【Fix】 remove text_after_process & raw_prediction (#4421)

* remove text_after_process &  raw_prediction

* remove text_after_process &  raw_prediction

docs/get_started/installation/intel_gaudi.md

@@ -71,5 +71,5 @@ curl -X POST "http://0.0.0.0:8188/v1/chat/completions" \
 
 ### 3. Successfully returns the result
 ```json
-{"id":"chatcmpl-3bd98ae2-fafe-46ae-a552-d653a8526503","object":"chat.completion","created":1757653575,"model":"ERNIE-4.5-21B-A3B-Paddle","choices":[{"index":0,"message":{"role":"assistant","content":"**AI (Artificial Intelligence)** refers to the development of computer systems that can perform tasks typically requiring human intelligence.","multimodal_content":null,"reasoning_content":null,"tool_calls":null,"prompt_token_ids":null,"completion_token_ids":null,"text_after_process":null,"raw_prediction":null,"prompt_tokens":null,"completion_tokens":null},"logprobs":null,"finish_reason":"length"}],"usage":{"prompt_tokens":11,"total_tokens":35,"completion_tokens":24,"prompt_tokens_details":{"cached_tokens":0}}}
+{"id":"chatcmpl-3bd98ae2-fafe-46ae-a552-d653a8526503","object":"chat.completion","created":1757653575,"model":"ERNIE-4.5-21B-A3B-Paddle","choices":[{"index":0,"message":{"role":"assistant","content":"**AI (Artificial Intelligence)** refers to the development of computer systems that can perform tasks typically requiring human intelligence.","multimodal_content":null,"reasoning_content":null,"tool_calls":null,"prompt_token_ids":null,"completion_token_ids":null,"prompt_tokens":null,"completion_tokens":null},"logprobs":null,"finish_reason":"length"}],"usage":{"prompt_tokens":11,"total_tokens":35,"completion_tokens":24,"prompt_tokens_details":{"cached_tokens":0}}}
 ```

docs/online_serving/README.md

@@ -231,8 +231,18 @@ ChatMessage:
     role: str
     content: str
     reasoning_content: Optional[str] = None
+    tool_calls: Optional[List[DeltaToolCall | ToolCall]] = None
     prompt_token_ids: Optional[List[int]] = None
     completion_token_ids: Optional[List[int]] = None
+    prompt_tokens: Optional[str] = None
+    completion_tokens: Optional[str] = None
+ToolCall:
+    id: str = None
+    type: Literal["function"] = "function"
+    function: FunctionCall
+FunctionCall:
+    name: str
+    arguments: str
 
 # Fields returned for streaming responses
 ChatCompletionStreamResponse:
@@ -254,6 +264,17 @@ DeltaMessage:
     prompt_token_ids: Optional[List[int]] = None
     completion_token_ids: Optional[List[int]] = None
     reasoning_content: Optional[str] = None
+    tool_calls: Optional[List[DeltaToolCall | ToolCall]] = None
+    prompt_tokens: Optional[str] = None
+    completion_tokens: Optional[str] = None
+DeltaToolCall:
+    id: Optional[str] = None
+    type: Optional[Literal["function"]] = None
+    index: int
+    function: Optional[DeltaFunctionCall] = None
+DeltaFunctionCall:
+    name: Optional[str] = None
+    arguments: Optional[str] = None
 ```
 
 ## Completion API
@@ -384,10 +405,20 @@ CompletionResponseChoice:
     text: str
     prompt_token_ids: Optional[List[int]] = None
     completion_token_ids: Optional[List[int]] = None
+    prompt_tokens: Optional[str] = None
+    completion_tokens: Optional[str] = None
     arrival_time: Optional[float] = None
     logprobs: Optional[int] = None
     reasoning_content: Optional[str] = None
     finish_reason: Optional[Literal["stop", "length", "tool_calls"]]
+    tool_calls: Optional[List[DeltaToolCall | ToolCall]] = None
+ToolCall:
+    id: str = None
+    type: Literal["function"] = "function"
+    function: FunctionCall
+FunctionCall:
+    name: str
+    arguments: str
 
 # Fields returned for streaming responses
 CompletionStreamResponse：
@@ -403,8 +434,18 @@ CompletionResponseStreamChoice:
     arrival_time: float = None
     prompt_token_ids: Optional[List[int]] = None
     completion_token_ids: Optional[List[int]] = None
+    prompt_tokens: Optional[str] = None
+    completion_tokens: Optional[str] = None
     logprobs: Optional[float] = None
     reasoning_content: Optional[str] = None
     finish_reason: Optional[Literal["stop", "length", "tool_calls"]] = None
-
+    tool_calls: Optional[List[DeltaToolCall | ToolCall]] = None
+DeltaToolCall:
+    id: Optional[str] = None
+    type: Optional[Literal["function"]] = None
+    index: int
+    function: Optional[DeltaFunctionCall] = None
+DeltaFunctionCall:
+    name: Optional[str] = None
+    arguments: Optional[str] = None
 ```

