[`from_pretrained`] Simpler code for peft (#25726)
* refactor complicated from pretrained for peft * nits * more nits * Update src/transformers/modeling_utils.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * make tests happy * fixup after merge --------- Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
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@ -2391,33 +2391,23 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
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else:
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commit_hash = getattr(config, "_commit_hash", None)
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if is_peft_available() and _adapter_model_path is None:
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maybe_adapter_model_path = find_adapter_config_file(
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pretrained_model_name_or_path,
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cache_dir=cache_dir,
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force_download=force_download,
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resume_download=resume_download,
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proxies=proxies,
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local_files_only=local_files_only,
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token=token,
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revision=revision,
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subfolder=subfolder,
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_commit_hash=commit_hash,
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)
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elif is_peft_available() and _adapter_model_path is not None:
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maybe_adapter_model_path = _adapter_model_path
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else:
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maybe_adapter_model_path = None
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has_adapter_config = maybe_adapter_model_path is not None
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if has_adapter_config:
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if _adapter_model_path is not None:
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adapter_model_id = _adapter_model_path
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else:
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with open(maybe_adapter_model_path, "r", encoding="utf-8") as f:
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adapter_model_id = pretrained_model_name_or_path
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pretrained_model_name_or_path = json.load(f)["base_model_name_or_path"]
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if is_peft_available():
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if _adapter_model_path is None:
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_adapter_model_path = find_adapter_config_file(
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pretrained_model_name_or_path,
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cache_dir=cache_dir,
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force_download=force_download,
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resume_download=resume_download,
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proxies=proxies,
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local_files_only=local_files_only,
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token=token,
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revision=revision,
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subfolder=subfolder,
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_commit_hash=commit_hash,
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)
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if _adapter_model_path is not None and os.path.isfile(_adapter_model_path):
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with open(_adapter_model_path, "r", encoding="utf-8"):
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_adapter_model_path = pretrained_model_name_or_path
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# change device_map into a map if we passed an int, a str or a torch.device
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if isinstance(device_map, torch.device):
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@ -3246,9 +3236,9 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
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if quantization_method_from_config == QuantizationMethod.GPTQ:
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model = quantizer.post_init_model(model)
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if has_adapter_config:
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if _adapter_model_path is not None:
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model.load_adapter(
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adapter_model_id,
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_adapter_model_path,
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adapter_name=adapter_name,
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revision=revision,
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token=token,
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