Merge pull request #217 from Xilinx/bump_to_5325d3e6

[AutoBump] Merge with 5325d3e (1)

python/torch_mlir/extras/fx_importer.py

@@ -27,6 +27,7 @@
     Tuple,
     TYPE_CHECKING,
     Union,
+    Iterable,
 )
 import weakref
 
@@ -1173,7 +1174,7 @@ def return_node_values(self, loc, nodes: List[Node]):
             func_dialect.ReturnOp(operands, loc=loc)
 
     def import_nodes(
-        self, nodes: Sequence[Node], *, skip_placeholders_outputs: bool = False
+        self, nodes: Iterable[Node], *, skip_placeholders_outputs: bool = False
     ):
         with InsertionPoint(self._b):
             loc = Location.unknown()
@@ -1266,7 +1267,7 @@ def _import_symbolic_torch_op(
                 (arg.meta["val"].node.pytype if isinstance(arg, Node) else type(arg))
                 for arg in node.args
             ]
-            is_int = [item == int for item in arg_types]
+            is_int = [item is int for item in arg_types]
             if all(is_int):
                 op_overload = "int"
             elif any(is_int):
@@ -1546,15 +1547,15 @@ def _import_scalar_as_tensor(self, loc: Location, arg: NodeArgument) -> Value:
         ).result
 
     def _import_list_argument(
-        self, loc: Location, arg: NodeArgument, expected_jit_type
+        self, loc: Location, arg: Sequence[NodeArgument], expected_jit_type
     ) -> Value:
         assert (
             isinstance(expected_jit_type, torch.ListType)
             or (
                 isinstance(expected_jit_type, torch.OptionalType)
                 and isinstance(expected_jit_type.getElementType(), torch.ListType)
             )
-            or isinstance(expected_jit_type, NoneType)
+            or (expected_jit_type is None)
         ), f"Unexpected jit type as list argument: {arg} of type {expected_jit_type}"
 
         # parse list type
@@ -1630,7 +1631,7 @@ def _import_default_value(self, loc: Location, arg, expected_jit_type) -> Value:
         with loc:
             return cvt(arg, self, self._cc)
 
-    def _unpack_node_result_types(self, node: torch.fx.Node, schema: FunctionSchema):
+    def _unpack_node_result_types(self, node: torch.fx.Node, schema: FunctionSchema) -> List[IrType]:
         return_count = len(schema.returns)
         if return_count == 1:
             # Unary return directly maps a single meta["val"] and cannot be subscripted.
@@ -1649,7 +1650,6 @@ def _unpack_node_result_types(self, node: torch.fx.Node, schema: FunctionSchema)
             result_types = []
             for v in node.meta["val"]:
                 result_types.append(self._cc.value_info_to_type(v))
-            result_types = tuple(result_types)
         return result_types