[Feature] Add return_token_ids, prompt_token_ids, and delete training, raw_request in request body (#2940)

* [feat] add return_token_ids, prompt_token_ids, delete raw_request in request body

* [fix] return_token_ids not working in curl request

* [test] improve some test cases of return_token_ids and prompt_token_ids

* [fix] the server responds ok even if request.messages is an empty list

fastdeploy/engine/request.py

@@ -46,7 +46,6 @@ class Request:
         preprocess_end_time: Optional[float] = None,
         multimodal_inputs: Optional[dict] = None,
         multimodal_data: Optional[dict] = None,
-        raw_request: bool = True,
         disaggregate_info: Optional[dict] = None,
         draft_token_ids: Optional[list[int]] = None,
         guided_json: Optional[Any] = None,
@@ -74,7 +73,6 @@ class Request:
         self.arrival_time = arrival_time
         self.preprocess_start_time = preprocess_start_time
         self.preprocess_end_time = preprocess_end_time
-        self.raw_request = raw_request
         self.disaggregate_info = disaggregate_info
 
         # speculative method in disaggregate-mode
@@ -117,7 +115,6 @@ class Request:
             multimodal_data=d.get("multimodal_data"),
             disaggregate_info=d.get("disaggregate_info"),
             draft_token_ids=d.get("draft_token_ids"),
-            raw_request=d.get("raw_request", True),
             guided_json=d.get("guided_json", None),
             guided_regex=d.get("guided_regex", None),
             guided_choice=d.get("guided_choice", None),
@@ -145,7 +142,6 @@ class Request:
             "preprocess_end_time": self.preprocess_end_time,
             "multimodal_inputs": self.multimodal_inputs,
             "multimodal_data": self.multimodal_data,
-            "raw_request": self.raw_request,
             "disaggregate_info": self.disaggregate_info,
             "draft_token_ids": self.draft_token_ids,
             "enable_thinking": self.enable_thinking,

fastdeploy/entrypoints/openai/protocol.py

@@ -124,6 +124,8 @@ class ChatMessage(BaseModel):
     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
 
 
 class ChatCompletionResponseChoice(BaseModel):
@@ -177,7 +179,8 @@ class DeltaMessage(BaseModel):
 
     role: Optional[str] = None
     content: Optional[str] = None
-    token_ids: Optional[List[int]] = None
+    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
 
@@ -214,7 +217,8 @@ class CompletionResponseChoice(BaseModel):
 
     index: int
     text: str
-    token_ids: Optional[List[int]] = None
+    prompt_token_ids: Optional[List[int]] = None
+    completion_token_ids: Optional[List[int]] = None
     arrival_time: Optional[float] = None
     logprobs: Optional[int] = None
     reasoning_content: Optional[str] = None
@@ -243,7 +247,8 @@ class CompletionResponseStreamChoice(BaseModel):
     index: int
     text: str
     arrival_time: float = None
-    token_ids: Optional[List[int]] = None
+    prompt_token_ids: Optional[List[int]] = None
+    completion_token_ids: Optional[List[int]] = None
     logprobs: Optional[float] = None
     reasoning_content: Optional[str] = None
     finish_reason: Optional[Literal["stop", "length", "tool_calls"]] = None
@@ -341,6 +346,9 @@ class CompletionRequest(BaseModel):
     top_k: Optional[int] = None
     min_p: Optional[float] = None
     user: Optional[str] = None
+    extra_body: Optional[dict] = None
+    return_token_ids: Optional[bool] = False
+    prompt_token_ids: Optional[List[int]] = None
 
     response_format: Optional[AnyResponseFormat] = None
     guided_json: Optional[Union[str, dict, BaseModel]] = None
@@ -373,9 +381,13 @@ class CompletionRequest(BaseModel):
         if prompt is not None:
             req_dict["prompt"] = prompt
 
-        if isinstance(prompt[0], int):
+        if self.prompt_token_ids is not None or \
-            req_dict["prompt_token_ids"] = prompt
+            (self.extra_body is not None and self.extra_body.get("prompt_token_ids") is not None):
-            del req_dict["prompt"]
+            req_dict["prompt_token_ids"] = self.prompt_token_ids
+            if "prompt" in req_dict:
+                del req_dict["prompt"]
+        else:
+            assert len(prompt) > 0
 
         guided_json_object = None
         if self.response_format is not None:
@@ -464,6 +476,9 @@ class ChatCompletionRequest(BaseModel):
     min_p: Optional[float] = None
     user: Optional[str] = None
     metadata: Optional[dict] = None
+    extra_body: Optional[dict] = None
+    return_token_ids: Optional[bool] = False
+    prompt_token_ids: Optional[List[int]] = None
 
     response_format: Optional[AnyResponseFormat] = None
     guided_json: Optional[Union[str, dict, BaseModel]] = None
@@ -499,12 +514,14 @@ class ChatCompletionRequest(BaseModel):
         for key, value in self.dict().items():
             if value is not None:
                 req_dict[key] = value
-        if isinstance(self.messages[0], int):
+
-            req_dict["prompt_token_ids"] = self.messages
+        if self.prompt_token_ids is not None or \
-            del req_dict["messages"]
+            (self.extra_body is not None and self.extra_body.get("prompt_token_ids") is not None):
-        if "raw_request" in req_dict and not req_dict["raw_request"]:
+            req_dict["prompt_token_ids"] = self.prompt_token_ids
-            req_dict["prompt"] = req_dict["messages"][0]["content"]
+            if "messages" in req_dict:
-            del req_dict["messages"]
+                del req_dict["messages"]
+        else:
+            assert len(self.messages) > 0
 
         guided_json_object = None
         if self.response_format is not None:

fastdeploy/entrypoints/openai/serving_chat.py

@@ -144,6 +144,7 @@ class OpenAIServingChat:
             if request.metadata is not None:
                 enable_thinking = request.metadata.get("enable_thinking")
                 include_stop_str_in_output = request.metadata.get("include_stop_str_in_output", False)
+            enable_return_token_ids = request.return_token_ids or (request.extra_body is not None and request.extra_body.get('return_token_ids', False))
             while num_choices > 0:
                 try:
                     raw_data = await asyncio.wait_for(dealer.read(), timeout=10)
@@ -189,10 +190,12 @@ class OpenAIServingChat:
                                     content="", 
                                     reasoning_content="", 
                                     tool_calls=None,
-                                ),
+                                    prompt_token_ids=None,
+                                    completion_token_ids=None,
+                                )
                             )
-                            if request.metadata is not None and request.metadata.get("training", False):
+                            if enable_return_token_ids:
-                                choice.delta.token_ids = prompt_token_ids
+                                choice.delta.prompt_token_ids = list(prompt_token_ids)
                             chunk = ChatCompletionStreamResponse(
                                 id=request_id,
                                 object=chunk_object_type,
@@ -229,8 +232,9 @@ class OpenAIServingChat:
                     previous_num_tokens += len(output["token_ids"])
                     delta_message = DeltaMessage(
                         content=delta_text, 
-                        reasoning_content=output.get("reasoning_content"),
+                        reasoning_content=output.get("reasoning_content"), \
-                        token_ids=output.get("token_ids"),
+                        prompt_token_ids=None,
+                        completion_token_ids=None, 
                         tool_calls=output.get("tool_call_content", []),
                     )
 
@@ -260,8 +264,8 @@ class OpenAIServingChat:
                         if res.get("error_msg") is not None and "Recover" in res["error_msg"]:
                             choice.finish_reason = "recover_stop"
 
-                    if request.metadata is not None and request.metadata.get("training", False) and delta_text != "":
+                    if enable_return_token_ids:
-                        choice.delta.token_ids = output["token_ids"]
+                        choice.delta.completion_token_ids = list(output["token_ids"])
                     if include_continuous_usage:
                         chunk.usage = UsageInfo(
                             prompt_tokens=num_prompt_tokens,
@@ -318,6 +322,7 @@ class OpenAIServingChat:
         final_res = None
         enable_thinking = None
         include_stop_str_in_output = False
+        enable_return_token_ids = request.return_token_ids or (request.extra_body is not None and request.extra_body.get('return_token_ids', False))
         try:
             dealer = await aiozmq.create_zmq_stream(zmq.DEALER, connect=f"ipc:///dev/shm/router_{self.pid}.ipc")
             dealer.write([b"", request_id.encode("utf-8")])
@@ -388,7 +393,8 @@ class OpenAIServingChat:
             content=output["text"],
             reasoning_content=output.get("reasoning_content"),
             tool_calls=output.get("tool_call_content"),
-            token_ids=output.get("token_ids"),
+            prompt_token_ids=prompt_token_ids if enable_return_token_ids else None,
+            completion_token_ids=output.get("token_ids") if enable_return_token_ids else None,
         )
         logprobs_full_res = None
         if logprob_contents:

fastdeploy/entrypoints/openai/serving_completion.py

@@ -226,7 +226,7 @@ class OpenAIServingCompletion:
                 model=model_name,
                 choices=choices,
             )
-
+            enable_return_token_ids = request.return_token_ids or (request.extra_body is not None and request.extra_body.get('return_token_ids', False))
             current_waiting_time = 0
             while num_choices > 0:
                 try:
@@ -250,18 +250,17 @@ class OpenAIServingCompletion:
                         raise ValueError("{}".format(res["error_msg"]))
 
                     if first_iteration[idx]:
-                        if request.suffix is not None and request.suffix.get("training", False):
+                        if enable_return_token_ids:
                             chunk = CompletionStreamResponse(
                                 id=request_id,
                                 created=created_time,
                                 model=model_name,
-                                choices=[
+                                choices=[CompletionResponseStreamChoice(
-                                    CompletionResponseStreamChoice(
+                                    index=idx,
-                                        index=idx,
+                                    text="",
-                                        text="",
+                                    prompt_token_ids=list(prompt_batched_token_ids[idx]) if enable_return_token_ids else None,
-                                        token_ids=list(prompt_batched_token_ids[idx]),
+                                    completion_token_ids=None,
-                                    )
+                                )]
-                                ],
                             )
                             yield f"data: {chunk.model_dump_json(exclude_unset=True)}\n\n"
                         first_iteration[idx] = False
@@ -275,16 +274,15 @@ class OpenAIServingCompletion:
 
                     output = res["outputs"]
 
-                    choices.append(
+                    choices.append(CompletionResponseStreamChoice(
-                        CompletionResponseStreamChoice(
+                        index=idx,
-                            index=idx,
+                        text=output["text"],
-                            text=output["text"],
+                        prompt_token_ids=None,
-                            token_ids=output.get("token_ids"),
+                        completion_token_ids=output.get("token_ids") if enable_return_token_ids else None,
-                            tool_calls=output.get("tool_call_content"),
+                        tool_calls=output.get("tool_call_content"),
-                            reasoning_content=output.get("reasoning_content"),
+                        reasoning_content=output.get("reasoning_content"),
-                            arrival_time=arrival_time,
+                        arrival_time=arrival_time
-                        )
+                    ))
-                    )
                     if res["finished"]:
                         if request.max_tokens is None or output_tokens[idx] + 1 != request.max_tokens:
                             chunk.choices[0].finish_reason = "stop"
@@ -347,6 +345,7 @@ class OpenAIServingCompletion:
         choices: List[CompletionResponseChoice] = []
         num_prompt_tokens = 0
         num_generated_tokens = 0
+        enable_return_token_ids = request.return_token_ids or (request.extra_body is not None and request.extra_body.get('return_token_ids', False))
 
         for idx in range(len(final_res_batch)):
             final_res = final_res_batch[idx]
@@ -371,7 +370,9 @@ class OpenAIServingCompletion:
                 token_ids=token_ids,
                 index=len(choices),
                 text=output_text,
-                reasoning_content=output.get("reasoning_content"),
+                prompt_token_ids=prompt_token_ids if enable_return_token_ids else None,
+                completion_token_ids=output["token_ids"] if enable_return_token_ids else None,
+                reasoning_content=output.get('reasoning_content'),
                 tool_calls=output.get("tool_call_content"),
                 logprobs=None,
                 finish_reason=None,

fastdeploy/input/ernie_processor.py

@@ -99,8 +99,9 @@ class ErnieProcessor(BaseDataProcessor):
 
         if request.prompt_token_ids is None or len(request.prompt_token_ids) == 0:
             if request.prompt is None and request.messages is None:
-                raise ValueError(f"The request should have `input_ids`, `text` or `messages`: {request}.")
+                raise ValueError(
-            if request.prompt is not None or not request.raw_request:
+                    f"The request should have `prompt_token_ids`, `prompt` or `messages`: {request}.")
+            if request.prompt is not None:
                 prompt = request.prompt if request.prompt is not None else request.messages[0]
                 prompt = prompt[0] if isinstance(prompt, list) else prompt
                 tokens = self.tokenizer.tokenize(prompt)

fastdeploy/input/text_processor.py

@@ -231,7 +231,7 @@ class DataProcessor(BaseDataProcessor):
 
         if request.prompt_token_ids is None or len(request.prompt_token_ids) == 0:
             if request.prompt is not None:
-                request.prompt_token_ids = self.text2ids(request.prompt, max_model_len, request.raw_request)
+                request.prompt_token_ids = self.text2ids(request.prompt, max_model_len)
             elif request.messages is not None:
                 if self.tokenizer.chat_template is None:
                     raise ValueError("This model does not support chat_template.")
@@ -266,7 +266,7 @@ class DataProcessor(BaseDataProcessor):
         if not request.get("eos_token_ids"):
             request["eos_token_ids"] = self.eos_token_ids
 
-        # 处理stop_sequences
+        # processing stop_sequences
         stop_sequences = request.get("stop", [])
         if stop_sequences:
             stop_seqs, stop_seqs_len = self.update_stop_seq(stop_sequences)
@@ -274,12 +274,11 @@ class DataProcessor(BaseDataProcessor):
             request["stop_seqs_len"] = stop_seqs_len
 
         data_processor_logger.info(f"Processing request {request}")
-        # 处理prompt_token_ids
+        # processing prompt_token_ids
-        if not request.get("prompt_token_ids"):
+        if not request.get('prompt_token_ids'):
-            if "prompt" in request:
+            if 'prompt' in request:
-                raw_request = request.get("raw_request", True)
+                request['prompt_token_ids'] = self.text2ids(request['prompt'], max_model_len).tolist()
-                request["prompt_token_ids"] = self.text2ids(request["prompt"], max_model_len, raw_request).tolist()
+            elif 'messages' in request:
-            elif "messages" in request:
                 if self.tokenizer.chat_template is None:
                     raise ValueError("This model does not support chat_template.")
                 request["prompt_token_ids"] = self.messages2ids(request)
@@ -416,7 +415,7 @@ class DataProcessor(BaseDataProcessor):
                 **kwargs,
             )
 
-    def text2ids(self, text, max_model_len, raw_request=True):
+    def text2ids(self, text, max_model_len):
         """
         text to token ids
 

test/ci_use/EB_Lite/test_EB_Lite_serving.py

@@ -342,6 +342,9 @@ def test_streaming(openai_client, capsys):
         output.append(chunk.choices[0].text)
     assert len(output) > 0
 
+# ==========================
+# OpenAI Client additional chat/completions test
+# ==========================
 
 def test_non_streaming_with_stop_str(openai_client):
     """
@@ -405,3 +408,256 @@ def test_streaming_with_stop_str(openai_client):
     for chunk in response:
         last_token = chunk.choices[0].delta.content
     assert last_token != "</s>"
+
+
+def test_non_streaming_chat_with_return_token_ids(openai_client, capsys):
+    """
+    Test return_token_ids option in non-streaming chat functionality with the local service
+    """
+    #  enable return_token_ids
+    response = openai_client.chat.completions.create(
+        model="default",
+        messages=[{"role": "user", "content": "Hello, how are you?"}],
+        temperature=1,
+        max_tokens=5,
+        extra_body={"return_token_ids": True},
+        stream=False,
+    )
+    assert hasattr(response, 'choices')
+    assert len(response.choices) > 0
+    assert hasattr(response.choices[0], 'message')
+    assert hasattr(response.choices[0].message, 'prompt_token_ids')
+    assert isinstance(response.choices[0].message.prompt_token_ids, list)
+    assert hasattr(response.choices[0].message, 'completion_token_ids')
+    assert isinstance(response.choices[0].message.completion_token_ids, list)
+
+    #  disable return_token_ids
+    response = openai_client.chat.completions.create(
+        model="default",
+        messages=[{"role": "user", "content": "Hello, how are you?"}],
+        temperature=1,
+        max_tokens=5,
+        extra_body={"return_token_ids": False},
+        stream=False,
+    )
+    assert hasattr(response, 'choices')
+    assert len(response.choices) > 0
+    assert hasattr(response.choices[0], 'message')
+    assert hasattr(response.choices[0].message, 'prompt_token_ids')
+    assert response.choices[0].message.prompt_token_ids is None
+    assert hasattr(response.choices[0].message, 'completion_token_ids')
+    assert response.choices[0].message.completion_token_ids is None
+
+
+def test_streaming_chat_with_return_token_ids(openai_client, capsys):
+    """
+    Test return_token_ids option in streaming chat functionality with the local service
+    """
+    # enable return_token_ids
+    response = openai_client.chat.completions.create(
+        model="default",
+        messages=[{"role": "user", "content": "Hello, how are you?"}],
+        temperature=1,
+        max_tokens=5,
+        extra_body={"return_token_ids": True},
+        stream=True,
+    )
+    is_first_chunk = True
+    for chunk in response:
+        assert hasattr(chunk, 'choices')
+        assert len(chunk.choices) > 0
+        assert hasattr(chunk.choices[0], 'delta')
+        assert hasattr(chunk.choices[0].delta, 'prompt_token_ids')
+        assert hasattr(chunk.choices[0].delta, 'completion_token_ids')
+        if is_first_chunk:
+            is_first_chunk = False
+            assert isinstance(chunk.choices[0].delta.prompt_token_ids, list)
+            assert chunk.choices[0].delta.completion_token_ids is None
+        else:
+            assert chunk.choices[0].delta.prompt_token_ids is None
+            assert isinstance(chunk.choices[0].delta.completion_token_ids, list)
+
+    # disable return_token_ids
+    response = openai_client.chat.completions.create(
+        model="default",
+        messages=[{"role": "user", "content": "Hello, how are you?"}],
+        temperature=1,
+        max_tokens=5,
+        extra_body={"return_token_ids": False},
+        stream=True,
+    )
+    for chunk in response:
+        assert hasattr(chunk, 'choices')
+        assert len(chunk.choices) > 0
+        assert hasattr(chunk.choices[0], 'delta')
+        assert hasattr(chunk.choices[0].delta, 'prompt_token_ids')
+        assert chunk.choices[0].delta.prompt_token_ids is None
+        assert hasattr(chunk.choices[0].delta, 'completion_token_ids')
+        assert chunk.choices[0].delta.completion_token_ids is None
+
+
+def test_non_streaming_completion_with_return_token_ids(openai_client, capsys):
+    """
+    Test return_token_ids option in non-streaming completion functionality with the local service
+    """
+    # enable return_token_ids
+    response = openai_client.completions.create(
+        model="default",
+        prompt="Hello, how are you?",
+        temperature=1,
+        max_tokens=5,
+        extra_body={"return_token_ids": True},
+        stream=False,
+    )
+    assert hasattr(response, 'choices')
+    assert len(response.choices) > 0
+    assert hasattr(response.choices[0], 'prompt_token_ids')
+    assert isinstance(response.choices[0].prompt_token_ids, list)
+    assert hasattr(response.choices[0], 'completion_token_ids')
+    assert isinstance(response.choices[0].completion_token_ids, list)
+
+    # disable return_token_ids
+    response = openai_client.completions.create(
+        model="default",
+        prompt="Hello, how are you?",
+        temperature=1,
+        max_tokens=5,
+        extra_body={"return_token_ids": False},
+        stream=False,
+    )
+    assert hasattr(response, 'choices')
+    assert len(response.choices) > 0
+    assert hasattr(response.choices[0], 'prompt_token_ids')
+    assert response.choices[0].prompt_token_ids is None
+    assert hasattr(response.choices[0], 'completion_token_ids')
+    assert response.choices[0].completion_token_ids is None
+
+
+def test_streaming_completion_with_return_token_ids(openai_client, capsys):
+    """
+    Test return_token_ids option in streaming completion functionality with the local service
+    """
+    # enable return_token_ids
+    response = openai_client.completions.create(
+        model="default",
+        prompt="Hello, how are you?",
+        temperature=1,
+        max_tokens=5,
+        extra_body={"return_token_ids": True},
+        stream=True,
+    )
+    is_first_chunk = True
+    for chunk in response:
+        assert hasattr(chunk, 'choices')
+        assert len(chunk.choices) > 0
+        assert hasattr(chunk.choices[0], 'prompt_token_ids')
+        assert hasattr(chunk.choices[0], 'completion_token_ids')
+        if is_first_chunk:
+            is_first_chunk = False
+            assert isinstance(chunk.choices[0].prompt_token_ids, list)
+            assert chunk.choices[0].completion_token_ids is None
+        else:
+            assert chunk.choices[0].prompt_token_ids is None
+            assert isinstance(chunk.choices[0].completion_token_ids, list)
+
+    # disable return_token_ids
+    response = openai_client.completions.create(
+        model="default",
+        prompt="Hello, how are you?",
+        temperature=1,
+        max_tokens=5,
+        extra_body={"return_token_ids": False},
+        stream=True,
