feat: add ChatCompletion endpoint in OpenAI demo server.

vllm/entrypoints/openai/api_server.py

@@ -4,7 +4,7 @@
 from http import HTTPStatus
 import json
 import time
-from typing import AsyncGenerator, Dict, List, Optional
+from typing import AsyncGenerator, Dict, List, Optional, Union, Any
 
 import fastapi
 from fastapi import BackgroundTasks, Request
@@ -17,8 +17,12 @@
 from vllm.engine.async_llm_engine import AsyncLLMEngine
 from vllm.entrypoints.openai.protocol import (
     CompletionRequest, CompletionResponse, CompletionResponseChoice,
-    CompletionResponseStreamChoice, CompletionStreamResponse, ErrorResponse,
-    LogProbs, ModelCard, ModelList, ModelPermission, UsageInfo)
+    CompletionResponseStreamChoice, CompletionStreamResponse,
+    ChatCompletionRequest, ChatCompletionResponse, ChatCompletionResponseChoice,
+    ChatCompletionResponseStreamChoice, ChatCompletionStreamResponse,
+    ChatMessage, DeltaMessage, ErrorResponse, LogProbs,
+    ModelCard, ModelList, ModelPermission, UsageInfo)
+from fastchat.conversation import Conversation, SeparatorStyle, get_conv_template
 from vllm.logger import init_logger
 from vllm.outputs import RequestOutput
 from vllm.sampling_params import SamplingParams
@@ -55,6 +59,70 @@ async def check_model(request) -> Optional[JSONResponse]:
     return ret
 
 
+async def get_gen_prompt(request) -> str:
+    conv = get_conv_template(request.model)
+    conv = Conversation(
+        name=conv.name,
+        system=conv.system,
+        roles=conv.roles,
+        messages=list(conv.messages),  # prevent in-place modification
+        offset=conv.offset,
+        sep_style=SeparatorStyle(conv.sep_style),
+        sep=conv.sep,
+        sep2=conv.sep2,
+        stop_str=conv.stop_str,
+        stop_token_ids=conv.stop_token_ids,
+    )
+
+    if isinstance(request.messages, str):
+        prompt = request.messages
+    else:
+        for message in request.messages:
+            msg_role = message["role"]
+            if msg_role == "system":
+                conv.system = message["content"]
+            elif msg_role == "user":
+                conv.append_message(conv.roles[0], message["content"])
+            elif msg_role == "assistant":
+                conv.append_message(conv.roles[1], message["content"])
+            else:
+                raise ValueError(f"Unknown role: {msg_role}")
+
+        # Add a blank message for the assistant.
+        conv.append_message(conv.roles[1], None)
+        prompt = conv.get_prompt()
+
+    return prompt
+
+
+async def check_length(request, prompt, engine):
+    if hasattr(engine.engine.model_config.hf_config, "max_sequence_length"):
+        context_len = engine.engine.model_config.hf_config.max_sequence_length
+    elif hasattr(engine.engine.model_config.hf_config, "seq_length"):
+        context_len = engine.engine.model_config.hf_config.seq_length
+    elif hasattr(engine.engine.model_config.hf_config, "max_position_embeddings"):
+        context_len = engine.engine.model_config.hf_config.max_position_embeddings
+    elif hasattr(engine.engine.model_config.hf_config, "seq_length"):
+        context_len = engine.engine.model_config.hf_config.seq_length
+    else:
+        context_len = 2048
+
+    input_ids = tokenizer(prompt).input_ids
+    token_num = len(input_ids)
+
+    if token_num + request.max_tokens > context_len:
+        return create_error_response(
+            HTTPStatus.BAD_REQUEST,
+            f"This model's maximum context length is {context_len} tokens. "
+            f"However, you requested {request.max_tokens + token_num} tokens "
+            f"({token_num} in the messages, "
+            f"{request.max_tokens} in the completion). "
+            f"Please reduce the length of the messages or completion.",
+        )
+    else:
+        return None
+
+
 @app.get("/v1/models")
 async def show_available_models():
     """Show available models. Right now we only have one model."""
@@ -85,6 +153,171 @@ def create_logprobs(token_ids: List[int],
     return logprobs
 
