[FX][export][dynamo] use tuple instead of list in normalized args_spec (#138212)

Pull Request resolved: https://github.com/pytorch/pytorch/pull/138212
Approved by: https://github.com/jansel

torch/_inductor/pattern_matcher.py

@@ -582,18 +582,25 @@ class _TargetArgsExpr(_TargetExpr):
     def pytree_flatten(
         args: Sequence[Any], kwargs: Mapping[Any, Any]
     ) -> Tuple[Sequence[Any], Union[_SimpleSpec, pytree.TreeSpec]]:
-        def norm_spec(s: pytree.TreeSpec) -> pytree.TreeSpec:
-            if s.type is None:
-                return s
-            mapping = {immutable_list: list, tuple: list, immutable_dict: dict}
-            return pytree.TreeSpec(
-                mapping.get(s.type, s.type),
-                s.context,
-                list(map(norm_spec, s.children_specs)),
-            )
+        type_mapping = {immutable_list: tuple, list: tuple, immutable_dict: dict}
 
-        flat, spec = pytree.tree_flatten([args, kwargs])
-        spec = norm_spec(spec)
+        def convert_type(x: Any) -> Any:
+            cls = type(x)
+            convert_fn = type_mapping.get(cls)
+            if convert_fn is not None:
+                return pytree.tree_map(
+                    convert_type,
+                    convert_fn(x),
+                    is_leaf=lambda x: type(x) in type_mapping,
+                )
+            return x
+
+        normalized_args_tree = pytree.tree_map(
+            convert_type,
+            (args, kwargs),
+            is_leaf=lambda x: type(x) in type_mapping,
+        )
+        flat, spec = pytree.tree_flatten(normalized_args_tree)
         return flat, spec
 
     def __repr__(self) -> str:

torch/onnx/_internal/io_adapter.py

@@ -136,15 +136,35 @@ class OutputAdapter:
 # TODO: make_fx lose stack info https://github.com/pytorch/pytorch/issues/90276
 
 
-def _replace_tuple_with_list(spec: pytree.TreeSpec) -> pytree.TreeSpec:
-    _type = list if spec.type == tuple else spec.type
-    return pytree.TreeSpec(
-        _type, spec.context, list(map(_replace_tuple_with_list, spec.children_specs))
+# TODO(XuehaiPan): Dynamo does not support `dummy_leaf = object()` as a sentinel value in the frame.
+class _DummyLeaf:  # use a class instead.
+    pass
+
+
+def _replace_list_with_tuple(spec: pytree.TreeSpec) -> pytree.TreeSpec:
+    def replace_list_with_tuple(x: Any) -> Any:
+        if type(x) is list:
+            return pytree.tree_map(
+                replace_list_with_tuple,
+                tuple(x),
+                is_leaf=lambda x: type(x) is list,
+            )
+        return x
+
+    dummy_leaf = _DummyLeaf()
+    dummy_tree = pytree.tree_unflatten([dummy_leaf] * spec.num_leaves, spec)
+    dummy_tree = pytree.tree_map(
+        replace_list_with_tuple,
+        dummy_tree,
+        is_leaf=lambda x: type(x) is list,
     )
+    return pytree.tree_structure(dummy_tree)
 
 
-def _open_top_level_list_if_single_element(spec: pytree.TreeSpec) -> pytree.TreeSpec:
-    if spec.type == list and spec.num_children == 1:
+def _open_top_level_sequence_if_single_element(
+    spec: pytree.TreeSpec,
+) -> pytree.TreeSpec:
+    if spec.type in (tuple, list) and spec.num_children == 1:
         return spec.children_specs[0]
     return spec
 
@@ -167,10 +187,10 @@ def _assert_identical_pytree_spec(
     pass_if_any_checks: Sequence[Callable[[], bool]] = [
         lambda: spec1 == spec2,
         # FIXME: Bug in `dynamo.export`. Sometimes outputs returned in 'list' instead of 'tuple'.
-        lambda: _replace_tuple_with_list(spec1) == _replace_tuple_with_list(spec2),
+        lambda: _replace_list_with_tuple(spec1) == _replace_list_with_tuple(spec2),
         # FIXME: Bug in `dynamo.export`. Sometimes single function return is wrapped in list.
-        lambda: _open_top_level_list_if_single_element(spec1) == spec2,
-        lambda: spec1 == _open_top_level_list_if_single_element(spec2),
+        lambda: _open_top_level_sequence_if_single_element(spec1) == spec2,
+        lambda: spec1 == _open_top_level_sequence_if_single_element(spec2),
     ]
 
     if not any(check() for check in pass_if_any_checks):