+    )
+    for chunk in response:
+        assert hasattr(chunk, 'choices')
+        assert len(chunk.choices) > 0
+        assert hasattr(chunk.choices[0], 'prompt_token_ids')
+        assert chunk.choices[0].prompt_token_ids is None
+        assert hasattr(chunk.choices[0], 'completion_token_ids')
+        assert chunk.choices[0].completion_token_ids is None
+
+
+def test_non_streaming_chat_with_prompt_token_ids(openai_client, capsys):
+    """
+    Test prompt_token_ids option in non-streaming chat functionality with the local service
+    """
+    response = openai_client.chat.completions.create(
+        model="default",
+        messages=[],
+        temperature=1,
+        max_tokens=5,
+        extra_body={"prompt_token_ids": [5209,626,274,45954,1071,3265,3934,1869,93937]},
+        stream=False,
+    )
+    assert hasattr(response, 'choices')
+    assert len(response.choices) > 0
+    assert hasattr(response, 'usage')
+    assert hasattr(response.usage, 'prompt_tokens')
+    assert response.usage.prompt_tokens == 9
+
+
+def test_streaming_chat_with_prompt_token_ids(openai_client, capsys):
+    """
+    Test prompt_token_ids option in streaming chat functionality with the local service
+    """
+    response = openai_client.chat.completions.create(
+        model="default",
+        messages=[],
+        temperature=1,
+        max_tokens=5,
+        extra_body={"prompt_token_ids": [5209,626,274,45954,1071,3265,3934,1869,93937]},
+        stream=True,
+        stream_options={"include_usage": True},
+    )
+    for chunk in response:
+        assert hasattr(chunk, 'choices')
+        assert hasattr(chunk, 'usage')
+        if len(chunk.choices) > 0:
+            assert chunk.usage is None
+        else:
+            assert hasattr(chunk.usage, 'prompt_tokens')
+            assert chunk.usage.prompt_tokens == 9
+
+
+def test_non_streaming_completion_with_prompt_token_ids(openai_client, capsys):
+    """
+    Test prompt_token_ids option in streaming completion functionality with the local service
+    """
+    response = openai_client.completions.create(
+        model="default",
+        prompt="",
+        temperature=1,
+        max_tokens=5,
+        extra_body={"prompt_token_ids": [5209,626,274,45954,1071,3265,3934,1869,93937]},
+        stream=False,
+    )
+    assert hasattr(response, 'choices')
+    assert len(response.choices) > 0
+    assert hasattr(response, 'usage')
+    assert hasattr(response.usage, 'prompt_tokens')
+    assert response.usage.prompt_tokens == 9
+
+
+def test_streaming_completion_with_prompt_token_ids(openai_client, capsys):
+    """
+    Test prompt_token_ids option in non-streaming completion functionality with the local service
+    """
+    response = openai_client.completions.create(
+        model="default",
+        prompt="",
+        temperature=1,
+        max_tokens=5,
+        extra_body={"prompt_token_ids": [5209,626,274,45954,1071,3265,3934,1869,93937]},
+        stream=True,
+        stream_options={"include_usage": True},
+    )
+    for chunk in response:
+        assert hasattr(chunk, 'choices')
+        assert hasattr(chunk, 'usage')
+        if len(chunk.choices) > 0:
+            assert chunk.usage is None
+        else:
+            assert hasattr(chunk.usage, 'prompt_tokens')
+            assert chunk.usage.prompt_tokens == 9
+

