diff --git a/test/export/test_unflatten.py b/test/export/test_unflatten.py
index 5e1872c249ed..13b234c173e5 100644
--- a/test/export/test_unflatten.py
+++ b/test/export/test_unflatten.py
@@ -1060,6 +1060,27 @@ def forward(self, x, y):
         inp = (torch.randn(3), None)
         self.assertTrue(torch.allclose(unf(*inp), M1()(*inp)))
 
+    def test_unflatten_root_module_type(self) -> None:
+        class M(torch.nn.Module):
+            def forward(self, x: torch.Tensor) -> torch.Tensor:
+                return x + x
+
+        class M1(torch.nn.Module):
+            def __init__(self) -> None:
+                super().__init__()
+                self.m = M()
+
+            def forward(self, x: torch.Tensor) -> torch.Tensor:
+                return self.m(x)
+
+        inp = (torch.randn(3),)
+        ep = torch.export.export(M1(), inp)
+        unf = torch.export.unflatten(ep)
+        self.assertIsNotNone(unf.type_name())
+        self.assertEqual(unf.type_name().split(".")[-1], "M1")
+        self.assertEqual(unf.m.type_name().split(".")[-1], "M")
+        self.assertTrue(torch.allclose(unf(*inp), M1()(*inp)))
+
 
 if __name__ == "__main__":
     run_tests()
diff --git a/torch/export/unflatten.py b/torch/export/unflatten.py
index bdea191190e3..de72c8f505d9 100644
--- a/torch/export/unflatten.py
+++ b/torch/export/unflatten.py
@@ -301,7 +301,7 @@ def get_flat_arg_paths(self) -> list[str]:
         return []
 
 
-class UnflattenedModule(torch.nn.Module):
+class UnflattenedModule(_SubmoduleBase, torch.nn.Module):
     def __init__(
         self,
         export_module: ExportedProgram,
@@ -340,6 +340,7 @@ def _id(obj):
 
         _inplace_buffer_and_input_mutations(export_graph, self.graph_signature)
         _fix_nn_module_stacks(export_graph)
+        self._ty = _root_module_type(export_graph)
 
         self.ivals = _IVals()
         # for any intermediate value of a mutation that is read, track the mutation
@@ -858,6 +859,17 @@ def forward(self, buffer, x):
     output_node.args = ((user_outputs),)
 
 
+def _root_module_type(graph: torch.fx.Graph) -> Optional[str]:
+    for node in graph.nodes:
+        if "nn_module_stack" not in node.meta:
+            continue
+
+        for path, ty in node.meta["nn_module_stack"].values():
+            if not path:
+                return ty
+    return None
+
+
 def _fix_nn_module_stacks(graph):
     # For each nn module stack in the graph, check if the fqns in it represent a stack:
     # 1. Each fqn must be a prefix of the next fqn.