259 lines
9.6 KiB
Python
259 lines
9.6 KiB
Python
"""
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调用示例: python llm_api_stale.py --model-path-address THUDM/chatglm2-6b@localhost@7650 THUDM/chatglm2-6b-32k@localhost@7651
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其他fastchat.server.controller/worker/openai_api_server参数可按照fastchat文档调用
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但少数非关键参数如--worker-address,--allowed-origins,--allowed-methods,--allowed-headers不支持
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"""
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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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import subprocess
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import re
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import logging
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import argparse
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LOG_PATH = "./logs/"
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LOG_FORMAT = "%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s: %(message)s"
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logger = logging.getLogger()
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logger.setLevel(logging.INFO)
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logging.basicConfig(format=LOG_FORMAT)
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parser = argparse.ArgumentParser()
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# ------multi worker-----------------
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parser.add_argument('--model-path-address',
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default="THUDM/chatglm2-6b@localhost@20002",
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nargs="+",
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type=str,
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help="model path, host, and port, formatted as model-path@host@port")
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# ---------------controller-------------------------
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parser.add_argument("--controller-host", type=str, default="localhost")
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parser.add_argument("--controller-port", type=int, default=21001)
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parser.add_argument(
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"--dispatch-method",
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type=str,
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choices=["lottery", "shortest_queue"],
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default="shortest_queue",
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)
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controller_args = ["controller-host", "controller-port", "dispatch-method"]
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# ----------------------worker------------------------------------------
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parser.add_argument("--worker-host", type=str, default="localhost")
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parser.add_argument("--worker-port", type=int, default=21002)
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# parser.add_argument("--worker-address", type=str, default="http://localhost:21002")
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# parser.add_argument(
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# "--controller-address", type=str, default="http://localhost:21001"
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# )
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parser.add_argument(
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"--model-path",
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type=str,
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default="lmsys/vicuna-7b-v1.3",
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help="The path to the weights. This can be a local folder or a Hugging Face repo ID.",
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)
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parser.add_argument(
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"--revision",
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type=str,
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default="main",
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help="Hugging Face Hub model revision identifier",
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)
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parser.add_argument(
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"--device",
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type=str,
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choices=["cpu", "cuda", "mps", "xpu"],
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default="cuda",
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help="The device type",
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)
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parser.add_argument(
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"--gpus",
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type=str,
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default="0",
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help="A single GPU like 1 or multiple GPUs like 0,2",
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)
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parser.add_argument("--num-gpus", type=int, default=1)
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parser.add_argument(
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"--max-gpu-memory",
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type=str,
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default="20GiB",
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help="The maximum memory per gpu. Use a string like '13Gib'",
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)
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parser.add_argument(
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"--load-8bit", action="store_true", help="Use 8-bit quantization"
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)
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parser.add_argument(
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"--cpu-offloading",
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action="store_true",
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help="Only when using 8-bit quantization: Offload excess weights to the CPU that don't fit on the GPU",
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)
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parser.add_argument(
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"--gptq-ckpt",
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type=str,
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default=None,
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help="Load quantized model. The path to the local GPTQ checkpoint.",
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)
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parser.add_argument(
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"--gptq-wbits",
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type=int,
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default=16,
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choices=[2, 3, 4, 8, 16],
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help="#bits to use for quantization",
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)
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parser.add_argument(
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"--gptq-groupsize",
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type=int,
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default=-1,
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help="Groupsize to use for quantization; default uses full row.",
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)
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parser.add_argument(
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"--gptq-act-order",
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action="store_true",
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help="Whether to apply the activation order GPTQ heuristic",
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)
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parser.add_argument(
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"--model-names",
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type=lambda s: s.split(","),
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help="Optional display comma separated names",
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)
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parser.add_argument(
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"--limit-worker-concurrency",
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type=int,
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default=5,
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help="Limit the model concurrency to prevent OOM.",
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)
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parser.add_argument("--stream-interval", type=int, default=2)
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parser.add_argument("--no-register", action="store_true")
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worker_args = [
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"worker-host", "worker-port",
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"model-path", "revision", "device", "gpus", "num-gpus",
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"max-gpu-memory", "load-8bit", "cpu-offloading",
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"gptq-ckpt", "gptq-wbits", "gptq-groupsize",
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"gptq-act-order", "model-names", "limit-worker-concurrency",
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"stream-interval", "no-register",
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"controller-address", "worker-address"
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]
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# -----------------openai server---------------------------
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parser.add_argument("--server-host", type=str, default="localhost", help="host name")
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parser.add_argument("--server-port", type=int, default=8888, help="port number")
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parser.add_argument(
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"--allow-credentials", action="store_true", help="allow credentials"
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)
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# parser.add_argument(
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# "--allowed-origins", type=json.loads, default=["*"], help="allowed origins"
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# )
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# parser.add_argument(
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# "--allowed-methods", type=json.loads, default=["*"], help="allowed methods"
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# )
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# parser.add_argument(
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# "--allowed-headers", type=json.loads, default=["*"], help="allowed headers"
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# )
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parser.add_argument(
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"--api-keys",
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type=lambda s: s.split(","),
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help="Optional list of comma separated API keys",
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)
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server_args = ["server-host", "server-port", "allow-credentials", "api-keys",
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"controller-address"
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]
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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 5s;
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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, args, worker_args=worker_args):
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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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args.worker_address = f"http://{args.worker_host}:{args.worker_port}"
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print("*" * 80)
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print(f"如长时间未启动，请到{LOG_PATH}{log_name}.log下查看日志")
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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(args,
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controller_args=controller_args,
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worker_args=worker_args,
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server_args=server_args
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):
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print(f"Launching llm service,logs are located in {LOG_PATH}...")
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print(f"开始启动LLM服务,请到{LOG_PATH}下监控各模块日志...")
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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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print(f"worker启动时间视设备不同而不同，约需3-10分钟，请耐心等待...")
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if isinstance(args.model_path_address, str):
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launch_worker(args.model_path_address, args=args, worker_args=worker_args)
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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, args=args, worker_args=worker_args)
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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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print("Launching LLM service done!")
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print("LLM服务启动完毕。")
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if __name__ == "__main__":
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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),
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**{"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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launch_all(args=args)
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