[Fix]fix empty prompt_token_ids,update the parser's triggering condit… (#2891)

fastdeploy/input/ernie_processor.py

@@ -111,6 +111,8 @@ class ErnieProcessor(BaseDataProcessor):
             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[: max_model_len - 1]
         if request.get("max_tokens") is None:
@@ -160,7 +162,9 @@ class ErnieProcessor(BaseDataProcessor):
                 req_id = request.get("request_id", None)
                 data_processor_logger.info(f"req_id:{req_id}, tokens:{tokens}, token_ids: {token_ids}")
             else:
-                request["prompt_token_ids"] = self.messages2ids(request)
+                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")
 
         # truncate prompts that exceed the length limit
         if max_model_len is not None and len(request["prompt_token_ids"]) > max_model_len:
@@ -184,7 +188,6 @@ class ErnieProcessor(BaseDataProcessor):
         Returns:
             Dict: response contain text fields
         """
-
         req_id = response_dict.request_id
         token_ids = response_dict.outputs.token_ids
 
@@ -228,6 +231,7 @@ class ErnieProcessor(BaseDataProcessor):
         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"]
@@ -237,8 +241,9 @@ class ErnieProcessor(BaseDataProcessor):
         delta_text, _, previous_texts = self.ids2tokens(token_ids, req_id)
         if is_end:
             full_text = previous_texts + delta_text
-            if self.reasoning_parser:
-                reasoning_content, text = self.reasoning_parser.extract_reasoning_content(full_text, response_dict)
+            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
                 response_dict["outputs"]["reasoning_content"] = reasoning_content
             else:

fastdeploy/input/mm_processor/process.py

@@ -27,6 +27,7 @@ from PIL import Image
 
 from fastdeploy.entrypoints.chat_utils import parse_chat_messages
 from fastdeploy.input.ernie_tokenizer import ErnieBotTokenizer
+from fastdeploy.utils import data_processor_logger
 
 from .image_preprocessor.image_preprocessor_adaptive import AdaptiveImageProcessor
 from .process_video import read_frames_decord, read_video_decord
@@ -252,6 +253,8 @@ class DataProcessor:
                     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)):
@@ -503,4 +506,6 @@ class DataProcessor:
         )
         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

@@ -239,7 +239,11 @@ class DataProcessor(BaseDataProcessor):
                 task["enable_thinking"] = kwargs.get("enable_thinking", True)
                 request.prompt_token_ids = self.messages2ids(task)
             else:
-                raise ValueError(f"The request should have `input_ids`, `text` or `messages`: {request}.")
+                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",
@@ -283,8 +287,11 @@ class DataProcessor(BaseDataProcessor):
                     raise ValueError("This model does not support chat_template.")
                 request["prompt_token_ids"] = self.messages2ids(request)
             else:
-                raise ValueError(f"Request must contain 'prompt_token_ids', 'prompt', or 'messages': {request}")
-
+                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"]))
         if request.get("temperature") < _SAMPLING_EPS:
@@ -335,6 +342,7 @@ class DataProcessor(BaseDataProcessor):
         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"]
@@ -344,8 +352,9 @@ class DataProcessor(BaseDataProcessor):
         delta_text, _, previous_texts = self.ids2tokens(token_ids, req_id)
         if is_end:
             full_text = previous_texts + delta_text
-            if self.reasoning_parser:
-                reasoning_content, text = self.reasoning_parser.extract_reasoning_content(full_text, response_dict)
+            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
                 response_dict["outputs"]["reasoning_content"] = reasoning_content
             else: