Llama Factory 笔记

本地环境：cuda 11.7    torch2.1.0

项目文件结构：

1. 项目文件结构：

如果利用llama factory 进行微调主要会用到  llama-factory/src 中的文件

2. src 下的目录结构

本地推理的demo    

通过api.py 进行 llama-factory 项目文件下运行，会有一个 web的demo 

(可能需要修改 gradio 下面一个包的权限，创建一个公共的端口就可以)

cuda_visible_devices=1  python src/api.py     --model_name_or_path  llama/llama3-8b-chinese-chat     --template llama3 

我运行之后打不开  网址 所以 根据之前的 为了简单起见 还是用 cli_demo.py 放在 src 路径下

from llamafactory.chat import chatmodel
from llamafactory.extras.misc import torch_gc

try:
    import platform

    if platform.system() != "windows":
        import readline  # noqa: f401
except importerror:
    print("install `readline` for a better experience.")


def main():
    chat_model = chatmodel()
    messages = []
    print("welcome to the cli application, use `clear` to remove the history, use `exit` to exit the application.")

    while true:
        try:
            query = input("\nuser: ")
        except unicodedecodeerror:
            print("detected decoding error at the inputs, please set the terminal encoding to utf-8.")
            continue
        except exception:
            raise

        if query.strip() == "exit":
            break

        if query.strip() == "clear":
            messages = []
            torch_gc()
            print("history has been removed.")
            continue

        messages.append({"role": "user", "content": query})
        print("assistant: ", end="", flush=true)

        response = ""
        for new_text in chat_model.stream_chat(messages):
            print(new_text, end="", flush=true)
            response += new_text
        print()
        messages.append({"role": "assistant", "content": response})


if __name__ == "__main__":
    main()

cuda_visible_devices=0  python src/cli_demo.py     --model_name_or_path  自己模型地址    --template 和模型有关（看github 的 readme）

遇到的问题：如果torch的版本低会有一个 bfloat16 的问题(开始是 2.0.1 报错了)

                        升级成 2.1.0 就好了

pytorch 官网 2.1.0 应该最低是cuda11.8 的 直接升级成这个就行 conda install 速度会快一些

可以在命令行进行展示：效果如下：

=============   以上是 2024.05.29 的 最新 llama factory 版本   =====================

本地微调：

再进行微调的时，主要就是 运行train.py 这个文件，但是需要指定一些参数 比如模型路径 数据集 微调方式等

train.py 内容

from llamafactory.train.tuner import run_exp

def main():
    run_exp()

def _mp_fn(index):
    # for xla_spawn (tpus)
    run_exp()

if __name__ == "__main__":
    main()

可以看到 train.py  就是用到了 llamafactory.train.tuner ，所以进一步看一下 llamafactory 文件的目录结构

llamafactory/train 的 结构：

tuner.py 内容如下：python 相对导入：python 相对导入-csdn博客

from typing import type_checking, any, dict, list, optional

import torch
from transformers import pretrainedmodel

from ..data import get_template_and_fix_tokenizer
from ..extras.callbacks import logcallback
from ..extras.logging import get_logger
from ..hparams import get_infer_args, get_train_args
from ..model import load_model, load_tokenizer
from .dpo import run_dpo
from .kto import run_kto
from .ppo import run_ppo
from .pt import run_pt
from .rm import run_rm
from .sft import run_sft


if type_checking:
    from transformers import trainercallback


logger = get_logger(__name__)


def run_exp(args: optional[dict[str, any]] = none, callbacks: list["trainercallback"] = []) -> none:
    model_args, data_args, training_args, finetuning_args, generating_args = get_train_args(args)
    callbacks.append(logcallback(training_args.output_dir))

    if finetuning_args.stage == "pt":
        run_pt(model_args, data_args, training_args, finetuning_args, callbacks)
    elif finetuning_args.stage == "sft":
        run_sft(model_args, data_args, training_args, finetuning_args, generating_args, callbacks)
    elif finetuning_args.stage == "rm":
        run_rm(model_args, data_args, training_args, finetuning_args, callbacks)
    elif finetuning_args.stage == "ppo":
        run_ppo(model_args, data_args, training_args, finetuning_args, generating_args, callbacks)
    elif finetuning_args.stage == "dpo":
        run_dpo(model_args, data_args, training_args, finetuning_args, callbacks)
    elif finetuning_args.stage == "kto":
        run_kto(model_args, data_args, training_args, finetuning_args, callbacks)
    else:
        raise valueerror("unknown task.")


