vllm.entrypoints.openai.api_server ¶

async def _convert_stream_to_sse_events(
    generator: AsyncGenerator[StreamingResponsesResponse, None],
) -> AsyncGenerator[str, None]:
    """Convert the generator to a stream of events in SSE format"""
    async for event in generator:
        event_type = getattr(event, "type", "unknown")
        # https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#event_stream_format
        event_data = (
            f"event: {event_type}\ndata: {event.model_dump_json(indent=None)}\n\n"
        )
        yield event_data

def _extract_content_from_chunk(chunk_data: dict) -> str:
    """Extract content from a streaming response chunk."""
    try:
        from vllm.entrypoints.openai.protocol import (
            ChatCompletionStreamResponse,
            CompletionStreamResponse,
        )

        # Try using Completion types for type-safe parsing
        if chunk_data.get("object") == "chat.completion.chunk":
            chat_response = ChatCompletionStreamResponse.model_validate(chunk_data)
            if chat_response.choices and chat_response.choices[0].delta.content:
                return chat_response.choices[0].delta.content
        elif chunk_data.get("object") == "text_completion":
            completion_response = CompletionStreamResponse.model_validate(chunk_data)
            if completion_response.choices and completion_response.choices[0].text:
                return completion_response.choices[0].text
    except pydantic.ValidationError:
        # Fallback to manual parsing
        if "choices" in chunk_data and chunk_data["choices"]:
            choice = chunk_data["choices"][0]
            if "delta" in choice and choice["delta"].get("content"):
                return choice["delta"]["content"]
            elif choice.get("text"):
                return choice["text"]
    return ""

def _log_non_streaming_response(response_body: list) -> None:
    """Log non-streaming response."""
    try:
        decoded_body = response_body[0].decode()
        logger.info("response_body={%s}", decoded_body)
    except UnicodeDecodeError:
        logger.info("response_body={<binary_data>}")

def _log_streaming_response(response, response_body: list) -> None:
    """Log streaming response with robust SSE parsing."""
    from starlette.concurrency import iterate_in_threadpool

    sse_decoder = SSEDecoder()
    chunk_count = 0

    def buffered_iterator():
        nonlocal chunk_count

        for chunk in response_body:
            chunk_count += 1
            yield chunk

            # Parse SSE events from chunk
            events = sse_decoder.decode_chunk(chunk)

            for event in events:
                if event["type"] == "data":
                    content = sse_decoder.extract_content(event["data"])
                    sse_decoder.add_content(content)
                elif event["type"] == "done":
                    # Log complete content when done
                    full_content = sse_decoder.get_complete_content()
                    if full_content:
                        # Truncate if too long
                        if len(full_content) > 2048:
                            full_content = full_content[:2048] + ""
                            "...[truncated]"
                        logger.info(
                            "response_body={streaming_complete: content=%r, chunks=%d}",
                            full_content,
                            chunk_count,
                        )
                    else:
                        logger.info(
                            "response_body={streaming_complete: no_content, chunks=%d}",
                            chunk_count,
                        )
                    return

    response.body_iterator = iterate_in_threadpool(buffered_iterator())
    logger.info("response_body={streaming_started: chunks=%d}", len(response_body))

def base(request: Request) -> OpenAIServing:
    # Reuse the existing instance
    return tokenization(request)

def build_app(args: Namespace) -> FastAPI:
    if args.disable_fastapi_docs:
        app = FastAPI(
            openapi_url=None, docs_url=None, redoc_url=None, lifespan=lifespan
        )
    else:
        app = FastAPI(lifespan=lifespan)
    app.state.args = args
    from vllm.entrypoints.serve import register_vllm_serve_api_routers

    register_vllm_serve_api_routers(app)

    from vllm.entrypoints.sagemaker.routes import register_sagemaker_routes

    register_sagemaker_routes(router)
    app.include_router(router)

    app.root_path = args.root_path

    from vllm.entrypoints.pooling import register_pooling_api_routers

    register_pooling_api_routers(app)

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

    @app.exception_handler(HTTPException)
    async def http_exception_handler(_: Request, exc: HTTPException):
        err = ErrorResponse(
            error=ErrorInfo(
                message=exc.detail,
                type=HTTPStatus(exc.status_code).phrase,
                code=exc.status_code,
            )
        )
        return JSONResponse(err.model_dump(), status_code=exc.status_code)

    @app.exception_handler(RequestValidationError)
    async def validation_exception_handler(_: Request, exc: RequestValidationError):
        exc_str = str(exc)
        errors_str = str(exc.errors())

        if exc.errors() and errors_str and errors_str != exc_str:
            message = f"{exc_str} {errors_str}"
        else:
            message = exc_str

        err = ErrorResponse(
            error=ErrorInfo(
                message=message,
                type=HTTPStatus.BAD_REQUEST.phrase,
                code=HTTPStatus.BAD_REQUEST,
            )
        )
        return JSONResponse(err.model_dump(), status_code=HTTPStatus.BAD_REQUEST)

    # Ensure --api-key option from CLI takes precedence over VLLM_API_KEY
    if tokens := [key for key in (args.api_key or [envs.VLLM_API_KEY]) if key]:
        app.add_middleware(AuthenticationMiddleware, tokens=tokens)

    if args.enable_request_id_headers:
        app.add_middleware(XRequestIdMiddleware)

    # Add scaling middleware to check for scaling state
    app.add_middleware(ScalingMiddleware)

    if envs.VLLM_DEBUG_LOG_API_SERVER_RESPONSE:
        logger.warning(
            "CAUTION: Enabling log response in the API Server. "
            "This can include sensitive information and should be "
            "avoided in production."
        )

        @app.middleware("http")
        async def log_response(request: Request, call_next):
            response = await call_next(request)
            response_body = [section async for section in response.body_iterator]
            response.body_iterator = iterate_in_threadpool(iter(response_body))
            # Check if this is a streaming response by looking at content-type
            content_type = response.headers.get("content-type", "")
            is_streaming = content_type == "text/event-stream; charset=utf-8"

            # Log response body based on type
            if not response_body:
                logger.info("response_body={<empty>}")
            elif is_streaming:
                _log_streaming_response(response, response_body)
            else:
                _log_non_streaming_response(response_body)
            return response

    for middleware in args.middleware:
        module_path, object_name = middleware.rsplit(".", 1)
        imported = getattr(importlib.import_module(module_path), object_name)
        if inspect.isclass(imported):
            app.add_middleware(imported)  # type: ignore[arg-type]
        elif inspect.iscoroutinefunction(imported):
            app.middleware("http")(imported)
        else:
            raise ValueError(
                f"Invalid middleware {middleware}. Must be a function or a class."
            )

    app = sagemaker_standards.bootstrap(app)

    return app

@asynccontextmanager
async def build_async_engine_client(
    args: Namespace,
    *,
    usage_context: UsageContext = UsageContext.OPENAI_API_SERVER,
    disable_frontend_multiprocessing: bool | None = None,
    client_config: dict[str, Any] | None = None,
) -> AsyncIterator[EngineClient]:
    if os.getenv("VLLM_WORKER_MULTIPROC_METHOD") == "forkserver":
        # The executor is expected to be mp.
        # Pre-import heavy modules in the forkserver process
        logger.debug("Setup forkserver with pre-imports")
        multiprocessing.set_start_method("forkserver")
        multiprocessing.set_forkserver_preload(["vllm.v1.engine.async_llm"])
        forkserver.ensure_running()
        logger.debug("Forkserver setup complete!")

    # Context manager to handle engine_client lifecycle
    # Ensures everything is shutdown and cleaned up on error/exit
    engine_args = AsyncEngineArgs.from_cli_args(args)
    if client_config:
        engine_args._api_process_count = client_config.get("client_count", 1)
        engine_args._api_process_rank = client_config.get("client_index", 0)

    if disable_frontend_multiprocessing is None:
        disable_frontend_multiprocessing = bool(args.disable_frontend_multiprocessing)

    async with build_async_engine_client_from_engine_args(
        engine_args,
        usage_context=usage_context,
        disable_frontend_multiprocessing=disable_frontend_multiprocessing,
        client_config=client_config,
    ) as engine:
        yield engine

