Jared WenandHongsheng Liu
6e35bfbbae
Signed-off-by: JaredforReal <w13431838023@gmail.com> Signed-off-by: Hongsheng Liu <liuhongsheng4@huawei.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>
1216 lines
50 KiB
Python
1216 lines
50 KiB
Python
"""
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Orchestrator for vLLM-Omni multi-stage runtime.
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Runs inside a background thread with its own asyncio event loop.
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Owns all StageEngineCoreClient instances, input/output processors,
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and handles stage-to-stage transfer logic.
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"""
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from __future__ import annotations
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import asyncio
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import copy
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import time as _time
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from dataclasses import dataclass, field
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from typing import Any
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import janus
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import torch
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from vllm.config import ModelConfig
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from vllm.logger import init_logger
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from vllm.outputs import RequestOutput
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from vllm.pooling_params import PoolingParams
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from vllm.sampling_params import SamplingParams
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from vllm.v1.engine import EngineCoreOutputs
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from vllm.v1.engine.exceptions import EngineDeadError
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from vllm_omni.distributed.omni_connectors.adapter import compute_talker_prompt_ids_length
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from vllm_omni.engine import (
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OmniEngineCoreRequest,
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)
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from vllm_omni.engine.cfg_companion_tracker import CfgCompanionTracker
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from vllm_omni.engine.serialization import serialize_additional_information
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from vllm_omni.metrics.stats import StageRequestStats as StageRequestMetrics
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from vllm_omni.metrics.stats import StageStats
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from vllm_omni.metrics.utils import count_tokens_from_outputs
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from vllm_omni.outputs import OmniRequestOutput
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logger = init_logger(__name__)
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def build_engine_core_request_from_tokens(
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request_id: str,
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prompt: dict[str, Any],
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params: SamplingParams | PoolingParams,
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arrival_time: float | None = None,
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model_config: ModelConfig | None = None,
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resumable: bool = False,
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mm_features: list | None = None,
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) -> OmniEngineCoreRequest:
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"""Build an OmniEngineCoreRequest directly from an OmniTokensPrompt.
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Lightweight alternative to the full InputProcessor pipeline - skips
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tokenization, multimodal preprocessing, LoRA validation, and platform
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validation. Intended for stage 1+ where the upstream stage has already
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produced token IDs and optional embeddings.
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"""
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if arrival_time is None:
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arrival_time = _time.time()
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prompt_token_ids = prompt["prompt_token_ids"]
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# Clone params and set max_tokens if needed
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sampling_params = None
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pooling_params = None
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if isinstance(params, SamplingParams):
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sampling_params = params.clone()
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if sampling_params.max_tokens is None and model_config is not None:
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sampling_params.max_tokens = model_config.max_model_len - len(prompt_token_ids)
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else:
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pooling_params = params.clone()
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prompt_embeds: torch.Tensor | None = prompt.get("prompt_embeds")
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# Serialize additional_information if present
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additional_info_payload = serialize_additional_information(
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prompt.get("additional_information"),
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log_prefix=f"build_engine_core_request_from_tokens req={request_id}",
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)
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return OmniEngineCoreRequest(
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request_id=request_id,
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prompt_token_ids=prompt_token_ids,
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mm_features=mm_features,
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sampling_params=sampling_params,
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pooling_params=pooling_params,
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arrival_time=arrival_time,
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lora_request=getattr(params, "lora_request", None),
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cache_salt=None,
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data_parallel_rank=None,
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prompt_embeds=prompt_embeds,
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resumable=resumable,
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additional_information=additional_info_payload,
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)
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# ============================================================
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# Orchestrator internals (run inside the background thread)
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# ============================================================
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@dataclass
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class OrchestratorRequestState:
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"""Per-request bookkeeping inside the Orchestrator."""
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request_id: str
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prompt: Any = None
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sampling_params_list: list[Any] = field(default_factory=list)
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final_stage_id: int = -1
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# Metrics: timestamp when request was submitted to each stage
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stage_submit_ts: dict[int, float] = field(default_factory=dict)
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mm_processor_kwargs: dict | None = None
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mm_features: list | None = None
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streaming: StreamingInputState = field(default_factory=lambda: StreamingInputState())
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# Per-request pipeline timing accumulator (milliseconds)
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pipeline_timings: dict[str, float] = field(default_factory=dict)
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@dataclass
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class StreamingInputState:
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# Flag of streaming input request
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enabled: bool = False
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# Flag of segment of streaming input finished
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segment_finished: bool = False
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# Streaming update prompt length
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new_prompt_len_snapshot: int | None = None
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# Model/bridge-specific runtime states (e.g., thinker->talker)
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bridge_states: dict[str, Any] = field(default_factory=dict)
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class Orchestrator:
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"""Runs inside a background thread's asyncio event loop.
