import asyncio import multiprocessing as mp import os import subprocess import sys from multiprocessing import Process from datetime import datetime 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, log_verbose, TEXT_SPLITTER_NAME from configs.server_config import (WEBUI_SERVER, API_SERVER, FSCHAT_CONTROLLER, FSCHAT_OPENAI_API, HTTPX_DEFAULT_TIMEOUT) 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) -> 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 args.embed_in_truncate = 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, embed_in_truncate=args.embed_in_truncate, ) 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_event(app: FastAPI, started_event: mp.Event = None): @app.on_event("startup") async def on_startup(): set_httpx_timeout() if started_event is not None: started_event.set() def run_controller(log_level: str = "INFO", started_event: mp.Event = None): 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_event(app, started_event) # 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 = HTTPX_DEFAULT_TIMEOUT * 2 # wait 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 = "", log_level: str = "INFO", q: mp.Queue = None, started_event: mp.Event = None, ): 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_event(app, started_event) 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([model_name, "start", new_model_name]) else: if new_model_name: q.put([model_name, "replace", new_model_name]) else: q.put([model_name, "stop", None]) return {"code": 200, "msg": "done"} uvicorn.run(app, host=host, port=port, log_level=log_level.lower()) def run_openai_api(log_level: str = "INFO", started_event: mp.Event = None): 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_event(app, started_event) 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(started_event: mp.Event = None): from server.api import create_app import uvicorn app = create_app() _set_app_event(app, started_event) host = API_SERVER["host"] port = API_SERVER["port"] uvicorn.run(app, host=host, port=port) def run_webui(started_event: mp.Event = None): host = WEBUI_SERVER["host"] port = WEBUI_SERVER["port"] p = subprocess.Popen(["streamlit", "run", "webui.py", "--server.address", host, "--server.port", str(port)]) started_event.set() 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, nargs="+", default=[LLM_MODEL], help="specify model name for model worker. add addition names with space seperated to start multiple model workers.", 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") models = [LLM_MODEL] if args and args.model_name: models = args.model_name print(f"当前使用的分词器：{TEXT_SPLITTER_NAME}") print(f"当前启动的LLM模型：{models} @ {llm_device()}") for model in models: 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 import signal def handler(signalname): """ Python 3.9 has `signal.strsignal(signalnum)` so this closure would not be needed. Also, 3.8 includes `signal.valid_signals()` that can be used to create a mapping for the same purpose. """ def f(signal_received, frame): raise KeyboardInterrupt(f"{signalname} received") return f # This will be inherited by the child process if it is forked (not spawned) signal.signal(signal.SIGINT, handler("SIGINT")) signal.signal(signal.SIGTERM, handler("SIGTERM")) mp.set_start_method("spawn") manager = mp.Manager() queue = manager.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": {}, "model_worker": {}} def process_count(): return len(processes) + len(processes["online_api"]) + len(processes["model_worker"]) - 2 if args.quiet or not log_verbose: log_level = "ERROR" else: log_level = "INFO" controller_started = manager.Event() if args.openai_api: process = Process( target=run_controller, name=f"controller", kwargs=dict(log_level=log_level, started_event=controller_started), daemon=True, ) processes["controller"] = process process = Process( target=run_openai_api, name=f"openai_api", daemon=True, ) processes["openai_api"] = process model_worker_started = [] if args.model_worker: for model_name in args.model_name: config = get_model_worker_config(model_name) if not config.get("online_api"): e = manager.Event() model_worker_started.append(e) process = Process( target=run_model_worker, name=f"model_worker - {model_name}", kwargs=dict(model_name=model_name, controller_address=args.controller_address, log_level=log_level, q=queue, started_event=e), daemon=True, ) processes["model_worker"][model_name] = 