""" 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 copy import dataclasses import json import os import queue import threading import time import uuid import weakref from collections.abc import Awaitable, Callable, Mapping, Sequence from contextlib import ExitStack from dataclasses import asdict, dataclass 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.stage_config import 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.diffusion.inline_stage_diffusion_client import InlineStageDiffusionClient from vllm_omni.diffusion.stage_diffusion_client import StageDiffusionClient from vllm_omni.diffusion.stage_diffusion_proc import ( complete_diffusion_handshake, spawn_diffusion_proc, ) from vllm_omni.distributed.omni_connectors.utils.initialization import ( resolve_omni_kv_config_for_stage, ) from vllm_omni.distributed.omni_coordinator import ( LoadBalancer, build_load_balancer_factory, ) from vllm_omni.engine import OmniEngineCoreRequest from vllm_omni.engine.messages import ( AbortRequestMessage, AddCompanionRequestMessage, CollectiveRPCRequestMessage, CollectiveRPCResultMessage, EngineQueueMessage, ErrorMessage, RegisterRemoteReplicaMessage, ShutdownRequestMessage, StageSubmissionMessage, ) from vllm_omni.engine.orchestrator import Orchestrator from vllm_omni.engine.output_modality import FinalOutputModalityType from vllm_omni.engine.serialization import ( deserialize_additional_information, serialize_additional_information, ) from vllm_omni.engine.stage_client import StageClient from vllm_omni.engine.stage_engine_core_client import StageEngineCoreClientBase from vllm_omni.engine.stage_engine_core_proc import ( complete_stage_handshake, spawn_stage_core, ) from vllm_omni.engine.stage_engine_startup import ( OmniMasterServer, connect_remote_engine_cores, launch_omni_core_engines, register_stage_with_omni_master, ) from vllm_omni.engine.stage_init_utils import ( LogicalStageInitPlan, ReplicaInitPlan, StageRemoteFactoryContext, _inject_inferred_kv_tp_topology, acquire_device_locks, acquire_diffusion_device_locks, build_diffusion_config, build_engine_args_dict, build_llm_stage_output_processor, build_stage0_input_processor, build_vllm_config, capture_stage_factory_contexts, compute_replica_layout, extract_stage_metadata, get_stage_connector_spec, initialize_diffusion_stage, inject_kv_stage_info, load_omni_transfer_config_for_model, prepare_engine_environment, release_device_locks, stage_runtime_setup, terminate_alive_proc, ) from vllm_omni.engine.stage_pool import StagePool, StagePoolClient from vllm_omni.entrypoints.pd_utils import PDDisaggregationMixin from vllm_omni.entrypoints.utils import ( inject_omni_kv_config, load_and_resolve_stage_configs, ) from vllm_omni.inputs.data import OmniSamplingParams from vllm_omni.platforms import current_omni_platform if TYPE_CHECKING: from vllm_omni.engine.arg_utils import OmniEngineArgs logger = init_logger(__name__) _STARTUP_POLL_INTERVAL_S = 1.0 @dataclass(frozen=True, slots=True) class StageRuntimeInfo: final_output: bool final_output_type: FinalOutputModalityType | None stage_type: str # ============================================================================ # 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"}) def _inject_global_id(target: Any, request_id: str) -> None: """Inject global_request_id into a prompt dict's additional_information.""" if isinstance(target, dict): if "additional_information" not in target: target["additional_information"] = {} if target["additional_information"] is None: target["additional_information"] = {} if isinstance(target["additional_information"], dict): target["additional_information"]["global_request_id"] = [str(request_id)] def _upgrade_to_omni_request( request: EngineCoreRequest, raw_prompt: Any, ) -> EngineCoreRequest: """Restore omni-only fields omitted by upstream InputProcessor.""" prompt_embeds = request.prompt_embeds additional_information = None if isinstance(raw_prompt, dict): if prompt_embeds is None: raw_prompt_embeds = raw_prompt.get("prompt_embeds") if isinstance(raw_prompt_embeds, torch.Tensor): prompt_embeds = raw_prompt_embeds additional_information = serialize_additional_information( raw_prompt.get("additional_information"), log_prefix="AsyncOmniEngine", ) if prompt_embeds is None and additional_information is None: return request return OmniEngineCoreRequest.from_request( request, prompt_embeds=prompt_embeds, additional_information=additional_information, ) def _apply_omni_final_stage_metadata( request: EngineCoreRequest, final_stage_id: int, ) -> EngineCoreRequest: """Tag EngineCoreRequest so OmniARScheduler can skip DiT KV when final_stage_id is 0.""" merged: dict[str, Any] = {} if isinstance(request, OmniEngineCoreRequest) and request.additional_information is not None: merged = deserialize_additional_information(request.additional_information) merged["omni_final_stage_id"] = final_stage_id payload = serialize_additional_information(merged) return OmniEngineCoreRequest.from_request( request, additional_information=payload, ) def _weak_shutdown_async_omni_engine( orchestrator_thread: threading.Thread | None, request_queue: janus.Queue[EngineQueueMessage] | None, output_queue: janus.Queue[EngineQueueMessage] | None, rpc_output_queue: janus.Queue[EngineQueueMessage] | None, ) -> None: """Best-effort orchestrator cleanup for GC finalization.""" try: if request_queue is not None: request_queue.sync_q.put_nowait(ShutdownRequestMessage()) except Exception: pass try: if orchestrator_thread is not None and orchestrator_thread.is_alive(): orchestrator_thread.join(timeout=10) except Exception: pass for q in (request_queue, output_queue, rpc_output_queue): try: if q is not None: q.close() except Exception: pass 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 """ def __init__( self, model: str, engine_args: OmniEngineArgs | None = None, stage_init_timeout: int = 300, init_timeout: int = 600, diffusion_batch_size: int = 1, single_stage_mode: bool = False, **kwargs: Any, ) -> None: self.model = model self.diffusion_batch_size = diffusion_batch_size startup_timeout = int(init_timeout) logger.info(f"[AsyncOmniEngine] Initializing with model {model}") # Merge tracked engine_args fields into kwargs; explicit kwargs take priority. if engine_args is not None: if not hasattr(engine_args, "_explicit_fields"): raise TypeError( "engine_args=OmniEngineArgs(...) is ambiguous under " "sentinel-default precedence. Use " "OmniEngineArgs.create(**explicit) or pass explicit kwargs " "directly." ) ea_dict = engine_args.explicit_kwargs() # Remove model since it is passed as a positional arg already. ea_dict.pop("model", None) kwargs = {**ea_dict, **kwargs} self.tokenizer: str | None = kwargs.get("tokenizer") # ------------------------------------------------------------------ # # 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") self._omni_master_server: OmniMasterServer | None = None # 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}") # Coordinator runtime (head-distributed only). self._coordinator_runtime: Any | None = None # Per-stage construction context, captured after _initialize_stages # and used by ``_build_remote_replica`` (the RemoteReplicaFactory # passed to Orchestrator) when a headless replica registers. self._stage_remote_factory_contexts: dict[int, StageRemoteFactoryContext] = {} 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, ) self.config_path, self.stage_configs = self._resolve_stage_configs(model, kwargs) self._validate_single_stage_mode_replica_constraints() 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.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() self.output_queue: janus.Queue[EngineQueueMessage] = janus.Queue() self.rpc_output_queue: janus.Queue[EngineQueueMessage] = janus.Queue() self._shutdown_called = False self._stage_init_executor: concurrent.futures.ThreadPoolExecutor | None = None self._weak_finalizer: weakref.finalize | None = None self._rpc_lock = threading.Lock() 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) # 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, ) logger.info(f"[AsyncOmniEngine] Orchestrator ready with {self.num_stages} stages") @staticmethod def _cleanup_launched_llm_resources( *, stage_id: int, proc: Any = None, engine_manager: Any = None, coordinator: Any = None, ) -> None: """Release launch-only LLM resources when client creation never completed.""" if proc is not None: try: terminate_alive_proc(proc) except Exception as cleanup_error: logger.warning( "[AsyncOmniEngine] Failed to terminate process for stage %s: %s", stage_id, cleanup_error, ) for resource, resource_name in ( (engine_manager, "engine manager"), (coordinator, "coordinator"), ): if resource is None: continue shutdown = getattr(resource, "shutdown", None) close = getattr(resource, "close", None) try: if callable(shutdown): shutdown() elif callable(close): close() except Exception as cleanup_error: logger.warning( "[AsyncOmniEngine] Failed to cleanup launched %s for stage %s: %s", resource_name, stage_id, cleanup_error, ) @staticmethod def _collect_initialized_clients_for_cleanup( stage_pools: Sequence[StagePool], initialized_clients_by_stage: Mapping[int, Sequence[StagePoolClient | None]], ) -> list[StageClient]: """Collect initialized clients exactly once for failure cleanup.""" collected: list[StageClient] = [] seen: set[int] = set() def _add_client(client: StageClient | None) -> None: if client is None: return client_id = id(client) if client_id in seen: return seen.add(client_id) collected.append(client) for pool in stage_pools: for client in getattr(pool, "clients", ()): _add_client(client) for clients in initialized_clients_by_stage.values(): for client in clients: _add_client(client) return collected @staticmethod def _shutdown_initialized_clients(clients: Sequence[StageClient]) -> None: """Best-effort shutdown for attached clients after init failure.""" for client in reversed(list(clients)): if client is None: continue try: client.shutdown() except Exception as cleanup_error: logger.warning( "[AsyncOmniEngine] Failed to shutdown initialized client after init failure: %s", cleanup_error, ) def _validate_single_stage_mode_replica_constraints(self) -> None: """Apply --omni-dp-size-local to the local stage's runtime.num_replicas. In the previous revision this method rejected LLM stages with ``num_replicas > 1``. The whole point of ``--omni-dp-size-local`` is to lift that restriction for the *local* stage, so the rejection is gone. We now use this hook to write ``--omni-dp-size-local`` onto the self-stage's runtime config so downstream code (``compute_replica_layout`` → ``_build_logical_stage_init_plans``) sees a consistent view. """ if not self.single_stage_mode: return target_stage_id = self._single_stage_id_filter if target_stage_id is None: return for idx, stage_cfg in enumerate(self.stage_configs): stage_id = int(getattr(stage_cfg, "stage_id", idx)) runtime_cfg = getattr(stage_cfg, "runtime", None) if runtime_cfg is None: continue if stage_id == target_stage_id: # Self stage: take --omni-dp-size-local from this process. try: runtime_cfg.num_replicas = self._omni_dp_size_local except Exception: if hasattr(runtime_cfg, "__setitem__"): runtime_cfg["num_replicas"] = self._omni_dp_size_local # Other stages keep their config-declared num_replicas; in # head-distributed mode they will be launched as ``launch_mode # == "remote"`` with the configured count. def _build_logical_stage_init_plans( self, omni_transfer_config: Any, replicas_per_stage: Sequence[int], replica_devices_map: Mapping[int, Sequence[str]], ) -> tuple[list[LogicalStageInitPlan], Any]: """Build startup plans for every logical stage and replica.""" prompt_expand_func = None stage_plans: list[LogicalStageInitPlan] = [] for stage_idx, stage_cfg in enumerate(self.stage_configs): base_metadata = extract_stage_metadata(stage_cfg) configured_stage_id = base_metadata.stage_id if base_metadata.prompt_expand_func is not None: prompt_expand_func = base_metadata.prompt_expand_func stage_connector_spec = get_stage_connector_spec( omni_transfer_config=omni_transfer_config, stage_id=configured_stage_id, async_chunk=self.async_chunk, ) omni_kv_connector = resolve_omni_kv_config_for_stage(omni_transfer_config, configured_stage_id) num_replicas = replicas_per_stage[stage_idx] launch_mode = "local" if ( self.single_stage_mode and self._single_stage_id_filter is not None and configured_stage_id != self._single_stage_id_filter ): launch_mode = "remote" replicas: list[ReplicaInitPlan] = [] stage_vllm_config = None executor_class = None if base_metadata.stage_type != "diffusion": engine_args_dict = build_engine_args_dict( stage_cfg, self.model, stage_connector_spec=stage_connector_spec, cli_tokenizer=getattr(self, "tokenizer", None), ) omni_conn_cfg, omni_from, omni_to = omni_kv_connector if omni_conn_cfg: omni_kv = engine_args_dict.get("omni_kv_config") or {} if not isinstance(omni_kv, dict): omni_kv = dict(omni_kv) omni_kv["connector_config"] = omni_conn_cfg omni_kv["omni_from_stage"] = omni_from omni_kv["omni_to_stage"] = omni_to omni_kv.setdefault("stage_id", configured_stage_id) engine_args_dict["omni_kv_config"] = omni_kv if self.stage_configs: _inject_inferred_kv_tp_topology( engine_args_dict.get("omni_kv_config"), configured_stage_id, self.stage_configs, ) stage_vllm_config, executor_class = build_vllm_config( stage_cfg, self.model, stage_connector_spec=stage_connector_spec, engine_args_dict=engine_args_dict, ) for replica_id in range(num_replicas): replica_cfg = copy.deepcopy(stage_cfg) if replica_id > 0 else stage_cfg if stage_idx in replica_devices_map: replica_cfg.runtime.devices = replica_devices_map[stage_idx][replica_id] replica_metadata = extract_stage_metadata(replica_cfg) replica_metadata.replica_id = replica_id # In single_stage_mode the head only owns its self stage's # replicas; the remote-stage metadata exists only so the # orchestrator can route requests through StagePool. Wiping # ``runtime_cfg`` there makes sense (we don't manage those # devices). For the *self stage* we MUST keep the # per-replica runtime so ``setup_stage_devices`` can apply # the device split from ``compute_replica_layout`` — # otherwise every replica inherits the parent's full # CUDA_VISIBLE_DEVICES and stacks on cuda:0 (OOM with any # model whose footprint exceeds ~1/(2N) of the card). if launch_mode == "remote" and replica_metadata.stage_type != "diffusion": replica_metadata.runtime_cfg = None replicas.append( ReplicaInitPlan( replica_id=replica_id, num_replicas=num_replicas, launch_mode=launch_mode, stage_cfg=replica_cfg, metadata=replica_metadata, stage_connector_spec=stage_connector_spec, omni_kv_connector=omni_kv_connector, stage_vllm_config=stage_vllm_config, executor_class=executor_class, ) ) stage_plans.append( LogicalStageInitPlan( stage_idx=stage_idx, configured_stage_id=configured_stage_id, replicas=replicas, ) ) return stage_plans, prompt_expand_func def _start_omni_master_server(self, stage_plans: Sequence[LogicalStageInitPlan]) -> None: """Start OmniMasterServer for single-stage mode.""" if not self._omni_master_address or not self._omni_master_port: raise ValueError( "AsyncOmniEngine single_stage_mode requires both omni_master_address and omni_master_port to be set." ) all_stage_ids: list[int] = [] stage_replica_counts: dict[int, int] = {} # Slots that the head itself will register (launch_mode == "local") # — auto-assign on the master must not hand these out to remote # headless registrations even within the race window before the # head's own register_stage_with_omni_master call completes. head_local_replicas: dict[int, list[int]] = {} seen_stage_ids: set[int] = set() for plan in stage_plans: stage_id = plan.configured_stage_id if stage_id in seen_stage_ids: raise ValueError( f"Duplicate stage_id {stage_id!r} detected among configured stages; stage_ids must be unique." ) seen_stage_ids.add(stage_id) all_stage_ids.append(stage_id) stage_replica_counts[stage_id] = len(plan.replicas) local_rids = [rep.replica_id for rep in plan.replicas if rep.launch_mode == "local"] if local_rids: head_local_replicas[stage_id] = local_rids # Start the OmniCoordinator runtime first so its router address is # available to publish in every registration reply. from vllm_omni.distributed.omni_coordinator import OmniCoordinatorRuntime self._coordinator_runtime = OmniCoordinatorRuntime( host=self._omni_master_address, heartbeat_timeout=self._omni_heartbeat_timeout, ) self._omni_master_server = OmniMasterServer( master_address=self._omni_master_address, master_port=self._omni_master_port, stage_ids=all_stage_ids, stage_replica_counts=stage_replica_counts, coordinator_router_address=self._coordinator_runtime.router_address, on_register=self._dispatch_master_register, head_local_replicas=head_local_replicas, ) self._omni_master_server.start() logger.info( "[AsyncOmniEngine] OmniMasterServer started for stages %s", all_stage_ids, ) # ------------------------------------------------------------------ # Remote replica factory (head-side client construction) # ------------------------------------------------------------------ async def _build_remote_replica(self, stage_id: int, replica_id: int) -> Any: """Construct a head-side stage client for a newly-registered remote replica. Used by :class:`Orchestrator` as its ``remote_replica_factory``. The orchestrator awaits this from its own asyncio loop, so the client is created in the same loop that owns ZMQ sockets — no cross-thread setup is required. Raises if the stage is not known or if its construction context was not captured (e.g. the stage was empty at bring-up time). """ ctx = self._stage_remote_factory_contexts.get(stage_id) if ctx is None: raise RuntimeError( f"no factory context captured for stage {stage_id}; " f"known stages: {sorted(self._stage_remote_factory_contexts.keys())}" ) if self._omni_master_server is None: raise RuntimeError("OmniMasterServer is not running; cannot build remote replica") alloc = self._omni_master_server.get_allocation(stage_id, replica_id=replica_id) # Build a per-replica copy of the base metadata so ``replica_id`` # is correct (StageMetadata is a plain dataclass-like). metadata = copy.copy(ctx.base_metadata) try: metadata.replica_id = replica_id except Exception: # Best-effort: if the metadata object is frozen / unusual, the # downstream client will fall back to ``replica_id = 0``. pass if ctx.stage_type == "diffusion": client = StageDiffusionClient.from_addresses( metadata, request_address=alloc.input_bind_address, response_address=alloc.output_bind_address, batch_size=ctx.diffusion_batch_size, ) logger.info( "[AsyncOmniEngine] Built remote diffusion client for stage=%d replica=%d (req=%s resp=%s)", stage_id, replica_id, alloc.input_bind_address, alloc.output_bind_address, ) return client # LLM path if ctx.vllm_config is None or ctx.executor_class is None: raise RuntimeError(f"stage {stage_id} factory context is missing vllm_config / executor_class") # The headless's StageEngineCoreProc subprocess calls # vllm.v1.engine.core.startup_handshake at boot and blocks until the # head's handshake ROUTER answers — without this step it hits the # built-in 5-minute timeout and exits. The bootstrap (pre-allocated) # path runs `connect_remote_engine_cores` to perform that # handshake; dynamic attach must do the same, otherwise every # replica that comes in via `on_register` (auto-assigned or # explicit-id-beyond-pre-alloc) deadlocks. Run the blocking # handshake in a thread so the orchestrator loop stays responsive, # then build the async client on this loop where `make_async_mp_client` # expects to be invoked. master_server = self._omni_master_server ctx_vllm_config = ctx.vllm_config ctx_executor_class = ctx.executor_class def _run_handshake() -> Any: with connect_remote_engine_cores( vllm_config=ctx_vllm_config, omni_master_server=master_server, stage_id=stage_id, replica_id=replica_id, ) as remote_resources: _engine_manager, _coordinator, addresses, _ = remote_resources return _engine_manager, _coordinator, addresses engine_manager, coordinator, addresses = await asyncio.to_thread(_run_handshake) client_addresses: dict[str, str] = { "input_address": addresses.inputs[0], "output_address": addresses.outputs[0], } if addresses.frontend_stats_publish_address is not None: client_addresses["stats_update_address"] = addresses.frontend_stats_publish_address client = StageEngineCoreClientBase.make_async_mp_client( vllm_config=ctx_vllm_config, executor_class=ctx_executor_class, metadata=metadata, client_addresses=client_addresses, proc=None, engine_manager=engine_manager, coordinator=coordinator, ) logger.info( "[AsyncOmniEngine] Built remote LLM client for stage=%d replica=%d (input=%s)", stage_id, replica_id, client_addresses["input_address"], ) return client # ------------------------------------------------------------------ # OmniCoordinator on_register proxy # ------------------------------------------------------------------ def _dispatch_master_register(self, stage_id: int, replica_id: int, alloc: Any) -> None: """Forward a master-server registration to the orchestrator queue. Called on :class:`OmniMasterServer`'s ROUTER thread (not the orchestrator loop). Must return promptly. ``self.request_queue`` is initialized in :meth:`_initialize_janus_queues` (running inside the orchestrator thread) before the master server starts, so it is non-None here; ``janus.Queue.sync_q`` is thread-safe by construction. """ if self.request_queue is None: logger.warning( "[AsyncOmniEngine] request_queue not initialized; dropping register_remote_replica stage=%d replica=%d", stage_id, replica_id, ) return msg = RegisterRemoteReplicaMessage( stage_id=int(stage_id), replica_id=int(replica_id), ) try: self.request_queue.sync_q.put_nowait(msg) except Exception: logger.exception( "[AsyncOmniEngine] Failed to enqueue register_remote_replica for stage=%d replica=%d", stage_id, replica_id, ) def _initialize_llm_replica( self, plan: ReplicaInitPlan, stage_init_timeout: int, llm_stage_launch_lock: threading.Lock, ) -> StageEngineCoreClientBase: """Initialize one LLM replica end-to-end.""" proc = None engine_manager = None coordinator = None stage_client = None lock_fds: list[int] = [] device_control_env = current_omni_platform.device_control_env_var stage_cfg = plan.stage_cfg try: if plan.launch_mode == "remote": assert self._omni_master_server is not None raw_stage_cfg = self._omni_master_server.get_stage_config( plan.metadata.stage_id, timeout_s=stage_init_timeout, replica_id=plan.replica_id, ) if raw_stage_cfg is None: raise ValueError(f"Remote stage {plan.metadata.stage_id} registered without stage config") vllm_config = plan.stage_vllm_config executor_class = plan.executor_class assert vllm_config is not None assert executor_class is not None vllm_config.parallel_config.data_parallel_size_local = 0 launch_cm = connect_remote_engine_cores( vllm_config=vllm_config, omni_master_server=self._omni_master_server, stage_id=plan.metadata.stage_id, replica_id=plan.replica_id, ) logger.info( "[AsyncOmniEngine] Stage %s remote engine handshake started", plan.metadata.stage_id, ) with launch_cm as remote_resources: engine_manager, coordinator, addresses, _tensor_queue = remote_resources logger.info( "[AsyncOmniEngine] Stage %s remote engine startup completed", plan.metadata.stage_id, ) client_addresses: dict[str, str] = { "input_address": addresses.inputs[0], "output_address": addresses.outputs[0], } if addresses.frontend_stats_publish_address is not None: client_addresses["stats_update_address"] = addresses.frontend_stats_publish_address stage_client = StageEngineCoreClientBase.make_async_mp_client( vllm_config=vllm_config, executor_class=executor_class, metadata=plan.metadata, client_addresses=client_addresses, engine_manager=engine_manager, coordinator=coordinator, ) else: handshake_address = None with ExitStack() as launch_stack: with llm_stage_launch_lock: previous_visible_devices = os.environ.get(device_control_env) try: with stage_runtime_setup( plan.metadata.stage_id, plan.metadata.runtime_cfg, ): vllm_config = plan.stage_vllm_config executor_class = plan.executor_class assert vllm_config is not None assert executor_class is not None engine_args_dict = build_engine_args_dict( stage_cfg, self.model, stage_connector_spec=plan.stage_connector_spec, cli_tokenizer=getattr(self, "tokenizer", None), ) lock_fds = acquire_device_locks( plan.metadata.stage_id, engine_args_dict, stage_init_timeout, ) if self.single_stage_mode and self._omni_master_server is not None: coord_router_addr: str | None = ( self._coordinator_runtime.router_address if self._coordinator_runtime is not None else None ) engine_manager, coordinator, addresses = launch_stack.enter_context( launch_omni_core_engines( vllm_config=vllm_config, executor_class=executor_class, log_stats=False, omni_master_server=self._omni_master_server, stage_id=plan.metadata.stage_id, stage_config=stage_cfg, replica_id=plan.replica_id, omni_coordinator_address=coord_router_addr, ) ) else: addresses, proc, handshake_address = spawn_stage_core( vllm_config=vllm_config, executor_class=executor_class, log_stats=False, ) logger.info( "[AsyncOmniEngine] Stage %s engine launch started", plan.metadata.stage_id, ) finally: if previous_visible_devices is None: current_omni_platform.unset_device_control_env_var() else: current_omni_platform.set_device_control_env_var(previous_visible_devices) if self.single_stage_mode and self._omni_master_server is not None: launch_stack.close() else: assert proc is not None assert handshake_address is not None complete_stage_handshake(proc, handshake_address, addresses, vllm_config, stage_init_timeout) logger.info( "[AsyncOmniEngine] Stage %s engine startup completed", plan.metadata.stage_id, ) client_addresses: dict[str, str] = { "input_address": addresses.inputs[0], "output_address": addresses.outputs[0], } if addresses.frontend_stats_publish_address is not None: client_addresses["stats_update_address"] = addresses.frontend_stats_publish_address stage_client = StageEngineCoreClientBase.make_async_mp_client( vllm_config=vllm_config, executor_class=executor_class, metadata=plan.metadata, client_addresses=client_addresses, proc=proc, engine_manager=engine_manager, coordinator=coordinator, ) logger.info("[AsyncOmniEngine] Stage %s initialized", plan.metadata.stage_id) return stage_client except Exception: if stage_client is not None: try: stage_client.shutdown() except Exception as cleanup_error: logger.warning( "[AsyncOmniEngine] Failed to cleanup stage %s after attach failure: %s", plan.metadata.stage_id, cleanup_error, ) else: self._cleanup_launched_llm_resources( stage_id=plan.metadata.stage_id, proc=proc, engine_manager=engine_manager, coordinator=coordinator, ) raise finally: if lock_fds: release_device_locks(lock_fds) def _initialize_diffusion_replica( self, plan: ReplicaInitPlan, stage_init_timeout: int, stage_launch_lock: threading.Lock, ) -> StageDiffusionClient | InlineStageDiffusionClient: """Initialize one diffusion replica end-to-end.""" client = None proc = None lock_fds: list[int] = [] try: if plan.launch_mode == "remote": assert self._omni_master_server is not None remote_stage_cfg = OmegaConf.create( self._omni_master_server.get_stage_config( plan.metadata.stage_id, timeout_s=stage_init_timeout, replica_id=plan.replica_id, ) ) remote_metadata = extract_stage_metadata(remote_stage_cfg) addresses = self._omni_master_server.get_zmq_addresses( plan.metadata.stage_id, replica_id=plan.replica_id, ) logger.info( "[AsyncOmniEngine] Stage %s remote diffusion startup completed", plan.metadata.stage_id, ) client = StageDiffusionClient.from_addresses( remote_metadata, request_address=addresses.inputs[0], response_address=addresses.outputs[0], batch_size=self.diffusion_batch_size, ) else: device_control_env = current_omni_platform.device_control_env_var with stage_launch_lock: previous_visible_devices = os.environ.get(device_control_env) try: with stage_runtime_setup( plan.metadata.stage_id, plan.metadata.runtime_cfg, ): omni_conn_cfg, omni_from, omni_to = plan.omni_kv_connector if omni_conn_cfg: inject_omni_kv_config(plan.stage_cfg, omni_conn_cfg, omni_from, omni_to) inject_kv_stage_info(plan.stage_cfg, plan.metadata.stage_id, self.stage_configs) if