""" Orchestrator for vLLM-Omni multi-stage runtime. Runs inside a background thread with its own asyncio event loop. Owns logical request progression across stage pools and handles stage-to-stage transfer logic. Distributed membership (replica attach/detach, hub monitoring) is handled by :class:`MembershipController`, which is injected optionally. """ from __future__ import annotations import asyncio import time as _time from dataclasses import dataclass, field from typing import Any import janus import torch from vllm.config import ModelConfig from vllm.logger import init_logger from vllm.outputs import CompletionOutput, RequestOutput from vllm.pooling_params import PoolingParams from vllm.sampling_params import SamplingParams from vllm.v1.engine import EngineCoreOutputs from vllm.v1.engine.exceptions import EngineDeadError from vllm.v1.metrics.stats import IterationStats from vllm_omni.engine import OmniEngineCoreRequest from vllm_omni.engine.cfg_companion_tracker import CfgCompanionTracker from vllm_omni.engine.membership_controller import MembershipController from vllm_omni.engine.messages import ( AbortRequestMessage, AddCompanionRequestMessage, CollectiveRPCRequestMessage, CollectiveRPCResultMessage, EngineQueueMessage, ErrorMessage, OutputMessage, RegisterRemoteReplicaMessage, ShutdownRequestMessage, StageMetricsMessage, StageSubmissionMessage, UnregisterRemoteReplicaMessage, ) from vllm_omni.engine.orchestrator_monitor import create_orch_monitor, replica_key from vllm_omni.engine.serialization import serialize_additional_information from vllm_omni.engine.stage_pool import StagePool from vllm_omni.metrics.prometheus import OmniRequestCounter from vllm_omni.metrics.stat_logger import OmniPrometheusStatLogger from vllm_omni.outputs import OmniRequestOutput logger = init_logger(__name__) def _build_terminal_empty_output( request_id: str, *, final_output_type: str | None, audio_sample_rate: int = 24000, ) -> RequestOutput: """Build a terminal empty output when no downstream stage input exists.""" completion = CompletionOutput( index=0, text="", token_ids=[], cumulative_logprob=None, logprobs=None, finish_reason="stop", stop_reason=None, ) if final_output_type == "audio": completion.multimodal_output = { "audio": torch.zeros((0,), dtype=torch.float32), "sr": audio_sample_rate, } return RequestOutput( request_id=request_id, prompt=None, prompt_token_ids=[], prompt_logprobs=None, outputs=[completion], finished=True, ) def _coerce_int_scalar(value: Any) -> int: if value is None: return 0 if isinstance(value, (list, tuple)): for item in value: coerced = _coerce_int_scalar(item) if coerced > 0: return coerced return 0 if hasattr(value, "item"): value = value.item() try: coerced = int(value) except (TypeError, ValueError): return 0 return coerced if coerced > 0 else 0 def _infer_stage_audio_sample_rate(stage_pool: StagePool, default: int = 24000) -> int: """Infer the final audio stage sample rate from stage metadata when possible.""" sample_rate_attrs = ("audio_sample_rate", "sample_rate", "sampling_rate", "output_sample_rate", "sr") stage_client = getattr(stage_pool, "stage_client", None) stage_config = getattr(stage_pool, "_stage_vllm_config", None) for source in (stage_client, stage_config): for attr in sample_rate_attrs: sample_rate = _coerce_int_scalar(getattr(source, attr, None)) if sample_rate > 0: return sample_rate return default def build_engine_core_request_from_tokens( request_id: str, prompt: dict[str, Any], params: SamplingParams | PoolingParams, arrival_time: float | None = None, model_config: ModelConfig | None = None, resumable: bool = False, mm_features: list | None = None, ) -> OmniEngineCoreRequest: """Build an OmniEngineCoreRequest directly from an OmniTokensPrompt.""" if arrival_time is None: arrival_time = _time.time() prompt_token_ids = prompt["prompt_token_ids"] sampling_params = None pooling_params = None if isinstance(params, SamplingParams): sampling_params = params.clone() if sampling_params.max_tokens is None and model_config is not None: sampling_params.max_tokens = model_config.max_model_len - len(prompt_token_ids) else: pooling_params = params.clone() prompt_embeds: torch.Tensor | None = prompt.get("prompt_embeds") additional_info_payload = serialize_additional_information( prompt.get("additional_information"), log_prefix=f"build_engine_core_request_from_tokens req={request_id}", ) return OmniEngineCoreRequest( request_id=request_id, prompt_token_ids=prompt_token_ids, mm_features=mm_features, sampling_params=sampling_params, pooling_params=pooling_params, arrival_time=arrival_time, lora_request=getattr(params, "lora_request", None), cache_salt=prompt.get("cache_salt"), data_parallel_rank=None, prompt_embeds=prompt_embeds, resumable=resumable, additional_information=additional_info_payload, ) @dataclass class OrchestratorRequestState: """Per-request bookkeeping inside the Orchestrator.""" request_id: str prompt: Any = None sampling_params_list: list[Any] = field(default_factory=list) final_stage_id: int = -1 final_output_stage_ids: set[int] = field(default_factory=set) finished_final_output_stage_ids: set[int] = field(default_factory=set) # Wall-clock timestamp when the client-facing engine request was accepted. request_timestamp: float = 0.0 # Metrics: timestamp when request was submitted to each stage. stage_submit_ts: dict[int, float] = field(default_factory=dict) mm_processor_kwargs: dict | None = None mm_features: list | None = None pd_prefill_multimodal_output: dict[str, Any] | None = None streaming: StreamingInputState = field(default_factory=lambda: StreamingInputState()) # Per-request pipeline timing accumulator (milliseconds) pipeline_timings: dict[str, float] = field(default_factory=dict) @dataclass class StreamingInputState: # Flag of streaming input request enabled: bool = False # Flag of segment of streaming input finished segment_finished: bool = False # Streaming update prompt length new_prompt_len_snapshot: int | None = None # Model/bridge-specific runtime states (e.g., thinker->talker) bridge_states: dict[str, Any] = field(default_factory=dict) class Orchestrator: """Runs inside a background thread's asyncio event loop.""" # Class-level defaults so tests that bypass __init__ via object.__new__ # don't AttributeError when transfer / counter emit paths access them. _running_counter: OmniRequestCounter | None = None _transfer_emitter: Any = None _stat_logger: OmniPrometheusStatLogger | None = None def __init__( self, request_async_queue: janus.AsyncQueue[EngineQueueMessage], output_async_queue: janus.AsyncQueue[dict[str, Any]], rpc_async_queue: janus.AsyncQueue[dict[str, Any]], stage_pools: list[StagePool], *, async_chunk: bool = False, pd_config: dict[str, Any] | None = None, membership_controller: MembershipController | None = None, running_counter: OmniRequestCounter | None = None, transfer_emitter: Any = None, log_stats: bool = False, enable_orch_monitor: bool = False, ) -> None: self.request_async_queue = request_async_queue self.output_async_queue = output_async_queue self.rpc_async_queue = rpc_async_queue self.async_chunk = bool(async_chunk) self.num_stages = len(stage_pools) self.stage_pools: list[StagePool] = stage_pools self._orch_monitor = create_orch_monitor( enabled=enable_orch_monitor, replica_sampler=self._sample_replica_metrics, ) for stage_id, pool in enumerate(self.stage_pools): for replica_id in pool.live_replica_ids(): self._orch_monitor.register_replica(stage_id, replica_id) # PD disaggregation state self._pd_pair: tuple[int, int] | None = None self._pd_bootstrap_addr: str | None = None self._pd_prefill_engine_id: str | None = None self._pd_kv_params: dict[str, Any] = {} if pd_config is not None: self._pd_pair = pd_config.get("pd_pair") self._pd_bootstrap_addr = pd_config.get("bootstrap_addr") self._pd_prefill_engine_id = pd_config.get("prefill_engine_id") self.request_states: dict[str, OrchestratorRequestState] = {} self._init_metrics_state(stage_pools, running_counter, transfer_emitter, log_stats=log_stats) self._cfg_tracker = CfgCompanionTracker() self._stage_input_processors: dict[int, Any] = {} self._shutdown_event = asyncio.Event() self._stages_shutdown = False self._fatal_error: str | None = None self._fatal_error_stage_id: int | None = None # Distributed membership (optional, injected by DistStageRuntime) self._membership = membership_controller def _init_metrics_state( self, stage_pools: list[StagePool], running_counter: OmniRequestCounter | None, transfer_emitter: Any, log_stats: bool = False, ) -> None: """Wire up all metric-related orchestrator state. Sets ``self._running_counter`` and ``self._transfer_emitter`` (both optional, used by request-add / forward paths), builds the ``(stage_id, replica_id) ↔ engine_idx`` lookup used at record() time, and best-effort constructs the ``OmniPrometheusStatLogger`` wrap that exposes ~37 upstream ``vllm:*`` families with per-(stage, replica) labels. Failure to build the wrap is logged and metrics are simply disabled — orchestrator construction continues so unit tests with a minimal ``vllm_config`` still pass. ``log_stats=False`` short-circuits the wrap entirely so the ~65 upstream ``vllm:*`` families are not registered in the Prometheus default registry at all. The per-step record() path already no-ops on ``scheduler_stats is None`` (which is what the upstream scheduler returns when its own log_stats is False), so this gate is mainly to keep the ``/metrics`` surface clean when the user did not request stats. """ self._running_counter = running_counter self._transfer_emitter = transfer_emitter # Flat engine_idx ↔ (stage, replica) maps. The reverse map is # consulted at record() time to translate the orchestrator's # (stage_id, replica_id) loop variables into an engine_idx the # underlying PrometheusStatLogger can address. stage_replica_map: dict[int, tuple[str, str]] = {} self._stage_replica_to_engine_idx: dict[tuple[int, int], int] = {} flat_idx = 0 for stage_id, pool in enumerate(stage_pools): for replica_id in range(pool.num_replicas): stage_replica_map[flat_idx] = (str(stage_id), str(replica_id)) self._stage_replica_to_engine_idx[(stage_id, replica_id)] = flat_idx flat_idx += 1 if not log_stats: self._stat_logger = None return vllm_config_for_stats = next( (p.stage_vllm_config for p in stage_pools if p.stage_vllm_config is not None), None, ) if vllm_config_for_stats is None: self._stat_logger = None return try: self._stat_logger = OmniPrometheusStatLogger( vllm_config=vllm_config_for_stats, stage_replica_map=stage_replica_map, ) except Exception: # Minimal vllm_config in unit-test contexts can lack fields the # upstream PrometheusStatLogger expects. Skip wrap rather than # break orchestrator construction. logger.exception("[Orchestrator] OmniPrometheusStatLogger init failed; metrics wrap disabled") self._stat_logger = None async def run(self) -> None: """Main entry point for the Orchestrator event loop.""" logger.info("[Orchestrator] Starting event loop") request_task = asyncio.create_task(self._request_handler(), name="orchestrator-request-handler") output_task = asyncio.create_task( self._orchestration_output_handler(), name="orchestrator-stage-output-handler", ) # Start membership watcher if distributed mode is active. membership_watcher: asyncio.Task[None] | None = None if self._membership is not None: self._membership.install_unregister_handlers( output_queue=self.output_async_queue, cleanup_callback=lambda ids: self._cleanup_request_ids(ids, abort=True), ) membership_watcher = self._membership.start() tasks = [request_task, output_task] if membership_watcher is not None: tasks.append(membership_watcher) try: await asyncio.gather(*tasks) except asyncio.CancelledError: raise except EngineDeadError as e: # EngineDeadError from _orchestration_loop means the diffusion # engine died. All pending requests were already notified and # _shutdown_event was already set by the loop's handler. # During teardown this is expected; the finally block handles # proper cleanup. Do not re-raise. logger.info("[Orchestrator] Engine dead during shutdown: %s", e) except Exception: logger.exception("[Orchestrator] Fatal error in orchestrator tasks") raise finally: self._shutdown_event.set() for task in tasks: if not task.done(): task.cancel() try: await asyncio.gather(*tasks, return_exceptions=True) except Exception: pass if self._fatal_error is not None: await self._drain_pending_requests_on_fatal() if self._membership is not None: await