""" 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. """ 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.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_omni.engine import OmniEngineCoreRequest from vllm_omni.engine.cfg_companion_tracker import CfgCompanionTracker from vllm_omni.engine.serialization import serialize_additional_information from vllm_omni.engine.stage_pool import StagePool from vllm_omni.outputs import OmniRequestOutput logger = init_logger(__name__) 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=None, 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 # 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.""" def __init__( self, request_async_queue: janus.AsyncQueue[dict[str, Any]], 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, ) -> 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 # 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._cfg_tracker = CfgCompanionTracker() self._shutdown_event = asyncio.Event() self._stages_shutdown = False self._fatal_error: str | None = None self._fatal_error_stage_id: int | None = 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", ) try: await asyncio.gather(request_task, output_task) except asyncio.CancelledError: raise except Exception: logger.exception("[Orchestrator] Fatal error in orchestrator tasks") raise finally: self._shutdown_event.set() for task in (request_task, output_task): if not task.done(): task.cancel() try: await asyncio.gather(request_task, output_task, return_exceptions=True) except Exception: pass # If a fatal error caused the shutdown, drain any pending # add_request messages that were never processed and broadcast # fatal error responses so callers are not left hanging. if self._fatal_error is not None: await self._drain_pending_requests_on_fatal() 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.get("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 msg_type == "shutdown": logger.info("[Orchestrator] Received shutdown signal") self._shutdown_event.set() self._shutdown_stages() break else: logger.warning("[Orchestrator] Unknown message type: %s", msg_type) async def _handle_add_request(self, msg: dict[str, Any]) -> None: """Handle an add_request message from the main thread.""" stage_id = 0 request_id = msg["request_id"] prompt = msg["prompt"] original_prompt = msg.get("original_prompt", 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"] logger.info( "[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, mm_features=getattr(prompt, "mm_features", None), ) self.request_states[request_id] = req_state req_state.streaming.enabled = bool(getattr(prompt, "resumable", False)) req_state.stage_submit_ts[stage_id] = _time.time() enqueue_ts = msg.get("enqueue_ts", 0.0) if enqueue_ts > 0: req_state.pipeline_timings["queue_wait_ms"] = (_time.perf_counter() - enqueue_ts) * 1000.0 preprocess_ms = msg.get("preprocess_ms", 0.0) 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.get("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: dict[str, Any]) -> None: """Handle a streaming_update message for an existing request.""" stage_id = 0 request_id = msg["request_id"] request = msg["prompt"] 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 = dict(msg) fallback_msg["type"] = "add_request" await self._handle_add_request(fallback_msg) return if "sampling_params_list" in msg and 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.get("output_prompt_text"), ) async def _handle_add_companion(self, msg: dict[str, Any]) -> 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, ) 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.get("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: dict[str, Any]) -> 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: dict[str, Any]) -> None: """Handle a control-plane RPC request from the main thread.""" rpc_id = msg["rpc_id"] method = msg["method"] timeout = msg.get("timeout") args = tuple(msg.get("args", ())) kwargs = dict(msg.get("kwargs") or {}) requested_stage_ids = msg.get("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 range(pool.num_replicas): 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( { "type": "collective_rpc_result", "rpc_id": rpc_id, "method": method, "stage_ids": stage_ids, "results": results, } ) # ---- Orchestration loop ---- 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 range(pool.num_replicas): if self._shutdown_event.is_set(): return if pool.stage_type == "diffusion": output = pool.poll_diffusion_output(replica_id) if output is None: continue 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: 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, ) raw_output = await pool.process_llm_raw_outputs(replica_id, raw_outputs) 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( { "type": "error", "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 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()), replica_id=replica_id, ) stage_metrics.pipeline_timings = dict(req_state.pipeline_timings) await self._route_output(stage_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( { "type": "error", "request_id": parent_id, "stage_id": stage_id, "error": output.error, } ) 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) self.request_states.pop(request_id, None) 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, 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) if finished and self._cfg_tracker.is_companion(req_id): await self._handle_cfg_companion_ready(req_id) await self._cleanup_request_ids([req_id]) return if self.stage_pools[stage_id].final_output: await self.output_async_queue.put( { "type": "output", "request_id": req_id, "stage_id": stage_id, "engine_outputs": output, "metrics": stage_metrics, "finished": finished and stage_id == req_state.final_stage_id, "stage_submit_ts": submit_ts, } ) elif stage_metrics is not None: await self.output_async_queue.put( { "type": "stage_metrics", "request_id": req_id, "stage_id": stage_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, 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, is_streaming_session=True, is_final_update=True, ) if finished and stage_id == req_state.final_stage_id: 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 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 async def _forward_to_next_stage( self, req_id: str, src_stage_id: int, output: Any, req_state: OrchestratorRequestState, *, 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) if next_pool.stage_type == "diffusion": if next_client.custom_process_input_func is not None: _t_ar2d = _time.perf_counter() diffusion_prompt = next_client.custom_process_input_func( source_outputs, req_state.prompt, requires_multimodal_data, ) _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 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( { "type": "output", "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 already_submitted and 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() 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() 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 # 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(next_client, "model_stage", None) mm_features = req_state.mm_features if model_stage == "thinker" else None 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=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() 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() 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 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, ) request.external_req_id = request.request_id await next_pool.submit_initial(request_id, req_state, request, prompt_text=None) 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.get("type") == "add_request": req_id = msg["request_id"] await self.output_async_queue.put( { "type": "error", "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( { "type": "error", "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.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 range(pool.num_replicas): pool.shutdown_replica(replica_id)