add "enable_prefix_caching" args for vllm engine.

docs/source/Instruction/命令行参数.md

@@ -276,6 +276,7 @@ Vera使用`target_modules`, `target_regex`, `modules_to_save`三个参数.
 - enforce_eager: vllm使用pytorch eager模式还是建立cuda graph. 默认为`False`. 设置为True可以节约显存, 但会影响效率.
 - 🔥limit_mm_per_prompt: 控制vllm使用多图, 默认为`None`. 例如传入`--limit_mm_per_prompt '{"image": 10, "video": 5}'`
 - vllm_max_lora_rank: 默认为`16`. vllm对于lora支持的参数
+- enable_prefix_caching: 是否开启 vllm 的 Prefix Caching 能力. 默认为`False`. 设置为 True 可以节约重复请求前缀（例如 System Prompt，长文档或多轮对话）处理时间。
 
 
 ### 合并参数

docs/source_en/Instruction/Command-line-parameters.md

@@ -281,6 +281,7 @@ Parameter meanings can be found in the [vllm documentation](https://docs.vllm.ai
 - enforce_eager: Whether vllm uses pytorch eager mode or establishes a cuda graph. Default is `False`. Setting to True can save memory but may affect efficiency.
 - 🔥limit_mm_per_prompt: Controls vllm using multiple images, default is `None`. For example, use `--limit_mm_per_prompt '{"image": 10, "video": 5}'`.
 - vllm_max_lora_rank: Default value is `16`. Parameters supported by vllm for LoRA.
+- enable_prefix_caching: Whether enable `Automatic Prefix Caching` feature for vllm. Default is `False`. Setting to True can save processing time for repeatable request prefix(such as system prompt, long docs, or multi-turn dialog, etc).
 
 ### Merge Arguments
 

swift/llm/argument/infer_args.py

@@ -61,6 +61,7 @@ class VllmArguments:
         enforce_eager (bool): Flag to enforce eager execution. Default is False.
         limit_mm_per_prompt (Optional[str]): Limit multimedia per prompt. Default is None.
         vllm_max_lora_rank (int): Maximum LoRA rank. Default is 16.
+        enable_prefix_caching (bool): Flag to enable automatic prefix caching. Default is False.
     """
     # vllm
     gpu_memory_utilization: float = 0.9
@@ -72,6 +73,7 @@ class VllmArguments:
     enforce_eager: bool = False
     limit_mm_per_prompt: Optional[Union[dict, str]] = None  # '{"image": 10, "video": 5}'
     vllm_max_lora_rank: int = 16
+    enable_prefix_caching: bool = False
 
     def __post_init__(self):
         self.limit_mm_per_prompt = ModelArguments.parse_to_dict(self.limit_mm_per_prompt)
@@ -92,6 +94,7 @@ def get_vllm_engine_kwargs(self):
             'max_lora_rank': self.vllm_max_lora_rank,
             'enable_lora': len(adapters) > 0,
             'max_loras': max(len(adapters), 1),
+            'enable_prefix_caching': self.enable_prefix_caching,
         }
 
 

swift/llm/infer/infer_engine/vllm_engine.py

@@ -34,28 +34,30 @@
 class VllmEngine(InferEngine):
 