docs/zh/get_started/installation/intel_gaudi.md

@@ -71,5 +71,5 @@ curl -X POST "http://0.0.0.0:8188/v1/chat/completions" \
 
 ### 3. 成功返回结果
 ```json
-{"id":"chatcmpl-3bd98ae2-fafe-46ae-a552-d653a8526503","object":"chat.completion","created":1757653575,"model":"ERNIE-4.5-21B-A3B-Paddle","choices":[{"index":0,"message":{"role":"assistant","content":"**AI (Artificial Intelligence)** refers to the development of computer systems that can perform tasks typically requiring human intelligence.","multimodal_content":null,"reasoning_content":null,"tool_calls":null,"prompt_token_ids":null,"completion_token_ids":null,"text_after_process":null,"raw_prediction":null,"prompt_tokens":null,"completion_tokens":null},"logprobs":null,"finish_reason":"length"}],"usage":{"prompt_tokens":11,"total_tokens":35,"completion_tokens":24,"prompt_tokens_details":{"cached_tokens":0}}}
+{"id":"chatcmpl-3bd98ae2-fafe-46ae-a552-d653a8526503","object":"chat.completion","created":1757653575,"model":"ERNIE-4.5-21B-A3B-Paddle","choices":[{"index":0,"message":{"role":"assistant","content":"**AI (Artificial Intelligence)** refers to the development of computer systems that can perform tasks typically requiring human intelligence.","multimodal_content":null,"reasoning_content":null,"tool_calls":null,"prompt_token_ids":null,"completion_token_ids":null,"prompt_tokens":null,"completion_tokens":null},"logprobs":null,"finish_reason":"length"}],"usage":{"prompt_tokens":11,"total_tokens":35,"completion_tokens":24,"prompt_tokens_details":{"cached_tokens":0}}}
 ```

docs/zh/online_serving/README.md

@@ -230,8 +230,18 @@ ChatMessage:
     role: str
     content: str
     reasoning_content: Optional[str] = None
+    tool_calls: Optional[List[DeltaToolCall | ToolCall]] = None
     prompt_token_ids: Optional[List[int]] = None
     completion_token_ids: Optional[List[int]] = None
+    prompt_tokens: Optional[str] = None
+    completion_tokens: Optional[str] = None
+ToolCall:
+    id: str = None
+    type: Literal["function"] = "function"
+    function: FunctionCall
+FunctionCall:
+    name: str
+    arguments: str
 
 # 返回流式响应的字段
 ChatCompletionStreamResponse:
@@ -253,6 +263,17 @@ DeltaMessage:
     prompt_token_ids: Optional[List[int]] = None
     completion_token_ids: Optional[List[int]] = None
     reasoning_content: Optional[str] = None
+    tool_calls: Optional[List[DeltaToolCall | ToolCall]] = None
+    prompt_tokens: Optional[str] = None
+    completion_tokens: Optional[str] = None
+DeltaToolCall:
+    id: Optional[str] = None
+    type: Optional[Literal["function"]] = None
+    index: int
+    function: Optional[DeltaFunctionCall] = None
+DeltaFunctionCall:
+    name: Optional[str] = None
+    arguments: Optional[str] = None
 ```
 
 ## Completion API
@@ -380,10 +401,20 @@ CompletionResponseChoice:
     text: str
     prompt_token_ids: Optional[List[int]] = None
     completion_token_ids: Optional[List[int]] = None
+    prompt_tokens: Optional[str] = None
+    completion_tokens: Optional[str] = None
     arrival_time: Optional[float] = None
     logprobs: Optional[int] = None
     reasoning_content: Optional[str] = None
     finish_reason: Optional[Literal["stop", "length", "tool_calls"]]
+    tool_calls: Optional[List[DeltaToolCall | ToolCall]] = None
+ToolCall:
+    id: str = None
+    type: Literal["function"] = "function"
+    function: FunctionCall
+FunctionCall:
+    name: str
+    arguments: str
 
 # 返回流式响应的字段
 CompletionStreamResponse：
@@ -399,8 +430,18 @@ CompletionResponseStreamChoice:
     arrival_time: float = None
     prompt_token_ids: Optional[List[int]] = None
     completion_token_ids: Optional[List[int]] = None
+    prompt_tokens: Optional[str] = None
+    completion_tokens: Optional[str] = None
     logprobs: Optional[float] = None
     reasoning_content: Optional[str] = None
     finish_reason: Optional[Literal["stop", "length", "tool_calls"]] = None
-
+    tool_calls: Optional[List[DeltaToolCall | ToolCall]] = None
+DeltaToolCall:
+    id: Optional[str] = None
+    type: Optional[Literal["function"]] = None
+    index: int
+    function: Optional[DeltaFunctionCall] = None
+DeltaFunctionCall:
+    name: Optional[str] = None
+    arguments: Optional[str] = None
 ```

fastdeploy/entrypoints/openai/protocol.py

@@ -193,8 +193,6 @@ class ChatMessage(BaseModel):
     tool_calls: Optional[List[DeltaToolCall | ToolCall]] = None
     prompt_token_ids: Optional[List[int]] = None
     completion_token_ids: Optional[List[int]] = None
-    text_after_process: Optional[str] = None
-    raw_prediction: Optional[str] = None
     prompt_tokens: Optional[str] = None
     completion_tokens: Optional[str] = None
 
@@ -255,8 +253,6 @@ class DeltaMessage(BaseModel):
     completion_token_ids: Optional[List[int]] = None
     reasoning_content: Optional[str] = None
     tool_calls: Optional[List[DeltaToolCall | ToolCall]] = None
-    text_after_process: Optional[str] = None
-    raw_prediction: Optional[str] = None
     prompt_tokens: Optional[str] = None
     completion_tokens: Optional[str] = None
 
