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

import torch

import vllm.envs as envs
from vllm import TokensPrompt
from vllm.config import VllmConfig
from vllm.distributed.weight_transfer.base import (
    WeightTransferInitRequest,
    WeightTransferUpdateRequest,
)
from vllm.engine.arg_utils import AsyncEngineArgs
from vllm.engine.protocol import EngineClient, StreamingInput
from vllm.inputs import ProcessorInputs, PromptType
from vllm.logger import init_logger
from vllm.lora.request import LoRARequest
from vllm.multimodal import MULTIMODAL_REGISTRY, MultiModalRegistry
from vllm.outputs import STREAM_FINISHED, PoolingRequestOutput, RequestOutput
from vllm.plugins.io_processors import get_io_processor
from vllm.pooling_params import PoolingParams
from vllm.renderers import renderer_from_config
from vllm.renderers.inputs.preprocess import extract_prompt_components
from vllm.sampling_params import RequestOutputKind, SamplingParams
from vllm.tasks import SupportedTask
from vllm.tokenizers import TokenizerLike
from vllm.tracing import init_tracer
from vllm.transformers_utils.config import maybe_register_config_serialize_by_value
from vllm.usage.usage_lib import UsageContext
from vllm.utils.async_utils import cancel_task_threadsafe
from vllm.utils.collection_utils import as_list
from vllm.v1.engine import EngineCoreRequest, PauseMode
from vllm.v1.engine.core_client import EngineCoreClient
from vllm.v1.engine.exceptions import EngineDeadError, EngineGenerateError
from vllm.v1.engine.input_processor import InputProcessor
from vllm.v1.engine.output_processor import OutputProcessor, RequestOutputCollector
from vllm.v1.engine.parallel_sampling import ParentRequest
from vllm.v1.executor import Executor
from vllm.v1.metrics.loggers import (
    StatLoggerFactory,
    StatLoggerManager,
    load_stat_logger_plugin_factories,
)
from vllm.v1.metrics.prometheus import shutdown_prometheus
from vllm.v1.metrics.stats import IterationStats

logger = init_logger(__name__)


class InputStreamError(Exception):
    """Wrapper for errors from the input stream generator.

    This is used to propagate errors from the user's input generator
    without wrapping them in EngineGenerateError.
    """

    def __init__(self, cause: Exception):
        self.cause = cause
        super().__init__(str(cause))


class AsyncLLM(EngineClient):
    """An asynchronous wrapper for the vLLM engine."""

    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: list[StatLoggerFactory] | None = None,
        aggregate_engine_logging: bool = False,
        client_addresses: dict[str, str] | None = 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
        """
        # Ensure we can serialize custom transformer configs
        maybe_register_config_serialize_by_value()

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

        tracing_endpoint = self.observability_config.otlp_traces_endpoint
        if tracing_endpoint is not None:
            init_tracer("vllm.llm_engine", tracing_endpoint)

        self.log_requests = log_requests

        custom_stat_loggers = list(stat_loggers or [])
        custom_stat_loggers.extend(load_stat_logger_plugin_factories())

        has_custom_loggers = bool(custom_stat_loggers)
        self.log_stats = log_stats or has_custom_loggers
        if not log_stats and has_custom_loggers:
            logger.info(
                "AsyncLLM created with log_stats=False, "
                "but custom stat loggers were found; "
                "enabling logging without default stat loggers."
            )

        self.renderer = renderer = renderer_from_config(self.vllm_config)
        self.io_processor = get_io_processor(
            self.vllm_config,
            self.model_config.io_processor_plugin,
        )

        # Convert TokPrompt --> EngineCoreRequest.
        self.input_processor = InputProcessor(self.vllm_config, renderer)

        # Converts EngineCoreOutputs --> RequestOutput.
        self.output_processor = OutputProcessor(
            renderer.tokenizer,
            log_stats=self.log_stats,
            stream_interval=self.vllm_config.scheduler_config.stream_interval,
            tracing_enabled=tracing_endpoint is not None,
        )