@asynccontextmanager
async def build_async_engine_client_from_engine_args(
    engine_args: AsyncEngineArgs,
    *,
    usage_context: UsageContext = UsageContext.OPENAI_API_SERVER,
    disable_frontend_multiprocessing: bool = False,
    client_config: dict[str, Any] | None = None,
) -> AsyncIterator[EngineClient]:
    """
    Create EngineClient, either:
        - in-process using the AsyncLLMEngine Directly
        - multiprocess using AsyncLLMEngine RPC

    Returns the Client or None if the creation failed.
    """

    # Create the EngineConfig (determines if we can use V1).
    vllm_config = engine_args.create_engine_config(usage_context=usage_context)

    if disable_frontend_multiprocessing:
        logger.warning("V1 is enabled, but got --disable-frontend-multiprocessing.")

    from vllm.v1.engine.async_llm import AsyncLLM

    async_llm: AsyncLLM | None = None

    # Don't mutate the input client_config
    client_config = dict(client_config) if client_config else {}
    client_count = client_config.pop("client_count", 1)
    client_index = client_config.pop("client_index", 0)

    try:
        async_llm = AsyncLLM.from_vllm_config(
            vllm_config=vllm_config,
            usage_context=usage_context,
            enable_log_requests=engine_args.enable_log_requests,
            aggregate_engine_logging=engine_args.aggregate_engine_logging,
            disable_log_stats=engine_args.disable_log_stats,
            client_addresses=client_config,
            client_count=client_count,
            client_index=client_index,
        )

        # Don't keep the dummy data in memory
        assert async_llm is not None
        await async_llm.reset_mm_cache()

        yield async_llm
    finally:
        if async_llm:
            async_llm.shutdown()

@router.post("/v1/responses/{response_id}/cancel")
async def cancel_responses(response_id: str, raw_request: Request):
    handler = responses(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Responses API"
        )

    try:
        response = await handler.cancel_responses(response_id)
    except Exception as e:
        raise HTTPException(
            status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value, detail=str(e)
        ) from e

    if isinstance(response, ErrorResponse):
        return JSONResponse(
            content=response.model_dump(), status_code=response.error.code
        )
    return JSONResponse(content=response.model_dump())

def chat(request: Request) -> OpenAIServingChat | None:
    return request.app.state.openai_serving_chat

@router.post("/collective_rpc")
async def collective_rpc(raw_request: Request):
    try:
        body = await raw_request.json()
    except json.JSONDecodeError as e:
        raise HTTPException(
            status_code=HTTPStatus.BAD_REQUEST.value,
            detail=f"JSON decode error: {e}",
        ) from e
    method = body.get("method")
    if method is None:
        raise HTTPException(
            status_code=HTTPStatus.BAD_REQUEST.value,
            detail="Missing 'method' in request body",
        )
    # For security reason, only serialized string args/kwargs are passed.
    # User-defined `method` is responsible for deserialization if needed.
    args: list[str] = body.get("args", [])
    kwargs: dict[str, str] = body.get("kwargs", {})
    timeout: float | None = body.get("timeout")
    results = await engine_client(raw_request).collective_rpc(
        method=method, timeout=timeout, args=tuple(args), kwargs=kwargs
    )
    if results is None:
        return Response(status_code=200)
    response: list[Any] = []
    for result in results:
        if result is None or isinstance(result, dict | list):
            response.append(result)
        else:
            response.append(str(result))
    return JSONResponse(content={"results": response})

def completion(request: Request) -> OpenAIServingCompletion | None:
    return request.app.state.openai_serving_completion