self.single_stage_mode: assert self._omni_master_server is not None od_config = build_diffusion_config(self.model, plan.stage_cfg, plan.metadata) lock_fds = acquire_diffusion_device_locks( plan.metadata.stage_id, od_config, stage_init_timeout, ) handshake_address, request_address, response_address = register_stage_with_omni_master( omni_master_address=self._omni_master_server.address, omni_master_port=self._omni_master_server.port, omni_stage_id=plan.metadata.stage_id, omni_stage_config=plan.stage_cfg, return_addresses=True, replica_id=plan.replica_id, ) logger.info( "[AsyncOmniEngine] Stage %s diffusion registration completed", plan.metadata.stage_id, ) coord_router_addr: str | None = ( self._coordinator_runtime.router_address if self._coordinator_runtime is not None else None ) proc, _, _, _ = spawn_diffusion_proc( self.model, od_config, handshake_address=handshake_address, request_address=request_address, response_address=response_address, omni_coordinator_address=coord_router_addr, omni_stage_id=plan.metadata.stage_id, omni_replica_id=plan.replica_id, ) complete_diffusion_handshake(proc, handshake_address, stage_init_timeout) logger.info( "[AsyncOmniEngine] Stage %s diffusion startup completed", plan.metadata.stage_id, ) client = StageDiffusionClient.from_addresses( plan.metadata, request_address=request_address, response_address=response_address, proc=proc, batch_size=self.diffusion_batch_size, ) else: client = initialize_diffusion_stage( plan.metadata.stage_id, self.model, plan.stage_cfg, plan.metadata, stage_init_timeout=stage_init_timeout, batch_size=self.diffusion_batch_size, use_inline=self.num_stages == 1 and plan.num_replicas == 1, ) finally: if previous_visible_devices is None: current_omni_platform.unset_device_control_env_var() else: current_omni_platform.set_device_control_env_var(previous_visible_devices) logger.info( "[AsyncOmniEngine] Stage %s replica %s initialized (diffusion, batch_size=%d, devices=%s)", plan.metadata.stage_id, plan.replica_id, self.diffusion_batch_size, getattr(getattr(plan.stage_cfg, "runtime", None), "devices", "default"), ) return client except Exception: if proc is not None: terminate_alive_proc(proc) raise finally: if lock_fds: release_device_locks(lock_fds) def _initialize_replica( self, plan: ReplicaInitPlan, stage_init_timeout: int, stage_launch_lock: threading.Lock, ) -> StagePoolClient: """Initialize one replica, regardless of backend type.""" if plan.metadata.stage_type == "diffusion": return self._initialize_diffusion_replica(plan, stage_init_timeout, stage_launch_lock) return self._initialize_llm_replica(plan, stage_init_timeout, stage_launch_lock) def _initialize_stage_replicas( self, stage_plans: Sequence[LogicalStageInitPlan], stage_init_timeout: int, ) -> dict[int, list[StagePoolClient | None]]: """Initialize all stage replicas. Diffusion replicas are launched **inline on the orchestrator thread** (the long-lived daemon thread created in ``__init__``). Their ``mp.Process`` workers are therefore parented by a thread whose lifetime equals the engine's lifetime. Submitting diffusion init to a scoped ``ThreadPoolExecutor`` causes the clone-parent Python thread to be destroyed at the end of init, which under Ray's actor subreaper leads the spawned ``DiffusionWorker`` processes to be silently ``SIGKILL``ed (exitcode -9). See git blame on this method. LLM replicas keep using the parallel init executor. """ stage_launch_lock = threading.Lock() initialized_clients_by_stage: dict[int, list[StagePoolClient | None]] = { plan.stage_idx: [None] * len(plan.replicas) for plan in stage_plans } primary_exc: Exception | None = None # Partition replicas: diffusion runs inline on the caller's thread; # LLM replicas are submitted to a scoped ThreadPoolExecutor. diffusion_replicas: list[tuple[int, ReplicaInitPlan]] = [] llm_replicas: list[tuple[int, ReplicaInitPlan]] = [] for plan in stage_plans: for replica in plan.replicas: if replica.metadata.stage_type == "diffusion": diffusion_replicas.append((plan.stage_idx, replica)) else: llm_replicas.append((plan.stage_idx, replica)) # --- 1) Diffusion replicas: inline on the orchestrator thread. --- for stage_idx, replica in diffusion_replicas: try: initialized_clients_by_stage[stage_idx][replica.replica_id] = self._initialize_replica( replica, stage_init_timeout, stage_launch_lock, ) except Exception as exc: primary_exc = exc break # --- 2) LLM replicas: parallel init via a long-lived ThreadPoolExecutor. --- if primary_exc is None and llm_replicas: future_to_replica: dict[concurrent.futures.Future[StagePoolClient], tuple[int, int]] = {} if getattr(self, "_stage_init_executor", None) is None: self._stage_init_executor = concurrent.futures.ThreadPoolExecutor( max_workers=max(1, len(llm_replicas)), thread_name_prefix="stage-init", ) init_executor = self._stage_init_executor for stage_idx, replica in llm_replicas: future = init_executor.submit( self._initialize_replica, replica, stage_init_timeout, stage_launch_lock, ) future_to_replica[future] = (stage_idx, replica.replica_id) for future in concurrent.futures.as_completed(future_to_replica): stage_idx, replica_id = future_to_replica[future] try: initialized_clients_by_stage[stage_idx][replica_id] = future.result() except concurrent.futures.CancelledError: continue except Exception as exc: if primary_exc is None: primary_exc = exc for other_future in future_to_replica: if other_future is future: continue other_future.cancel() if primary_exc is not None: setattr(primary_exc, "_initialized_clients_by_stage", initialized_clients_by_stage) raise primary_exc return initialized_clients_by_stage def _assemble_stage_pools( self, stage_plans: Sequence[LogicalStageInitPlan], initialized_clients_by_stage: Mapping[int, Sequence[StagePoolClient | None]], ) -> list[StagePool]: """Assemble logical stage pools and update top-level stage metadata.""" stage_pools: list[StagePool] = [] default_sampling_params_list: list[OmniSamplingParams] = [] stage_metadata_list: list[StageRuntimeInfo] = [] for plan in stage_plans: replica_clients = initialized_clients_by_stage[plan.stage_idx] first_client = replica_clients[0] if replica_clients else None if first_client is None: raise RuntimeError(f"Stage {plan.stage_idx} initialization completed with a missing client") clients: list[StagePoolClient] = [client for client in replica_clients if client is not None] stage_vllm_config = None output_processor = None if plan.replicas[0].metadata.stage_type != "diffusion": stage_vllm_config = plan.replicas[0].stage_vllm_config assert stage_vllm_config is not None output_processor = build_llm_stage_output_processor(plan, stage_vllm_config) stage_pools.append( StagePool( plan.stage_idx, clients, output_processor=output_processor, stage_vllm_config=stage_vllm_config, ) ) default_sampling_params_list.append(first_client.default_sampling_params) stage_metadata_list.append( StageRuntimeInfo( final_output=first_client.final_output, final_output_type=first_client.final_output_type, stage_type=first_client.stage_type, ) ) self.default_sampling_params_list = list(default_sampling_params_list) self.stage_metadata = list(stage_metadata_list) return stage_pools def _initialize_stages(self, stage_init_timeout: int) -> None: """Initialize stage clients/processors in orchestrator thread and assign to self. Phases: 1. Compute replica layout (counts + device splits). 2. Build per-stage/per-replica startup plans. 3. Initialize all replicas in parallel via backend-specific launchers. 4. Build logical StagePools and finalize runtime metadata. TODO(stage-pool): move per-stage launch + attach logic into a StagePool.build_from_config() classmethod so this method only iterates stage_configs, collects pools, and finalizes metadata. """ num_stages = len(self.stage_configs) self.num_stages = num_stages self._validate_single_stage_mode_replica_constraints() replicas_per_stage, replica_devices_map = compute_replica_layout(self.stage_configs) prepare_engine_environment() omni_transfer_config = load_omni_transfer_config_for_model(self.model, self.config_path) stage_plans, prompt_expand_func = self._build_logical_stage_init_plans( omni_transfer_config, replicas_per_stage, replica_devices_map, ) # Capture per-stage context now (before _start_omni_master_server) # so the on_register proxy can build head-side clients for # registrations that arrive immediately after the server starts. if self.single_stage_mode: self._stage_remote_factory_contexts = capture_stage_factory_contexts( stage_plans, diffusion_batch_size=self.diffusion_batch_size ) self._start_omni_master_server(stage_plans) stage_pools: list[StagePool] = [] input_processor: InputProcessor | None = None initialized_clients_by_stage: dict[int, list[StagePoolClient | None]] = { plan.stage_idx: [None] * len(plan.replicas) for plan in stage_plans } try: initialized_clients_by_stage = self._initialize_stage_replicas(stage_plans, stage_init_timeout) if stage_plans and stage_plans[0].replicas[0].metadata.stage_type != "diffusion": stage0_vllm_config = stage_plans[0].replicas[0].stage_vllm_config assert stage0_vllm_config is not None input_processor = build_stage0_input_processor(stage0_vllm_config) stage_pools = self._assemble_stage_pools(stage_plans, initialized_clients_by_stage) except Exception as exc: initialized_clients_by_stage = getattr( exc, "_initialized_clients_by_stage", initialized_clients_by_stage, ) cleanup_clients = self._collect_initialized_clients_for_cleanup( stage_pools, initialized_clients_by_stage, ) logger.exception( "[AsyncOmniEngine] Stage initialization failed; shutting down %s initialized client(s)", len(cleanup_clients), ) self._shutdown_initialized_clients(cleanup_clients) if self._omni_master_server is not None: try: self._omni_master_server.stop() except Exception: logger.exception("[AsyncOmniEngine] Failed to stop OmniMasterServer during stage-init cleanup") if self._coordinator_runtime is not None: try: self._coordinator_runtime.close() except Exception: logger.exception( "[AsyncOmniEngine] Failed to close OmniCoordinatorRuntime during stage-init cleanup" ) self._coordinator_runtime = None raise self.stage_pools = stage_pools self.input_processor = input_processor self.prompt_expand_func = prompt_expand_func # Derive logical-stage views for external readers (entrypoints/async_omni.py). self.stage_clients = [cast(StageClient, pool.stage_client) for pool in self.stage_pools] 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] # TODO(Peiqi): Hack here supported_tasks: set[str] = set() if any(getattr(pool.stage_client, "is_comprehension", False) for pool in self.stage_pools): supported_tasks.add("generate") if any(m.final_output_type == "audio" for m 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() coordinator_pub_address: str | None = None load_balancer_factory: Callable[[], LoadBalancer] | None = None remote_replica_factory: Callable[[int, int], Awaitable[Any]] | None = None if self._coordinator_runtime is not None: coordinator_pub_address = self._coordinator_runtime.pub_address load_balancer_factory = build_load_balancer_factory(self._omni_lb_policy) remote_replica_factory = self._build_remote_replica 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, coordinator_pub_address=coordinator_pub_address, load_balancer_factory=load_balancer_factory, remote_replica_factory=remote_replica_factory, ) 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: 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, 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.""" 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, preprocess_ms=_preprocess_ms, 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 # 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_nowait( 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, } 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 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 enable_expert_parallel = normalized_kwargs.get("enable_expert_parallel") or False use_hsdp = normalized_kwargs.get("use_hsdp") or False hsdp_shard_size = normalized_kwargs.get("hsdp_shard_size") or -1 hsdp_replicate_size = normalized_kwargs.get("hsdp_replicate_size") or 1 if sequence_parallel_size is None: sequence_parallel_size = 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, ulysses_mode=ulysses_mode, cfg_parallel_size=cfg_parallel_size, vae_patch_parallel_size=vae_patch_parallel_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), "additional_config": kwargs.get("additional_config", None), "step_execution": kwargs.get("step_execution", False), "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), "enforce_eager": False if kwargs.get("enforce_eager") is None else kwargs.get("enforce_eager"), "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), "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), "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, "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 _resolve_stage_configs(self, model: str, kwargs: dict[str, Any]) -> 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) stage_overrides_json = kwargs.pop("stage_overrides", None) kwargs.pop("_cli_explicit_keys", 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 = None if stage_overrides_json: if isinstance(stage_overrides_json, str): try: stage_overrides = json.loads(stage_overrides_json) except json.JSONDecodeError as exc: raise ValueError( f"--stage-overrides is not valid JSON: {exc}. Got: {stage_overrides_json!r}" ) from exc else: stage_overrides = stage_overrides_json config_path, stage_configs = load_and_resolve_stage_configs( model, stage_configs_path, base_kwargs, default_stage_cfg_factory=lambda: self._create_default_diffusion_stage_cfg(kwargs), deploy_config_path=deploy_config_path, stage_overrides=stage_overrides, ) # 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("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, 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, 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_nowait(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, 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, 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, arrival_time: float | None = 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, arrival_time=arrival_time, resumable=resumable, message_type="streaming_update", ) self.request_queue.sync_q.put_nowait(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, arrival_time: float | None = 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, arrival_time=arrival_time, resumable=resumable, ) 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_nowait(AbortRequestMessage(request_ids=request_ids)) async def abort_async(self, request_ids: list[str]) -> None: """Async abort API.""" self.abort(request_ids) 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, ) with self._rpc_lock: self.request_queue.sync_q.put_nowait(msg) deadline = None if timeout is None else time.monotonic() + timeout while True: remaining = None if deadline is None else max(0.0, deadline - time.monotonic()) try: result_msg = self.rpc_output_queue.sync_q.get(timeout=remaining) except queue.Empty as exc: raise TimeoutError(f"collective_rpc timed out after {timeout} seconds") from exc if isinstance(result_msg, ErrorMessage): raise RuntimeError(result_msg.error) if not isinstance(result_msg, CollectiveRPCResultMessage): logger.warning( "[AsyncOmniEngine] Dropping unexpected rpc queue message type=%s", getattr(result_msg, "type", type(result_msg).__name__), ) continue if result_msg.rpc_id != rpc_id: logger.warning( "[AsyncOmniEngine] Dropping mismatched rpc result rpc_id=%s expected=%s", result_msg.rpc_id, rpc_id, ) continue 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") try: if self.request_queue is not None: self.request_queue.sync_q.put_nowait(ShutdownRequestMessage()) except Exception: pass if self.is_alive(): self.orchestrator_thread.join(timeout=10) if self.orchestrator_thread.is_alive(): logger.warning("[AsyncOmniEngine] Orchestrator thread did not exit in time") stage_init_executor = getattr(self, "_stage_init_executor", None) if stage_init_executor is not None: stage_init_executor.shutdown(wait=False, cancel_futures=True) self._stage_init_executor = None for q in (self.request_queue, self.output_queue, self.rpc_output_queue): try: q.close() except Exception: pass if self._omni_master_server is not None: try: self._omni_master_server.stop() except Exception: logger.exception("[AsyncOmniEngine] Failed to stop OmniMasterServer during shutdown") self._omni_master_server = None if self._coordinator_runtime is not None: try: self._coordinator_runtime.close() except Exception: logger.exception("[AsyncOmniEngine] Failed to close OmniCoordinatorRuntime during shutdown") self._coordinator_runtime = None def _try_shutdown(self, *args, **kwargs) -> None: try: self.shutdown() except Exception: logger.exception(*args, **kwargs)