diff --git a/src/accelerate/accelerator.py b/src/accelerate/accelerator.py
index cc34cd5cb1c..e35c7e723b7 100755
--- a/src/accelerate/accelerator.py
+++ b/src/accelerate/accelerator.py
@@ -1357,8 +1357,8 @@ def prepare_model(self, model: torch.nn.Module, device_placement: bool = None, e
                 " Please rerun your script specifying `--num_processes=1` or by launching with `python {{myscript.py}}`."
             )
 
+        model._accelerate_original_forward_id = id(model.forward)
         if self.native_amp:
-            model._original_forward = model.forward
             model_forward_func = model.forward.__func__ if hasattr(model.forward, "__func__") else model.forward
             autocast_context = get_mixed_precision_context_manager(self.native_amp, self.autocast_handler)
             new_forward = autocast_context(model_forward_func)
@@ -1372,7 +1372,6 @@ def prepare_model(self, model: torch.nn.Module, device_placement: bool = None, e
                 with torch.no_grad():
                     convert_model(model)
                 model._converted_to_transformer_engine = True
-            model._original_forward = model.forward
 
             kwargs = self.fp8_recipe_handler.to_kwargs() if self.fp8_recipe_handler is not None else {}
             if "fp8_format" in kwargs:
diff --git a/src/accelerate/utils/other.py b/src/accelerate/utils/other.py
index 970b2969478..6dc84b54cfa 100644
--- a/src/accelerate/utils/other.py
+++ b/src/accelerate/utils/other.py
@@ -86,14 +86,16 @@ def extract_model_from_parallel(model, keep_fp32_wrapper: bool = True):
         model = model.module
 
     if not keep_fp32_wrapper:
+        original_forward_id = getattr(model, "_accelerate_original_forward_id", None)
         forward = getattr(model, "forward")
-        original_forward = model.__dict__.pop("_original_forward", None)
-        if original_forward is not None:
+        forward_func = getattr(forward, "__func__", None)
+        if (original_forward_id is not None) and (id(forward) != original_forward_id):
             while hasattr(forward, "__wrapped__"):
                 forward = forward.__wrapped__
-                if forward == original_forward:
+                forward_func = getattr(forward, "__func__", None)
+                if id(forward) == original_forward_id or id(forward_func) == original_forward_id:
                     break
-            model.forward = MethodType(forward, model)
+            model.forward = MethodType(forward_func if forward_func is not None else forward, model)
         if getattr(model, "_converted_to_transformer_engine", False):
             convert_model(model, to_transformer_engine=False)
 
diff --git a/tests/test_utils.py b/tests/test_utils.py
index 2bf86314eed..eb3c65ffe31 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -132,7 +132,7 @@ def test_patch_environment_key_exists(self):
 
     def test_can_undo_convert_outputs(self):
         model = RegressionModel()
-        model._original_forward = model.forward
+        model._accelerate_original_forward_id = id(model.forward)
         model.forward = convert_outputs_to_fp32(model.forward)
         model = extract_model_from_parallel(model, keep_fp32_wrapper=False)
         _ = pickle.dumps(model)
@@ -140,7 +140,7 @@ def test_can_undo_convert_outputs(self):
     @require_cuda
     def test_can_undo_fp16_conversion(self):
         model = RegressionModel()
-        model._original_forward = model.forward
+        model._accelerate_original_forward_id = id(model.forward)
         model.forward = torch.cuda.amp.autocast(dtype=torch.float16)(model.forward)
         model.forward = convert_outputs_to_fp32(model.forward)
         model = extract_model_from_parallel(model, keep_fp32_wrapper=False)
@@ -150,7 +150,7 @@ def test_can_undo_fp16_conversion(self):
     @require_torch_min_version(version="2.0")
     def test_dynamo(self):
         model = RegressionModel()
-        model._original_forward = model.forward
+        model._accelerate_original_forward_id = id(model.forward)
         model.forward = torch.cuda.amp.autocast(dtype=torch.float16)(model.forward)
         model.forward = convert_outputs_to_fp32(model.forward)
         model.forward = torch.compile(model.forward, backend="inductor")