@router.post(
    "/v1/chat/completions",
    dependencies=[Depends(validate_json_request)],
    responses={
        HTTPStatus.OK.value: {"content": {"text/event-stream": {}}},
        HTTPStatus.BAD_REQUEST.value: {"model": ErrorResponse},
        HTTPStatus.NOT_FOUND.value: {"model": ErrorResponse},
        HTTPStatus.INTERNAL_SERVER_ERROR.value: {"model": ErrorResponse},
    },
)
@with_cancellation
@load_aware_call
async def create_chat_completion(request: ChatCompletionRequest, raw_request: Request):
    metrics_header_format = raw_request.headers.get(
        ENDPOINT_LOAD_METRICS_FORMAT_HEADER_LABEL, ""
    )
    handler = chat(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Chat Completions API"
        )
    try:
        generator = await handler.create_chat_completion(request, raw_request)
    except Exception as e:
        raise HTTPException(
            status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value, detail=str(e)
        ) from e
    if isinstance(generator, ErrorResponse):
        return JSONResponse(
            content=generator.model_dump(), status_code=generator.error.code
        )

    elif isinstance(generator, ChatCompletionResponse):
        return JSONResponse(
            content=generator.model_dump(),
            headers=metrics_header(metrics_header_format),
        )

    return StreamingResponse(content=generator, media_type="text/event-stream")

@router.post(
    "/v1/completions",
    dependencies=[Depends(validate_json_request)],
    responses={
        HTTPStatus.OK.value: {"content": {"text/event-stream": {}}},
        HTTPStatus.BAD_REQUEST.value: {"model": ErrorResponse},
        HTTPStatus.NOT_FOUND.value: {"model": ErrorResponse},
        HTTPStatus.INTERNAL_SERVER_ERROR.value: {"model": ErrorResponse},
    },
)
@with_cancellation
@load_aware_call
async def create_completion(request: CompletionRequest, raw_request: Request):
    metrics_header_format = raw_request.headers.get(
        ENDPOINT_LOAD_METRICS_FORMAT_HEADER_LABEL, ""
    )
    handler = completion(raw_request)
    if handler is None:
        return base(raw_request).create_error_response(
            message="The model does not support Completions API"
        )

    try:
        generator = await handler.create_completion(request, raw_request)
    except OverflowError as e:
        raise HTTPException(
            status_code=HTTPStatus.BAD_REQUEST.value, detail=str(e)
        ) from e
    except Exception as e:
        raise HTTPException(
            status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value, detail=str(e)
        ) from e

    if isinstance(generator, ErrorResponse):
        return JSONResponse(
            content=generator.model_dump(), status_code=generator.error.code
        )
    elif isinstance(generator, CompletionResponse):
        return JSONResponse(
            content=generator.model_dump(),
            headers=metrics_header(metrics_header_format),
        )

    return StreamingResponse(content=generator, media_type="text/event-stream")

@router.post(
    "/v1/messages",
    dependencies=[Depends(validate_json_request)],
    responses={
        HTTPStatus.OK.value: {"content": {"text/event-stream": {}}},
        HTTPStatus.BAD_REQUEST.value: {"model": AnthropicErrorResponse},
        HTTPStatus.NOT_FOUND.value: {"model": AnthropicErrorResponse},
        HTTPStatus.INTERNAL_SERVER_ERROR.value: {"model": AnthropicErrorResponse},
    },
)
@with_cancellation
@load_aware_call
async def create_messages(request: AnthropicMessagesRequest, raw_request: Request):
    def translate_error_response(response: ErrorResponse) -> JSONResponse:
        anthropic_error = AnthropicErrorResponse(
            error=AnthropicError(
                type=response.error.type,
                message=response.error.message,
            )
        )
        return JSONResponse(
            status_code=response.error.code, content=anthropic_error.model_dump()
        )

    handler = messages(raw_request)
    if handler is None:
        error = base(raw_request).create_error_response(
            message="The model does not support Messages API"
        )
        return translate_error_response(error)

    try:
        generator = await handler.create_messages(request, raw_request)
    except Exception as e:
        logger.exception("Error in create_messages: %s", e)
        return JSONResponse(
            status_code=HTTPStatus.INTERNAL_SERVER_ERROR.value,
            content=AnthropicErrorResponse(
                error=AnthropicError(
                    type="internal_error",
                    message=str(e),
                )
            ).model_dump(),
        )

    if isinstance(generator, ErrorResponse):
        return translate_error_response(generator)

    elif isinstance(generator, AnthropicMessagesResponse):
        resp = generator.model_dump(exclude_none=True)
        logger.debug("Anthropic Messages Response: %s", resp)
        return JSONResponse(content=resp)

    return StreamingResponse(content=generator, media_type="text/event-stream")