self._membership.drain_tasks(timeout=10.0) self._membership.shutdown() self._orch_monitor.flush() self._shutdown_stages() loop = asyncio.get_running_loop() pending = [t for t in asyncio.all_tasks(loop) if t is not asyncio.current_task() and not t.done()] for task in pending: task.cancel() if pending: await asyncio.gather(*pending, return_exceptions=True) # ---- Request handling ---- async def _request_handler(self) -> None: """Read messages from the main thread via request_async_queue.""" while True: msg = await self.request_async_queue.get() msg_type = msg.type if msg_type == "add_request": await self._handle_add_request(msg) elif msg_type == "streaming_update": await self._handle_streaming_update(msg) elif msg_type == "add_companion_request": await self._handle_add_companion(msg) elif msg_type == "abort": await self._handle_abort(msg) elif msg_type == "collective_rpc": await self._handle_collective_rpc(msg) elif isinstance(msg, RegisterRemoteReplicaMessage): if self._membership is not None: await self._membership.handle_register(msg.stage_id, msg.replica_id) self._orch_monitor.register_replica(msg.stage_id, msg.replica_id) elif isinstance(msg, UnregisterRemoteReplicaMessage): if self._membership is not None: await self._membership.handle_unregister(msg.stage_id, msg.input_addr) elif isinstance(msg, ShutdownRequestMessage): logger.info("[Orchestrator] Received shutdown signal") self._shutdown_event.set() # Pre-mark stage clients as shutting down to prevent # proc_monitor daemon threads from flagging normal # process exit as EngineDeadError during teardown. for pool in self.stage_pools: for client in pool.clients: if hasattr(client, "_shutting_down"): client._shutting_down = True # Stage teardown runs once in run()'s finally after the # orchestration loop observes _shutdown_event and exits. break else: logger.warning("[Orchestrator] Unknown message type: %s", msg_type) async def _handle_add_request(self, msg: StageSubmissionMessage) -> None: """Handle an add_request message from the main thread.""" stage_id = 0 request_id = msg.request_id prompt = msg.prompt original_prompt = msg.original_prompt sampling_params_list = msg.sampling_params_list if not sampling_params_list: raise ValueError(f"Missing sampling params for stage 0. Got {len(sampling_params_list)} stage params.") final_stage_id = msg.final_stage_id final_output_stage_ids = set(msg.final_output_stage_ids or [final_stage_id]) logger.debug( "[Orchestrator] _handle_add_request: stage=%s req=%s " "prompt_type=%s original_prompt_type=%s final_stage=%s " "num_sampling_params=%d", stage_id, request_id, type(prompt).__name__, type(original_prompt).__name__, final_stage_id, len(sampling_params_list), ) req_state = OrchestratorRequestState( request_id=request_id, prompt=original_prompt, sampling_params_list=sampling_params_list, final_stage_id=final_stage_id, final_output_stage_ids=final_output_stage_ids, request_timestamp=float(msg.request_timestamp or _time.time()), mm_features=getattr(prompt, "mm_features", None), ) self.request_states[request_id] = req_state if self._running_counter is not None: self._running_counter.increment() req_state.streaming.enabled = bool(getattr(prompt, "resumable", False)) req_state.stage_submit_ts[stage_id] = _time.time() enqueue_ts = msg.enqueue_ts if enqueue_ts > 0: req_state.pipeline_timings["queue_wait_ms"] = (_time.perf_counter() - enqueue_ts) * 1000.0 preprocess_ms = msg.preprocess_ms if preprocess_ms > 0: req_state.pipeline_timings["preprocess_ms"] = preprocess_ms await self.stage_pools[stage_id].submit_initial( request_id, req_state, prompt, prompt_text=msg.output_prompt_text, ) if self.async_chunk and stage_id == 0 and final_stage_id > 0: await self._prewarm_async_chunk_stages(request_id, prompt, req_state) async def _handle_streaming_update(self, msg: StageSubmissionMessage) -> None: """Handle a streaming_update message for an existing request.""" stage_id = 0 request_id = msg.request_id request = msg.prompt final_stage_id = msg.final_stage_id req_state = self.request_states.get(request_id) if req_state is None: logger.warning( "[Orchestrator] streaming_update for unknown req=%s, falling back to add_request", request_id, ) fallback_msg = StageSubmissionMessage( type="add_request", request_id=msg.request_id, prompt=msg.prompt, original_prompt=msg.original_prompt, output_prompt_text=msg.output_prompt_text, sampling_params_list=msg.sampling_params_list, final_stage_id=msg.final_stage_id, final_output_stage_ids=msg.final_output_stage_ids, preprocess_ms=msg.preprocess_ms, request_timestamp=msg.request_timestamp, enqueue_ts=msg.enqueue_ts, ) await self._handle_add_request(fallback_msg) return if msg.sampling_params_list: req_state.sampling_params_list = msg.sampling_params_list req_state.streaming.enabled = True req_state.stage_submit_ts[stage_id] = _time.time() await self.stage_pools[stage_id].submit_update( request_id, req_state, request, prompt_text=msg.output_prompt_text, ) if self.async_chunk and stage_id == 0 and final_stage_id > 0: await self._prewarm_async_chunk_stages(request_id, request, req_state) async def _handle_add_companion(self, msg: AddCompanionRequestMessage) -> None: """Handle an add_companion_request message: submit companion to stage 0.""" companion_id = msg.companion_id parent_id = msg.parent_id role = msg.role companion_prompt = msg.prompt sampling_params_list = msg.sampling_params_list parent_state = self.request_states.get(parent_id) if parent_state is None: logger.info( "[Orchestrator] Dropping CFG companion %s (role=%s): parent %s is no longer active", companion_id, role, parent_id, ) return self._cfg_tracker.register_companion(parent_id, role, companion_id) companion_state = OrchestratorRequestState( request_id=companion_id, prompt=companion_prompt, sampling_params_list=sampling_params_list, final_stage_id=0, final_output_stage_ids={0}, request_timestamp=parent_state.request_timestamp, ) self.request_states[companion_id] = companion_state companion_state.stage_submit_ts[0] = _time.time() companion_replica_id = await self.stage_pools[0].submit_initial( companion_id, companion_state, companion_prompt, prompt_text=msg.companion_prompt_text, affinity_request_id=parent_id, ) logger.info( "[Orchestrator] CFG companion submitted: %s (role=%s, parent=%s, stage-0 