247 lines
6.5 KiB
Python
247 lines
6.5 KiB
Python
from multiprocessing import Process, Queue
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import sys
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import os
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sys.path.append(os.path.dirname(os.path.dirname(__file__)))
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from configs.model_config import llm_model_dict, LLM_MODEL, LLM_DEVICE, LOG_PATH, logger
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import asyncio
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host_ip = "0.0.0.0"
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controller_port = 20001
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model_worker_port = 20002
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openai_api_port = 8888
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base_url = "http://127.0.0.1:{}"
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queue = Queue()
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def set_httpx_timeout(timeout=60.0):
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import httpx
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httpx._config.DEFAULT_TIMEOUT_CONFIG.connect = timeout
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httpx._config.DEFAULT_TIMEOUT_CONFIG.read = timeout
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httpx._config.DEFAULT_TIMEOUT_CONFIG.write = timeout
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def create_controller_app(
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dispatch_method="shortest_queue",
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):
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from fastchat.serve.controller import app, Controller
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from loguru import logger
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logger.add(os.path.join(LOG_PATH, "controller.log"), level="INFO")
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controller = Controller(dispatch_method)
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sys.modules["fastchat.serve.controller"].controller = controller
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sys.modules["fastchat.serve.controller"].logger = logger
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logger.info(f"controller dispatch method: {dispatch_method}")
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return app
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def create_model_worker_app(
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model_path=llm_model_dict[LLM_MODEL].get("local_model_path"),
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model_names=[LLM_MODEL],
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device=LLM_DEVICE,
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load_8bit=False,
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gptq_ckpt=None,
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gptq_wbits=16,
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gpus=None,
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num_gpus=1,
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max_gpu_memory=None,
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cpu_offloading=None,
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worker_address=base_url.format(model_worker_port),
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controller_address=base_url.format(controller_port),
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limit_model_concurrency=5,
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stream_interval=2,
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no_register=False,
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):
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from fastchat.serve.model_worker import app, GptqConfig, ModelWorker, worker_id
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from fastchat.serve import model_worker
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import argparse
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from loguru import logger
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logger.add(os.path.join(LOG_PATH, "model_worker.log"), level="INFO")
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if gpus and num_gpus is None:
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num_gpus = len(gpus.split(','))
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gptq_config = GptqConfig(
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ckpt=gptq_ckpt or model_path,
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wbits=gptq_wbits,
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groupsize=-1,
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act_order=None,
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)
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worker = ModelWorker(
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controller_address,
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worker_address,
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worker_id,
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no_register,
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model_path,
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model_names,
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device,
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num_gpus,
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max_gpu_memory,
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load_8bit,
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cpu_offloading,
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gptq_config,
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)
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parser = argparse.ArgumentParser()
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args = parser.parse_args()
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args.model_path=model_path
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args.model_names=model_names
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args.device=device
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args.load_8bit=load_8bit
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args.gptq_ckpt=gptq_ckpt
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args.gptq_wbits=gptq_wbits
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args.gpus=gpus
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args.num_gpus=num_gpus
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args.max_gpu_memory=max_gpu_memory
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args.cpu_offloading=cpu_offloading
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args.worker_address=worker_address
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args.controller_address=controller_address
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args.limit_model_concurrency=limit_model_concurrency
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args.stream_interval=stream_interval
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args.no_register=no_register
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sys.modules["fastchat.serve.model_worker"].worker = worker
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sys.modules["fastchat.serve.model_worker"].args = args
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sys.modules["fastchat.serve.model_worker"].gptq_config = gptq_config
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sys.modules["fastchat.serve.model_worker"].logger = logger
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return app
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def create_openai_api_app(
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host=host_ip,
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port=openai_api_port,
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controller_address=base_url.format(controller_port),
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api_keys=[],
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):
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from fastchat.serve.openai_api_server import app, CORSMiddleware, app_settings
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from loguru import logger
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logger.add(os.path.join(LOG_PATH, "openai_api.log"), level="INFO")
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app.add_middleware(
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CORSMiddleware,
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allow_credentials=True,
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allow_origins=["*"],
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allow_methods=["*"],
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allow_headers=["*"],
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)
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app_settings.controller_address = controller_address
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app_settings.api_keys = api_keys
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sys.modules["fastchat.serve.openai_api_server"].logger = logger
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return app
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def run_controller(q):
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import uvicorn
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app = create_controller_app()
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@app.on_event("startup")
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async def on_startup():
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set_httpx_timeout()
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q.put(1)
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uvicorn.run(app, host=host_ip, port=controller_port)
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def run_model_worker(q):
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import uvicorn
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app = create_model_worker_app()
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@app.on_event("startup")
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async def on_startup():
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set_httpx_timeout()
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while True:
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no = q.get()
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if no != 1:
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q.put(no)
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else:
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break
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q.put(2)
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uvicorn.run(app, host=host_ip, port=model_worker_port)
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def run_openai_api(q):
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import uvicorn
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app = create_openai_api_app()
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@app.on_event("startup")
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async def on_startup():
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set_httpx_timeout()
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while True:
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no = q.get()
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if no != 2:
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q.put(no)
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else:
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break
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q.put(3)
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uvicorn.run(app, host=host_ip, port=openai_api_port)
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# 1. llm_model_dict精简；
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# 2. 不同任务的日志还是分开；
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# 3. 在model_config.py里定义args；
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# 4. 用logger.removeHandler把它添加的handler删掉，添加我们自己的handler;
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# 5. 用watchdog监控第二步的执行情况；
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# 6. requirements指定fastchat版本号。
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if __name__ == "__main__":
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logger.info(llm_model_dict[LLM_MODEL])
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model_path = llm_model_dict[LLM_MODEL]["local_model_path"]
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model_path = "d:\\chatglm\\models\\chatglm-6b"
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logger.info(f"如需查看 llm_api 日志，请前往 {LOG_PATH}")
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if not model_path:
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logger.error("local_model_path 不能为空")
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else:
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controller_process = Process(
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target=run_controller,
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name=f"controller({os.getpid()})",
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args=(queue,),
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daemon=True,
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)
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controller_process.start()
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model_worker_process = Process(
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target=run_model_worker,
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name=f"model_worker({os.getpid()})",
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args=(queue,),
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daemon=True,
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)
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model_worker_process.start()
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openai_api_process = Process(
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target=run_openai_api,
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name=f"openai_api({os.getpid()})",
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args=(queue,),
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daemon=True,
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)
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openai_api_process.start()
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controller_process.join()
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model_worker_process.join()
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openai_api_process.join()
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# 服务启动后接口调用示例：
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# import openai
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# openai.api_key = "EMPTY" # Not support yet
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# openai.api_base = "http://0.0.0.0:8000/v1"
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# model = "chatglm2-6b"
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# # create a chat completion
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# completion = openai.ChatCompletion.create(
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# model=model,
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# messages=[{"role": "user", "content": "Hello! What is your name?"}]
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# )
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# # print the completion
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# print(completion.choices[0].message.content)
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