@@ -295,8 +291,6 @@ class CompletionResponseChoice(BaseModel):
     text: str
     prompt_token_ids: Optional[List[int]] = None
     completion_token_ids: Optional[List[int]] = None
-    text_after_process: Optional[str] = None
-    raw_prediction: Optional[str] = None
     prompt_tokens: Optional[str] = None
     completion_tokens: Optional[str] = None
     arrival_time: Optional[float] = None
@@ -341,8 +335,6 @@ class CompletionResponseStreamChoice(BaseModel):
     logprobs: Optional[CompletionLogprobs] = None
     prompt_token_ids: Optional[List[int]] = None
     completion_token_ids: Optional[List[int]] = None
-    text_after_process: Optional[str] = None
-    raw_prediction: Optional[str] = None
     prompt_tokens: Optional[str] = None
     completion_tokens: Optional[str] = None
     reasoning_content: Optional[str] = None

fastdeploy/entrypoints/openai/serving_chat.py

@@ -118,14 +118,14 @@ class OpenAIServingChat:
             else:
                 request_id = f"chatcmpl-{uuid.uuid4()}"
             api_server_logger.info(f"create chat completion request: {request_id}")
-            text_after_process = None
+            prompt_tokens = None
             try:
                 current_req_dict = request.to_dict_for_infer(request_id)
                 if "chat_template" not in current_req_dict:
                     current_req_dict["chat_template"] = self.chat_template
                 current_req_dict["arrival_time"] = time.time()
                 prompt_token_ids = await self.engine_client.format_and_add_data(current_req_dict)
-                text_after_process = current_req_dict.get("text_after_process")
+                prompt_tokens = current_req_dict.get("prompt_tokens")
                 if isinstance(prompt_token_ids, np.ndarray):
                     prompt_token_ids = prompt_token_ids.tolist()
             except ParameterError as e:
@@ -143,12 +143,12 @@ class OpenAIServingChat:
 
             if request.stream:
                 return self.chat_completion_stream_generator(
-                    request, request_id, request.model, prompt_token_ids, text_after_process
+                    request, request_id, request.model, prompt_token_ids, prompt_tokens
                 )
             else:
                 try:
                     return await self.chat_completion_full_generator(
-                        request, request_id, request.model, prompt_token_ids, text_after_process
+                        request, request_id, request.model, prompt_token_ids, prompt_tokens
                     )
                 except Exception as e:
                     error_msg = f"request[{request_id}]full generator error: {str(e)}, {str(traceback.format_exc())}"
@@ -175,7 +175,7 @@ class OpenAIServingChat:
         request_id: str,
         model_name: str,
         prompt_token_ids: list(),
-        text_after_process: str,
+        prompt_tokens: str,
     ):
         """
         Streaming chat completion generator.
@@ -289,8 +289,7 @@ class OpenAIServingChat:
 
                             if request.return_token_ids:
                                 choice.delta.prompt_token_ids = list(prompt_token_ids)
-                                choice.delta.text_after_process = text_after_process
+                                choice.delta.prompt_tokens = prompt_tokens
-                                choice.delta.prompt_tokens = text_after_process
                             chunk = ChatCompletionStreamResponse(
                                 id=request_id,
                                 object=chunk_object_type,
@@ -368,8 +367,7 @@ class OpenAIServingChat:
                             choice.delta.multimodal_content[0]["completion_token_ids"] = list(output["token_ids"])
                         else:
                             choice.delta.completion_token_ids = list(output["token_ids"])
-                        choice.delta.raw_prediction = output.get("raw_prediction")
+                        choice.delta.completion_tokens = output.get("completion_tokens")
-                        choice.delta.completion_tokens = output.get("raw_prediction")
                     if include_continuous_usage:
                         chunk.usage = UsageInfo(
                             prompt_tokens=num_prompt_tokens,
@@ -419,7 +417,7 @@ class OpenAIServingChat:
         request_id: str,
         model_name: str,
         prompt_token_ids: list(),
-        text_after_process: str,
+        prompt_tokens: str,
     ):
         """
         Full chat completion generator.
@@ -509,10 +507,8 @@ class OpenAIServingChat:
             tool_calls=output.get("tool_call"),
             prompt_token_ids=prompt_token_ids if request.return_token_ids else None,
             completion_token_ids=completion_token_ids if request.return_token_ids else None,
-            text_after_process=text_after_process if request.return_token_ids else None,
+            prompt_tokens=prompt_tokens if request.return_token_ids else None,
-            prompt_tokens=text_after_process if request.return_token_ids else None,
+            completion_tokens=output.get("completion_tokens") if request.return_token_ids else None,
-            raw_prediction=output.get("raw_prediction") if request.return_token_ids else None,
-            completion_tokens=output.get("raw_prediction") if request.return_token_ids else None,
         )
 
         if response_processor.enable_multimodal_content():

