Reduce the error log when using core models that need their weights r…

…enamed, and provide a step forward (#32656)

* Fin

* Modify msg

* Finish up nits

src/transformers/modeling_utils.py

@@ -104,7 +104,6 @@
 
 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():
@@ -692,17 +691,30 @@ def _load_state_dict_into_model(model_to_load, state_dict, start_prefix, assign_
     # Convert old format to new format if needed from a PyTorch state_dict
     old_keys = []
     new_keys = []
+    renamed_keys = {}
+    renamed_gamma = {}
+    renamed_beta = {}
+    warning_msg = f"A pretrained model of type `{model_to_load.__class__.__name__}` "
     for key in state_dict.keys():
         new_key = None
         if "gamma" in key:
-            logger.warning(PARAM_RENAME_WARNING.format("gamma", "weight"))
+            # We add only the first key as an example
             new_key = key.replace("gamma", "weight")
+            renamed_gamma[key] = new_key if not renamed_gamma else renamed_gamma
         if "beta" in key:
-            logger.warning(PARAM_RENAME_WARNING.format("beta", "bias"))
+            # We add only the first key as an example
             new_key = key.replace("beta", "bias")
+            renamed_beta[key] = new_key if not renamed_beta else renamed_beta
         if new_key:
             old_keys.append(key)
             new_keys.append(new_key)
+    renamed_keys = {**renamed_gamma, **renamed_beta}
+    if renamed_keys:
+        warning_msg += "contains parameters that have been renamed internally (a few are listed below but more are present in the model):\n"
+        for old_key, new_key in renamed_keys.items():
+            warning_msg += f"* `{old_key}` -> `{new_key}`\n"
+        warning_msg += "If you are using a model from the Hub, consider submitting a PR to adjust these weights and help future users."
+        logger.info_once(warning_msg)
     for old_key, new_key in zip(old_keys, new_keys):
         state_dict[new_key] = state_dict.pop(old_key)
 
@@ -818,6 +830,7 @@ def _load_state_dict_into_meta_model(
     is_safetensors=False,
     keep_in_fp32_modules=None,
     unexpected_keys=None,  # passing `unexpected` for cleanup from quantization items
+    pretrained_model_name_or_path=None,  # for flagging the user when the model contains renamed keys
 ):
     """
     This is somewhat similar to `_load_state_dict_into_model`, but deals with a model that has some or all of its
@@ -840,18 +853,30 @@ def _load_state_dict_into_meta_model(
 
     old_keys = []
     new_keys = []
+    renamed_gamma = {}
+    renamed_beta = {}
     is_quantized = hf_quantizer is not None
+    warning_msg = f"This model {type(model)}"
     for key in state_dict.keys():
         new_key = None
         if "gamma" in key:
-            logger.warning(PARAM_RENAME_WARNING.format("gamma", "weight"))
+            # We add only the first key as an example
             new_key = key.replace("gamma", "weight")
+            renamed_gamma[key] = new_key if not renamed_gamma else renamed_gamma
         if "beta" in key:
-            logger.warning(PARAM_RENAME_WARNING.format("beta", "bias"))
+            # We add only the first key as an example
             new_key = key.replace("beta", "bias")
+            renamed_beta[key] = new_key if not renamed_beta else renamed_beta
         if new_key:
             old_keys.append(key)
             new_keys.append(new_key)
+    renamed_keys = {**renamed_gamma, **renamed_beta}
+    if renamed_keys:
+        warning_msg += "contains parameters that have been renamed internally (a few are listed below but more are present in the model):\n"
+        for old_key, new_key in renamed_keys.items():
+            warning_msg += f"* `{old_key}` -> `{new_key}`\n"
+        warning_msg += "If you are using a model from the Hub, consider submitting a PR to adjust these weights and help future users."
+        logger.info_once(warning_msg)
     for old_key, new_key in zip(old_keys, new_keys):
         state_dict[new_key] = state_dict.pop(old_key)
 
@@ -4534,7 +4559,12 @@ def retrieve_modules_from_names(self, names, add_prefix=False, remove_prefix=Fal
 
     @staticmethod
     def _load_pretrained_model_low_mem(
-        model, loaded_state_dict_keys, resolved_archive_file, start_prefix="", hf_quantizer=None
+        model,
+        loaded_state_dict_keys,
+        resolved_archive_file,
+        start_prefix="",
+        hf_quantizer=None,
+        pretrained_model_name_or_path=None,
     ):
         """
         This is an experimental function that loads the model using ~1.x model size CPU memory

src/transformers/utils/logging.py

@@ -331,6 +331,21 @@ def warning_once(self, *args, **kwargs):
 logging.Logger.warning_once = warning_once
 
 
+@functools.lru_cache(None)
+def info_once(self, *args, **kwargs):
+    """
+    This method is identical to `logger.info()`, but will emit the info with the same message only once
+
+    Note: The cache is for the function arguments, so 2 different callers using the same arguments will hit the cache.
+    The assumption here is that all warning messages are unique across the code. If they aren't then need to switch to
+    another type of cache that includes the caller frame information in the hashing function.
+    """
+    self.info(*args, **kwargs)
+
+
+logging.Logger.info_once = info_once
+
+
 class EmptyTqdm:
     """Dummy tqdm which doesn't do anything."""
 

tests/utils/test_modeling_utils.py

@@ -1640,17 +1640,18 @@ def forward(self):
 
         logger = logging.get_logger("transformers.modeling_utils")
         config = PretrainedConfig()
-        warning_msg_gamma = "A parameter name that contains `gamma` will be renamed internally"
+        warning_msg_gamma = "`gamma_param` -> `weight_param`"
         model = TestModelGamma(config)
 
         with tempfile.TemporaryDirectory() as tmp_dir:
             model.save_pretrained(tmp_dir)
-            with LoggingLevel(logging.WARNING):
+            with LoggingLevel(logging.INFO):
                 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("`TestModelGamma`", cl1.out)
         self.assertIn(warning_msg_gamma, cl1.out)
         self.assertIn("gamma_param", missing_keys)
         self.assertIn("weight_param", unexpected_keys)
@@ -1664,17 +1665,18 @@ def __init__(self, config):
             def forward(self):
                 return self.beta_param.sum()
 
-        warning_msg_beta = "A parameter name that contains `beta` will be renamed internally"
+        warning_msg_beta = "`beta_param` -> `bias_param`"
         model = TestModelBeta(config)
 
         with tempfile.TemporaryDirectory() as tmp_dir:
             model.save_pretrained(tmp_dir)
-            with LoggingLevel(logging.WARNING):
+            with LoggingLevel(logging.INFO):
                 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("`TestModelBeta`", cl2.out)
         self.assertIn(warning_msg_beta, cl2.out)
         self.assertIn("beta_param", missing_keys)
         self.assertIn("bias_param", unexpected_keys)