codefuse-chatbot/examples/llm_api.py

971 lines
34 KiB
Python
Raw Blame History

This file contains ambiguous Unicode characters

This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

############################# Attention ########################
# Code copied from
# https://github.com/chatchat-space/Langchain-Chatchat/blob/master/server/llm_api.py
#################################################################
from multiprocessing import Process, Queue
import multiprocessing as mp
import sys
import os
from typing import List, Union, Dict
import httpx
import asyncio
import datetime
import argparse
src_dir = os.path.join(
os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
)
sys.path.append(src_dir)
sys.path.append(os.path.dirname(os.path.dirname(__file__)))
from configs.model_config import llm_model_dict, LLM_MODEL, LLM_DEVICE, LOG_PATH, logger, LLM_MODELs
from configs.server_config import (
FSCHAT_CONTROLLER, FSCHAT_MODEL_WORKERS, FSCHAT_OPENAI_API
)
from examples.utils import get_model_worker_config
from coagent.utils.server_utils import (
MakeFastAPIOffline,
)
from fastapi import FastAPI
host_ip = "0.0.0.0"
controller_port = 20001
model_worker_port = 20002
openai_api_port = 8888
base_url = "http://127.0.0.1:{}"
os.environ['PATH'] = os.environ.get("PATH", "") + os.pathsep
log_verbose = True
def set_httpx_timeout(timeout=60.0):
import httpx
httpx._config.DEFAULT_TIMEOUT_CONFIG.connect = timeout
httpx._config.DEFAULT_TIMEOUT_CONFIG.read = timeout
httpx._config.DEFAULT_TIMEOUT_CONFIG.write = timeout
def get_all_model_worker_configs() -> dict:
result = {}
model_names = set(FSCHAT_MODEL_WORKERS.keys())
for name in model_names:
if name != "default":
result[name] = get_model_worker_config(name)
return result
def fschat_controller_address() -> str:
from configs.server_config import FSCHAT_CONTROLLER
host = FSCHAT_CONTROLLER["host"]
if host == "0.0.0.0":
host = "127.0.0.1"
port = FSCHAT_CONTROLLER["port"]
return f"http://{host}:{port}"
def fschat_model_worker_address(model_name: str = LLM_MODEL) -> str:
if model := get_model_worker_config(model_name): # TODO: depends fastchat
host = model["host"]
if host == "0.0.0.0":
host = "127.0.0.1"
port = model["port"]
return f"http://{host}:{port}"
return ""
def fschat_openai_api_address() -> str:
from configs.server_config import FSCHAT_OPENAI_API
host = FSCHAT_OPENAI_API["host"]
if host == "0.0.0.0":
host = "127.0.0.1"
port = FSCHAT_OPENAI_API["port"]
return f"http://{host}:{port}/v1"
def set_httpx_config(
timeout: float = 300,
proxy: Union[str, Dict] = None,
):
'''
设置httpx默认timeout。httpx默认timeout是5秒在请求LLM回答时不够用。
将本项目相关服务加入无代理列表避免fastchat的服务器请求错误。(windows下无效)
对于chatgpt等在线API如要使用代理需要手动配置。搜索引擎的代理如何处置还需考虑。
'''
import httpx
import os
httpx._config.DEFAULT_TIMEOUT_CONFIG.connect = timeout
httpx._config.DEFAULT_TIMEOUT_CONFIG.read = timeout
httpx._config.DEFAULT_TIMEOUT_CONFIG.write = timeout
# 在进程范围内设置系统级代理
proxies = {}
if isinstance(proxy, str):
for n in ["http", "https", "all"]:
proxies[n + "_proxy"] = proxy
elif isinstance(proxy, dict):
for n in ["http", "https", "all"]:
if p := proxy.get(n):
proxies[n + "_proxy"] = p
elif p := proxy.get(n + "_proxy"):
proxies[n + "_proxy"] = p
for k, v in proxies.items():
os.environ[k] = v
# set host to bypass proxy
no_proxy = [x.strip() for x in os.environ.get("no_proxy", "").split(",") if x.strip()]
no_proxy += [
# do not use proxy for locahost
"http://127.0.0.1",
"http://localhost",
]
# do not use proxy for user deployed fastchat servers
for x in [
fschat_controller_address(),
fschat_model_worker_address(),
fschat_openai_api_address(),
]:
host = ":".join(x.split(":")[:2])
if host not in no_proxy:
no_proxy.append(host)
os.environ["NO_PROXY"] = ",".join(no_proxy)
