1. debug vllm加速框架完毕；2. 修改requirements,requirements_api对vllm的依赖；3.注释掉serve_config中baichuan-7b的device为cpu的配置

configs/server_config.py.example

@@ -88,6 +88,11 @@ FSCHAT_MODEL_WORKERS = {
         # 'disable_log_requests': False
 
     },
+    # 可以如下示例方式更改默认配置
+    # "baichuan-7b": { # 使用default中的IP和端口
+    #    "device": "cpu",
+    # },
+
     "zhipu-api": { # 请为每个要运行的在线API设置不同的端口
         "port": 21001,
     },

docs/FAQ.md

@@ -185,18 +185,10 @@ A16.pymilvus版本需要匹配和milvus对应否则会超时参考pymilvus==2.1.
 
 Q16: 使用vllm推理加速框架时，已经下载了模型但出现HuggingFace通信问题
 
-A16: 参照如下代码修改python环境下/site-packages/vllm/model_executor/weight_utils.py文件的prepare_hf_model_weights函数如下：
+A16: 参照如下代码修改python环境下/site-packages/vllm/model_executor/weight_utils.py文件的prepare_hf_model_weights函数如下对应代码：
 
 ```python
-def prepare_hf_model_weights(
-    model_name_or_path: str,
-    cache_dir: Optional[str] = None,
-    use_safetensors: bool = False,
-    fall_back_to_pt: bool = True,
-):
-    # Download model weights from huggingface.
-    is_local = os.path.isdir(model_name_or_path)
-    allow_patterns = "*.safetensors" if use_safetensors else "*.bin"
+
     if not is_local:
         # Use file lock to prevent multiple processes from
         # downloading the same model weights at the same time.
@@ -225,22 +217,7 @@ def prepare_hf_model_weights(
                                             tqdm_class=Disabledtqdm)
     else:
         hf_folder = model_name_or_path
-    hf_weights_files = glob.glob(os.path.join(hf_folder, allow_patterns))
-    if not use_safetensors:
-        hf_weights_files = [
-            x for x in hf_weights_files if not x.endswith("training_args.bin")
-        ]
 
-    if len(hf_weights_files) == 0 and use_safetensors and fall_back_to_pt:
-        return prepare_hf_model_weights(model_name_or_path,
-                                        cache_dir=cache_dir,
-                                        use_safetensors=False,
-                                        fall_back_to_pt=False)
 
-    if len(hf_weights_files) == 0:
-        raise RuntimeError(
-            f"Cannot find any model weights with `{model_name_or_path}`")
-
-    return hf_folder, hf_weights_files, use_safetensors
 
 ```

requirements.txt

@@ -23,7 +23,7 @@ pathlib
 pytest
 scikit-learn
 numexpr
-
+vllm==0.1.7
 # online api libs
 # zhipuai
 # dashscope>=1.10.0 # qwen

requirements_api.txt

@@ -23,7 +23,7 @@ pathlib
 pytest
 scikit-learn
 numexpr
-
+vllm==0.1.7
 # online api libs
 # zhipuai
 # dashscope>=1.10.0 # qwen

startup.py

@@ -75,10 +75,10 @@ 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 app, GptqConfig, AWQConfig, ModelWorker, worker_id, logger
+    from fastchat.serve.model_worker import worker_id, logger
     import argparse
-    import threading
     import fastchat.serve.model_worker
+    import fastchat.serve.vllm_worker
     logger.setLevel(log_level)
 
     parser = argparse.ArgumentParser()
@@ -89,6 +89,7 @@ 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
         worker = worker_class(model_names=args.model_names,
                               controller_addr=args.controller_address,
                               worker_addr=args.worker_address)
@@ -97,15 +98,10 @@ def create_model_worker_app(log_level: str = "INFO", **kwargs) -> FastAPI:
     else:
         from configs.model_config import VLLM_MODEL_DICT
         if kwargs["model_names"][0] in VLLM_MODEL_DICT and args.infer_turbo == "vllm":
-            from fastchat.serve.vllm_worker import VLLMWorker
+            from fastchat.serve.vllm_worker import VLLMWorker,app
             from vllm import AsyncLLMEngine
             from vllm.engine.arg_utils import AsyncEngineArgs,EngineArgs
-            #! -------------似乎会在这个地方加入tokenizer------------
-
-            # parser = AsyncEngineArgs.add_cli_args(args)
-            # # args = parser.parse_args()
-
-
+            
             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
@@ -150,9 +146,11 @@ def create_model_worker_app(log_level: str = "INFO", **kwargs) -> FastAPI:
                         llm_engine =  engine,
                         conv_template = args.conv_template,
                         )
+            sys.modules["fastchat.serve.vllm_worker"].engine = engine
             sys.modules["fastchat.serve.vllm_worker"].worker = worker
-
+            
         else:
+            from fastchat.serve.model_worker import app, GptqConfig, AWQConfig, ModelWorker
             args.gpus = "1"
             args.max_gpu_memory = "20GiB"
             args.load_8bit = False