from multiprocessing import Process, Queue import multiprocessing as mp import subprocess import asyncio import sys import os from pprint import pprint # 设置numexpr最大线程数，默认为CPU核心数 try: import numexpr n_cores = numexpr.utils.detect_number_of_cores() os.environ["NUMEXPR_MAX_THREADS"] = str(n_cores) except: pass sys.path.append(os.path.dirname(os.path.dirname(__file__))) from configs.model_config import EMBEDDING_MODEL, llm_model_dict, LLM_MODEL, LOG_PATH, \ logger from configs.server_config import (WEBUI_SERVER, API_SERVER, FSCHAT_CONTROLLER, FSCHAT_OPENAI_API, ) from server.utils import (fschat_controller_address, fschat_model_worker_address, fschat_openai_api_address, set_httpx_timeout, get_model_worker_config, get_all_model_worker_configs, MakeFastAPIOffline, FastAPI, llm_device, embedding_device) import argparse from typing import Tuple, List, Dict from configs import VERSION def create_controller_app( dispatch_method: str, log_level: str = "INFO", ) -> FastAPI: import fastchat.constants fastchat.constants.LOGDIR = LOG_PATH from fastchat.serve.controller import app, Controller, logger logger.setLevel(log_level) controller = Controller(dispatch_method) sys.modules["fastchat.serve.controller"].controller = controller MakeFastAPIOffline(app) app.title = "FastChat Controller" app._controller = controller return app def create_model_worker_app(log_level: str = "INFO", **kwargs) -> Tuple[argparse.ArgumentParser, FastAPI]: import fastchat.constants fastchat.constants.LOGDIR = LOG_PATH from fastchat.serve.model_worker import app, GptqConfig, AWQConfig, ModelWorker, worker_id, logger import argparse import threading import fastchat.serve.model_worker logger.setLevel(log_level) # workaround to make program exit with Ctrl+c # it should be deleted after pr is merged by fastchat def _new_init_heart_beat(self): self.register_to_controller() self.heart_beat_thread = threading.Thread( target=fastchat.serve.model_worker.heart_beat_worker, args=(self,), daemon=True, ) self.heart_beat_thread.start() ModelWorker.init_heart_beat = _new_init_heart_beat parser = argparse.ArgumentParser() args = parser.parse_args([]) # default args. should be deleted after pr is merged by fastchat args.gpus = None args.max_gpu_memory = "20GiB" args.load_8bit = False args.cpu_offloading = None args.gptq_ckpt = None args.gptq_wbits = 16 args.gptq_groupsize = -1 args.gptq_act_order = False args.awq_ckpt = None args.awq_wbits = 16 args.awq_groupsize = -1 args.num_gpus = 1 args.model_names = [] args.conv_template = None args.limit_worker_concurrency = 5 args.stream_interval = 2 args.no_register = False for k, v in kwargs.items(): setattr(args, k, v) if args.gpus: if args.num_gpus is None: args.num_gpus = len(args.gpus.split(',')) if len(args.gpus.split(",")) < args.num_gpus: raise ValueError( f"Larger --num-gpus ({args.num_gpus}) than --gpus {args.gpus}!" ) os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus # 在线模型API if worker_class := kwargs.get("worker_class"): worker = worker_class(model_names=args.model_names, controller_addr=args.controller_address, worker_addr=args.worker_address) # 本地模型 else: # workaround to make program exit with Ctrl+c # it should be deleted after pr is merged by fastchat def _new_init_heart_beat(self): self.register_to_controller() self.heart_beat_thread = threading.Thread( target=fastchat.serve.model_worker.heart_beat_worker, args=(self,), daemon=True, ) self.heart_beat_thread.start() ModelWorker.init_heart_beat = _new_init_heart_beat gptq_config = GptqConfig( ckpt=args.gptq_ckpt or args.model_path, wbits=args.gptq_wbits, groupsize=args.gptq_groupsize, act_order=args.gptq_act_order, ) awq_config = AWQConfig( ckpt=args.awq_ckpt or args.model_path, wbits=args.awq_wbits, groupsize=args.awq_groupsize, ) worker = ModelWorker( controller_addr=args.controller_address, worker_addr=args.worker_address, worker_id=worker_id, model_path=args.model_path, model_names=args.model_names, limit_worker_concurrency=args.limit_worker_concurrency, no_register=args.no_register, device=args.device, num_gpus=args.num_gpus, max_gpu_memory=args.max_gpu_memory, load_8bit=args.load_8bit, cpu_offloading=args.cpu_offloading, gptq_config=gptq_config, awq_config=awq_config, stream_interval=args.stream_interval, conv_template=args.conv_template, ) sys.modules["fastchat.serve.model_worker"].args = args sys.modules["fastchat.serve.model_worker"].gptq_config = gptq_config sys.modules["fastchat.serve.model_worker"].worker = worker MakeFastAPIOffline(app) app.title = f"FastChat LLM Server ({args.model_names[0]})" app._worker = worker return app def create_openai_api_app( controller_address: str, api_keys: List = [], log_level: str = "INFO", ) -> FastAPI: import fastchat.constants fastchat.constants.LOGDIR = LOG_PATH from fastchat.serve.openai_api_server import app, CORSMiddleware, app_settings from fastchat.utils import build_logger logger = build_logger("openai_api", "openai_api.log") logger.setLevel(log_level) app.add_middleware( CORSMiddleware, allow_credentials=True, allow_origins=["*"], allow_methods=["*"], allow_headers=["*"], ) sys.modules["fastchat.serve.openai_api_server"].logger = logger app_settings.controller_address = controller_address app_settings.api_keys = api_keys MakeFastAPIOffline(app) app.title = "FastChat OpeanAI API Server" return app def _set_app_seq(app: FastAPI, q: Queue, run_seq: int): if q is None or not isinstance(run_seq, int): return if run_seq == 1: @app.on_event("startup") async def on_startup(): set_httpx_timeout() q.put(run_seq) elif run_seq > 1: @app.on_event("startup") async def on_startup(): set_httpx_timeout() while True: no = q.get() if no != run_seq - 1: q.put(no) else: break q.put(run_seq) def run_controller(q: Queue, run_seq: int = 1, log_level: str = "INFO"): import uvicorn import httpx from fastapi import Body import time import sys app = create_controller_app( dispatch_method=FSCHAT_CONTROLLER.get("dispatch_method"), log_level=log_level, ) _set_app_seq(app, q, run_seq) # add interface to release and load model worker @app.post("/release_worker") def release_worker( model_name: str = Body(..., description="要释放模型的名称", samples=["chatglm-6b"]), # worker_address: str = Body(None, description="要释放模型的地址，与名称二选一", samples=[fschat_controller_address()]), new_model_name: str = Body(None, description="释放后加载该模型"), keep_origin: bool = Body(False, description="不释放原模型，加载新模型") ) -> Dict: available_models = app._controller.list_models() if new_model_name in available_models: msg = f"要切换的LLM模型 {new_model_name} 已经存在" logger.info(msg) return {"code": 500, "msg": msg} if new_model_name: logger.info(f"开始切换LLM模型：从 {model_name} 到 {new_model_name}") else: logger.info(f"即将停止LLM模型： {model_name}") if model_name not in available_models: msg = f"the model {model_name} is not available" logger.error(msg) return {"code": 500, "msg": msg} worker_address = app._controller.get_worker_address(model_name) if not worker_address: msg = f"can not find model_worker address for {model_name}" logger.error(msg) return {"code": 500, "msg": msg} r = httpx.post(worker_address + "/release", json={"new_model_name": new_model_name, "keep_origin": keep_origin}) if r.status_code != 200: msg = f"failed to release model: {model_name}" logger.error(msg) return {"code": 500, "msg": msg} if new_model_name: timer = 300 # wait 5 minutes for new model_worker register while timer > 0: models = app._controller.list_models() if new_model_name in models: break time.sleep(1) timer -= 1 if timer > 0: msg = f"sucess change model from {model_name} to {new_model_name}" logger.info(msg) return {"code": 200, "msg": msg} else: msg = f"failed change model from {model_name} to {new_model_name}" logger.error(msg) return {"code": 500, "msg": msg} else: msg = f"sucess to release model: {model_name}" logger.info(msg) return {"code": 200, "msg": msg} host = FSCHAT_CONTROLLER["host"] port = FSCHAT_CONTROLLER["port"] if log_level == "ERROR": sys.stdout = sys.__stdout__ sys.stderr = sys.