Fix number of minimal calls to the Hub with peft integration (#25715)

* Fix number of minimal calls to the Hub with peft integration

* Alternate design

* And this way?

* Revert

* Address comments

src/transformers/modeling_utils.py

@@ -51,6 +51,7 @@ from .pytorch_utils import (  # noqa: F401
 from .utils import (
     ADAPTER_SAFE_WEIGHTS_NAME,
     ADAPTER_WEIGHTS_NAME,
+    CONFIG_NAME,
     DUMMY_INPUTS,
     FLAX_WEIGHTS_NAME,
     SAFE_WEIGHTS_INDEX_NAME,
@@ -65,6 +66,7 @@ from .utils import (
     cached_file,
     copy_func,
     download_url,
+    extract_commit_hash,
     has_file,
     is_accelerate_available,
     is_auto_gptq_available,
@@ -2368,13 +2370,39 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
                 " ignored."
             )
 
+        if commit_hash is None:
+            if not isinstance(config, PretrainedConfig):
+                # We make a call to the config file first (which may be absent) to get the commit hash as soon as possible
+                resolved_config_file = cached_file(
+                    pretrained_model_name_or_path,
+                    CONFIG_NAME,
+                    cache_dir=cache_dir,
+                    force_download=force_download,
+                    resume_download=resume_download,
+                    proxies=proxies,
+                    local_files_only=local_files_only,
+                    token=token,
+                    revision=revision,
+                    subfolder=subfolder,
+                    _raise_exceptions_for_missing_entries=False,
+                    _raise_exceptions_for_connection_errors=False,
+                )
+                commit_hash = extract_commit_hash(resolved_config_file, commit_hash)
+            else:
+                commit_hash = getattr(config, "_commit_hash", None)
+
         if is_peft_available() and _adapter_model_path is None:
             maybe_adapter_model_path = find_adapter_config_file(
                 pretrained_model_name_or_path,
+                cache_dir=cache_dir,
+                force_download=force_download,
+                resume_download=resume_download,
+                proxies=proxies,
+                local_files_only=local_files_only,
+                token=token,
                 revision=revision,
                 subfolder=subfolder,
-                token=token,
-                commit_hash=commit_hash,
+                _commit_hash=commit_hash,
             )
         elif is_peft_available() and _adapter_model_path is not None:
             maybe_adapter_model_path = _adapter_model_path
@@ -2622,9 +2650,6 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
                 " `pip install --upgrade bitsandbytes`."
             )
 
-        if commit_hash is None:
-            commit_hash = getattr(config, "_commit_hash", None)
-
         # This variable will flag if we're loading a sharded checkpoint. In this case the archive file is just the
         # index of the files.
         is_sharded = False

src/transformers/models/auto/auto_factory.py

@@ -22,7 +22,16 @@ from collections import OrderedDict
 
 from ...configuration_utils import PretrainedConfig
 from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
-from ...utils import copy_func, find_adapter_config_file, is_peft_available, logging, requires_backends
+from ...utils import (
+    CONFIG_NAME,
+    cached_file,
+    copy_func,
+    extract_commit_hash,
+    find_adapter_config_file,
+    is_peft_available,
+    logging,
+    requires_backends,
+)
 from .configuration_auto import AutoConfig, model_type_to_module_name, replace_list_option_in_docstrings
 
 
@@ -443,7 +452,6 @@ class _BaseAutoModelClass:
         kwargs["_from_auto"] = True
         hub_kwargs_names = [
             "cache_dir",
-            "code_revision",
             "force_download",
             "local_files_only",
             "proxies",
@@ -454,6 +462,8 @@ class _BaseAutoModelClass:
             "token",
         ]
         hub_kwargs = {name: kwargs.pop(name) for name in hub_kwargs_names if name in kwargs}
+        code_revision = kwargs.pop("code_revision", None)
+        commit_hash = kwargs.pop("_commit_hash", None)
 
         token = hub_kwargs.pop("token", None)
         use_auth_token = hub_kwargs.pop("use_auth_token", None)
@@ -470,12 +480,23 @@ class _BaseAutoModelClass:
         if token is not None:
             hub_kwargs["token"] = token
 
-        if is_peft_available():
-            revision = kwargs.get("revision", None)
-            subfolder = kwargs.get("subfolder", None)
+        if commit_hash is None:
+            if not isinstance(config, PretrainedConfig):
+                # We make a call to the config file first (which may be absent) to get the commit hash as soon as possible
+                resolved_config_file = cached_file(
+                    pretrained_model_name_or_path,
+                    CONFIG_NAME,
+                    _raise_exceptions_for_missing_entries=False,
+                    _raise_exceptions_for_connection_errors=False,
+                    **hub_kwargs,
+                )
+                commit_hash = extract_commit_hash(resolved_config_file, commit_hash)
+            else:
+                commit_hash = getattr(config, "_commit_hash", None)
 