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Owns all StageEngineCoreClient instances, input/output processors,
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and handles stage-to-stage transfer logic.
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"""
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def __init__(
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self,
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request_async_queue: janus.AsyncQueue[dict[str, Any]],
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output_async_queue: janus.AsyncQueue[dict[str, Any]],
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rpc_async_queue: janus.AsyncQueue[dict[str, Any]],
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stage_clients: list[Any],
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output_processors: list[Any],
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stage_vllm_configs: list[Any],
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*,
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async_chunk: bool = False,
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pd_config: dict[str, Any] | None = None,
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) -> None:
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self.request_async_queue = request_async_queue
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self.output_async_queue = output_async_queue
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self.rpc_async_queue = rpc_async_queue
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self.num_stages = len(stage_clients)
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self.async_chunk = bool(async_chunk)
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self.stage_clients: list[Any] = stage_clients
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self.output_processors: list[Any] = output_processors
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self.stage_vllm_configs: list[Any] = stage_vllm_configs
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# PD disaggregation state
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self._pd_pair: tuple[int, int] | None = None
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self._pd_bootstrap_addr: str | None = None
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self._pd_prefill_engine_id: str | None = None
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self._pd_kv_params: dict[str, Any] = {}
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if pd_config is not None:
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self._pd_pair = pd_config.get("pd_pair")
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self._pd_bootstrap_addr = pd_config.get("bootstrap_addr")
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self._pd_prefill_engine_id = pd_config.get("prefill_engine_id")
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# Per-request state
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self.request_states: dict[str, OrchestratorRequestState] = {}
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# CFG companion tracking
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self._cfg_tracker = CfgCompanionTracker()
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# Per-stage metrics accumulators.
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self._batch_seq: list[int] = [0] * self.num_stages
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self._agg_total_tokens: list[int] = [0] * self.num_stages
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self._agg_total_gen_time_ms: list[float] = [0.0] * self.num_stages
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# Shutdown coordination
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self._shutdown_event = asyncio.Event()
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self._stages_shutdown = False
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self._fatal_error: str | None = None
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self._fatal_error_stage_id: int | None = None
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async def run(self) -> None:
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"""Main entry point for the Orchestrator event loop."""
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logger.info("[Orchestrator] Starting event loop")
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request_task = asyncio.create_task(self._request_handler(), name="orchestrator-request-handler")
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output_task = asyncio.create_task(
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self._orchestration_output_handler(),
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name="orchestrator-stage-output-handler",
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)
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try:
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# Run both tasks concurrently; if either fails the other is cancelled.
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await asyncio.gather(request_task, output_task)
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except asyncio.CancelledError:
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raise
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except Exception:
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logger.exception("[Orchestrator] Fatal error in orchestrator tasks")
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raise
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finally:
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self._shutdown_event.set()
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for t in (request_task, output_task):
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if not t.done():
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t.cancel()
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try:
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await asyncio.gather(request_task, output_task, return_exceptions=True)
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except Exception:
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pass
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# If a fatal error caused the shutdown, drain any pending
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# add_request messages that were never processed and broadcast
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# fatal error responses so callers are not left hanging.
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if self._fatal_error is not None:
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await self._drain_pending_requests_on_fatal()
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self._shutdown_stages()
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# Cancel any remaining tasks spawned by wait_for / gather so
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# the event loop can close cleanly without "pending task" errors.
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loop = asyncio.get_running_loop()
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pending = [t for t in asyncio.all_tasks(loop) if t is not asyncio.current_task() and not t.done()]
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for t in pending:
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t.cancel()
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if pending:
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await asyncio.gather(*pending, return_exceptions=True)
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async def _request_handler(self) -> None:
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"""Read messages from the main thread via request_async_queue."""