fastdeploy/entrypoints/openai/serving_completion.py

@@ -132,7 +132,7 @@ class OpenAIServingCompletion:
         num_choices = len(request_prompts)
         api_server_logger.info(f"Start preprocessing request: req_id={request_id}), num_choices={num_choices}")
         prompt_batched_token_ids = []
-        text_after_process_list = []
+        prompt_tokens_list = []
         try:
             if self.max_waiting_time < 0:
                 await self.engine_client.semaphore.acquire()
@@ -157,7 +157,7 @@ class OpenAIServingCompletion:
                     prompt_token_ids = await self.engine_client.format_and_add_data(current_req_dict)  # tokenize
                     if isinstance(prompt_token_ids, np.ndarray):
                         prompt_token_ids = prompt_token_ids.tolist()
-                    text_after_process_list.append(current_req_dict.get("text_after_process"))
+                    prompt_tokens_list.append(current_req_dict.get("prompt_tokens"))
                     prompt_batched_token_ids.append(prompt_token_ids)
                     del current_req_dict
             except ParameterError as e:
@@ -180,7 +180,7 @@ class OpenAIServingCompletion:
                     created_time=created_time,
                     model_name=request.model,
                     prompt_batched_token_ids=prompt_batched_token_ids,
-                    text_after_process_list=text_after_process_list,
+                    prompt_tokens_list=prompt_tokens_list,
                 )
             else:
                 try:
@@ -191,7 +191,7 @@ class OpenAIServingCompletion:
                         created_time=created_time,
                         model_name=request.model,
                         prompt_batched_token_ids=prompt_batched_token_ids,
-                        text_after_process_list=text_after_process_list,
+                        prompt_tokens_list=prompt_tokens_list,
                     )
                 except Exception as e:
                     error_msg = (
@@ -213,7 +213,7 @@ class OpenAIServingCompletion:
         created_time: int,
         model_name: str,
         prompt_batched_token_ids: list(),
-        text_after_process_list: list(),
+        prompt_tokens_list: list(),
     ):
         """
         Process the full completion request with multiple choices.
@@ -292,7 +292,7 @@ class OpenAIServingCompletion:
                 model_name=model_name,
                 prompt_batched_token_ids=prompt_batched_token_ids,
                 completion_batched_token_ids=completion_batched_token_ids,
-                text_after_process_list=text_after_process_list,
+                prompt_tokens_list=prompt_tokens_list,
             )
             api_server_logger.info(f"Completion response: {res.model_dump_json()}")
             return res
@@ -344,7 +344,7 @@ class OpenAIServingCompletion:
         created_time: int,
         model_name: str,
         prompt_batched_token_ids: list(),
-        text_after_process_list: list(),
+        prompt_tokens_list: list(),
     ):
         """
         Process the stream completion request.
@@ -408,8 +408,7 @@ class OpenAIServingCompletion:
                                         index=idx,
                                         text="",
                                         prompt_token_ids=list(prompt_batched_token_ids[idx]),
-                                        text_after_process=text_after_process_list[idx],
+                                        prompt_tokens=prompt_tokens_list[idx],
-                                        prompt_tokens=text_after_process_list[idx],
                                         completion_token_ids=None,
                                     )
                                 ],
@@ -443,8 +442,7 @@ class OpenAIServingCompletion:
                         prompt_token_ids=None,
                         completion_token_ids=output.get("token_ids") if request.return_token_ids else None,
                         tool_calls=None,
-                        raw_prediction=output.get("raw_prediction") if request.return_token_ids else None,
+                        completion_tokens=output.get("completion_tokens") if request.return_token_ids else None,
-                        completion_tokens=output.get("raw_prediction") if request.return_token_ids else None,
                         reasoning_content="",
                         arrival_time=arrival_time,
                         logprobs=logprobs_res,
@@ -522,7 +520,7 @@ class OpenAIServingCompletion:
         model_name: str,
         prompt_batched_token_ids: list(),
         completion_batched_token_ids: list(),
-        text_after_process_list: list(),
+        prompt_tokens_list: list(),
     ) -> CompletionResponse:
         choices: List[CompletionResponseChoice] = []
         num_prompt_tokens = 0
@@ -556,10 +554,8 @@ class OpenAIServingCompletion:
                 text=output_text,
                 prompt_token_ids=prompt_token_ids if request.return_token_ids else None,
                 completion_token_ids=completion_token_ids if request.return_token_ids else None,
-                raw_prediction=output.get("raw_prediction") if request.return_token_ids else None,
+                completion_tokens=output.get("completion_tokens") if request.return_token_ids else None,
-                completion_tokens=output.get("raw_prediction") if request.return_token_ids else None,
+                prompt_tokens=prompt_tokens_list[idx] if request.return_token_ids else None,
-                text_after_process=text_after_process_list[idx] if request.return_token_ids else None,
-                prompt_tokens=text_after_process_list[idx] if request.return_token_ids else None,
                 reasoning_content=output.get("reasoning_content"),
                 tool_calls=output.get("tool_call"),
                 logprobs=aggregated_logprobs,

fastdeploy/input/ernie4_5_processor.py

@@ -197,7 +197,7 @@ class Ernie4_5Processor(BaseDataProcessor):
                 if isinstance(prompt, list):  # if prompt is a token id list
                     request["prompt_token_ids"] = prompt
                 else:
-                    request["text_after_process"] = prompt
+                    request["prompt_tokens"] = prompt
                     tokens = self.tokenizer.tokenize(prompt)
                     token_ids = self.tokenizer.convert_tokens_to_ids(tokens)
                     request["prompt_token_ids"] = token_ids
@@ -318,7 +318,7 @@ class Ernie4_5Processor(BaseDataProcessor):
                 if tool_call_info.tools_called:
                     response_dict["outputs"]["tool_call"] = tool_call_info.tool_calls
                     response_dict["outputs"]["text"] = tool_call_info.content
-            response_dict["outputs"]["raw_prediction"] = full_text
+            response_dict["outputs"]["completion_tokens"] = full_text
             data_processor_logger.info(f"req_id:{req_id}, decode_status: {self.decode_status[req_id]}")
             del self.decode_status[req_id]
         return response_dict
@@ -342,7 +342,7 @@ class Ernie4_5Processor(BaseDataProcessor):
             if token_ids[-1] == self.tokenizer.eos_token_id:
                 token_ids = token_ids[:-1]
         delta_text, previous_token_ids, previous_texts = self.ids2tokens(token_ids, req_id)
-        response_dict["outputs"]["raw_prediction"] = delta_text
+        response_dict["outputs"]["completion_tokens"] = delta_text
         if self.reasoning_parser and (
             enable_thinking or self.reasoning_parser.__class__.__name__ == "ErnieX1ReasoningParser"
         ):
@@ -398,7 +398,7 @@ class Ernie4_5Processor(BaseDataProcessor):
             add_special_tokens=False,
             **kwargs,
         )
-        request_or_messages["text_after_process"] = spliced_message
+        request_or_messages["prompt_tokens"] = spliced_message
         req_id = None
         if isinstance(request_or_messages, dict):
             req_id = request_or_messages.get("request_id", None)

fastdeploy/input/ernie4_5_vl_processor/ernie4_5_vl_processor.py

@@ -222,7 +222,7 @@ class Ernie4_5_VLProcessor(Ernie4_5Processor):
             self._check_mm_limits(multimodal_data)
             images = multimodal_data.get("image", None)
             videos = multimodal_data.get("video", None)
-            request["text_after_process"] = request.get("prompt")
+            request["prompt_tokens"] = request.get("prompt")
             outputs = self.ernie4_5_processor.text2ids(request["prompt"], images, videos)
         elif request.get("messages"):
             messages = request["messages"]

fastdeploy/input/ernie4_5_vl_processor/process.py

@@ -503,7 +503,7 @@ class DataProcessor:
         prompt_token_str = prompt_token_template.replace("<|image@placeholder|>", "").replace(
             "<|video@placeholder|>", ""
         )
-        request["text_after_process"] = prompt_token_template
+        request["prompt_tokens"] = prompt_token_template
         tokens = self.tokenizer.tokenize(prompt_token_str)
         token_ids = self.tokenizer.convert_tokens_to_ids(tokens)
         data_processor_logger.info(

fastdeploy/input/qwen_vl_processor/process.py

@@ -495,7 +495,7 @@ class DataProcessor:
             add_generation_prompt=request.get("add_generation_prompt", True),
         )
         prompt_token_str = raw_prompt.replace(self.image_token, "").replace(self.video_token, "")
-        request["text_after_process"] = raw_prompt
+        request["prompt_tokens"] = raw_prompt
 
         tokens = self.tokenizer.tokenize(prompt_token_str)
         token_ids = self.tokenizer.convert_tokens_to_ids(tokens)

fastdeploy/input/text_processor.py

@@ -403,7 +403,7 @@ class DataProcessor(BaseDataProcessor):
         delta_text, _, previous_texts = self.ids2tokens(token_ids, req_id)
         if is_end:
             full_text = previous_texts + delta_text
-            response_dict["outputs"]["raw_prediction"] = full_text
+            response_dict["outputs"]["completion_tokens"] = full_text
             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
@@ -439,7 +439,7 @@ class DataProcessor(BaseDataProcessor):
             if token_ids[-1] in self.eos_token_ids:
                 token_ids = token_ids[:-1]
         delta_text, previous_token_ids, previous_texts = self.ids2tokens(token_ids, req_id)
-        response_dict["outputs"]["raw_prediction"] = delta_text
+        response_dict["outputs"]["completion_tokens"] = delta_text
         if self.reasoning_parser and (
             enable_thinking or self.reasoning_parser.__class__.__name__ == "ErnieX1ReasoningParser"
         ):
@@ -548,7 +548,7 @@ class DataProcessor(BaseDataProcessor):
             return_tensors="pd",
             **kwargs,
         )
-        request["text_after_process"] = spliced_message
+        request["prompt_tokens"] = spliced_message
         req_id = None
         tokens = self.tokenizer.tokenize(spliced_message)
         if isinstance(request, dict):

tests/ce/server/test_return_token_ids.py

@@ -14,10 +14,10 @@ from core import TEMPLATE, URL, build_request_payload, send_request
 COMPLETIONS_URL = URL.replace("/v1/chat/completions", "/v1/completions")
 
 
-def test_completion_stream_text_after_process_raw_prediction():
+def test_completion_stream_prompt_tokens_completion_tokens():
     """
     /v1/completions接口, stream=True
-    返回属性"text_after_process"和"reasoning_content"
+    return "prompt_tokens"和"reasoning_content"
     """
     data = {
         "prompt": "你是谁",
@@ -39,55 +39,55 @@ def test_completion_stream_text_after_process_raw_prediction():
 
         choice = response_data["choices"][0]
         if "prompt_token_ids" in choice and choice["prompt_token_ids"] is not None:
-            text_after_process = choice["text_after_process"]
+            prompt_tokens = choice["prompt_tokens"]
-            assert data["prompt"] in text_after_process, "text_after_process取值结果不正确"
+            assert data["prompt"] in prompt_tokens, "prompt_tokens取值结果不正确"
         else:
-            raw_prediction = choice["raw_prediction"]
+            completion_tokens = choice["completion_tokens"]
             reasoning_content = choice["reasoning_content"]
             text = choice["text"]
-            assert reasoning_content or text in raw_prediction, "raw_prediction取值结果不正确"
+            assert reasoning_content or text in completion_tokens, "completion_tokens取值结果不正确"
         if "finish_reason" in line.strip():
             break
 
 
-def test_completion_text_after_process_raw_predictio_return_token_ids():
+def test_completion_prompt_tokens_completion_tokens_return_token_ids():
     """
     /v1/completions接口,非流式接口
-    返回属性"text_after_process"和"reasoning_content"
+    return "prompt_tokens"和"reasoning_content"
     """
     data = {"stream": False, "prompt": "你是谁", "max_tokens": 50, "return_token_ids": True}
     payload = build_request_payload(TEMPLATE, data)
     resp = send_request(COMPLETIONS_URL, payload).json()
 
-    text_after_process = resp["choices"][0]["text_after_process"]
+    prompt_tokens = resp["choices"][0]["prompt_tokens"]
-    assert data["prompt"] in text_after_process, "text_after_process取值结果不正确"
+    assert data["prompt"] in prompt_tokens, "prompt_tokens取值结果不正确"
 
-    raw_prediction = resp["choices"][0]["raw_prediction"]
+    completion_tokens = resp["choices"][0]["completion_tokens"]
     reasoning_content = resp["choices"][0]["reasoning_content"]
     text = resp["choices"][0]["text"]
-    assert reasoning_content or text in raw_prediction, "raw_prediction取值结果不正确"
+    assert reasoning_content or text in completion_tokens, "completion_tokens取值结果不正确"
 
