vllm.entrypoints.openai.run_batch ¶

async def main(args: Namespace):
    from vllm.entrypoints.openai.api_server import build_async_engine_client
    from vllm.usage.usage_lib import UsageContext

    validate_run_batch_args(args)

    async with build_async_engine_client(
        args,
        usage_context=UsageContext.OPENAI_BATCH_RUNNER,
        disable_frontend_multiprocessing=False,
    ) as engine_client:
        await run_batch(engine_client, args)

def make_arg_parser(parser: FlexibleArgumentParser):
    parser.add_argument(
        "-i",
        "--input-file",
        required=True,
        type=str,
        help="The path or url to a single input file. Currently supports local file "
        "paths, or the http protocol (http or https). If a URL is specified, "
        "the file should be available via HTTP GET.",
    )
    parser.add_argument(
        "-o",
        "--output-file",
        required=True,
        type=str,
        help="The path or url to a single output file. Currently supports "
        "local file paths, or web (http or https) urls. If a URL is specified,"
        " the file should be available via HTTP PUT.",
    )
    parser.add_argument(
        "--output-tmp-dir",
        type=str,
        default=None,
        help="The directory to store the output file before uploading it "
        "to the output URL.",
    )
    parser.add_argument(
        "--response-role",
        type=optional_type(str),
        default="assistant",
        help="The role name to return if `request.add_generation_prompt=True`.",
    )

    parser = AsyncEngineArgs.add_cli_args(parser)

    parser.add_argument(
        "--max-log-len",
        type=int,
        default=None,
        help="Max number of prompt characters or prompt "
        "ID numbers being printed in log."
        "\n\nDefault: Unlimited",
    )

    parser.add_argument(
        "--enable-metrics", action="store_true", help="Enable Prometheus metrics"
    )
    parser.add_argument(
        "--url",
        type=str,
        default="0.0.0.0",
        help="URL to the Prometheus metrics server "
        "(only needed if enable-metrics is set).",
    )
    parser.add_argument(
        "--port",
        type=int,
        default=8000,
        help="Port number for the Prometheus metrics server "
        "(only needed if enable-metrics is set).",
    )
    parser.add_argument(
        "--enable-prompt-tokens-details",
        action="store_true",
        default=False,
        help="If set to True, enable prompt_tokens_details in usage.",
    )
    parser.add_argument(
        "--enable-force-include-usage",
        action="store_true",
        default=False,
        help="If set to True, include usage on every request "
        "(even when stream_options is not specified)",
    )

    return parser

async def make_async_error_request_output(
    request: BatchRequestInput, error_msg: str
) -> BatchRequestOutput:
    return make_error_request_output(request, error_msg)

def make_error_request_output(
    request: BatchRequestInput, error_msg: str
) -> BatchRequestOutput:
    batch_output = BatchRequestOutput(
        id=f"vllm-{random_uuid()}",
        custom_id=request.custom_id,
        response=BatchResponseData(
            status_code=HTTPStatus.BAD_REQUEST,
            request_id=f"vllm-batch-{random_uuid()}",
        ),
        error=error_msg,
    )
    return batch_output

def parse_args():
    parser = FlexibleArgumentParser(description="vLLM OpenAI-Compatible batch runner.")
    return make_arg_parser(parser).parse_args()

async def read_file(path_or_url: str) -> str:
    if path_or_url.startswith("http://") or path_or_url.startswith("https://"):
        async with aiohttp.ClientSession() as session, session.get(path_or_url) as resp:
            return await resp.text()
    else:
        with open(path_or_url, encoding="utf-8") as f:
            return f.read()

async def run_batch(
    engine_client: EngineClient,
    args: Namespace,
) -> None:
    if args.served_model_name is not None:
        served_model_names = args.served_model_name
    else:
        served_model_names = [args.model]

    if args.enable_log_requests:
        request_logger = RequestLogger(max_log_len=args.max_log_len)
    else:
        request_logger = None

    base_model_paths = [
        BaseModelPath(name=name, model_path=args.model) for name in served_model_names
    ]

    model_config = engine_client.model_config
    supported_tasks = await engine_client.get_supported_tasks()
    logger.info("Supported tasks: %s", supported_tasks)

