[fix] non-streaming api now returns full output ids if return_token_ids is enabled (#2951)

fastdeploy/entrypoints/openai/protocol.py

@@ -372,19 +372,22 @@ class CompletionRequest(BaseModel):
         req_dict = {}
         if request_id is not None:
             req_dict["request_id"] = request_id
+
+        # parse request model into dict, priority: request > extra_body > suffix
         for key, value in self.dict().items():
             if value is not None:
                 req_dict[key] = value
+        if self.extra_body is not None:
+            for key, value in self.extra_body.items():
+                req_dict.setdefault(key, value)
         if self.suffix is not None:
             for key, value in self.suffix.items():
-                req_dict[key] = value
+                req_dict.setdefault(key, value)
+
         if prompt is not None:
             req_dict["prompt"] = prompt
 
-        if self.prompt_token_ids is not None or (
-            self.extra_body is not None and self.extra_body.get("prompt_token_ids") is not None
-        ):
-            req_dict["prompt_token_ids"] = self.prompt_token_ids
+        if "prompt_token_ids" in req_dict:
             if "prompt" in req_dict:
                 del req_dict["prompt"]
         else:
@@ -508,21 +511,21 @@ class ChatCompletionRequest(BaseModel):
         req_dict["max_tokens"] = self.max_completion_tokens or self.max_tokens
         req_dict["logprobs"] = self.top_logprobs if self.logprobs else None
 
+        # parse request model into dict, priority: request > extra_body > metadata
+        for key, value in self.dict().items():
+            if value is not None:
+                req_dict[key] = value
+        if self.extra_body is not None:
+            for key, value in self.extra_body.items():
+                req_dict.setdefault(key, value)
         if self.metadata is not None:
             assert (
                 "raw_request" not in self.metadata
             ), "The parameter `raw_request` is not supported now, please use completion api instead."
             for key, value in self.metadata.items():
-                req_dict[key] = value
+                req_dict.setdefault(key, value)
 
-        for key, value in self.dict().items():
-            if value is not None:
-                req_dict[key] = value
-
-        if self.prompt_token_ids is not None or (
-            self.extra_body is not None and self.extra_body.get("prompt_token_ids") is not None
-        ):
-            req_dict["prompt_token_ids"] = self.prompt_token_ids
+        if "prompt_token_ids" in req_dict:
             if "messages" in req_dict:
                 del req_dict["messages"]
         else:

fastdeploy/entrypoints/openai/serving_chat.py

@@ -330,6 +330,7 @@ class OpenAIServingChat:
             previous_num_tokens = 0
             current_waiting_time = 0
             logprob_contents = []
+            completion_token_ids = []
             while True:
                 try:
                     raw_data = await asyncio.wait_for(dealer.read(), timeout=10)
@@ -361,6 +362,7 @@ class OpenAIServingChat:
                     )
                     # api_server_logger.debug(f"Client {request_id} received: {data}")
                     previous_num_tokens += len(data["outputs"]["token_ids"])
+                    completion_token_ids.extend(data["outputs"]["token_ids"])
                     # The logprob for handling the response
                     output = data["outputs"]
                     raw_top_logprobs = output["top_logprobs"]
@@ -394,7 +396,7 @@ class OpenAIServingChat:
             reasoning_content=output.get("reasoning_content"),
             tool_calls=output.get("tool_call_content"),
             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,
+            completion_token_ids=completion_token_ids if enable_return_token_ids else None,
         )
         logprobs_full_res = None
         if logprob_contents:

fastdeploy/entrypoints/openai/serving_completion.py

@@ -151,6 +151,7 @@ class OpenAIServingCompletion:
 
             valid_results = [dict()] * num_choices
             output_tokens = [0] * num_choices
+            completion_batched_token_ids = [[] for _ in range(num_choices)]
             current_waiting_time = 0
             while num_choices > 0:
                 try:
@@ -174,6 +175,7 @@ class OpenAIServingCompletion:
 
                     self.engine_client.data_processor.process_response_dict(data, stream=False)
                     output_tokens[rid] += len(data["outputs"]["token_ids"])
+                    completion_batched_token_ids[rid].extend(data["outputs"]["token_ids"])
                     if data.get("finished", False):
                         data["output_token_ids"] = output_tokens[rid]
                         valid_results[rid] = data
@@ -187,6 +189,7 @@ class OpenAIServingCompletion:
                 created_time=created_time,
                 model_name=model_name,
                 prompt_batched_token_ids=prompt_batched_token_ids,
+                completion_batched_token_ids=completion_batched_token_ids,
             )
         except Exception as e:
             api_server_logger.error(f"Error in completion_full_generator: {e}", exc_info=True)
@@ -341,6 +344,7 @@ class OpenAIServingCompletion:
         created_time: int,
         model_name: str,
         prompt_batched_token_ids: list(),
+        completion_batched_token_ids: list()
     ) -> CompletionResponse:
         choices: List[CompletionResponseChoice] = []
         num_prompt_tokens = 0
@@ -352,6 +356,7 @@ class OpenAIServingCompletion:
             prompt_token_ids = prompt_batched_token_ids[idx]
             assert prompt_token_ids is not None
             prompt_text = final_res["prompt"]
+            completion_token_ids = completion_batched_token_ids[idx]
 
             output = final_res["outputs"]
             if request.echo:
@@ -371,7 +376,7 @@ class OpenAIServingCompletion:
                 index=len(choices),
                 text=output_text,
                 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,
+                completion_token_ids=completion_token_ids if enable_return_token_ids else None,
                 reasoning_content=output.get('reasoning_content'),
                 tool_calls=output.get("tool_call_content"),
                 logprobs=None,

fastdeploy/input/ernie_processor.py

@@ -138,14 +138,15 @@ class ErnieProcessor(BaseDataProcessor):
         request = self._apply_default_parameters(request)
         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)
             request["stop_token_ids"] = stop_seqs
             request["stop_seqs_len"] = stop_seqs_len
 
-        # 处理prompt_token_ids
+        # processing prompt_token_ids
         if not request.get("prompt_token_ids"):
             if request.get("prompt") is None and request.get("messages") is None:
                 raise ValueError(f"Request must contain 'prompt_token_ids', 'prompt', or 'messages': {request}")
@@ -161,7 +162,7 @@ class ErnieProcessor(BaseDataProcessor):
             else:
                 request["prompt_token_ids"] = self.messages2ids(request)
 
-        # 截断超过长度限制的prompt
+        # truncate prompts that exceed the length limit
         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: