vllm-omni/vllm_omni/engine/async_omni_engine.py

"""
Async Omni Engine for vLLM-Omni multi-stage runtime.

AsyncOmniEngine in the caller's thread is a thin proxy that communicates
with the Orchestrator (running in a background thread) via janus queues.
"""

from __future__ import annotations

import asyncio
import concurrent.futures
import dataclasses
import json
import queue
import threading
import time
import uuid
import weakref
from collections.abc import Mapping, Sequence
from dataclasses import asdict
from typing import TYPE_CHECKING, Any, Literal, cast

import janus
import torch
from omegaconf import OmegaConf
from vllm import envs as vllm_envs
from vllm.engine.arg_utils import EngineArgs
from vllm.inputs import PromptType
from vllm.logger import init_logger
from vllm.v1.engine import EngineCoreRequest
from vllm.v1.engine.input_processor import InputProcessor

from vllm_omni.config.config_factory import StageConfigFactory, with_trust_remote_code_override
from vllm_omni.config.stage_config import (
    DuplexSessionRuntimeConfig,
    load_deploy_config,
    strip_parent_engine_args,
)
from vllm_omni.diffusion.data import DiffusionParallelConfig, parse_attention_config
from vllm_omni.diffusion.diffusion_engine import supports_audio_output
from vllm_omni.engine.async_engine_utils import (
    SHUTDOWN_ENQUEUE_TIMEOUT_S,
    SHUTDOWN_JOIN_TIMEOUT_S,
    apply_omni_final_stage_metadata,
    enqueue_orchestrator_shutdown,
    inject_global_id,
    shutdown_runtime_after_orchestrator,
    upgrade_to_omni_request,
    weak_shutdown_async_omni_engine,
)
from vllm_omni.engine.messages import (
    AbortRequestMessage,
    AddCompanionRequestMessage,
    CollectiveRPCRequestMessage,
    CollectiveRPCResultMessage,
    EngineQueueMessage,
    ErrorMessage,
    InteractionMessage,
    StageSubmissionMessage,
)
from vllm_omni.engine.orchestrator import Orchestrator
from vllm_omni.engine.rpc_result_router import CorrelatedRpcClient
from vllm_omni.engine.stage_client import StageClient
from vllm_omni.engine.stage_init_utils import build_stage0_input_processor
from vllm_omni.engine.stage_pool import StagePool
from vllm_omni.engine.stage_runtime import (
    StageRuntimeInfo,
    create_stage_runtime,
)
from vllm_omni.entrypoints.pd_utils import PDDisaggregationMixin
from vllm_omni.entrypoints.utils import (
    load_and_resolve_stage_configs,
    parse_stage_overrides,
)
from vllm_omni.inputs.data import OmniInteractionPrompt, OmniSamplingParams
from vllm_omni.metrics.prometheus import OmniRequestCounter

logger = init_logger(__name__)

if TYPE_CHECKING:
    from vllm_omni.experimental.fullduplex.engine.duplex_control_client import DuplexControlClient
    from vllm_omni.experimental.fullduplex.engine.lease import DuplexLeaseActivity
    from vllm_omni.experimental.fullduplex.engine.messages import DuplexFence

_STARTUP_POLL_INTERVAL_S = 1.0
_REQUEST_QUEUE_MAXSIZE = 256
# ============================================================================
# Parent-EngineArgs field-routing contracts (consumed by
# AsyncOmniEngine._strip_parent_engine_args when ``stage_configs_path`` is set).
# ============================================================================

# Fields that must survive the "equal to default → strip" filter because
# diffusion stages need them even when equal to vllm's default value
# (e.g. colocate worker setup relies on worker_extension_cls being forwarded).
_PARENT_ARGS_KEEP: frozenset[str] = frozenset(
    {
        "worker_extension_cls",
        "allowed_local_media_path",
        "allowed_media_domains",
        # Legacy stage-config YAMLs may intentionally leave parallel or
        # distributed knobs unspecified at the stage level and rely on
        # top-level CLI values to fill them in during the per-stage merge.
        # Keep these fields so stages that omit them can inherit CLI values,
        # while stages with explicit YAML values still win because the legacy
        # stage-config loader prefers stage-local engine args.
        "tensor_parallel_size",
    }
)

# Omni orchestrator-level fields consumed by ``_resolve_stage_configs`` that
# must never leak into per-stage EngineArgs (``stage_configs_path`` would
# trigger the ``create_model_config`` guard).
_PARENT_ARGS_STRIP: frozenset[str] = frozenset({"stage_configs_path"})


# Fields always populated by callers (via ``from_cli_args`` / ``asdict``) so
# their presence as an override is never a surprise — suppress the
# "override ignored" warning for these.
_PARENT_ARGS_NO_WARN: frozenset[str] = frozenset({"model"})


class AsyncOmniEngine:
    """Thin proxy that launches an Orchestrator in a background thread.

    All stage clients, input/output processors, and stage-to-stage transfer
    logic live inside the Orchestrator coroutine (running in its own thread
    with a dedicated asyncio event loop). This class communicates with it
    via janus queues (sync side for callers, async side for orchestrator).

    Args:
        model: Model name or path
        init_timeout: Total timeout waiting for orchestrator startup (seconds).
        stage_init_timeout: Timeout for stage initialization (seconds)
        **kwargs: Additional arguments
    """

    # Class-level defaults so tests that bypass __init__ via object.__new__
    # don't AttributeError when stage-init / forward paths touch these attrs.
    _log_stats: bool = False
    _coordinator_runtime: Any = None
    _transfer_emitter: Any = None
    _enable_orch_monitor: bool = False

    def __init__(
        self,
        model: str,
        stage_init_timeout: int = 300,
        init_timeout: int = 600,
        diffusion_batch_size: int = 1,
        single_stage_mode: bool = False,
        transfer_emitter: Any = None,
        log_stats: bool = False,
        tokenizer: str | None = None,
        trust_remote_code: bool | None = None,
        **kwargs: Any,
    ) -> None:
        self.model = model
        self.tokenizer = tokenizer
        self.diffusion_batch_size = diffusion_batch_size
        # Cached by get_diffusion_od_config().
        self._diffusion_od_config_view: Any = None
        startup_timeout = int(init_timeout)
        # Forwarded into Orchestrator so its _forward_to_next_stage path can
        # emit per-edge transfer_tx_s / transfer_size_bytes histograms.
        # Optional: when None, Orchestrator silently skips TX emit (existing
        # RX path still works via OrchestratorAggregator).
        self._transfer_emitter = transfer_emitter
        # Drives upstream EngineCore + scheduler stats production. When False
        # the engine skips SchedulerStats / IterationStats; the per-(stage,
        # replica) vllm:* wrap stays registered but reads zero. Respects the
        # --log-stats CLI flag set by the user via OmniBase.
        self._log_stats = log_stats
        self._enable_orch_monitor = bool(kwargs.pop("enable_orch_monitor", False))

        logger.info(f"[AsyncOmniEngine] Initializing with model {model}")

        # ------------------------------------------------------------------ #
        # Single-stage mode detection                                        #
        # ------------------------------------------------------------------ #
        # Single-stage mode is enabled when the caller explicitly passes      #
        # single_stage_mode=True, or when a stage_id is provided in the args. #
        _stage_id_kwarg = kwargs.get("stage_id")
        if isinstance(_stage_id_kwarg, int) and not single_stage_mode:
            single_stage_mode = True

        self.single_stage_mode: bool = single_stage_mode
        self._single_stage_id_filter: int | None = (
            int(_stage_id_kwarg) if single_stage_mode and isinstance(_stage_id_kwarg, int) else None
        )
        self._omni_master_address: str | None = kwargs.get("omni_master_address")
        self._omni_master_port: int | None = kwargs.get("omni_master_port")

        # New omni-coordinator flags. Consumed only in single_stage_mode.
        # ``omni_dp_size_local`` is process-local: each invocation (head and
        # every headless) launches that many replicas for its own stage.
        self._omni_dp_size_local: int = int(kwargs.get("omni_dp_size_local") or 1)
        if self._omni_dp_size_local < 1:
            raise ValueError(f"--omni-dp-size-local must be >= 1, got {self._omni_dp_size_local}")
        self._omni_lb_policy: str = str(kwargs.get("omni_lb_policy") or "random")
        self._omni_heartbeat_timeout: float = float(kwargs.get("omni_heartbeat_timeout") or 30.0)
        if self._omni_heartbeat_timeout <= 0:
            raise ValueError(f"--omni-heartbeat-timeout must be > 0, got {self._omni_heartbeat_timeout}")

        if single_stage_mode:
            logger.info(
                "[AsyncOmniEngine] Single-stage mode enabled (stage_id_filter=%s, master=%s:%s)",
                self._single_stage_id_filter,
                self._omni_master_address,
                self._omni_master_port,
            )

        # Stage resolution pops deploy_config, so get pipeline-wide settings
        # beforehand. The stage CLI exposes the same deploy YAML through
        # stage_configs_path.
        deploy_config_path = kwargs.get("deploy_config") or kwargs.get("stage_configs_path")
        # ``trust_remote_code`` is tri-state (bool | None): ``None`` means "not
        # specified" so stage-config resolution can defer to the deploy yaml's
        # per-stage value (see ``with_trust_remote_code_override``). The
        # restriction path below loads the top-level HF config via vLLM's
        # ``get_config``, which needs a real bool, so collapse ``None`` to the
        # default ``False`` here (#5495).
        pipeline_config = StageConfigFactory.get_pipeline_config(
            model=model,
            trust_remote_code=bool(trust_remote_code),
            deploy_config_path=deploy_config_path,
        )
        self.endpoint_restrictions = pipeline_config.endpoint_restrictions if pipeline_config is not None else ()
        self._duplex_runtime_extension_path = (
            pipeline_config.duplex_runtime_extension if pipeline_config is not None else None
        )
        self.duplex_serving_adapter_path = (
            pipeline_config.duplex_serving_adapter if pipeline_config is not None else None
        )
        self._duplex_control_enabled = bool(pipeline_config and pipeline_config.duplex_control_enabled)
        self.duplex_session_config = DuplexSessionRuntimeConfig()
        if deploy_config_path is not None:
            self.duplex_session_config = load_deploy_config(deploy_config_path).duplex_session

        # Tri-state: None means "not specified" — the deploy yaml's per-stage
        # trust_remote_code stays in effect. An explicit True/False here is a
        # global override (precedence: caller > deploy yaml > default False);
        # the merge rule lives in with_trust_remote_code_override.
        kwargs = with_trust_remote_code_override(kwargs, trust_remote_code)
        self.config_path, self.stage_configs = self._resolve_stage_configs(
            model,
            kwargs,
            trust_remote_code=trust_remote_code,
        )

        self.num_stages = len(self.stage_configs)
        stage0_args = getattr(self.stage_configs[0], "engine_args", None) if self.num_stages > 0 else None
        self.async_chunk = bool(getattr(stage0_args, "async_chunk", False))
        self.stage_pools: list[StagePool] = []
        self.stage_clients: list[StageClient] = []  # logical-stage view for external readers
        self.input_processor: InputProcessor | None = None
        self.prompt_expand_func: Any | None = None
        self.supported_tasks: tuple[str, ...] = ("generate",)
        self.default_sampling_params_list: list[OmniSamplingParams] = []
        self.stage_metadata: list[StageRuntimeInfo] = []
        # Janus queues are constructed eagerly here (not deferred to the
        # orchestrator thread) so the master server's ROUTER thread always
        # sees a non-None ``self.request_queue`` when on_register fires.
        # ``async_q`` lazily binds to whatever event loop first awaits on
        # it (the orchestrator loop), so cross-thread use stays correct.
        self.request_queue: janus.Queue[EngineQueueMessage] = janus.Queue(maxsize=_REQUEST_QUEUE_MAXSIZE)
        self.output_queue: janus.Queue[EngineQueueMessage] = janus.Queue()
        self.rpc_output_queue: janus.Queue[EngineQueueMessage] = janus.Queue()
        self._shutdown_called = False
        self._weak_finalizer: weakref.finalize | None = None
        self._correlated_rpc_client: CorrelatedRpcClient | None = None
        self._duplex_control_client: DuplexControlClient | None = None
        self._running_counter = OmniRequestCounter()

        logger.info(f"[AsyncOmniEngine] Launching Orchestrator thread with {self.num_stages} stages")

        # Launch orchestrator background thread
        startup_future: concurrent.futures.Future = concurrent.futures.Future()

        self.orchestrator_thread = threading.Thread(
            target=self._bootstrap_orchestrator,
            args=(
                stage_init_timeout,
                startup_future,
            ),
            daemon=True,
            name="orchestrator",
        )
        self.orchestrator_thread.start()
        self._wait_for_orchestrator_init(startup_future, startup_timeout)
        self._correlated_rpc_client = CorrelatedRpcClient(
            self.request_queue.sync_q,
            self.rpc_output_queue.sync_q,
        )

        # Stage runtime fields are assigned directly on self by the bootstrap thread.
        self._weak_finalizer = weakref.finalize(
            self,
            weak_shutdown_async_omni_engine,
            self.orchestrator_thread,
            self.request_queue,
            self.output_queue,
            self.rpc_output_queue,
            self._correlated_rpc_client,
        )

        logger.info(f"[AsyncOmniEngine] Orchestrator ready with {self.num_stages} stages")

    def get_diffusion_od_config(self) -> Any:
        """Expose the diffusion ``model_class_name`` to client-side model-extras.

        The worker holds the full config; here we just resolve the pipeline class
        name from the model config (cached). ``model_class_name`` may be ``None``.
        """
        if self._diffusion_od_config_view is None:
            from types import SimpleNamespace

            from vllm_omni.diffusion.data import resolve_model_class_name
            from vllm_omni.diffusion.model_metadata import get_diffusion_model_metadata

            model_class_name = resolve_model_class_name(self.model)
            metadata = get_diffusion_model_metadata(model_class_name)
            self._diffusion_od_config_view = SimpleNamespace(
                model_class_name=model_class_name,
                supports_multimodal_inputs=metadata.supports_multimodal_inputs,
                max_multimodal_image_inputs=metadata.max_multimodal_image_inputs,
                supports_mixed_reference_inputs=metadata.supports_mixed_reference_inputs,
            )
        return self._diffusion_od_config_view

    def _initialize_stages(self, stage_init_timeout: int) -> None:
        """Initialize stage clients/processors via StageRuntime and assign to self."""
        self._runtime = create_stage_runtime(
            stage_configs=self.stage_configs,
            model=self.model,
            config_path=self.config_path,
            single_stage_mode=self.single_stage_mode,
            stage_init_timeout=stage_init_timeout,
            diffusion_batch_size=self.diffusion_batch_size,
            async_chunk=self.async_chunk,
            tokenizer=self.tokenizer,
            single_stage_id_filter=self._single_stage_id_filter,
            omni_master_address=self._omni_master_address,
            omni_master_port=self._omni_master_port,
            omni_dp_size_local=self._omni_dp_size_local,
            omni_heartbeat_timeout=self._omni_heartbeat_timeout,
            omni_lb_policy=self._omni_lb_policy,
            request_queue=self.request_queue,
            log_stats=self._log_stats,
        )
        self._runtime.initialize()

        self.num_stages = len(self.stage_configs)
        self.stage_pools = self._runtime.stage_pools
        self.stage_clients = [
            cast(StageClient, pool.stage_client) for pool in self.stage_pools if pool.stage_client is not None
        ]
        self.stage_vllm_configs = [pool.stage_vllm_config for pool in self.stage_pools]
        self.output_processors = [pool.output_processor for pool in self.stage_pools]
        self.input_processor = (
            build_stage0_input_processor(self.stage_vllm_configs[0])
            if self.stage_vllm_configs and self.stage_vllm_configs[0] is not None
            else None
        )
        self.prompt_expand_func = next(
            (
                getattr(client, "prompt_expand_func", None)
                for client in self.stage_clients
                if getattr(client, "prompt_expand_func", None) is not None
            ),
            None,
        )
        self.default_sampling_params_list = [client.default_sampling_params for client in self.stage_clients]
        self.stage_metadata = [
            StageRuntimeInfo(
                final_output=client.final_output,
                final_output_type=client.final_output_type,
                stage_type=client.stage_type,
                model_stage=getattr(client, "model_stage", None),
            )
            for client in self.stage_clients
        ]
        supported_tasks: set[str] = set()
        if any(getattr(client, "is_comprehension", False) for client in self.stage_clients):
            supported_tasks.add("generate")
        if any(meta.final_output_type == "audio" for meta in self.stage_metadata):
            supported_tasks.add("speech")
        self.supported_tasks = tuple(supported_tasks) if supported_tasks else ("generate",)

    def _bootstrap_orchestrator(
        self,
        stage_init_timeout: int,
        startup_future: concurrent.futures.Future,
    ) -> None:
        """Create loop, initialize stages, then run Orchestrator."""

        loop = asyncio.new_event_loop()
        asyncio.set_event_loop(loop)

        async def _run_orchestrator() -> None:
            self._initialize_stages(stage_init_timeout)

            pd_config = self._detect_pd_config()

            membership_controller = self._runtime.create_membership_controller()
            duplex_runtime_extension = None
            if self._duplex_control_enabled:
                from vllm_omni.experimental.fullduplex.engine.duplex_runtime import (
                    load_duplex_runtime_extension,
                    validate_duplex_runtime_extension,
                )

                duplex_runtime_extension = load_duplex_runtime_extension(
                    getattr(self, "_duplex_runtime_extension_path", None)
                )
                if duplex_runtime_extension is not None:
                    validate_duplex_runtime_extension(
                        duplex_runtime_extension,
                        sampling_defaults=tuple(pool.stage_client.default_sampling_params for pool in self.stage_pools),
                    )

            orchestrator = Orchestrator(
                request_async_queue=self.request_queue.async_q,
                output_async_queue=self.output_queue.async_q,
                rpc_async_queue=self.rpc_output_queue.async_q,
                stage_pools=self.stage_pools,
                async_chunk=self.async_chunk,
                pd_config=pd_config,
                membership_controller=membership_controller,
                running_counter=self._running_counter,
                transfer_emitter=self._transfer_emitter,
                log_stats=self._log_stats,
                enable_orch_monitor=self._enable_orch_monitor,
                duplex_runtime_extension=duplex_runtime_extension,
                enable_duplex_control=self._duplex_control_enabled,
                duplex_session_config=self.duplex_session_config,
            )
            if not startup_future.done():
                startup_future.set_result(asyncio.get_running_loop())
            await orchestrator.run()

        try:
            loop.run_until_complete(_run_orchestrator())
        except Exception as e:
            if not startup_future.done():
                wrapped = RuntimeError(f"Orchestrator initialization failed: {e}")
                wrapped.__cause__ = e
                startup_future.set_exception(wrapped)
            logger.exception("[AsyncOmniEngine] Orchestrator thread crashed")
            error_text = str(e) or "Orchestrator thread crashed"
            try:
                error_msg = ErrorMessage(error=error_text, fatal=True)
                if self.output_queue is not None:
                    self.output_queue.sync_q.put_nowait(error_msg)
                if self.rpc_output_queue is not None:
                    self.rpc_output_queue.sync_q.put_nowait(error_msg)
            except Exception:
                pass
            raise
        finally:
            try:
                pending = [task for task in asyncio.all_tasks(loop) if not task.done()]
                for task in pending:
                    task.cancel()
                if pending:
                    loop.run_until_complete(asyncio.gather(*pending, return_exceptions=True))
                loop.run_until_complete(loop.shutdown_asyncgens())
                if hasattr(loop, "shutdown_default_executor"):
                    loop.run_until_complete(loop.shutdown_default_executor())
            except Exception:
                logger.exception("[AsyncOmniEngine] Failed during orchestrator loop cleanup")
            finally:
                asyncio.set_event_loop(None)
                loop.close()

    def _wait_for_orchestrator_init(self, startup_future: concurrent.futures.Future, startup_timeout: int) -> None:
        """
        Wait for orchestrator startup future to return ready. Raises exception on any failures to the init process.
        """
        deadline = time.monotonic() + startup_timeout
        while True:
            remaining = deadline - time.monotonic()
            if remaining <= 0:
                logger.warning(
                    "[AsyncOmniEngine] Orchestrator startup timed out after %ss. "
                    "Multi-stage deployments that initialize stages sequentially on one device "
                    "or load checkpoints from slow storage may need larger --init-timeout and "
                    "--stage-init-timeout values.",
                    startup_timeout,
                )
                self._try_shutdown("[AsyncOmniEngine] Failed to cleanup after orchestrator startup timeout")
                raise TimeoutError(f"Orchestrator did not become ready within {startup_timeout}s")
            try:
                startup_future.result(
                    timeout=min(remaining, _STARTUP_POLL_INTERVAL_S),
                )
                break
            except concurrent.futures.TimeoutError:
                if not self.orchestrator_thread.is_alive():
                    self._try_shutdown("[AsyncOmniEngine] Failed to cleanup after orchestrator startup failure")
                    if startup_future.done():
                        startup_future.result()  # re-raises the real exception
                    raise RuntimeError("Orchestrator thread died during startup")
            except Exception:
                self._try_shutdown("[AsyncOmniEngine] Failed to cleanup after orchestrator startup failure")
                raise

    # ---- request helpers ----

    @staticmethod
    def _iter_multimodal_items(value: Any) -> list[Any]:
        if value is None:
            return []
        if isinstance(value, list):
            return value
        return [value]

    def _ensure_stage_replica_mm_uuids(
        self,
        prompt: Any,
        *,
        stage_id: int,
        replica_id: int,
    ) -> None:
        """Make multimodal processor-cache keys local to a stage replica.

        vLLM's frontend multimodal sender cache is process-global, while each
        vllm-omni stage replica owns a separate EngineCore receiver cache. If
        two requests with the same image are routed to different stage-0
        replicas, a plain content hash can make the sender omit the tensor for
        a replica that has never received it. Prefixing user/content UUIDs with
        the selected replica keeps cache reuse within the receiver that owns it.
        """

        if not isinstance(prompt, dict):
            return

        mm_data = prompt.get("multi_modal_data")
        if not isinstance(mm_data, dict) or not mm_data:
            return

        from vllm.multimodal.hasher import MultiModalHasher

        existing_uuids = prompt.get("multi_modal_uuids")
        if not isinstance(existing_uuids, dict):
            existing_uuids = {}

        model_id = str(getattr(self, "model", ""))
        scoped_uuids: dict[str, list[str | None]] = dict(existing_uuids)
        for modality, raw_items in mm_data.items():
            items = self._iter_multimodal_items(raw_items)
            if not items:
                continue

            modality_existing = existing_uuids.get(modality)
            if not isinstance(modality_existing, list):
                modality_existing = [modality_existing] if modality_existing is not None else []

            modality_uuids: list[str | None] = []
            for idx, item in enumerate(items):
                user_uuid = modality_existing[idx] if idx < len(modality_existing) else None
                if user_uuid is not None:
                    base_uuid = str(user_uuid)
                elif item is None:
                    base_uuid = None
                else:
                    base_uuid = MultiModalHasher.hash_kwargs(
                        model_id=model_id,
                        **{modality: item},
                    )

                if base_uuid is None:
                    modality_uuids.append(None)
                else:
                    modality_uuids.append(f"stage{stage_id}:rep{replica_id}:{base_uuid}")

            scoped_uuids[modality] = modality_uuids

        if scoped_uuids:
            prompt["multi_modal_uuids"] = scoped_uuids

    @staticmethod
    def _stage_pool_replica_count(stage_pool: Any) -> int:
        try:
            live_num_replicas = getattr(stage_pool, "live_num_replicas", None)
            if live_num_replicas is not None:
                return int(live_num_replicas)
        except Exception:
            pass

        try:
            live_replica_ids = getattr(stage_pool, "live_replica_ids", None)
            if callable(live_replica_ids):
                return len(live_replica_ids())
        except Exception:
            pass

        try:
            clients = getattr(stage_pool, "clients", None)
            if clients is not None:
                return sum(1 for client in clients if client is not None)
        except Exception:
            pass

        return int(getattr(stage_pool, "num_replicas", 1) or 1)

    @staticmethod
    def _stage_pool_is_distributed(stage_pool: Any) -> bool:
        try:
            is_distributed = getattr(stage_pool, "is_distributed", None)
            if is_distributed is not None:
                return bool(is_distributed() if callable(is_distributed) else is_distributed)
        except Exception:
            pass

        return getattr(stage_pool, "_hub", None) is not None

    def _scope_stage0_multimodal_cache_to_replica(
        self,
        request_id: str,
        prompt: Any,
    ) -> int | None:
        stage_pools = getattr(self, "stage_pools", None)
        if isinstance(prompt, EngineCoreRequest) or not stage_pools:
            return None

        stage0_pool = stage_pools[0]
        # TODO: Currently only supports the ar -> dit process.
        # Future scenarios (e.g., dit -> ar) need to be added, which will require modifications here.
        if stage0_pool.stage_type == "diffusion" or self._stage_pool_replica_count(stage0_pool) <= 1:
            return None

        prompts = prompt if isinstance(prompt, list) else [prompt]
        if not any(isinstance(p, dict) and p.get("multi_modal_data") for p in prompts):
            return None

        if self._stage_pool_is_distributed(stage0_pool):
            preselect_replica_id = getattr(stage0_pool, "preselect_replica_id", None)
            if not callable(preselect_replica_id):
                logger.debug(
                    "[AsyncOmniEngine] Skipping stage-0 multimodal cache scoping for distributed routing "
                    "without preselect support req=%s",
                    request_id,
                )
                return None
            replica_id = preselect_replica_id(request_id)
            if replica_id is None:
                logger.debug(
                    "[AsyncOmniEngine] Skipping stage-0 multimodal cache scoping for distributed routing "
                    "because no serviceable replica is available yet req=%s",
                    request_id,
                )
                return None
        else:
            replica_id = stage0_pool.select_replica_id(request_id)

        for p in prompts:
            self._ensure_stage_replica_mm_uuids(
                p,
                stage_id=0,
                replica_id=replica_id,
            )

        logger.debug(
            "[AsyncOmniEngine] Scoped multimodal cache keys to stage-0 replica-%s for req=%s",
            replica_id,
            request_id,
        )
        return replica_id

    def _build_add_request_message(
        self,
        request_id: str,
        prompt: EngineCoreRequest | PromptType,
        prompt_text: str | None = None,
        sampling_params_list: Sequence[Any] | None = None,
        final_stage_id: int = 0,
        final_output_stage_ids: Sequence[int] | None = None,
        arrival_time: float | None = None,
        lora_request: Any = 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,
        *,
        resumable: bool = False,
        message_type: Literal["add_request", "streaming_update"] = "add_request",
    ) -> StageSubmissionMessage:
        """Build an add_request message after stage-0 preprocessing."""
        request_timestamp = float(arrival_time) if arrival_time is not None else time.time()
        effective_sampling_params_list: list[OmniSamplingParams] = (
            list(cast(Sequence[OmniSamplingParams], sampling_params_list))
            if sampling_params_list is not None
            else list(self.default_sampling_params_list)
        )
        if not effective_sampling_params_list:
            raise ValueError(
                f"Missing sampling params for stage 0. Got {len(effective_sampling_params_list)} stage params."
            )
        params = effective_sampling_params_list[0]

        # Keep the original prompt for downstream stages (they need the raw
        # dict, e.g. for multi_modal_data).
        original_prompt = prompt
        preselected_stage0_replica: int | None = None

        stage_type = self.stage_metadata[0].stage_type
        output_prompt_text: Any = None
        _preprocess_ms = 0.0
        if stage_type != "diffusion" and not isinstance(prompt, EngineCoreRequest):
            # Inject global_request_id into the raw prompt.
            if isinstance(prompt, dict):
                inject_global_id(prompt, request_id)
            elif isinstance(prompt, list):
                for item in prompt:
                    inject_global_id(item, request_id)

            preselected_stage0_replica = self._scope_stage0_multimodal_cache_to_replica(
                request_id,
                prompt,
            )

            # Full input processing (tokenization, multimodal, etc.)
            _t_preprocess = time.perf_counter()
            try:
                request = self.input_processor.process_inputs(
                    request_id=request_id,
                    prompt=prompt,
                    params=params,
                    supported_tasks=self.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,
                    resumable=resumable,
                )
            except Exception:
                if preselected_stage0_replica is not None and self.stage_pools:
                    self.stage_pools[0].release_binding(request_id)
                raise
            _preprocess_ms = (time.perf_counter() - _t_preprocess) * 1000.0
            # TODO (Peiqi): add this for Qwen3-TTS only. Other models don't have
            # additional_information field in the prompt.
            request = upgrade_to_omni_request(request, prompt)

            if reasoning_ended is not None:
                request.reasoning_ended = reasoning_ended

            # Restore external_req_id to the original user-facing request_id.
            # InputProcessor.process_inputs() renames request_id to an internal
            # UUID (saving the original in external_req_id), but then overwrites
            # external_req_id with the new internal ID. We need external_req_id
            # to match the key used in Orchestrator.request_states so that
            # output routing (output.request_id lookup) can find the req_state.
            request.external_req_id = request_id
            request = apply_omni_final_stage_metadata(request, final_stage_id)

            # Registration with stage 0's output processor is deferred to the
            # orchestrator thread (see Orchestrator._handle_add_request), which
            # now routes admission through StagePool.submit_initial().
            output_prompt_text = prompt_text
            if output_prompt_text is None and isinstance(original_prompt, dict):
                output_prompt_text = original_prompt.get("prompt")
            prompt = request

        return StageSubmissionMessage(
            type=message_type,
            request_id=request_id,
            prompt=prompt,
            original_prompt=original_prompt,
            output_prompt_text=output_prompt_text,
            sampling_params_list=effective_sampling_params_list,
            final_stage_id=final_stage_id,
            final_output_stage_ids=list(final_output_stage_ids) if final_output_stage_ids is not None else None,
            preprocess_ms=_preprocess_ms,
            request_timestamp=request_timestamp,
            enqueue_ts=time.perf_counter(),
        )

    def _enqueue_cfg_companions(
        self,
        parent_id: str,
        original_prompt: Any,
        stage0_params: Any,
        sampling_params_list: list[Any],
    ) -> None:
        """Expand prompt into CFG companions, process through InputProcessor, and enqueue."""
        try:
            expanded = self.prompt_expand_func(original_prompt, stage0_params)
        except Exception:
            logger.exception("[AsyncOmniEngine] prompt_expand_func failed for req %s", parent_id)
            return

        if not expanded:
            return

        for ep in expanded:
            cid = f"{parent_id}{ep.request_id_suffix}"
            companion_prompt = ep.prompt

            companion_params, companion_spl = ep.apply_overrides(stage0_params, sampling_params_list)

            if isinstance(companion_prompt, dict):
                inject_global_id(companion_prompt, cid)

            request = self.input_processor.process_inputs(
                request_id=cid,
                prompt=companion_prompt,
                params=companion_params,
                supported_tasks=self.supported_tasks,
            )
            request.external_req_id = cid
            # Companions are stage-0-final for ordinary downstream payloads,
            # but diffusion still needs their CFG KV caches.
            request = apply_omni_final_stage_metadata(request, 0, force_kv_transfer=True)

            # Registration of this companion on stage-0's output processor is
            # deferred to Orchestrator._handle_add_companion, which routes
            # admission through StagePool.submit_initial(..., affinity_request_id=...).
            self.request_queue.sync_q.put(
                AddCompanionRequestMessage(
                    companion_id=cid,
                    parent_id=parent_id,
                    role=ep.role,
                    prompt=request,
                    companion_prompt_text=companion_prompt,
                    sampling_params_list=companion_spl,
                )
            )

        logger.info(
            "[AsyncOmniEngine] CFG expansion for req %s: %d companions",
            parent_id,
            len(expanded),
        )

    @staticmethod
    def _get_default_cache_config(cache_backend: str | None) -> dict[str, Any] | None:
        if cache_backend == "cache_dit":
            return {
                "Fn_compute_blocks": 1,
                "Bn_compute_blocks": 0,
                "max_warmup_steps": 4,
                "residual_diff_threshold": 0.24,
                "max_continuous_cached_steps": 3,
                "enable_taylorseer": False,
                "taylorseer_order": 1,
                "scm_steps_mask_policy": None,
                "scm_steps_policy": "dynamic",
            }
        if cache_backend == "tea_cache":
            return {
                "rel_l1_thresh": 0.2,
            }
        if cache_backend == "mag_cache":
            return {
                "mag_threshold": 0.24,
                "mag_max_skip_steps": 5,
                "mag_retention_ratio": 0.1,
            }
        if cache_backend in ("step_cache"):
            return {
                "step_cache_dit_enabled": True,
                "velocity_sim_thresholds": [0.95, 0.93],
                "velocity_skip_countdowns": [4, 2],
                "step_cache_dit_min_history": 2,
                "step_cache_dit_max_history": 2,
            }
        return None

    @staticmethod
    def _normalize_cache_config(cache_backend: str | None, cache_config: Any | None) -> Any | None:
        if isinstance(cache_config, str):
            try:
                cache_config = json.loads(cache_config)
            except json.JSONDecodeError:
                logger.warning("Invalid cache_config JSON, using defaults.")
                cache_config = None
        if cache_config is None and cache_backend not in (None, "", "none"):
            cache_config = AsyncOmniEngine._get_default_cache_config(cache_backend)
        return cache_config

    def _detect_pd_config(self) -> dict[str, Any] | None:
        """Detect PD (Prefill-Decode) disaggregation config from stage_configs.
        Returns a dict with 'pd_pair' and 'bootstrap_addr', or None.
        """
        pd_pair = PDDisaggregationMixin.detect_pd_separation_from_stage_configs(self.stage_configs)
        if pd_pair is None:
            return None
        prefill_idx, decode_idx = pd_pair

        # Extract bootstrap address from prefill stage engine_args
        bootstrap_addr: str | None = None
        try:
            prefill_cfg = self.stage_configs[prefill_idx]
            ea = getattr(prefill_cfg, "engine_args", None)
            kv_cfg = getattr(ea, "kv_transfer_config", None) if ea is not None else None
            if kv_cfg is not None:
                port = vllm_envs.VLLM_MOONCAKE_BOOTSTRAP_PORT
                kv_ip = getattr(kv_cfg, "kv_ip", None) or "127.0.0.1"
                bootstrap_addr = f"http://{kv_ip}:{port}"
        except Exception as exc:
            logger.warning("[AsyncOmniEngine] Could not extract PD bootstrap address: %s", exc)

        logger.info(
            "[AsyncOmniEngine] PD disaggregation detected: prefill=stage-%d, decode=stage-%d, bootstrap=%s",
            prefill_idx,
            decode_idx,
            bootstrap_addr,
        )
        prefill_engine_id: str | None = None
        try:
            prefill_client = self.stage_clients[prefill_idx]
            kv_cfg = getattr(getattr(prefill_client, "vllm_config", None), "kv_transfer_config", None)
            prefill_engine_id = getattr(kv_cfg, "engine_id", None)
        except Exception as exc:
            logger.warning("[AsyncOmniEngine] Could not extract prefill engine_id: %s", exc)

        return {
            "pd_pair": (prefill_idx, decode_idx),
            "bootstrap_addr": bootstrap_addr,
            "prefill_engine_id": prefill_engine_id,
        }

    @staticmethod
    def _create_default_diffusion_stage_cfg(kwargs: dict[str, Any]) -> list:
        """Create a default single-stage diffusion config from kwargs."""
        # We temporally create a default config for diffusion stage.
        # In the future, we should merge the default config with the user-provided config.
        normalized_kwargs = dict(kwargs)
        default_sampling_params = normalized_kwargs.get("default_sampling_params")
        if isinstance(default_sampling_params, str):
            try:
                default_sampling_params = json.loads(default_sampling_params)
            except json.JSONDecodeError:
                logger.warning("Invalid default_sampling_params JSON, ignoring stage defaults.")
                default_sampling_params = None
        if not isinstance(default_sampling_params, dict):
            default_sampling_params = None
        stage_default_sampling_params = default_sampling_params.get("0", {}) if default_sampling_params else {}
        if normalized_kwargs.get("dtype") is None:
            normalized_kwargs["dtype"] = "auto"

        # TODO: hack, convert dtype to string to avoid non-premitive omegaconf create error.
        if "dtype" in normalized_kwargs and not isinstance(normalized_kwargs["dtype"], str):
            if not isinstance(normalized_kwargs["dtype"], torch.dtype):
                raise TypeError(
                    f"Provided dtype must be a string or torch.dtype, got {type(normalized_kwargs['dtype']).__name__}"
                )
            normalized_kwargs["dtype"] = str(normalized_kwargs["dtype"]).removeprefix("torch.")

        cache_backend = normalized_kwargs.get("cache_backend", "none")
        cache_config = AsyncOmniEngine._normalize_cache_config(
            cache_backend,
            normalized_kwargs.get("cache_config", None),
        )

        parallel_config = normalized_kwargs.get("parallel_config")
        if isinstance(parallel_config, dict):
            parallel_config = DiffusionParallelConfig.from_dict(parallel_config)
        if parallel_config is None:
            ulysses_degree = normalized_kwargs.get("ulysses_degree") or 1
            ring_degree = normalized_kwargs.get("ring_degree") or 1
            allgather_degree = normalized_kwargs.get("allgather_degree") or 1
            ulysses_mode = normalized_kwargs.get("ulysses_mode") or "strict"
            sequence_parallel_size = normalized_kwargs.get("sequence_parallel_size")
            pipeline_parallel_size = normalized_kwargs.get("pipeline_parallel_size") or 1
            data_parallel_size = normalized_kwargs.get("data_parallel_size") or 1
            tensor_parallel_size = normalized_kwargs.get("tensor_parallel_size") or 1
            cfg_parallel_size = normalized_kwargs.get("cfg_parallel_size") or 1
            pipeline_parallel_size = normalized_kwargs.get("pipeline_parallel_size") or 1
            vae_patch_parallel_size = normalized_kwargs.get("vae_patch_parallel_size") or 1
            vae_parallel_mode = normalized_kwargs.get("vae_parallel_mode") or "auto"
            text_encoder_tp_size = normalized_kwargs.get("text_encoder_tp_size") or 1
            enable_expert_parallel = normalized_kwargs.get("enable_expert_parallel") or False
            use_hsdp = normalized_kwargs.get("use_hsdp", False)
            hsdp_shard_size = normalized_kwargs.get("hsdp_shard_size", -1)
            hsdp_replicate_size = normalized_kwargs.get("hsdp_replicate_size", 1)
            if sequence_parallel_size is None:
                sequence_parallel_size = allgather_degree if allgather_degree > 1 else ulysses_degree * ring_degree

            parallel_config = DiffusionParallelConfig(
                pipeline_parallel_size=pipeline_parallel_size,
                data_parallel_size=data_parallel_size,
                tensor_parallel_size=tensor_parallel_size,
                enable_expert_parallel=enable_expert_parallel,
                sequence_parallel_size=sequence_parallel_size,
                ulysses_degree=ulysses_degree,
                ring_degree=ring_degree,
                allgather_degree=allgather_degree,
                ulysses_mode=ulysses_mode,
                cfg_parallel_size=cfg_parallel_size,
                vae_patch_parallel_size=vae_patch_parallel_size,
                vae_parallel_mode=vae_parallel_mode,
                text_encoder_tp_size=text_encoder_tp_size,
                use_hsdp=use_hsdp,
                hsdp_shard_size=hsdp_shard_size,
                hsdp_replicate_size=hsdp_replicate_size,
            )

        num_devices = max(1, int(parallel_config.world_size))
        devices = ",".join(str(i) for i in range(num_devices))
        model_class_name = kwargs.get("model_class_name", None)
        final_output_type = "audio" if model_class_name and supports_audio_output(model_class_name) else "image"

        attention_config = None
        if (
            kwargs.get("diffusion_attention_config") is not None
            or kwargs.get("diffusion_attention_backend") is not None
        ):
            attention_config = parse_attention_config(
                kwargs.get("diffusion_attention_config"),
                attention_backend=kwargs.get("diffusion_attention_backend"),
            )

        stage_engine_args = {
            "max_num_seqs": kwargs.get("max_num_seqs") or 1,
            "parallel_config": parallel_config,
            "model_class_name": kwargs.get("model_class_name", None),
            "task_type": kwargs.get("task_type", None),
            "model_config": kwargs.get("model_config", None),
            "additional_config": kwargs.get("additional_config", None),
            "step_execution": kwargs.get("step_execution", False),
            "request_batch_max_wait_ms": kwargs.get("request_batch_max_wait_ms", 0.0),
            "vae_use_slicing": kwargs.get("vae_use_slicing", False),
            "vae_use_tiling": kwargs.get("vae_use_tiling", False),
            "cache_backend": cache_backend,
            "cache_config": cache_config,
            "enable_cache_dit_summary": kwargs.get("enable_cache_dit_summary", False),
            "enable_cpu_offload": kwargs.get("enable_cpu_offload", False),
            "enable_layerwise_offload": kwargs.get("enable_layerwise_offload", False),
            "enable_distributed_layerwise_offload": kwargs.get("enable_distributed_layerwise_offload", False),
            "dlo_use_allgather": kwargs.get("dlo_use_allgather", True),
            "dlo_resident_layers": kwargs.get("dlo_resident_layers", 0),
            "enforce_eager": False if kwargs.get("enforce_eager") is None else kwargs.get("enforce_eager"),
            "diffusion_compile_granularity": (
                "regional"
                if kwargs.get("diffusion_compile_granularity") is None
                else kwargs["diffusion_compile_granularity"]
            ),
            "diffusion_compile_dynamic": (
                True if kwargs.get("diffusion_compile_dynamic") is None else kwargs["diffusion_compile_dynamic"]
            ),
            "fa_deterministic": bool(kwargs.get("fa_deterministic", False)),
            "boundary_ratio": kwargs.get("boundary_ratio", None),
            "flow_shift": kwargs.get("flow_shift", None),
            "diffusion_load_format": kwargs.get("diffusion_load_format", "default"),
            "custom_pipeline_args": kwargs.get("custom_pipeline_args", None),
            "worker_extension_cls": kwargs.get("worker_extension_cls", None),
            "trust_remote_code": (False if kwargs.get("trust_remote_code") is None else kwargs["trust_remote_code"]),
            "distributed_executor_backend": (
                "mp" if kwargs.get("distributed_executor_backend") is None else kwargs["distributed_executor_backend"]
            ),
            "enable_sleep_mode": kwargs.get("enable_sleep_mode", False),
            "enable_prompt_embed_cache": kwargs.get("enable_prompt_embed_cache", False),
            "prompt_embed_cache_size": kwargs.get("prompt_embed_cache_size", 32),
            "enable_multithread_weight_load": kwargs.get("enable_multithread_weight_load", True),
            "num_weight_load_threads": kwargs.get("num_weight_load_threads", 4),
            "quantization": kwargs.get("quantization", None),
            "quantization_config": kwargs.get("quantization_config", None),
            "diffusion_kv_cache_dtype": kwargs.get("diffusion_kv_cache_dtype", None),
            "diffusion_kv_cache_skip_steps": kwargs.get("diffusion_kv_cache_skip_steps", None),
            "diffusion_kv_cache_skip_layers": kwargs.get("diffusion_kv_cache_skip_layers", None),
            **({"diffusion_attention_config": attention_config} if attention_config is not None else {}),
            "force_cutlass_fp8": bool(kwargs.get("force_cutlass_fp8", False)),
            "enable_diffusion_pipeline_profiler": kwargs.get("enable_diffusion_pipeline_profiler", False),
            "streaming_output": kwargs.get("diffusion_streaming_output", False),
            "enable_ar_profiler": kwargs.get("enable_ar_profiler", False),
            "extras": {
                "auxiliary_text_encoder": kwargs.get("auxiliary_text_encoder", None),
                "default_llama_model_id": kwargs.get("default_llama_model_id", "meta-llama/Meta-Llama-3.1-8B-Instruct"),
            },
            **(
                {
                    "profiler_config": asdict(kwargs["profiler_config"])
                    if hasattr(kwargs["profiler_config"], "__dataclass_fields__")
                    else kwargs["profiler_config"]
                }
                if kwargs.get("profiler_config") is not None
                else {}
            ),
        }
        # Only set dtype if it was already explicitly passed and normalized
        if "dtype" in normalized_kwargs:
            stage_engine_args["dtype"] = normalized_kwargs["dtype"]

        # New split fields for diffusers adapter kwargs.
        if kwargs.get("diffusers_load_kwargs") is not None:
            stage_engine_args["diffusers_load_kwargs"] = kwargs["diffusers_load_kwargs"]
        if kwargs.get("diffusers_call_kwargs") is not None:
            stage_engine_args["diffusers_call_kwargs"] = kwargs["diffusers_call_kwargs"]

        default_stage_cfg = [
            {
                "stage_id": 0,
                "stage_type": "diffusion",
                "runtime": {
                    "process": True,
                    "devices": devices,
                },
                "engine_args": stage_engine_args,
                "engine_input_source": [],
                "default_sampling_params": stage_default_sampling_params,
                "final_output": True,
                "final_output_type": final_output_type,
            }
        ]
        default_stage_cfg[0]["engine_args"]["model_stage"] = "diffusion"
        return default_stage_cfg

    @staticmethod
    def _strip_single_engine_args(kwargs: dict[str, Any]) -> dict[str, Any]:
        """Remove parent ``EngineArgs`` fields from *kwargs*.

        When ``stage_configs_path`` is set, per-stage engine args are defined
        in the YAML.  Top-level single-engine fields (``compilation_config``,
        ``tensor_parallel_size``, …) must not leak into per-stage configs via
        the ``base_engine_args`` merge in ``load_stage_configs_from_yaml`` —
        they can cause type errors (e.g. ``compilation_config`` as a JSON
        string rejected by ``VllmConfig``) or silently override YAML values.

        Logs a warning for any parent field whose value differs from the
        dataclass default, so users know their explicit overrides are ignored.
        See the module-level ``_PARENT_ARGS_*`` constants for the routing
        contracts this method enforces.
        """
        parent_fields: dict[str, dataclasses.Field] = {f.name: f for f in dataclasses.fields(EngineArgs)}
        result, overridden = strip_parent_engine_args(
            kwargs,
            parent_fields=parent_fields,
            keep_keys=_PARENT_ARGS_KEEP,
            strip_keys=_PARENT_ARGS_STRIP,
            no_warn_keys=_PARENT_ARGS_NO_WARN,
        )

        if overridden:
            logger.warning(
                "stage_configs_path is set — the following top-level engine "
                "args are ignored (per-stage YAML takes precedence): %s",
                ", ".join(sorted(overridden)),
            )

        return result

    def _apply_strategy_lb_policy(self, derived: str | None, kwargs: dict[str, Any]) -> None:
        """Apply a strategy-derived ``omni_lb_policy`` to the engine.

        Precedence: an explicit ``--omni-lb-policy`` always wins. ``"random"`` is
        the engine default and is treated as "unset" (indistinguishable from no
        flag), so a strategy value overrides it. If the user explicitly passed a
        non-default policy that conflicts with the strategy-derived one, raise so
        the mismatch is not silently ignored.
        """
        if not derived:
            return
        explicit = kwargs.get("omni_lb_policy")
        user_set = explicit is not None and str(explicit) != "random"
        if user_set:
            if str(explicit) != str(derived):
                raise ValueError(
                    f"Conflicting load-balancer policy: --omni-lb-policy={explicit!r} was given "
                    f"but the composable-parallel strategy derived omni_lb_policy={derived!r}. "
                    "Drop --omni-lb-policy to use the strategy value, or make them match."
                )
            return
        if self._omni_lb_policy != str(derived):
            logger.info(
                "[composable_parallel] applying strategy-derived omni_lb_policy=%r (was %r).",
                derived,
                self._omni_lb_policy,
            )
            self._omni_lb_policy = str(derived)

    def _resolve_stage_configs(
        self,
        model: str,
        kwargs: dict[str, Any],
        *,
        trust_remote_code: bool | None,
    ) -> tuple[str, list[Any]]:
        """Resolve stage configs and inject defaults shared by orchestrator/headless."""

        stage_configs_path = kwargs.get("stage_configs_path", None)
        deploy_config_path = kwargs.pop("deploy_config", None)
        strategy_config_path = kwargs.pop("strategy_config", None)
        stage_overrides_json = kwargs.pop("stage_overrides", None)
        explicit_stage_configs = kwargs.pop("stage_configs", None)
        if explicit_stage_configs is not None:
            logger.warning(
                "`stage_configs` is not part of the public API. "
                "Ignoring it and resolving stages from stage_configs_path/model factory."
            )

        if stage_configs_path is not None:
            base_kwargs = self._strip_single_engine_args(kwargs)
        else:
            base_kwargs = kwargs

        # Parse --stage-overrides JSON string if provided
        stage_overrides = parse_stage_overrides(stage_overrides_json)

        config_path, stage_configs, strategy_lb_policy = load_and_resolve_stage_configs(
            model,
            stage_configs_path,
            base_kwargs,
            trust_remote_code=trust_remote_code,
            default_stage_cfg_factory=lambda: self._create_default_diffusion_stage_cfg(kwargs),
            deploy_config_path=deploy_config_path,
            stage_overrides=stage_overrides,
            strategy_config_path=strategy_config_path,
        )

        # A strategy.yaml may derive a pipeline-wide load-balancer policy. It is
        # an orchestrator-level knob (read once at construction), so apply it here
        # rather than as a per-stage config field.
        self._apply_strategy_lb_policy(strategy_lb_policy, kwargs)

        # Inject diffusion LoRA-related knobs from kwargs if not present in the stage config.
        for cfg in stage_configs:
            try:
                if not hasattr(cfg, "engine_args") or cfg.engine_args is None:
                    cfg.engine_args = OmegaConf.create({})
                global_sleep_mode = kwargs.get("enable_sleep_mode")
                if global_sleep_mode is not None:
                    if not hasattr(cfg.engine_args, "enable_sleep_mode") or cfg.engine_args.enable_sleep_mode is None:
                        cfg.engine_args.enable_sleep_mode = global_sleep_mode
                if getattr(cfg, "stage_type", None) != "diffusion":
                    continue
                if not hasattr(cfg, "engine_args") or cfg.engine_args is None:
                    cfg.engine_args = OmegaConf.create({})
                additional_config = kwargs.get("additional_config")
                if additional_config is not None:
                    current_additional_config = getattr(cfg.engine_args, "additional_config", None)
                    if current_additional_config in (None, {}):
                        cfg.engine_args.additional_config = additional_config
                if kwargs.get("lora_path") is not None:
                    if not hasattr(cfg.engine_args, "lora_path") or cfg.engine_args.lora_path is None:
                        cfg.engine_args.lora_path = kwargs["lora_path"]
                lora_scale = kwargs.get("lora_scale")
                if lora_scale is None:
                    # Backwards compatibility for older callers.
                    lora_scale = kwargs.get("static_lora_scale")
                if lora_scale is not None:
                    if not hasattr(cfg.engine_args, "lora_scale") or cfg.engine_args.lora_scale is None:
                        cfg.engine_args.lora_scale = lora_scale
                if (
                    kwargs.get("diffusion_attention_config") is not None
                    or kwargs.get("diffusion_attention_backend") is not None
                ):
                    has_stage_attention = (
                        hasattr(cfg.engine_args, "diffusion_attention_config")
                        and cfg.engine_args.diffusion_attention_config is not None
                    )
                    if not has_stage_attention:
                        cfg.engine_args.diffusion_attention_config = parse_attention_config(
                            kwargs.get("diffusion_attention_config"),
                            attention_backend=kwargs.get("diffusion_attention_backend"),
                        )
                quantization_config = kwargs.get("diffusion_quantization_config") or kwargs.get("quantization_config")
                if quantization_config is not None:
                    if (
                        not hasattr(cfg.engine_args, "quantization_config")
                        or cfg.engine_args.quantization_config is None
                    ):
                        cfg.engine_args.quantization_config = quantization_config
                # Inject profiler flags for diffusion stages
                for profiler_key in (
                    "enable_diffusion_pipeline_profiler",
                    "enable_ar_profiler",
                ):
                    val = kwargs.get(profiler_key)
                    if val:
                        if not hasattr(cfg.engine_args, profiler_key) or not getattr(
                            cfg.engine_args, profiler_key, False
                        ):
                            setattr(cfg.engine_args, profiler_key, val)
                quantization = kwargs.get("quantization")
                if quantization is not None:
                    if not hasattr(cfg.engine_args, "quantization") or cfg.engine_args.quantization is None:
                        cfg.engine_args.quantization = quantization
                diffusion_kv_cache_dtype = kwargs.get("diffusion_kv_cache_dtype")
                if diffusion_kv_cache_dtype is not None:
                    if (
                        not hasattr(cfg.engine_args, "diffusion_kv_cache_dtype")
                        or cfg.engine_args.diffusion_kv_cache_dtype is None
                    ):
                        cfg.engine_args.diffusion_kv_cache_dtype = diffusion_kv_cache_dtype
                diffusion_kv_cache_skip_steps = kwargs.get("diffusion_kv_cache_skip_steps")
                if diffusion_kv_cache_skip_steps is not None:
                    if (
                        not hasattr(cfg.engine_args, "diffusion_kv_cache_skip_steps")
                        or cfg.engine_args.diffusion_kv_cache_skip_steps is None
                    ):
                        cfg.engine_args.diffusion_kv_cache_skip_steps = diffusion_kv_cache_skip_steps
                diffusion_kv_cache_skip_layers = kwargs.get("diffusion_kv_cache_skip_layers")
                if diffusion_kv_cache_skip_layers is not None:
                    if (
                        not hasattr(cfg.engine_args, "diffusion_kv_cache_skip_layers")
                        or cfg.engine_args.diffusion_kv_cache_skip_layers is None
                    ):
                        cfg.engine_args.diffusion_kv_cache_skip_layers = diffusion_kv_cache_skip_layers
            except Exception as e:
                logger.warning("Failed to inject LoRA config for stage: %s", e)

        return config_path, stage_configs

    # ==================== Public API ====================

    def add_request(
        self,
        request_id: str,
        prompt: EngineCoreRequest | PromptType,
        prompt_text: str | None = None,
        sampling_params_list: Sequence[Any] | None = None,
        final_stage_id: int = 0,
        final_output_stage_ids: Sequence[int] | None = None,
        arrival_time: float | None = None,
        lora_request: Any = 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,
        *,
        resumable: bool = False,
    ) -> None:
        """Process stage-0 input locally, then send to the Orchestrator.

        Input processing and output
        processor registration happen here in the caller's thread, avoiding
        a queue + coroutine-switch round-trip.  The Orchestrator receives a
        ready-to-submit OmniEngineCoreRequest.
        """
        msg = self._build_add_request_message(
            request_id=request_id,
            prompt=prompt,
            prompt_text=prompt_text,
            sampling_params_list=sampling_params_list,
            final_stage_id=final_stage_id,
            final_output_stage_ids=final_output_stage_ids,
            arrival_time=arrival_time,
            lora_request=lora_request,
            tokenization_kwargs=tokenization_kwargs,
            trace_headers=trace_headers,
            priority=priority,
            data_parallel_rank=data_parallel_rank,
            reasoning_ended=reasoning_ended,
            resumable=resumable,
        )
        self.request_queue.sync_q.put(msg)

        # CFG companion expansion: create and enqueue companion requests
        # so the AR stage also generates their KV caches.
        if self.prompt_expand_func is not None and final_stage_id > 0:
            original_prompt = msg.original_prompt
            effective_spl = msg.sampling_params_list
            stage0_params = effective_spl[0] if effective_spl else None
            if stage0_params is not None:
                self._enqueue_cfg_companions(request_id, original_prompt, stage0_params, effective_spl)

    async def add_request_async(
        self,
        request_id: str,
        prompt: EngineCoreRequest | PromptType,
        prompt_text: str | None = None,
        sampling_params_list: Sequence[Any] | None = None,
        final_stage_id: int = 0,
        final_output_stage_ids: Sequence[int] | None = None,
        arrival_time: float | None = None,
        lora_request: Any = 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,
        *,
        resumable: bool = False,
    ) -> None:
        """Async add_request API."""
        self.add_request(
            request_id=request_id,
            prompt=prompt,
            prompt_text=prompt_text,
            sampling_params_list=sampling_params_list,
            final_stage_id=final_stage_id,
            final_output_stage_ids=final_output_stage_ids,
            arrival_time=arrival_time,
            lora_request=lora_request,
            tokenization_kwargs=tokenization_kwargs,
            trace_headers=trace_headers,
            priority=priority,
            data_parallel_rank=data_parallel_rank,
            reasoning_ended=reasoning_ended,
            resumable=resumable,
        )

    def add_streaming_update(
        self,
        request_id: str,
        prompt: EngineCoreRequest | PromptType,
        prompt_text: str | None = None,
        sampling_params_list: Sequence[Any] | None = None,
        final_stage_id: int = 0,
        final_output_stage_ids: Sequence[int] | None = None,
        arrival_time: float | None = None,
        lora_request: Any = None,
        *,
        resumable: bool = True,
    ) -> None:
        """Send an incremental streaming update for an existing request."""
        msg = self._build_add_request_message(
            request_id=request_id,
            prompt=prompt,
            prompt_text=prompt_text,
            sampling_params_list=sampling_params_list,
            final_stage_id=final_stage_id,
            final_output_stage_ids=final_output_stage_ids,
            arrival_time=arrival_time,
            lora_request=lora_request,
            resumable=resumable,
            message_type="streaming_update",
        )
        self.request_queue.sync_q.put(msg)

    async def add_streaming_update_async(
        self,
        request_id: str,
        prompt: EngineCoreRequest | PromptType,
        prompt_text: str | None = None,
        sampling_params_list: Sequence[Any] | None = None,
        final_stage_id: int = 0,
        final_output_stage_ids: Sequence[int] | None = None,
        arrival_time: float | None = None,
        lora_request: Any = None,
        *,
        resumable: bool = True,
    ) -> None:
        """Async wrapper for add_streaming_update()."""
        self.add_streaming_update(
            request_id=request_id,
            prompt=prompt,
            prompt_text=prompt_text,
            sampling_params_list=sampling_params_list,
            final_stage_id=final_stage_id,
            final_output_stage_ids=final_output_stage_ids,
            arrival_time=arrival_time,
            lora_request=lora_request,
            resumable=resumable,
        )

    def open_duplex_session(
        self,
        session_id: str,
        *,
        session_mode: str = "duplex",
        capabilities: dict[str, object] | None = None,
        session_config: dict[str, object] | None = None,
        runtime_config: dict[str, object] | None = None,
        fence: DuplexFence,
        timeout: float | None = 10.0,
    ) -> dict[str, object]:
        """Open an engine-level duplex session."""
        return self._get_duplex_control_client().open(
            session_id,
            session_mode=session_mode,
            capabilities=capabilities,
            session_config=session_config,
            runtime_config=runtime_config,
            fence=fence,
            timeout=timeout,
        )

    async def open_duplex_session_async(
        self,
        session_id: str,
        *,
        session_mode: str = "duplex",
        capabilities: dict[str, object] | None = None,
        session_config: dict[str, object] | None = None,
        runtime_config: dict[str, object] | None = None,
        fence: DuplexFence,
        timeout: float | None = 10.0,
    ) -> dict[str, object]:
        """Async wrapper for opening an engine-level duplex session."""
        loop = asyncio.get_running_loop()
        return await loop.run_in_executor(
            None,
            lambda: self.open_duplex_session(
                session_id,
                session_mode=session_mode,
                capabilities=capabilities,
                session_config=session_config,
                runtime_config=runtime_config,
                fence=fence,
                timeout=timeout,
            ),
        )

    def append_duplex_input(
        self,
        session_id: str,
        *,
        mode: str,
        payload: object,
        operation_id: str | None = None,
        final: bool = False,
        expected_epoch: int | None = None,
        fence: DuplexFence,
        timeout: float | None = 10.0,
    ) -> dict[str, object]:
        """Append input to an engine-level duplex session."""
        return self._get_duplex_control_client().append(
            session_id,
            mode=mode,
            payload=payload,
            operation_id=operation_id,
            final=final,
            expected_epoch=expected_epoch,
            fence=fence,
            timeout=timeout,
        )

    async def append_duplex_input_async(
        self,
        session_id: str,
        *,
        mode: str,
        payload: object,
        operation_id: str | None = None,
        final: bool = False,
        expected_epoch: int | None = None,
        fence: DuplexFence,
        timeout: float | None = 10.0,
    ) -> dict[str, object]:
        """Async wrapper for appending duplex input."""
        loop = asyncio.get_running_loop()
        return await loop.run_in_executor(
            None,
            lambda: self.append_duplex_input(
                session_id,
                mode=mode,
                payload=payload,
                operation_id=operation_id,
                final=final,
                expected_epoch=expected_epoch,
                fence=fence,
                timeout=timeout,
            ),
        )

    def signal_duplex_turn(
        self,
        session_id: str,
        *,
        event: str,
        fence: DuplexFence,
        next_fence: DuplexFence | None = None,
        session_config: dict[str, object] | None = None,
        runtime_config: dict[str, object] | None = None,
        timeout: float | None = 10.0,
    ) -> dict[str, object]:
        """Signal an engine-level duplex turn."""
        return self._get_duplex_control_client().signal(
            session_id,
            event=event,
            fence=fence,
            next_fence=next_fence,
            session_config=session_config,
            runtime_config=runtime_config,
            timeout=timeout,
        )

    async def signal_duplex_turn_async(
        self,
        session_id: str,
        *,
        event: str,
        fence: DuplexFence,
        next_fence: DuplexFence | None = None,
        session_config: dict[str, object] | None = None,
        runtime_config: dict[str, object] | None = None,
        timeout: float | None = 10.0,
    ) -> dict[str, object]:
        """Async wrapper for signaling a duplex turn."""
        loop = asyncio.get_running_loop()
        return await loop.run_in_executor(
            None,
            lambda: self.signal_duplex_turn(
                session_id,
                event=event,
                fence=fence,
                next_fence=next_fence,
                session_config=session_config,
                runtime_config=runtime_config,
                timeout=timeout,
            ),
        )

    def close_duplex_session(
        self,
        session_id: str,
        *,
        reason: str = "client_close",
        fence: DuplexFence,
        timeout: float | None = 10.0,
    ) -> dict[str, object]:
        """Close an engine-level duplex session."""
        return self._get_duplex_control_client().close(
            session_id,
            reason=reason,
            fence=fence,
            timeout=timeout,
        )

    def touch_duplex_session(
        self,
        session_id: str,
        *,
        fence: DuplexFence,
        activity: DuplexLeaseActivity,
        timeout: float | None = 10.0,
    ) -> dict[str, object]:
        return self._get_duplex_control_client().touch(
            session_id,
            fence=fence,
            activity=activity,
            timeout=timeout,
        )

    async def touch_duplex_session_async(
        self,
        session_id: str,
        *,
        fence: DuplexFence,
        activity: DuplexLeaseActivity,
        timeout: float | None = 10.0,
    ) -> dict[str, object]:
        loop = asyncio.get_running_loop()
        return await loop.run_in_executor(
            None,
            lambda: self.touch_duplex_session(
                session_id,
                fence=fence,
                activity=activity,
                timeout=timeout,
            ),
        )