replica-%s)", companion_id, role, parent_id, companion_replica_id, ) async def _handle_abort(self, msg: AbortRequestMessage) -> None: """Handle an abort message from the main thread.""" request_ids = msg.request_ids await self._cleanup_request_ids( self._cfg_tracker.abort_parents(request_ids), abort=True, ) logger.info("[Orchestrator] Aborted request(s) %s", request_ids) async def _abort_request_ids(self, request_ids: list[str]) -> None: """Forward abort requests to all stage pools.""" if not request_ids: return for pool in self.stage_pools: await pool.abort_requests(request_ids) def _release_request_bindings(self, request_ids: list[str]) -> None: """Release all stage-local route bindings for the given request ids.""" for pool in self.stage_pools: pool.release_bindings(request_ids) async def _handle_collective_rpc(self, msg: CollectiveRPCRequestMessage) -> None: """Handle a control-plane RPC request from the main thread.""" rpc_id = msg.rpc_id method = msg.method timeout = msg.timeout args = tuple(msg.args) kwargs = dict(msg.kwargs or {}) requested_stage_ids = msg.stage_ids target_pools: list[StagePool] = [] if requested_stage_ids is None: target_pools.extend(self.stage_pools) else: for lid in requested_stage_ids: if not (0 <= lid < self.num_stages): logger.warning("[Orchestrator] collective_rpc: ignoring invalid stage_id %s", lid) continue target_pools.append(self.stage_pools[lid]) results: list[Any] = [] stage_ids: list[int] = [] for pool in target_pools: for replica_id in pool.live_replica_ids(): stage_result = await pool.collective_rpc( replica_id=replica_id, method=method, timeout=timeout, args=args, kwargs=kwargs, ) stage_ids.append(pool.stage_id) results.append(stage_result) await self.rpc_async_queue.put( CollectiveRPCResultMessage( rpc_id=rpc_id, method=method, stage_ids=stage_ids, results=results, ) ) # ---- Orchestration loop ---- def _sample_replica_metrics(self) -> dict[str, tuple[int, int]]: samples: dict[str, tuple[int, int]] = {} for stage_id, pool in enumerate(self.stage_pools): for replica_id in pool.live_replica_ids(): key = replica_key(stage_id, replica_id) samples[key] = pool.replica_monitor_sample(replica_id) return samples async def _orchestration_output_handler(self) -> None: """Poll all stages, handle transfers, send final outputs to main.""" try: await self._orchestration_loop() except asyncio.CancelledError: logger.debug("[Orchestrator] _orchestration_output_handler cancelled") return async def _orchestration_loop(self) -> None: """Poll stage pools and route logical outputs.""" while not self._shutdown_event.is_set(): idle = True for stage_id in range(self.num_stages): pool = self.stage_pools[stage_id] for replica_id in pool.live_replica_ids(): if self._shutdown_event.is_set(): return if pool.stage_type == "diffusion": output = pool.poll_diffusion_output(replica_id) if output is None: continue pool.record_output_timestamps([output]) await self._handle_processed_outputs(stage_id, replica_id, [output]) idle = False else: try: raw_outputs = await pool.poll_llm_raw_output(replica_id, timeout_s=0.001) if raw_outputs is None: continue await self._handle_kv_ready_raw_outputs(stage_id, raw_outputs) for eco in raw_outputs.outputs: req_state = self.request_states.get(getattr(eco, "request_id", None)) if req_state is None or not req_state.streaming.enabled: continue req_state.streaming.segment_finished = bool(getattr(eco, "is_segment_finished", False)) req_state.streaming.new_prompt_len_snapshot = getattr( eco, "new_prompt_len_snapshot", None, ) if req_state.streaming.enabled: await self._apply_raw_terminal_stage_finish(stage_id, eco, req_state) # OmniSchedulerMixin.make_stats() already throttles # per-scheduler at 1 Hz, so raw_outputs.scheduler_stats # being non-None means this replica passed its own gate. # A second global throttle here would drop stats for # other (stage, replica) pairs in the same 1s window. record_stats = self._stat_logger is not None and raw_outputs.scheduler_stats is not None iteration_stats = IterationStats() if record_stats else None raw_output = await pool.process_llm_raw_outputs( replica_id, raw_outputs, iteration_stats=iteration_stats, ) if record_stats: self._stat_logger.record( raw_outputs.scheduler_stats, iteration_stats, engine_idx=self._stage_replica_to_engine_idx[(stage_id, replica_id)], ) except asyncio.CancelledError: raise except EngineDeadError as e: logger.error( "[Orchestrator] Stage-%s is dead: %s", stage_id, e, ) # TODO: Fault handling is intentionally fail-stop at # the orchestrator level today. If one replica in a # logical stage dies, we promote it to `_fatal_error`, # notify requests already admitted to that stage, and # re-raise so `run()` shuts down all stages. This is # conservative but means a single unhealthy replica in # a multi-replica deployment can take down otherwise # healthy replicas in other stages. Revisit this when # adding per-replica fault isolation / eviction. self._fatal_error = str(e) self._fatal_error_stage_id = stage_id for req_id, req_state in list(self.request_states.items()): if stage_id in req_state.stage_submit_ts: await self.output_async_queue.put( ErrorMessage( error=str(e), fatal=True, request_id=req_id, stage_id=stage_id, ) ) self.request_states.pop(req_id, None) self._shutdown_event.set() raise except Exception: if self._shutdown_event.is_set(): return logger.exception( "[Orchestrator] Stage-%s replica-%s processing failed", stage_id, replica_id, ) raise await self._handle_processed_outputs(stage_id, replica_id, raw_output) idle = False self._orch_monitor.note_loop(idle=idle) if idle: await asyncio.sleep(0.001) else: await asyncio.sleep(0) async def _handle_processed_outputs(self, stage_id: int, replica_id: int, outputs: list[Any]) -> None: """Route processed stage outputs produced by one stage poll.""" pool = self.stage_pools[stage_id] for output in outputs: req_state = self.request_states.get(output.request_id) if req_state is None: logger.warning( "[Orchestrator] Dropping output for unknown req %s at stage-%s (known reqs: %s)", output.request_id, stage_id, list(self.request_states.keys()), ) continue if getattr(output, "error", None) is not None: await self._handle_stage_error(stage_id, output) continue stage_metrics = None if output.finished: stage_metrics = pool.build_stage_metrics( [output], submit_ts=req_state.stage_submit_ts.get(stage_id, _time.time()), request_timestamp=req_state.request_timestamp, replica_id=replica_id, sampling_params=req_state.sampling_params_list[stage_id], ) stage_metrics.pipeline_timings = dict(req_state.pipeline_timings) await self._route_output(stage_id, replica_id, output, req_state, stage_metrics) async def _handle_stage_error(self, stage_id: int, output: Any) -> None: """Emit a frontend-visible error and clean up request state.""" if self._cfg_tracker.is_companion(output.request_id): parent_id = self._cfg_tracker.get_parent_id(output.request_id) or output.request_id else: parent_id = output.request_id await self.output_async_queue.put( ErrorMessage( request_id=parent_id, stage_id=stage_id, error=output.error, status_code=getattr(output, "error_status_code", None), error_type=getattr(output, "error_type", None), ) ) await self._cleanup_request_ids( [parent_id, *self._cfg_tracker.cleanup_parent(parent_id)], abort=True, ) # ---- Shared helpers ---- async def _cleanup_request_ids(self, request_ids: list[str], *, abort: bool = False) -> None: """Release pool bindings and logical request state for the given ids.""" if not request_ids: return if abort: await self._abort_request_ids(request_ids) self._release_request_bindings(request_ids) for request_id in request_ids: self._pd_kv_params.pop(request_id, None) if self.request_states.pop(request_id, None) is not None and self._running_counter is not None: self._running_counter.decrement() async def _apply_raw_terminal_stage_finish( self, stage_id: int, eco: Any, req_state: OrchestratorRequestState, ) -> None: """Record session-level finish markers dropped by the streaming output processor. Streaming segment stops set ``is_segment_finished=True`` and are handled via processed outputs. Session termination (e.g. ``finish_requests`` after ``resumable=False``) emits a terminal ``finish_reason`` with ``is_segment_finished=False``, but vLLM's output processor may remove the request state before that EngineCoreOutput is processed. Only update ``finished_final_output_stage_ids`` here. Request cleanup stays in ``_route_output`` so downstream async-chunk stages can still deliver outputs after stage-0 session end. """ if getattr(eco, "finish_reason", None) is None: return if getattr(eco, "is_segment_finished", False): return final_output_stage_ids = req_state.final_output_stage_ids or {req_state.final_stage_id} if stage_id not in final_output_stage_ids: return req_state.finished_final_output_stage_ids.add(stage_id) def _maybe_clone_diffusion_params_for_cfg(self, request_id: str, params: Any) -> Any: """Attach CFG companion ids to diffusion sampling params when needed.""" companion_request_ids = self._cfg_tracker.get_companion_request_ids(request_id) if not companion_request_ids: return params import copy from vllm_omni.inputs.data import OmniDiffusionSamplingParams if not isinstance(params, OmniDiffusionSamplingParams): return params params = copy.deepcopy(params) params.cfg_kv_request_ids = companion_request_ids return params async def _route_output( self, stage_id: int, replica_id: int, output: Any, req_state: OrchestratorRequestState, stage_metrics: Any, ) -> None: """Route a processed output: send to frontend and/or forward.""" req_id = output.request_id finished = output.finished submit_ts = req_state.stage_submit_ts.get(stage_id) # CFG companion: stash output so parent can bundle [parent, *companions] # into source_outputs for the bridge (e.g. thinker2imagegen). if finished and self._cfg_tracker.is_companion(req_id): self._cfg_tracker.set_companion_output(req_id, output) await self._handle_cfg_companion_ready(req_id) await self._cleanup_request_ids([req_id]) return request_finished = False if finished and self.stage_pools[stage_id].final_output: req_state.finished_final_output_stage_ids.add(stage_id) final_output_stage_ids = req_state.final_output_stage_ids or {req_state.final_stage_id} request_finished = final_output_stage_ids.issubset(req_state.finished_final_output_stage_ids) if self.stage_pools[stage_id].final_output: await self.output_async_queue.put( OutputMessage( request_id=req_id, stage_id=stage_id, replica_id=replica_id, engine_outputs=output, metrics=stage_metrics, finished=request_finished, stage_submit_ts=submit_ts, ) ) elif stage_metrics is not None: await self.output_async_queue.put( StageMetricsMessage( request_id=req_id, stage_id=stage_id, replica_id=replica_id, metrics=stage_metrics, stage_submit_ts=submit_ts, ) ) if self._pd_pair is not None and finished and stage_id == self._pd_pair[0]: kv_params = getattr(output, "kv_transfer_params", None) if kv_params is not None: self._pd_kv_params[req_id] = kv_params if isinstance(kv_params, dict) else dict(kv_params) req_state.pd_prefill_multimodal_output = getattr(output, "multimodal_output", None) if ( (finished or (req_state.streaming.enabled and req_state.streaming.segment_finished)) and stage_id < req_state.final_stage_id and not self.async_chunk and (not self._next_stage_already_submitted(stage_id, req_state) or req_state.streaming.enabled) ): if ( finished and self._cfg_tracker.has_companions(req_id) and not self._cfg_tracker.all_companions_done(req_id) ): self._cfg_tracker.defer_parent(req_id, output, stage_id) else: await self._forward_to_next_stage( req_id, stage_id, output, req_state, src_replica_id=replica_id, is_streaming_session=req_state.streaming.enabled, is_final_update=False, ) if req_state.streaming.enabled and finished: # For streaming sessions, send the terminal (resumable=False) update only on a finish await self._forward_to_next_stage( req_id, stage_id, output, req_state, src_replica_id=replica_id, is_streaming_session=True, is_final_update=True, ) if request_finished: await self._cleanup_request_ids([req_id, *self._cfg_tracker.cleanup_parent(req_id)]) def _next_stage_already_submitted(self, stage_id: int, req_state: OrchestratorRequestState) -> bool: return (stage_id + 1) in req_state.stage_submit_ts def _get_stage_input_processor(self, stage_id: int) -> Any: processor = self._stage_input_processors.get(stage_id) if processor is None: from vllm_omni.engine.stage_init_utils import build_stage0_input_processor processor = build_stage0_input_processor(self.stage_pools[stage_id].stage_vllm_config) self._stage_input_processors[stage_id] = processor return processor def _upgrade_processed_stage_request(self, request: Any, raw_prompt: Any) -> Any: prompt_embeds = getattr(request, "prompt_embeds", None) 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="Orchestrator stage input", ) 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 _next_stage_input_is_tokens(self, next_input: Any) -> bool: return isinstance(next_input, dict) and "prompt_token_ids" in next_input def _build_next_stage_request( self, req_id: str, next_stage_id: int, next_input: Any, params: SamplingParams | PoolingParams, *, mm_features: list | None = None, resumable: bool = False, ) -> Any: next_pool = self.stage_pools[next_stage_id] if self._next_stage_input_is_tokens(next_input): request = build_engine_core_request_from_tokens( request_id=req_id, prompt=next_input, params=params, model_config=next_pool.stage_vllm_config.model_config, mm_features=mm_features, resumable=resumable, ) request.external_req_id = request.request_id return request processor = self._get_stage_input_processor(next_stage_id) request = processor.process_inputs( request_id=req_id, prompt=next_input, params=params, supported_tasks=("generate",), arrival_time=_time.time(), resumable=resumable, ) request = self._upgrade_processed_stage_request(request, next_input) request.external_req_id = req_id return request async def _handle_cfg_companion_ready(self, req_id: str) -> None: """Mark a CFG companion as done; if all companions are done, flush deferred parent.""" parent_id = self._cfg_tracker.on_companion_completed(req_id) if parent_id is None: return deferred = self._cfg_tracker.pop_pending_parent(parent_id) if deferred is None: return parent_state = self.request_states.get(parent_id) if parent_state is None: return stage_id = deferred["stage_id"] if (stage_id + 1) in parent_state.stage_submit_ts: return await self._forward_to_next_stage( parent_id, stage_id, deferred["engine_outputs"], parent_state, ) async def _handle_kv_ready_raw_outputs( self, stage_id: int, raw_outputs: EngineCoreOutputs, ) -> None: """Forward split requests once stage-0 KV is ready.""" if self.async_chunk: return for raw_output in raw_outputs.outputs: kv_params = getattr(raw_output, "kv_transfer_params", None) if not (isinstance(kv_params, dict) and kv_params.get("kv_ready")): continue req_id = raw_output.request_id req_state = self.request_states.get(req_id) if req_state is None: continue if self._cfg_tracker.is_companion(req_id): await self._handle_cfg_companion_ready(req_id) continue if stage_id >= req_state.final_stage_id: continue if (stage_id + 1) in req_state.stage_submit_ts: continue if self._cfg_tracker.has_companions(req_id) and not self._cfg_tracker.all_companions_done(req_id): self._cfg_tracker.defer_parent(req_id, raw_output, stage_id) else: await self._forward_to_next_stage(req_id, stage_id, raw_output, req_state) def _build_pd_decode_params(self, req_id: str, sp: Any) -> Any: """Build decode-side sampling params with KV transfer params for PD routing. Clones the sampling params and injects kv_transfer_params that tell the decode engine where to pull the KV cache from (prefill engine's bootstrap addr). """ sp = sp.clone() if sp.extra_args is None: sp.extra_args = {} # Get KV params captured from the prefill output (must include remote_request_id). kv_prefill_params = self._pd_kv_params.pop(req_id, None) if not kv_prefill_params or "remote_request_id" not in kv_prefill_params: raise RuntimeError( f"[Orchestrator][PD] Missing prefill kv_transfer_params.remote_request_id for req={req_id}" ) decode_kv_params: dict[str, Any] = { "transfer_id": f"xfer-{req_id}", } if self._pd_bootstrap_addr: decode_kv_params["remote_bootstrap_addr"] = self._pd_bootstrap_addr if self._pd_prefill_engine_id: decode_kv_params["remote_engine_id"] = self._pd_prefill_engine_id # Overlay params from prefill side (includes remote_request_id set by monkey patch). decode_kv_params.update(kv_prefill_params) # Ensure these flags are set correctly after any overlay. decode_kv_params["do_remote_prefill"] = True decode_kv_params["do_remote_decode"] = False if not decode_kv_params.get("transfer_id"): decode_kv_params["transfer_id"] = f"xfer-{req_id}" sp.extra_args["kv_transfer_params"] = decode_kv_params logger.debug( "[Orchestrator][PD] decode kv_transfer_params for req=%s: %s", req_id, decode_kv_params, ) return sp def _emit_tx_edge( self, *, from_stage: int, from_replica: int, to_stage: int, to_pool: StagePool, request_id: str, tx_ms: float, ) -> None: """Emit per-edge transfer_tx_s + transfer_size_bytes histograms. ``tx_ms`` is the orchestrator-side wall-clock spent in ``next_pool. submit_*`` (serialize + queue submit to the receiving worker). Best- effort size_bytes left at 0 — orchestrator doesn't have a cheap handle on the serialized payload size; a follow-up can plumb that from the connector adapter. """ if self._transfer_emitter is None: return to_replica = to_pool.get_bound_replica_id(request_id) if to_replica is None: return try: self._transfer_emitter.observe_size(from_stage, from_replica, to_stage, to_replica, 0) self._transfer_emitter.observe_tx_time(from_stage, from_replica, to_stage, to_replica, tx_ms / 1000.0) except Exception: logger.debug( "[Orchestrator] transfer_tx emit failed for edge %d->%d req=%s", from_stage, to_stage, request_id, exc_info=True, ) async def _forward_to_next_stage( self, req_id: str, src_stage_id: int, output: Any, req_state: OrchestratorRequestState, *, src_replica_id: int | None = None, is_streaming_session: bool = False, is_final_update: bool = False, ) -> None: """Forward output from the current logical stage to the next one.""" next_logical = src_stage_id + 1 next_pool = self.stage_pools[next_logical] next_client = next_pool.stage_client params = req_state.sampling_params_list[next_logical] source_outputs = [output] next_stage_resumable = is_streaming_session and not is_final_update already_submitted = self._next_stage_already_submitted(src_stage_id, req_state) requires_multimodal_data = getattr(next_client, "requires_multimodal_data", False) _t_submit_start = _time.perf_counter() if next_pool.stage_type == "diffusion": companion_outputs = self._cfg_tracker.pop_companion_outputs(req_id) expected = len(self._cfg_tracker.get_companion_request_ids(req_id)) if expected > len(companion_outputs): logger.warning( "[Orchestrator] req=%s: only %d/%d CFG companion outputs arrived; " "downstream CFG conditioning may degrade", req_id, len(companion_outputs), expected, ) diffusion_source_outputs = [output, *companion_outputs] if next_client.custom_process_input_func is not None: _t_ar2d = _time.perf_counter() _fn = next_client.custom_process_input_func _extra_kwargs: dict[str, Any] = {} # TODO: replace signature probe with explicit kwarg contract. try: import inspect as _inspect if "sampling_params" in _inspect.signature(_fn).parameters: _extra_kwargs["sampling_params"] = params except (TypeError, ValueError): pass diffusion_prompt = _fn( diffusion_source_outputs, req_state.prompt, requires_multimodal_data, **_extra_kwargs, ) _dt_ar2d = (_time.perf_counter() - _t_ar2d) * 1000 req_state.pipeline_timings["ar2diffusion_ms"] = _dt_ar2d logger.info( "[Orchestrator] ar2diffusion req=%s wall_time=%.3fms stage=%d->%d", req_id, _dt_ar2d, src_stage_id, next_logical, ) if diffusion_prompt is None: error_output = OmniRequestOutput.from_error( req_id, f"Stage-{src_stage_id} produced no valid inputs for diffusion stage-{next_logical}", ) logger.warning( "[Orchestrator] req=%s stage=%d produced empty diffusion inputs for stage=%d; " "routing terminal error output", req_id, src_stage_id, next_logical, ) await self.output_async_queue.put( OutputMessage( request_id=req_id, stage_id=next_logical, engine_outputs=error_output, metrics=None, finished=True, ) ) await self._cleanup_request_ids( [req_id, *self._cfg_tracker.cleanup_parent(req_id)], ) return if isinstance(diffusion_prompt, list): if not diffusion_prompt: error_output = OmniRequestOutput.from_error( req_id, f"Stage-{src_stage_id} produced no valid inputs for diffusion stage-{next_logical}", ) logger.warning( "[Orchestrator] req=%s stage=%d produced empty diffusion inputs for stage=%d; " "routing terminal error output", req_id, src_stage_id, next_logical, ) await self.output_async_queue.put( OutputMessage( request_id=req_id, stage_id=next_logical, engine_outputs=error_output, metrics=None, finished=True, ) ) await self._cleanup_request_ids( [req_id, *self._cfg_tracker.cleanup_parent(req_id)], ) return if len(diffusion_prompt) == 1: diffusion_prompt = diffusion_prompt[0] else: diffusion_prompt = req_state.prompt if already_submitted: await next_pool.submit_update(req_id, req_state, diffusion_prompt) else: await next_pool.submit_initial( req_id, req_state, diffusion_prompt, submit_kwargs={ "kv_sender_info": self._build_kv_sender_info( list(getattr(next_client, "engine_input_source", None) or [src_stage_id]), request_id=req_id, ) }, params_override=self._maybe_clone_diffusion_params_for_cfg(req_id, params), ) req_state.stage_submit_ts[next_logical] = _time.time() _tx_ms = (_time.perf_counter() - _t_submit_start) * 1000.0 self._emit_tx_edge( from_stage=src_stage_id, from_replica=src_replica_id if src_replica_id is not None else 0, to_stage=next_logical, to_pool=next_pool, request_id=req_id, tx_ms=_tx_ms, ) return # PD disaggregation: prefill → decode routing uses original prompt + KV transfer params if self._pd_pair is not None and (src_stage_id, next_logical) == self._pd_pair: params = self._build_pd_decode_params(req_id, params) # Use the original user prompt for the decode stage (not processed embeddings) original_prompt = req_state.prompt raw_decode_inputs = [original_prompt] if not isinstance(original_prompt, list) else original_prompt decode_inputs: list[dict[str, Any]] = [] for decode_input in raw_decode_inputs: if isinstance(decode_input, dict): decode_inputs.append(decode_input) continue prompt_token_ids = getattr(decode_input, "prompt_token_ids", None) if prompt_token_ids is None: raise TypeError( "[Orchestrator][PD] decode input must be dict or have prompt_token_ids, " f"got {type(decode_input).__name__} for req={req_id}" ) decode_inputs.append({"prompt_token_ids": list(prompt_token_ids)}) for decode_input in decode_inputs: request = build_engine_core_request_from_tokens( request_id=req_id, prompt=decode_input, params=params, model_config=next_pool.stage_vllm_config.model_config, mm_features=req_state.mm_features, resumable=next_stage_resumable, ) request.external_req_id = request.request_id if already_submitted: await next_pool.submit_update(req_id, req_state, request) else: await next_pool.submit_initial(req_id, req_state, request, prompt_text=None) req_state.stage_submit_ts[next_logical] = _time.time() _tx_ms = (_time.perf_counter() - _t_submit_start) * 1000.0 self._emit_tx_edge( from_stage=src_stage_id, from_replica=src_replica_id if src_replica_id is not None else 0, to_stage=next_logical, to_pool=next_pool, request_id=req_id, tx_ms=_tx_ms, ) return if req_state.pd_prefill_multimodal_output is not None: req_state.streaming.bridge_states.setdefault( "pd_prefill_multimodal_output_by_req", {}, )[req_id] = req_state.pd_prefill_multimodal_output try: next_inputs = next_client.process_engine_inputs( source_outputs, req_state.prompt, streaming_context=req_state.streaming, ) except Exception: logger.exception( "[Orchestrator] req=%s process_engine_inputs FAILED for stage-%s", req_id, next_logical, ) raise if not next_inputs: if not getattr(output, "finished", False): logger.debug( "[Orchestrator] req=%s stage-%s produced no inputs for stage-%s; waiting for more outputs", req_id, src_stage_id, next_logical, ) return final_stage_id = req_state.final_stage_id final_pool = self.stage_pools[final_stage_id] final_output_type = getattr(final_pool.stage_client, "final_output_type", None) terminal_output = _build_terminal_empty_output( req_id, final_output_type=final_output_type, audio_sample_rate=_infer_stage_audio_sample_rate(final_pool), ) submit_ts = _time.time() req_state.stage_submit_ts[final_stage_id] = submit_ts logger.info( "[Orchestrator] req=%s stage-%s produced no terminal inputs for stage-%s; " "returning empty %s output from final stage-%s", req_id, src_stage_id, next_logical, final_output_type or "text", final_stage_id, ) await self.output_async_queue.put( OutputMessage( request_id=req_id, stage_id=final_stage_id, replica_id=0, engine_outputs=terminal_output, metrics=None, finished=True, stage_submit_ts=submit_ts, ) ) await