Preserve hub-related kwargs in AutoModel.from_pretrained (#18545)

* Preserve hub-related kwargs in AutoModel.from_pretrained

* Fix tests

* Remove debug statement

src/transformers/models/auto/auto_factory.py

@@ -419,9 +419,24 @@ class _BaseAutoModelClass:
         config = kwargs.pop("config", None)
         trust_remote_code = kwargs.pop("trust_remote_code", False)
         kwargs["_from_auto"] = True
+        hub_kwargs_names = [
+            "cache_dir",
+            "force_download",
+            "local_files_only",
+            "proxies",
+            "resume_download",
+            "revision",
+            "subfolder",
+            "use_auth_token",
+        ]
+        hub_kwargs = {name: kwargs.pop(name) for name in hub_kwargs_names if name in kwargs}
         if not isinstance(config, PretrainedConfig):
             config, kwargs = AutoConfig.from_pretrained(
-                pretrained_model_name_or_path, return_unused_kwargs=True, trust_remote_code=trust_remote_code, **kwargs
+                pretrained_model_name_or_path,
+                return_unused_kwargs=True,
+                trust_remote_code=trust_remote_code,
+                **hub_kwargs,
+                **kwargs,
             )
         if hasattr(config, "auto_map") and cls.__name__ in config.auto_map:
             if not trust_remote_code:
@@ -430,7 +445,7 @@ class _BaseAutoModelClass:
                     "on your local machine. Make sure you have read the code there to avoid malicious use, then set "
                     "the option `trust_remote_code=True` to remove this error."
                 )
-            if kwargs.get("revision", None) is None:
+            if hub_kwargs.get("revision", None) is None:
                 logger.warning(
                     "Explicitly passing a `revision` is encouraged when loading a model with custom code to ensure "
                     "no malicious code has been contributed in a newer revision."
@@ -438,12 +453,16 @@ class _BaseAutoModelClass:
             class_ref = config.auto_map[cls.__name__]
             module_file, class_name = class_ref.split(".")
             model_class = get_class_from_dynamic_module(
-                pretrained_model_name_or_path, module_file + ".py", class_name, **kwargs
+                pretrained_model_name_or_path, module_file + ".py", class_name, **hub_kwargs, **kwargs
+            )
+            return model_class.from_pretrained(
+                pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs
             )
-            return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
         elif type(config) in cls._model_mapping.keys():
             model_class = _get_model_class(config, cls._model_mapping)
-            return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, config=config, **kwargs)
+            return model_class.from_pretrained(
+                pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs
+            )
         raise ValueError(
             f"Unrecognized configuration class {config.__class__} for this kind of AutoModel: {cls.__name__}.\n"
             f"Model type should be one of {', '.join(c.__name__ for c in cls._model_mapping.keys())}."

src/transformers/models/auto/configuration_auto.py

@@ -728,7 +728,7 @@ class AutoConfig:
         kwargs["_from_auto"] = True
         kwargs["name_or_path"] = pretrained_model_name_or_path
         trust_remote_code = kwargs.pop("trust_remote_code", False)
-        config_dict, _ = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
+        config_dict, unused_kwargs = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
         if "auto_map" in config_dict and "AutoConfig" in config_dict["auto_map"]:
             if not trust_remote_code:
                 raise ValueError(
@@ -749,13 +749,13 @@ class AutoConfig:
             return config_class.from_pretrained(pretrained_model_name_or_path, **kwargs)
         elif "model_type" in config_dict:
             config_class = CONFIG_MAPPING[config_dict["model_type"]]
-            return config_class.from_dict(config_dict, **kwargs)
+            return config_class.from_dict(config_dict, **unused_kwargs)
         else:
             # Fallback: use pattern matching on the string.
             # We go from longer names to shorter names to catch roberta before bert (for instance)
             for pattern in sorted(CONFIG_MAPPING.keys(), key=len, reverse=True):
                 if pattern in str(pretrained_model_name_or_path):
-                    return CONFIG_MAPPING[pattern].from_dict(config_dict, **kwargs)
+                    return CONFIG_MAPPING[pattern].from_dict(config_dict, **unused_kwargs)
 
         raise ValueError(
             f"Unrecognized model in {pretrained_model_name_or_path}. "