[Feature] Pass through the chat_template_kwargs to the data processing module

fastdeploy/engine/engine.py

@@ -465,10 +465,7 @@ def add_requests(self, task, sampling_params=None, **kwargs):
             request.sampling_params = sampling_params
         request.preprocess_start_time = time.time()
 
-        enable_thinking = None
-        if kwargs is not None:
-            enable_thinking = kwargs.get("enable_thinking", None)
-        request = self.data_processor.process_request(request, self.cfg.max_model_len, enable_thinking=enable_thinking)
+        request = self.data_processor.process_request(request, self.cfg.max_model_len, **kwargs)
         request.prompt_token_ids_len = len(request.prompt_token_ids)
         request.need_prefill_tokens = request.prompt_token_ids_len
         input_ids_len = request.prompt_token_ids_len

fastdeploy/entrypoints/llm.py

@@ -248,7 +248,7 @@ def _add_request(
         self,
         prompts,
         sampling_params,
-        chat_template_kwargs: Optional[dict[str, Any]] = None,
+        **kwargs,
     ):
         """
             添加一个请求到 LLM Engine，并返回该请求的 ID。
@@ -289,10 +289,7 @@ def _add_request(
                 current_sampling_params = sampling_params[i]
             else:
                 current_sampling_params = sampling_params
-            enable_thinking = None
-            if chat_template_kwargs is not None:
-                enable_thinking = chat_template_kwargs.get("enable_thinking", None)
-            self.llm_engine.add_requests(tasks, current_sampling_params, enable_thinking=enable_thinking)
+            self.llm_engine.add_requests(tasks, current_sampling_params, **kwargs)
         return req_ids
 
     def _decode_token(self, token_id: int) -> str:

fastdeploy/input/ernie_processor.py

@@ -107,7 +107,16 @@ def process_request(self, request, max_model_len=None, **kwargs):
                 request.prompt_token_ids = token_ids
                 data_processor_logger.info(f"req_id:{request.request_id}, tokens:{tokens}, token_ids: {token_ids}")
             else:
-                request.prompt_token_ids = self.messages2ids(request.to_dict())
+                task = request.to_dict()
+                chat_template_kwargs = kwargs.get("chat_template_kwargs")
+                if chat_template_kwargs:
+                    if isinstance(chat_template_kwargs, dict):
+                        for k, v in chat_template_kwargs.items():
+                            if k not in task:
+                                task[k] = v
+                    else:
+                        raise ValueError("Invalid input: chat_template_kwargs must be a dict")
+                request.prompt_token_ids = self.messages2ids(task)
 
         if len(request.prompt_token_ids) == 0:
             raise ValueError("Invalid input: prompt_token_ids must be a non-empty sequence of token IDs")
@@ -162,6 +171,14 @@ def process_request_dict(self, request, max_model_len=None):
                 req_id = request.get("request_id", None)
                 data_processor_logger.info(f"req_id:{req_id}, tokens:{tokens}, token_ids: {token_ids}")
             else:
+                chat_template_kwargs = request.get("chat_template_kwargs")
+                if chat_template_kwargs:
+                    if isinstance(chat_template_kwargs, dict):
+                        for k, v in chat_template_kwargs.items():
+                            if k not in request:
+                                request[k] = v
+                    else:
+                        raise ValueError("Invalid input: chat_template_kwargs must be a dict")
                 request["prompt_token_ids"] = self.messages2ids(request)
         if len(request["prompt_token_ids"]) == 0:
             raise ValueError("Invalid input: prompt_token_ids must be a non-empty sequence of token IDs")

fastdeploy/input/ernie_vl_processor.py

@@ -110,7 +110,7 @@ def set_value(req, key, value):
     def process_request(self, request, max_model_len=None, **kwargs):
         """process the input data"""
         task = request.to_dict()
-        task["enable_thinking"] = kwargs.get("enable_thinking", True)
+        task["chat_template_kwargs"] = kwargs.get("chat_template_kwargs")
         self.process_request_dict(task, max_model_len)
         request = Request.from_dict(task)
         request = self._apply_default_parameters(request)
@@ -217,6 +217,15 @@ def process_request_dict(self, request, max_model_len=None):
         elif request.get("messages"):
             messages = request["messages"]
             self._check_mm_limits(messages)
+            chat_template_kwargs = request.get("chat_template_kwargs")
+            if chat_template_kwargs:
+                if isinstance(chat_template_kwargs, dict):
+                    for k, v in chat_template_kwargs.items():
+                        if k not in request:
+                            request[k] = v
+                else:
+                    raise ValueError("Invalid input: chat_template_kwargs must be a dict")
+            request.setdefault("enable_thinking", True)
             outputs = self.ernie_processor.request2ids(request)
         else:
             raise ValueError(f"Request must contain 'prompt', or 'messages': {request}")

fastdeploy/input/text_processor.py

@@ -207,7 +207,6 @@ def process_request(self, request, max_model_len=None, **kwargs):
         request = self._apply_default_parameters(request)
         if request.get("eos_token_ids") is None or len(request.eos_token_ids) == 0:
             request.eos_token_ids = self.eos_token_ids
-
         stop_sequences = request.get("stop", [])
         if stop_sequences is not None and len(stop_sequences) != 0:
             stop_seqs, stop_seqs_len = self.update_stop_seq(stop_sequences)
@@ -221,7 +220,15 @@ def process_request(self, request, max_model_len=None, **kwargs):
                 if self.tokenizer.chat_template is None:
                     raise ValueError("This model does not support chat_template.")
                 task = request.to_dict()
-                task["enable_thinking"] = kwargs.get("enable_thinking", True)
+                chat_template_kwargs = kwargs.get("chat_template_kwargs")
+                if chat_template_kwargs:
+                    if isinstance(chat_template_kwargs, dict):
+                        for k, v in chat_template_kwargs.items():
+                            if k not in task:
+                                task[k] = v
+                    else:
+                        raise ValueError("Invalid input: chat_template_kwargs must be a dict")
+                task.setdefault("enable_thinking", True)
                 request.prompt_token_ids = self.messages2ids(task)
             else:
                 raise ValueError(f"The request should have `input_ids`, `text` or `messages`: {request}.")
@@ -271,6 +278,15 @@ def process_request_dict(self, request, max_model_len=None, **kwargs):
             elif "messages" in request:
                 if self.tokenizer.chat_template is None:
                     raise ValueError("This model does not support chat_template.")
+                chat_template_kwargs = request.get("chat_template_kwargs")
+                if chat_template_kwargs:
+                    if isinstance(chat_template_kwargs, dict):
+                        for k, v in chat_template_kwargs.items():
+                            if k not in request:
+                                request[k] = v
+                    else:
+                        raise ValueError("Invalid input: chat_template_kwargs must be a dict")
+                request.setdefault("enable_thinking", True)
                 request["prompt_token_ids"] = self.messages2ids(request)
             else:
                 raise ValueError(f"Request must contain 'prompt_token_ids', 'prompt', or 'messages': {request}")

test/ci_use/EB_VL_Lite/test_EB_VL_Lite_serving.py

@@ -508,6 +508,7 @@ def test_chat_with_thinking(openai_client, capsys):
         extra_body={"chat_template_kwargs": {"enable_thinking": False}},
     )
     assert response.choices[0].message.reasoning_content is None
+    assert "</think>" not in response.choices[0].message.content
 
     # enable thinking, streaming
     reasoning_max_tokens = 3