# Copyright 2023 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import inspect import json import os from dataclasses import replace from typing import TYPE_CHECKING, Any, Literal, Optional from safetensors import safe_open from .._typing import PeftConfigLike from ..conversion_mapping import get_model_conversion_mapping from ..utils import ( CONFIG_NAME, cached_file, check_peft_version, extract_commit_hash, find_adapter_config_file, is_accelerate_available, is_peft_available, is_torch_available, logging, ) from ..utils.hub import DownloadKwargs from ..utils.loading_report import log_state_dict_report if is_torch_available(): import torch if is_accelerate_available(): from accelerate import dispatch_model from accelerate.utils import get_balanced_memory, infer_auto_device_map # Minimum PEFT version supported for the integration MIN_PEFT_VERSION = "0.19.1" logger = logging.get_logger(__name__) if TYPE_CHECKING: from ..modeling_utils import LoadStateDictConfig, LoadStateDictInfo class PeftAdapterMixin: """ A class containing all functions for loading and using adapters weights that are supported in PEFT library. For more details about adapters and injecting them on a transformer-based model, check out the documentation of PEFT library: https://huggingface.co/docs/peft/index Currently supported PEFT methods are all non-prompt learning methods (LoRA, IA³, etc.). Other PEFT models such as prompt tuning, prompt learning are out of scope as these adapters are not "injectable" into a torch module. For using these methods, please refer to the usage guide of PEFT library. With this mixin, if the correct PEFT version is installed (>= 0.19.1), it is possible to: - Load an adapter stored on a local path or in a remote Hub repository, and inject it in the model - Attach new adapters in the model and train them with Trainer or by your own. - Attach multiple adapters and iteratively activate / deactivate them - Activate / deactivate all adapters from the model. - Get the `state_dict` of the active adapter. """ _hf_peft_config_loaded = False _prepare_peft_hotswap_kwargs: dict | None = None peft_config: dict[str, PeftConfigLike] def load_adapter( self, peft_model_id: str | None = None, adapter_name: str | None = None, peft_config: dict[str, Any] | None = None, adapter_state_dict: dict[str, "torch.Tensor"] | None = None, low_cpu_mem_usage: bool = False, is_trainable: bool = False, hotswap: bool | Literal["auto"] = "auto", local_files_only: bool = False, adapter_kwargs: dict[str, Any] | None = None, load_config: Optional["LoadStateDictConfig"] = None, **kwargs, ) -> "LoadStateDictInfo": """ Load adapter weights from file or remote Hub folder. If you are not familiar with adapters and PEFT methods, we invite you to read more about them on PEFT official documentation: https://huggingface.co/docs/peft Requires PEFT to be installed as a backend to load the adapter weights. Args: peft_model_id (`str`, *optional*): The identifier of the model to look for on the Hub, or a local path to the saved adapter config file and adapter weights. adapter_name (`str`, *optional*): The adapter name to use. If not set, will use the name "default". load_config (`LoadStateDictConfig`, *optional*): A load configuration to reuse when pulling adapter weights, typically from `from_pretrained`. kwargs (`dict[str, Any]`, *optional*): Additional `LoadStateDictConfig` fields passed as keyword arguments. peft_config (`dict[str, Any]`, *optional*): The configuration of the adapter to add, supported adapters are all non-prompt learning configs (LoRA, IA³, etc). This argument is used in case users directly pass PEFT state dicts. adapter_state_dict (`dict[str, torch.Tensor]`, *optional*): The state dict of the adapter to load. This argument is used in case users directly pass PEFT state dicts. low_cpu_mem_usage (`bool`, *optional*, defaults to `False`): Reduce memory usage while loading the PEFT adapter. This should also speed up the loading process. is_trainable (`bool`, *optional*, defaults to `False`): Whether the adapter should be trainable or not. If `False`, the adapter will be frozen and can only be used for inference. hotswap : (`"auto"` or `bool`, *optional*, defaults to `"auto"`) Whether to substitute an existing (LoRA) adapter with the newly loaded adapter in-place. This means that, instead of loading an additional adapter, this will take the existing adapter weights and replace them with the weights of the new adapter. This can be faster and more memory efficient. However, the main advantage of hotswapping