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while True:
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msg = await self.request_async_queue.get()
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msg_type = msg.get("type")
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if msg_type == "add_request":
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await self._handle_add_request(msg)
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elif msg_type == "streaming_update":
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await self._handle_streaming_update(msg)
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elif msg_type == "add_companion_request":
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await self._handle_add_companion(msg)
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elif msg_type == "abort":
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await self._handle_abort(msg)
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elif msg_type == "collective_rpc":
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await self._handle_collective_rpc(msg)
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elif msg_type == "shutdown":
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logger.info("[Orchestrator] Received shutdown signal")
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self._shutdown_event.set()
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self._shutdown_stages()
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break
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else:
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logger.warning(f"[Orchestrator] Unknown message type: {msg_type}")
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async def _orchestration_output_handler(self) -> None:
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"""Poll all stages, handle transfers, send final outputs to main."""
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try:
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await self._orchestration_loop()
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except asyncio.CancelledError:
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logger.debug("[Orchestrator] _orchestration_output_handler cancelled")
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return
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async def _orchestration_loop(self) -> None:
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"""Inner loop for _orchestration_output_handler (clean cancellation).
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Control flow: poll raw → process through output processor → route.
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"""
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while not self._shutdown_event.is_set():
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idle = True
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for stage_id in range(self.num_stages):
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if self._shutdown_event.is_set():
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return
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# 1) Diffusion stage: poll non-blocking queue
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# TODO (Peiqi): the output of diffusion stage is OmniRequestOutput,
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# which is different from EngineCoreOutputs (LLM stages). We may want to unify
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# the output format in the future to simplify the processing logic in Orchestrator.
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stage_client = self.stage_clients[stage_id]
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if stage_client.stage_type == "diffusion":
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output = stage_client.get_diffusion_output_nowait()
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if output is not None:
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idle = False
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if getattr(output, "error", None) is not None:
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await self.output_async_queue.put(
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{
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"type": "output",
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"request_id": output.request_id,
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"stage_id": stage_id,
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"engine_outputs": output,
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"metrics": None,
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"finished": True,
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}
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)
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self.request_states.pop(output.request_id, None)
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continue
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req_state = self.request_states.get(output.request_id)
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if req_state is not None:
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if getattr(output, "error", None) is not None:
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parent_id = self._companion_to_parent.get(output.request_id, output.request_id)
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await self.output_async_queue.put(
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{
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"type": "error",
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"request_id": parent_id,
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"stage_id": stage_id,
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"error": output.error,
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}
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)
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role_map = self._companion_map.get(parent_id, {})
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for cid in role_map.values():
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self.request_states.pop(cid, None)
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self._cleanup_companion_state(parent_id)
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self.request_states.pop(parent_id, None)
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continue
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stage_metrics = self._build_stage_metrics(stage_id, output.request_id, [output], req_state)
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await self._route_output(stage_id, output, req_state, stage_metrics)
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continue
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# 1) Poll raw outputs from the stage
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try:
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raw_outputs = await asyncio.wait_for(self._poll_stage_raw(stage_id), timeout=0.001)
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except asyncio.TimeoutError:
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continue
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except asyncio.CancelledError:
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raise
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except EngineDeadError as e:
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logger.error(
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"[Orchestrator] Stage-%s is dead: %s",
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stage_id,
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e,
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)
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self._fatal_error = str(e)
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self._fatal_error_stage_id = stage_id
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for req_id, req_state in list(self.request_states.items()):
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if stage_id in req_state.stage_submit_ts:
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await self.output_async_queue.put(
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{
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"type": "error",
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"error": str(e),
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"fatal": True,
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"request_id": req_id,
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"stage_id": stage_id,
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}
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)
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self.request_states.pop(req_id, None)
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self._shutdown_event.set()
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raise
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except Exception:
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if self._shutdown_event.is_set():
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return
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logger.exception(
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"[Orchestrator] _poll_stage_raw failed for stage-%s",
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stage_id,
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)
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raise
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if raw_outputs is None:
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continue
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idle = False
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# Handle prefill-finished KV-ready signals before finished outputs.
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await self._handle_kv_ready_raw_outputs(stage_id, raw_outputs)
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# 2) Process raw outputs through the output processor
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request_outputs = await self._process_stage_outputs(stage_id, raw_outputs)
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# 3) Route each processed output
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for output in request_outputs:
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req_state = self.request_states.get(output.request_id)
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if req_state is None:
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logger.warning(
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"[Orchestrator] Dropping output for unknown req %s at stage-%s (known reqs: %s)",
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output.request_id,
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stage_id,
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list(self.request_states.keys()),
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)
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continue
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stage_metrics = None
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if output.finished:
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stage_metrics = self._build_stage_metrics(
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stage_id,
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output.request_id,
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[output],
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req_state,
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)
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await self._route_output(stage_id, output, req_state, stage_metrics)
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if idle:
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await asyncio.sleep(0.001)
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else:
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await asyncio.sleep(0)
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|
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async def _route_output(
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self,
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stage_id: int,
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output: Any,
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req_state: OrchestratorRequestState,
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stage_metrics: Any,
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) -> None:
|
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"""Route a processed output: send to main thread and/or forward to next stage."""
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req_id = output.request_id
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finished = output.finished
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submit_ts = req_state.stage_submit_ts.get(stage_id)
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stage_client = self.stage_clients[stage_id]
|
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|
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# CFG companion handling: companions don't produce user-visible output
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# and don't forward to the next stage directly.
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if finished and self._cfg_tracker.is_companion(req_id):
|
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await self._handle_cfg_companion_ready(req_id)
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self.request_states.pop(req_id, None)
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return
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|
|
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if stage_client.final_output:
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await self.output_async_queue.put(
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{
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"type": "output",
|
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"request_id": req_id,
|
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"stage_id": stage_id,
|
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"engine_outputs": output,
|
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"metrics": stage_metrics,
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"finished": finished and stage_id == req_state.final_stage_id,
|
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"stage_submit_ts": submit_ts,
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}
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)
|
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elif stage_metrics is not None:
|
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await self.output_async_queue.put(
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{
|
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"type": "stage_metrics",
|
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"request_id": req_id,
|
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"stage_id": stage_id,
|
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"metrics": stage_metrics,
|
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"stage_submit_ts": submit_ts,
|
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}
|
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)
|
|
|
|
# PD disaggregation: extract KV transfer params from prefill stage output
|
|
if self._pd_pair is not None and finished and stage_id == self._pd_pair[0]:
|
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kv_params = getattr(output, "kv_transfer_params", None)
|
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if kv_params is not None:
|
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self._pd_kv_params[req_id] = kv_params if isinstance(kv_params, dict) else dict(kv_params)
|
|
logger.debug(
|
|
"[Orchestrator][PD] stored kv_transfer_params for req=%s (keys=%s)",
|
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req_id,
|
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list(self._pd_kv_params[req_id].keys()),
|
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)
|
|
else:
|
|
logger.warning(
|
|
"[Orchestrator][PD] prefill stage output for req=%s has no kv_transfer_params; "
|
|
"KV transfer may fail. Ensure apply_mooncake_connector_patch() was called.",
|
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req_id,
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)
|
|
|
|
if (
|
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(finished or (req_state.streaming.enabled and req_state.streaming.segment_finished))
|
|
and stage_id < req_state.final_stage_id
|
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and not self.async_chunk
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|
and (not self._next_stage_already_submitted(stage_id, req_state) or req_state.streaming.enabled)
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):
|
|
if (
|
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finished
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and self._cfg_tracker.has_companions(req_id)
|
|
and not self._cfg_tracker.all_companions_done(req_id)
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):
|
|
self._cfg_tracker.defer_parent(req_id, output, stage_id)
|
|
else:
|
|
await self._forward_to_next_stage(
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req_id,
|
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stage_id,
|
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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(
|
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req_id,
|
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stage_id,
|
|
output,
|
|
req_state,
|
|
is_streaming_session=True,
|
|
is_final_update=True,
|
|
)
|
|
|
|
if finished and stage_id == req_state.final_stage_id:
|
|
# PD: clean up any lingering KV params for this request
|
|
self._pd_kv_params.pop(req_id, None)
|
|
self._cfg_tracker.cleanup_parent(req_id)
|
|
self.request_states.pop(req_id, None)
|
|
|
|
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 not None and not self._next_stage_already_submitted(deferred["stage_id"], parent_state):
|
|
await self._forward_to_next_stage(
|
|
parent_id,
|
|
deferred["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, not only when decode fully finishes."""
|
|
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 self._next_stage_already_submitted(stage_id, req_state):
|
|
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 _build_stage_metrics(
|
|
self,
|
|
stage_id: int,
|
|
req_id: str,
|
|
request_outputs: list[RequestOutput],
|
|
req_state: OrchestratorRequestState,
|
|
) -> StageRequestMetrics:
|
|
"""Build StageRequestMetrics for a finished request at a stage.
|
|
|
|
Reuses StageRequestMetrics so OrchestratorMetrics and downstream
|
|
metric handlers can consume a stable schema.
|
|
"""
|
|
now = _time.time()
|
|
submit_ts = req_state.stage_submit_ts.get(stage_id, now)
|
|
stage_gen_time_ms = (now - submit_ts) * 1000.0
|
|
|
|
num_tokens_out = count_tokens_from_outputs(request_outputs)
|
|
num_tokens_in = 0
|
|
if stage_id == 0:
|
|
for ro in request_outputs:
|
|
ptids = getattr(ro, "prompt_token_ids", None)
|
|
if ptids is not None:
|
|
num_tokens_in += len(ptids)
|
|
|
|
# Monotonic batch counter per stage.
|
|
self._batch_seq[stage_id] += 1
|
|
batch_id = self._batch_seq[stage_id]
|
|
|
|
# Accumulate for running-average stage_stats
|
|
self._agg_total_tokens[stage_id] += num_tokens_out
|
|
self._agg_total_gen_time_ms[stage_id] += stage_gen_time_ms
|
|
|
|
return StageRequestMetrics(
|
|
num_tokens_in=num_tokens_in,
|
|
num_tokens_out=num_tokens_out,
|
|
stage_gen_time_ms=stage_gen_time_ms,
|
|
batch_id=batch_id,
|
|
batch_size=1,
|
|
rx_decode_time_ms=0.0,
|
|
rx_transfer_bytes=0,
|
|
rx_in_flight_time_ms=0.0,
|
|
stage_stats=StageStats(
|
|
total_token=self._agg_total_tokens[stage_id],
|
|
total_gen_time_ms=self._agg_total_gen_time_ms[stage_id],
|
|
),
|
|
pipeline_timings=dict(req_state.pipeline_timings),
|
|
)
|
|
|
|
def _build_kv_sender_info(self, sender_stage_ids: list[int]) -> 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 >= self.num_stages:
|
|
continue
|
|
|
|
sender_stage = self.stage_clients[sender_stage_id]
|
|
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
|
|
|
|
async def _forward_to_next_stage(
|
|
self,
|
|
req_id: str,
|
|
stage_id: int,
|
|
output: Any,
|
|
req_state: OrchestratorRequestState,
|
|
*,
|
|
is_streaming_session: bool = False,
|
|
is_final_update: bool = False,
|
|
) -> None:
|
|
"""Forward output from current stage to the next stage.
|
|
|
|
Handles the full pipeline: set outputs on current stage, compute
|
|
next-stage inputs, build lightweight requests, and submit them.
|
|
"""
|
|
next_stage_id = stage_id + 1
|
|
next_client = self.stage_clients[next_stage_id]
|
|
params = req_state.sampling_params_list[next_stage_id]
|
|
next_stage_resumable = is_streaming_session and not is_final_update
|
|
|
|
if next_client.stage_type == "diffusion":
|
|
self.stage_clients[stage_id].set_engine_outputs([output])
|
|
if next_client.custom_process_input_func is not None:
|
|
_t_ar2d = _time.perf_counter()
|
|
diffusion_prompt = next_client.custom_process_input_func(
|
|
self.stage_clients,
|
|
next_client.engine_input_source,
|
|
req_state.prompt,
|
|
False,
|
|
)
|
|
_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,
|
|
stage_id,
|
|
next_stage_id,
|
|
)
|
|
if isinstance(diffusion_prompt, list):
|
|
if not diffusion_prompt:
|
|
error_output = OmniRequestOutput.from_error(
|
|
req_id,
|
|
f"Stage-{stage_id} produced no valid inputs for diffusion stage-{next_stage_id}",
|
|
)
|
|
logger.warning(
|
|
"[Orchestrator] req=%s stage=%d produced empty diffusion inputs for stage=%d; "
|
|
"routing terminal error output",
|
|
req_id,
|
|
stage_id,
|
|
next_stage_id,
|
|
)
|
|
await self.output_async_queue.put(
|
|
{
|
|
"type": "output",
|
|
"request_id": req_id,
|
|
"stage_id": next_stage_id,
|
|
"engine_outputs": error_output,
|
|
"metrics": None,
|
|
"finished": True,
|
|
}
|
|
)
|
|
self._pd_kv_params.pop(req_id, None)
|
|
self._cfg_tracker.cleanup_parent(req_id)
|
|
self.request_states.pop(req_id, None)
|
|
return
|
|
diffusion_prompt = diffusion_prompt[0]
|
|
else:
|
|
diffusion_prompt = req_state.prompt
|
|
|
|
params = self._cfg_tracker.attach_cfg_request_ids(req_id, params)
|
|
|
|
source_stage_ids = list(getattr(next_client, "engine_input_source", None) or [stage_id])
|
|
kv_sender_info = self._build_kv_sender_info(sender_stage_ids=source_stage_ids)
|
|
if isinstance(diffusion_prompt, list):
|
|
await next_client.add_batch_request_async(
|
|
req_id,
|
|
diffusion_prompt,
|
|
params,
|
|
kv_sender_info=kv_sender_info,
|
|
)
|
|
else:
|
|
await next_client.add_request_async(
|
|
req_id,
|
|
diffusion_prompt,
|
|
params,
|
|
kv_sender_info=kv_sender_info,
|
|
)
|
|
req_state.stage_submit_ts[next_stage_id] = _time.time()
|
|
return
|
|
|
|
# PD disaggregation: prefill → decode routing uses original prompt + KV transfer params
|
|
if self._pd_pair is not None and (stage_id, next_stage_id) == self._pd_pair:
|
|
# Save prefill stage outputs so thinker2talker can merge embeddings later
|
|
self.stage_clients[stage_id].set_engine_outputs([output])
|
|
|
|
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=self.stage_vllm_configs[next_stage_id].model_config,
|
|
mm_features=req_state.mm_features, # Pass mm_features for M-RoPE
|
|
)
|
|
request.external_req_id = request.request_id
|
|
|
|
self.output_processors[next_stage_id].add_request(
|
|
request=request,
|
|
prompt=None,
|
|
parent_req=None,
|
|
request_index=0,
|
|
queue=None,
|
|
)
|
|
await next_client.add_request_async(request)
|
|
|
|
req_state.stage_submit_ts[next_stage_id] = _time.time()
|
|
return
|
|
|
|
self.stage_clients[stage_id].set_engine_outputs([output])
|
|
|
|
# Process inputs for next stage
|
|
try:
|
|
next_inputs = next_client.process_engine_inputs(
|
|
stage_list=self.stage_clients,
|
|
prompt=req_state.prompt,
|
|
streaming_context=(req_state.streaming if req_state.streaming.enabled else None),
|
|
)
|
|
except Exception:
|
|
logger.exception(
|
|
"[Orchestrator] req=%s process_engine_inputs FAILED for stage-%s",
|
|
req_id,
|
|
next_stage_id,
|
|
)
|
|
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).
|
|
_ms = getattr(next_client, "model_stage", None)
|
|
_mm_features = req_state.mm_features if _ms == "thinker" else None
|
|
request = build_engine_core_request_from_tokens(
|
|
request_id=req_id,
|
|
prompt=next_input,
|
|
params=params,
|
|
model_config=self.stage_vllm_configs[next_stage_id].model_config,
|
|
mm_features=_mm_features,
|
|
resumable=next_stage_resumable,
|
|
)
|
|
|
|
# TODO: Here we directly use the req id to assign.
|
|
request.external_req_id = request.request_id
|
|
|
|
self.output_processors[next_stage_id].add_request(
|
|
request=request,
|
|
prompt=None,
|
|
parent_req=None,
|
|
request_index=0,
|
|
queue=None,
|
|
)
|
|
|
|
await next_client.add_request_async(request)
|
|
|
|
# Record submit timestamp for the next stage
|
|
req_state.stage_submit_ts[next_stage_id] = _time.time()
|
|
|
|
async def _poll_stage_raw(self, stage_id: int) -> EngineCoreOutputs | None:
|
|
"""Pull raw EngineCoreOutputs from a stage client without processing.
|
|
|
|
Returns the raw outputs object, or None when there is nothing
|
|
to consume.
|
|
"""
|
|
outputs = await self.stage_clients[stage_id].get_output_async()
|
|
if not outputs.outputs:
|
|
return None
|
|
return outputs
|
|
|
|
async def _process_stage_outputs(self, stage_id: int, raw_outputs: EngineCoreOutputs) -> list[RequestOutput]:
|
|
"""Run the output processor on raw outputs, returning RequestOutputs.
|
|
|
|
Also handles abort forwarding and scheduler stats updates.
|
|
"""
|
|
processor = self.output_processors[stage_id]
|
|
|
|
processed = processor.process_outputs(
|
|
raw_outputs.outputs,
|
|
raw_outputs.timestamp,
|
|
None,
|
|
)
|
|
for eco in raw_outputs.outputs:
|
|
if not hasattr(eco, "request_id"):
|
|
continue
|
|
req_state = self.request_states.get(eco.request_id)
|
|
if req_state:
|
|
req_state.streaming.segment_finished = eco.is_segment_finished
|
|
req_state.streaming.new_prompt_len_snapshot = eco.new_prompt_len_snapshot
|
|
|
|
if processed.reqs_to_abort:
|
|
await self.stage_clients[stage_id].abort_requests_async(processed.reqs_to_abort)
|
|
|
|
if raw_outputs.scheduler_stats is not None:
|
|
processor.update_scheduler_stats(raw_outputs.scheduler_stats)
|
|
|
|
return processed.request_outputs
|
|
|
|
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.")
|
|
params = sampling_params_list[0]
|
|
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),
|
|
)
|
|
|
|
# Track request state - use original_prompt so downstream stages
|
|
# (e.g. thinker2talker) can access the raw dict with multi_modal_data.
|
|
request = prompt
|
|
is_streaming = bool(getattr(request, "resumable", False))
|
|
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), # Save mm_features for PD
|
|
)
|
|
req_state.streaming.enabled = is_streaming
|
|
req_state.stage_submit_ts[stage_id] = _time.time()
|
|
|
|
# Per-request pipeline timings from caller thread
|
|
_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
|
|
|
|
self.request_states[request_id] = req_state
|
|
|
|
# Stage-0 prompt is already a fully-formed OmniEngineCoreRequest
|
|
# (pre-processed by AsyncOmniEngine.add_request, output processor
|
|
# already registered there) - submit directly.
|
|
stage_client = self.stage_clients[stage_id]
|
|
if stage_client.stage_type == "diffusion":
|
|
if isinstance(prompt, list):
|
|
await stage_client.add_batch_request_async(
|
|
request_id,
|
|
prompt,
|
|
params,
|
|
)
|
|
else:
|
|
await stage_client.add_request_async(request_id, prompt, params)
|
|
else:
|
|
await stage_client.add_request_async(request)
|
|
|
|
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_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()
|
|
stage_client = self.stage_clients[stage_id]
|
|
if stage_client.stage_type == "diffusion":
|
|
params = req_state.sampling_params_list[stage_id]
|
|
await stage_client.add_request_async(request_id, request, params)
|
|
else:
|
|
await stage_client.add_request_async(request)
|
|
|
|
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.
|
|
|
|
In async-chunk mode, stages exchange data through connectors/chunk adapters,
|
|
so downstream stages should be armed once at request start instead of waiting
|
|
for stage-finished forwarding.
|
|
"""
|
|
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
|
|
|
|
# Pre-arm stage-1+ with placeholder prompt IDs.
|
|
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
|
|
|
|
for next_stage_id in range(1, req_state.final_stage_id + 1):
|
|
next_client = self.stage_clients[next_stage_id]
|
|
params = req_state.sampling_params_list[next_stage_id]
|
|
|
|
if next_client.stage_type == "diffusion":
|
|
source_stage_ids = list(getattr(next_client, "engine_input_source", None) or [next_stage_id - 1])
|
|
kv_sender_info = self._build_kv_sender_info(sender_stage_ids=source_stage_ids)
|
|
await next_client.add_request_async(
|
|
request_id,
|
|
req_state.prompt,
|
|
params,
|
|
kv_sender_info=kv_sender_info,
|
|
)
|
|
req_state.stage_submit_ts[next_stage_id] = _time.time()
|
|
continue
|
|
|
|
request = build_engine_core_request_from_tokens(
|
|
request_id=request_id,
|
|
prompt=base_input,
|
|
params=params,
|
|
model_config=self.stage_vllm_configs[next_stage_id].model_config,
|
|
)
|
|
request.external_req_id = request.request_id
|
|
|
|
self.output_processors[next_stage_id].add_request(
|
|
request=request,
|
|
prompt=None,
|
|
parent_req=None,
|
|
request_index=0,
|
|
queue=None,
|
|
)
|
|
await next_client.add_request_async(request)
|
|
req_state.stage_submit_ts[next_stage_id] = _time.time()
|
|
|
|
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"]
|
|
|
|
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,
|
|
)
|
|
companion_state.stage_submit_ts[0] = _time.time()
|
|
self.request_states[companion_id] = companion_state
|
|
|
|
request = companion_prompt # Already a processed OmniEngineCoreRequest
|
|
stage_client = self.stage_clients[0]
|
|
await stage_client.add_request_async(request)
|
|
|
|
logger.info(
|
|
"[Orchestrator] CFG companion submitted: %s (role=%s, parent=%s)",
|
|
companion_id,
|
|
role,
|
|
parent_id,
|
|
)
|
|
|
|
async def _handle_abort(self, msg: dict[str, Any]) -> None:
|
|
"""Handle an abort message from the main thread."""
|
|
request_ids = msg["request_ids"]
|
|
all_ids_to_abort = self._cfg_tracker.abort_parents(request_ids)
|
|
for stage_id in range(self.num_stages):
|
|
await self.stage_clients[stage_id].abort_requests_async(all_ids_to_abort)
|
|
for req_id in all_ids_to_abort:
|
|
self.request_states.pop(req_id, None)
|
|
logger.info("[Orchestrator] Aborted request(s) %s", request_ids)
|
|
|
|
async def _handle_collective_rpc(self, msg: dict[str, Any]) -> None:
|
|
"""Handle a control-plane RPC request from the main thread.
|
|
|
|
TODO(AsyncOmni): parallelize stage dispatch if control latency becomes
|
|
noticeable. The current sequential fanout keeps the first version simple
|
|
and deterministic.
|
|
"""
|
|
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")
|
|
stage_ids = list(range(self.num_stages)) if requested_stage_ids is None else list(requested_stage_ids)
|
|
|
|
results: list[Any] = []
|
|
for stage_id in stage_ids:
|
|
if stage_id < 0 or stage_id >= self.num_stages:
|
|
results.append(
|
|
{
|
|
"supported": False,
|
|
"todo": True,
|
|
"error": f"Invalid stage id {stage_id}",
|
|
}
|
|
)
|
|
continue
|
|
|
|
stage_client = self.stage_clients[stage_id]
|
|
try:
|
|
if hasattr(stage_client, "collective_rpc_async"):
|
|
stage_result = await stage_client.collective_rpc_async(
|
|
method=method,
|
|
timeout=timeout,
|
|
args=args,
|
|
kwargs=kwargs,
|
|
)
|
|
else:
|
|
stage_result = {
|
|
"supported": False,
|
|
"todo": True,
|
|
"reason": (f"{stage_client.__class__.__name__}.collective_rpc_async is not implemented yet"),
|
|
}
|
|
except Exception as exc:
|
|
logger.exception(
|
|
"[Orchestrator] collective_rpc failed: stage=%s method=%s",
|
|
stage_id,
|
|
method,
|
|
)
|
|
stage_result = {
|
|
"supported": False,
|
|
"error": str(exc),
|
|
}
|
|
|
|
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,
|
|
}
|
|
)
|
|
|
|
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 clients."""
|
|
if self._stages_shutdown:
|
|
return
|
|
|
|
self._stages_shutdown = True
|
|
logger.info("[Orchestrator] Shutting down all stages")
|
|
for stage_id, stage_client in enumerate(self.stage_clients):
|
|
try:
|
|
stage_client.shutdown()
|
|
logger.info(f"[Orchestrator] Stage {stage_id} shut down")
|
|
except Exception as e:
|
|
logger.warning(f"[Orchestrator] Failed to shutdown stage {stage_id}: {e}")
|