diff --git a/exir/delegate.py b/exir/delegate.py
index de2be14a8e3..77049a8848b 100644
--- a/exir/delegate.py
+++ b/exir/delegate.py
@@ -123,44 +123,13 @@ def call_delegate_fake_tensor_mode(mode, lowered_module, *args):
         return lowered_module.original_module(*args)
 
 
-@executorch_call_delegate.py_impl(torch._C.DispatchKey.Functionalize)
+@executorch_call_delegate.py_functionalize_impl
 # pyre-ignore
-def call_delegate_func(lowered_module, *args):
-    reapply_views = torch._C._functionalization_reapply_views_tls()
-    # At this point, we will see functionalized tensors, so need to unwrap them first
-    unwrapped_args = tuple(
-        _unwrap_all_tensors_from_functional(arg, reapply_views=reapply_views)
-        for arg in args
-    )
-    guard = torch._C.ExcludeDispatchKeyGuard(
-        torch._C.DispatchKeySet(torch._C.DispatchKey.Functionalize)
-    )
-    try:
-        delegate_return = executorch_call_delegate(lowered_module, *unwrapped_args)
-        return _wrap_all_tensors_to_functional(delegate_return, level=0)
-    finally:
-        del guard
-
-
-# pyre-ignore
-@executorch_call_delegate.py_impl(torch._C._functorch.TransformType.Functionalize)
-# pyre-ignore
-def call_delegate_functionalize(interpreter, lowered_module, *args):
-    """
-    Functionalization implementation for torch.ops.executorch_call_delegate. We
-    don't need to do anything since the delegated program is controlled by
-    users.
-    """
-    reapply_views = interpreter.functionalize_add_back_views()
-    # At this point, we will see functionalized tensors, so need to unwrap them first
-    unwrapped_args = tuple(
-        _unwrap_all_tensors_from_functional(arg, reapply_views=reapply_views)
-        for arg in args
-    )
-
-    with interpreter.lower():
+def call_delegate_functionalize(ctx, lowered_module, *args):
+    unwrapped_args = tuple(ctx.unwrap_tensors(arg) for arg in args)
+    with ctx.redispatch_to_next():
         res = executorch_call_delegate(lowered_module, *unwrapped_args)
-        return _wrap_all_tensors_to_functional(res, level=interpreter.level())
+        return ctx.wrap_tensors(res)
 
 
 # pyre-ignore: Missing parameter annotation [2]: Parameter `obj` must have a type other than `Any`.Pyre