warning was in info mode.

src/transformers/modeling_utils.py

@@ -595,12 +595,13 @@ def _load_state_dict_into_model(model_to_load, state_dict, start_prefix):
         state_dict._metadata = metadata
 
     error_msgs = []
+    unexpected_keys, missing_keys = [], []
 
     # PyTorch's `_load_from_state_dict` does not copy parameters in a module's descendants
     # so we need to apply the function recursively.
     def load(module: nn.Module, state_dict, prefix=""):
         local_metadata = {} if metadata is None else metadata.get(prefix[:-1], {})
-        args = (state_dict, prefix, local_metadata, True, [], [], error_msgs)
+        args = (state_dict, prefix, local_metadata, True, missing_keys, unexpected_keys, error_msgs)
         # Parameters of module and children will start with prefix. We can exit early if there are none in this
         # state_dict
         if len([key for key in state_dict if key.startswith(prefix)]) > 0:
@@ -630,7 +631,7 @@ def _load_state_dict_into_model(model_to_load, state_dict, start_prefix):
     # it's safe to delete it.
     del state_dict
 
-    return error_msgs
+    return error_msgs, unexpected_keys, missing_keys
 
 
 def find_submodule_and_param_name(model, long_key, start_prefix):
@@ -3901,7 +3902,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
                 remove_prefix_from_model,
                 ignore_mismatched_sizes,
             )
-            error_msgs = _load_state_dict_into_model(model_to_load, state_dict, start_prefix)
+            error_msgs, unexpected_keys, missing_keys = _load_state_dict_into_model(model_to_load, state_dict, start_prefix)
             offload_index = None
         else:
             # Sharded checkpoint or whole but low_cpu_mem_usage==True
@@ -3974,8 +3975,8 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
                         )
                         error_msgs += new_error_msgs
                 else:
-                    error_msgs += _load_state_dict_into_model(model_to_load, state_dict, start_prefix)
-
+                    mmsg, unexpected_keys, missing_keys = _load_state_dict_into_model(model_to_load, state_dict, start_prefix)
+                    error_msgs += mmsg
                 # force memory release
                 del state_dict
                 gc.collect()
@@ -4009,9 +4010,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
             raise RuntimeError(f"Error(s) in loading state_dict for {model.__class__.__name__}:\n\t{error_msg}")
 
         if len(unexpected_keys) > 0:
-            archs = [] if model.config.architectures is None else model.config.architectures
-            warner = logger.warning if model.__class__.__name__ in archs else logger.info
-            warner(
+            logger.warning(
                 f"Some weights of the model checkpoint at {pretrained_model_name_or_path} were not used when"
                 f" initializing {model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are"
                 f" initializing {model.__class__.__name__} from the checkpoint of a model trained on another task or"