    # Create the openai serving objects.
    openai_serving_models = OpenAIServingModels(
        engine_client=engine_client,
        base_model_paths=base_model_paths,
        lora_modules=None,
    )

    openai_serving_chat = (
        OpenAIServingChat(
            engine_client,
            openai_serving_models,
            args.response_role,
            request_logger=request_logger,
            chat_template=None,
            chat_template_content_format="auto",
            reasoning_parser=args.structured_outputs_config.reasoning_parser,
            enable_prompt_tokens_details=args.enable_prompt_tokens_details,
            enable_force_include_usage=args.enable_force_include_usage,
        )
        if "generate" in supported_tasks
        else None
    )

    openai_serving_embedding = (
        OpenAIServingEmbedding(
            engine_client,
            openai_serving_models,
            request_logger=request_logger,
            chat_template=None,
            chat_template_content_format="auto",
        )
        if "embed" in supported_tasks
        else None
    )

    enable_serving_reranking = (
        "classify" in supported_tasks
        and getattr(model_config.hf_config, "num_labels", 0) == 1
    )

    openai_serving_scores = (
        ServingScores(
            engine_client,
            openai_serving_models,
            request_logger=request_logger,
        )
        if ("embed" in supported_tasks or enable_serving_reranking)
        else None
    )

    tracker = BatchProgressTracker()
    logger.info("Reading batch from %s...", args.input_file)

    # Submit all requests in the file to the engine "concurrently".
    response_futures: list[Awaitable[BatchRequestOutput]] = []
    for request_json in (await read_file(args.input_file)).strip().split("\n"):
        # Skip empty lines.
        request_json = request_json.strip()
        if not request_json:
            continue

        request = BatchRequestInput.model_validate_json(request_json)

        # Determine the type of request and run it.
        if request.url == "/v1/chat/completions":
            chat_handler_fn = (
                openai_serving_chat.create_chat_completion
                if openai_serving_chat is not None
                else None
            )
            if chat_handler_fn is None:
                response_futures.append(
                    make_async_error_request_output(
                        request,
                        error_msg="The model does not support Chat Completions API",
                    )
                )
                continue

            response_futures.append(run_request(chat_handler_fn, request, tracker))
            tracker.submitted()
        elif request.url == "/v1/embeddings":
            embed_handler_fn = (
                openai_serving_embedding.create_embedding
                if openai_serving_embedding is not None
                else None
            )
            if embed_handler_fn is None:
                response_futures.append(
                    make_async_error_request_output(
                        request,
                        error_msg="The model does not support Embeddings API",
                    )
                )
                continue

            response_futures.append(run_request(embed_handler_fn, request, tracker))
            tracker.submitted()
        elif request.url.endswith("/score"):
            score_handler_fn = (
                openai_serving_scores.create_score
                if openai_serving_scores is not None
                else None
            )
            if score_handler_fn is None:
                response_futures.append(
                    make_async_error_request_output(
                        request,
                        error_msg="The model does not support Scores API",
                    )
                )
                continue

            response_futures.append(run_request(score_handler_fn, request, tracker))
            tracker.submitted()
        elif request.url.endswith("/rerank"):
            rerank_handler_fn = (
                openai_serving_scores.do_rerank
                if openai_serving_scores is not None
                else None
            )
            if rerank_handler_fn is None:
                response_futures.append(
                    make_async_error_request_output(
                        request,
                        error_msg="The model does not support Rerank API",
                    )
                )
                continue

            response_futures.append(run_request(rerank_handler_fn, request, tracker))
            tracker.submitted()
        else:
            response_futures.append(
                make_async_error_request_output(
                    request,
                    error_msg=f"URL {request.url} was used. "
                    "Supported endpoints: /v1/chat/completions, /v1/embeddings,"
                    " /score, /rerank ."
                    "See vllm/entrypoints/openai/api_server.py for supported "
                    "score/rerank versions.",
                )
            )

    with tracker.pbar():
        responses = await asyncio.gather(*response_futures)

    await write_file(args.output_file, responses, args.output_tmp_dir)

async def run_request(
    serving_engine_func: Callable,
    request: BatchRequestInput,
    tracker: BatchProgressTracker,
) -> BatchRequestOutput:
    response = await serving_engine_func(request.body)

    if isinstance(
        response,
        (ChatCompletionResponse, EmbeddingResponse, ScoreResponse, RerankResponse),
    ):
        batch_output = BatchRequestOutput(
            id=f"vllm-{random_uuid()}",
            custom_id=request.custom_id,
            response=BatchResponseData(
                body=response, request_id=f"vllm-batch-{random_uuid()}"
            ),
            error=None,
        )
    elif isinstance(response, ErrorResponse):
        batch_output = BatchRequestOutput(
            id=f"vllm-{random_uuid()}",
            custom_id=request.custom_id,
            response=BatchResponseData(
                status_code=response.error.code,
                request_id=f"vllm-batch-{random_uuid()}",
            ),
            error=response,
        )
    else:
        batch_output = make_error_request_output(
            request, error_msg="Request must not be sent in stream mode"
        )

