codefuse-chatbot/dev_opsgpt/service/llm_api.py

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# Attention: code copied from https://github.com/chatchat-space/Langchain-Chatchat/blob/master/server/llm_api.py
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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
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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
from configs.server_config import (
FSCHAT_CONTROLLER, FSCHAT_MODEL_WORKERS, FSCHAT_OPENAI_API
)
from dev_opsgpt.utils.server_utils import (
MakeFastAPIOffline,
)
from fastapi import FastAPI
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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
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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_model_worker_config(model_name: str = None) -> dict:
'''
加载model worker的配置项
优先级:FSCHAT_MODEL_WORKERS[model_name] > ONLINE_LLM_MODEL[model_name] > FSCHAT_MODEL_WORKERS["default"]
'''
from configs.model_config import ONLINE_LLM_MODEL
from configs.server_config import FSCHAT_MODEL_WORKERS
# from server import model_workers
config = FSCHAT_MODEL_WORKERS.get("default", {}).copy()
# config.update(ONLINE_LLM_MODEL.get(model_name, {}).copy())
config.update(FSCHAT_MODEL_WORKERS.get(model_name, {}).copy())
# if model_name in ONLINE_LLM_MODEL:
# config["online_api"] = True
# if provider := config.get("provider"):
# try:
# config["worker_class"] = getattr(model_workers, provider)
# except Exception as e:
# msg = f"在线模型 {model_name} 的provider没有正确配置"
# logger.error(f'{e.__class__.__name__}: {msg}',
# exc_info=e if log_verbose else None)
# 本地模型
if model_name in llm_model_dict:
path = llm_model_dict[model_name]["local_model_path"]
config["model_path"] = path
if path and os.path.isdir(path):
config["model_path_exists"] = True
config["device"] = LLM_DEVICE
return config
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,
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):
'''
设置httpx默认timeouthttpx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:
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import fastchat.constants
fastchat.constants.LOGDIR = LOG_PATH
from fastchat.serve.controller import app, Controller, logger
logger.setLevel(log_level)
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controller = Controller(dispatch_method)
sys.modules["fastchat.serve.controller"].controller = controller
MakeFastAPIOffline(app)
app.title = "FastChat Controller"
app._controller = controller
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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`
"""
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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,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 = 4 # 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,
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)
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)
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MakeFastAPIOffline(app)
app.title = f"FastChat LLM Server ({args.model_names[0]})"
app._worker = worker
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return app
def create_openai_api_app(
controller_address: str,
api_keys: List = [],
log_level: str = "INFO",
) -> FastAPI:
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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)
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app.add_middleware(
CORSMiddleware,
allow_credentials=True,
allow_origins=["*"],
allow_methods=["*"],
allow_headers=["*"],
)
sys.modules["fastchat.serve.openai_api_server"].logger = logger
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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):
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@app.on_event("startup")
async def on_startup():
if started_event is not None:
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
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
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])
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else:
q.put([model_name, "stop", None])
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
import sys
set_httpx_config()
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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)
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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)
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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_MODEL],
help="specify model name for model worker. "
"add addition names with space seperated to start multiple model workers.",
dest="model_name",
)
parser.add_argument(
"-c",
"--controller",
type=str,
help="specify controller address the worker is registered to. default is 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_MODEL]
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"))
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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
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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)
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
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if process_count() == 0:
parser.print_help()
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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)
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if __name__ == "__main__":
if sys.version_info < (3, 10):
loop = asyncio.get_event_loop()
else:
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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())
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# 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)