894 lines
31 KiB
Python
894 lines
31 KiB
Python
import asyncio
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import multiprocessing as mp
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import os
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import subprocess
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import sys
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from multiprocessing import Process
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from datetime import datetime
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from pprint import pprint
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# 设置numexpr最大线程数，默认为CPU核心数
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try:
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import numexpr
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n_cores = numexpr.utils.detect_number_of_cores()
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os.environ["NUMEXPR_MAX_THREADS"] = str(n_cores)
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except:
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pass
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sys.path.append(os.path.dirname(os.path.dirname(__file__)))
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from configs import (
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LOG_PATH,
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log_verbose,
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logger,
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LLM_MODELS,
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EMBEDDING_MODEL,
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TEXT_SPLITTER_NAME,
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FSCHAT_CONTROLLER,
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FSCHAT_OPENAI_API,
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FSCHAT_MODEL_WORKERS,
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API_SERVER,
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WEBUI_SERVER,
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HTTPX_DEFAULT_TIMEOUT,
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)
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from server.utils import (fschat_controller_address, fschat_model_worker_address,
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fschat_openai_api_address, set_httpx_config, get_httpx_client,
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get_model_worker_config, get_all_model_worker_configs,
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MakeFastAPIOffline, FastAPI, llm_device, embedding_device)
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import argparse
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from typing import Tuple, List, Dict
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from configs import VERSION
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def create_controller_app(
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dispatch_method: str,
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log_level: str = "INFO",
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) -> FastAPI:
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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, logger
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logger.setLevel(log_level)
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controller = Controller(dispatch_method)
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sys.modules["fastchat.serve.controller"].controller = controller
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MakeFastAPIOffline(app)
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app.title = "FastChat Controller"
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app._controller = controller
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return app
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def create_model_worker_app(log_level: str = "INFO", **kwargs) -> FastAPI:
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"""
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kwargs包含的字段如下：
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host:
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port:
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model_names:[`model_name`]
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controller_address:
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worker_address:
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对于Langchain支持的模型：
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langchain_model:True
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不会使用fschat
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对于online_api:
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online_api:True
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worker_class: `provider`
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对于离线模型：
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model_path: `model_name_or_path`,huggingface的repo-id或本地路径
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device:`LLM_DEVICE`
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"""
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import fastchat.constants
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fastchat.constants.LOGDIR = LOG_PATH
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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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for k, v in kwargs.items():
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setattr(args, k, v)
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if worker_class := kwargs.get("langchain_model"): #Langchian支持的模型不用做操作
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from fastchat.serve.base_model_worker import app
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worker = ""
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# 在线模型API
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elif worker_class := kwargs.get("worker_class"):
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from fastchat.serve.base_model_worker import app
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worker = worker_class(model_names=args.model_names,
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controller_addr=args.controller_address,
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worker_addr=args.worker_address)
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# sys.modules["fastchat.serve.base_model_worker"].worker = worker
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sys.modules["fastchat.serve.base_model_worker"].logger.setLevel(log_level)
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# 本地模型
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else:
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from configs.model_config import VLLM_MODEL_DICT
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if kwargs["model_names"][0] in VLLM_MODEL_DICT and args.infer_turbo == "vllm":
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import fastchat.serve.vllm_worker
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from fastchat.serve.vllm_worker import VLLMWorker, app
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from vllm import AsyncLLMEngine
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from vllm.engine.arg_utils import AsyncEngineArgs,EngineArgs
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args.tokenizer = args.model_path # 如果tokenizer与model_path不一致在此处添加
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args.tokenizer_mode = 'auto'
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args.trust_remote_code= True
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args.download_dir= None
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args.load_format = 'auto'
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args.dtype = 'auto'
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args.seed = 0
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args.worker_use_ray = False
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args.pipeline_parallel_size = 1
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args.tensor_parallel_size = 1
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args.block_size = 16
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args.swap_space = 4 # GiB
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args.gpu_memory_utilization = 0.90
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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
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args.max_num_seqs = 256
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args.disable_log_stats = False
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args.conv_template = None
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args.limit_worker_concurrency = 5
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args.no_register = False
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args.num_gpus = 4 # vllm worker的切分是tensor并行，这里填写显卡的数量
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args.engine_use_ray = False
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args.disable_log_requests = False
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# 0.2.1 vllm后要加的参数, 但是这里不需要
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args.max_model_len = None
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args.revision = None
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args.quantization = None
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args.max_log_len = None
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args.tokenizer_revision = None
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if args.model_path:
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args.model = args.model_path
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if args.num_gpus > 1:
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args.tensor_parallel_size = args.num_gpus
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for k, v in kwargs.items():
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setattr(args, k, v)
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engine_args = AsyncEngineArgs.from_cli_args(args)
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engine = AsyncLLMEngine.from_engine_args(engine_args)
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worker = VLLMWorker(
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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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llm_engine = engine,
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conv_template = args.conv_template,
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)
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sys.modules["fastchat.serve.vllm_worker"].engine = engine
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# sys.modules["fastchat.serve.vllm_worker"].worker = worker
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sys.modules["fastchat.serve.vllm_worker"].logger.setLevel(log_level)
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else:
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from fastchat.serve.model_worker import app, GptqConfig, AWQConfig, ModelWorker, worker_id
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args.gpus = "0" # GPU的编号,如果有多个GPU，可以设置为"0,1,2,3"
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args.max_gpu_memory = "22GiB"
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args.num_gpus = 1 # model worker的切分是model并行，这里填写显卡的数量
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args.load_8bit = False
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args.cpu_offloading = None
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args.gptq_ckpt = None
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args.gptq_wbits = 16
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args.gptq_groupsize = -1
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args.gptq_act_order = False
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args.awq_ckpt = None
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args.awq_wbits = 16
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args.awq_groupsize = -1
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args.model_names = [""]
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args.conv_template = None
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args.limit_worker_concurrency = 5
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args.stream_interval = 2
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args.no_register = False
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args.embed_in_truncate = False
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for k, v in kwargs.items():
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setattr(args, k, v)
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if args.gpus:
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if args.num_gpus is None:
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args.num_gpus = len(args.gpus.split(','))
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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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gptq_config = GptqConfig(
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ckpt=args.gptq_ckpt or args.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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awq_config = AWQConfig(
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ckpt=args.awq_ckpt or args.model_path,
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wbits=args.awq_wbits,
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groupsize=args.awq_groupsize,
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)
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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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awq_config=awq_config,
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stream_interval=args.stream_interval,
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conv_template=args.conv_template,
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embed_in_truncate=args.embed_in_truncate,
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)
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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"].worker = worker
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sys.modules["fastchat.serve.model_worker"].logger.setLevel(log_level)
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MakeFastAPIOffline(app)
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app.title = f"FastChat LLM Server ({args.model_names[0]})"
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app._worker = worker
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return app
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def create_openai_api_app(
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controller_address: str,
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api_keys: List = [],
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log_level: str = "INFO",
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) -> FastAPI:
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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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from fastchat.utils import build_logger
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logger = build_logger("openai_api", "openai_api.log")
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logger.setLevel(log_level)
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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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sys.modules["fastchat.serve.openai_api_server"].logger = logger
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app_settings.controller_address = controller_address
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app_settings.api_keys = api_keys
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MakeFastAPIOffline(app)
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app.title = "FastChat OpeanAI API Server"
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return app
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def _set_app_event(app: FastAPI, started_event: mp.Event = None):
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@app.on_event("startup")
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async def on_startup():
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if started_event is not None:
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started_event.set()
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def run_controller(log_level: str = "INFO", started_event: mp.Event = None):
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import uvicorn
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import httpx
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from fastapi import Body
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import time
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import sys
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from server.utils import set_httpx_config
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set_httpx_config()
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app = create_controller_app(
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dispatch_method=FSCHAT_CONTROLLER.get("dispatch_method"),
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log_level=log_level,
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)
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_set_app_event(app, started_event)
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# add interface to release and load model worker
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@app.post("/release_worker")
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def release_worker(
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model_name: str = Body(..., description="要释放模型的名称", samples=["chatglm-6b"]),
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# worker_address: str = Body(None, description="要释放模型的地址，与名称二选一", samples=[FSCHAT_CONTROLLER_address()]),
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new_model_name: str = Body(None, description="释放后加载该模型"),
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keep_origin: bool = Body(False, description="不释放原模型，加载新模型")
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) -> Dict:
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available_models = app._controller.list_models()
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if new_model_name in available_models:
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msg = f"要切换的LLM模型 {new_model_name} 已经存在"
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logger.info(msg)
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return {"code": 500, "msg": msg}
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if new_model_name:
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logger.info(f"开始切换LLM模型：从 {model_name} 到 {new_model_name}")
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else:
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logger.info(f"即将停止LLM模型： {model_name}")
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if model_name not in available_models:
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msg = f"the model {model_name} is not available"
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logger.error(msg)
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return {"code": 500, "msg": msg}
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worker_address = app._controller.get_worker_address(model_name)
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if not worker_address:
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msg = f"can not find model_worker address for {model_name}"
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logger.error(msg)
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return {"code": 500, "msg": msg}
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with get_httpx_client() as client:
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r = client.post(worker_address + "/release",
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json={"new_model_name": new_model_name, "keep_origin": keep_origin})
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if r.status_code != 200:
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msg = f"failed to release model: {model_name}"
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logger.error(msg)
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return {"code": 500, "msg": msg}
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if new_model_name:
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timer = HTTPX_DEFAULT_TIMEOUT # wait for new model_worker register
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while timer > 0:
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models = app._controller.list_models()
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if new_model_name in models:
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break
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time.sleep(1)
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timer -= 1
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if timer > 0:
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msg = f"sucess change model from {model_name} to {new_model_name}"
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logger.info(msg)
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return {"code": 200, "msg": msg}
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else:
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msg = f"failed change model from {model_name} to {new_model_name}"
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logger.error(msg)
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return {"code": 500, "msg": msg}
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else:
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msg = f"sucess to release model: {model_name}"
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logger.info(msg)
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return {"code": 200, "msg": msg}
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host = FSCHAT_CONTROLLER["host"]
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port = FSCHAT_CONTROLLER["port"]
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if log_level == "ERROR":
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sys.stdout = sys.__stdout__
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sys.stderr = sys.__stderr__
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uvicorn.run(app, host=host, port=port, log_level=log_level.lower())
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def run_model_worker(
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model_name: str = LLM_MODELS[0],
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controller_address: str = "",
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log_level: str = "INFO",
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q: mp.Queue = None,
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started_event: mp.Event = None,
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):
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import uvicorn
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from fastapi import Body
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import sys
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from server.utils import set_httpx_config
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set_httpx_config()
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kwargs = get_model_worker_config(model_name)
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host = kwargs.pop("host")
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port = kwargs.pop("port")
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kwargs["model_names"] = [model_name]
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kwargs["controller_address"] = controller_address or fschat_controller_address()
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kwargs["worker_address"] = fschat_model_worker_address(model_name)
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model_path = kwargs.get("model_path", "")
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kwargs["model_path"] = model_path
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app = create_model_worker_app(log_level=log_level, **kwargs)
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_set_app_event(app, started_event)
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if log_level == "ERROR":
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sys.stdout = sys.__stdout__
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sys.stderr = sys.__stderr__
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# add interface to release and load model
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@app.post("/release")
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def release_model(
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new_model_name: str = Body(None, description="释放后加载该模型"),
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keep_origin: bool = Body(False, description="不释放原模型，加载新模型")
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) -> Dict:
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if keep_origin:
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if new_model_name:
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q.put([model_name, "start", new_model_name])
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else:
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if new_model_name:
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q.put([model_name, "replace", new_model_name])
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else:
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q.put([model_name, "stop", None])
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return {"code": 200, "msg": "done"}
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uvicorn.run(app, host=host, port=port, log_level=log_level.lower())
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def run_openai_api(log_level: str = "INFO", started_event: mp.Event = None):
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import uvicorn
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import sys
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from server.utils import set_httpx_config
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set_httpx_config()
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controller_addr = fschat_controller_address()
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app = create_openai_api_app(controller_addr, log_level=log_level) # TODO: not support keys yet.
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_set_app_event(app, started_event)
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host = FSCHAT_OPENAI_API["host"]
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port = FSCHAT_OPENAI_API["port"]
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if log_level == "ERROR":
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sys.stdout = sys.__stdout__
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sys.stderr = sys.__stderr__
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uvicorn.run(app, host=host, port=port)
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def run_api_server(started_event: mp.Event = None, run_mode: str = None):
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from server.api import create_app
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import uvicorn
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from server.utils import set_httpx_config
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set_httpx_config()
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app = create_app(run_mode=run_mode)
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_set_app_event(app, started_event)
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host = API_SERVER["host"]
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port = API_SERVER["port"]
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uvicorn.run(app, host=host, port=port)
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def run_webui(started_event: mp.Event = None, run_mode: str = None):
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from server.utils import set_httpx_config
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set_httpx_config()
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host = WEBUI_SERVER["host"]
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port = WEBUI_SERVER["port"]
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cmd = ["streamlit", "run", "webui.py",
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"--server.address", host,
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"--server.port", str(port),
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"--theme.base", "light",
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"--theme.primaryColor", "#165dff",
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"--theme.secondaryBackgroundColor", "#f5f5f5",
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"--theme.textColor", "#000000",
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]
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if run_mode == "lite":
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cmd += [
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"--",
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"lite",
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]
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p = subprocess.Popen(cmd)
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started_event.set()
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p.wait()
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def parse_args() -> argparse.ArgumentParser:
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"-a",
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"--all-webui",
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action="store_true",
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help="run fastchat's controller/openai_api/model_worker servers, run api.py and webui.py",
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dest="all_webui",
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)
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parser.add_argument(
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"--all-api",
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action="store_true",
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help="run fastchat's controller/openai_api/model_worker servers, run api.py",
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dest="all_api",
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)
|
||
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__":
|
||
|
||
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)
|