+        if is_peft_available():
             maybe_adapter_path = find_adapter_config_file(
-                pretrained_model_name_or_path, revision=revision, token=token, subfolder=subfolder
+                pretrained_model_name_or_path, _commit_hash=commit_hash, **hub_kwargs
             )
 
             if maybe_adapter_path is not None:
@@ -499,6 +520,8 @@ class _BaseAutoModelClass:
                 pretrained_model_name_or_path,
                 return_unused_kwargs=True,
                 trust_remote_code=trust_remote_code,
+                code_revision=code_revision,
+                _commit_hash=commit_hash,
                 **hub_kwargs,
                 **kwargs,
             )
@@ -517,7 +540,7 @@ class _BaseAutoModelClass:
         if has_remote_code and trust_remote_code:
             class_ref = config.auto_map[cls.__name__]
             model_class = get_class_from_dynamic_module(
-                class_ref, pretrained_model_name_or_path, **hub_kwargs, **kwargs
+                class_ref, pretrained_model_name_or_path, code_revision=code_revision, **hub_kwargs, **kwargs
             )
             _ = hub_kwargs.pop("code_revision", None)
             if os.path.isdir(pretrained_model_name_or_path):

src/transformers/models/auto/configuration_auto.py

@@ -1007,6 +1007,8 @@ class AutoConfig:
         kwargs["_from_auto"] = True
         kwargs["name_or_path"] = pretrained_model_name_or_path
         trust_remote_code = kwargs.pop("trust_remote_code", None)
+        code_revision = kwargs.pop("code_revision", None)
+
         config_dict, unused_kwargs = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
         has_remote_code = "auto_map" in config_dict and "AutoConfig" in config_dict["auto_map"]
         has_local_code = "model_type" in config_dict and config_dict["model_type"] in CONFIG_MAPPING
@@ -1016,10 +1018,11 @@ class AutoConfig:
 
         if has_remote_code and trust_remote_code:
             class_ref = config_dict["auto_map"]["AutoConfig"]
-            config_class = get_class_from_dynamic_module(class_ref, pretrained_model_name_or_path, **kwargs)
+            config_class = get_class_from_dynamic_module(
+                class_ref, pretrained_model_name_or_path, code_revision=code_revision, **kwargs
+            )
             if os.path.isdir(pretrained_model_name_or_path):
                 config_class.register_for_auto_class()
-            _ = kwargs.pop("code_revision", None)
             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"]]

src/transformers/utils/peft_utils.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 import importlib
 import os
-from typing import Optional
+from typing import Dict, Optional, Union
 
 from packaging import version
 
@@ -28,10 +28,15 @@ ADAPTER_SAFE_WEIGHTS_NAME = "adapter_model.safetensors"
 
 def find_adapter_config_file(
     model_id: str,
-    revision: str = None,
-    subfolder: str = None,
-    token: Optional[str] = None,
-    commit_hash: Optional[str] = None,
+    cache_dir: Optional[Union[str, os.PathLike]] = None,
+    force_download: bool = False,
+    resume_download: bool = False,
+    proxies: Optional[Dict[str, str]] = None,
+    token: Optional[Union[bool, str]] = None,
+    revision: Optional[str] = None,
+    local_files_only: bool = False,
+    subfolder: str = "",
+    _commit_hash: Optional[str] = None,
 ) -> Optional[str]:
     r"""
     Simply checks if the model stored on the Hub or locally is an adapter model or not, return the path the the adapter
@@ -40,6 +45,20 @@ def find_adapter_config_file(
     Args:
         model_id (`str`):
             The identifier of the model to look for, can be either a local path or an id to the repository on the Hub.
+        cache_dir (`str` or `os.PathLike`, *optional*):
+            Path to a directory in which a downloaded pretrained model configuration should be cached if the standard
+            cache should not be used.
+        force_download (`bool`, *optional*, defaults to `False`):
+            Whether or not to force to (re-)download the configuration files and override the cached versions if they
+            exist.
+        resume_download (`bool`, *optional*, defaults to `False`):
+            Whether or not to delete incompletely received file. Attempts to resume the download if such a file exists.
+        proxies (`Dict[str, str]`, *optional*):
+            A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
+            'http://hostname': 'foo.bar:4012'}.` The proxies are used on each request.
+        token (`str` or *bool*, *optional*):
+            The token to use as HTTP bearer authorization for remote files. If `True`, will use the token generated
+            when running `huggingface-cli login` (stored in `~/.huggingface`).
         revision (`str`, *optional*, defaults to `"main"`):
             The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
             git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
@@ -51,12 +70,11 @@ def find_adapter_config_file(
 
             </Tip>
 
+        local_files_only (`bool`, *optional*, defaults to `False`):
+            If `True`, will only try to load the tokenizer configuration from local files.
         subfolder (`str`, *optional*, defaults to `""`):
             In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can
             specify the folder name here.
-        token (`str`, `optional`):
-            Whether to use authentication token to load the remote folder. Userful to load private repositories that
-            are on HuggingFace Hub. You might need to call `huggingface-cli login` and paste your tokens to cache it.
     """
     adapter_cached_filename = None
     if model_id is None:
@@ -69,10 +87,15 @@ def find_adapter_config_file(
         adapter_cached_filename = cached_file(
             model_id,
             ADAPTER_CONFIG_NAME,
-            revision=revision,
+            cache_dir=cache_dir,
+            force_download=force_download,
+            resume_download=resume_download,
+            proxies=proxies,
             token=token,
-            _commit_hash=commit_hash,
+            revision=revision,
+            local_files_only=local_files_only,
             subfolder=subfolder,
+            _commit_hash=_commit_hash,
             _raise_exceptions_for_missing_entries=False,
             _raise_exceptions_for_connection_errors=False,
         )