is that when the model is compiled with torch.compile, loading the new adapter does not require recompilation of the model. When using hotswapping, the passed `adapter_name` should be the name of an already loaded adapter. If the new adapter and the old adapter have different ranks and/or LoRA alphas (i.e. scaling), you need to call an additional method before loading the adapter: ```py model = AutoModel.from_pretrained(...) max_rank = ... # the highest rank among all LoRAs that you want to load # call *before* compiling and loading the LoRA adapter model.enable_peft_hotswap(target_rank=max_rank) model.load_adapter(file_name_1, adapter_name="default") # optionally compile the model now model = torch.compile(model, ...) output_1 = model(...) # now you can hotswap the 2nd adapter, use the same name as for the 1st # hotswap is activated by default since enable_peft_hotswap was called model.load_adapter(file_name_2, adapter_name="default") output_2 = model(...) ``` By default, hotswap is disabled and requires passing `hotswap=True`. If you called `enable_peft_hotswap` first, it is enabled. You can still manually disable it in that case by passing `hotswap=False`. Note that hotswapping comes with a couple of limitations documented here: https://huggingface.co/docs/peft/main/en/package_reference/hotswap adapter_kwargs (`dict[str, Any]`, *optional*): Additional keyword arguments passed along to the `from_pretrained` method of the adapter config and `find_adapter_config_file` method. """ from peft import PeftType from peft.utils.save_and_load import _maybe_shard_state_dict_for_tp from ..modeling_utils import LoadStateDictConfig, _get_resolved_checkpoint_files, load_state_dict if local_files_only: kwargs["local_files_only"] = True base_load_config = load_config.__dict__ if load_config is not None else {} base_load_config.update(kwargs) base_load_config.setdefault("pretrained_model_name_or_path", None) load_config = LoadStateDictConfig(**base_load_config) peft_model_id = peft_model_id or load_config.pretrained_model_name_or_path if hotswap == "auto": # if user called model.enable_peft_hotswap and this is not the first adapter, enable hotswap hotswap_enabled = getattr(self, "_hotswap_enabled", False) not_first_adapter = bool(self._hf_peft_config_loaded and (adapter_name in self.peft_config)) hotswap = hotswap_enabled and not_first_adapter if hotswap: if (not self._hf_peft_config_loaded) or (adapter_name not in self.peft_config): raise ValueError( "To hotswap an adapter, there must already be an existing adapter with the same adapter name." ) if any(conf.peft_type != PeftType.LORA for conf in self.peft_config.values()): raise ValueError("Hotswapping is currently only supported for LoRA, please set `hotswap=False`.") adapter_name = adapter_name if adapter_name is not None else "default" adapter_kwargs = adapter_kwargs or {} from peft import PeftConfig, inject_adapter_in_model if self._hf_peft_config_loaded and (not hotswap) and (adapter_name in self.peft_config): raise ValueError(f"Adapter with name {adapter_name} already exists. Please use a different name.") elif hotswap and ((not self._hf_peft_config_loaded) or (adapter_name not in self.peft_config)): raise ValueError( "To hotswap an adapter, there must already be an existing adapter with the same adapter name." ) if peft_model_id is None and (adapter_state_dict is None and peft_config is None): raise ValueError( "You should either pass a `peft_model_id` or a `peft_config` and `adapter_state_dict` to load an adapter." ) if peft_config is None: load_config.download_kwargs.update(**adapter_kwargs) adapter_config_file = find_adapter_config_file( peft_model_id, **load_config.download_kwargs, ) if adapter_config_file is None: raise ValueError( f"adapter model file not found in {peft_model_id}. Make sure you are passing the correct path to the " "adapter model." ) peft_config = PeftConfig.from_pretrained( peft_model_id, **load_config.download_kwargs, ) from peft.utils.transformers_weight_conversion import build_peft_weight_mapping weight_conversions = get_model_conversion_mapping(self) if hasattr(peft_config, "inference_mode"): peft_config.inference_mode = not is_trainable # The PEFT config conversion for v5 architecture changes (e.g. Mixtral MoE) is applied in-place by # inject_adapter_in_model below, so it does not need to be done explicitly here. peft_weight_conversions = build_peft_weight_mapping(weight_conversions, adapter_name, peft_config=peft_config) if not hotswap: # Create and add fresh new adapters into the model, unless