250 lines
6.9 KiB
Python
250 lines
6.9 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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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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import fastchat.constants
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fastchat.constants.LOGDIR = LOG_PATH
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from fastchat.serve.controller import app, Controller
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controller = Controller(dispatch_method)
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sys.modules["fastchat.serve.controller"].controller = controller
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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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gptq_groupsize=-1,
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gptq_act_order=None,
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gpus=None,
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num_gpus=1,
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max_gpu_memory="20GiB",
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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_worker_concurrency=5,
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stream_interval=2,
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no_register=False,
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):
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import fastchat.constants
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fastchat.constants.LOGDIR = LOG_PATH
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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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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.gptq_groupsize = gptq_groupsize
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args.gptq_act_order = gptq_act_order
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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_worker_concurrency = limit_worker_concurrency
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args.stream_interval = stream_interval
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args.no_register = no_register
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if args.gpus:
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if len(args.gpus.split(",")) < args.num_gpus:
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raise ValueError(
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f"Larger --num-gpus ({args.num_gpus}) than --gpus {args.gpus}!"
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)
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os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
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if gpus and num_gpus is None:
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num_gpus = len(gpus.split(','))
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args.num_gpus = num_gpus
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gptq_config = GptqConfig(
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ckpt=gptq_ckpt or model_path,
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wbits=args.gptq_wbits,
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groupsize=args.gptq_groupsize,
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act_order=args.gptq_act_order,
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)
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# torch.multiprocessing.set_start_method('spawn')
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worker = ModelWorker(
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controller_addr=args.controller_address,
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worker_addr=args.worker_address,
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worker_id=worker_id,
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model_path=args.model_path,
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model_names=args.model_names,
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limit_worker_concurrency=args.limit_worker_concurrency,
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no_register=args.no_register,
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device=args.device,
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num_gpus=args.num_gpus,
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max_gpu_memory=args.max_gpu_memory,
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load_8bit=args.load_8bit,
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cpu_offloading=args.cpu_offloading,
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gptq_config=gptq_config,
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stream_interval=args.stream_interval,
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)
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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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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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import fastchat.constants
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fastchat.constants.LOGDIR = LOG_PATH
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from fastchat.serve.openai_api_server import app, CORSMiddleware, app_settings
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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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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, *args, **kwargs):
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import uvicorn
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app = create_model_worker_app(*args, **kwargs)
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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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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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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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# cuda 没办法用在fork的多进程中
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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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# # kwargs={"load_8bit": True},
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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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run_model_worker(queue)
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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://localhost:8888/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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