     def __init__(
-            self,
-            model_id_or_path: str,
-            torch_dtype: Optional[torch.dtype] = None,
-            *,
-            model_type: Optional[str] = None,
-            use_hf: Optional[bool] = None,
-            hub_token: Optional[str] = None,
-            revision: Optional[str] = None,
-            # engine_kwargs
-            gpu_memory_utilization: float = 0.9,
-            tensor_parallel_size: int = 1,
-            pipeline_parallel_size: int = 1,
-            max_model_len: Optional[int] = None,
-            max_num_seqs: int = 256,
-            disable_custom_all_reduce: bool = False,
-            enforce_eager: bool = False,
-            limit_mm_per_prompt: Optional[Dict[str, Any]] = None,
-            # lora
-            enable_lora: bool = False,
-            max_loras: int = 1,
-            max_lora_rank: int = 16,
-            engine_kwargs: Optional[Dict[str, Any]] = None) -> None:
+        self,
+        model_id_or_path: str,
+        torch_dtype: Optional[torch.dtype] = None,
+        *,
+        model_type: Optional[str] = None,
+        use_hf: Optional[bool] = None,
+        hub_token: Optional[str] = None,
+        revision: Optional[str] = None,
+        # engine_kwargs
+        gpu_memory_utilization: float = 0.9,
+        tensor_parallel_size: int = 1,
+        pipeline_parallel_size: int = 1,
+        max_model_len: Optional[int] = None,
+        max_num_seqs: int = 256,
+        disable_custom_all_reduce: bool = False,
+        enforce_eager: bool = False,
+        limit_mm_per_prompt: Optional[Dict[str, Any]] = None,
+        # lora
+        enable_lora: bool = False,
+        max_loras: int = 1,
+        max_lora_rank: int = 16,
+        enable_prefix_caching: bool = False,
+        engine_kwargs: Optional[Dict[str, Any]] = None,
+    ) -> None:
         self.processor = get_model_tokenizer(
             model_id_or_path,
             torch_dtype,
@@ -79,7 +81,9 @@ def __init__(
             enable_lora=enable_lora,
             max_loras=max_loras,
             max_lora_rank=max_lora_rank,
-            engine_kwargs=engine_kwargs)
+            enable_prefix_caching=enable_prefix_caching,
+            engine_kwargs=engine_kwargs,
+        )
 
         self._prepare_engine()
         self._load_generation_config()
@@ -91,19 +95,22 @@ def _prepare_engine(self) -> None:
             engine = AsyncLLMEngine.from_engine_args(self.engine_args)
         self.engine = engine
 
-    def _prepare_engine_kwargs(self,
-                               gpu_memory_utilization: float = 0.9,
-                               tensor_parallel_size: int = 1,
-                               pipeline_parallel_size: int = 1,
-                               max_model_len: Optional[int] = None,
-                               max_num_seqs: int = 256,
-                               disable_custom_all_reduce: bool = False,
-                               enforce_eager: bool = False,
-                               limit_mm_per_prompt: Optional[Dict[str, Any]] = None,
-                               enable_lora: bool = False,
-                               max_loras: int = 1,
-                               max_lora_rank: int = 16,
-                               engine_kwargs: Optional[Dict[str, Any]] = None) -> None:
+    def _prepare_engine_kwargs(
+        self,
+        gpu_memory_utilization: float = 0.9,
+        tensor_parallel_size: int = 1,
+        pipeline_parallel_size: int = 1,
+        max_model_len: Optional[int] = None,
+        max_num_seqs: int = 256,
+        disable_custom_all_reduce: bool = False,
+        enforce_eager: bool = False,
+        limit_mm_per_prompt: Optional[Dict[str, Any]] = None,
+        enable_lora: bool = False,
+        max_loras: int = 1,
+        max_lora_rank: int = 16,
+        enable_prefix_caching: bool = False,
+        engine_kwargs: Optional[Dict[str, Any]] = None,
+    ) -> None:
         if engine_kwargs is None:
             engine_kwargs = {}
         disable_log_stats = engine_kwargs.pop('disable_log_stats', True)
@@ -136,7 +143,9 @@ def _prepare_engine_kwargs(self,
             disable_custom_all_reduce=disable_custom_all_reduce,
             enforce_eager=enforce_eager,
             trust_remote_code=True,
-            **engine_kwargs)
+            enable_prefix_caching=enable_prefix_caching,
+            **engine_kwargs,
+        )
         self.engine_args = engine_args
         self.enable_lora = enable_lora
         if max_model_len is not None: