mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-04 20:52:59 +08:00
112 lines
4.6 KiB
Python
112 lines
4.6 KiB
Python
import torch
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from typing import TYPE_CHECKING
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from peft import PeftModel, TaskType, LoraConfig, get_peft_model
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from llmtuner.extras.logging import get_logger
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from llmtuner.model.utils import find_all_linear_modules
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if TYPE_CHECKING:
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from transformers.modeling_utils import PreTrainedModel
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from llmtuner.hparams import ModelArguments, FinetuningArguments
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logger = get_logger(__name__)
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def init_adapter(
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model: "PreTrainedModel",
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model_args: "ModelArguments",
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finetuning_args: "FinetuningArguments",
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is_trainable: bool
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) -> "PreTrainedModel":
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r"""
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Initializes the adapters.
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Support full-parameter, freeze and LoRA training.
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Note that the trainable parameters must be cast to float32.
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"""
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if (not is_trainable) and model_args.checkpoint_dir is None:
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logger.info("Checkpoint is not found at evaluation, load the original model.")
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return model
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if finetuning_args.finetuning_type == "full" and is_trainable:
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logger.info("Fine-tuning method: Full")
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model = model.float()
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if finetuning_args.finetuning_type == "freeze" and is_trainable:
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logger.info("Fine-tuning method: Freeze")
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num_layers = (
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getattr(model.config, "num_hidden_layers", None)
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or getattr(model.config, "num_layers", None)
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or getattr(model.config, "n_layer", None)
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)
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if not num_layers:
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raise ValueError("Current model does not support freeze tuning.")
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if finetuning_args.num_layer_trainable > 0: # fine-tuning the last n layers if num_layer_trainable > 0
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trainable_layer_ids = [num_layers - k - 1 for k in range(finetuning_args.num_layer_trainable)]
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else: # fine-tuning the first n layers if num_layer_trainable < 0
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trainable_layer_ids = [k for k in range(-finetuning_args.num_layer_trainable)]
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trainable_layers = []
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for module_name in finetuning_args.name_module_trainable:
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for idx in trainable_layer_ids:
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trainable_layers.append("{:d}.{}".format(idx, module_name))
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for name, param in model.named_parameters():
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if not any(trainable_layer in name for trainable_layer in trainable_layers):
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param.requires_grad_(False)
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else:
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param.data = param.data.to(torch.float32)
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if finetuning_args.finetuning_type == "lora":
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logger.info("Fine-tuning method: LoRA")
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checkpoint_to_resume = None
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if model_args.checkpoint_dir is not None:
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is_mergeable = True
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if getattr(model, "quantization_method", None): # merge lora in quantized model is unstable
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assert len(model_args.checkpoint_dir) == 1, "Quantized model only accepts a single checkpoint."
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is_mergeable = False
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if (is_trainable and finetuning_args.resume_lora_training) or (not is_mergeable):
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checkpoints_to_merge, checkpoint_to_resume = model_args.checkpoint_dir[:-1], model_args.checkpoint_dir[-1]
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else:
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checkpoints_to_merge = model_args.checkpoint_dir
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for checkpoint in checkpoints_to_merge:
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model = PeftModel.from_pretrained(model, checkpoint)
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model = model.merge_and_unload()
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if len(checkpoints_to_merge) > 0:
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logger.info("Merged {} model checkpoint(s).".format(len(checkpoints_to_merge)))
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if checkpoint_to_resume is not None: # resume lora training
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model = PeftModel.from_pretrained(model, checkpoint_to_resume, is_trainable=is_trainable)
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if is_trainable and checkpoint_to_resume is None: # create new lora weights while training
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if len(finetuning_args.lora_target) == 1 and finetuning_args.lora_target[0] == "all":
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target_modules = find_all_linear_modules(model)
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else:
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target_modules = finetuning_args.lora_target
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lora_config = LoraConfig(
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task_type=TaskType.CAUSAL_LM,
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inference_mode=False,
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r=finetuning_args.lora_rank,
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lora_alpha=finetuning_args.lora_alpha,
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lora_dropout=finetuning_args.lora_dropout,
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target_modules=target_modules,
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modules_to_save=finetuning_args.additional_target
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)
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model = get_peft_model(model, lora_config)
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for param in filter(lambda p: p.requires_grad, model.parameters()):
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param.data = param.data.to(torch.float32)
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if model_args.checkpoint_dir is not None:
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logger.info("Loaded fine-tuned model from checkpoint(s): {}".format(",".join(model_args.checkpoint_dir)))
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return model
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