytes)
+        self.assertEqual(EXAMPLE_HEADER_DATA, to_bytes)

exir/program/_program.py

@@ -80,6 +80,7 @@ def _get_updated_graph_signature(
         arg = (
             old_input_spec.arg
             if isinstance(old_input_spec.arg, ConstantArgument)
+            # pyre-fixme[20]: Argument `class_fqn` expected.
             else type(old_input_spec.arg)(node.name)
         )
         new_input_specs.append(
@@ -104,6 +105,7 @@ def _get_updated_graph_signature(
         arg = (
             old_output_spec.arg
             if isinstance(old_output_spec.arg, ConstantArgument)
+            # pyre-fixme[20]: Argument `class_fqn` expected.
             else type(old_output_spec.arg)(node.name)
         )
         new_output_specs.append(

exir/serde/export_serialize.py

@@ -702,6 +702,7 @@ def serialize_optional_tensor_args(a):
             # serialize/deserialize function.
             custom_obj_name = f"_custom_obj_{len(self.custom_objs)}"
             self.custom_objs[custom_obj_name] = arg
+            # pyre-fixme[20]: Argument `class_fqn` expected.
             return Argument.create(as_custom_obj=CustomObjArgument(custom_obj_name))
         else:
             raise SerializeError(f"Unsupported argument type: {type(arg)}")

exir/sym_util.py

@@ -42,7 +42,7 @@ def eval_upper_bound(maybe_symint: Union[int, torch.SymInt]) -> Optional[int]:
     var_range: ValueRanges = bound_sympy(expr, shape_env.var_to_range)
     upper_bound = var_range.upper
     if isinstance(upper_bound, sympy.Integer):
-        concrete_upper = int(var_range.upper)  # pyre-ignore
+        concrete_upper = int(var_range.upper)
         assert isinstance(
             concrete_upper, int
         ), f"Expect upper bound to be a concrete int but got {concrete_upper}"

exir/tests/models.py

@@ -193,6 +193,7 @@ def __init__(self) -> None:
         super().__init__()
 
     def forward(self, input: Tensor, sections: int, dim: int = 0) -> List[Tensor]:
+        # pyre-fixme[7]: Expected `List[Tensor]` but got `Tuple[Tensor, ...]`.
         return torch.tensor_split(input, sections, dim)