使用Python语言部署 QWQ-32B 大语言模型

大模型部署

福将～白鹿

819人浏览 · 2025-03-25 09:33:23

福将～白鹿 · 2025-03-25 09:33:23 发布

以下是使用 Python + Flask + vLLM 部署 QWQ-32B 大语言模型的完整实现代码，包含详细注释和优化配置：

# -*- coding: utf-8 -*-
# qwq32b_deploy.py
import os
import time
from typing import Dict, List
from flask import Flask, request, jsonify
from vllm import AsyncLLMEngine, SamplingParams
from vllm.engine.arg_utils import AsyncEngineArgs
from concurrent.futures import ThreadPoolExecutor

# ========== 配置区 ==========
class Config:
    MODEL_PATH = "/path/to/QWQ-32B"  # 模型路径 (需包含config.json和模型权重)
    HOST = "0.0.0.0"
    PORT = 8000
    MAX_TOKENS = 1024                # 默认生成最大token数
    TEMPERATURE = 0.7                # 默认温度参数
    TOP_P = 0.9                      # 默认top-p采样
    GPU_MEM_UTIL = 0.85              # GPU显存利用率
    TENSOR_PARALLEL = 2              # 张量并行度 (根据GPU数量调整)
    MAX_CONCURRENT = 100             # 最大并发请求数

# ========== 初始化 ==========
def create_engine():
    """初始化vLLM异步推理引擎"""
    engine_args = AsyncEngineArgs(
        model=Config.MODEL_PATH,
        tensor_parallel_size=Config.TENSOR_PARALLEL,
        gpu_memory_utilization=Config.GPU_MEM_UTIL,
        max_num_seqs=Config.MAX_CONCURRENT,
        max_model_len=4096,           # 最大上下文长度
        trust_remote_code=True,       # 允许自定义模型代码
        quantization="awq",           # 使用AWQ量化 (如果模型支持)
        enable_prefix_caching=True,    # 启用前缀缓存
        block_size=16,                # KV缓存块大小
        swap_space=8                  # CPU-GPU交换空间(GB)
    )
    return AsyncLLMEngine.from_engine_args(engine_args)

# ========== Flask应用 ==========
app = Flask(__name__)
engine = create_engine()
executor = ThreadPoolExecutor(max_workers=Config.MAX_CONCURRENT)

# ========== 工具函数 ==========
def generate_request_id():
    """生成唯一请求ID"""
    return f"{int(time.time())}-{os.urandom(4).hex()}"

def validate_input(data: Dict) -> Dict:
    """验证输入参数"""
    defaults = {
        "prompt": "",
        "max_tokens": Config.MAX_TOKENS,
        "temperature": Config.TEMPERATURE,
        "top_p": Config.TOP_P,
        "stop": None
    }
    return {k: data.get(k, v) for k, v in defaults.items()}

# ========== 核心接口 ==========
# 备注此处也可以调换为标准OpenAI接口风格
@app.route('/generate', methods=['POST'])
async def generate():
    """文本生成主接口"""
    try:
        # 参数验证
        data = validate_input(request.json)
        
        # 构建采样参数
        sampling_params = SamplingParams(
            n=1,
            max_tokens=data["max_tokens"],
            temperature=data["temperature"],
            top_p=data["top_p"],
            stop=data["stop"],
            frequency_penalty=0.5,
            presence_penalty=0.5
        )

        # 异步执行推理
        request_id = generate_request_id()
        output_generator = engine.generate(
            data["prompt"],
            sampling_params,
            request_id=request_id
        )

        # 获取结果
        final_output = None
        async for output in output_generator:
            final_output = output

        # 返回格式化结果
        return jsonify({
            "status": "success",
            "output": final_output.outputs[0].text,
            "request_id": request_id,
            "metrics": {
                "total_tokens": len(final_output.outputs[0].token_ids),
                "generation_time": final_output.metrics.generation_time
            }
        })

    except Exception as e:
        return jsonify({
            "status": "error",
            "message": str(e),
            "request_id": request_id if 'request_id' in locals() else None
        }), 500

# ========== 管理接口 ==========
@app.route('/health', methods=['GET'])
def health_check():
    """健康检查接口"""
    return jsonify({
        "status": "healthy",
        "gpu_utilization": engine.statistics.gpu_memory_used,
        "pending_requests": engine.statistics.pending_requests
    })

@app.route('/stats', methods=['GET'])
def get_stats():
    """性能统计接口"""
    stats = engine.statistics
    return jsonify({
        "throughput": stats.throughput,
        "avg_latency": stats.avg_latency,
        "gpu_mem_used": f"{stats.gpu_memory_used} GB",
        "active_requests": stats.running_requests
    })

# ========== 启动服务 ==========
if __name__ == '__main__':
    # 打印配置信息
    print(f"""
    ===== QWQ-32B 部署配置 =====
    Model Path: {Config.MODEL_PATH}
    Tensor Parallel: {Config.TENSOR_PARALLEL}
    Max Concurrent: {Config.MAX_CONCURRENT}
    Server: {Config.HOST}:{Config.PORT}
    """)
    
    # 启动Flask应用
    app.run(
        host=Config.HOST,
        port=Config.PORT,
        threaded=True,
        debug=False
    )

部署说明

环境准备：

# 基础环境
pip install flask vllm>=0.3.3 torch>=2.1.0 transformers

# 可选监控工具
pip install prometheus_client  # 如需添加监控

模型准备：

确保模型目录包含：

QWQ-32B/
  ├── config.json
  ├── model.safetensors
  ├── tokenizer.model
  └── special_tokens_map.json

如果使用量化版本，需要对应修改quantization参数

启动命令：

# 单GPU启动
CUDA_VISIBLE_DEVICES=0 python qwq32b_deploy.py

# 多GPU启动 (示例使用2卡)
CUDA_VISIBLE_DEVICES=0,1 python qwq32b_deploy.py --tensor_parallel 2

性能优化建议：

调整block_size参数优化显存使用
使用--quantization "awq"启用4bit量化
监控/stats接口调整max_concurrent参数

生产环境建议：

使用Gunicorn+Gevent部署：

pip install gunicorn gevent
gunicorn -w 4 -k gevent -b :8000 qwq32b_deploy:app

添加Nginx反向代理和SSL加密
实现API密钥认证

接口调用示例

import requests

url = "http://localhost:8000/generate"
headers = {"Content-Type": "application/json"}

data = {
    "prompt": "解释量子计算的基本原理",
    "max_tokens": 256,
    "temperature": 0.8
}

response = requests.post(url, json=data, headers=headers)
print(response.json())