__stderr__ uvicorn.run(app, host=host, port=port, log_level=log_level.lower()) def run_model_worker( model_name: str = LLM_MODEL, controller_address: str = "", q: Queue = None, run_seq: int = 2, log_level: str = "INFO", ): import uvicorn from fastapi import Body import sys kwargs = get_model_worker_config(model_name) host = kwargs.pop("host") port = kwargs.pop("port") kwargs["model_names"] = [model_name] kwargs["controller_address"] = controller_address or fschat_controller_address() kwargs["worker_address"] = fschat_model_worker_address(model_name) model_path = kwargs.get("local_model_path", "") kwargs["model_path"] = model_path app = create_model_worker_app(log_level=log_level, **kwargs) _set_app_seq(app, q, run_seq) if log_level == "ERROR": sys.stdout = sys.__stdout__ sys.stderr = sys.__stderr__ # add interface to release and load model @app.post("/release") def release_model( new_model_name: str = Body(None, description="释放后加载该模型"), keep_origin: bool = Body(False, description="不释放原模型，加载新模型") ) -> Dict: if keep_origin: if new_model_name: q.put(["start", new_model_name]) else: if new_model_name: q.put(["replace", new_model_name]) else: q.put(["stop"]) return {"code": 200, "msg": "done"} uvicorn.run(app, host=host, port=port, log_level=log_level.lower()) def run_openai_api(q: Queue, run_seq: int = 3, log_level: str = "INFO"): import uvicorn import sys controller_addr = fschat_controller_address() app = create_openai_api_app(controller_addr, log_level=log_level) # TODO: not support keys yet. _set_app_seq(app, q, run_seq) host = FSCHAT_OPENAI_API["host"] port = FSCHAT_OPENAI_API["port"] if log_level == "ERROR": sys.stdout = sys.__stdout__ sys.stderr = sys.__stderr__ uvicorn.run(app, host=host, port=port) def run_api_server(q: Queue, run_seq: int = 4): from server.api import create_app import uvicorn app = create_app() _set_app_seq(app, q, run_seq) host = API_SERVER["host"] port = API_SERVER["port"] uvicorn.run(app, host=host, port=port) def run_webui(q: Queue, run_seq: int = 5): host = WEBUI_SERVER["host"] port = WEBUI_SERVER["port"] if q is not None and isinstance(run_seq, int): while True: no = q.get() if no != run_seq - 1: q.put(no) else: break q.put(run_seq) p = subprocess.Popen(["streamlit", "run", "webui.py", "--server.address", host, "--server.port", str(port)]) p.wait() def parse_args() -> argparse.ArgumentParser: parser = argparse.ArgumentParser() parser.add_argument( "-a", "--all-webui", action="store_true", help="run fastchat's controller/openai_api/model_worker servers, run api.py and webui.py", dest="all_webui", ) parser.add_argument( "--all-api", action="store_true", help="run fastchat's controller/openai_api/model_worker servers, run api.py", dest="all_api", ) parser.add_argument( "--llm-api", action="store_true", help="run fastchat's controller/openai_api/model_worker servers", dest="llm_api", ) parser.add_argument( "-o", "--openai-api", action="store_true", help="run fastchat's controller/openai_api servers", dest="openai_api", ) parser.add_argument( "-m", "--model-worker", action="store_true", help="run fastchat's model_worker server with specified model name. specify --model-name if not using default LLM_MODEL", dest="model_worker", ) parser.add_argument( "-n", "--model-name", type=str, default=LLM_MODEL, help="specify model name for model worker.", dest="model_name", ) parser.add_argument( "-c", "--controller", type=str, help="specify controller address the worker is registered to. default is server_config.FSCHAT_CONTROLLER", dest="controller_address", ) parser.add_argument( "--api", action="store_true", help="run api.py server", dest="api", ) parser.add_argument( "-p", "--api-worker", action="store_true", help="run online model api such as zhipuai", dest="api_worker", ) parser.add_argument( "-w", "--webui", action="store_true", help="run webui.py server", dest="webui", ) parser.add_argument( "-q", "--quiet", action="store_true", help="减少fastchat服务log信息", dest="quiet", ) args = parser.parse_args() return args, parser def dump_server_info(after_start=False, args=None): import platform import langchain import fastchat from server.utils import api_address, webui_address print("\n") print("=" * 30 + "Langchain-Chatchat Configuration" + "=" * 30) print(f"操作系统：{platform.platform()}.") print(f"python版本：{sys.version}") print(f"项目版本：{VERSION}") print(f"langchain版本：{langchain.__version__}. fastchat版本：{fastchat.