def export_model(args: optional[dict[str, any]] = none) -> none:
    model_args, data_args, finetuning_args, _ = get_infer_args(args)

    if model_args.export_dir is none:
        raise valueerror("please specify `export_dir` to save model.")

    if model_args.adapter_name_or_path is not none and model_args.export_quantization_bit is not none:
        raise valueerror("please merge adapters before quantizing the model.")

    tokenizer_module = load_tokenizer(model_args)
    tokenizer = tokenizer_module["tokenizer"]
    processor = tokenizer_module["processor"]
    get_template_and_fix_tokenizer(tokenizer, data_args.template)
    model = load_model(tokenizer, model_args, finetuning_args)  # must after fixing tokenizer to resize vocab

    if getattr(model, "quantization_method", none) and model_args.adapter_name_or_path is not none:
        raise valueerror("cannot merge adapters to a quantized model.")

    if not isinstance(model, pretrainedmodel):
        raise valueerror("the model is not a `pretrainedmodel`, export aborted.")

    if getattr(model, "quantization_method", none) is none:  # cannot convert dtype of a quantized model
        output_dtype = getattr(model.config, "torch_dtype", torch.float16)
        setattr(model.config, "torch_dtype", output_dtype)
        model = model.to(output_dtype)
    else:
        setattr(model.config, "torch_dtype", torch.float16)

    model.save_pretrained(
        save_directory=model_args.export_dir,
        max_shard_size="{}gb".format(model_args.export_size),
        safe_serialization=(not model_args.export_legacy_format),
    )
    if model_args.export_hub_model_id is not none:
        model.push_to_hub(
            model_args.export_hub_model_id,
            token=model_args.hf_hub_token,
            max_shard_size="{}gb".format(model_args.export_size),
            safe_serialization=(not model_args.export_legacy_format),
        )

    try:
        tokenizer.padding_side = "left"  # restore padding side
        tokenizer.init_kwargs["padding_side"] = "left"
        tokenizer.save_pretrained(model_args.export_dir)
        if model_args.export_hub_model_id is not none:
            tokenizer.push_to_hub(model_args.export_hub_model_id, token=model_args.hf_hub_token)

        if model_args.visual_inputs and processor is not none:
            getattr(processor, "image_processor").save_pretrained(model_args.export_dir)
            if model_args.export_hub_model_id is not none:
                getattr(processor, "image_processor").push_to_hub(
                    model_args.export_hub_model_id, token=model_args.hf_hub_token
                )

    except exception:
        logger.warning("cannot save tokenizer, please copy the files manually.")

可以看到 包含两个函数:

  1. run_exp()   根据传入参数的不同选择不同的方式 

  2. export_model: 将原来的模型和微调之后的checkpoint 进行合并  

到这里就基本上完成了  流程上的梳理  具体的微调方法需要到每个函数内部自行查看

=======================  以上 2024/05/27 ========================

怎么finetuning起来？

写一个脚本 train.sh ，放在 llama-factory 根目录下：终端运行   bash train.sh 即可

cuda_visible_devices=0 python src/train.py \
    --stage sft \
    --do_train true \
    --model_name_or_path 自己模型的路径\
    --finetuning_type lora \
    --template default \
    --flash_attn auto \
    --dataset_dir data \
    --dataset 自己的数据集\
    --cutoff_len 1024 \
    --learning_rate 5e-05 \
    --num_train_epochs 1.0 \
    --max_samples 100000 \
    --per_device_train_batch_size 2 \
    --gradient_accumulation_steps 8 \
    --lr_scheduler_type cosine \
    --max_grad_norm 1.0 \
    --logging_steps 5 \
    --save_steps 100 \
    --warmup_steps 0 \
    --optim adamw_torch \
    --report_to none \
    --output_dir 模型微调完成之后adapter的输出位置 \
    --fp16 true \
    --lora_rank 8 \
    --lora_alpha 16 \
    --lora_dropout 0 \
    --lora_target q_proj,v_proj \
    --plot_loss true

具体的参数  batch_size ，lora_rank 需自行确定

推理： 

cuda_visible_devices=0 python src/cli_demo.py
--model_name_or_path  模型地址
--adapter_name_or_path  训练出来的适配器的位置
--template  提示词模版和模型相关

即可成功 ！

注：暂时没用 vllm 框架，用的话可能问题较多