Add warning message for beta and gamma parameters (huggingface#31654)

* Add warning message for  and  parameters

* Fix when the warning is raised

* Formatting changes

* Improve testing and remove duplicated warning from _fix_key

src/transformers/modeling_utils.py

@@ -104,6 +104,8 @@
 
 XLA_USE_BF16 = os.environ.get("XLA_USE_BF16", "0").upper()
 XLA_DOWNCAST_BF16 = os.environ.get("XLA_DOWNCAST_BF16", "0").upper()
+PARAM_RENAME_WARNING = "A parameter name that contains `{}` will be renamed internally to `{}`. Please use a different name to suppress this warning."
+
 
 if is_accelerate_available():
     from accelerate import dispatch_model, infer_auto_device_map, init_empty_weights
@@ -662,8 +664,10 @@ def _load_state_dict_into_model(model_to_load, state_dict, start_prefix):
     for key in state_dict.keys():
         new_key = None
         if "gamma" in key:
+            logger.warning(PARAM_RENAME_WARNING.format("gamma", "weight"))
             new_key = key.replace("gamma", "weight")
         if "beta" in key:
+            logger.warning(PARAM_RENAME_WARNING.format("beta", "bias"))
             new_key = key.replace("beta", "bias")
         if new_key:
             old_keys.append(key)
@@ -807,8 +811,10 @@ def _load_state_dict_into_meta_model(
     for key in state_dict.keys():
         new_key = None
         if "gamma" in key:
+            logger.warning(PARAM_RENAME_WARNING.format("gamma", "weight"))
             new_key = key.replace("gamma", "weight")
         if "beta" in key:
+            logger.warning(PARAM_RENAME_WARNING.format("beta", "bias"))
             new_key = key.replace("beta", "bias")
         if new_key:
             old_keys.append(key)

tests/utils/test_modeling_utils.py

@@ -1511,6 +1511,57 @@ def test_model_from_pretrained_from_mlx(self):
             outputs_from_saved = new_model(input_ids)
             self.assertTrue(torch.allclose(outputs_from_saved["logits"], outputs["logits"]))
 
+    def test_warning_for_beta_gamma_parameters(self):
+        class TestModelGamma(PreTrainedModel):
+            def __init__(self, config):
+                super().__init__(config)
+                self.gamma_param = nn.Parameter(torch.ones(10))
+                self.post_init()
+
+            def forward(self):
+                return self.gamma_param.sum()
+
+        logger = logging.get_logger("transformers.modeling_utils")
+        config = PretrainedConfig()
+        warning_msg_gamma = "A parameter name that contains `gamma` will be renamed internally"
+        model = TestModelGamma(config)
+
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model.save_pretrained(tmp_dir)
+            with LoggingLevel(logging.WARNING):
+                with CaptureLogger(logger) as cl1:
+                    _, loading_info = TestModelGamma.from_pretrained(tmp_dir, config=config, output_loading_info=True)
+
+        missing_keys = loading_info["missing_keys"]
+        unexpected_keys = loading_info["unexpected_keys"]
+        self.assertIn(warning_msg_gamma, cl1.out)
+        self.assertIn("gamma_param", missing_keys)
+        self.assertIn("weight_param", unexpected_keys)
+
+        class TestModelBeta(PreTrainedModel):
+            def __init__(self, config):
+                super().__init__(config)
+                self.beta_param = nn.Parameter(torch.ones(10))
+                self.post_init()
+
+            def forward(self):
+                return self.beta_param.sum()
+
+        warning_msg_beta = "A parameter name that contains `beta` will be renamed internally"
+        model = TestModelBeta(config)
+
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model.save_pretrained(tmp_dir)
+            with LoggingLevel(logging.WARNING):
+                with CaptureLogger(logger) as cl2:
+                    _, loading_info = TestModelBeta.from_pretrained(tmp_dir, config=config, output_loading_info=True)
+
+        missing_keys = loading_info["missing_keys"]
+        unexpected_keys = loading_info["unexpected_keys"]
+        self.assertIn(warning_msg_beta, cl2.out)
+        self.assertIn("beta_param", missing_keys)
+        self.assertIn("bias_param", unexpected_keys)
+
 
 @slow
 @require_torch