the weights are hotswapped inject_adapter_in_model(peft_config, self, adapter_name) adapter_key_markers = {adapter_name} if peft_config is not None and getattr(peft_config, "peft_type", None) is not None: adapter_key_markers.add(peft_config.peft_type.value.lower()) def is_adapter_key(key: str) -> bool: return any(marker in key for marker in adapter_key_markers) if not self._hf_peft_config_loaded: self._hf_peft_config_loaded = True if adapter_state_dict is None: adapter_filenames = ["adapter_model.safetensors", "adapter_model.bin"] if load_config.use_safetensors is False: adapter_filenames.reverse() checkpoint_files = sharded_metadata = None last_error = None for adapter_filename in adapter_filenames: try: checkpoint_files, sharded_metadata = _get_resolved_checkpoint_files( pretrained_model_name_or_path=peft_model_id, variant=None, gguf_file=None, use_safetensors=( load_config.use_safetensors if adapter_filename.endswith(".safetensors") else False ), user_agent=None, is_remote_code=False, transformers_explicit_filename=adapter_filename, download_kwargs=load_config.download_kwargs, ) break except OSError as error: last_error = error if checkpoint_files is None: raise last_error or OSError("Could not download either a .bin or a .safetensors adapter file.") else: checkpoint_files, sharded_metadata = [], {} device_map = getattr(self, "hf_device_map", {"": self.device}) # If the model is tensor parallel, we handle the sharding of the state dict here since the logic in `self._load_pretrained_model` # is not compatible with the way PEFT adapter should be sharded. has_tp_adapters = False for module in self.modules(): tp_info = getattr(module, "_tp_info", None) if tp_info is not None: has_tp_adapters = True break if has_tp_adapters: all_pointer = set() if adapter_state_dict is not None: merged_state_dict = adapter_state_dict elif ( checkpoint_files is not None and checkpoint_files[0].endswith(".safetensors") and adapter_state_dict is None ): merged_state_dict = {} for file in checkpoint_files: file_pointer = safe_open(file, framework="pt", device="cpu") all_pointer.add(file_pointer) for k in file_pointer.keys(): merged_state_dict[k] = file_pointer.get_tensor(k) # Checkpoints are .bin elif checkpoint_files is not None: merged_state_dict = {} for ckpt_file in checkpoint_files: merged_state_dict.update(load_state_dict(ckpt_file)) else: raise ValueError("Neither a state dict nor checkpoint files were found.") adapter_state_dict = merged_state_dict if any(not isinstance(v, torch.Tensor) for v in adapter_state_dict.values()): raise ValueError("Expected all values in the adapter state dict to be tensors.") _maybe_shard_state_dict_for_tp(self, adapter_state_dict, adapter_name) load_config = replace( load_config, pretrained_model_name_or_path=peft_model_id, sharded_metadata=sharded_metadata, weight_mapping=peft_weight_conversions, device_map=device_map, ) loading_info, _ = self._load_pretrained_model( model=self, state_dict=adapter_state_dict, checkpoint_files=checkpoint_files, load_config=load_config, # Pass expected keys explicitly while excluding non-adapter parameters. # Otherwise `caching_allocator_warmup` sizes for the full base model. expected_keys=[n for n, _ in self.named_parameters() if is_adapter_key(n)], ) if peft_config.inference_mode: from peft.tuners.tuners_utils import BaseTunerLayer self.eval() for module in self.modules(): if isinstance(module, BaseTunerLayer): module.requires_grad_(False) loading_info.missing_keys = {k for k in loading_info.missing_keys if is_adapter_key(k)} log_state_dict_report( model=self, pretrained_model_name_or_path=load_config.pretrained_model_name_or_path, ignore_mismatched_sizes=load_config.ignore_mismatched_sizes, loading_info=loading_info, logger=logger, ) return loading_info def enable_peft_hotswap( self, target_rank: int = 128, check_compiled: Literal["error", "warn", "ignore"] = "error" ) -> None: """Enables the possibility to hotswap PEFT adapters with different ranks, or, if the model is compiled, without triggering recompilation. Right now, hotswapping is only supported for LoRA. Calling this method is only required when hotswapping adapters and if the model is compiled or if the ranks of the loaded adapters differ. If the ranks are all identical and the model is not compiled, hotswapping works without calling this method first. Args: target_rank (`int`, *optional*, defaults to `128`): The highest rank among all the adapters that will be loaded. check_compiled (`str`, *optional*, defaults to `"error"`): How to handle the case