LLaMA-Factory/src/llmtuner/tuner/core/adapter.py

import os
import torch
from typing import TYPE_CHECKING

from transformers.utils import cached_file
from transformers.trainer import WEIGHTS_NAME, SAFE_WEIGHTS_NAME
from peft import (
    PeftModel,
    TaskType,
    LoraConfig,
    get_peft_model
)

from llmtuner.extras.logging import get_logger
from llmtuner.tuner.core.utils import find_all_linear_modules

if TYPE_CHECKING:
    from transformers.modeling_utils import PreTrainedModel
    from llmtuner.hparams import ModelArguments, FinetuningArguments


logger = get_logger(__name__)


def init_adapter(
    model: "PreTrainedModel",
    model_args: "ModelArguments",
    finetuning_args: "FinetuningArguments",
    is_trainable: bool
) -> "PreTrainedModel":
    r"""
    Initializes the adapters.

    Support full-parameter, freeze and LoRA training.

    Note that the trainable parameters must be cast to float32.
    """

    if (not is_trainable) and model_args.checkpoint_dir is None:
        logger.info("Checkpoint is not found at evaluation, load the original model.")

    if finetuning_args.finetuning_type == "full" and is_trainable:
        logger.info("Fine-tuning method: Full")
        model = model.float()

    if finetuning_args.finetuning_type == "freeze":
        logger.info("Fine-tuning method: Freeze")
        num_layers = getattr(model.config, "num_layers")
        if finetuning_args.num_layer_trainable > 0: # fine-tuning the last n layers if num_layer_trainable > 0
            trainable_layer_ids = [num_layers - k - 1 for k in range(finetuning_args.num_layer_trainable)]
        else: # fine-tuning the first n layers if num_layer_trainable < 0
            trainable_layer_ids = [k for k in range(-finetuning_args.num_layer_trainable)]

        trainable_layers = ["{:d}.{}".format(idx, finetuning_args.name_module_trainable) for idx in trainable_layer_ids]
        for name, param in model.named_parameters():
            if not any(trainable_layer in name for trainable_layer in trainable_layers):
                param.requires_grad_(False)
            else:
                param.data = param.data.to(torch.float32)

    if finetuning_args.finetuning_type == "lora":
        logger.info("Fine-tuning method: LoRA")
        checkpoint_to_resume = None

        if model_args.checkpoint_dir is not None:
            if is_trainable and finetuning_args.resume_lora_training:
                checkpoints_to_merge, checkpoint_to_resume = model_args.checkpoint_dir[:-1], model_args.checkpoint_dir[-1]
            else:
                checkpoints_to_merge = model_args.checkpoint_dir

            for checkpoint in checkpoints_to_merge:
                model = PeftModel.from_pretrained(model, checkpoint)
                model = model.merge_and_unload()

            if len(checkpoints_to_merge) > 0:
                logger.info("Merged {} model checkpoint(s).".format(len(checkpoints_to_merge)))

            if checkpoint_to_resume is not None: # resume lora training
                model = PeftModel.from_pretrained(model, checkpoint_to_resume, is_trainable=is_trainable)

        if is_trainable and checkpoint_to_resume is None: # create new lora weights while training
            if len(finetuning_args.lora_target) == 1 and finetuning_args.lora_target[0] == "all":
                target_modules = find_all_linear_modules(model, model_args.quantization_bit)
            else:
                target_modules = finetuning_args.lora_target

            lora_config = LoraConfig(
                task_type=TaskType.CAUSAL_LM,
                inference_mode=False,
                r=finetuning_args.lora_rank,
                lora_alpha=finetuning_args.lora_alpha,
                lora_dropout=finetuning_args.lora_dropout,
                target_modules=target_modules,
                modules_to_save=finetuning_args.additional_target
            )
            model = get_peft_model(model, lora_config)

    if model_args.checkpoint_dir is not None:
        logger.info("Loaded fine-tuned model from checkpoint(s): {}".format(",".join(model_args.checkpoint_dir)))

    return model


def load_valuehead_params(
    model: "PreTrainedModel",
    model_args: "ModelArguments"
) -> bool:
    kwargs = {
        "path_or_repo_id": model_args.reward_model,
        "cache_dir": model_args.cache_dir,
        "token": model_args.hf_hub_token,
        "revision": model_args.model_revision
    }
    try:
        vhead_file = cached_file(filename=WEIGHTS_NAME, **kwargs)
    except:
        try:
            vhead_file = cached_file(filename=SAFE_WEIGHTS_NAME, **kwargs)
        except:
            logger.warning("Provided path ({}) does not contain valuehead weights.".format(model_args.reward_model))
            return False

    vhead_params = torch.load(vhead_file, map_location="cpu")
    model.register_buffer("reward_head_weight", vhead_params["v_head.summary.weight"], persistent=False)
    model.register_buffer("reward_head_bias", vhead_params["v_head.summary.bias"], persistent=False)
    model.register_buffer("default_head_weight", torch.zeros_like(vhead_params["v_head.summary.weight"]), persistent=False)
    model.register_buffer("default_head_bias", torch.zeros_like(vhead_params["v_head.summary.bias"]), persistent=False)
    return True