fix: correct model_worker's logger and semaphor

server/model_workers/base.py

@@ -7,7 +7,7 @@ import json
 import sys
 from pydantic import BaseModel
 import fastchat
-import threading
+import asyncio
 from typing import Dict, List
 
 
@@ -40,6 +40,7 @@ class ApiModelWorker(BaseModelWorker):
                         worker_addr=worker_addr,
                         **kwargs)
         self.context_len = context_len
+        self.semaphore = asyncio.Semaphore(self.limit_worker_concurrency)
         self.init_heart_beat()
 
     def count_token(self, params):

startup.py

@@ -78,9 +78,7 @@ def create_model_worker_app(log_level: str = "INFO", **kwargs) -> FastAPI:
     """
     import fastchat.constants
     fastchat.constants.LOGDIR = LOG_PATH
-    from fastchat.serve.model_worker import worker_id, logger
     import argparse
-    logger.setLevel(log_level)
 
     parser = argparse.ArgumentParser()
     args = parser.parse_args([])
@@ -90,19 +88,22 @@ def create_model_worker_app(log_level: str = "INFO", **kwargs) -> FastAPI:
 
     # 在线模型API
     if worker_class := kwargs.get("worker_class"):
-        from fastchat.serve.model_worker import app
+        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.model_worker"].worker = worker
+        # 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
+            from fastchat.serve.vllm_worker import VLLMWorker, app
             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
@@ -155,10 +156,12 @@ def create_model_worker_app(log_level: str = "INFO", **kwargs) -> FastAPI:
                         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"].worker = worker
+            sys.modules["fastchat.serve.vllm_worker"].logger.setLevel(log_level)
 
         else:
-            from fastchat.serve.model_worker import app, GptqConfig, AWQConfig, ModelWorker
+            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
@@ -221,8 +224,8 @@ def create_model_worker_app(log_level: str = "INFO", **kwargs) -> FastAPI:
             )
             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"].worker = worker
+            sys.modules["fastchat.serve.model_worker"].logger.setLevel(log_level)
 
     MakeFastAPIOffline(app)
     app.title = f"FastChat LLM Server ({args.model_names[0]})"