93 lines
3.8 KiB
Python
93 lines
3.8 KiB
Python
import sys
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import os
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sys.path.append(os.path.dirname(os.path.dirname(__file__)))
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from configs.model_config import LOG_PATH,controller_args,worker_args,server_args,parser
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import subprocess
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import re
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import argparse
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args = parser.parse_args()
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# 必须要加http//:，否则InvalidSchema: No connection adapters were found
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args = argparse.Namespace(**vars(args),**{"controller-address":f"http://{args.controller_host}:{str(args.controller_port)}"})
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if args.gpus:
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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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# 0,controller, model_worker, openai_api_server
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# 1, 命令行选项
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# 2,LOG_PATH
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# 3, log的文件名
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base_launch_sh = "nohup python3 -m fastchat.serve.{0} {1} >{2}/{3}.log 2>&1 &"
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# 0 log_path
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#! 1 log的文件名，必须与bash_launch_sh一致
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# 2 controller, worker, openai_api_server
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base_check_sh = """while [ `grep -c "Uvicorn running on" {0}/{1}.log` -eq '0' ];do
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sleep 1s;
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echo "wait {2} running"
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done
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echo '{2} running' """
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def string_args(args,args_list):
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"""将args中的key转化为字符串"""
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args_str = ""
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for key, value in args._get_kwargs():
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# args._get_kwargs中的key以_为分隔符,先转换，再判断是否在指定的args列表中
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key = key.replace("_","-")
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if key not in args_list:
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continue
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# fastchat中port,host没有前缀，去除前缀
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key = key.split("-")[-1] if re.search("port|host",key) else key
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if not value:
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pass
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# 1==True -> True
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elif isinstance(value,bool) and value == True:
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args_str += f" --{key} "
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elif isinstance(value, list) or isinstance(value, tuple) or isinstance(value, set):
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value = " ".join(value)
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args_str += f" --{key} {value} "
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else:
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args_str += f" --{key} {value} "
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return args_str
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def launch_worker(item):
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log_name = item.split("/")[-1].split("\\")[-1].replace("-","_").replace("@","_").replace(".","_")
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# 先分割model-path-address,在传到string_args中分析参数
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args.model_path,args.worker_host, args.worker_port = item.split("@")
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print("*"*80)
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worker_str_args = string_args(args,worker_args)
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print(worker_str_args)
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worker_sh = base_launch_sh.format("model_worker",worker_str_args,LOG_PATH,f"worker_{log_name}")
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worker_check_sh = base_check_sh.format(LOG_PATH,f"worker_{log_name}","model_worker")
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subprocess.run(worker_sh,shell=True,check=True)
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subprocess.run(worker_check_sh,shell=True,check=True)
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def launch_all():
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controller_str_args = string_args(args,controller_args)
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controller_sh = base_launch_sh.format("controller",controller_str_args,LOG_PATH,"controller")
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controller_check_sh = base_check_sh.format(LOG_PATH,"controller","controller")
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subprocess.run(controller_sh,shell=True,check=True)
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subprocess.run(controller_check_sh,shell=True,check=True)
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if isinstance(args.model_path_address, str):
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launch_worker(args.model_path_address)
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else:
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for idx,item in enumerate(args.model_path_address):
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print(f"开始加载第{idx}个模型:{item}")
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launch_worker(item)
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server_str_args = string_args(args,server_args)
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server_sh = base_launch_sh.format("openai_api_server",server_str_args,LOG_PATH,"openai_api_server")
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server_check_sh = base_check_sh.format(LOG_PATH,"openai_api_server","openai_api_server")
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subprocess.run(server_sh,shell=True,check=True)
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subprocess.run(server_check_sh,shell=True,check=True)
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if __name__ == "__main__":
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launch_all() |