[Fix]fix empty prompt_token_ids,update the parser's triggering condit…

fastdeploy/input/ernie_processor.py

@@ -114,6 +114,8 @@ def process_request(self, request, max_model_len=None, **kwargs):
             else:
                 request.prompt_token_ids = self.messages2ids(request.to_dict())
 
+        if len(request.prompt_token_ids) == 0:
+            raise ValueError("Invalid input: prompt_token_ids must be a non-empty sequence of token IDs")
         if max_model_len is not None and len(
                 request.prompt_token_ids) > max_model_len:
             request.prompt_token_ids = request.prompt_token_ids[:
@@ -170,6 +172,8 @@ def process_request_dict(self, request, max_model_len=None):
                 )
             else:
                 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")
 
         # 截断超过长度限制的prompt
         if max_model_len is not None and len(
@@ -196,7 +200,6 @@ def process_response(self, response_dict, **kwargs):
         Returns:
             Dict: response contain text fields
         """
-
         req_id = response_dict.request_id
         token_ids = response_dict.outputs.token_ids
 
@@ -245,6 +248,7 @@ def process_response_dict_normal(self, response_dict, **kwargs):
         Returns:
             Dict: response contain text fields
         """
+        enable_thinking = kwargs.get("enable_thinking")
         token_ids = response_dict["outputs"]["token_ids"]
         is_end = response_dict["finished"]
         req_id = response_dict["request_id"]
@@ -254,7 +258,7 @@ def process_response_dict_normal(self, response_dict, **kwargs):
         delta_text, _, previous_texts = self.ids2tokens(token_ids, req_id)
         if is_end:
             full_text = previous_texts + delta_text
-            if self.reasoning_parser:
+            if enable_thinking and self.reasoning_parser:
                 reasoning_content, text = self.reasoning_parser.extract_reasoning_content(
                     full_text, response_dict)
                 response_dict["outputs"]["text"] = text

fastdeploy/input/mm_processor/process.py

@@ -34,6 +34,7 @@
 from .utils.render_timestamp import render_frame_timestamp
 from fastdeploy.input.ernie_tokenizer import ErnieBotTokenizer
 from fastdeploy.entrypoints.chat_utils import parse_chat_messages
+from fastdeploy.utils import data_processor_logger
 
 IDS_TYPE_FLAG = {"text": 0, "image": 1, "video": 2, "audio": 3}
 
@@ -241,6 +242,8 @@ def request2ids(self, request: Dict[str, Any],tgts: List[str]=None) -> Dict[str,
                     image_message_list.append(item)
 
         prompt_token_ids = self.apply_chat_template(request)
+        if len(prompt_token_ids) == 0:
+            raise ValueError("Invalid input: prompt_token_ids must be a non-empty sequence of token IDs")
         image_start_index = 0
         image_message_index = 0
         for i in range(len(prompt_token_ids)):
@@ -481,4 +484,6 @@ def apply_chat_template(self, request):
         ).replace("<|image@placeholder|>", "").replace("<|video@placeholder|>", "")
         tokens = self.tokenizer.tokenize(prompt_token_str)
         token_ids = self.tokenizer.convert_tokens_to_ids(tokens)
+        data_processor_logger.info(
+            f"req_id:{request.get('request_id', ''),} tokens:{tokens}, token_ids: {token_ids}")
         return token_ids

fastdeploy/input/text_processor.py

@@ -252,6 +252,8 @@ def process_request(self, request, max_model_len=None, **kwargs):
                 raise ValueError(
                     f"The request should have `input_ids`, `text` or `messages`: {request}."
                 )
+        if len(request.prompt_token_ids) == 0:
+            raise ValueError("Invalid input: prompt_token_ids must be a non-empty sequence of token IDs")
         if request.get("max_tokens") is None:
             request.set("max_tokens",
                         max(1, max_model_len - len(request.prompt_token_ids)))
@@ -299,7 +301,8 @@ def process_request_dict(self, request, max_model_len=None, **kwargs):
                 raise ValueError(
                     f"Request must contain 'prompt_token_ids', 'prompt', or 'messages': {request}"
                 )
-
+        if len(request['prompt_token_ids']) == 0:
+            raise ValueError("Invalid input: prompt_token_ids must be a non-empty sequence of token IDs")
         if request.get("max_tokens") is None:
             request["max_tokens"] = max(
                 1, max_model_len - len(request['prompt_token_ids']))
@@ -352,6 +355,7 @@ def process_response_dict_normal(self, response_dict, **kwargs):
         Returns:
             Dict: response contain text fields
         """
+        enable_thinking = kwargs.get("enable_thinking")
         token_ids = response_dict["outputs"]["token_ids"]
         is_end = response_dict["finished"]
         req_id = response_dict["request_id"]
@@ -361,7 +365,7 @@ def process_response_dict_normal(self, response_dict, **kwargs):
         delta_text, _, previous_texts = self.ids2tokens(token_ids, req_id)
         if is_end:
             full_text = previous_texts + delta_text
-            if self.reasoning_parser:
+            if enable_thinking and self.reasoning_parser:
                 reasoning_content, text = self.reasoning_parser.extract_reasoning_content(
                     full_text, response_dict)
                 response_dict["outputs"]["text"] = text