 
-def test_completion_text_after_process_raw_prediction():
+def test_completion_prompt_tokens_completion_tokens():
     """
     /v1/completions接口,无return_token_ids参数
-    非流式接口中,无return token ids 属性"text_after_process"和"reasoning_content"值为null
+    非流式接口中,无return token ids 属性"prompt_tokens"和"reasoning_content"值为null
     """
     data = {"stream": False, "prompt": "你是谁", "max_tokens": 50}
     payload = build_request_payload(TEMPLATE, data)
     resp = send_request(COMPLETIONS_URL, payload).json()
 
-    text_after_process = resp["choices"][0]["text_after_process"]
+    prompt_tokens = resp["choices"][0]["prompt_tokens"]
-    assert text_after_process is None, "text_after_process取值结果不正确"
+    assert prompt_tokens is None, "prompt_tokens取值结果不正确"
 
-    raw_prediction = resp["choices"][0]["raw_prediction"]
+    completion_tokens = resp["choices"][0]["completion_tokens"]
-    assert raw_prediction is None, "raw_prediction取值结果不正确"
+    assert completion_tokens is None, "completion_tokens取值结果不正确"
 
 
-def test_stream_text_after_process_raw_prediction():
+def test_stream_prompt_tokens_completion_tokens():
     """
     /v1/chat/completions接口,"stream": True
-    返回属性"text_after_process"和"reasoning_content"
+    返回属性"prompt_tokens"和"reasoning_content"
     """
     data = {
         "messages": [{"role": "user", "content": "你是谁"}],
@@ -109,21 +109,21 @@ def test_stream_text_after_process_raw_prediction():
 
         choice = response_data["choices"][0]
         if "prompt_token_ids" in choice["delta"] and choice["delta"]["prompt_token_ids"] is not None:
-            text_after_process = choice["delta"]["text_after_process"]
+            prompt_tokens = choice["delta"]["prompt_tokens"]
-            assert data["messages"][0]["content"] in text_after_process, "text_after_process取值结果不正确"
+            assert data["messages"][0]["content"] in prompt_tokens, "prompt_tokens取值结果不正确"
         else:
-            raw_prediction = choice["delta"]["raw_prediction"]
+            completion_tokens = choice["delta"]["completion_tokens"]
             reasoning_content = choice["delta"]["reasoning_content"]
             content = choice["delta"]["content"]
-            assert reasoning_content or content in raw_prediction, "raw_prediction取值结果不正确"
+            assert reasoning_content or content in completion_tokens, "completion_tokens取值结果不正确"
         if "finish_reason" in line.strip():
             break
 
 
-def test_text_after_process_raw_prediction_return_token_ids():
+def test_prompt_tokens_completion_tokens_return_token_ids():
     """
     /v1/chat/completions接口,非流式接口
-    返回属性"text_after_process"和"reasoning_content"
+    返回属性"prompt_tokens"和"reasoning_content"
     """
     data = {
         "stream": False,
@@ -136,19 +136,19 @@ def test_text_after_process_raw_prediction_return_token_ids():
     payload = build_request_payload(TEMPLATE, data)
     resp = send_request(URL, payload).json()
 
-    text_after_process = resp["choices"][0]["message"]["text_after_process"]
+    prompt_tokens = resp["choices"][0]["message"]["prompt_tokens"]
-    assert data["messages"][0]["content"] in text_after_process, "text_after_process取值结果不正确"
+    assert data["messages"][0]["content"] in prompt_tokens, "prompt_tokens取值结果不正确"
 
-    raw_prediction = resp["choices"][0]["message"]["raw_prediction"]
+    completion_tokens = resp["choices"][0]["message"]["completion_tokens"]
     reasoning_content = resp["choices"][0]["message"]["reasoning_content"]
     text = resp["choices"][0]["message"]["content"]
-    assert reasoning_content or text in raw_prediction, "raw_prediction取值结果不正确"
+    assert reasoning_content or text in completion_tokens, "completion_tokens取值结果不正确"
 
 
-def test_text_after_process_raw_prediction():
+def test_prompt_tokens_completion_tokens():
     """
     /v1/chat/completions接口,无return_token_ids参数
-    无return token ids 属性"text_after_process"和"reasoning_content"值为null
+    无return token ids 属性"prompt_tokens"和"reasoning_content"值为null
     """
     data = {
         "stream": False,
@@ -160,8 +160,8 @@ def test_text_after_process_raw_prediction():
     payload = build_request_payload(TEMPLATE, data)
     resp = send_request(URL, payload).json()
 
-    text_after_process = resp["choices"][0]["message"]["text_after_process"]
+    prompt_tokens = resp["choices"][0]["message"]["prompt_tokens"]
-    assert text_after_process is None, "text_after_process取值结果不正确"
+    assert prompt_tokens is None, "prompt_tokens取值结果不正确"
 
-    raw_prediction = resp["choices"][0]["message"]["raw_prediction"]
+    completion_tokens = resp["choices"][0]["message"]["completion_tokens"]
-    assert raw_prediction is None, "raw_prediction取值结果不正确"
+    assert completion_tokens is None, "completion_tokens取值结果不正确"

tests/entrypoints/openai/test_completion_echo.py

@@ -57,7 +57,7 @@ class TestCompletionEcho(unittest.IsolatedAsyncioTestCase):
             model_name="test_model",
             prompt_batched_token_ids=[[1, 2]],
             completion_batched_token_ids=[[3, 4, 5]],
-            text_after_process_list=["test prompt"],
+            prompt_tokens_list=["test prompt"],
         )
 
         self.assertEqual(response.choices[0].text, "test prompt generated text")
@@ -90,7 +90,7 @@ class TestCompletionEcho(unittest.IsolatedAsyncioTestCase):
             model_name="test_model",
             prompt_batched_token_ids=[[1, 2]],
             completion_batched_token_ids=[[3, 4, 5]],
-            text_after_process_list=["test prompt"],
+            prompt_tokens_list=["test prompt"],
         )
         self.assertEqual(response.choices[0].text, "decoded_[1, 2, 3] generated text")
 