 
+@app.post("/v1/chat/completions")
+async def create_chat_completion(raw_request: Request):
+    """Completion API similar to OpenAI's API.
+
+    See  https://platform.openai.com/docs/api-reference/chat/create
+    for the API specification. This API mimics the OpenAI ChatCompletion API.
+
+    NOTE: Currently we do not support the following features:
+        - function_call (Users should implement this by themselves)
+        - logit_bias (to be supported by vLLM engine)
+    """
+    request = ChatCompletionRequest(**await raw_request.json())
+    logger.info(f"Received chat completion request: {request}")
+
+    error_check_ret = await check_model(request)
+    if error_check_ret is not None:
+        return error_check_ret
+
+    if request.logit_bias is not None:
+        # TODO: support logit_bias in vLLM engine.
+        return create_error_response(HTTPStatus.BAD_REQUEST,
+                                     "logit_bias is not currently supported")
+
+    prompt = await get_gen_prompt(request)
+    error_check_ret = await check_length(request, prompt, engine)
+    if error_check_ret is not None:
+        return error_check_ret
+
+    model_name = request.model
+    request_id = f"cmpl-{random_uuid()}"
+    created_time = int(time.time())
+    try:
+        sampling_params = SamplingParams(
+            n=request.n,
+            presence_penalty=request.presence_penalty,
+            frequency_penalty=request.frequency_penalty,
+            temperature=request.temperature,
+            top_p=request.top_p,
+            stop=request.stop,
+            max_tokens=request.max_tokens,
+            best_of=request.best_of,
+            top_k=request.top_k,
+            ignore_eos=request.ignore_eos,
+            use_beam_search=request.use_beam_search,
+        )
+    except ValueError as e:
+        return create_error_response(HTTPStatus.BAD_REQUEST, str(e))
+
+    result_generator = engine.generate(prompt, sampling_params,
+                                       request_id)
+
+    async def abort_request() -> None:
+        await engine.abort(request_id)
+
+    def create_stream_response_json(index: int,
+                                    text: str,
+                                    finish_reason: Optional[str] = None) -> str:
+        choice_data = ChatCompletionResponseStreamChoice(
+            index=index,
+            delta=DeltaMessage(content=text),
+            finish_reason=finish_reason,
+        )
+        response = ChatCompletionStreamResponse(
+            id=request_id,
+            created=created_time,
+            model=model_name,
+            choices=[choice_data],
+        )
+        response_json = response.json(ensure_ascii=False)
+
+        return response_json
+
+    async def completion_stream_generator() -> AsyncGenerator[str, None]:
+        # First chunk with role
+        for i in range(request.n):
+            choice_data = ChatCompletionResponseStreamChoice(
+                index=i,
+                delta=DeltaMessage(role="assistant"),
+                finish_reason=None,
+            )
+            chunk = ChatCompletionStreamResponse(
+                id=request_id, choices=[choice_data], model=model_name
+            )
+            yield f"data: {chunk.json(exclude_unset=True, ensure_ascii=False)}\n\n"
+
+        previous_texts = [""] * request.n
+        previous_num_tokens = [0] * request.n
+        async for res in result_generator:
+            res: RequestOutput
+            for output in res.outputs:
+                i = output.index
+                delta_text = output.text[len(previous_texts[i]):]
+                previous_texts[i] = output.text
+                previous_num_tokens[i] = len(output.token_ids)
+                response_json = create_stream_response_json(
+                    index=i,
+                    text=delta_text,
+                )
+                yield f"data: {response_json}\n\n"
+                if output.finish_reason is not None:
+                    response_json = create_stream_response_json(
+                        index=i,
+                        text="",
+                        finish_reason=output.finish_reason,
+                    )
+                    yield f"data: {response_json}\n\n"
+            yield "data: [DONE]\n\n"
+
+    # Streaming response
+    if request.stream:
+        background_tasks = BackgroundTasks()
+        # Abort the request if the client disconnects.
+        background_tasks.add_task(abort_request)
+        return StreamingResponse(completion_stream_generator(),
+                                 media_type="text/event-stream",
+                                 background=background_tasks)
+
+    # Non-streaming response
+    final_res: RequestOutput = None
+    async for res in result_generator:
+        if await raw_request.is_disconnected():
+            # Abort the request if the client disconnects.
+            await abort_request()
+            return create_error_response(HTTPStatus.BAD_REQUEST,
+                                         "Client disconnected")
+        final_res = res
+    assert final_res is not None
+    choices = []
+    for output in final_res.outputs:
+        choice_data = ChatCompletionResponseChoice(
+            index=output.index,
+            message=ChatMessage(role="assistant", content=output.text),
+            finish_reason=output.finish_reason,
+        )
+        choices.append(choice_data)
+
+    num_prompt_tokens = len(final_res.prompt_token_ids)
+    num_generated_tokens = sum(len(output.token_ids)
+                               for output in final_res.outputs)
+    usage = UsageInfo(
+        prompt_tokens=num_prompt_tokens,
+        completion_tokens=num_generated_tokens,
+        total_tokens=num_prompt_tokens + num_generated_tokens,
+    )
+    response = ChatCompletionResponse(
+        id=request_id,
+        created=created_time,
+        model=model_name,
+        choices=choices,
+        usage=usage,
+    )
+
+    if request.stream:
+        # When user requests streaming but we don't stream, we still need to
+        # return a streaming response with a single event.
+        response_json = response.json(ensure_ascii=False)
+        async def fake_stream_generator() -> AsyncGenerator[str, None]:
+            yield f"data: {response_json}\n\n"
+            yield "data: [DONE]\n\n"
+        return StreamingResponse(fake_stream_generator(),
+                                 media_type="text/event-stream")
+
+    return response
+
+
 @app.post("/v1/completions")
 async def create_completion(raw_request: Request):
     """Completion API similar to OpenAI's API.