        # 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: StatLoggerManager | None = None
        if self.log_stats:
            self.logger_manager = StatLoggerManager(
                vllm_config=vllm_config,
                engine_idxs=self.engine_core.engine_ranks_managed,
                custom_stat_loggers=custom_stat_loggers,
                enable_default_loggers=log_stats,
                client_count=client_count,
                aggregate_engine_logging=aggregate_engine_logging,
            )
            self.logger_manager.log_engine_initialized()

        self._client_count = client_count

        self.output_handler: asyncio.Task | None = 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 (
            vllm_config.profiler_config.profiler == "torch"
            and not vllm_config.profiler_config.ignore_frontend
        ):
            profiler_dir = vllm_config.profiler_config.torch_profiler_dir
            logger.info(
                "Torch profiler enabled. AsyncLLM CPU traces will be collected under %s",  # noqa: E501
                profiler_dir,
            )
            worker_name = f"{socket.gethostname()}_{os.getpid()}.async_llm"
            self.profiler = torch.profiler.profile(
                activities=[
                    torch.profiler.ProfilerActivity.CPU,
                ],
                with_stack=vllm_config.profiler_config.torch_profiler_with_stack,
                on_trace_ready=torch.profiler.tensorboard_trace_handler(
                    profiler_dir,
                    worker_name=worker_name,
                    use_gzip=vllm_config.profiler_config.torch_profiler_use_gzip,
                ),
            )
        else:
            self.profiler = None

    @classmethod
    def from_vllm_config(
        cls,
        vllm_config: VllmConfig,
        start_engine_loop: bool = True,
        usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
        stat_loggers: list[StatLoggerFactory] | None = None,
        enable_log_requests: bool = False,
        aggregate_engine_logging: bool = False,
        disable_log_stats: bool = False,
        client_addresses: dict[str, str] | None = None,
        client_count: int = 1,
        client_index: int = 0,
    ) -> "AsyncLLM":
        # 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,
            aggregate_engine_logging=aggregate_engine_logging,
            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: list[StatLoggerFactory] | None = 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 renderer := getattr(self, "renderer", None):
            renderer.shutdown()

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

        handler = getattr(self, "output_handler", None)
        if handler is not None:
            cancel_task_threadsafe(handler)

    async def get_supported_tasks(self) -> tuple[SupportedTask, ...]:
        if not hasattr(self, "_supported_tasks"):
            # Cache the result
            self._supported_tasks = await self.engine_core.get_supported_tasks_async()

        return self._supported_tasks

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

        if self.errored:
            raise EngineDeadError()

        is_pooling = isinstance(params, PoolingParams)

        if (
            self.vllm_config.cache_config.kv_sharing_fast_prefill
            and not is_pooling
            and params.prompt_logprobs
        ):
            raise ValueError(
                "--kv-sharing-fast-prefill produces incorrect logprobs for "
                "prompt tokens, please disable it when the requests need "
                "prompt logprobs"
            )

        if isinstance(prompt, AsyncGenerator):
            if reasoning_ended is not None:
                raise NotImplementedError

            # Streaming input case.
            return await self._add_streaming_input_request(
                request_id,
                prompt,
                params,
                arrival_time,
                lora_request,
                tokenization_kwargs,
                trace_headers,
                priority,
                data_parallel_rank,
            )

        # Convert Input --> Request.
        if isinstance(prompt, EngineCoreRequest):
            logger.warning_once(
                "Passing EngineCoreRequest to AsyncLLM.generate() and .add_requests() "
                "is deprecated and will be removed in v0.18. You should instead pass "
                "the outputs of Renderer.render_cmpl() or Renderer.render_chat()."
            )

            request = prompt
            if request_id != request.request_id:
                logger.warning_once(
                    "AsyncLLM.add_request() was passed a request_id parameter that "
                    "does not match the EngineCoreRequest.request_id attribute. The "
                    "latter will be used, and the former will be ignored."
                )
        else:
            request = self.input_processor.process_inputs(
                request_id,
                prompt,
                params,
                supported_tasks=await self.get_supported_tasks(),
                arrival_time=arrival_time,
                lora_request=lora_request,
                tokenization_kwargs=tokenization_kwargs,
                trace_headers=trace_headers,
                priority=priority,
                data_parallel_rank=data_parallel_rank,
            )
            prompt_text, _, _ = extract_prompt_components(self.model_config, prompt)

        if reasoning_ended is not None:
            request.reasoning_ended = reasoning_ended

        self.input_processor.assign_request_id(request)

        # We start the output_handler on the first call to add_request() 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()

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

        # Use cloned params that may have been updated in process_inputs()
        params = request.params

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

        parent_params = params
        assert isinstance(parent_params, SamplingParams)

        # Fan out child requests (for n>1).
        parent_request = ParentRequest(request)
        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: str | None,
        parent_req: ParentRequest | None,
        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)

    async def _add_streaming_input_request(
        self,
        request_id: str,
        input_stream: AsyncGenerator[StreamingInput, None],
        sampling_params: SamplingParams | PoolingParams,
        arrival_time: float | None = None,
        lora_request: LoRARequest | None = None,
        tokenization_kwargs: dict[str, Any] | None = None,
        trace_headers: Mapping[str, str] | None = None,
        priority: int = 0,
        data_parallel_rank: int | None = None,
    ) -> RequestOutputCollector:
        self._validate_streaming_input_sampling_params(sampling_params)

        inputs = dict(
            supported_tasks=await self.get_supported_tasks(),
            arrival_time=arrival_time,
            lora_request=lora_request,
            tokenization_kwargs=tokenization_kwargs,
            trace_headers=trace_headers,
            priority=priority,
            data_parallel_rank=data_parallel_rank,
        )

        if not sampling_params.skip_clone:
            sampling_params = sampling_params.clone()
            sampling_params.skip_clone = True

        # Create request for validation, also used as the finished signal
        # once the input stream is closed.
        final_req = self.input_processor.process_inputs(
            request_id=request_id,
            prompt=TokensPrompt(prompt_token_ids=[0]),
            params=sampling_params,
            **inputs,  # type: ignore[arg-type]
        )
        self.input_processor.assign_request_id(final_req)
        internal_req_id = final_req.request_id

        queue = RequestOutputCollector(sampling_params.output_kind, internal_req_id)

        async def handle_inputs():
            cancelled = False
            try:
                async for input_chunk in input_stream:
                    sp = input_chunk.sampling_params
                    if sp:
                        self._validate_streaming_input_sampling_params(sp)
                    else:
                        sp = sampling_params
                    # TODO(nick): Avoid re-validating reused sampling parameters
                    req = self.input_processor.process_inputs(
                        request_id=internal_req_id,
                        prompt=input_chunk.prompt,
                        params=sp,
                        resumable=True,
                        **inputs,  # type: ignore[arg-type]
                    )
                    req.external_req_id = request_id
                    if req.prompt_embeds is not None:
                        raise ValueError(
                            "prompt_embeds not supported for streaming inputs"
                        )
                    prompt_text, _, _ = extract_prompt_components(
                        self.model_config, input_chunk.prompt
                    )
                    await self._add_request(req, prompt_text, None, 0, queue)
            except (asyncio.CancelledError, GeneratorExit):
                cancelled = True
            except Exception as error:
                # Wrap in InputStreamError so generate() can propagate it
                # without wrapping in EngineGenerateError.
                queue.put(InputStreamError(error))
            finally:
                queue._input_stream_task = None
                if not cancelled:
                    # Send empty final request to indicate that inputs have
                    # finished. Don't send if cancelled (session was aborted).
                    await self._add_request(final_req, None, None, 0, queue)

        # Ensure output handler is running.
        self._run_output_handler()

        queue._input_stream_task = asyncio.create_task(handle_inputs())
        return queue