__version__}") print("\n") model = LLM_MODEL if args and args.model_name: model = args.model_name print(f"当前LLM模型：{model} @ {llm_device()}") pprint(llm_model_dict[model]) print(f"当前Embbedings模型： {EMBEDDING_MODEL} @ {embedding_device()}") if after_start: print("\n") print(f"服务端运行信息：") if args.openai_api: print(f" OpenAI API Server: {fschat_openai_api_address()}/v1") print(" (请确认llm_model_dict中配置的api_base_url与上面地址一致。)") if args.api: print(f" Chatchat API Server: {api_address()}") if args.webui: print(f" Chatchat WEBUI Server: {webui_address()}") print("=" * 30 + "Langchain-Chatchat Configuration" + "=" * 30) print("\n") async def start_main_server(): import time mp.set_start_method("spawn") # TODO 链式启动的队列，确实可以用于控制启动顺序， # 但目前引入proxy_worker后，启动的独立于框架的work processes无法确认当前的位置， # 导致注册器未启动时，无法注册。整个启动链因为异常被终止 # 使用await asyncio.sleep(3)可以让后续代码等待一段时间，但不是最优解 queue = Queue() args, parser = parse_args() if args.all_webui: args.openai_api = True args.model_worker = True args.api = True args.api_worker = True args.webui = True elif args.all_api: args.openai_api = True args.model_worker = True args.api = True args.api_worker = True args.webui = False elif args.llm_api: args.openai_api = True args.model_worker = True args.api_worker = True args.api = False args.webui = False dump_server_info(args=args) if len(sys.argv) > 1: logger.info(f"正在启动服务：") logger.info(f"如需查看 llm_api 日志，请前往 {LOG_PATH}") processes = {"online-api": []} def process_count(): return len(processes) + len(processes["online-api"]) - 1 if args.quiet: log_level = "ERROR" else: log_level = "INFO" if args.openai_api: process = Process( target=run_controller, name=f"controller({os.getpid()})", args=(queue, process_count() + 1, log_level), daemon=True, ) process.start() await asyncio.sleep(3) processes["controller"] = process process = Process( target=run_openai_api, name=f"openai_api({os.getpid()})", args=(queue, process_count() + 1), daemon=True, ) process.start() processes["openai_api"] = process if args.model_worker: config = get_model_worker_config(args.model_name) if not config.get("online_api"): process = Process( target=run_model_worker, name=f"model_worker - {args.model_name} ({os.getpid()})", args=(args.model_name, args.controller_address, queue, process_count() + 1, log_level), daemon=True, ) process.start() processes["model_worker"] = process if args.api_worker: configs = get_all_model_worker_configs() for model_name, config in configs.items(): if config.get("online_api") and config.get("worker_class"): process = Process( target=run_model_worker, name=f"model_worker - {model_name} ({os.getpid()})", args=(model_name, args.controller_address, queue, process_count() + 1, log_level), daemon=True, ) process.start() processes["online-api"].append(process) if args.api: process = Process( target=run_api_server, name=f"API Server{os.getpid()})", args=(queue, process_count() + 1), daemon=True, ) process.start() processes["api"] = process if args.webui: process = Process( target=run_webui, name=f"WEBUI Server{os.getpid()})", args=(queue, process_count() + 1), daemon=True, ) process.start() processes["webui"] = process if process_count() == 0: parser.print_help() else: try: while True: no = queue.get() if no == process_count(): time.sleep(0.5) dump_server_info(after_start=True, args=args) break else: queue.put(no) if model_worker_process := processes.pop("model_worker", None): model_worker_process.join() for process in processes.pop("online-api", []): process.join() for name, process in processes.items(): process.join() except: if model_worker_process := processes.pop("model_worker", None): model_worker_process.terminate() for process in processes.pop("online-api", []): process.terminate() for name, process in processes.items(): process.terminate() if __name__ == "__main__": # 同步调用协程代码 asyncio.get_event_loop().run_until_complete(start_main_server()) # 服务启动后接口调用示例： # import openai # openai.api_key = "EMPTY" # Not support yet # openai.api_base = "http://localhost:8888/v1" # model = "chatglm2-6b" # # create a chat completion # completion = openai.ChatCompletion.create( # model=model, # messages=[{"role": "user", "content": "Hello! What is your name?"}] # ) # # print the completion # print(completion.choices[0].message.content)