基于 LlamaFactory 的 LoRA 微调模型支持 vllm 批量推理的实现

背景

LlamaFactory 的 LoRA 微调功能非常便捷，微调后的模型，没有直接支持 vllm 推理，故导致推理速度不够快。

LlamaFactory 目前支持通过 VLLM API 进行部署，调用 API 时的响应速度，仍然没有vllm批量推理的速度快。

如果模型是通过 LlamaFactory 微调的，为了确保数据集的一致性，建议在推理时也使用 LlamaFactory 提供的封装数据集。

简介

在上述的背景下，我们使用 LlamaFactory 原生数据集，支持 lora的 vllm 批量推理。

完整代码如下：

c 复制代码

import json
import os
from typing import List

from vllm import LLM, SamplingParams
from vllm.lora.request import LoRARequest

from llamafactory.data import get_dataset, get_template_and_fix_tokenizer
from llamafactory.extras.constants import IGNORE_INDEX
from llamafactory.hparams import get_train_args
from llamafactory.model import load_tokenizer

def vllm_infer():
    model_args, data_args, training_args, finetuning_args, generating_args = (
        get_train_args()
    )
    tokenizer = load_tokenizer(model_args)["tokenizer"]
    template = get_template_and_fix_tokenizer(tokenizer, data_args)

    eval_dataset = get_dataset(
        template, model_args, data_args, training_args, finetuning_args.stage, tokenizer
    )["eval_dataset"]

    prompts = [item["input_ids"] for item in eval_dataset]
    prompts = tokenizer.batch_decode(prompts, skip_special_tokens=False)

    labels = [
        list(filter(lambda x: x != IGNORE_INDEX, item["labels"]))
        for item in eval_dataset
    ]
    labels = tokenizer.batch_decode(labels, skip_special_tokens=True)

    sampling_params = SamplingParams(
        temperature=generating_args.temperature,
        top_k=generating_args.top_k,
        top_p=generating_args.top_p,
        max_tokens=2048,
    )

    if model_args.adapter_name_or_path:
        if isinstance(model_args.adapter_name_or_path, list):
            lora_requests = []
            for i, _lora_path in enumerate(model_args.adapter_name_or_path):
                lora_requests.append(
                    LoRARequest(f"lora_adapter_{i}", i, lora_path=_lora_path)
                )
        else:
            lora_requests = LoRARequest(
                "lora_adapter_0", 0, lora_path=model_args.adapter_name_or_path
            )

        enable_lora = True
    else:
        lora_requests = None
        enable_lora = False

    llm = LLM(
        model=model_args.model_name_or_path,
        trust_remote_code=True,
        tokenizer=model_args.model_name_or_path,
        enable_lora=enable_lora,
    )

    outputs = llm.generate(prompts, sampling_params, lora_request=lora_requests)

    if not os.path.exists(training_args.output_dir):
        os.makedirs(training_args.output_dir, exist_ok=True)

    output_prediction_file = os.path.join(
        training_args.output_dir, "generated_predictions.jsonl"
    )

    with open(output_prediction_file, "w", encoding="utf-8") as writer:
        res: List[str] = []
        for text, pred, label in zip(prompts, outputs, labels):
            res.append(
                json.dumps(
                    {"prompt": text, "predict": pred.outputs[0].text, "label": label},
                    ensure_ascii=False,
                )
            )
        writer.write("\n".join(res))

vllm.yaml 示例:

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## model
model_name_or_path: qwen/Qwen2.5-7B-Instruct
# adapter_name_or_path: lora模型

### method
stage: sft
do_predict: true
finetuning_type: lora

### dataset
dataset_dir: 数据集路径
eval_dataset: 数据集
template: qwen
cutoff_len: 1024
max_samples: 1000
overwrite_cache: true
preprocessing_num_workers: 16

### output
output_dir: output/
overwrite_output_dir: true

### eval
predict_with_generate: true

程序调用:

shell 复制代码

python vllm_infer.py vllm.yaml

程序运行速度：

shell 复制代码

Processed prompts: 100%|█| 1000/1000 [01:56<00:00,  8.60it/s, est. speed input: 5169.35 toks/s, output: 811.57

总结

本方案在原生 LlamaFactory 数据集的基础上，支持 LoRA 的 vllm 批量推理，能提升了推理效率。