    @staticmethod
    def _validate_streaming_input_sampling_params(
        params: SamplingParams | PoolingParams,
    ):
        if (
            not isinstance(params, SamplingParams)
            or params.n > 1
            or params.output_kind == RequestOutputKind.FINAL_ONLY
            or params.stop
        ):
            raise ValueError(
                "Input streaming not currently supported "
                "for pooling models, n > 1, request_kind = FINAL_ONLY "
                "or with stop strings."
            )

    # 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: EngineCoreRequest
        | PromptType
        | ProcessorInputs
        | AsyncGenerator[StreamingInput, None],
        sampling_params: SamplingParams,
        request_id: str,
        *,
        prompt_text: str | None = None,
        lora_request: LoRARequest | None = None,
        tokenization_kwargs: dict[str, Any] | None = None,
        trace_headers: Mapping[str, str] | None = None,
        priority: int = 0,
        data_parallel_rank: int | None = None,
        reasoning_ended: bool | None = 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.
        """

        q: RequestOutputCollector | None = None
        try:
            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,
                reasoning_ended=reasoning_ended,
            )

            # 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.
                assert isinstance(out, RequestOutput)
                finished = out.finished
                if out is not STREAM_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):
            if q is not None:
                await self.abort(q.request_id, internal=True)
            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 as e:
            if self.log_requests:
                logger.info("Request %s failed (bad request): %s.", request_id, e)
            raise

        # Error from input stream generator - propagate directly.
        except InputStreamError as e:
            if q is not None:
                await self.abort(q.request_id, internal=True)
            if self.log_requests:
                logger.info("Request %s failed (input error): %s.", request_id, e)
            raise e.cause from e

        # Unexpected error in the generate() task (possibly recoverable).
        except Exception as e:
            if q is not None:
                await self.abort(q.request_id, internal=True)
            if self.log_requests:
                try:
                    s = f"{e.__class__.__name__}: {e}"
                except Exception as e2:
                    s = (
                        f"{e.__class__.__name__}: "
                        "error during printing an exception of class"
                        + e2.__class__.__name__
                    )
                logger.info("Request %s failed due to %s.", request_id, s)
            raise EngineGenerateError() from e
        finally:
            if q is not None:
                q.close()

    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
        renderer = self.renderer
        chunk_size = envs.VLLM_V1_OUTPUT_PROC_CHUNK_SIZE

        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.
                    engine_core_outputs = outputs.outputs
                    for start in range(0, num_outputs, chunk_size):
                        end = start + chunk_size
                        outputs_slice = engine_core_outputs[start:end]
                        # 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 end < num_outputs:
                            await asyncio.sleep(0)

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

                    output_processor.update_scheduler_stats(outputs.scheduler_stats)

                    # 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,
                            mm_cache_stats=renderer.stat_mm_cache(),
                        )
            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: str | Iterable[str], internal: bool = False
    ) -> 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, internal)
        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 pause_generation(
        self,
        *,
        mode: PauseMode = "abort",
        wait_for_inflight_requests: bool | None = None,
        clear_cache: bool = True,
    ) -> None:
        """
        Pause generation to allow model weight updates.

        All mode handling (abort / wait / keep) and cache clearing is done
        in the engine. New generation/encoding requests will not be scheduled
        until resume is called.

        Args:
            mode: How to handle in-flight requests:
                - ``"abort"``: Abort all in-flight requests immediately
                  (default).
                - ``"wait"``: Wait for in-flight requests to complete.
                - ``"keep"``: Freeze requests in queue; they resume on
                  :meth:`resume_generation`.
            wait_for_inflight_requests: DEPRECATED: use mode argument.
            clear_cache: Whether to clear KV cache and prefix cache after
                draining. Set to ``False`` to preserve cache for faster resume.
        """
        if wait_for_inflight_requests:
            warnings.warn(
                "The `wait_for_inflight_requests` parameter in "
                "`AsyncLLM.pause_generation()` is deprecated. "
                "Please use `mode` argument instead.",
                DeprecationWarning,
                stacklevel=2,
            )
            mode = "wait"
        await self.engine_core.pause_scheduler_async(mode=mode, clear_cache=clear_cache)
        # Small sleep to help ensure that final outputs from any in-flight requests are
        # returned prior to this method returning. These outputs come out of the engine
        # prior to the wait-for-idle completion event, but involve additional async
        # tasks in output processing.
        # Note that this is not required for correctness, just more intuitive ordering
        # of events from caller's pov.
        await asyncio.sleep(0.02)

