async_llm.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import asyncio
import os
import socket
import time
from collections.abc import AsyncGenerator, Iterable, Mapping
from copy import copy
from typing import Any, Optional, Union

import numpy as np
import torch

import vllm.envs as envs
from vllm.config import ModelConfig, VllmConfig
from vllm.engine.arg_utils import AsyncEngineArgs
from vllm.engine.protocol import EngineClient
from vllm.entrypoints.utils import _validate_truncation_size
from vllm.envs import VLLM_V1_OUTPUT_PROC_CHUNK_SIZE
from vllm.inputs import PromptType
from vllm.inputs.preprocess import InputPreprocessor
from vllm.logger import init_logger
from vllm.lora.request import LoRARequest
from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
from vllm.outputs import PoolingRequestOutput, RequestOutput
from vllm.pooling_params import PoolingParams
from vllm.sampling_params import SamplingParams
from vllm.tasks import SupportedTask
from vllm.tracing import init_tracer
from vllm.transformers_utils.config import maybe_register_config_serialize_by_value
from vllm.transformers_utils.tokenizer import AnyTokenizer
from vllm.usage.usage_lib import UsageContext
from vllm.utils import Device, as_list, cancel_task_threadsafe, cdiv, deprecate_kwargs
from vllm.v1.engine import EngineCoreRequest
from vllm.v1.engine.core_client import EngineCoreClient
from vllm.v1.engine.exceptions import EngineDeadError, EngineGenerateError
from vllm.v1.engine.output_processor import OutputProcessor, RequestOutputCollector
from vllm.v1.engine.parallel_sampling import ParentRequest
from vllm.v1.engine.processor import Processor
from vllm.v1.executor.abstract import Executor
from vllm.v1.metrics.loggers import StatLoggerFactory, StatLoggerManager
from vllm.v1.metrics.prometheus import shutdown_prometheus
from vllm.v1.metrics.stats import IterationStats

logger = init_logger(__name__)


class AsyncLLM(EngineClient):
    def __init__(
        self,
        vllm_config: VllmConfig,
        executor_class: type[Executor],
        log_stats: bool,
        usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
        mm_registry: MultiModalRegistry = MULTIMODAL_REGISTRY,
        use_cached_outputs: bool = False,
        log_requests: bool = True,
        start_engine_loop: bool = True,
        stat_loggers: Optional[list[StatLoggerFactory]] = None,
        client_addresses: Optional[dict[str, str]] = None,
        client_count: int = 1,
        client_index: int = 0,
    ) -> None:
        """
        Create an AsyncLLM.

        Args:
            vllm_config: global configuration.
            executor_class: an Executor impl, e.g. MultiprocExecutor.
            log_stats: Whether to log stats.
            usage_context: Usage context of the LLM.
            mm_registry: Multi-modal registry.
            use_cached_outputs: Whether to use cached outputs.
            log_requests: Whether to log requests.
            start_engine_loop: Whether to start the engine loop.
            stat_loggers: customized stat loggers for the engine.
                If not provided, default stat loggers will be used.
                PLEASE BE AWARE THAT STAT LOGGER IS NOT STABLE
                IN V1, AND ITS BASE CLASS INTERFACE MIGHT CHANGE.

        Returns:
            None
        """
        if not envs.VLLM_USE_V1:
            raise ValueError(
                "Using V1 AsyncLLMEngine, but envs.VLLM_USE_V1=False. "
                "This should not happen. As a workaround, try using "
                "AsyncLLMEngine.from_vllm_config(...) or explicitly set "
                "VLLM_USE_V1=0 or 1 and report this issue on Github."
            )

        # Ensure we can serialize custom transformer configs
        maybe_register_config_serialize_by_value()

        self.model_config = vllm_config.model_config
        self.vllm_config = vllm_config
        self.observability_config = vllm_config.observability_config
        self.log_requests = log_requests

        self.log_stats = log_stats or (stat_loggers is not None)
        if not log_stats and stat_loggers is not None:
            logger.info(
                "AsyncLLM created with log_stats=False and non-empty custom "
                "logger list; enabling logging without default stat loggers"
            )

        # Processor (converts Inputs --> EngineCoreRequests).
        self.processor = Processor(vllm_config, mm_registry=mm_registry)

        # OutputProcessor (converts EngineCoreOutputs --> RequestOutput).
        self.output_processor = OutputProcessor(
            self.tokenizer, log_stats=self.log_stats
        )
        if self.observability_config.otlp_traces_endpoint is not None:
            tracer = init_tracer(
                "vllm.llm_engine", self.observability_config.otlp_traces_endpoint
            )
            self.output_processor.tracer = tracer

        # EngineCore (starts the engine in background process).
        self.engine_core = EngineCoreClient.make_async_mp_client(
            vllm_config=vllm_config,
            executor_class=executor_class,
            log_stats=self.log_stats,
            client_addresses=client_addresses,
            client_count=client_count,
            client_index=client_index,
        )

        # Loggers.
        self.logger_manager: Optional[StatLoggerManager] = None
        if self.log_stats:
            self.logger_manager = StatLoggerManager(
                vllm_config=vllm_config,
                engine_idxs=self.engine_core.engine_ranks_managed,
                custom_stat_loggers=stat_loggers,
                enable_default_loggers=log_stats,
                client_count=client_count,
            )
            self.logger_manager.log_engine_initialized()

        self.output_handler: Optional[asyncio.Task] = None
        try:
            # Start output handler eagerly if we are in the asyncio eventloop.
            asyncio.get_running_loop()
            self._run_output_handler()
        except RuntimeError:
            pass

        if envs.VLLM_TORCH_PROFILER_DIR:
            logger.info(
                "Torch profiler enabled. AsyncLLM CPU traces will be collected under %s",  # noqa: E501
                envs.VLLM_TORCH_PROFILER_DIR,
            )
            worker_name = f"{socket.gethostname()}_{os.getpid()}.async_llm"
            self.profiler = torch.profiler.profile(
                activities=[
                    torch.profiler.ProfilerActivity.CPU,
                ],
                with_stack=envs.VLLM_TORCH_PROFILER_WITH_STACK,
                on_trace_ready=torch.profiler.tensorboard_trace_handler(
                    envs.VLLM_TORCH_PROFILER_DIR, worker_name=worker_name, use_gzip=True
                ),
            )
        else:
            self.profiler = None

    @classmethod
    @deprecate_kwargs(
        "disable_log_requests",
        additional_message=(
            "This argument will have no effect. Use `enable_log_requests` instead."
        ),
    )
    def from_vllm_config(
        cls,
        vllm_config: VllmConfig,
        start_engine_loop: bool = True,
        usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
        stat_loggers: Optional[list[StatLoggerFactory]] = None,
        enable_log_requests: bool = False,
        disable_log_stats: bool = False,
        client_addresses: Optional[dict[str, str]] = None,
        client_count: int = 1,
        client_index: int = 0,
        disable_log_requests: bool = True,  # Deprecated, will be removed
    ) -> "AsyncLLM":
        if not envs.VLLM_USE_V1:
            raise ValueError(
                "Using V1 AsyncLLMEngine, but envs.VLLM_USE_V1=False. "
                "This should not happen. As a workaround, try using "
                "AsyncLLMEngine.from_vllm_config(...) or explicitly set "
                "VLLM_USE_V1=0 or 1 and report this issue on Github."
            )

        # Create the LLMEngine.
        return cls(
            vllm_config=vllm_config,
            executor_class=Executor.get_class(vllm_config),
            start_engine_loop=start_engine_loop,
            stat_loggers=stat_loggers,
            log_requests=enable_log_requests,
            log_stats=not disable_log_stats,
            usage_context=usage_context,
            client_addresses=client_addresses,
            client_count=client_count,
            client_index=client_index,
        )

    @classmethod
    def from_engine_args(
        cls,
        engine_args: AsyncEngineArgs,
        start_engine_loop: bool = True,
        usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
        stat_loggers: Optional[list[StatLoggerFactory]] = None,
    ) -> "AsyncLLM":
        """Create an AsyncLLM from the EngineArgs."""

        # Create the engine configs.
        vllm_config = engine_args.create_engine_config(usage_context)
        executor_class = Executor.get_class(vllm_config)

        # Create the AsyncLLM.
        return cls(
            vllm_config=vllm_config,
            executor_class=executor_class,
            log_requests=engine_args.enable_log_requests,
            log_stats=not engine_args.disable_log_stats,
            start_engine_loop=start_engine_loop,
            usage_context=usage_context,
            stat_loggers=stat_loggers,
        )

    def __del__(self):
        self.shutdown()

    def shutdown(self):
        """Shutdown, cleaning up the background proc and IPC."""

        shutdown_prometheus()

        if engine_core := getattr(self, "engine_core", None):
            engine_core.shutdown()

        cancel_task_threadsafe(getattr(self, "output_handler", None))

    @property
    def tokenizer(self) -> Optional[AnyTokenizer]:
        return self.processor.tokenizer

    async def get_supported_tasks(self) -> tuple[SupportedTask, ...]:
        return await self.engine_core.get_supported_tasks_async()

    async def add_request(
        self,
        request_id: str,
        prompt: Union[EngineCoreRequest, PromptType],
        params: Union[SamplingParams, PoolingParams],
        arrival_time: Optional[float] = None,
        lora_request: Optional[LoRARequest] = None,
        tokenization_kwargs: Optional[dict[str, Any]] = None,
        trace_headers: Optional[Mapping[str, str]] = None,
        priority: int = 0,
        data_parallel_rank: Optional[int] = None,
        prompt_text: Optional[str] = None,
    ) -> RequestOutputCollector:
        """Add new request to the AsyncLLM."""

        if self.errored:
            raise EngineDeadError()

        is_pooling = isinstance(params, PoolingParams)

        # Create a new output collector for the request.
        queue = RequestOutputCollector(output_kind=params.output_kind)

        # Convert Input --> Request.
        if isinstance(prompt, EngineCoreRequest):
            request = prompt
        else:
            assert prompt_text is None
            logger.warning_once(
                "Processor has been moved under OpenAIServing and will "
                "be removed from AsyncLLM in v0.13."
            )
            request = self.processor.process_inputs(
                request_id,
                prompt,
                params,
                arrival_time,
                lora_request,
                tokenization_kwargs,
                trace_headers,
                priority,
                data_parallel_rank,
            )
            prompt_text = prompt if isinstance(prompt, str) else prompt.get("prompt")

        if is_pooling or params.n == 1:
            await self._add_request(request, prompt_text, None, 0, queue)
            return queue

        # Get the updated SamplingParams from the request, which
        # were cloned/updated in processor.process_inputs above.
        parent_params = request.sampling_params
        assert parent_params is not None

        # Fan out child requests (for n>1).
        parent_request = ParentRequest(request_id, parent_params)
        for idx in range(parent_params.n):
            request_id, child_params = parent_request.get_child_info(idx)
            child_request = request if idx == parent_params.n - 1 else copy(request)
            child_request.request_id = request_id
            child_request.sampling_params = child_params
            await self._add_request(
                child_request, prompt_text, parent_request, idx, queue
            )
        return queue

    async def _add_request(
        self,
        request: EngineCoreRequest,
        prompt: Optional[str],
        parent_req: Optional[ParentRequest],
        index: int,
        queue: RequestOutputCollector,
    ):
        # Add the request to OutputProcessor (this process).
        self.output_processor.add_request(request, prompt, parent_req, index, queue)

        # Add the EngineCoreRequest to EngineCore (separate process).
        await self.engine_core.add_request_async(request)

        if self.log_requests:
            logger.info("Added request %s.", request.request_id)

    # TODO: we should support multiple prompts in one call, as you
    # can do with LLM.generate. So that for multi-prompt completion
    # requests we don't need to send multiple messages to core proc,
    # and so we don't need multiple streams which then get
    # re-multiplexed in the API server anyhow.
    async def generate(
        self,
        prompt: Union[EngineCoreRequest, PromptType],
        sampling_params: SamplingParams,
        request_id: str,
        *,
        prompt_text: Optional[str] = None,
        lora_request: Optional[LoRARequest] = None,
        tokenization_kwargs: Optional[dict[str, Any]] = None,
        trace_headers: Optional[Mapping[str, str]] = None,
        priority: int = 0,
        data_parallel_rank: Optional[int] = None,
    ) -> AsyncGenerator[RequestOutput, None]:
        """
        Main function called by the API server to kick off a request
            * 1) Making an AsyncStream corresponding to the Request.
            * 2) Processing the Input.
            * 3) Adding the Request to the Detokenizer.
            * 4) Adding the Request to the EngineCore (separate process).

        A separate output_handler loop runs in a background AsyncIO task,
        pulling outputs from EngineCore and putting them into the
        per-request AsyncStream.

        The caller of generate() iterates the returned AsyncGenerator,
        returning the RequestOutput back to the caller.
        """

        if (
            self.vllm_config.cache_config.kv_sharing_fast_prefill
            and sampling_params.prompt_logprobs
        ):
            raise ValueError(
                "--kv-sharing-fast-prefill produces incorrect logprobs for "
                "prompt tokens, please disable it when the requests need "
                "prompt logprobs"
            )

        try:
            # We start the output_handler on the first call to generate() so
            # we can call __init__ before the event loop, which enables us
            # to handle startup failure gracefully in the OpenAI server.
            self._run_output_handler()

            if tokenization_kwargs is None:
                tokenization_kwargs = {}
                truncate_prompt_tokens = sampling_params.truncate_prompt_tokens

                _validate_truncation_size(
                    self.model_config.max_model_len,
                    truncate_prompt_tokens,
                    tokenization_kwargs,
                )

            q = await self.add_request(
                request_id,
                prompt,
                sampling_params,
                lora_request=lora_request,
                tokenization_kwargs=tokenization_kwargs,
                trace_headers=trace_headers,
                priority=priority,
                data_parallel_rank=data_parallel_rank,
                prompt_text=prompt_text,
            )

            # The output_handler task pushes items into the queue.
            # This task pulls from the queue and yields to caller.
            finished = False
            while not finished:
                # Note: drain queue without await if possible (avoids
                # task switching under load which helps performance).
                out = q.get_nowait() or await q.get()

                # Note: both OutputProcessor and EngineCore handle their
                # own request cleanup based on finished.
                finished = out.finished
                yield out

        # If the request is disconnected by the client, generate()
        # is cancelled or the generator is garbage collected. So,
        # we abort the request if we end up here.
        except (asyncio.CancelledError, GeneratorExit):
            await self.abort(request_id)
            if self.log_requests:
                logger.info("Request %s aborted.", request_id)
            raise

        # Engine is dead. Do not abort since we shut down.
        except EngineDeadError:
            if self.log_requests:
                logger.info("Request %s failed (engine dead).", request_id)
            raise

        # Request validation error.
        except ValueError:
            if self.log_requests:
                logger.info("Request %s failed (bad request).", request_id)
            raise

        # Unexpected error in the generate() task (possibly recoverable).
        except Exception as e:
            await self.abort(request_id)
            if self.log_requests:
                logger.info("Request %s failed.", request_id)
            raise EngineGenerateError() from e

    def _run_output_handler(self):
        """Background loop: pulls from EngineCore and pushes to AsyncStreams."""

        if self.output_handler is not None:
            return

        # Ensure that the task doesn't have a circular ref back to the AsyncLLM
        # object, or else it won't be garbage collected and cleaned up properly.
        engine_core = self.engine_core
        output_processor = self.output_processor
        log_stats = self.log_stats
        logger_manager = self.logger_manager

        async def output_handler():
            try:
                while True:
                    # 1) Pull EngineCoreOutputs from the EngineCore.
                    outputs = await engine_core.get_output_async()
                    num_outputs = len(outputs.outputs)

                    iteration_stats = (
                        IterationStats() if (log_stats and num_outputs) else None
                    )

                    # Split outputs into chunks of at most
                    # VLLM_V1_OUTPUT_PROC_CHUNK_SIZE, so that we don't block the
                    # event loop for too long.
                    if num_outputs <= VLLM_V1_OUTPUT_PROC_CHUNK_SIZE:
                        slices = (outputs.outputs,)
                    else:
                        slices = np.array_split(
                            outputs.outputs,
                            cdiv(num_outputs, VLLM_V1_OUTPUT_PROC_CHUNK_SIZE),
                        )

                    for i, outputs_slice in enumerate(slices):
                        # 2) Process EngineCoreOutputs.
                        processed_outputs = output_processor.process_outputs(
                            outputs_slice, outputs.timestamp, iteration_stats
                        )
                        # NOTE: RequestOutputs are pushed to their queues.
                        assert not processed_outputs.request_outputs

                        # Allow other asyncio tasks to run between chunks
                        if i + 1 < len(slices):
                            await asyncio.sleep(0)

                        # 3) Abort any reqs that finished due to stop strings.
                        await engine_core.abort_requests_async(
                            processed_outputs.reqs_to_abort
                        )

                    # 4) Logging.
                    # TODO(rob): make into a coroutine and launch it in
                    # background thread once Prometheus overhead is non-trivial.
                    if logger_manager:
                        logger_manager.record(
                            engine_idx=outputs.engine_index,
                            scheduler_stats=outputs.scheduler_stats,
                            iteration_stats=iteration_stats,
                        )
            except Exception as e:
                logger.exception("AsyncLLM output_handler failed.")
                output_processor.propagate_error(e)

        self.output_handler = asyncio.create_task(output_handler())

    async def abort(self, request_id: Union[str, Iterable[str]]) -> None:
        """Abort RequestId in OutputProcessor and EngineCore."""

        request_ids = (
            (request_id,) if isinstance(request_id, str) else as_list(request_id)
        )
        all_request_ids = self.output_processor.abort_requests(request_ids)
        await self.engine_core.abort_requests_async(all_request_ids)

        if self.log_requests:
            logger.info("Aborted request(s) %s.", ",".join(request_ids))

    async def encode(
        self,
        prompt: PromptType,
        pooling_params: PoolingParams,
        request_id: str,
        lora_request: Optional[LoRARequest] = None,
        trace_headers: Optional[Mapping[str, str]] = None,
        priority: int = 0,
        truncate_prompt_tokens: Optional[int] = None,
        tokenization_kwargs: Optional[dict[str, Any]] = None,
    ) -> AsyncGenerator[PoolingRequestOutput, None]:
        """
        Main function called by the API server to kick off a request
            * 1) Making an AsyncStream corresponding to the Request.
            * 2) Processing the Input.
            * 3) Adding the Request to the EngineCore (separate process).

        A separate output_handler loop runs in a background AsyncIO task,
        pulling outputs from EngineCore and putting them into the
        per-request AsyncStream.

        The caller of generate() iterates the returned AsyncGenerator,
        returning the RequestOutput back to the caller.
        """

        try:
            # We start the output_handler on the first call to generate() so
            # we can call __init__ before the event loop, which enables us
            # to handle startup failure gracefully in the OpenAI server.
            self._run_output_handler()

            if tokenization_kwargs is None:
                tokenization_kwargs = {}
            _validate_truncation_size(
                self.model_config.max_model_len,
                truncate_prompt_tokens,
                tokenization_kwargs,
            )

            q = await self.add_request(
                request_id,
                prompt,
                pooling_params,
                lora_request=lora_request,
                tokenization_kwargs=tokenization_kwargs,
                trace_headers=trace_headers,
                priority=priority,
            )

            # The output_handler task pushes items into the queue.
            # This task pulls from the queue and yields to caller.
            finished = False
            while not finished:
                # Note: drain queue without await if possible (avoids
                # task switching under load which helps performance).
                out = q.get_nowait() or await q.get()
                assert isinstance(out, PoolingRequestOutput)
                # Note: both OutputProcessor and EngineCore handle their
                # own request cleanup based on finished.
                finished = out.finished
                yield out

        # If the request is disconnected by the client, generate()
        # is cancelled. So, we abort the request if we end up here.
        except asyncio.CancelledError:
            await self.abort(request_id)
            if self.log_requests:
                logger.info("Request %s aborted.", request_id)
            raise

        # Engine is dead. Do not abort since we shut down.
        except EngineDeadError:
            if self.log_requests:
                logger.info("Request %s failed (engine dead).", request_id)
            raise

        # Request validation error.
        except ValueError:
            if self.log_requests:
                logger.info("Request %s failed (bad request).", request_id)
            raise

        # Unexpected error in the generate() task (possibly recoverable).
        except Exception as e:
            await self.abort(request_id)
            if self.log_requests:
                logger.info("Request %s failed.", request_id)
            raise EngineGenerateError() from e

    async def get_vllm_config(self) -> VllmConfig:
        return self.vllm_config

    async def get_model_config(self) -> ModelConfig:
        return self.model_config

    async def get_input_preprocessor(self) -> InputPreprocessor:
        return self.processor.input_preprocessor

    async def get_tokenizer(self) -> AnyTokenizer:
        if self.tokenizer is None:
            raise ValueError(
                "Unable to get tokenizer because skip_tokenizer_init is True"
            )

        return self.tokenizer

    async def is_tracing_enabled(self) -> bool:
        return self.observability_config.otlp_traces_endpoint is not None

    async def do_log_stats(self) -> None:
        if self.logger_manager:
            self.logger_manager.log()

    async def check_health(self) -> None:
        logger.debug("Called check_health.")
        if self.errored:
            raise self.dead_error

    async def start_profile(self) -> None:
        coros = [self.engine_core.profile_async(True)]
        if self.profiler is not None:
            coros.append(asyncio.to_thread(self.profiler.start))
        await asyncio.gather(*coros)

    async def stop_profile(self) -> None:
        coros = [self.engine_core.profile_async(False)]
        if self.profiler is not None:
            coros.append(asyncio.to_thread(self.profiler.stop))
        await asyncio.gather(*coros)

    async def reset_mm_cache(self) -> None:
        self.processor.clear_cache()
        await self.engine_core.reset_mm_cache_async()

    async def reset_prefix_cache(self, device: Optional[Device] = None) -> None:
        if device == Device.CPU:
            raise ValueError("Not supported on CPU.")
        await self.engine_core.reset_prefix_cache_async()

    async def sleep(self, level: int = 1) -> None:
        await self.reset_prefix_cache()
        await self.engine_core.sleep_async(level)

    async def wake_up(self, tags: Optional[list[str]] = None) -> None:
        await self.engine_core.wake_up_async(tags)

    async def is_sleeping(self) -> bool:
        return await self.engine_core.is_sleeping_async()

    async def add_lora(self, lora_request: LoRARequest) -> bool:
        """Load a new LoRA adapter into the engine for future requests."""
        return await self.engine_core.add_lora_async(lora_request)

    async def remove_lora(self, lora_id: int) -> bool:
        """Remove an already loaded LoRA adapter."""
        return await self.engine_core.remove_lora_async(lora_id)

    async def list_loras(self) -> set[int]:
        """List all registered adapters."""
        return await self.engine_core.list_loras_async()

    async def pin_lora(self, lora_id: int) -> bool:
        """Prevent an adapter from being evicted."""
        return await self.engine_core.pin_lora_async(lora_id)

    async def collective_rpc(
        self,
        method: str,
        timeout: Optional[float] = None,
        args: tuple = (),
        kwargs: Optional[dict] = None,
    ):
        """
        Perform a collective RPC call to the given path.
        """
        return await self.engine_core.collective_rpc_async(
            method, timeout, args, kwargs
        )

    async def wait_for_requests_to_drain(self, drain_timeout: int = 300):
        """Wait for all requests to be drained."""
        start_time = time.time()
        while time.time() - start_time < drain_timeout:
            if not self.engine_core.dp_engines_running():
                logger.info("Engines are idle, requests have been drained")
                return

            logger.info("Engines are still running, waiting for requests to drain...")
            await asyncio.sleep(1)  # Wait 1 second before checking again

        raise TimeoutError(
            f"Timeout reached after {drain_timeout} seconds "
            "waiting for requests to drain."
        )

    async def scale_elastic_ep(
        self, new_data_parallel_size: int, drain_timeout: int = 300
    ):
        """
        Scale up or down the data parallel size by adding or removing
        engine cores.
        Args:
            new_data_parallel_size: The new number of data parallel workers
            drain_timeout:
                Maximum time to wait for requests to drain (seconds)
        """
        old_data_parallel_size = self.vllm_config.parallel_config.data_parallel_size
        if old_data_parallel_size == new_data_parallel_size:
            logger.info(
                "Data parallel size is already %s, skipping scale",
                new_data_parallel_size,
            )
            return
        logger.info(
            "Waiting for requests to drain before scaling up to %s engines...",
            new_data_parallel_size,
        )
        await self.wait_for_requests_to_drain(drain_timeout)
        logger.info(
            "Requests have been drained, proceeding with scale to %s engines",
            new_data_parallel_size,
        )
        await self.engine_core.scale_elastic_ep(new_data_parallel_size)
        self.vllm_config.parallel_config.data_parallel_size = new_data_parallel_size

        # recreate stat loggers
        if new_data_parallel_size > old_data_parallel_size and self.log_stats:
            # TODO(rob): fix this after talking with Ray team.
            # This resets all the prometheus metrics since we
            # unregister during initialization. Need to understand
            # the intended behavior here better.
            self.logger_manager = StatLoggerManager(
                vllm_config=self.vllm_config,
                engine_idxs=list(range(new_data_parallel_size)),
                custom_stat_loggers=None,
            )

    @property
    def is_running(self) -> bool:
        # Is None before the loop is started.
        return self.output_handler is None or not self.output_handler.done()

    @property
    def is_stopped(self) -> bool:
        return self.errored

    @property
    def errored(self) -> bool:
        return self.engine_core.resources.engine_dead or not self.is_running

    @property
    def dead_error(self) -> BaseException:
        return EngineDeadError()