api_server.py 24.8 KB
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# Adapted from
# https://github.com/lm-sys/FastChat/blob/168ccc29d3f7edc50823016105c024fe2282732a/fastchat/serve/openai_api_server.py
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import argparse
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import asyncio
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import json
import time
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from http import HTTPStatus
from typing import AsyncGenerator, Dict, List, Optional, Tuple, Union
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import fastapi
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import uvicorn
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from fastapi import Request
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from fastapi.exceptions import RequestValidationError
from fastapi.middleware.cors import CORSMiddleware
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from fastapi.responses import JSONResponse, StreamingResponse, Response
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from packaging import version
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from vllm.engine.arg_utils import AsyncEngineArgs
from vllm.engine.async_llm_engine import AsyncLLMEngine
from vllm.entrypoints.openai.protocol import (
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    CompletionRequest, CompletionResponse, CompletionResponseChoice,
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    CompletionResponseStreamChoice, CompletionStreamResponse,
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    ChatCompletionRequest, ChatCompletionResponse,
    ChatCompletionResponseChoice, ChatCompletionResponseStreamChoice,
    ChatCompletionStreamResponse, ChatMessage, DeltaMessage, ErrorResponse,
    LogProbs, ModelCard, ModelList, ModelPermission, UsageInfo)
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from vllm.logger import init_logger
from vllm.outputs import RequestOutput
from vllm.sampling_params import SamplingParams
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from vllm.transformers_utils.tokenizer import get_tokenizer
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from vllm.utils import random_uuid
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try:
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    import fastchat
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    from fastchat.conversation import Conversation, SeparatorStyle
    from fastchat.model.model_adapter import get_conversation_template
    _fastchat_available = True
except ImportError:
    _fastchat_available = False

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TIMEOUT_KEEP_ALIVE = 5  # seconds
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logger = init_logger(__name__)
served_model = None
app = fastapi.FastAPI()
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engine = None
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def create_error_response(status_code: HTTPStatus,
                          message: str) -> JSONResponse:
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    return JSONResponse(ErrorResponse(message=message,
                                      type="invalid_request_error").dict(),
                        status_code=status_code.value)
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@app.exception_handler(RequestValidationError)
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async def validation_exception_handler(_, exc):
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    return create_error_response(HTTPStatus.BAD_REQUEST, str(exc))


async def check_model(request) -> Optional[JSONResponse]:
    if request.model == served_model:
        return
    ret = create_error_response(
        HTTPStatus.NOT_FOUND,
        f"The model `{request.model}` does not exist.",
    )
    return ret


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async def get_gen_prompt(request) -> str:
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    if not _fastchat_available:
        raise ModuleNotFoundError(
            "fastchat is not installed. Please install fastchat to use "
            "the chat completion and conversation APIs: `$ pip install fschat`"
        )
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    if version.parse(fastchat.__version__) < version.parse("0.2.23"):
        raise ImportError(
            f"fastchat version is low. Current version: {fastchat.__version__} "
            "Please upgrade fastchat to use: `$ pip install -U fschat`")

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    conv = get_conversation_template(request.model)
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    conv = Conversation(
        name=conv.name,
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        system_template=conv.system_template,
        system_message=conv.system_message,
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        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":
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                conv.system_message = message["content"]
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            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


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async def check_length(
    request: Union[ChatCompletionRequest, CompletionRequest],
    prompt: Optional[str] = None,
    prompt_ids: Optional[List[int]] = None
) -> Tuple[List[int], Optional[JSONResponse]]:
    assert (not (prompt is None and prompt_ids is None)
            and not (prompt is not None and prompt_ids is not None)
            ), "Either prompt or prompt_ids should be provided."
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    input_ids = prompt_ids if prompt_ids is not None else tokenizer(
        prompt).input_ids
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    token_num = len(input_ids)

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    if request.max_tokens is None:
        request.max_tokens = max_model_len - token_num
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    if token_num + request.max_tokens > max_model_len:
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        return input_ids, create_error_response(
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            HTTPStatus.BAD_REQUEST,
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            f"This model's maximum context length is {max_model_len} tokens. "
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            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:
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        return input_ids, None
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@app.get("/health")
async def health() -> Response:
    """Health check."""
    return Response(status_code=200)


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@app.get("/v1/models")
async def show_available_models():
    """Show available models. Right now we only have one model."""
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    model_cards = [
        ModelCard(id=served_model,
                  root=served_model,
                  permission=[ModelPermission()])
    ]
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    return ModelList(data=model_cards)


def create_logprobs(token_ids: List[int],
                    id_logprobs: List[Dict[int, float]],
                    initial_text_offset: int = 0) -> LogProbs:
    """Create OpenAI-style logprobs."""
    logprobs = LogProbs()
    last_token_len = 0
    for token_id, id_logprob in zip(token_ids, id_logprobs):
        token = tokenizer.convert_ids_to_tokens(token_id)
        logprobs.tokens.append(token)
        logprobs.token_logprobs.append(id_logprob[token_id])
        if len(logprobs.text_offset) == 0:
            logprobs.text_offset.append(initial_text_offset)
        else:
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            logprobs.text_offset.append(logprobs.text_offset[-1] +
                                        last_token_len)
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        last_token_len = len(token)

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        logprobs.top_logprobs.append({
            tokenizer.convert_ids_to_tokens(i): p
            for i, p in id_logprob.items()
        })
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    return logprobs


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@app.post("/v1/chat/completions")
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async def create_chat_completion(request: ChatCompletionRequest,
                                 raw_request: Request):
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    """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)
    """
    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

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    if request.logit_bias is not None and len(request.logit_bias) > 0:
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        # 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)
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    token_ids, error_check_ret = await check_length(request, prompt=prompt)
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    if error_check_ret is not None:
        return error_check_ret

    model_name = request.model
    request_id = f"cmpl-{random_uuid()}"
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    created_time = int(time.monotonic())
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    try:
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        spaces_between_special_tokens = request.spaces_between_special_tokens
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        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,
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            stop_token_ids=request.stop_token_ids,
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            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,
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            skip_special_tokens=request.skip_special_tokens,
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            spaces_between_special_tokens=spaces_between_special_tokens,
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        )
    except ValueError as e:
        return create_error_response(HTTPStatus.BAD_REQUEST, str(e))

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    result_generator = engine.generate(prompt, sampling_params, request_id,
                                       token_ids)
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    def create_stream_response_json(
        index: int,
        text: str,
        finish_reason: Optional[str] = None,
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        usage: Optional[UsageInfo] = None,
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    ) -> str:
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        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],
        )
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        if usage is not None:
            response.usage = usage
        # exclude unset to leave details out of each sse
        response_json = response.json(exclude_unset=True, ensure_ascii=False)
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        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,
            )
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            chunk = ChatCompletionStreamResponse(id=request_id,
                                                 choices=[choice_data],
                                                 model=model_name)
            data = chunk.json(exclude_unset=True, ensure_ascii=False)
            yield f"data: {data}\n\n"
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        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
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                completion_tokens = len(output.token_ids)
                previous_num_tokens[i] = completion_tokens
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                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:
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                    prompt_tokens = len(res.prompt_token_ids)
                    final_usage = UsageInfo(
                        prompt_tokens=prompt_tokens,
                        completion_tokens=completion_tokens,
                        total_tokens=prompt_tokens + completion_tokens,
                    )
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                    response_json = create_stream_response_json(
                        index=i,
                        text="",
                        finish_reason=output.finish_reason,
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                        usage=final_usage,
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                    )
                    yield f"data: {response_json}\n\n"
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        yield "data: [DONE]\n\n"
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    # Streaming response
    if request.stream:
        return StreamingResponse(completion_stream_generator(),
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                                 media_type="text/event-stream")
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    # 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.
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            await engine.abort(request_id)
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            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)
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    num_generated_tokens = sum(
        len(output.token_ids) for output in final_res.outputs)
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    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)
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        async def fake_stream_generator() -> AsyncGenerator[str, None]:
            yield f"data: {response_json}\n\n"
            yield "data: [DONE]\n\n"
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        return StreamingResponse(fake_stream_generator(),
                                 media_type="text/event-stream")

    return response


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@app.post("/v1/completions")
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async def create_completion(request: CompletionRequest, raw_request: Request):
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    """Completion API similar to OpenAI's API.

    See https://platform.openai.com/docs/api-reference/completions/create
    for the API specification. This API mimics the OpenAI Completion API.

    NOTE: Currently we do not support the following features:
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        - echo (since the vLLM engine does not currently support
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          getting the logprobs of prompt tokens)
        - suffix (the language models we currently support do not support
          suffix)
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        - logit_bias (to be supported by vLLM engine)
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    """
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    logger.info(f"Received completion request: {request}")

    error_check_ret = await check_model(request)
    if error_check_ret is not None:
        return error_check_ret

    if request.echo:
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        # We do not support echo since the vLLM engine does not
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        # currently support getting the logprobs of prompt tokens.
        return create_error_response(HTTPStatus.BAD_REQUEST,
                                     "echo is not currently supported")

    if request.suffix is not None:
        # The language models we currently support do not support suffix.
        return create_error_response(HTTPStatus.BAD_REQUEST,
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                                     "suffix is not currently supported")
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    if request.logit_bias is not None and len(request.logit_bias) > 0:
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        # TODO: support logit_bias in vLLM engine.
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        return create_error_response(HTTPStatus.BAD_REQUEST,
                                     "logit_bias is not currently supported")

    model_name = request.model
    request_id = f"cmpl-{random_uuid()}"
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    use_token_ids = False
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    if isinstance(request.prompt, list):
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        if len(request.prompt) == 0:
            return create_error_response(HTTPStatus.BAD_REQUEST,
                                         "please provide at least one prompt")
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        first_element = request.prompt[0]
        if isinstance(first_element, int):
            use_token_ids = True
            prompt = request.prompt
        elif isinstance(first_element, (str, list)):
            # TODO: handles multiple prompt case in list[list[int]]
            if len(request.prompt) > 1:
                return create_error_response(
                    HTTPStatus.BAD_REQUEST,
                    "multiple prompts in a batch is not currently supported")
            use_token_ids = not isinstance(first_element, str)
            prompt = request.prompt[0]
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    else:
        prompt = request.prompt
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    if use_token_ids:
        _, error_check_ret = await check_length(request, prompt_ids=prompt)
    else:
        token_ids, error_check_ret = await check_length(request, prompt=prompt)
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    if error_check_ret is not None:
        return error_check_ret

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    created_time = int(time.monotonic())
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    try:
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        spaces_between_special_tokens = request.spaces_between_special_tokens
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        sampling_params = SamplingParams(
            n=request.n,
            best_of=request.best_of,
            presence_penalty=request.presence_penalty,
            frequency_penalty=request.frequency_penalty,
            temperature=request.temperature,
            top_p=request.top_p,
            top_k=request.top_k,
            stop=request.stop,
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            stop_token_ids=request.stop_token_ids,
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            ignore_eos=request.ignore_eos,
            max_tokens=request.max_tokens,
            logprobs=request.logprobs,
            use_beam_search=request.use_beam_search,
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            skip_special_tokens=request.skip_special_tokens,
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            spaces_between_special_tokens=spaces_between_special_tokens,
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        )
    except ValueError as e:
        return create_error_response(HTTPStatus.BAD_REQUEST, str(e))

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    if use_token_ids:
        result_generator = engine.generate(None,
                                           sampling_params,
                                           request_id,
                                           prompt_token_ids=prompt)
    else:
        result_generator = engine.generate(prompt, sampling_params, request_id,
                                           token_ids)
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    # Similar to the OpenAI API, when n != best_of, we do not stream the
    # results. In addition, we do not stream the results when use beam search.
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    stream = (request.stream
              and (request.best_of is None or request.n == request.best_of)
              and not request.use_beam_search)
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    def create_stream_response_json(
        index: int,
        text: str,
        logprobs: Optional[LogProbs] = None,
        finish_reason: Optional[str] = None,
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        usage: Optional[UsageInfo] = None,
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    ) -> str:
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        choice_data = CompletionResponseStreamChoice(
            index=index,
            text=text,
            logprobs=logprobs,
            finish_reason=finish_reason,
        )
        response = CompletionStreamResponse(
            id=request_id,
            created=created_time,
            model=model_name,
            choices=[choice_data],
        )
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        if usage is not None:
            response.usage = usage
        response_json = response.json(exclude_unset=True, ensure_ascii=False)
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        return response_json

