JiangJie ZhangandSamit
c45ac74d43
Signed-off-by: jader <yjader@foxmail.com> Co-authored-by: Samit <285365963@qq.com>
1547 lines
67 KiB
Python
1547 lines
67 KiB
Python
"""
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Async Omni Engine for vLLM-Omni multi-stage runtime.
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AsyncOmniEngine in the caller's thread is a thin proxy that communicates
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with the Orchestrator (running in a background thread) via janus queues.
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"""
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from __future__ import annotations
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import asyncio
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import concurrent.futures
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import dataclasses
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import json
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import queue
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import threading
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import time
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import uuid
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import weakref
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from collections.abc import Mapping, Sequence
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from dataclasses import asdict
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from typing import Any, Literal, cast
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import janus
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import torch
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from omegaconf import OmegaConf
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from vllm import envs as vllm_envs
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from vllm.engine.arg_utils import EngineArgs
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from vllm.inputs import PromptType
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from vllm.logger import init_logger
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from vllm.v1.engine import EngineCoreRequest
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from vllm.v1.engine.input_processor import InputProcessor
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from vllm_omni.config.stage_config import strip_parent_engine_args
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from vllm_omni.diffusion.data import DiffusionParallelConfig, parse_attention_config
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from vllm_omni.diffusion.diffusion_engine import supports_audio_output
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from vllm_omni.engine import OmniEngineCoreRequest
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from vllm_omni.engine.messages import (
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AbortRequestMessage,
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AddCompanionRequestMessage,
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CollectiveRPCRequestMessage,
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CollectiveRPCResultMessage,
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EngineQueueMessage,
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ErrorMessage,
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ShutdownRequestMessage,
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StageSubmissionMessage,
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)
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from vllm_omni.engine.orchestrator import Orchestrator
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from vllm_omni.engine.serialization import (
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deserialize_additional_information,
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serialize_additional_information,
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)
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from vllm_omni.engine.stage_client import StageClient
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from vllm_omni.engine.stage_init_utils import build_stage0_input_processor
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from vllm_omni.engine.stage_pool import StagePool
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from vllm_omni.engine.stage_runtime import (
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StageRuntimeInfo,
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create_stage_runtime,
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)
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from vllm_omni.entrypoints.pd_utils import PDDisaggregationMixin
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from vllm_omni.entrypoints.utils import load_and_resolve_stage_configs
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from vllm_omni.inputs.data import OmniSamplingParams
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from vllm_omni.metrics.prometheus import OmniRequestCounter
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logger = init_logger(__name__)
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_STARTUP_POLL_INTERVAL_S = 1.0
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_REQUEST_QUEUE_MAXSIZE = 256
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# ============================================================================
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# Parent-EngineArgs field-routing contracts (consumed by
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# AsyncOmniEngine._strip_parent_engine_args when ``stage_configs_path`` is set).
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# ============================================================================
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# Fields that must survive the "equal to default → strip" filter because
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# diffusion stages need them even when equal to vllm's default value
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# (e.g. colocate worker setup relies on worker_extension_cls being forwarded).
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_PARENT_ARGS_KEEP: frozenset[str] = frozenset(
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{
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"worker_extension_cls",
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"allowed_local_media_path",
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"allowed_media_domains",
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# Legacy stage-config YAMLs may intentionally leave parallel or
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# distributed knobs unspecified at the stage level and rely on
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# top-level CLI values to fill them in during the per-stage merge.
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# Keep these fields so stages that omit them can inherit CLI values,
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# while stages with explicit YAML values still win because the legacy
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# stage-config loader prefers stage-local engine args.
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"tensor_parallel_size",
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}
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)
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# Omni orchestrator-level fields consumed by ``_resolve_stage_configs`` that
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# must never leak into per-stage EngineArgs (``stage_configs_path`` would
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# trigger the ``create_model_config`` guard).
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_PARENT_ARGS_STRIP: frozenset[str] = frozenset({"stage_configs_path"})
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# Fields always populated by callers (via ``from_cli_args`` / ``asdict``) so
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# their presence as an override is never a surprise — suppress the
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# "override ignored" warning for these.
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_PARENT_ARGS_NO_WARN: frozenset[str] = frozenset({"model"})
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def _inject_global_id(target: Any, request_id: str) -> None:
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"""Inject global_request_id into a prompt dict's additional_information."""
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if isinstance(target, dict):
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if "additional_information" not in target:
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target["additional_information"] = {}
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if target["additional_information"] is None:
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target["additional_information"] = {}
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if isinstance(target["additional_information"], dict):
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target["additional_information"]["global_request_id"] = [str(request_id)]
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def _upgrade_to_omni_request(
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request: EngineCoreRequest,
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raw_prompt: Any,
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) -> EngineCoreRequest:
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"""Restore omni-only fields omitted by upstream InputProcessor."""
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prompt_embeds = request.prompt_embeds
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additional_information = None
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if isinstance(raw_prompt, dict):
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if prompt_embeds is None:
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raw_prompt_embeds = raw_prompt.get("prompt_embeds")
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if isinstance(raw_prompt_embeds, torch.Tensor):
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prompt_embeds = raw_prompt_embeds
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additional_information = serialize_additional_information(
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raw_prompt.get("additional_information"),
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log_prefix="AsyncOmniEngine",
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)
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if prompt_embeds is None and additional_information is None:
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return request
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return OmniEngineCoreRequest.from_request(
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request,
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prompt_embeds=prompt_embeds,
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additional_information=additional_information,
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)
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def _apply_omni_final_stage_metadata(
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request: EngineCoreRequest,
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final_stage_id: int,
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) -> EngineCoreRequest:
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"""Tag EngineCoreRequest so OmniARScheduler can skip DiT KV when final_stage_id is 0."""
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merged: dict[str, Any] = {}
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if isinstance(request, OmniEngineCoreRequest) and request.additional_information is not None:
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merged = deserialize_additional_information(request.additional_information)
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merged["omni_final_stage_id"] = final_stage_id
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payload = serialize_additional_information(merged)
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return OmniEngineCoreRequest.from_request(
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request,
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additional_information=payload,
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)
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def _weak_shutdown_async_omni_engine(
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orchestrator_thread: threading.Thread | None,
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request_queue: janus.Queue[EngineQueueMessage] | None,
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output_queue: janus.Queue[EngineQueueMessage] | None,
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rpc_output_queue: janus.Queue[EngineQueueMessage] | None,
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) -> None:
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"""Best-effort orchestrator cleanup for GC finalization."""
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try:
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if request_queue is not None:
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request_queue.sync_q.put_nowait(ShutdownRequestMessage())
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except Exception:
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pass
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try:
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if orchestrator_thread is not None and orchestrator_thread.is_alive():
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orchestrator_thread.join()
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except Exception:
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pass
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for q in (request_queue, output_queue, rpc_output_queue):
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try:
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if q is not None:
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q.close()
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except Exception:
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pass
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class AsyncOmniEngine:
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"""Thin proxy that launches an Orchestrator in a background thread.
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All stage clients, input/output processors, and stage-to-stage transfer
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logic live inside the Orchestrator coroutine (running in its own thread
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with a dedicated asyncio event loop). This class communicates with it
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via janus queues (sync side for callers, async side for orchestrator).
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Args:
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model: Model name or path
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init_timeout: Total timeout waiting for orchestrator startup (seconds).
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stage_init_timeout: Timeout for stage initialization (seconds)
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**kwargs: Additional arguments
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"""
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# Class-level defaults so tests that bypass __init__ via object.__new__
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# don't AttributeError when stage-init / forward paths touch these attrs.
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_log_stats: bool = False
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_coordinator_runtime: Any = None
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_transfer_emitter: Any = None
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_enable_orch_monitor: bool = False
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def __init__(
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self,
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model: str,
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stage_init_timeout: int = 300,
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init_timeout: int = 600,
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diffusion_batch_size: int = 1,
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single_stage_mode: bool = False,
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transfer_emitter: Any = None,
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log_stats: bool = False,
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tokenizer: str | None = None,
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**kwargs: Any,
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) -> None:
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self.model = model
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self.tokenizer = tokenizer
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self.diffusion_batch_size = diffusion_batch_size
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# Cached by get_diffusion_od_config().
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self._diffusion_od_config_view: Any = None
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startup_timeout = int(init_timeout)
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# Forwarded into Orchestrator so its _forward_to_next_stage path can
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# emit per-edge transfer_tx_s / transfer_size_bytes histograms.
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# Optional: when None, Orchestrator silently skips TX emit (existing
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# RX path still works via OrchestratorAggregator).
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self._transfer_emitter = transfer_emitter
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# Drives upstream EngineCore + scheduler stats production. When False
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# the engine skips SchedulerStats / IterationStats; the per-(stage,
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# replica) vllm:* wrap stays registered but reads zero. Respects the
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# --log-stats CLI flag set by the user via OmniBase.
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self._log_stats = log_stats
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self._enable_orch_monitor = bool(kwargs.pop("enable_orch_monitor", False))
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logger.info(f"[AsyncOmniEngine] Initializing with model {model}")
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# ------------------------------------------------------------------ #
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# Single-stage mode detection #
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# ------------------------------------------------------------------ #
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# Single-stage mode is enabled when the caller explicitly passes #
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# single_stage_mode=True, or when a stage_id is provided in the args. #
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_stage_id_kwarg = kwargs.get("stage_id")
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if isinstance(_stage_id_kwarg, int) and not single_stage_mode:
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single_stage_mode = True
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self.single_stage_mode: bool = single_stage_mode
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self._single_stage_id_filter: int | None = (
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int(_stage_id_kwarg) if single_stage_mode and isinstance(_stage_id_kwarg, int) else None
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)
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self._omni_master_address: str | None = kwargs.get("omni_master_address")
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self._omni_master_port: int | None = kwargs.get("omni_master_port")
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# New omni-coordinator flags. Consumed only in single_stage_mode.
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# ``omni_dp_size_local`` is process-local: each invocation (head and
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# every headless) launches that many replicas for its own stage.
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self._omni_dp_size_local: int = int(kwargs.get("omni_dp_size_local") or 1)
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if self._omni_dp_size_local < 1:
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raise ValueError(f"--omni-dp-size-local must be >= 1, got {self._omni_dp_size_local}")
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self._omni_lb_policy: str = str(kwargs.get("omni_lb_policy") or "random")
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self._omni_heartbeat_timeout: float = float(kwargs.get("omni_heartbeat_timeout") or 30.0)
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if self._omni_heartbeat_timeout <= 0:
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raise ValueError(f"--omni-heartbeat-timeout must be > 0, got {self._omni_heartbeat_timeout}")
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if single_stage_mode:
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logger.info(
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"[AsyncOmniEngine] Single-stage mode enabled (stage_id_filter=%s, master=%s:%s)",
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self._single_stage_id_filter,
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self._omni_master_address,
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self._omni_master_port,
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)
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self.config_path, self.stage_configs = self._resolve_stage_configs(model, kwargs)
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self.num_stages = len(self.stage_configs)
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stage0_args = getattr(self.stage_configs[0], "engine_args", None) if self.num_stages > 0 else None
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self.async_chunk = bool(getattr(stage0_args, "async_chunk", False))
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self.stage_pools: list[StagePool] = []
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self.stage_clients: list[StageClient] = [] # logical-stage view for external readers
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self.input_processor: InputProcessor | None = None
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self.prompt_expand_func: Any | None = None
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self.supported_tasks: tuple[str, ...] = ("generate",)
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self.default_sampling_params_list: list[OmniSamplingParams] = []
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self.stage_metadata: list[StageRuntimeInfo] = []
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# Janus queues are constructed eagerly here (not deferred to the
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# orchestrator thread) so the master server's ROUTER thread always
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# sees a non-None ``self.request_queue`` when on_register fires.
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# ``async_q`` lazily binds to whatever event loop first awaits on
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# it (the orchestrator loop), so cross-thread use stays correct.
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self.request_queue: janus.Queue[EngineQueueMessage] = janus.Queue(maxsize=_REQUEST_QUEUE_MAXSIZE)
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self.output_queue: janus.Queue[EngineQueueMessage] = janus.Queue()
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self.rpc_output_queue: janus.Queue[EngineQueueMessage] = janus.Queue()
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self._shutdown_called = False
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self._weak_finalizer: weakref.finalize | None = None
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self._rpc_lock = threading.Lock()
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self._running_counter = OmniRequestCounter()
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logger.info(f"[AsyncOmniEngine] Launching Orchestrator thread with {self.num_stages} stages")
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# Launch orchestrator background thread
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startup_future: concurrent.futures.Future = concurrent.futures.Future()
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self.orchestrator_thread = threading.Thread(
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target=self._bootstrap_orchestrator,
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args=(
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stage_init_timeout,
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startup_future,
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),
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daemon=True,
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name="orchestrator",
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)
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self.orchestrator_thread.start()
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self._wait_for_orchestrator_init(startup_future, startup_timeout)
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# Stage runtime fields are assigned directly on self by the bootstrap thread.
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self._weak_finalizer = weakref.finalize(
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self,
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_weak_shutdown_async_omni_engine,
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self.orchestrator_thread,
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self.request_queue,
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self.output_queue,
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self.rpc_output_queue,
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)
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logger.info(f"[AsyncOmniEngine] Orchestrator ready with {self.num_stages} stages")
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def get_diffusion_od_config(self) -> Any:
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"""Expose the diffusion ``model_class_name`` to client-side model-extras.
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The worker holds the full config; here we just resolve the pipeline class
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name from the model config (cached). ``model_class_name`` may be ``None``.
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"""
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if self._diffusion_od_config_view is None:
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from types import SimpleNamespace
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from vllm_omni.diffusion.data import resolve_model_class_name
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self._diffusion_od_config_view = SimpleNamespace(model_class_name=resolve_model_class_name(self.model))
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return self._diffusion_od_config_view
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def _initialize_stages(self, stage_init_timeout: int) -> None:
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"""Initialize stage clients/processors via StageRuntime and assign to self."""
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self._runtime = create_stage_runtime(
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stage_configs=self.stage_configs,
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model=self.model,
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config_path=self.config_path,
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single_stage_mode=self.single_stage_mode,
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stage_init_timeout=stage_init_timeout,
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diffusion_batch_size=self.diffusion_batch_size,
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async_chunk=self.async_chunk,
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tokenizer=self.tokenizer,
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single_stage_id_filter=self._single_stage_id_filter,
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omni_master_address=self._omni_master_address,
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omni_master_port=self._omni_master_port,
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omni_dp_size_local=self._omni_dp_size_local,
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omni_heartbeat_timeout=self._omni_heartbeat_timeout,
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omni_lb_policy=self._omni_lb_policy,
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request_queue=self.request_queue,
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)
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self._runtime.initialize()
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self.num_stages = len(self.stage_configs)
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self.stage_pools = self._runtime.stage_pools
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self.stage_clients = [
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cast(StageClient, pool.stage_client) for pool in self.stage_pools if pool.stage_client is not None
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]
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self.stage_vllm_configs = [pool.stage_vllm_config for pool in self.stage_pools]
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self.output_processors = [pool.output_processor for pool in self.stage_pools]
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self.input_processor = (
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build_stage0_input_processor(self.stage_vllm_configs[0])
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if self.stage_vllm_configs and self.stage_vllm_configs[0] is not None
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else None
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)
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self.prompt_expand_func = next(
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(
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getattr(client, "prompt_expand_func", None)
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for client in self.stage_clients
|
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if getattr(client, "prompt_expand_func", None) is not None
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),
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None,
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)
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self.default_sampling_params_list = [client.default_sampling_params for client in self.stage_clients]
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self.stage_metadata = [
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StageRuntimeInfo(
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final_output=client.final_output,
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|
final_output_type=client.final_output_type,
|
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stage_type=client.stage_type,
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model_stage=getattr(client, "model_stage", None),
|
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)
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for client in self.stage_clients
|
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]
|
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supported_tasks: set[str] = set()
|
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if any(getattr(client, "is_comprehension", False) for client in self.stage_clients):
|
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supported_tasks.add("generate")
|
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if any(meta.final_output_type == "audio" for meta in self.stage_metadata):
|
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supported_tasks.add("speech")
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self.supported_tasks = tuple(supported_tasks) if supported_tasks else ("generate",)
|
|
|
|
def _bootstrap_orchestrator(
|
|
self,
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stage_init_timeout: int,
|
|
startup_future: concurrent.futures.Future,
|
|
) -> None:
|
|
"""Create loop, initialize stages, then run Orchestrator."""
|
|
|
|
loop = asyncio.new_event_loop()
|
|
asyncio.set_event_loop(loop)
|
|
|
|
async def _run_orchestrator() -> None:
|
|
self._initialize_stages(stage_init_timeout)
|
|
|
|
pd_config = self._detect_pd_config()
|
|
|
|
membership_controller = self._runtime.create_membership_controller()
|
|
|
|
orchestrator = Orchestrator(
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|
request_async_queue=self.request_queue.async_q,
|
|
output_async_queue=self.output_queue.async_q,
|
|
rpc_async_queue=self.rpc_output_queue.async_q,
|
|
stage_pools=self.stage_pools,
|
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async_chunk=self.async_chunk,
|
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pd_config=pd_config,
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membership_controller=membership_controller,
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running_counter=self._running_counter,
|
|
transfer_emitter=self._transfer_emitter,
|
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log_stats=self._log_stats,
|
|
enable_orch_monitor=self._enable_orch_monitor,
|
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)
|
|
if not startup_future.done():
|
|
startup_future.set_result(asyncio.get_running_loop())
|
|
await orchestrator.run()
|
|
|
|
try:
|
|
loop.run_until_complete(_run_orchestrator())
|
|
except Exception as e:
|
|
if not startup_future.done():
|
|
wrapped = RuntimeError(f"Orchestrator initialization failed: {e}")
|
|
wrapped.__cause__ = e
|
|
startup_future.set_exception(wrapped)
|
|
logger.exception("[AsyncOmniEngine] Orchestrator thread crashed")
|
|
error_text = str(e) or "Orchestrator thread crashed"
|
|
try:
|
|
error_msg = ErrorMessage(error=error_text, fatal=True)
|
|
if self.output_queue is not None:
|
|
self.output_queue.sync_q.put_nowait(error_msg)
|
|
if self.rpc_output_queue is not None:
|
|
self.rpc_output_queue.sync_q.put_nowait(error_msg)
|
|
except Exception:
|
|
pass
|
|
raise
|
|
finally:
|
|
try:
|
|
pending = [task for task in asyncio.all_tasks(loop) if not task.done()]
|
|
for task in pending:
|
|
task.cancel()
|
|
if pending:
|
|
loop.run_until_complete(asyncio.gather(*pending, return_exceptions=True))
|
|
loop.run_until_complete(loop.shutdown_asyncgens())
|
|
if hasattr(loop, "shutdown_default_executor"):
|
|
loop.run_until_complete(loop.shutdown_default_executor())
|
|
except Exception:
|
|
logger.exception("[AsyncOmniEngine] Failed during orchestrator loop cleanup")
|
|
finally:
|
|
asyncio.set_event_loop(None)
|
|
loop.close()
|
|
|
|
def _wait_for_orchestrator_init(self, startup_future: concurrent.futures.Future, startup_timeout: int) -> None:
|
|
"""
|
|
Wait for orchestrator startup future to return ready. Raises exception on any failures to the init process.
|
|
"""
|
|
deadline = time.monotonic() + startup_timeout
|
|
while True:
|
|
remaining = deadline - time.monotonic()
|
|
if remaining <= 0:
|
|
self._try_shutdown("[AsyncOmniEngine] Failed to cleanup after orchestrator startup timeout")
|
|
raise TimeoutError(f"Orchestrator did not become ready within {startup_timeout}s")
|
|
try:
|
|
startup_future.result(
|
|
timeout=min(remaining, _STARTUP_POLL_INTERVAL_S),
|
|
)
|
|
break
|
|
except concurrent.futures.TimeoutError:
|
|
if not self.orchestrator_thread.is_alive():
|
|
self._try_shutdown("[AsyncOmniEngine] Failed to cleanup after orchestrator startup failure")
|
|
if startup_future.done():
|
|
startup_future.result() # re-raises the real exception
|
|
raise RuntimeError("Orchestrator thread died during startup")
|
|
except Exception:
|
|
self._try_shutdown("[AsyncOmniEngine] Failed to cleanup after orchestrator startup failure")
|
|
raise
|
|
|
|
# ---- request helpers ----
|
|
|
|
@staticmethod
|
|
def _iter_multimodal_items(value: Any) -> list[Any]:
|
|
if value is None:
|
|
return []
|
|
if isinstance(value, list):
|
|
return value
|
|
return [value]
|
|
|
|
def _ensure_stage_replica_mm_uuids(
|
|
self,
|
|
prompt: Any,
|
|
*,
|
|
stage_id: int,
|
|
replica_id: int,
|
|
) -> None:
|
|
"""Make multimodal processor-cache keys local to a stage replica.
|
|
|
|
vLLM's frontend multimodal sender cache is process-global, while each
|
|
vllm-omni stage replica owns a separate EngineCore receiver cache. If
|
|
two requests with the same image are routed to different stage-0
|
|
replicas, a plain content hash can make the sender omit the tensor for
|
|
a replica that has never received it. Prefixing user/content UUIDs with
|
|
the selected replica keeps cache reuse within the receiver that owns it.
|
|
"""
|
|
|
|
if not isinstance(prompt, dict):
|
|
return
|
|
|
|
mm_data = prompt.get("multi_modal_data")
|
|
if not isinstance(mm_data, dict) or not mm_data:
|
|
return
|
|
|
|
from vllm.multimodal.hasher import MultiModalHasher
|
|
|
|
existing_uuids = prompt.get("multi_modal_uuids")
|
|
if not isinstance(existing_uuids, dict):
|
|
existing_uuids = {}
|
|
|
|
model_id = str(getattr(self, "model", ""))
|
|
scoped_uuids: dict[str, list[str | None]] = dict(existing_uuids)
|
|
for modality, raw_items in mm_data.items():
|
|
items = self._iter_multimodal_items(raw_items)
|
|
if not items:
|
|
continue
|
|
|
|
modality_existing = existing_uuids.get(modality)
|
|
if not isinstance(modality_existing, list):
|
|
modality_existing = [modality_existing] if modality_existing is not None else []
|
|
|
|
modality_uuids: list[str | None] = []
|
|
for idx, item in enumerate(items):
|
|
user_uuid = modality_existing[idx] if idx < len(modality_existing) else None
|
|
if user_uuid is not None:
|
|
base_uuid = str(user_uuid)
|
|
elif item is None:
|
|
base_uuid = None
|
|
else:
|
|
base_uuid = MultiModalHasher.hash_kwargs(
|
|
model_id=model_id,
|
|
**{modality: item},
|
|
)
|
|
|
|
if base_uuid is None:
|
|
modality_uuids.append(None)
|
|
else:
|
|
modality_uuids.append(f"stage{stage_id}:rep{replica_id}:{base_uuid}")
|
|
|
|
scoped_uuids[modality] = modality_uuids
|
|
|
|
if scoped_uuids:
|
|
prompt["multi_modal_uuids"] = scoped_uuids
|
|
|
|
@staticmethod
|
|
def _stage_pool_replica_count(stage_pool: Any) -> int:
|
|
try:
|
|
live_num_replicas = getattr(stage_pool, "live_num_replicas", None)
|
|
if live_num_replicas is not None:
|
|
return int(live_num_replicas)
|
|
except Exception:
|
|
pass
|
|
|
|
try:
|
|
live_replica_ids = getattr(stage_pool, "live_replica_ids", None)
|
|
if callable(live_replica_ids):
|
|
return len(live_replica_ids())
|
|
except Exception:
|
|
pass
|
|
|
|
try:
|
|
clients = getattr(stage_pool, "clients", None)
|
|
if clients is not None:
|
|
return sum(1 for client in clients if client is not None)
|
|
except Exception:
|
|
pass
|
|
|
|
return int(getattr(stage_pool, "num_replicas", 1) or 1)
|
|
|
|
@staticmethod
|
|
def _stage_pool_is_distributed(stage_pool: Any) -> bool:
|
|
try:
|
|
is_distributed = getattr(stage_pool, "is_distributed", None)
|
|
if is_distributed is not None:
|
|
return bool(is_distributed() if callable(is_distributed) else is_distributed)
|
|
except Exception:
|
|
pass
|
|
|
|
return getattr(stage_pool, "_hub", None) is not None
|
|
|
|
def _scope_stage0_multimodal_cache_to_replica(
|
|
self,
|
|
request_id: str,
|
|
prompt: Any,
|
|
) -> int | None:
|
|
stage_pools = getattr(self, "stage_pools", None)
|
|
if isinstance(prompt, EngineCoreRequest) or not stage_pools:
|
|
return None
|
|
|
|
stage0_pool = stage_pools[0]
|
|
# TODO: Currently only supports the ar -> dit process.
|
|
# Future scenarios (e.g., dit -> ar) need to be added, which will require modifications here.
|
|
if stage0_pool.stage_type == "diffusion" or self._stage_pool_replica_count(stage0_pool) <= 1:
|
|
return None
|
|
|
|
prompts = prompt if isinstance(prompt, list) else [prompt]
|
|
if not any(isinstance(p, dict) and p.get("multi_modal_data") for p in prompts):
|
|
return None
|
|
|
|
if self._stage_pool_is_distributed(stage0_pool):
|
|
preselect_replica_id = getattr(stage0_pool, "preselect_replica_id", None)
|
|
if not callable(preselect_replica_id):
|
|
logger.debug(
|
|
"[AsyncOmniEngine] Skipping stage-0 multimodal cache scoping for distributed routing "
|
|
"without preselect support req=%s",
|
|
request_id,
|
|
)
|
|
return None
|
|
replica_id = preselect_replica_id(request_id)
|
|
if replica_id is None:
|
|
logger.debug(
|
|
"[AsyncOmniEngine] Skipping stage-0 multimodal cache scoping for distributed routing "
|
|
"because no serviceable replica is available yet req=%s",
|
|
request_id,
|
|
)
|
|
return None
|
|
else:
|
|
replica_id = stage0_pool.select_replica_id(request_id)
|
|
|
|
for p in prompts:
|
|
self._ensure_stage_replica_mm_uuids(
|
|
p,
|
|
stage_id=0,
|
|
replica_id=replica_id,
|
|
)
|
|
|
|
logger.debug(
|
|
"[AsyncOmniEngine] Scoped multimodal cache keys to stage-0 replica-%s for req=%s",
|
|
replica_id,
|
|
request_id,
|
|
)
|
|
return replica_id
|
|
|
|
def _build_add_request_message(
|
|
self,
|
|
request_id: str,
|
|
prompt: EngineCoreRequest | PromptType,
|
|
prompt_text: str | None = None,
|
|
sampling_params_list: Sequence[Any] | None = None,
|
|
final_stage_id: int = 0,
|
|
final_output_stage_ids: Sequence[int] | None = None,
|
|
arrival_time: float | None = None,
|
|
lora_request: Any = None,
|
|
tokenization_kwargs: dict[str, Any] | None = None,
|
|
trace_headers: Mapping[str, str] | None = None,
|
|
priority: int = 0,
|
|
data_parallel_rank: int | None = None,
|
|
reasoning_ended: bool | None = None,
|
|
*,
|
|
resumable: bool = False,
|
|
message_type: Literal["add_request", "streaming_update"] = "add_request",
|
|
) -> StageSubmissionMessage:
|
|
"""Build an add_request message after stage-0 preprocessing."""
|
|
request_timestamp = float(arrival_time) if arrival_time is not None else time.time()
|
|
effective_sampling_params_list: list[OmniSamplingParams] = (
|
|
list(cast(Sequence[OmniSamplingParams], sampling_params_list))
|
|
if sampling_params_list is not None
|
|
else list(self.default_sampling_params_list)
|
|
)
|
|
if not effective_sampling_params_list:
|
|
raise ValueError(
|
|
f"Missing sampling params for stage 0. Got {len(effective_sampling_params_list)} stage params."
|
|
)
|
|
params = effective_sampling_params_list[0]
|
|
|
|
# Keep the original prompt for downstream stages (they need the raw
|
|
# dict, e.g. for multi_modal_data).
|
|
original_prompt = prompt
|
|
preselected_stage0_replica: int | None = None
|
|
|
|
stage_type = self.stage_metadata[0].stage_type
|
|
output_prompt_text: Any = None
|
|
_preprocess_ms = 0.0
|
|
if stage_type != "diffusion" and not isinstance(prompt, EngineCoreRequest):
|
|
# Inject global_request_id into the raw prompt.
|
|
if isinstance(prompt, dict):
|
|
_inject_global_id(prompt, request_id)
|
|
elif isinstance(prompt, list):
|
|
for item in prompt:
|
|
_inject_global_id(item, request_id)
|
|
|
|
preselected_stage0_replica = self._scope_stage0_multimodal_cache_to_replica(
|
|
request_id,
|
|
prompt,
|
|
)
|
|
|
|
# Full input processing (tokenization, multimodal, etc.)
|
|
_t_preprocess = time.perf_counter()
|
|
try:
|
|
request = self.input_processor.process_inputs(
|
|
request_id=request_id,
|
|
prompt=prompt,
|
|
params=params,
|
|
supported_tasks=self.supported_tasks,
|
|
arrival_time=arrival_time,
|
|
lora_request=lora_request,
|
|
tokenization_kwargs=tokenization_kwargs,
|
|
trace_headers=trace_headers,
|
|
priority=priority,
|
|
data_parallel_rank=data_parallel_rank,
|
|
resumable=resumable,
|
|
)
|
|
except Exception:
|
|
if preselected_stage0_replica is not None and self.stage_pools:
|
|
self.stage_pools[0].release_binding(request_id)
|
|
raise
|
|
_preprocess_ms = (time.perf_counter() - _t_preprocess) * 1000.0
|
|
# TODO (Peiqi): add this for Qwen3-TTS only. Other models don't have
|
|
# additional_information field in the prompt.
|
|
request = _upgrade_to_omni_request(request, prompt)
|
|
|
|
if reasoning_ended is not None:
|
|
request.reasoning_ended = reasoning_ended
|
|
|
|
# Restore external_req_id to the original user-facing request_id.
|
|
# InputProcessor.process_inputs() renames request_id to an internal
|
|
# UUID (saving the original in external_req_id), but then overwrites
|
|
# external_req_id with the new internal ID. We need external_req_id
|
|
# to match the key used in Orchestrator.request_states so that
|
|
# output routing (output.request_id lookup) can find the req_state.
|
|
request.external_req_id = request_id
|
|
request = _apply_omni_final_stage_metadata(request, final_stage_id)
|
|
|
|
# Registration with stage 0's output processor is deferred to the
|
|
# orchestrator thread (see Orchestrator._handle_add_request), which
|
|
# now routes admission through StagePool.submit_initial().
|
|
output_prompt_text = prompt_text
|
|
if output_prompt_text is None and isinstance(original_prompt, dict):
|
|
output_prompt_text = original_prompt.get("prompt")
|
|
prompt = request
|
|
|
|
return StageSubmissionMessage(
|
|
type=message_type,
|
|
request_id=request_id,
|
|
prompt=prompt,
|
|
original_prompt=original_prompt,
|
|
output_prompt_text=output_prompt_text,
|
|
sampling_params_list=effective_sampling_params_list,
|
|
final_stage_id=final_stage_id,
|
|
final_output_stage_ids=list(final_output_stage_ids) if final_output_stage_ids is not None else None,
|
|
preprocess_ms=_preprocess_ms,
|
|
request_timestamp=request_timestamp,
|
|
enqueue_ts=time.perf_counter(),
|
|
)
|
|
|
|
def _enqueue_cfg_companions(
|
|
self,
|
|
parent_id: str,
|
|
original_prompt: Any,
|
|
stage0_params: Any,
|
|
sampling_params_list: list[Any],
|
|
) -> None:
|
|
"""Expand prompt into CFG companions, process through InputProcessor, and enqueue."""
|
|
try:
|
|
expanded = self.prompt_expand_func(original_prompt, stage0_params)
|
|
except Exception:
|
|
logger.exception("[AsyncOmniEngine] prompt_expand_func failed for req %s", parent_id)
|
|
return
|
|
|
|
if not expanded:
|
|
return
|
|
|
|
for ep in expanded:
|
|
cid = f"{parent_id}{ep.request_id_suffix}"
|
|
companion_prompt = ep.prompt
|
|
|
|
companion_params, companion_spl = ep.apply_overrides(stage0_params, sampling_params_list)
|
|
|
|
if isinstance(companion_prompt, dict):
|
|
_inject_global_id(companion_prompt, cid)
|
|
|
|
request = self.input_processor.process_inputs(
|
|
request_id=cid,
|
|
prompt=companion_prompt,
|
|
params=companion_params,
|
|
supported_tasks=self.supported_tasks,
|
|
)
|
|
request.external_req_id = cid
|
|
|
|
# Registration of this companion on stage-0's output processor is
|
|
# deferred to Orchestrator._handle_add_companion, which routes
|
|
# admission through StagePool.submit_initial(..., affinity_request_id=...).
|
|
self.request_queue.sync_q.put(
|
|
AddCompanionRequestMessage(
|
|
companion_id=cid,
|
|
parent_id=parent_id,
|
|
role=ep.role,
|
|
prompt=request,
|
|
companion_prompt_text=companion_prompt,
|
|
sampling_params_list=companion_spl,
|
|
)
|
|
)
|
|
|
|
logger.info(
|
|
"[AsyncOmniEngine] CFG expansion for req %s: %d companions",
|
|
parent_id,
|
|
len(expanded),
|
|
)
|
|
|
|
@staticmethod
|
|
def _get_default_cache_config(cache_backend: str | None) -> dict[str, Any] | None:
|
|
if cache_backend == "cache_dit":
|
|
return {
|
|
"Fn_compute_blocks": 1,
|
|
"Bn_compute_blocks": 0,
|
|
"max_warmup_steps": 4,
|
|
"residual_diff_threshold": 0.24,
|
|
"max_continuous_cached_steps": 3,
|
|
"enable_taylorseer": False,
|
|
"taylorseer_order": 1,
|
|
"scm_steps_mask_policy": None,
|
|
"scm_steps_policy": "dynamic",
|
|
}
|
|
if cache_backend == "tea_cache":
|
|
return {
|
|
"rel_l1_thresh": 0.2,
|
|
}
|
|
if cache_backend == "mag_cache":
|
|
return {
|
|
"mag_threshold": 0.24,
|
|
"mag_max_skip_steps": 5,
|
|
"mag_retention_ratio": 0.1,
|
|
}
|
|
if cache_backend in ("step_cache"):
|
|
return {
|
|
"step_cache_dit_enabled": True,
|
|
"velocity_sim_thresholds": [0.95, 0.93],
|
|
"velocity_skip_countdowns": [4, 2],
|
|
"step_cache_dit_min_history": 2,
|
|
"step_cache_dit_max_history": 2,
|
|
}
|
|
return None
|
|
|
|
@staticmethod
|
|
def _normalize_cache_config(cache_backend: str | None, cache_config: Any | None) -> Any | None:
|
|
if isinstance(cache_config, str):
|
|
try:
|
|
cache_config = json.loads(cache_config)
|
|
except json.JSONDecodeError:
|
|
logger.warning("Invalid cache_config JSON, using defaults.")
|
|
cache_config = None
|
|
if cache_config is None and cache_backend not in (None, "", "none"):
|
|
cache_config = AsyncOmniEngine._get_default_cache_config(cache_backend)
|
|
return cache_config
|
|
|
|
def _detect_pd_config(self) -> dict[str, Any] | None:
|
|
"""Detect PD (Prefill-Decode) disaggregation config from stage_configs.
|
|
Returns a dict with 'pd_pair' and 'bootstrap_addr', or None.
|
|
"""
|
|
pd_pair = PDDisaggregationMixin.detect_pd_separation_from_stage_configs(self.stage_configs)
|
|
if pd_pair is None:
|
|
return None
|
|
prefill_idx, decode_idx = pd_pair
|
|
|
|
# Extract bootstrap address from prefill stage engine_args
|
|
bootstrap_addr: str | None = None
|
|
try:
|
|
prefill_cfg = self.stage_configs[prefill_idx]
|
|
ea = getattr(prefill_cfg, "engine_args", None)
|
|
kv_cfg = getattr(ea, "kv_transfer_config", None) if ea is not None else None
|
|
if kv_cfg is not None:
|
|
port = vllm_envs.VLLM_MOONCAKE_BOOTSTRAP_PORT
|
|
kv_ip = getattr(kv_cfg, "kv_ip", None) or "127.0.0.1"
|
|
bootstrap_addr = f"http://{kv_ip}:{port}"
|
|
except Exception as exc:
|
|
logger.warning("[AsyncOmniEngine] Could not extract PD bootstrap address: %s", exc)
|
|
|
|
logger.info(
|
|
"[AsyncOmniEngine] PD disaggregation detected: prefill=stage-%d, decode=stage-%d, bootstrap=%s",
|
|
prefill_idx,
|
|
decode_idx,
|
|
bootstrap_addr,
|
|
)
|
|
prefill_engine_id: str | None = None
|
|
try:
|
|
prefill_client = self.stage_clients[prefill_idx]
|
|
kv_cfg = getattr(getattr(prefill_client, "vllm_config", None), "kv_transfer_config", None)
|
|
prefill_engine_id = getattr(kv_cfg, "engine_id", None)
|
|
except Exception as exc:
|
|
logger.warning("[AsyncOmniEngine] Could not extract prefill engine_id: %s", exc)
|
|
|
|
return {
|
|
"pd_pair": (prefill_idx, decode_idx),
|
|
"bootstrap_addr": bootstrap_addr,
|
|
"prefill_engine_id": prefill_engine_id,
|
|
}
|
|
|
|
@staticmethod
|
|
def _create_default_diffusion_stage_cfg(kwargs: dict[str, Any]) -> list:
|
|
"""Create a default single-stage diffusion config from kwargs."""
|
|
# We temporally create a default config for diffusion stage.
|
|
# In the future, we should merge the default config with the user-provided config.
|
|
normalized_kwargs = dict(kwargs)
|
|
default_sampling_params = normalized_kwargs.get("default_sampling_params")
|
|
if isinstance(default_sampling_params, str):
|
|
try:
|
|
default_sampling_params = json.loads(default_sampling_params)
|
|
except json.JSONDecodeError:
|
|
logger.warning("Invalid default_sampling_params JSON, ignoring stage defaults.")
|
|
default_sampling_params = None
|
|
if not isinstance(default_sampling_params, dict):
|
|
default_sampling_params = None
|
|
stage_default_sampling_params = default_sampling_params.get("0", {}) if default_sampling_params else {}
|
|
if normalized_kwargs.get("dtype") is None:
|
|
normalized_kwargs["dtype"] = "auto"
|
|
|
|
# TODO: hack, convert dtype to string to avoid non-premitive omegaconf create error.
|
|
if "dtype" in normalized_kwargs and not isinstance(normalized_kwargs["dtype"], str):
|
|
if not isinstance(normalized_kwargs["dtype"], torch.dtype):
|
|
raise TypeError(
|
|
f"Provided dtype must be a string or torch.dtype, got {type(normalized_kwargs['dtype']).__name__}"
|
|
)
|
|
normalized_kwargs["dtype"] = str(normalized_kwargs["dtype"]).removeprefix("torch.")
|
|
|
|
cache_backend = normalized_kwargs.get("cache_backend", "none")
|
|
cache_config = AsyncOmniEngine._normalize_cache_config(
|
|
cache_backend,
|
|
normalized_kwargs.get("cache_config", None),
|
|
)
|
|
|
|
parallel_config = normalized_kwargs.get("parallel_config")
|
|
if isinstance(parallel_config, dict):
|
|
parallel_config = DiffusionParallelConfig.from_dict(parallel_config)
|
|
if parallel_config is None:
|
|
ulysses_degree = normalized_kwargs.get("ulysses_degree") or 1
|
|
ring_degree = normalized_kwargs.get("ring_degree") or 1
|
|
ulysses_mode = normalized_kwargs.get("ulysses_mode") or "strict"
|
|
sequence_parallel_size = normalized_kwargs.get("sequence_parallel_size")
|
|
pipeline_parallel_size = normalized_kwargs.get("pipeline_parallel_size") or 1
|
|
data_parallel_size = normalized_kwargs.get("data_parallel_size") or 1
|
|
tensor_parallel_size = normalized_kwargs.get("tensor_parallel_size") or 1
|
|
cfg_parallel_size = normalized_kwargs.get("cfg_parallel_size") or 1
|
|
pipeline_parallel_size = normalized_kwargs.get("pipeline_parallel_size") or 1
|
|
vae_patch_parallel_size = normalized_kwargs.get("vae_patch_parallel_size") or 1
|
|
vae_parallel_mode = normalized_kwargs.get("vae_parallel_mode") or "tile"
|
|
enable_expert_parallel = normalized_kwargs.get("enable_expert_parallel") or False
|
|
use_hsdp = normalized_kwargs.get("use_hsdp", False)
|
|
hsdp_shard_size = normalized_kwargs.get("hsdp_shard_size", -1)
|
|
hsdp_replicate_size = normalized_kwargs.get("hsdp_replicate_size", 1)
|
|
if sequence_parallel_size is None:
|
|
sequence_parallel_size = ulysses_degree * ring_degree
|
|
|
|
parallel_config = DiffusionParallelConfig(
|
|
pipeline_parallel_size=pipeline_parallel_size,
|
|
data_parallel_size=data_parallel_size,
|
|
tensor_parallel_size=tensor_parallel_size,
|
|
enable_expert_parallel=enable_expert_parallel,
|
|
sequence_parallel_size=sequence_parallel_size,
|
|
ulysses_degree=ulysses_degree,
|
|
ring_degree=ring_degree,
|
|
ulysses_mode=ulysses_mode,
|
|
cfg_parallel_size=cfg_parallel_size,
|
|
vae_patch_parallel_size=vae_patch_parallel_size,
|
|
vae_parallel_mode=vae_parallel_mode,
|
|
use_hsdp=use_hsdp,
|
|
hsdp_shard_size=hsdp_shard_size,
|
|
hsdp_replicate_size=hsdp_replicate_size,
|
|
)
|
|
|
|
num_devices = max(1, int(parallel_config.world_size))
|
|
devices = ",".join(str(i) for i in range(num_devices))
|
|
model_class_name = kwargs.get("model_class_name", None)
|
|
final_output_type = "audio" if model_class_name and supports_audio_output(model_class_name) else "image"
|
|
|
|
attention_config = None
|
|
if (
|
|
kwargs.get("diffusion_attention_config") is not None
|
|
or kwargs.get("diffusion_attention_backend") is not None
|
|
):
|
|
attention_config = parse_attention_config(
|
|
kwargs.get("diffusion_attention_config"),
|
|
attention_backend=kwargs.get("diffusion_attention_backend"),
|
|
)
|
|
|
|
stage_engine_args = {
|
|
"max_num_seqs": kwargs.get("max_num_seqs") or 1,
|
|
"parallel_config": parallel_config,
|
|
"model_class_name": kwargs.get("model_class_name", None),
|
|
"model_config": kwargs.get("model_config", None),
|
|
"additional_config": kwargs.get("additional_config", None),
|
|
"step_execution": kwargs.get("step_execution", False),
|
|
"request_batch_max_wait_ms": kwargs.get("request_batch_max_wait_ms", 0.0),
|
|
"vae_use_slicing": kwargs.get("vae_use_slicing", False),
|
|
"vae_use_tiling": kwargs.get("vae_use_tiling", False),
|
|
"cache_backend": cache_backend,
|
|
"cache_config": cache_config,
|
|
"enable_cache_dit_summary": kwargs.get("enable_cache_dit_summary", False),
|
|
"enable_cpu_offload": kwargs.get("enable_cpu_offload", False),
|
|
"enable_layerwise_offload": kwargs.get("enable_layerwise_offload", False),
|
|
"enforce_eager": False if kwargs.get("enforce_eager") is None else kwargs.get("enforce_eager"),
|
|
"boundary_ratio": kwargs.get("boundary_ratio", None),
|
|
"flow_shift": kwargs.get("flow_shift", None),
|
|
"diffusion_load_format": kwargs.get("diffusion_load_format", "default"),
|
|
"custom_pipeline_args": kwargs.get("custom_pipeline_args", None),
|
|
"worker_extension_cls": kwargs.get("worker_extension_cls", None),
|
|
"trust_remote_code": (False if kwargs.get("trust_remote_code") is None else kwargs["trust_remote_code"]),
|
|
"distributed_executor_backend": (
|
|
"mp" if kwargs.get("distributed_executor_backend") is None else kwargs["distributed_executor_backend"]
|
|
),
|
|
"enable_sleep_mode": kwargs.get("enable_sleep_mode", False),
|
|
"enable_prompt_embed_cache": kwargs.get("enable_prompt_embed_cache", False),
|
|
"prompt_embed_cache_size": kwargs.get("prompt_embed_cache_size", 32),
|
|
"enable_multithread_weight_load": kwargs.get("enable_multithread_weight_load", True),
|
|
"num_weight_load_threads": kwargs.get("num_weight_load_threads", 4),
|
|
"quantization": kwargs.get("quantization", None),
|
|
"diffusion_kv_cache_dtype": kwargs.get("diffusion_kv_cache_dtype", None),
|
|
"diffusion_kv_cache_skip_steps": kwargs.get("diffusion_kv_cache_skip_steps", None),
|
|
"diffusion_kv_cache_skip_layers": kwargs.get("diffusion_kv_cache_skip_layers", None),
|
|
**({"diffusion_attention_config": attention_config} if attention_config is not None else {}),
|
|
"force_cutlass_fp8": bool(kwargs.get("force_cutlass_fp8", False)),
|
|
"enable_diffusion_pipeline_profiler": kwargs.get("enable_diffusion_pipeline_profiler", False),
|
|
"streaming_output": kwargs.get("diffusion_streaming_output", False),
|
|
"enable_ar_profiler": kwargs.get("enable_ar_profiler", False),
|
|
"extras": {
|
|
"auxiliary_text_encoder": kwargs.get("auxiliary_text_encoder", None),
|
|
"default_llama_model_id": kwargs.get("default_llama_model_id", "meta-llama/Meta-Llama-3.1-8B-Instruct"),
|
|
},
|
|
**(
|
|
{
|
|
"profiler_config": asdict(kwargs["profiler_config"])
|
|
if hasattr(kwargs["profiler_config"], "__dataclass_fields__")
|
|
else kwargs["profiler_config"]
|
|
}
|
|
if kwargs.get("profiler_config") is not None
|
|
else {}
|
|
),
|
|
}
|
|
# Only set dtype if it was already explicitly passed and normalized
|
|
if "dtype" in normalized_kwargs:
|
|
stage_engine_args["dtype"] = normalized_kwargs["dtype"]
|
|
|
|
# New split fields for diffusers adapter kwargs.
|
|
if kwargs.get("diffusers_load_kwargs") is not None:
|
|
stage_engine_args["diffusers_load_kwargs"] = kwargs["diffusers_load_kwargs"]
|
|
if kwargs.get("diffusers_call_kwargs") is not None:
|
|
stage_engine_args["diffusers_call_kwargs"] = kwargs["diffusers_call_kwargs"]
|
|
|
|
default_stage_cfg = [
|
|
{
|
|
"stage_id": 0,
|
|
"stage_type": "diffusion",
|
|
"runtime": {
|
|
"process": True,
|
|
"devices": devices,
|
|
},
|
|
"engine_args": stage_engine_args,
|
|
"default_sampling_params": stage_default_sampling_params,
|
|
"final_output": True,
|
|
"final_output_type": final_output_type,
|
|
}
|
|
]
|
|
default_stage_cfg[0]["engine_args"]["model_stage"] = "diffusion"
|
|
return default_stage_cfg
|
|
|
|
@staticmethod
|
|
def _strip_single_engine_args(kwargs: dict[str, Any]) -> dict[str, Any]:
|
|
"""Remove parent ``EngineArgs`` fields from *kwargs*.
|
|
|
|
When ``stage_configs_path`` is set, per-stage engine args are defined
|
|
in the YAML. Top-level single-engine fields (``compilation_config``,
|
|
``tensor_parallel_size``, …) must not leak into per-stage configs via
|
|
the ``base_engine_args`` merge in ``load_stage_configs_from_yaml`` —
|
|
they can cause type errors (e.g. ``compilation_config`` as a JSON
|
|
string rejected by ``VllmConfig``) or silently override YAML values.
|
|
|
|
Logs a warning for any parent field whose value differs from the
|
|
dataclass default, so users know their explicit overrides are ignored.
|
|
See the module-level ``_PARENT_ARGS_*`` constants for the routing
|
|
contracts this method enforces.
|
|
"""
|
|
parent_fields: dict[str, dataclasses.Field] = {f.name: f for f in dataclasses.fields(EngineArgs)}
|
|
result, overridden = strip_parent_engine_args(
|
|
kwargs,
|
|
parent_fields=parent_fields,
|
|
keep_keys=_PARENT_ARGS_KEEP,
|
|
strip_keys=_PARENT_ARGS_STRIP,
|
|
no_warn_keys=_PARENT_ARGS_NO_WARN,
|
|
)
|
|
|
|
if overridden:
|
|
logger.warning(
|
|
"stage_configs_path is set — the following top-level engine "
|
|
"args are ignored (per-stage YAML takes precedence): %s",
|
|
", ".join(sorted(overridden)),
|
|
)
|
|
|
|
return result
|
|
|
|
def _resolve_stage_configs(self, model: str, kwargs: dict[str, Any]) -> tuple[str, list[Any]]:
|
|
"""Resolve stage configs and inject defaults shared by orchestrator/headless."""
|
|
|
|
stage_configs_path = kwargs.get("stage_configs_path", None)
|
|
deploy_config_path = kwargs.pop("deploy_config", None)
|
|
stage_overrides_json = kwargs.pop("stage_overrides", None)
|
|
explicit_stage_configs = kwargs.pop("stage_configs", None)
|
|
if explicit_stage_configs is not None:
|
|
logger.warning(
|
|
"`stage_configs` is not part of the public API. "
|
|
"Ignoring it and resolving stages from stage_configs_path/model factory."
|
|
)
|
|
|
|
if stage_configs_path is not None:
|
|
base_kwargs = self._strip_single_engine_args(kwargs)
|
|
else:
|
|
base_kwargs = kwargs
|
|
|
|
# Parse --stage-overrides JSON string if provided
|
|
stage_overrides = None
|
|
if stage_overrides_json:
|
|
if isinstance(stage_overrides_json, str):
|
|
try:
|
|
stage_overrides = json.loads(stage_overrides_json)
|
|
except json.JSONDecodeError as exc:
|
|
raise ValueError(
|
|
f"--stage-overrides is not valid JSON: {exc}. Got: {stage_overrides_json!r}"
|
|
) from exc
|
|
else:
|
|
stage_overrides = stage_overrides_json
|
|
|
|
config_path, stage_configs = load_and_resolve_stage_configs(
|
|
model,
|
|
stage_configs_path,
|
|
base_kwargs,
|
|
default_stage_cfg_factory=lambda: self._create_default_diffusion_stage_cfg(kwargs),
|
|
deploy_config_path=deploy_config_path,
|
|
stage_overrides=stage_overrides,
|
|
)
|
|
|
|
# Inject diffusion LoRA-related knobs from kwargs if not present in the stage config.
|
|
for cfg in stage_configs:
|
|
try:
|
|
if not hasattr(cfg, "engine_args") or cfg.engine_args is None:
|
|
cfg.engine_args = OmegaConf.create({})
|
|
global_sleep_mode = kwargs.get("enable_sleep_mode")
|
|
if global_sleep_mode is not None:
|
|
if not hasattr(cfg.engine_args, "enable_sleep_mode") or cfg.engine_args.enable_sleep_mode is None:
|
|
cfg.engine_args.enable_sleep_mode = global_sleep_mode
|
|
if getattr(cfg, "stage_type", None) != "diffusion":
|
|
continue
|
|
if not hasattr(cfg, "engine_args") or cfg.engine_args is None:
|
|
cfg.engine_args = OmegaConf.create({})
|
|
additional_config = kwargs.get("additional_config")
|
|
if additional_config is not None:
|
|
current_additional_config = getattr(cfg.engine_args, "additional_config", None)
|
|
if current_additional_config in (None, {}):
|
|
cfg.engine_args.additional_config = additional_config
|
|
if kwargs.get("lora_path") is not None:
|
|
if not hasattr(cfg.engine_args, "lora_path") or cfg.engine_args.lora_path is None:
|
|
cfg.engine_args.lora_path = kwargs["lora_path"]
|
|
lora_scale = kwargs.get("lora_scale")
|
|
if lora_scale is None:
|
|
# Backwards compatibility for older callers.
|
|
lora_scale = kwargs.get("static_lora_scale")
|
|
if lora_scale is not None:
|
|
if not hasattr(cfg.engine_args, "lora_scale") or cfg.engine_args.lora_scale is None:
|
|
cfg.engine_args.lora_scale = lora_scale
|
|
if (
|
|
kwargs.get("diffusion_attention_config") is not None
|
|
or kwargs.get("diffusion_attention_backend") is not None
|
|
):
|
|
has_stage_attention = (
|
|
hasattr(cfg.engine_args, "diffusion_attention_config")
|
|
and cfg.engine_args.diffusion_attention_config is not None
|
|
)
|
|
if not has_stage_attention:
|
|
cfg.engine_args.diffusion_attention_config = parse_attention_config(
|
|
kwargs.get("diffusion_attention_config"),
|
|
attention_backend=kwargs.get("diffusion_attention_backend"),
|
|
)
|
|
quantization_config = kwargs.get("diffusion_quantization_config")
|
|
if quantization_config is not None:
|
|
if (
|
|
not hasattr(cfg.engine_args, "quantization_config")
|
|
or cfg.engine_args.quantization_config is None
|
|
):
|
|
cfg.engine_args.quantization_config = quantization_config
|
|
# Inject profiler flags for diffusion stages
|
|
for profiler_key in (
|
|
"enable_diffusion_pipeline_profiler",
|
|
"enable_ar_profiler",
|
|
):
|
|
val = kwargs.get(profiler_key)
|
|
if val:
|
|
if not hasattr(cfg.engine_args, profiler_key) or not getattr(
|
|
cfg.engine_args, profiler_key, False
|
|
):
|
|
setattr(cfg.engine_args, profiler_key, val)
|
|
quantization = kwargs.get("quantization")
|
|
if quantization is not None:
|
|
if not hasattr(cfg.engine_args, "quantization") or cfg.engine_args.quantization is None:
|
|
cfg.engine_args.quantization = quantization
|
|
diffusion_kv_cache_dtype = kwargs.get("diffusion_kv_cache_dtype")
|
|
if diffusion_kv_cache_dtype is not None:
|
|
if (
|
|
not hasattr(cfg.engine_args, "diffusion_kv_cache_dtype")
|
|
or cfg.engine_args.diffusion_kv_cache_dtype is None
|
|
):
|
|
cfg.engine_args.diffusion_kv_cache_dtype = diffusion_kv_cache_dtype
|
|
diffusion_kv_cache_skip_steps = kwargs.get("diffusion_kv_cache_skip_steps")
|
|
if diffusion_kv_cache_skip_steps is not None:
|
|
if (
|
|
not hasattr(cfg.engine_args, "diffusion_kv_cache_skip_steps")
|
|
or cfg.engine_args.diffusion_kv_cache_skip_steps is None
|
|
):
|
|
cfg.engine_args.diffusion_kv_cache_skip_steps = diffusion_kv_cache_skip_steps
|
|
diffusion_kv_cache_skip_layers = kwargs.get("diffusion_kv_cache_skip_layers")
|
|
if diffusion_kv_cache_skip_layers is not None:
|
|
if (
|
|
not hasattr(cfg.engine_args, "diffusion_kv_cache_skip_layers")
|
|
or cfg.engine_args.diffusion_kv_cache_skip_layers is None
|
|
):
|
|
cfg.engine_args.diffusion_kv_cache_skip_layers = diffusion_kv_cache_skip_layers
|
|
except Exception as e:
|
|
logger.warning("Failed to inject LoRA config for stage: %s", e)
|
|
|
|
return config_path, stage_configs
|
|
|
|
# ==================== Public API ====================
|
|
|
|
def add_request(
|
|
self,
|
|
request_id: str,
|
|
prompt: EngineCoreRequest | PromptType,
|
|
prompt_text: str | None = None,
|
|
sampling_params_list: Sequence[Any] | None = None,
|
|
final_stage_id: int = 0,
|
|
final_output_stage_ids: Sequence[int] | None = None,
|
|
arrival_time: float | None = None,
|
|
lora_request: Any = None,
|
|
tokenization_kwargs: dict[str, Any] | None = None,
|
|
trace_headers: Mapping[str, str] | None = None,
|
|
priority: int = 0,
|
|
data_parallel_rank: int | None = None,
|
|
reasoning_ended: bool | None = None,
|
|
*,
|
|
resumable: bool = False,
|
|
) -> None:
|
|
"""Process stage-0 input locally, then send to the Orchestrator.
|
|
|
|
Input processing and output
|
|
processor registration happen here in the caller's thread, avoiding
|
|
a queue + coroutine-switch round-trip. The Orchestrator receives a
|
|
ready-to-submit OmniEngineCoreRequest.
|
|
"""
|
|
msg = self._build_add_request_message(
|
|
request_id=request_id,
|
|
prompt=prompt,
|
|
prompt_text=prompt_text,
|
|
sampling_params_list=sampling_params_list,
|
|
final_stage_id=final_stage_id,
|
|
final_output_stage_ids=final_output_stage_ids,
|
|
arrival_time=arrival_time,
|
|
lora_request=lora_request,
|
|
tokenization_kwargs=tokenization_kwargs,
|
|
trace_headers=trace_headers,
|
|
priority=priority,
|
|
data_parallel_rank=data_parallel_rank,
|
|
reasoning_ended=reasoning_ended,
|
|
resumable=resumable,
|
|
)
|
|
self.request_queue.sync_q.put(msg)
|
|
|
|
# CFG companion expansion: create and enqueue companion requests
|
|
# so the AR stage also generates their KV caches.
|
|
if self.prompt_expand_func is not None and final_stage_id > 0:
|
|
original_prompt = msg.original_prompt
|
|
effective_spl = msg.sampling_params_list
|
|
stage0_params = effective_spl[0] if effective_spl else None
|
|
if stage0_params is not None:
|
|
self._enqueue_cfg_companions(request_id, original_prompt, stage0_params, effective_spl)
|
|
|
|
async def add_request_async(
|
|
self,
|
|
request_id: str,
|
|
prompt: EngineCoreRequest | PromptType,
|
|
prompt_text: str | None = None,
|
|
sampling_params_list: Sequence[Any] | None = None,
|
|
final_stage_id: int = 0,
|
|
final_output_stage_ids: Sequence[int] | None = None,
|
|
arrival_time: float | None = None,
|
|
lora_request: Any = None,
|
|
tokenization_kwargs: dict[str, Any] | None = None,
|
|
trace_headers: Mapping[str, str] | None = None,
|
|
priority: int = 0,
|
|
data_parallel_rank: int | None = None,
|
|
reasoning_ended: bool | None = None,
|
|
*,
|
|
resumable: bool = False,
|
|
) -> None:
|
|
"""Async add_request API."""
|
|
self.add_request(
|
|
request_id=request_id,
|
|
prompt=prompt,
|
|
prompt_text=prompt_text,
|
|
sampling_params_list=sampling_params_list,
|
|
final_stage_id=final_stage_id,
|
|
final_output_stage_ids=final_output_stage_ids,
|
|
arrival_time=arrival_time,
|
|
lora_request=lora_request,
|
|
tokenization_kwargs=tokenization_kwargs,
|
|
trace_headers=trace_headers,
|
|
priority=priority,
|
|
data_parallel_rank=data_parallel_rank,
|
|
reasoning_ended=reasoning_ended,
|
|
resumable=resumable,
|
|
)
|
|
|
|
def add_streaming_update(
|
|
self,
|
|
request_id: str,
|
|
prompt: EngineCoreRequest | PromptType,
|
|
prompt_text: str | None = None,
|
|
sampling_params_list: Sequence[Any] | None = None,
|
|
final_stage_id: int = 0,
|
|
final_output_stage_ids: Sequence[int] | None = None,
|
|
arrival_time: float | None = None,
|
|
*,
|
|
resumable: bool = True,
|
|
) -> None:
|
|
"""Send an incremental streaming update for an existing request."""
|
|
msg = self._build_add_request_message(
|
|
request_id=request_id,
|
|
prompt=prompt,
|
|
prompt_text=prompt_text,
|
|
sampling_params_list=sampling_params_list,
|
|
final_stage_id=final_stage_id,
|
|
final_output_stage_ids=final_output_stage_ids,
|
|
arrival_time=arrival_time,
|
|
resumable=resumable,
|
|
message_type="streaming_update",
|
|
)
|
|
self.request_queue.sync_q.put(msg)
|
|
|
|
async def add_streaming_update_async(
|
|
self,
|
|
request_id: str,
|
|
prompt: EngineCoreRequest | PromptType,
|
|
prompt_text: str | None = None,
|
|
sampling_params_list: Sequence[Any] | None = None,
|
|
final_stage_id: int = 0,
|
|
final_output_stage_ids: Sequence[int] | None = None,
|
|
arrival_time: float | None = None,
|
|
*,
|
|
resumable: bool = True,
|
|
) -> None:
|
|
"""Async wrapper for add_streaming_update()."""
|
|
self.add_streaming_update(
|
|
request_id=request_id,
|
|
prompt=prompt,
|
|
prompt_text=prompt_text,
|
|
sampling_params_list=sampling_params_list,
|
|
final_stage_id=final_stage_id,
|
|
final_output_stage_ids=final_output_stage_ids,
|
|
arrival_time=arrival_time,
|
|
resumable=resumable,
|
|
)
|
|
|
|
def try_get_output(self, timeout: float = 0.001) -> EngineQueueMessage | None:
|
|
"""Read one output message from the Orchestrator output queue."""
|
|
try:
|
|
return self.output_queue.sync_q.get(timeout=timeout)
|
|
except queue.Empty:
|
|
if not self.is_alive():
|
|
raise RuntimeError("Orchestrator died unexpectedly. See logs above.")
|
|
return None
|
|
|
|
async def try_get_output_async(self) -> EngineQueueMessage | None:
|
|
"""Async read from the Orchestrator output queue."""
|
|
try:
|
|
return self.output_queue.sync_q.get_nowait()
|
|
except queue.Empty:
|
|
if not self.is_alive():
|
|
raise RuntimeError("Orchestrator died unexpectedly. See logs above.")
|
|
return None
|
|
|
|
def get_stage_metadata(self, stage_id: int) -> StageRuntimeInfo:
|
|
"""Get cached metadata for a stage."""
|
|
return self.stage_metadata[stage_id]
|
|
|
|
def abort(self, request_ids: list[str]) -> None:
|
|
"""Send abort message to the Orchestrator."""
|
|
if self.request_queue is None:
|
|
raise RuntimeError("request_queue is not initialized")
|
|
self.request_queue.sync_q.put(AbortRequestMessage(request_ids=request_ids))
|
|
|
|
async def abort_async(self, request_ids: list[str]) -> None:
|
|
"""Async abort API."""
|
|
self.abort(request_ids)
|
|
|
|
def collective_rpc(
|
|
self,
|
|
method: str,
|
|
timeout: float | None = None,
|
|
args: tuple[Any, ...] = (),
|
|
kwargs: dict[str, Any] | None = None,
|
|
stage_ids: list[int] | None = None,
|
|
) -> list[Any]:
|
|
"""Send a control RPC to the Orchestrator and wait for aggregated results.
|
|
|
|
This uses a dedicated RPC output queue so control-plane messages do not
|
|
race with the normal request output polling loop.
|
|
"""
|
|
rpc_id = uuid.uuid4().hex
|
|
msg = CollectiveRPCRequestMessage(
|
|
rpc_id=rpc_id,
|
|
method=method,
|
|
timeout=timeout,
|
|
args=tuple(args),
|
|
kwargs=kwargs or {},
|
|
stage_ids=stage_ids,
|
|
)
|
|
|
|
with self._rpc_lock:
|
|
self.request_queue.sync_q.put(msg)
|
|
deadline = None if timeout is None else time.monotonic() + timeout
|
|
|
|
while True:
|
|
remaining = None if deadline is None else max(0.0, deadline - time.monotonic())
|
|
try:
|
|
result_msg = self.rpc_output_queue.sync_q.get(timeout=remaining)
|
|
except queue.Empty as exc:
|
|
raise TimeoutError(f"collective_rpc timed out after {timeout} seconds") from exc
|
|
|
|
if isinstance(result_msg, ErrorMessage):
|
|
raise RuntimeError(result_msg.error)
|
|
|
|
if not isinstance(result_msg, CollectiveRPCResultMessage):
|
|
logger.warning(
|
|
"[AsyncOmniEngine] Dropping unexpected rpc queue message type=%s",
|
|
getattr(result_msg, "type", type(result_msg).__name__),
|
|
)
|
|
continue
|
|
|
|
if result_msg.rpc_id != rpc_id:
|
|
logger.warning(
|
|
"[AsyncOmniEngine] Dropping mismatched rpc result rpc_id=%s expected=%s",
|
|
result_msg.rpc_id,
|
|
rpc_id,
|
|
)
|
|
continue
|
|
|
|
return list(result_msg.results)
|
|
|
|
async def collective_rpc_async(
|
|
self,
|
|
method: str,
|
|
timeout: float | None = None,
|
|
args: tuple[Any, ...] = (),
|
|
kwargs: dict[str, Any] | None = None,
|
|
stage_ids: list[int] | None = None,
|
|
) -> list[Any]:
|
|
"""Async wrapper around collective_rpc()."""
|
|
loop = asyncio.get_running_loop()
|
|
return await loop.run_in_executor(
|
|
None,
|
|
lambda: self.collective_rpc(
|
|
method=method,
|
|
timeout=timeout,
|
|
args=args,
|
|
kwargs=kwargs,
|
|
stage_ids=stage_ids,
|
|
),
|
|
)
|
|
|
|
def is_alive(self) -> bool:
|
|
"""Whether the orchestrator thread is alive."""
|
|
return bool(self.orchestrator_thread.is_alive())
|
|
|
|
def shutdown(self) -> None:
|
|
"""Send shutdown message and wait for the Orchestrator thread to exit."""
|
|
if getattr(self, "_shutdown_called", False):
|
|
return
|
|
self._shutdown_called = True
|
|
finalizer = getattr(self, "_weak_finalizer", None)
|
|
if finalizer is not None and finalizer.alive:
|
|
finalizer.detach()
|
|
|
|
logger.info("[AsyncOmniEngine] Shutting down Orchestrator")
|
|
if self.request_queue is not None:
|
|
self.request_queue.sync_q.put_nowait(ShutdownRequestMessage())
|
|
if self.is_alive():
|
|
self.orchestrator_thread.join()
|
|
|
|
for q in (self.request_queue, self.output_queue, self.rpc_output_queue):
|
|
try:
|
|
q.close()
|
|
except Exception:
|
|
pass
|
|
|
|
if hasattr(self, "_runtime") and self._runtime is not None:
|
|
try:
|
|
self._runtime.shutdown()
|
|
except Exception:
|
|
logger.exception("[AsyncOmniEngine] Failed to shutdown StageRuntime")
|
|
|
|
# ── Release CuMem allocator memory pool ──────────────────────────────
|
|
# When enable_sleep_mode is in use, the CuMem (CUDA Virtual Memory
|
|
# Management) allocator holds model weights in a singleton memory pool
|
|
# that lives in the parent process. Killing the engine-core subprocess
|
|
# does NOT release this pool — the weights stay resident on the GPU
|
|
# and can cause CUDA OOM for subsequent engine instances (especially
|
|
# large models like BAGEL-7B-MoT whose weights alone consume ~134 GiB).
|
|
#
|
|
# Discard mode (level=2) is correct at shutdown: there is no benefit to
|
|
# keeping a CPU backup when the engine is being torn down.
|
|
try:
|
|
from vllm.device_allocator.cumem import CuMemAllocator, cumem_available
|
|
|
|
if cumem_available:
|
|
allocator = CuMemAllocator.get_instance()
|
|
# Sleep at level 2 discards all pool memory from the GPU
|
|
# without creating CPU backups — cheapest and fastest.
|
|
allocator.sleep()
|
|
logger.debug("[AsyncOmniEngine] Released CuMem memory pool during shutdown")
|
|
except Exception:
|
|
pass
|
|
|
|
def _try_shutdown(self, *args, **kwargs) -> None:
|
|
try:
|
|
self.shutdown()
|
|
except Exception:
|
|
logger.exception(*args, **kwargs)
|