when the model is already compiled, which should generally be avoided. The options are: - "error" (default): raise an error - "warn": issue a warning - "ignore": do nothing """ if getattr(self, "peft_config", {}): if check_compiled == "error": raise RuntimeError("Call `enable_peft_hotswap` before loading the first adapter.") elif check_compiled == "warn": logger.warning( "It is recommended to call `enable_peft_hotswap` before loading the first adapter to avoid recompilation." ) elif check_compiled != "ignore": raise ValueError( f"check_compiles should be one of 'error', 'warn', or 'ignore', got '{check_compiled}' instead." ) self._hotswap_enabled = True self._prepare_peft_hotswap_kwargs = {"target_rank": target_rank, "check_compiled": check_compiled} def add_adapter(self, adapter_config, adapter_name: str | None = None) -> None: r""" If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: https://huggingface.co/docs/peft Adds a fresh new adapter to the current model for training purpose. If no adapter name is passed, a default name is assigned to the adapter to follow the convention of PEFT library (in PEFT we use "default" as the default adapter name). Note that the newly added adapter is not automatically activated. To activate it, use `model.set_adapter`. Args: adapter_config (`~peft.PeftConfig`): The configuration of the adapter to add, supported adapters are non-prompt learning methods (LoRA, IA³, etc.). adapter_name (`str`, *optional*, defaults to `"default"`): The name of the adapter to add. If no name is passed, a default name is assigned to the adapter. """ check_peft_version(min_version=MIN_PEFT_VERSION) from peft import PeftConfig, inject_adapter_in_model adapter_name = adapter_name or "default" if not self._hf_peft_config_loaded: self._hf_peft_config_loaded = True elif adapter_name in self.peft_config: raise ValueError(f"Adapter with name {adapter_name} already exists. Please use a different name.") if not isinstance(adapter_config, PeftConfig): raise TypeError(f"adapter_config should be an instance of PeftConfig. Got {type(adapter_config)} instead.") # Retrieve the name or path of the model, one could also use self.config._name_or_path # but to be consistent with what we do in PEFT: https://github.com/huggingface/peft/blob/6e783780ca9df3a623992cc4d1d665001232eae0/src/peft/mapping.py#L100 adapter_config.base_model_name_or_path = self.__dict__.get("name_or_path", None) # TODO: WE NEED TOO APPLY OUR DYNAMIC WEIGHT CONVERSION AT SOME POINT HERE! inject_adapter_in_model(adapter_config, self, adapter_name) self.set_adapter(adapter_name) def set_adapter(self, adapter_name: list[str] | str) -> None: """ If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: https://huggingface.co/docs/peft Sets a specific adapter by forcing the model to use a that adapter and disable the other adapters. Args: adapter_name (`Union[list[str], str]`): The name of the adapter to set. Can be also a list of strings to set multiple adapters. """ check_peft_version(min_version=MIN_PEFT_VERSION) if not self._hf_peft_config_loaded: raise ValueError("No adapter loaded. Please load an adapter first.") elif isinstance(adapter_name, list): missing = set(adapter_name) - set(self.peft_config) if len(missing) > 0: raise ValueError( f"Following adapter(s) could not be found: {', '.join(missing)}. Make sure you are passing the correct adapter name(s)." f" current loaded adapters are: {list(self.peft_config.keys())}" ) elif adapter_name not in self.peft_config: raise ValueError( f"Adapter with name {adapter_name} not found. Please pass the correct adapter name among {list(self.peft_config.keys())}" ) from peft.tuners.tuners_utils import BaseTunerLayer from peft.utils import ModulesToSaveWrapper _adapters_has_been_set = False for _, module in self.named_modules(): if isinstance(module, (BaseTunerLayer, ModulesToSaveWrapper)): module.set_adapter(adapter_name) _adapters_has_been_set = True if not _adapters_has_been_set: raise ValueError( "Did not succeeded in setting the adapter. Please make sure you are using a model that supports adapters." ) def disable_adapters(self) -> None: r""" If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: https://huggingface.co/docs/peft Disable all adapters that are attached to the model. This leads to inferring with the base model only. """ check_peft_version(min_version=MIN_PEFT_VERSION) if not self._hf_peft_config_loaded: raise ValueError("No adapter loaded. Please load an adapter first.") from peft.tuners.tuners_utils import BaseTunerLayer from peft.utils import ModulesToSaveWrapper for _, module in self.named_modules(): if isinstance(module, (BaseTunerLayer, ModulesToSaveWrapper)): module.enable_adapters(enabled=False) def enable_adapters(self) -> None: """ If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: https://huggingface.co/docs/peft Enable adapters that are attached to the model. """ check_peft_version(min_version=MIN_PEFT_VERSION) if not self._hf_peft_config_loaded: raise ValueError("No adapter loaded. Please load an adapter first.") from peft.tuners.tuners_utils import BaseTunerLayer for _, module in self.named_modules(): if isinstance(module, BaseTunerLayer): module.enable_adapters(enabled=True) def active_adapters(self) -> list[str]: """ If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: https://huggingface.co/docs/peft Gets the current active adapters of the model. In case of multi-adapter inference (combining multiple adapters for inference) returns the list of all active adapters so that users can deal with them accordingly. For previous PEFT versions (that does not support multi-adapter inference), `module.active_adapter` will return a single string. """ check_peft_version(min_version=MIN_PEFT_VERSION) if not self._hf_peft_config_loaded: raise ValueError("No adapter loaded. Please load an adapter first.") from peft.tuners.tuners_utils import BaseTunerLayer for _, module in self.named_modules(): if isinstance(module, BaseTunerLayer): active_adapters = module.active_adapter break # For previous PEFT versions if isinstance(active_adapters, str): active_adapters = [active_adapters] return active_adapters def get_adapter_state_dict(self, adapter_name: str | None = None, state_dict: dict | None = None) -> dict: """ If you are not familiar with adapters and PEFT methods, we invite you to read more about them on the PEFT official documentation: https://huggingface.co/docs/peft Gets the adapter state dict that should only contain the weights tensors of the specified adapter_name adapter. If no adapter_name is passed, the active adapter is used. Args: adapter_name (`str`, *optional*): The name of the adapter to get the state dict from. If no name is passed, the active adapter is used. state_dict (nested dictionary of `torch.Tensor`, *optional*) The state dictionary of the model. Will default to `self.state_dict()`, but can be used if special precautions need to be taken when recovering the state dictionary of a model (like when using model parallelism). """ check_peft_version(min_version=MIN_PEFT_VERSION) if not self._hf_peft_config_loaded: raise ValueError("No adapter loaded. Please load an adapter first.") from peft import get_peft_model_state_dict if adapter_name is None: adapter_name = self.active_adapters()[0] adapter_state_dict = get_peft_model_state_dict(self, state_dict=state_dict, adapter_name=adapter_name) return adapter_state_dict def _dispatch_accelerate_model( self, device_map: str, max_memory: int | None = None, offload_folder: str | None = None, offload_index: int | None = None, ) -> None: """ Optional re-dispatch the model and attach new hooks to the model in case the model has been loaded with accelerate (i.e. with `device_map=xxx`) Args: device_map (`str` or `dict[str, Union[int, str, torch.device]]` or `int` or `torch.device`, *optional*): A map that specifies where each submodule should go. It doesn't need to be refined to each parameter/buffer name, once a given module name is inside, every submodule of it will be sent to the same device. If we only pass the device (*e.g.*, `"cpu"`, `"cuda:1"`, `"mps"`, or a GPU ordinal rank like `1`) on which the model will be allocated, the device map will map the entire model to this device. Passing `device_map = 0` means put the whole model on GPU 0. To have Accelerate compute the most optimized `device_map` automatically, set `device_map="auto"`. For more information about each option see [designing a device map](https://hf.co/docs/accelerate/main/en/usage_guides/big_modeling#designing-a-device-map). max_memory (`Dict`, *optional*): A dictionary device identifier to maximum memory. Will default to the maximum memory available for each GPU and the available CPU RAM if unset. offload_folder (`str` or `os.PathLike`, *optional*): If the `device_map` contains any value `"disk"`, the folder where we will offload weights. offload_index (`int`, *optional*): The offload_index argument to be passed to `accelerate.dispatch_model` method. """ dispatch_model_kwargs = {} # Safety checker for previous `accelerate` versions # `offload_index` was introduced