    def resume_duplex_session(
        self,
        session_id: str,
        *,
        fence: DuplexFence,
        expected_lease_generation: int,
        timeout: float | None = 10.0,
    ) -> dict[str, object]:
        return self._get_duplex_control_client().resume(
            session_id,
            fence=fence,
            expected_lease_generation=expected_lease_generation,
            timeout=timeout,
        )

    async def resume_duplex_session_async(
        self,
        session_id: str,
        *,
        fence: DuplexFence,
        expected_lease_generation: int,
        timeout: float | None = 10.0,
    ) -> dict[str, object]:
        loop = asyncio.get_running_loop()
        return await loop.run_in_executor(
            None,
            lambda: self.resume_duplex_session(
                session_id,
                fence=fence,
                expected_lease_generation=expected_lease_generation,
                timeout=timeout,
            ),
        )

    def _get_duplex_control_client(self) -> DuplexControlClient:
        from vllm_omni.experimental.fullduplex.engine.duplex_control_client import DuplexControlClient

        client = getattr(self, "_duplex_control_client", None)
        if client is None:
            transport = getattr(self, "_correlated_rpc_client", None)
            if transport is None:
                raise RuntimeError("correlated RPC client is not initialized")
            client = DuplexControlClient(
                transport,
                control_id_factory=lambda: uuid.uuid4().hex,
            )
            self._duplex_control_client = client
        return client

    async def close_duplex_session_async(
        self,
        session_id: str,
        *,
        reason: str = "client_close",
        fence: DuplexFence,
        timeout: float | None = 10.0,
    ) -> dict[str, object]:
        """Async wrapper for closing an engine-level duplex session."""
        loop = asyncio.get_running_loop()
        return await loop.run_in_executor(
            None,
            lambda: self.close_duplex_session(session_id, reason=reason, fence=fence, timeout=timeout),
        )

    def try_get_output(self, timeout: float = 0.001) -> EngineQueueMessage | None:
        """Read one output message from the Orchestrator output queue."""
        try:
            return self.output_queue.sync_q.get(timeout=timeout)
        except queue.Empty:
            if not self.is_alive():
                raise RuntimeError("Orchestrator died unexpectedly. See logs above.")
            return None

    async def try_get_output_async(self) -> EngineQueueMessage | None:
        """Async read from the Orchestrator output queue."""
        try:
            return self.output_queue.sync_q.get_nowait()
        except queue.Empty:
            if not self.is_alive():
                raise RuntimeError("Orchestrator died unexpectedly. See logs above.")
            return None

    def get_stage_metadata(self, stage_id: int) -> StageRuntimeInfo:
        """Get cached metadata for a stage."""
        return self.stage_metadata[stage_id]

    def abort(self, request_ids: list[str]) -> None:
        """Send abort message to the Orchestrator."""
        if self.request_queue is None:
            raise RuntimeError("request_queue is not initialized")
        self.request_queue.sync_q.put(AbortRequestMessage(request_ids=request_ids))

    async def abort_async(self, request_ids: list[str]) -> None:
        """Async abort API."""
        self.abort(request_ids)

    def submit_interaction(
        self,
        request_id: str,
        interaction: OmniInteractionPrompt,
    ) -> None:
        """Send an interaction control message to the Orchestrator."""
        if self.request_queue is None:
            raise RuntimeError("request_queue is not initialized")

        self.request_queue.sync_q.put_nowait(
            InteractionMessage(
                request_id=request_id,
                interaction=interaction,
            )
        )

    async def submit_interaction_async(
        self,
        request_id: str,
        interaction: OmniInteractionPrompt,
    ) -> None:
        """Async interaction API."""
        self.submit_interaction(request_id, interaction)

    def collective_rpc(
        self,
        method: str,
        timeout: float | None = None,
        args: tuple[Any, ...] = (),
        kwargs: dict[str, Any] | None = None,
        stage_ids: list[int] | None = None,
    ) -> list[Any]:
        """Send a control RPC to the Orchestrator and wait for aggregated results.

        This uses a dedicated RPC output queue so control-plane messages do not
        race with the normal request output polling loop.
        """
        rpc_id = uuid.uuid4().hex
        msg = CollectiveRPCRequestMessage(
            rpc_id=rpc_id,
            method=method,
            timeout=timeout,
            args=tuple(args),
            kwargs=kwargs or {},
            stage_ids=stage_ids,
        )

        transport = self._correlated_rpc_client
        if transport is None:
            raise RuntimeError("correlated RPC client is not initialized")
        result_msg = transport.execute(
            ("collective", rpc_id),
            msg,
            timeout=timeout,
            timeout_message=f"collective_rpc timed out after {timeout} seconds",
            block_on_submit=True,
        )
        if not isinstance(result_msg, CollectiveRPCResultMessage):
            raise RuntimeError(f"unexpected collective RPC result type: {type(result_msg).__name__}")
        return list(result_msg.results)

    async def collective_rpc_async(
        self,
        method: str,
        timeout: float | None = None,
        args: tuple[Any, ...] = (),
        kwargs: dict[str, Any] | None = None,
        stage_ids: list[int] | None = None,
    ) -> list[Any]:
        """Async wrapper around collective_rpc()."""
        loop = asyncio.get_running_loop()
        return await loop.run_in_executor(
            None,
            lambda: self.collective_rpc(
                method=method,
                timeout=timeout,
                args=args,
                kwargs=kwargs,
                stage_ids=stage_ids,
            ),
        )

    def is_alive(self) -> bool:
        """Whether the orchestrator thread is alive."""
        return bool(self.orchestrator_thread.is_alive())

    def shutdown(self) -> None:
        """Send shutdown message and wait for the Orchestrator thread to exit."""
        if getattr(self, "_shutdown_called", False):
            return
        self._shutdown_called = True
        finalizer = getattr(self, "_weak_finalizer", None)
        if finalizer is not None and finalizer.alive:
            finalizer.detach()

        logger.info("[AsyncOmniEngine] Shutting down Orchestrator")
        request_queue_closed = False
        shutdown_enqueued = enqueue_orchestrator_shutdown(
            self.request_queue,
            timeout=SHUTDOWN_ENQUEUE_TIMEOUT_S,
        )
        if self.request_queue is not None and not shutdown_enqueued:
            logger.error(
                "[AsyncOmniEngine] Failed to enqueue orchestrator shutdown; "
                "closing the request queue to wake the request handler"
            )
            try:
                self.request_queue.close()
                request_queue_closed = True
            except Exception:
                logger.exception("[AsyncOmniEngine] Failed to close the request queue")

        if self._correlated_rpc_client is not None:
            try:
                self._correlated_rpc_client.close()
            except Exception:
                logger.exception("[AsyncOmniEngine] Failed to close correlated RPC client")

        orchestrator_stopped = False
        try:
            if self.is_alive():
                self.orchestrator_thread.join(timeout=SHUTDOWN_JOIN_TIMEOUT_S)
            orchestrator_stopped = not self.is_alive()
            if not orchestrator_stopped:
                logger.error(
                    "[AsyncOmniEngine] Orchestrator did not stop within %.1f seconds; continuing cleanup",
                    SHUTDOWN_JOIN_TIMEOUT_S,
                )
        except Exception:
            logger.exception("[AsyncOmniEngine] Failed to join Orchestrator thread")

        for q in (self.request_queue, self.output_queue, self.rpc_output_queue):
            try:
                if not (q is self.request_queue and request_queue_closed):
                    q.close()
            except Exception:
                pass

        if hasattr(self, "_runtime") and self._runtime is not None and orchestrator_stopped:
            try:
                self._runtime.shutdown()
            except Exception:
                logger.exception("[AsyncOmniEngine] Failed to shutdown StageRuntime")
        elif hasattr(self, "_runtime") and self._runtime is not None:
            logger.warning("[AsyncOmniEngine] Deferring StageRuntime shutdown until the Orchestrator exits")
            threading.Thread(
                target=shutdown_runtime_after_orchestrator,
                args=(self.orchestrator_thread, self._runtime),
                daemon=True,
                name="omni-stage-runtime-shutdown",
            ).start()

        # ── Release CuMem allocator memory pool ──────────────────────────────
        # When enable_sleep_mode is in use, the CuMem (CUDA Virtual Memory
        # Management) allocator holds model weights in a singleton memory pool
        # that lives in the parent process.  Killing the engine-core subprocess
        # does NOT release this pool — the weights stay resident on the GPU
        # and can cause CUDA OOM for subsequent engine instances (especially
        # large models like BAGEL-7B-MoT whose weights alone consume ~134 GiB).
        #
        # CuMemAllocator.sleep() is NOT idempotent — calling it on already-
        # slept entries causes CUDA_ERROR_INVALID_VALUE at cumem_allocator
        # cuMemRelease (double-free of the memory handle).  Use release_pools()
        # instead, which is the designed cleanup path: it drops MemPool refs
        # and lets the destructor/free path handle asleep entries correctly
        # (returns a null handle so the C extension skips unmap/release).
        try:
            from vllm.device_allocator.cumem import CuMemAllocator, cumem_available

            if cumem_available:
                allocator = CuMemAllocator.get_instance()
                allocator.release_pools()
                logger.debug("[AsyncOmniEngine] Released CuMem memory pool during shutdown")
        except Exception:
            pass

    def _try_shutdown(self, *args, **kwargs) -> None:
        try:
            self.shutdown()
        except Exception:
            logger.exception(*args, **kwargs)