import asyncio import multiprocessing as mp import os import subprocess import sys from multiprocessing import Process from datetime import datetime from pprint import pprint from langchain_core._api import deprecated 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 import ( LOG_PATH, log_verbose, logger, LLM_MODELS, EMBEDDING_MODEL, TEXT_SPLITTER_NAME, FSCHAT_CONTROLLER, FSCHAT_OPENAI_API, FSCHAT_MODEL_WORKERS, API_SERVER, WEBUI_SERVER, HTTPX_DEFAULT_TIMEOUT, ) from server.utils import (fschat_controller_address, fschat_model_worker_address, fschat_openai_api_address, get_httpx_client, get_model_worker_config, MakeFastAPIOffline, FastAPI, llm_device, embedding_device) from server.knowledge_base.migrate import create_tables import argparse from typing import List, Dict from configs import VERSION @deprecated( since="0.3.0", message="模型启动功能将于 Langchain-Chatchat 0.3.x重写,支持更多模式和加速启动，0.2.x中相关功能将废弃", removal="0.3.0") 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: """ kwargs包含的字段如下： host: port: model_names:[`model_name`] controller_address: worker_address: 对于Langchain支持的模型： langchain_model:True 不会使用fschat 对于online_api: online_api:True worker_class: `provider` 对于离线模型： model_path: `model_name_or_path`,huggingface的repo-id或本地路径 device:`LLM_DEVICE` """ import fastchat.constants fastchat.constants.LOGDIR = LOG_PATH import argparse parser = argparse.ArgumentParser() args = parser.parse_args([]) for k, v in kwargs.items(): setattr(args, k, v) if worker_class := kwargs.get("langchain_model"): # Langchian支持的模型不用做操作 from fastchat.serve.base_model_worker import app worker = "" # 在线模型API elif worker_class := kwargs.get("worker_class"): from fastchat.serve.base_model_worker import app worker = worker_class(model_names=args.model_names, controller_addr=args.controller_address, worker_addr=args.worker_address) # sys.modules["fastchat.serve.base_model_worker"].worker = worker sys.modules["fastchat.serve.base_model_worker"].logger.setLevel(log_level) # 本地模型 else: from configs.model_config import VLLM_MODEL_DICT if kwargs["model_names"][0] in VLLM_MODEL_DICT and args.infer_turbo == "vllm": import fastchat.serve.vllm_worker from fastchat.serve.vllm_worker import VLLMWorker, app, worker_id from vllm import AsyncLLMEngine from vllm.engine.arg_utils import AsyncEngineArgs args.tokenizer = args.model_path args.tokenizer_mode = 'auto' args.trust_remote_code = True args.download_dir = None args.load_format = 'auto' args.dtype = 'auto' args.seed = 0 args.worker_use_ray = False args.pipeline_parallel_size = 1 args.tensor_parallel_size = 1 args.block_size = 16 args.swap_space = 4 # GiB args.gpu_memory_utilization = 0.90 args.max_num_batched_tokens = None # 一个批次中的最大令牌（tokens）数量，这个取决于你的显卡和大模型设置，设置太大显存会不够 args.max_num_seqs = 256 args.disable_log_stats = False args.conv_template = None args.limit_worker_concurrency = 5 args.no_register = False args.num_gpus = 1 # vllm worker的切分是tensor并行，这里填写显卡的数量 args.engine_use_ray = False args.disable_log_requests = False # 0.2.1 vllm后要加的参数, 但是这里不需要 args.max_model_len = None args.revision = None args.quantization = None args.max_log_len = None args.tokenizer_revision = None # 0.2.2 vllm需要新加的参数 args.max_paddings = 256 if args.model_path: args.model = args.model_path if args.num_gpus > 1: args.tensor_parallel_size = args.num_gpus for k, v in kwargs.items(): setattr(args, k, v) engine_args = AsyncEngineArgs.from_cli_args(args) engine = AsyncLLMEngine.from_engine_args(engine_args) worker = VLLMWorker( 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, llm_engine=engine, conv_template=args.conv_template, ) sys.modules["fastchat.serve.vllm_worker"].engine = engine sys.modules["fastchat.serve.vllm_worker"].worker = worker sys.modules["fastchat.serve.vllm_worker"].logger.setLevel(log_level) else: from fastchat.serve.model_worker import app, GptqConfig, AWQConfig, ModelWorker, worker_id args.gpus = "0" # GPU的编号,如果有多个GPU，可以设置为"0,1,2,3" args.max_gpu_memory = "22GiB" args.num_gpus = 1 # model worker的切分是model并行，这里填写显卡的数量 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.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 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 sys.modules["fastchat.serve.model_worker"].logger.setLevel(log_level) 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(): 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 from server.utils import set_httpx_config set_httpx_config() 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} with get_httpx_client() as client: r = client.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 # 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_MODELS[0], controller_address: str = "", log_level: str = "INFO", q: mp.Queue = None, started_event: mp.Event = None, ): import uvicorn from fastapi import Body import sys from server.utils import set_httpx_config set_httpx_config() 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("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 from server.utils import set_httpx_config set_httpx_config() controller_addr = fschat_controller_address() app = create_openai_api_app(controller_addr, log_level=log_level) _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, run_mode: str = None): from server.api import create_app import uvicorn from server.utils import set_httpx_config set_httpx_config() app = create_app(run_mode=run_mode) _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, run_mode: str = None): from server.utils import set_httpx_config set_httpx_config() host = WEBUI_SERVER["host"] port = WEBUI_SERVER["port"] cmd = ["streamlit", "run", "webui.py", "--server.address", host, "--server.port", str(port), "--theme.base", "light", "--theme.primaryColor", "#165dff", "--theme.secondaryBackgroundColor", "#f5f5f5", "--theme.textColor", "#000000", ] if run_mode == "lite": cmd += [ "--", "lite", ] p = subprocess.Popen(cmd) 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_MODELS", dest="model_worker", ) parser.add_argument( "-n", "--model-name", type=str, nargs="+", default=LLM_MODELS, 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 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", ) parser.add_argument( "-i", "--lite", action="store_true", help="以Lite模式运行：仅支持在线API的LLM对话、搜索引擎对话", dest="lite", ) 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_MODELS 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(get_model_worker_config(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()}") 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() run_mode = None 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 if args.lite: args.model_worker = False run_mode = "lite" 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: for model_name in args.model_name: config = get_model_worker_config(model_name) if (config.get("online_api") and config.get("worker_class") and model_name in FSCHAT_MODEL_WORKERS): 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, run_mode=run_mode), 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, run_mode=run_mode), 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__": create_tables() 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) 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 = "chatglm3-6b" # # 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)