    async def completion_stream_generator() -> AsyncGenerator[str, None]:
        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]):]
                if request.logprobs is not None:
                    logprobs = create_logprobs(
                        output.token_ids[previous_num_tokens[i]:],
                        output.logprobs[previous_num_tokens[i]:],
                        len(previous_texts[i]))
                else:
                    logprobs = None
                previous_texts[i] = output.text
                previous_num_tokens[i] = len(output.token_ids)
                response_json = create_stream_response_json(
                    index=i,
                    text=delta_text,
                    logprobs=logprobs,
                )
                yield f"data: {response_json}\n\n"
                if output.finish_reason is not None:
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                    logprobs = (LogProbs()
                                if request.logprobs is not None else None)
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                    prompt_tokens = len(res.prompt_token_ids)
                    completion_tokens = len(output.token_ids)
                    final_usage = UsageInfo(
                        prompt_tokens=prompt_tokens,
                        completion_tokens=completion_tokens,
                        total_tokens=prompt_tokens + completion_tokens,
                    )
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                    response_json = create_stream_response_json(
                        index=i,
                        text="",
                        logprobs=logprobs,
                        finish_reason=output.finish_reason,
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                        usage=final_usage,
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                    )
                    yield f"data: {response_json}\n\n"
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        yield "data: [DONE]\n\n"
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    # Streaming response
    if stream:
        return StreamingResponse(completion_stream_generator(),
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                                 media_type="text/event-stream")
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    # Non-streaming response
    final_res: RequestOutput = None
    async for res in result_generator:
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        if await raw_request.is_disconnected():
            # Abort the request if the client disconnects.
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            await engine.abort(request_id)
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            return create_error_response(HTTPStatus.BAD_REQUEST,
                                         "Client disconnected")
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        final_res = res
    assert final_res is not None
    choices = []
    for output in final_res.outputs:
        if request.logprobs is not None:
            logprobs = create_logprobs(output.token_ids, output.logprobs)
        else:
            logprobs = None
        choice_data = CompletionResponseChoice(
            index=output.index,
            text=output.text,
            logprobs=logprobs,
            finish_reason=output.finish_reason,
        )
        choices.append(choice_data)

    num_prompt_tokens = len(final_res.prompt_token_ids)
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    num_generated_tokens = sum(
        len(output.token_ids) for output in final_res.outputs)
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    usage = UsageInfo(
        prompt_tokens=num_prompt_tokens,
        completion_tokens=num_generated_tokens,
        total_tokens=num_prompt_tokens + num_generated_tokens,
    )
    response = CompletionResponse(
        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)
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        async def fake_stream_generator() -> AsyncGenerator[str, None]:
            yield f"data: {response_json}\n\n"
            yield "data: [DONE]\n\n"
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        return StreamingResponse(fake_stream_generator(),
                                 media_type="text/event-stream")

    return response


if __name__ == "__main__":
    parser = argparse.ArgumentParser(
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        description="vLLM OpenAI-Compatible RESTful API server.")
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    parser.add_argument("--host", type=str, default=None, help="host name")
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    parser.add_argument("--port", type=int, default=8000, help="port number")
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    parser.add_argument("--allow-credentials",
                        action="store_true",
                        help="allow credentials")
    parser.add_argument("--allowed-origins",
                        type=json.loads,
                        default=["*"],
                        help="allowed origins")
    parser.add_argument("--allowed-methods",
                        type=json.loads,
                        default=["*"],
                        help="allowed methods")
    parser.add_argument("--allowed-headers",
                        type=json.loads,
                        default=["*"],
                        help="allowed headers")
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    parser.add_argument("--served-model-name",
                        type=str,
                        default=None,
                        help="The model name used in the API. If not "
                        "specified, the model name will be the same as "
                        "the huggingface name.")
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    parser = AsyncEngineArgs.add_cli_args(parser)
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    args = parser.parse_args()

    app.add_middleware(
        CORSMiddleware,
        allow_origins=args.allowed_origins,
        allow_credentials=args.allow_credentials,
        allow_methods=args.allowed_methods,
        allow_headers=args.allowed_headers,
    )

    logger.info(f"args: {args}")

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    if args.served_model_name is not None:
        served_model = args.served_model_name
    else:
        served_model = args.model

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    engine_args = AsyncEngineArgs.from_cli_args(args)
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    engine = AsyncLLMEngine.from_engine_args(engine_args)
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    engine_model_config = asyncio.run(engine.get_model_config())
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    max_model_len = engine_model_config.max_model_len
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    # A separate tokenizer to map token IDs to strings.
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    tokenizer = get_tokenizer(
        engine_model_config.tokenizer,
        tokenizer_mode=engine_model_config.tokenizer_mode,
        trust_remote_code=engine_model_config.trust_remote_code)
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    uvicorn.run(app,
                host=args.host,
                port=args.port,
                log_level="info",
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                timeout_keep_alive=TIMEOUT_KEEP_ALIVE)