pytorch
diff --git a/‎test/dynamo/test_repros.py
-34 b/‎test/dynamo/test_repros.py
-34
diff --git a/‎torch/_dynamo/eval_frame.py
+6-16 b/‎torch/_dynamo/eval_frame.py
+6-16
@@ -5853,40 +5853,6 @@ def test_optimized_module_training(self):
         mod.eval()
         self.assertFalse(opt_mod.training)
 
-    def test_optimized_module_patched_init(self):
-        # A regression test for #138157, and the pattern acame from deepspeed.
-        class MyModule(torch.nn.Module):
-            def __init__(self):
-                super().__init__()
-
-            def forward(self, x):
-                return x.mul(5.0)
-
-        def patch_init(init):
-            @functools.wraps(init)
-            def wrapper(module, *args, **kwargs):
-                if not hasattr(module, "_ds_child_entered"):
-                    # child's __init__ was called, since parents all see the same object they can now skip post_init
-                    module._ds_child_entered = True
-                init(module, *args, **kwargs)
-
-            return wrapper
-
-        def patch_init_for_class(cls):
-            if "__init__" in cls.__dict__:
-                cls._old_init = cls.__init__
-                cls.__init__ = patch_init(cls.__init__)
-
-        patch_init_for_class(MyModule)
-        mod = MyModule()
-        opt_mod = torch.compile(mod)
-
-        x = torch.rand(10)
-        ref = mod(x)
-        res = opt_mod(x)
-
-        self.assertEqual(ref, res)
-
     def test_os_fspath(self):
         @torch.compile(backend="eager", fullgraph=True)
         def fn(x):
 
@@ -313,23 +313,12 @@ class OptimizedModule(torch.nn.Module):
         "_forward",
         "__dict__",
         "named_children_walk",
-        "_super_module_initialized",
     }
 
     def __init__(self, mod: torch.nn.Module, dynamo_ctx) -> None:
-        # NOTE: this must go first, because attribute reads/writes of `self`
-        # uses `_orig_mod`, and sometimes users override `Module.__init__` to
-        # do attribute reads/writes on `self`.
-        #
-        # We also can't use regular setattr because `super().__setattr__` will
-        # complain for module value before `super().__init__()`
-        object.__setattr__(self, "_orig_mod", mod)
-        self._super_module_initialized = False
         super().__init__()
-        self._super_module_initialized = True
-
         # Installs the params/buffer
-        self._orig_mod = mod  # `super().__setattr__` will register this module
+        self._orig_mod = mod
         self.dynamo_ctx = dynamo_ctx
         self._initialize()
         self.training = self._orig_mod.training
@@ -390,11 +379,12 @@ def training(self):
 
     @training.setter
     def training(self, value):
-        # Ignore the `training` mutation in `super().__init__()`, since that's
-        # setting the default on `nn.Module`, but we are mirroring the
-        # `training` attr in `self._orig_mod`.
-        if self._super_module_initialized:
+        try:
+            super().__getattr__("_orig_mod")
             self._orig_mod.training = value
+        except AttributeError:
+            # still initializing
+            pass
 
     def __getattr__(self, name):
         if name == "_orig_mod":