@@ -123,7 +123,7 @@ class TestCompletionEcho(unittest.IsolatedAsyncioTestCase):
             model_name="test_model",
             prompt_batched_token_ids=[[1], [2]],
             completion_batched_token_ids=[[1, 2], [3, 4]],
-            text_after_process_list=["prompt1", "prompt2"],
+            prompt_tokens_list=["prompt1", "prompt2"],
         )
 
         self.assertEqual(len(response.choices), 2)
@@ -159,7 +159,7 @@ class TestCompletionEcho(unittest.IsolatedAsyncioTestCase):
             model_name="test_model",
             prompt_batched_token_ids=[[1], [2]],
             completion_batched_token_ids=[[1, 2], [3, 4]],
-            text_after_process_list=["prompt1", "prompt2"],
+            prompt_tokens_list=["prompt1", "prompt2"],
         )
 
         self.assertEqual(len(response.choices), 2)

tests/entrypoints/openai/test_max_streaming_tokens.py

@@ -160,7 +160,7 @@ class TestMaxStreamingResponseTokens(IsolatedAsyncioTestCase):
             request_id="test-request-id",
             model_name="test-model",
             prompt_token_ids=[1, 2, 3],
-            text_after_process="Hello",
+            prompt_tokens="Hello",
         )
 
         chunks = []
@@ -242,7 +242,7 @@ class TestMaxStreamingResponseTokens(IsolatedAsyncioTestCase):
             model_name="test-model",
             created_time=11,
             prompt_batched_token_ids=[[1, 2, 3]],
-            text_after_process_list=["Hello"],
+            prompt_tokens_list=["Hello"],
         )
 
         chunks = []

tests/entrypoints/openai/test_run_batch.py

@@ -54,8 +54,8 @@ INVALID_INPUT_BATCH = """
 """
 
 BATCH_RESPONSE = """
-{"id":"fastdeploy-7fcc30e2e4334fca806c4d01ee7ac4ab","custom_id":"req-00001","response":{"status_code":200,"request_id":"fastdeploy-batch-5f4017beded84b15aa3a8b0f1fce154c","body":{"id":"chatcmpl-33b09ae5-a8f1-40ad-9110-efa2b381eac9","object":"chat.completion","created":1758698637,"model":"/root/paddlejob/zhaolei36/ernie-4_5-0_3b-bf16-paddle","choices":[{"index":0,"message":{"role":"assistant","content":"In a sunlit meadow where dreams bloom,\\nA gentle breeze carries the breeze,\\nThe leaves rustle like ancient letters,\\nAnd in the sky, a song of hope and love.","multimodal_content":null,"reasoning_content":null,"tool_calls":null,"prompt_token_ids":null,"completion_token_ids":null,"text_after_process":null,"raw_prediction":null,"prompt_tokens":null,"completion_tokens":null},"logprobs":null,"finish_reason":"stop"}],"usage":{"prompt_tokens":19,"total_tokens":60,"completion_tokens":41,"prompt_tokens_details":{"cached_tokens":0}}}},"error":null}
+{"id":"fastdeploy-7fcc30e2e4334fca806c4d01ee7ac4ab","custom_id":"req-00001","response":{"status_code":200,"request_id":"fastdeploy-batch-5f4017beded84b15aa3a8b0f1fce154c","body":{"id":"chatcmpl-33b09ae5-a8f1-40ad-9110-efa2b381eac9","object":"chat.completion","created":1758698637,"model":"/root/paddlejob/zhaolei36/ernie-4_5-0_3b-bf16-paddle","choices":[{"index":0,"message":{"role":"assistant","content":"In a sunlit meadow where dreams bloom,\\nA gentle breeze carries the breeze,\\nThe leaves rustle like ancient letters,\\nAnd in the sky, a song of hope and love.","multimodal_content":null,"reasoning_content":null,"tool_calls":null,"prompt_token_ids":null,"completion_token_ids":null,"prompt_tokens":null,"completion_tokens":null},"logprobs":null,"finish_reason":"stop"}],"usage":{"prompt_tokens":19,"total_tokens":60,"completion_tokens":41,"prompt_tokens_details":{"cached_tokens":0}}}},"error":null}
-{"id":"fastdeploy-bf549849df2145598ae1758ba260f784","custom_id":"req-00002","response":{"status_code":200,"request_id":"fastdeploy-batch-81223f12fdc345efbfe85114ced10a1d","body":{"id":"chatcmpl-9479e36c-1542-45ff-b364-1dc6d34be9e7","object":"chat.completion","created":1758698637,"model":"/root/paddlejob/zhaolei36/ernie-4_5-0_3b-bf16-paddle","choices":[{"index":0,"message":{"role":"assistant","content":"Based on the given text, here are some possible actions you can take:\\n\\n1. **Read the question**: To understand what you can do, you can read the question (id=2) and analyze its requirements or constraints.\\n2. **Identify the keywords**: Look for specific keywords or phrases that describe what you can do. For example, if the question mentions \\"coding,\\" you can focus on coding skills or platforms.\\n3. **Brainstorm ideas**: You can think creatively about different ways to perform the action. For example, you could brainstorm different methods of communication, data analysis, or problem-solving.\\n4. **Explain your action**: If you have knowledge or skills in a particular area, you can explain how you would use those skills to achieve the desired outcome.\\n5. **Ask for help**: If you need assistance, you can ask for help from a friend, teacher, or mentor.","multimodal_content":null,"reasoning_content":null,"tool_calls":null,"prompt_token_ids":null,"completion_token_ids":null,"text_after_process":null,"raw_prediction":null,"prompt_tokens":null,"completion_tokens":null},"logprobs":null,"finish_reason":"stop"}],"usage":{"prompt_tokens":17,"total_tokens":211,"completion_tokens":194,"prompt_tokens_details":{"cached_tokens":0}}}},"error":null}
+{"id":"fastdeploy-bf549849df2145598ae1758ba260f784","custom_id":"req-00002","response":{"status_code":200,"request_id":"fastdeploy-batch-81223f12fdc345efbfe85114ced10a1d","body":{"id":"chatcmpl-9479e36c-1542-45ff-b364-1dc6d34be9e7","object":"chat.completion","created":1758698637,"model":"/root/paddlejob/zhaolei36/ernie-4_5-0_3b-bf16-paddle","choices":[{"index":0,"message":{"role":"assistant","content":"Based on the given text, here are some possible actions you can take:\\n\\n1. **Read the question**: To understand what you can do, you can read the question (id=2) and analyze its requirements or constraints.\\n2. **Identify the keywords**: Look for specific keywords or phrases that describe what you can do. For example, if the question mentions \\"coding,\\" you can focus on coding skills or platforms.\\n3. **Brainstorm ideas**: You can think creatively about different ways to perform the action. For example, you could brainstorm different methods of communication, data analysis, or problem-solving.\\n4. **Explain your action**: If you have knowledge or skills in a particular area, you can explain how you would use those skills to achieve the desired outcome.\\n5. **Ask for help**: If you need assistance, you can ask for help from a friend, teacher, or mentor.","multimodal_content":null,"reasoning_content":null,"tool_calls":null,"prompt_token_ids":null,"completion_token_ids":null,"prompt_tokens":null,"completion_tokens":null},"logprobs":null,"finish_reason":"stop"}],"usage":{"prompt_tokens":17,"total_tokens":211,"completion_tokens":194,"prompt_tokens_details":{"cached_tokens":0}}}},"error":null}
 """
 
