Add LogProbs for Chat Completions in OpenAI (vllm-project#2918)

Author: jlcmoore

tests/entrypoints/test_openai_server.py

@@ -155,15 +155,18 @@ async def test_single_chat_session(server, client: openai.AsyncOpenAI,
     }]
 
     # test single completion
-    chat_completion = await client.chat.completions.create(
-        model=model_name,
-        messages=messages,
-        max_tokens=10,
-    )
+    chat_completion = await client.chat.completions.create(model=model_name,
+                                                           messages=messages,
+                                                           max_tokens=10,
+                                                           logprobs=True,
+                                                           top_logprobs=10)
     assert chat_completion.id is not None
     assert chat_completion.choices is not None and len(
         chat_completion.choices) == 1
     assert chat_completion.choices[0].message is not None
+    assert chat_completion.choices[0].logprobs is not None
+    assert chat_completion.choices[0].logprobs.top_logprobs is not None
+    assert len(chat_completion.choices[0].logprobs.top_logprobs[0]) == 10
     message = chat_completion.choices[0].message
     assert message.content is not None and len(message.content) >= 10
     assert message.role == "assistant"
@@ -198,13 +201,11 @@ async def test_completion_streaming(server, client: openai.AsyncOpenAI,
     single_output = single_completion.choices[0].text
     single_usage = single_completion.usage
 
-    stream = await client.completions.create(
-        model=model_name,
-        prompt=prompt,
-        max_tokens=5,
-        temperature=0.0,
-        stream=True,
-    )
+    stream = await client.completions.create(model=model_name,
+                                             prompt=prompt,
+                                             max_tokens=5,
+                                             temperature=0.0,
+                                             stream=True)
     chunks = []
     async for chunk in stream:
         chunks.append(chunk.choices[0].text)

vllm/entrypoints/openai/protocol.py

@@ -63,6 +63,8 @@ class ChatCompletionRequest(BaseModel):
     seed: Optional[int] = None
     stop: Optional[Union[str, List[str]]] = Field(default_factory=list)
     stream: Optional[bool] = False
+    logprobs: Optional[bool] = False
+    top_logprobs: Optional[int] = None
     presence_penalty: Optional[float] = 0.0
     frequency_penalty: Optional[float] = 0.0
     logit_bias: Optional[Dict[str, float]] = None
@@ -84,6 +86,8 @@ class ChatCompletionRequest(BaseModel):
     length_penalty: Optional[float] = 1.0
 
     def to_sampling_params(self) -> SamplingParams:
+        if self.logprobs and not self.top_logprobs:
+            raise ValueError("Top logprobs must be set when logprobs is.")
         return SamplingParams(
             n=self.n,
             presence_penalty=self.presence_penalty,
@@ -96,6 +100,8 @@ def to_sampling_params(self) -> SamplingParams:
             stop=self.stop,
             stop_token_ids=self.stop_token_ids,
             max_tokens=self.max_tokens,
+            logprobs=self.top_logprobs if self.logprobs else None,
+            prompt_logprobs=self.top_logprobs if self.echo else None,
             best_of=self.best_of,
             top_k=self.top_k,
             ignore_eos=self.ignore_eos,
@@ -216,6 +222,7 @@ class ChatMessage(BaseModel):
 class ChatCompletionResponseChoice(BaseModel):
     index: int
     message: ChatMessage
+    logprobs: Optional[LogProbs] = None
     finish_reason: Optional[Literal["stop", "length"]] = None
 
 
@@ -236,6 +243,7 @@ class DeltaMessage(BaseModel):
 class ChatCompletionResponseStreamChoice(BaseModel):
     index: int
     delta: DeltaMessage
+    logprobs: Optional[LogProbs] = None
     finish_reason: Optional[Literal["stop", "length"]] = None
 
 

vllm/entrypoints/openai/serving_chat.py

@@ -101,7 +101,10 @@ async def chat_completion_stream_generator(
         role = self.get_chat_request_role(request)
         for i in range(request.n):
             choice_data = ChatCompletionResponseStreamChoice(
-                index=i, delta=DeltaMessage(role=role), finish_reason=None)
+                index=i,
+                delta=DeltaMessage(role=role),
+                logprobs=None,
+                finish_reason=None)
             chunk = ChatCompletionStreamResponse(id=request_id,
                                                  object=chunk_object_type,
                                                  created=created_time,
@@ -118,6 +121,7 @@ async def chat_completion_stream_generator(
                         "content") and request.messages[-1].get(
                             "role") == role:
                 last_msg_content = request.messages[-1]["content"]
+
             if last_msg_content:
                 for i in range(request.n):
                     choice_data = ChatCompletionResponseStreamChoice(
@@ -129,6 +133,7 @@ async def chat_completion_stream_generator(
                         object=chunk_object_type,
                         created=created_time,
                         choices=[choice_data],
+                        logprobs=None,
                         model=model_name)
                     data = chunk.model_dump_json(exclude_unset=True)
                     yield f"data: {data}\n\n"
@@ -145,15 +150,29 @@ async def chat_completion_stream_generator(
                 if finish_reason_sent[i]:
                     continue
 
+                delta_token_ids = output.token_ids[previous_num_tokens[i]:]
+                top_logprobs = output.logprobs[
+                    previous_num_tokens[i]:] if output.logprobs else None
+
+                if request.logprobs:
+                    logprobs = self._create_logprobs(
+                        token_ids=delta_token_ids,
+                        top_logprobs=top_logprobs,
+                        num_output_top_logprobs=request.logprobs,
+                        initial_text_offset=len(previous_texts[i]),
+                    )
+                else:
+                    logprobs = None
+
                 delta_text = output.text[len(previous_texts[i]):]
                 previous_texts[i] = output.text
                 previous_num_tokens[i] = len(output.token_ids)
-
                 if output.finish_reason is None:
                     # Send token-by-token response for each request.n
                     choice_data = ChatCompletionResponseStreamChoice(
                         index=i,
                         delta=DeltaMessage(content=delta_text),
+                        logprobs=logprobs,
                         finish_reason=None)
                     chunk = ChatCompletionStreamResponse(
                         id=request_id,
@@ -174,6 +193,7 @@ async def chat_completion_stream_generator(
                     choice_data = ChatCompletionResponseStreamChoice(
                         index=i,
                         delta=DeltaMessage(content=delta_text),
+                        logprobs=logprobs,
                         finish_reason=output.finish_reason)
                     chunk = ChatCompletionStreamResponse(
                         id=request_id,
@@ -208,11 +228,25 @@ async def chat_completion_full_generator(
         assert final_res is not None
 
         choices = []
+
         role = self.get_chat_request_role(request)
         for output in final_res.outputs:
+            token_ids = output.token_ids
+            top_logprobs = output.logprobs
+
+            if request.logprobs:
+                logprobs = self._create_logprobs(
+                    token_ids=token_ids,
+                    top_logprobs=top_logprobs,
+                    num_output_top_logprobs=request.logprobs,
+                )
+            else:
+                logprobs = None
+
             choice_data = ChatCompletionResponseChoice(
                 index=output.index,
                 message=ChatMessage(role=role, content=output.text),
+                logprobs=logprobs,
                 finish_reason=output.finish_reason,
             )
             choices.append(choice_data)