    async def resume_generation(self) -> None:
        """Resume generation after :meth:`pause_generation`."""
        await self.engine_core.resume_scheduler_async()

    async def is_paused(self) -> bool:
        """Return whether the engine is currently paused."""
        return await self.engine_core.is_scheduler_paused_async()

    async def encode(
        self,
        prompt: PromptType | ProcessorInputs,
        pooling_params: PoolingParams,
        request_id: str,
        lora_request: LoRARequest | None = None,
        trace_headers: Mapping[str, str] | None = None,
        priority: int = 0,
        tokenization_kwargs: dict[str, Any] | None = None,
        reasoning_ended: bool | None = 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.
        """

        q: RequestOutputCollector | None = None
        try:
            q = await self.add_request(
                request_id,
                prompt,
                pooling_params,
                lora_request=lora_request,
                tokenization_kwargs=tokenization_kwargs,
                trace_headers=trace_headers,
                priority=priority,
                reasoning_ended=reasoning_ended,
            )

            # 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:
            if q is not None:
                await self.abort(q.request_id, internal=True)
            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:
            if q is not None:
                await self.abort(q.request_id, internal=True)
            if self.log_requests:
                logger.info("Request %s failed.", request_id)
            raise EngineGenerateError() from e
        finally:
            if q is not None:
                q.close()

    @property
    def tokenizer(self) -> TokenizerLike | None:
        return self.renderer.tokenizer

    def get_tokenizer(self) -> TokenizerLike:
        return self.renderer.get_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, profile_prefix: str | None = None) -> None:
        coros = [self.engine_core.profile_async(True, profile_prefix)]
        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.renderer.clear_mm_cache()
        await self.engine_core.reset_mm_cache_async()

    async def reset_prefix_cache(
        self, reset_running_requests: bool = False, reset_connector: bool = False
    ) -> bool:
        return await self.engine_core.reset_prefix_cache_async(
            reset_running_requests, reset_connector
        )

    async def reset_encoder_cache(self) -> None:
        await self.engine_core.reset_encoder_cache_async()

    async def sleep(self, level: int = 1, mode: PauseMode = "abort") -> None:
        await self.engine_core.sleep_async(level, mode)

        if self.logger_manager is not None:
            self.logger_manager.record_sleep_state(1, level)

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

        if self.logger_manager is not None:
            self.logger_manager.record_sleep_state(0, 0)

    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: float | None = None,
        args: tuple = (),
        kwargs: dict | None = 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()

    async def init_weight_transfer_engine(
        self, request: WeightTransferInitRequest
    ) -> None:
        """
        Initialize weight transfer for RL training.

        Args:
            request: Weight transfer initialization request with backend-specific info
        """
        from vllm.distributed.weight_transfer.base import (
            WeightTransferInitRequest,
        )

        if isinstance(request, WeightTransferInitRequest):
            init_info_dict = request.init_info
        else:
            raise TypeError(f"Expected WeightTransferInitRequest, got {type(request)}")

        await self.collective_rpc(
            "init_weight_transfer_engine", kwargs={"init_info": init_info_dict}
        )

    async def update_weights(self, request: WeightTransferUpdateRequest) -> None:
        """
        Batched weight update for RL training.

        Args:
            request: Weight update request with backend-specific update info
        """

        if isinstance(request, WeightTransferUpdateRequest):
            update_info_dict = request.update_info
        else:
            raise TypeError(
                f"Expected WeightTransferUpdateRequest, got {type(request)}"
            )

        await self.collective_rpc(
            "update_weights", kwargs={"update_info": update_info_dict}
        )
