148 lines
4.5 KiB
Python
148 lines
4.5 KiB
Python
from typing import Union
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from fastapi import FastAPI, Header, BackgroundTasks,Request,Body
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from urllib.parse import unquote
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import re
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import pickle
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import os
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from fastapi.responses import JSONResponse
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import asyncio
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from pydantic import BaseModel
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import time
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from transformers import AutoTokenizer, AutoModel
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import uvicorn, json, datetime
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import torch
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DEVICE = "cuda"
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DEVICE_ID = "0"
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CUDA_DEVICE = f"{DEVICE}:{DEVICE_ID}" if DEVICE_ID else DEVICE
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def torch_gc():
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if torch.cuda.is_available():
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with torch.cuda.device(CUDA_DEVICE):
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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app = FastAPI()
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class Item(BaseModel):
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query: Union[str, None] = None
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@app.get("/")
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def index():
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return "hello"
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@app.get("/home")
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# def read_root():
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# return {"Hello": "World"}
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def read_items(
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appid: Union[str, None] = Header(default=None),
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bid: Union[str, None] = Header(default=None),
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requestid: Union[str, None] = Header(default=None),
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uid: Union[str, None] = Header(default=None),
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):
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print("console :,", "appid", appid, "bid", bid, "requestid", requestid, "uid", uid)
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return {"appid": appid, "bid": bid, "requestid": requestid, "uid": uid}
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@app.post("/glm")
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async def create_item(request: Request):
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# 获取请求参数
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global model, tokenizer
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json_post_raw = await request.json()
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json_post = json.dumps(json_post_raw)
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json_post_list = json.loads(json_post)
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prompt = json_post_list.get('prompt') #问题
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history = json_post_list.get('history') #默认空数组
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max_length = json_post_list.get('max_length')
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top_p = json_post_list.get('top_p')
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temperature = json_post_list.get('temperature')
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# 请求ai模型
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response, history = model.chat(tokenizer,
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prompt,
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history=history,
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max_length=max_length if max_length else 2048,
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top_p=top_p if top_p else 0.7,
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temperature=temperature if temperature else 0.95)
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now = datetime.datetime.now()
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time = now.strftime("%Y-%m-%d %H:%M:%S")
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answer = {
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"response": response,
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"history": history,
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"status": 200,
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"time": time
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}
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log = "[" + time + "] " + '", prompt:"' + prompt + '", response:"' + repr(response) + '"'
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print(log)
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torch_gc()
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return answer
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# 通过网址发送闪信
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@app.post("/callback")
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async def callback(
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# item: Item,
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# inputArguments: Union[str, None] = None,
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query=Body(None),
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appid: Union[str, None] = Header(default=None),
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bid: Union[str, None] = Header(default=None),
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requestid: Union[str, None] = Header(default=None),
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uid: Union[str, None] = Header(default=None),
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):
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print('query',query)
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print('query[query]',query["query"])
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# 获取请求参数
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global model, tokenizer
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# time.sleep(10)
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# print("item", item)
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history = [] #默认空数组
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prompt=''
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try:
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query = unquote(query["query"], "utf-8")
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prompt = query #问题
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except:
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prompt = ""
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max_length=None
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top_p=None
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temperature=None
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# 请求ai模型
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response, history = model.chat(tokenizer,
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prompt,
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history=history,
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max_length=max_length if max_length else 2048,
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top_p=top_p if top_p else 0.7,
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temperature=temperature if temperature else 0.95)
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# log日志
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now = datetime.datetime.now()
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time = now.strftime("%Y-%m-%d %H:%M:%S")
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log = "[" + time + "] " + '", prompt:"' + prompt + '", response:"' + repr(response) + '"'
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print(log)
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torch_gc()
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# return answer
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content = {
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"answer": response,
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"answer_type": "text",
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}
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headers = {"Content-Type": "application/json"}
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return JSONResponse(content=content, headers=headers)
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if __name__ == '__main__':
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tokenizer = AutoTokenizer.from_pretrained("THUDM/chatglm2-6b-int4", trust_remote_code=True)
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model = AutoModel.from_pretrained("THUDM/chatglm2-6b-int4", trust_remote_code=True).cuda()
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# 多显卡支持，使用下面三行代替上面两行，将num_gpus改为你实际的显卡数量
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# model_path = "THUDM/chatglm2-6b"
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# tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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# model = load_model_on_gpus(model_path, num_gpus=2)
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model.eval()
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uvicorn.run(app, host='0.0.0.0', port=8000, workers=1) |