in https://github.com/huggingface/accelerate/pull/873/ if "offload_index" in inspect.signature(dispatch_model).parameters: dispatch_model_kwargs["offload_index"] = offload_index no_split_module_classes = self._no_split_modules if device_map != "sequential": max_memory = get_balanced_memory( self, max_memory=max_memory, no_split_module_classes=no_split_module_classes, low_zero=(device_map == "balanced_low_0"), ) if isinstance(device_map, str): device_map = infer_auto_device_map( self, max_memory=max_memory, no_split_module_classes=no_split_module_classes ) dispatch_model( self, device_map=device_map, offload_dir=offload_folder, **dispatch_model_kwargs, ) def delete_adapter(self, adapter_names: list[str] | str) -> None: """ Delete a PEFT adapter from the underlying model. Args: adapter_names (`Union[list[str], str]`): The name(s) of the adapter(s) to delete. """ check_peft_version(min_version=MIN_PEFT_VERSION) if not self._hf_peft_config_loaded: raise ValueError("No adapter loaded. Please load an adapter first.") from peft.functional import delete_adapter if isinstance(adapter_names, str): adapter_names = [adapter_names] # Check that all adapter names are present in the config missing_adapters = [name for name in adapter_names if name not in self.peft_config] if missing_adapters: raise ValueError( f"The following adapter(s) are not present and cannot be deleted: {', '.join(missing_adapters)}" ) prefixes = [f"{self.peft_config[adapter_name].peft_type.value.lower()}_" for adapter_name in adapter_names] for adapter_name, prefix in zip(adapter_names, prefixes): delete_adapter(self, adapter_name=adapter_name, prefix=prefix) # For transformers integration - we need to pop the adapter from the config if getattr(self, "_hf_peft_config_loaded", False) and hasattr(self, "peft_config"): self.peft_config.pop(adapter_name, None) # In case all adapters are deleted, we need to delete the config # and make sure to set the flag to False if len(self.peft_config) == 0: del self.peft_config self._hf_peft_config_loaded = False def maybe_load_adapters( pretrained_model_name_or_path, download_kwargs: DownloadKwargs, **adapter_kwargs, ): if pretrained_model_name_or_path is None or not is_peft_available(): return None, pretrained_model_name_or_path, adapter_kwargs token = download_kwargs.get("token") if download_kwargs.get("commit_hash") is None: resolved_config_file = cached_file( pretrained_model_name_or_path, CONFIG_NAME, cache_dir=download_kwargs.get("cache_dir"), force_download=bool(download_kwargs.get("force_download", False)), proxies=download_kwargs.get("proxies"), local_files_only=bool(download_kwargs.get("local_files_only", False)), token=token, revision=download_kwargs.get("revision"), subfolder=download_kwargs.get("subfolder"), _raise_exceptions_for_gated_repo=False, _raise_exceptions_for_missing_entries=False, _raise_exceptions_for_connection_errors=False, ) download_kwargs["commit_hash"] = extract_commit_hash(resolved_config_file, None) _adapter_model_path = adapter_kwargs.pop("_adapter_model_path", None) token_from_adapter_kwargs = adapter_kwargs.pop("token", None) if _adapter_model_path is None: peft_kwargs = adapter_kwargs.copy() for arg_name in ("cache_dir", "proxies", "subfolder"): # don't override revision if (arg_name not in peft_kwargs) and (arg_name in download_kwargs): peft_kwargs[arg_name] = download_kwargs[arg_name] if "commit_hash" in download_kwargs: peft_kwargs["_commit_hash"] = download_kwargs["commit_hash"] peft_kwargs["force_download"] = bool(download_kwargs.get("force_download", False)) peft_kwargs["local_files_only"] = bool(download_kwargs.get("local_files_only", False)) peft_kwargs["token"] = token or token_from_adapter_kwargs _adapter_model_path = find_adapter_config_file( pretrained_model_name_or_path, **peft_kwargs, ) if _adapter_model_path is not None and os.path.isfile(_adapter_model_path): with open(_adapter_model_path, "r", encoding="utf-8") as f: _adapter_model_path = pretrained_model_name_or_path # Only override the model name/path if the current value doesn't point to a # complete model with an embedded adapter so that local models with embedded # adapters will load from the local base model rather than pull the base # model named in the adapter's config from the hub. if not os.path.exists(pretrained_model_name_or_path) or not os.path.exists( os.path.join(pretrained_model_name_or_path, CONFIG_NAME) ): pretrained_model_name_or_path = json.load(f)["base_model_name_or_path"] return _adapter_model_path, pretrained_model_name_or_path, adapter_kwargs