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"): e = manager.Event() model_worker_started.append(e) process = Process( target=run_model_worker, name=f"api_worker - {model_name}", kwargs=dict(model_name=model_name, controller_address=args.controller_address, log_level=log_level, q=queue, started_event=e), daemon=True, ) processes["online_api"][model_name] = process api_started = manager.Event() if args.api: process = Process( target=run_api_server, name=f"API Server", kwargs=dict(started_event=api_started), daemon=True, ) processes["api"] = process webui_started = manager.Event() if args.webui: process = Process( target=run_webui, name=f"WEBUI Server", kwargs=dict(started_event=webui_started), daemon=True, ) processes["webui"] = process if process_count() == 0: parser.print_help() else: try: # 保证任务收到SIGINT后，能够正常退出 if p:= processes.get("controller"): p.start() p.name = f"{p.name} ({p.pid})" controller_started.wait() # 等待controller启动完成 if p:= processes.get("openai_api"): p.start() p.name = f"{p.name} ({p.pid})" for n, p in processes.get("model_worker", {}).items(): p.start() p.name = f"{p.name} ({p.pid})" for n, p in processes.get("online_api", []).items(): p.start() p.name = f"{p.name} ({p.pid})" # 等待所有model_worker启动完成 for e in model_worker_started: e.wait() if p:= processes.get("api"): p.start() p.name = f"{p.name} ({p.pid})" api_started.wait() # 等待api.py启动完成 if p:= processes.get("webui"): p.start() p.name = f"{p.name} ({p.pid})" webui_started.wait() # 等待webui.py启动完成 dump_server_info(after_start=True, args=args) while True: cmd = queue.get() # 收到切换模型的消息 e = manager.Event() if isinstance(cmd, list): model_name, cmd, new_model_name = cmd if cmd == "start": # 运行新模型 logger.info(f"准备启动新模型进程：{new_model_name}") process = Process( target=run_model_worker, name=f"model_worker - {new_model_name}", kwargs=dict(model_name=new_model_name, controller_address=args.controller_address, log_level=log_level, q=queue, started_event=e), daemon=True, ) process.start() process.name = f"{process.name} ({process.pid})" processes["model_worker"][new_model_name] = process e.wait() logger.info(f"成功启动新模型进程：{new_model_name}") elif cmd == "stop": if process := processes["model_worker"].get(model_name): time.sleep(1) process.terminate() process.join() logger.info(f"停止模型进程：{model_name}") else: logger.error(f"未找到模型进程：{model_name}") elif cmd == "replace": if process := processes["model_worker"].pop(model_name, None): logger.info(f"停止模型进程：{model_name}") start_time = datetime.now() time.sleep(1) process.terminate() process.join() process = Process( target=run_model_worker, name=f"model_worker - {new_model_name}", kwargs=dict(model_name=new_model_name, controller_address=args.controller_address, log_level=log_level, q=queue, started_event=e), daemon=True, ) process.start() process.name = f"{process.name} ({process.pid})" processes["model_worker"][new_model_name] = process e.wait() timing = datetime.now() - start_time logger.info(f"成功启动新模型进程：{new_model_name}。用时：{timing}。") else: logger.error(f"未找到模型进程：{model_name}") # for process in processes.get("model_worker", {}).values(): # process.join() # for process in processes.get("online_api", {}).values(): # process.join() # for name, process in processes.items(): # if name not in ["model_worker", "online_api"]: # if isinstance(p, dict): # for work_process in p.values(): # work_process.join() # else: # process.join() except Exception as e: logger.error(e) logger.warning("Caught KeyboardInterrupt! Setting stop event...") finally: # Send SIGINT if process doesn't exit quickly enough, and kill it as last resort # .is_alive() also implicitly joins the process (good practice in linux) # while alive_procs := [p for p in processes.values() if p.is_alive()]: for p in processes.values(): logger.warning("Sending SIGKILL to %s", p) # Queues and other inter-process communication primitives can break when # process is killed, but we don't care here if isinstance(p, dict): for process in p.values(): process.kill() else: p.kill() for p in processes.values(): logger.info("Process status: %s", p) if __name__ == "__main__": if sys.version_info < (3, 10): loop = asyncio.get_event_loop() else: try: loop = asyncio.get_running_loop() except RuntimeError: loop = asyncio.new_event_loop() asyncio.set_event_loop(loop) # 同步调用协程代码 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)