test/ci_use/EB_VL_Lite/test_EB_VL_Lite_serving.py

@@ -323,3 +323,174 @@ def test_streaming_chat(openai_client, capsys):
         if hasattr(chunk.choices[0], "delta") and hasattr(chunk.choices[0].delta, "content"):
             output.append(chunk.choices[0].delta.content)
     assert len(output) > 2
+
+
+
+# ==========================
+# OpenAI Client additional chat/completions test
+# ==========================
+
+def test_non_streaming_chat_with_return_token_ids(openai_client, capsys):
+    """
+    Test return_token_ids option in non-streaming chat functionality with the local service
+    """
+    # 设定 return_token_ids
+    response = openai_client.chat.completions.create(
+        model="default",
+        messages=[
+            {
+                "role": "system",
+                "content": "You are a helpful AI assistant."
+            },  # system不是必需，可选
+            {
+                "role":
+                "user",
+                "content": [{
+                    "type": "image_url",
+                    "image_url": {
+                        "url":
+                        "https://paddlenlp.bj.bcebos.com/datasets/paddlemix/demo_images/example2.jpg",
+                        "detail": "high"
+                    }
+                }, {
+                    "type": "text",
+                    "text": "请描述图片内容"
+                }]
+            }
+        ],
+        temperature=1,
+        max_tokens=53,
+        extra_body={"return_token_ids": True},
+        stream=False,
+    )
+    assert hasattr(response, 'choices')
+    assert len(response.choices) > 0
+    assert hasattr(response.choices[0], 'message')
+    assert hasattr(response.choices[0].message, 'prompt_token_ids')
+    assert isinstance(response.choices[0].message.prompt_token_ids, list)
+    assert hasattr(response.choices[0].message, 'completion_token_ids')
+    assert isinstance(response.choices[0].message.completion_token_ids, list)
+
+    # 不设定 return_token_ids
+    response = openai_client.chat.completions.create(
+        model="default",
+        messages=[
+            {
+                "role": "system",
+                "content": "You are a helpful AI assistant."
+            },  # system不是必需，可选
+            {
+                "role":
+                "user",
+                "content": [{
+                    "type": "image_url",
+                    "image_url": {
+                        "url":
+                        "https://paddlenlp.bj.bcebos.com/datasets/paddlemix/demo_images/example2.jpg",
+                        "detail": "high"
+                    }
+                }, {
+                    "type": "text",
+                    "text": "请描述图片内容"
+                }]
+            }
+        ],
+        temperature=1,
+        max_tokens=53,
+        extra_body={"return_token_ids": False},
+        stream=False,
+    )
+    assert hasattr(response, 'choices')
+    assert len(response.choices) > 0
+    assert hasattr(response.choices[0], 'message')
+    assert hasattr(response.choices[0].message, 'prompt_token_ids')
+    assert response.choices[0].message.prompt_token_ids is None
+    assert hasattr(response.choices[0].message, 'completion_token_ids')
+    assert response.choices[0].message.completion_token_ids is None
+
+
+def test_streaming_chat_with_return_token_ids(openai_client, capsys):
+    """
+    Test return_token_ids option in streaming chat functionality with the local service
+    """
+    # enable return_token_ids
+    response = openai_client.chat.completions.create(
+        model="default",
+        messages=[
+            {
+                "role": "system",
+                "content": "You are a helpful AI assistant."
+            },  # system不是必需，可选
+            {
+                "role":
+                "user",
+                "content": [{
+                    "type": "image_url",
+                    "image_url": {
+                        "url":
+                        "https://paddlenlp.bj.bcebos.com/datasets/paddlemix/demo_images/example2.jpg",
+                        "detail": "high"
+                    }
+                }, {
+                    "type": "text",
+                    "text": "请描述图片内容"
+                }]
+            }
+        ],
+        temperature=1,
+        max_tokens=53,
+        extra_body={"return_token_ids": True},
+        stream=True,
+    )
+    is_first_chunk = True
+    for chunk in response:
+        assert hasattr(chunk, 'choices')
+        assert len(chunk.choices) > 0
+        assert hasattr(chunk.choices[0], 'delta')
+        assert hasattr(chunk.choices[0].delta, 'prompt_token_ids')
+        assert hasattr(chunk.choices[0].delta, 'completion_token_ids')
+        if is_first_chunk:
+            is_first_chunk = False
+            assert isinstance(chunk.choices[0].delta.prompt_token_ids, list)
+            assert chunk.choices[0].delta.completion_token_ids is None
+        else:
+            assert chunk.choices[0].delta.prompt_token_ids is None
+            assert isinstance(chunk.choices[0].delta.completion_token_ids, list)
+
+    # disable return_token_ids
+    response = openai_client.chat.completions.create(
+        model="default",
+        messages=[
+            {
+                "role": "system",
+                "content": "You are a helpful AI assistant."
+            },  # system不是必需，可选
+            {
+                "role":
+                "user",
+                "content": [{
+                    "type": "image_url",
+                    "image_url": {
+                        "url":
+                        "https://paddlenlp.bj.bcebos.com/datasets/paddlemix/demo_images/example2.jpg",
+                        "detail": "high"
+                    }
+                }, {
+                    "type": "text",
+                    "text": "请描述图片内容"
+                }]
+            }
+        ],
+        temperature=1,
+        max_tokens=53,
+        extra_body={"return_token_ids": False},
+        stream=True,
+    )
+    for chunk in response:
+        assert hasattr(chunk, 'choices')
+        assert len(chunk.choices) > 0
+        assert hasattr(chunk.choices[0], 'delta')
+        assert hasattr(chunk.choices[0].delta, 'prompt_token_ids')
+        assert chunk.choices[0].delta.prompt_token_ids is None
+        assert hasattr(chunk.choices[0].delta, 'completion_token_ids')
+        assert chunk.choices[0].delta.completion_token_ids is None