self._cleanup_request_ids([req_id, *self._cfg_tracker.cleanup_parent(req_id)]) return # Build and submit requests for each input for next_input in next_inputs: # Only AR thinker stages consume encoder mm_features; downstream # (talker/code2wav/…) must not see them (avoids encoder-cache misses). model_stage = getattr(getattr(next_pool.stage_vllm_config, "model_config", None), "model_stage", None) mm_features = req_state.mm_features if model_stage == "thinker" else None request = self._build_next_stage_request( req_id, next_logical, next_input, params=params, mm_features=mm_features, resumable=next_stage_resumable, ) if already_submitted: await next_pool.submit_update(req_id, req_state, request) else: await next_pool.submit_initial(req_id, req_state, request, prompt_text=None) req_state.stage_submit_ts[next_logical] = _time.time() _tx_ms = (_time.perf_counter() - _t_submit_start) * 1000.0 self._emit_tx_edge( from_stage=src_stage_id, from_replica=src_replica_id if src_replica_id is not None else 0, to_stage=next_logical, to_pool=next_pool, request_id=req_id, tx_ms=_tx_ms, ) async def _prewarm_async_chunk_stages( self, request_id: str, stage0_request: Any, req_state: OrchestratorRequestState, ) -> None: """Pre-submit downstream stages for async-chunk mode.""" if req_state.final_stage_id <= 0: return prompt_token_ids = getattr(stage0_request, "prompt_token_ids", None) if prompt_token_ids is None: logger.warning( "[Orchestrator] async_chunk prewarm skipped for req=%s: stage0 prompt_token_ids missing", request_id, ) return for next_stage_id in range(1, req_state.final_stage_id + 1): next_pool = self.stage_pools[next_stage_id] params = req_state.sampling_params_list[next_stage_id] req_state.stage_submit_ts[next_stage_id] = _time.time() _t_submit_start = _time.perf_counter() if next_pool.stage_type == "diffusion": await next_pool.submit_initial( request_id, req_state, req_state.prompt, submit_kwargs={ "kv_sender_info": self._build_kv_sender_info( list(getattr(next_pool.stage_client, "engine_input_source", None) or [next_stage_id - 1]), request_id=request_id, ) }, ) else: import copy from vllm_omni.distributed.omni_connectors.adapter import compute_talker_prompt_ids_length try: next_prompt_len = max(1, compute_talker_prompt_ids_length(prompt_token_ids)) except Exception: next_prompt_len = max(1, len(prompt_token_ids)) original_prompt = req_state.prompt if isinstance(original_prompt, dict): base_input = copy.deepcopy(original_prompt) else: base_input = {} base_input["prompt_token_ids"] = [0] * next_prompt_len base_input["multi_modal_data"] = None base_input["mm_processor_kwargs"] = None downstream_resumable = bool(getattr(stage0_request, "resumable", req_state.streaming.enabled)) request = build_engine_core_request_from_tokens( request_id=request_id, prompt=base_input, params=params, model_config=next_pool.stage_vllm_config.model_config, resumable=downstream_resumable, ) request.external_req_id = request.request_id await next_pool.submit_initial( request_id, req_state, request, prompt_text=None, ) # async_chunk pre-submit fires per stage edge (N-1 -> N). Source # replica is stage 0's bound replica (single-replica thinker in # all current configs); fall back to 0 if unknown. _tx_ms = (_time.perf_counter() - _t_submit_start) * 1000.0 src_replica = self.stage_pools[next_stage_id - 1].get_bound_replica_id(request_id) self._emit_tx_edge( from_stage=next_stage_id - 1, from_replica=src_replica if src_replica is not None else 0, to_stage=next_stage_id, to_pool=next_pool, request_id=request_id, tx_ms=_tx_ms, ) def _build_kv_sender_info( self, sender_stage_ids: list[int], *, request_id: str | None = None, ) -> dict[int, dict[str, Any]] | None: """Build per-request sender info for diffusion KV-transfer receivers.""" sender_infos: dict[int, dict[str, Any]] = {} for sender_stage_id in dict.fromkeys(sender_stage_ids): if sender_stage_id < 0 or sender_stage_id >= len(self.stage_pools): continue sender_pool = self.stage_pools[sender_stage_id] sender_stage = sender_pool.get_bound_client(request_id) if request_id is not None else None if sender_stage is None: sender_stage = sender_pool.stage_client get_sender_info = getattr(sender_stage, "get_kv_sender_info", None) if not callable(get_sender_info): continue sender_info = get_sender_info() if not sender_info: logger.warning( "[Orchestrator] Stage-%s has no KV sender info available", sender_stage_id, ) continue sender_infos[sender_stage_id] = sender_info return sender_infos or None # ---- Shutdown / lifecycle ---- async def _drain_pending_requests_on_fatal(self) -> None: """Drain the request queue and broadcast fatal errors for any pending add_request messages that were never processed. Called from the ``run()`` finally block when a fatal error (e.g. ``EngineDeadError``) caused the orchestrator to shut down before the request handler could process all queued messages. Also broadcasts for any already-tracked requests still in ``request_states`` that were not yet notified. """ assert self._fatal_error is not None notified: set[str] = set() # 1) Drain pending messages from the request queue. while True: try: msg = self.request_async_queue.get_nowait() except Exception: break if msg.type == "add_request": req_id = msg.request_id await self.output_async_queue.put( ErrorMessage( error=self._fatal_error, fatal=True, request_id=req_id, stage_id=self._fatal_error_stage_id, ) ) notified.add(req_id) # 2) Broadcast for any tracked requests not already notified # (e.g. request was registered but the EngineDeadError handler # missed it because it wasn't submitted to the dead stage yet). for req_id in list(self.request_states): if req_id not in notified: await self.output_async_queue.put( ErrorMessage( error=self._fatal_error, fatal=True, request_id=req_id, stage_id=self._fatal_error_stage_id, ) ) self.request_states.pop(req_id, None) def _shutdown_stages(self) -> None: """Shutdown all stage pools.""" if self._stages_shutdown: return self._stages_shutdown = True total = sum(pool.live_num_replicas for pool in self.stage_pools) logger.info("[Orchestrator] Shutting down all %d client(s)", total) for pool in self.stage_pools: for replica_id in pool.live_replica_ids(): pool.shutdown_replica(replica_id)