# TODO: 简单的清除系统代理不是个好的选择影响太多。似乎修改代理服务器的bypass列表更好。
# patch requests to use custom proxies instead of system settings
def _get_proxies():
return proxies
import urllib.request
urllib.request.getproxies = _get_proxies
# 自动检查torch可用的设备。分布式部署时不运行LLM的机器上可以不装torch
def get_httpx_client(
use_async: bool = False,
proxies: Union[str, Dict] = None,
timeout: float = 300,
**kwargs,
) -> Union[httpx.Client, httpx.AsyncClient]:
'''
helper to get httpx client with default proxies that bypass local addesses.
'''
default_proxies = {
# do not use proxy for locahost
"all://127.0.0.1": None,
"all://localhost": None,
}
# do not use proxy for user deployed fastchat servers
for x in [
fschat_controller_address(),
fschat_model_worker_address(),
fschat_openai_api_address(),
]:
host = ":".join(x.split(":")[:2])
default_proxies.update({host: None})
# get proxies from system envionrent
# proxy not str empty string, None, False, 0, [] or {}
default_proxies.update({
"http://": (os.environ.get("http_proxy")
if os.environ.get("http_proxy") and len(os.environ.get("http_proxy").strip())
else None),
"https://": (os.environ.get("https_proxy")
if os.environ.get("https_proxy") and len(os.environ.get("https_proxy").strip())
else None),
"all://": (os.environ.get("all_proxy")
if os.environ.get("all_proxy") and len(os.environ.get("all_proxy").strip())
else None),
})
for host in os.environ.get("no_proxy", "").split(","):
if host := host.strip():
# default_proxies.update({host: None}) # Origin code
default_proxies.update({'all://' + host: None}) # PR 1838 fix, if not add 'all://', httpx will raise error
# merge default proxies with user provided proxies
if isinstance(proxies, str):
proxies = {"all://": proxies}
if isinstance(proxies, dict):
default_proxies.update(proxies)
# construct Client
kwargs.update(timeout=timeout, proxies=default_proxies)
if log_verbose:
logger.info(f'{get_httpx_client.__class__.__name__}:kwargs: {kwargs}')
if use_async:
return httpx.AsyncClient(**kwargs)
else:
return httpx.Client(**kwargs)
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)
logger.error(f"可用模型有哪些: {args.model_names}")
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":
if kwargs["model_names"][0] in VLLM_MODEL_DICT:
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,EngineArgs
args.tokenizer = args.model_path # 如果tokenizer与model_path不一致在此处添加
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
args.max_parallel_loading_workers = 1
args.enforce_eager = True
args.max_context_len_to_capture = 8192
# 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{model_name}{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 = 300 # 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
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
# kwargs["gptq_wbits"] = 4 # int4 模型试用这个参数
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
set_httpx_config()
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 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
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"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"当前启动的LLM模型{models} @ {LLM_DEVICE}")
for model in models:
print(get_model_worker_config(model))
if after_start:
print("\n")
print(f"服务端运行信息:")
print(f" OpenAI API Server: {fschat_openai_api_address()}")
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()
logger.debug(f"args: {args}")
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()
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 = []
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
for model_name in args.model_name:
config = get_model_worker_config(model_name)
logger.error(f"config: {config}, {model_name}, {FSCHAT_MODEL_WORKERS.keys()}")
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
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()
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)