 
@@ -867,8 +867,6 @@ class TestFileOperations(unittest.TestCase):
                     tool_calls=message_data["tool_calls"],
                     prompt_token_ids=message_data["prompt_token_ids"],
                     completion_token_ids=message_data["completion_token_ids"],
-                    text_after_process=message_data["text_after_process"],
-                    raw_prediction=message_data["raw_prediction"],
                     prompt_tokens=message_data["prompt_tokens"],
                     completion_tokens=message_data["completion_tokens"],
                 )

tests/entrypoints/openai/test_serving_completion.py

@@ -155,7 +155,7 @@ class TestOpenAIServingCompletion(unittest.TestCase):
             model_name=model_name,
             prompt_batched_token_ids=prompt_batched_token_ids,
             completion_batched_token_ids=completion_batched_token_ids,
-            text_after_process_list=["1", "1"],
+            prompt_tokens_list=["1", "1"],
         )
 
         assert completion_response.id == request_id

tests/input/test_ernie_processor.py

@@ -61,7 +61,7 @@ class TestErnie4_5ProcessorProcessResponseDictStreaming(unittest.TestCase):
         result = self.processor.process_response_dict_streaming(response_dict, **kwargs)
 
         # 验证结果
-        self.assertEqual(result["outputs"]["raw_prediction"], "delta_text")
+        self.assertEqual(result["outputs"]["completion_tokens"], "delta_text")
 
     def test_process_request_dict(self):
         request_dict = {

tests/input/test_qwen_vl_processor.py

@@ -276,7 +276,7 @@ class TestQwenVLProcessor(unittest.TestCase):
         # Create equivalent request in prompt format
         prompt = {
             "request_id": "12345",
-            "prompt": request["text_after_process"],
+            "prompt": request["prompt_tokens"],
             "multimodal_data": {
                 "image": [mock_pil_image(480, 640)],
                 "video": [{"video": b"123"}],
@@ -300,7 +300,7 @@ class TestQwenVLProcessor(unittest.TestCase):
 
         This test verifies that:
         - The processor correctly handles multimodal messages (image, video, text)
-        - The text_after_process field matches the output from direct tokenizer application
+        - The prompt_tokens field matches the output from direct tokenizer application
         - The chat template application preserves the message structure and content
 
         Test Steps:
@@ -345,7 +345,7 @@ class TestQwenVLProcessor(unittest.TestCase):
 
         # Process request through the processor
         self.processor.process_request_dict(request, 1024 * 100)
-        prompt2 = request["text_after_process"]
+        prompt2 = request["prompt_tokens"]
 
         # Verify both methods produce identical prompt strings
         self.assertEqual(prompt, prompt2)

tests/utils/test_custom_chat_template.py

@@ -62,7 +62,7 @@ class TestLodChatTemplate(unittest.IsolatedAsyncioTestCase):
         )
 
         async def mock_chat_completion_full_generator(
-            request, request_id, model_name, prompt_token_ids, text_after_process
+            request, request_id, model_name, prompt_token_ids, prompt_tokens
         ):
             return prompt_token_ids
 
@@ -89,7 +89,7 @@ class TestLodChatTemplate(unittest.IsolatedAsyncioTestCase):
         )
 
         async def mock_chat_completion_full_generator(
-            request, request_id, model_name, prompt_token_ids, text_after_process
+            request, request_id, model_name, prompt_token_ids, prompt_tokens
         ):
             return prompt_token_ids