datawhalechina--self-llm
wehub-resource-sync
62 行
2.4 KiB
Python
62 行
2.4 KiB
Python
from fastapi import FastAPI, Request
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from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
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import uvicorn
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import json
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import datetime
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import torch
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# 设置设备参数
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DEVICE = "cuda" # 使用CUDA
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DEVICE_ID = "0" # CUDA设备ID，如果未设置则为空
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CUDA_DEVICE = f"{DEVICE}:{DEVICE_ID}" if DEVICE_ID else DEVICE # 组合CUDA设备信息
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# 清理GPU内存函数
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def torch_gc():
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if torch.cuda.is_available(): # 检查是否可用CUDA
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with torch.cuda.device(CUDA_DEVICE): # 指定CUDA设备
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torch.cuda.empty_cache() # 清空CUDA缓存
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torch.cuda.ipc_collect() # 收集CUDA内存碎片
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# 创建FastAPI应用
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app = FastAPI()
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# 处理POST请求的端点
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@app.post("/")
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async def create_item(request: Request):
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global model, tokenizer # 声明全局变量以便在函数内部使用模型和分词器
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json_post_raw = await request.json() # 获取POST请求的JSON数据
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json_post = json.dumps(json_post_raw) # 将JSON数据转换为字符串
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json_post_list = json.loads(json_post) # 将字符串转换为Python对象
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prompt = json_post_list.get('prompt') # 获取请求中的提示
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# 构建消息
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history = [{"role": "user", "content": prompt}]
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response = model.chat(tokenizer, history)
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now = datetime.datetime.now() # 获取当前时间
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time = now.strftime("%Y-%m-%d %H:%M:%S") # 格式化时间为字符串
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# 构建响应JSON
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answer = {
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"response": response,
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"status": 200,
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"time": time
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}
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# 构建日志信息
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log = "[" + time + "] " + '", prompt:"' + prompt + '", response:"' + repr(response) + '"'
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print(log) # 打印日志
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torch_gc() # 执行GPU内存清理
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return answer # 返回响应
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# 主函数入口
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if __name__ == '__main__':
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# 加载预训练的分词器和模型
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model_path = "xverse/XVERSE-7B-Chat"
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16, trust_remote_code=True).cuda()
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model.generation_config = GenerationConfig.from_pretrained(model_path)
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model = model.eval()
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# 启动FastAPI应用
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# 用6006端口可以将autodl的端口映射到本地，从而在本地使用api
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uvicorn.run(app, host='0.0.0.0', port=6006, workers=1) # 在指定端口和主机上启动应用 |