    tracker.completed()
    return batch_output

async def upload_data(output_url: str, data_or_file: str, from_file: bool) -> None:
    """
    Upload a local file to a URL.
    output_url: The URL to upload the file to.
    data_or_file: Either the data to upload or the path to the file to upload.
    from_file: If True, data_or_file is the path to the file to upload.
    """
    # Timeout is a common issue when uploading large files.
    # We retry max_retries times before giving up.
    max_retries = 5
    # Number of seconds to wait before retrying.
    delay = 5

    for attempt in range(1, max_retries + 1):
        try:
            # We increase the timeout to 1000 seconds to allow
            # for large files (default is 300).
            async with aiohttp.ClientSession(
                timeout=aiohttp.ClientTimeout(total=1000)
            ) as session:
                if from_file:
                    with open(data_or_file, "rb") as file:
                        async with session.put(output_url, data=file) as response:
                            if response.status != 200:
                                raise Exception(
                                    f"Failed to upload file.\n"
                                    f"Status: {response.status}\n"
                                    f"Response: {response.text()}"
                                )
                else:
                    async with session.put(output_url, data=data_or_file) as response:
                        if response.status != 200:
                            raise Exception(
                                f"Failed to upload data.\n"
                                f"Status: {response.status}\n"
                                f"Response: {response.text()}"
                            )

        except Exception as e:
            if attempt < max_retries:
                logger.error(
                    "Failed to upload data (attempt %d). Error message: %s.\nRetrying in %d seconds...",  # noqa: E501
                    attempt,
                    e,
                    delay,
                )
                await asyncio.sleep(delay)
            else:
                raise Exception(
                    f"Failed to upload data (attempt {attempt}). Error message: {str(e)}."  # noqa: E501
                ) from e

def validate_run_batch_args(args):
    valid_reasoning_parsers = ReasoningParserManager.list_registered()
    if (
        reasoning_parser := args.structured_outputs_config.reasoning_parser
    ) and reasoning_parser not in valid_reasoning_parsers:
        raise KeyError(
            f"invalid reasoning parser: {reasoning_parser} "
            f"(chose from {{ {','.join(valid_reasoning_parsers)} }})"
        )

async def write_file(
    path_or_url: str, batch_outputs: list[BatchRequestOutput], output_tmp_dir: str
) -> None:
    """
    Write batch_outputs to a file or upload to a URL.
    path_or_url: The path or URL to write batch_outputs to.
    batch_outputs: The list of batch outputs to write.
    output_tmp_dir: The directory to store the output file before uploading it
    to the output URL.
    """
    if path_or_url.startswith("http://") or path_or_url.startswith("https://"):
        if output_tmp_dir is None:
            logger.info("Writing outputs to memory buffer")
            output_buffer = StringIO()
            for o in batch_outputs:
                print(o.model_dump_json(), file=output_buffer)
            output_buffer.seek(0)
            logger.info("Uploading outputs to %s", path_or_url)
            await upload_data(
                path_or_url,
                output_buffer.read().strip().encode("utf-8"),
                from_file=False,
            )
        else:
            # Write responses to a temporary file and then upload it to the URL.
            with tempfile.NamedTemporaryFile(
                mode="w",
                encoding="utf-8",
                dir=output_tmp_dir,
                prefix="tmp_batch_output_",
                suffix=".jsonl",
            ) as f:
                logger.info("Writing outputs to temporary local file %s", f.name)
                await write_local_file(f.name, batch_outputs)
                logger.info("Uploading outputs to %s", path_or_url)
                await upload_data(path_or_url, f.name, from_file=True)
    else:
        logger.info("Writing outputs to local file %s", path_or_url)
        await write_local_file(path_or_url, batch_outputs)

async def write_local_file(
    output_path: str, batch_outputs: list[BatchRequestOutput]
) -> None:
    """
    Write the responses to a local file.
    output_path: The path to write the responses to.
    batch_outputs: The list of batch outputs to write.
    """
    # We should make this async, but as long as run_batch runs as a
    # standalone program, blocking the event loop won't affect performance.
    with open(output_path, "w", encoding="utf-8") as f:
        for o in batch_outputs:
            print(o.model_dump_json(), file=f)