vllm/entrypoints/openai/protocol.py

@@ -53,16 +53,22 @@ class UsageInfo(BaseModel):
 
 class ChatCompletionRequest(BaseModel):
     model: str
-    messages: List[Dict[str, str]]
+    messages: Union[str, List[Dict[str, str]]]
     temperature: Optional[float] = 0.7
     top_p: Optional[float] = 1.0
     n: Optional[int] = 1
-    max_tokens: Optional[int] = None
-    stop: Optional[Union[str, List[str]]] = None
+    max_tokens: Optional[int] = 16
+    stop: Optional[Union[str, List[str]]] = Field(default_factory=list)
     stream: Optional[bool] = False
     presence_penalty: Optional[float] = 0.0
     frequency_penalty: Optional[float] = 0.0
+    logit_bias: Optional[Dict[str, float]] = None
     user: Optional[str] = None
+    # Additional parameters supported by vLLM
+    best_of: Optional[int] = None
+    top_k: Optional[int] = -1
+    ignore_eos: Optional[bool] = False
+    use_beam_search: Optional[bool] = False
 
 
 class CompletionRequest(BaseModel):
@@ -124,3 +130,42 @@ class CompletionStreamResponse(BaseModel):
     created: int = Field(default_factory=lambda: int(time.time()))
     model: str
     choices: List[CompletionResponseStreamChoice]
+
+
+class ChatMessage(BaseModel):
+    role: str
+    content: str
+
+
+class ChatCompletionResponseChoice(BaseModel):
+    index: int
+    message: ChatMessage
+    finish_reason: Optional[Literal["stop", "length"]] = None
+
+
+class ChatCompletionResponse(BaseModel):
+    id: str = Field(default_factory=lambda: f"chatcmpl-{random_uuid()}")
+    object: str = "chat.completion"
+    created: int = Field(default_factory=lambda: int(time.time()))
+    model: str
+    choices: List[ChatCompletionResponseChoice]
+    usage: UsageInfo
+
+
+class DeltaMessage(BaseModel):
+    role: Optional[str] = None
+    content: Optional[str] = None
+
+
+class ChatCompletionResponseStreamChoice(BaseModel):
+    index: int
+    delta: DeltaMessage
+    finish_reason: Optional[Literal["stop", "length"]] = None
+
+
+class ChatCompletionStreamResponse(BaseModel):
+    id: str = Field(default_factory=lambda: f"chatcmpl-{random_uuid()}")
+    object: str = "chat.completion.chunk"
+    created: int = Field(default_factory=lambda: int(time.time()))
+    model: str
+    choices: List[ChatCompletionResponseStreamChoice]