1270 lines
53 KiB
Python
1270 lines
53 KiB
Python
"""
|
|
Async Omni Engine for vLLM-Omni multi-stage runtime.
|
|
|
|
AsyncOmniEngine in the caller's thread is a thin proxy that communicates
|
|
with the Orchestrator (running in a background thread) via janus queues.
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
|
|
import asyncio
|
|
import concurrent.futures
|
|
import dataclasses
|
|
import json
|
|
import os
|
|
import queue
|
|
import threading
|
|
import time
|
|
import uuid
|
|
import weakref
|
|
from collections.abc import Mapping, Sequence
|
|
from dataclasses import asdict
|
|
from typing import TYPE_CHECKING, Any
|
|
|
|
if TYPE_CHECKING:
|
|
from vllm_omni.engine.arg_utils import OmniEngineArgs
|
|
|
|
import janus
|
|
import torch
|
|
from omegaconf import OmegaConf
|
|
from vllm.inputs import PromptType
|
|
from vllm.logger import init_logger
|
|
from vllm.tokenizers import cached_tokenizer_from_config
|
|
from vllm.v1.engine import EngineCoreRequest
|
|
from vllm.v1.engine.input_processor import InputProcessor
|
|
|
|
from vllm_omni.diffusion.data import DiffusionParallelConfig
|
|
from vllm_omni.distributed.omni_connectors.utils.initialization import (
|
|
resolve_omni_kv_config_for_stage,
|
|
)
|
|
from vllm_omni.engine import (
|
|
OmniEngineCoreRequest,
|
|
)
|
|
from vllm_omni.engine.orchestrator import Orchestrator
|
|
from vllm_omni.engine.output_processor import MultimodalOutputProcessor
|
|
from vllm_omni.engine.serialization import serialize_additional_information
|
|
from vllm_omni.engine.stage_engine_core_client import StageEngineCoreClient
|
|
from vllm_omni.engine.stage_engine_core_proc import (
|
|
complete_stage_handshake,
|
|
spawn_stage_core,
|
|
)
|
|
from vllm_omni.engine.stage_init_utils import (
|
|
StartedLlmStage,
|
|
acquire_device_locks,
|
|
build_engine_args_dict,
|
|
build_vllm_config,
|
|
cleanup_failed_stage_initialization,
|
|
close_started_llm_stage,
|
|
extract_stage_metadata,
|
|
finalize_initialized_stages,
|
|
get_stage_connector_spec,
|
|
initialize_diffusion_stage,
|
|
load_omni_transfer_config_for_model,
|
|
prepare_engine_environment,
|
|
release_device_locks,
|
|
setup_stage_devices,
|
|
)
|
|
from vllm_omni.entrypoints.utils import (
|
|
load_and_resolve_stage_configs,
|
|
)
|
|
from vllm_omni.inputs.preprocess import OmniInputPreprocessor
|
|
from vllm_omni.platforms import current_omni_platform
|
|
|
|
logger = init_logger(__name__)
|
|
|
|
|
|
def _patch_generation_config_if_needed(model_config: Any) -> None:
|
|
"""Ensure try_get_generation_config won't crash for models whose HF
|
|
config.json lacks model_type (e.g. CosyVoice3). We probe it once;
|
|
if it raises, we monkey-patch the method to return None."""
|
|
try:
|
|
model_config.try_get_generation_config()
|
|
except Exception:
|
|
model_config.try_get_generation_config = lambda: {}
|
|
|
|
|
|
def _inject_kv_stage_info(stage_cfg: Any, stage_id: int) -> None:
|
|
"""Inject stage_id and engine_input_source into omni_kv_config.
|
|
|
|
OmniKVTransferManager needs stage_id to compute recv_stages for the
|
|
receiving side. In the old Omni architecture, OmniDiffusion.__init__
|
|
performed this injection; replicate it here for AsyncOmniEngine.
|
|
"""
|
|
try:
|
|
engine_args = stage_cfg.engine_args
|
|
if hasattr(engine_args, "get"):
|
|
omni_kv = engine_args.get("omni_kv_config", None)
|
|
else:
|
|
omni_kv = getattr(engine_args, "omni_kv_config", None)
|
|
|
|
if omni_kv is None:
|
|
return
|
|
|
|
if hasattr(omni_kv, "setdefault"):
|
|
omni_kv.setdefault("stage_id", stage_id)
|
|
elif hasattr(omni_kv, "__setitem__"):
|
|
if "stage_id" not in omni_kv:
|
|
omni_kv["stage_id"] = stage_id
|
|
|
|
engine_input_source = getattr(stage_cfg, "engine_input_source", None)
|
|
if engine_input_source is not None:
|
|
if hasattr(omni_kv, "setdefault"):
|
|
omni_kv.setdefault("engine_input_source", list(engine_input_source))
|
|
elif hasattr(omni_kv, "__setitem__") and "engine_input_source" not in omni_kv:
|
|
omni_kv["engine_input_source"] = list(engine_input_source)
|
|
except Exception as e:
|
|
logger.debug("Failed to inject stage info into omni_kv_config: %s", e)
|
|
|
|
|
|
def _inject_global_id(target: Any, request_id: str) -> None:
|
|
"""Inject global_request_id into a prompt dict's additional_information."""
|
|
if isinstance(target, dict):
|
|
if "additional_information" not in target:
|
|
target["additional_information"] = {}
|
|
if target["additional_information"] is None:
|
|
target["additional_information"] = {}
|
|
if isinstance(target["additional_information"], dict):
|
|
target["additional_information"]["global_request_id"] = [str(request_id)]
|
|
|
|
|
|
def _upgrade_to_omni_request(
|
|
request: EngineCoreRequest,
|
|
raw_prompt: Any,
|
|
) -> EngineCoreRequest:
|
|
"""Restore omni-only fields omitted by upstream InputProcessor."""
|
|
prompt_embeds = request.prompt_embeds
|
|
additional_information = None
|
|
|
|
if isinstance(raw_prompt, dict):
|
|
if prompt_embeds is None:
|
|
raw_prompt_embeds = raw_prompt.get("prompt_embeds")
|
|
if isinstance(raw_prompt_embeds, torch.Tensor):
|
|
prompt_embeds = raw_prompt_embeds
|
|
additional_information = serialize_additional_information(
|
|
raw_prompt.get("additional_information"),
|
|
log_prefix="AsyncOmniEngine",
|
|
)
|
|
|
|
if prompt_embeds is None and additional_information is None:
|
|
return request
|
|
|
|
return OmniEngineCoreRequest(
|
|
request_id=request.request_id,
|
|
prompt_token_ids=request.prompt_token_ids,
|
|
mm_features=request.mm_features,
|
|
sampling_params=request.sampling_params,
|
|
pooling_params=request.pooling_params,
|
|
arrival_time=request.arrival_time,
|
|
lora_request=request.lora_request,
|
|
cache_salt=request.cache_salt,
|
|
data_parallel_rank=request.data_parallel_rank,
|
|
prompt_embeds=prompt_embeds,
|
|
client_index=request.client_index,
|
|
current_wave=request.current_wave,
|
|
priority=request.priority,
|
|
trace_headers=request.trace_headers,
|
|
resumable=request.resumable,
|
|
external_req_id=request.external_req_id,
|
|
reasoning_ended=request.reasoning_ended,
|
|
additional_information=additional_information,
|
|
)
|
|
|
|
|
|
def _weak_shutdown_async_omni_engine(
|
|
orchestrator_thread: threading.Thread | None,
|
|
request_queue: janus.Queue[dict[str, Any]] | None,
|
|
output_queue: janus.Queue[dict[str, Any]] | None,
|
|
rpc_output_queue: janus.Queue[dict[str, Any]] | None,
|
|
) -> None:
|
|
"""Best-effort orchestrator cleanup for GC finalization."""
|
|
try:
|
|
if request_queue is not None:
|
|
request_queue.sync_q.put_nowait({"type": "shutdown"})
|
|
except Exception:
|
|
pass
|
|
|
|
try:
|
|
if orchestrator_thread is not None and orchestrator_thread.is_alive():
|
|
orchestrator_thread.join(timeout=10)
|
|
except Exception:
|
|
pass
|
|
|
|
for q in (request_queue, output_queue, rpc_output_queue):
|
|
if q is None:
|
|
continue
|
|
try:
|
|
q.close()
|
|
except Exception:
|
|
pass
|
|
|
|
|
|
class AsyncOmniEngine:
|
|
"""Thin proxy that launches an Orchestrator in a background thread.
|
|
|
|
All stage clients, input/output processors, and stage-to-stage transfer
|
|
logic live inside the Orchestrator coroutine (running in its own thread
|
|
with a dedicated asyncio event loop). This class communicates with it
|
|
via janus queues (sync side for callers, async side for orchestrator).
|
|
|
|
Args:
|
|
model: Model name or path
|
|
init_timeout: Total timeout waiting for orchestrator startup (seconds).
|
|
stage_init_timeout: Timeout for stage initialization (seconds)
|
|
**kwargs: Additional arguments
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
model: str,
|
|
engine_args: OmniEngineArgs | None = None,
|
|
stage_init_timeout: int = 300,
|
|
init_timeout: int = 600,
|
|
diffusion_batch_size: int = 1,
|
|
**kwargs: Any,
|
|
) -> None:
|
|
self.model = model
|
|
self.diffusion_batch_size = diffusion_batch_size
|
|
startup_timeout = int(init_timeout)
|
|
|
|
logger.info(f"[AsyncOmniEngine] Initializing with model {model}")
|
|
|
|
# Merge typed engine_args fields into kwargs; explicit kwargs take priority.
|
|
if engine_args is not None:
|
|
ea_dict = {
|
|
f.name: getattr(engine_args, f.name)
|
|
for f in dataclasses.fields(engine_args)
|
|
if not f.name.startswith("_")
|
|
}
|
|
# Remove model since it is passed as a positional arg already.
|
|
ea_dict.pop("model", None)
|
|
kwargs = {**ea_dict, **kwargs}
|
|
|
|
self.config_path, self.stage_configs = self._resolve_stage_configs(model, kwargs)
|
|
|
|
self.num_stages = len(self.stage_configs)
|
|
stage0_args = getattr(self.stage_configs[0], "engine_args", None) if self.num_stages > 0 else None
|
|
self.async_chunk = bool(getattr(stage0_args, "async_chunk", False))
|
|
self.stage_clients: list[Any] = []
|
|
self.stage_vllm_configs: list[Any] = []
|
|
self.output_processors: list[MultimodalOutputProcessor | None] = []
|
|
self.input_processor: InputProcessor | None = None
|
|
self.supported_tasks: tuple[str, ...] = ("generate",)
|
|
self.default_sampling_params_list: list[Any] = []
|
|
self.stage_metadata: list[dict[str, Any]] = []
|
|
self.request_queue: janus.Queue[dict[str, Any]] | None = None
|
|
self.output_queue: janus.Queue[dict[str, Any]] | None = None
|
|
self.rpc_output_queue: janus.Queue[dict[str, Any]] | None = None
|
|
self._shutdown_called = False
|
|
self._weak_finalizer: weakref.finalize | None = None
|
|
self._rpc_lock = threading.Lock()
|
|
|
|
logger.info(f"[AsyncOmniEngine] Launching Orchestrator thread with {self.num_stages} stages")
|
|
|
|
# Launch orchestrator background thread
|
|
startup_future: concurrent.futures.Future = concurrent.futures.Future()
|
|
|
|
self.orchestrator_thread = threading.Thread(
|
|
target=self._bootstrap_orchestrator,
|
|
args=(
|
|
stage_init_timeout,
|
|
startup_future,
|
|
),
|
|
daemon=True,
|
|
name="orchestrator",
|
|
)
|
|
self.orchestrator_thread.start()
|
|
|
|
# Wait for stage/runtime initialization result from orchestrator thread.
|
|
try:
|
|
startup_future.result(timeout=startup_timeout)
|
|
except concurrent.futures.TimeoutError as e:
|
|
try:
|
|
self.shutdown()
|
|
except Exception:
|
|
logger.exception("[AsyncOmniEngine] Failed to cleanup after orchestrator startup timeout")
|
|
raise TimeoutError(f"Orchestrator did not become ready within {startup_timeout}s") from e
|
|
except Exception:
|
|
try:
|
|
self.shutdown()
|
|
except Exception:
|
|
logger.exception("[AsyncOmniEngine] Failed to cleanup after orchestrator startup failure")
|
|
raise
|
|
|
|
# Stage runtime fields are assigned directly on self by the bootstrap thread.
|
|
self._weak_finalizer = weakref.finalize(
|
|
self,
|
|
_weak_shutdown_async_omni_engine,
|
|
self.orchestrator_thread,
|
|
self.request_queue,
|
|
self.output_queue,
|
|
self.rpc_output_queue,
|
|
)
|
|
|
|
logger.info(f"[AsyncOmniEngine] Orchestrator ready with {self.num_stages} stages")
|
|
|
|
def _launch_llm_stage(
|
|
self,
|
|
stage_cfg: Any,
|
|
metadata: Any,
|
|
stage_connector_spec: dict[str, Any],
|
|
stage_init_timeout: int,
|
|
llm_stage_launch_lock: threading.Lock,
|
|
omni_kv_connector: tuple[dict[str, Any] | None, str | None, str | None] = (None, None, None),
|
|
) -> StartedLlmStage:
|
|
"""Launch one LLM stage to READY state in a helper thread."""
|
|
started_stage: StartedLlmStage | None = None
|
|
lock_fds: list[int] = []
|
|
device_control_env = current_omni_platform.device_control_env_var
|
|
|
|
try:
|
|
with llm_stage_launch_lock:
|
|
previous_visible_devices = os.environ.get(device_control_env)
|
|
try:
|
|
setup_stage_devices(metadata.stage_id, metadata.runtime_cfg)
|
|
engine_args_dict = build_engine_args_dict(
|
|
stage_cfg,
|
|
self.model,
|
|
stage_connector_spec=stage_connector_spec,
|
|
)
|
|
omni_conn_cfg, omni_from, omni_to = omni_kv_connector
|
|
if omni_conn_cfg:
|
|
omni_kv = engine_args_dict.get("omni_kv_config") or {}
|
|
if not isinstance(omni_kv, dict):
|
|
omni_kv = dict(omni_kv)
|
|
omni_kv["connector_config"] = omni_conn_cfg
|
|
omni_kv["omni_from_stage"] = omni_from
|
|
omni_kv["omni_to_stage"] = omni_to
|
|
omni_kv.setdefault("stage_id", metadata.stage_id)
|
|
engine_args_dict["omni_kv_config"] = omni_kv
|
|
vllm_config, executor_class = build_vllm_config(
|
|
stage_cfg,
|
|
self.model,
|
|
stage_connector_spec=stage_connector_spec,
|
|
engine_args_dict=engine_args_dict,
|
|
)
|
|
lock_fds = acquire_device_locks(
|
|
metadata.stage_id,
|
|
engine_args_dict,
|
|
stage_init_timeout,
|
|
)
|
|
addresses, proc, handshake_address = spawn_stage_core(
|
|
vllm_config=vllm_config,
|
|
executor_class=executor_class,
|
|
log_stats=False,
|
|
)
|
|
started_stage = StartedLlmStage(
|
|
stage_id=metadata.stage_id,
|
|
metadata=metadata,
|
|
vllm_config=vllm_config,
|
|
executor_class=executor_class,
|
|
proc=proc,
|
|
addresses=addresses,
|
|
)
|
|
finally:
|
|
if previous_visible_devices is None:
|
|
current_omni_platform.unset_device_control_env_var()
|
|
else:
|
|
current_omni_platform.set_device_control_env_var(previous_visible_devices)
|
|
|
|
logger.info("[AsyncOmniEngine] Stage %s engine launch started", metadata.stage_id)
|
|
complete_stage_handshake(proc, handshake_address, addresses, vllm_config)
|
|
logger.info("[AsyncOmniEngine] Stage %s engine startup completed", metadata.stage_id)
|
|
assert started_stage is not None
|
|
return started_stage
|
|
except Exception:
|
|
if started_stage is not None:
|
|
close_started_llm_stage(started_stage)
|
|
raise
|
|
finally:
|
|
if lock_fds:
|
|
release_device_locks(lock_fds)
|
|
|
|
def _attach_llm_stage(
|
|
self,
|
|
started: StartedLlmStage,
|
|
) -> tuple[Any, Any, Any, InputProcessor | None]:
|
|
"""Attach a READY LLM stage to the orchestrator event loop."""
|
|
|
|
client_addresses = {
|
|
"input_address": started.addresses.inputs[0],
|
|
"output_address": started.addresses.outputs[0],
|
|
}
|
|
if started.addresses.frontend_stats_publish_address is not None:
|
|
client_addresses["stats_update_address"] = started.addresses.frontend_stats_publish_address
|
|
|
|
try:
|
|
stage_client = StageEngineCoreClient(
|
|
vllm_config=started.vllm_config,
|
|
executor_class=started.executor_class,
|
|
metadata=started.metadata,
|
|
client_addresses=client_addresses,
|
|
proc=started.proc,
|
|
)
|
|
started.proc = None
|
|
except Exception:
|
|
close_started_llm_stage(started)
|
|
raise
|
|
|
|
try:
|
|
if started.vllm_config.model_config.skip_tokenizer_init:
|
|
tokenizer = None
|
|
else:
|
|
tokenizer = cached_tokenizer_from_config(
|
|
model_config=started.vllm_config.model_config,
|
|
)
|
|
output_processor = MultimodalOutputProcessor(
|
|
tokenizer=tokenizer,
|
|
log_stats=False,
|
|
engine_core_output_type=started.metadata.engine_output_type,
|
|
)
|
|
input_processor = None
|
|
if started.stage_id == 0:
|
|
# Some omni models (e.g. CosyVoice3) have an empty HF
|
|
# config.json without model_type, which causes
|
|
# try_get_generation_config -> AutoConfig.from_pretrained
|
|
# to raise ValueError. Patch it to return None so
|
|
# InputProcessor doesn't crash.
|
|
_patch_generation_config_if_needed(started.vllm_config.model_config)
|
|
input_processor = InputProcessor(vllm_config=started.vllm_config)
|
|
# Use omni preprocessor so text-only prompts with
|
|
# mm_processor_kwargs (e.g. GLM-Image t2i target_h/target_w)
|
|
# still go through multimodal processor path.
|
|
input_processor.input_preprocessor = OmniInputPreprocessor(
|
|
vllm_config=started.vllm_config,
|
|
renderer=input_processor.renderer,
|
|
)
|
|
except Exception:
|
|
try:
|
|
stage_client.shutdown()
|
|
except Exception as cleanup_error:
|
|
logger.warning(
|
|
"[AsyncOmniEngine] Failed to cleanup stage %s after attach failure: %s",
|
|
started.stage_id,
|
|
cleanup_error,
|
|
)
|
|
raise
|
|
|
|
logger.info("[AsyncOmniEngine] Stage %s initialized", started.stage_id)
|
|
return stage_client, output_processor, started.vllm_config, input_processor
|
|
|
|
def _initialize_stages(self, stage_init_timeout: int) -> None:
|
|
"""Initialize stage clients/processors in orchestrator thread and assign to self."""
|
|
device_control_env = current_omni_platform.device_control_env_var
|
|
|
|
num_stages = self.num_stages
|
|
stage_clients: list[Any | None] = [None] * num_stages
|
|
output_processors: list[Any | None] = [None] * num_stages
|
|
stage_vllm_configs: list[Any | None] = [None] * num_stages
|
|
input_processor: InputProcessor | None = None
|
|
llm_stage_ids: list[int] = []
|
|
llm_launch_futures: dict[int, concurrent.futures.Future[StartedLlmStage]] = {}
|
|
started_llm_stages: dict[int, StartedLlmStage] = {}
|
|
llm_stage_launch_lock = threading.Lock()
|
|
|
|
async_chunk = self.async_chunk
|
|
prompt_expand_func = None
|
|
llm_stage_count = sum(
|
|
1 for stage_cfg in self.stage_configs if getattr(stage_cfg, "stage_type", "llm") != "diffusion"
|
|
)
|
|
|
|
prepare_engine_environment()
|
|
omni_transfer_config = load_omni_transfer_config_for_model(self.model, self.config_path)
|
|
|
|
try:
|
|
with concurrent.futures.ThreadPoolExecutor(
|
|
max_workers=max(1, llm_stage_count),
|
|
thread_name_prefix="llm-stage-launch",
|
|
) as launch_executor:
|
|
for stage_id, stage_cfg in enumerate(self.stage_configs):
|
|
logger.info("[AsyncOmniEngine] Initializing stage %s", stage_id)
|
|
metadata = extract_stage_metadata(stage_cfg)
|
|
if metadata.prompt_expand_func is not None:
|
|
prompt_expand_func = metadata.prompt_expand_func
|
|
|
|
stage_connector_spec = get_stage_connector_spec(
|
|
omni_transfer_config=omni_transfer_config,
|
|
stage_id=stage_id,
|
|
async_chunk=async_chunk,
|
|
)
|
|
|
|
omni_kv_connector = resolve_omni_kv_config_for_stage(omni_transfer_config, stage_id)
|
|
|
|
if metadata.stage_type == "diffusion":
|
|
with llm_stage_launch_lock:
|
|
previous_visible_devices = os.environ.get(device_control_env)
|
|
try:
|
|
setup_stage_devices(stage_id, metadata.runtime_cfg)
|
|
omni_conn_cfg, omni_from, omni_to = omni_kv_connector
|
|
if omni_conn_cfg:
|
|
from vllm_omni.entrypoints.utils import inject_omni_kv_config
|
|
|
|
inject_omni_kv_config(stage_cfg, omni_conn_cfg, omni_from, omni_to)
|
|
_inject_kv_stage_info(stage_cfg, stage_id)
|
|
stage_clients[stage_id] = initialize_diffusion_stage(
|
|
self.model,
|
|
stage_cfg,
|
|
metadata,
|
|
batch_size=self.diffusion_batch_size,
|
|
)
|
|
logger.info(
|
|
"[AsyncOmniEngine] Stage %s initialized (diffusion, batch_size=%d)",
|
|
stage_id,
|
|
self.diffusion_batch_size,
|
|
)
|
|
finally:
|
|
if previous_visible_devices is None:
|
|
current_omni_platform.unset_device_control_env_var()
|
|
else:
|
|
current_omni_platform.set_device_control_env_var(previous_visible_devices)
|
|
continue
|
|
|
|
llm_stage_ids.append(stage_id)
|
|
llm_launch_futures[stage_id] = launch_executor.submit(
|
|
self._launch_llm_stage,
|
|
stage_cfg,
|
|
metadata,
|
|
stage_connector_spec,
|
|
stage_init_timeout,
|
|
llm_stage_launch_lock,
|
|
omni_kv_connector,
|
|
)
|
|
|
|
concurrent.futures.wait(list(llm_launch_futures.values()))
|
|
|
|
for stage_id in llm_stage_ids:
|
|
started_llm_stages[stage_id] = llm_launch_futures[stage_id].result()
|
|
|
|
for stage_id in llm_stage_ids:
|
|
started = started_llm_stages[stage_id]
|
|
stage_client, output_processor, vllm_config, stage0_input_processor = self._attach_llm_stage(started)
|
|
stage_clients[stage_id] = stage_client
|
|
output_processors[stage_id] = output_processor
|
|
stage_vllm_configs[stage_id] = vllm_config
|
|
if stage0_input_processor is not None:
|
|
input_processor = stage0_input_processor
|
|
|
|
initialized_stage_clients, default_sampling_params_list, stage_metadata = finalize_initialized_stages(
|
|
stage_clients,
|
|
input_processor,
|
|
)
|
|
except Exception:
|
|
for stage_id, future in llm_launch_futures.items():
|
|
if not future.done() or future.cancelled() or future.exception() is not None:
|
|
continue
|
|
started_llm_stages.setdefault(stage_id, future.result())
|
|
logger.exception(
|
|
"[AsyncOmniEngine] Stage initialization failed; shutting down %s initialized stage(s)",
|
|
len([stage_client for stage_client in stage_clients if stage_client is not None]),
|
|
)
|
|
cleanup_failed_stage_initialization(
|
|
stage_clients,
|
|
[started_llm_stages[stage_id] for stage_id in llm_stage_ids if stage_id in started_llm_stages],
|
|
)
|
|
raise
|
|
|
|
self.stage_clients = initialized_stage_clients
|
|
self.output_processors = output_processors
|
|
self.stage_vllm_configs = stage_vllm_configs
|
|
self.input_processor = input_processor
|
|
self.prompt_expand_func = prompt_expand_func
|
|
# TODO(Peiqi): Hack here
|
|
supported_tasks: set[str] = set()
|
|
if any(getattr(stage_client, "is_comprehension", False) for stage_client in initialized_stage_clients):
|
|
supported_tasks.add("generate")
|
|
if any(metadata.get("final_output_type") == "audio" for metadata in stage_metadata):
|
|
supported_tasks.add("speech")
|
|
self.supported_tasks = tuple(supported_tasks) if supported_tasks else ("generate",)
|
|
|
|
self.default_sampling_params_list = default_sampling_params_list
|
|
self.stage_metadata = stage_metadata
|
|
|
|
def _initialize_janus_queues(self) -> None:
|
|
"""Initialize janus queues inside orchestrator thread loop context."""
|
|
self.request_queue = janus.Queue()
|
|
self.output_queue = janus.Queue()
|
|
self.rpc_output_queue = janus.Queue()
|
|
logger.debug("[AsyncOmniEngine] janus queues initialized in orchestrator thread loop")
|
|
|
|
def _bootstrap_orchestrator(
|
|
self,
|
|
stage_init_timeout: int,
|
|
startup_future: concurrent.futures.Future,
|
|
) -> None:
|
|
"""Create loop, initialize stages, then run Orchestrator."""
|
|
|
|
loop = asyncio.new_event_loop()
|
|
asyncio.set_event_loop(loop)
|
|
|
|
async def _run_orchestrator() -> None:
|
|
self._initialize_janus_queues()
|
|
|
|
self._initialize_stages(stage_init_timeout)
|
|
orchestrator = Orchestrator(
|
|
request_async_queue=self.request_queue.async_q,
|
|
output_async_queue=self.output_queue.async_q,
|
|
rpc_async_queue=self.rpc_output_queue.async_q,
|
|
async_chunk=self.async_chunk,
|
|
stage_clients=self.stage_clients,
|
|
output_processors=self.output_processors,
|
|
stage_vllm_configs=self.stage_vllm_configs,
|
|
)
|
|
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():
|
|
startup_future.set_exception(RuntimeError(f"Orchestrator initialization failed: {e}"))
|
|
logger.exception("[AsyncOmniEngine] Orchestrator thread crashed")
|
|
try:
|
|
if self.output_queue is not None:
|
|
self.output_queue.sync_q.put_nowait({"type": "error", "error": "Orchestrator thread crashed"})
|
|
if self.rpc_output_queue is not None:
|
|
self.rpc_output_queue.sync_q.put_nowait({"type": "error", "error": "Orchestrator thread crashed"})
|
|
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()
|
|
|
|
# ---- request helpers ----
|
|
|
|
def _build_add_request_message(
|
|
self,
|
|
request_id: str,
|
|
prompt: EngineCoreRequest | PromptType,
|
|
prompt_text: str | None = None,
|
|
sampling_params_list: Sequence[Any] | None = None,
|
|
final_stage_id: int = 0,
|
|
arrival_time: float | None = None,
|
|
lora_request: Any = None,
|
|
tokenization_kwargs: dict[str, Any] | None = None,
|
|
trace_headers: Mapping[str, str] | None = None,
|
|
priority: int = 0,
|
|
data_parallel_rank: int | None = None,
|
|
reasoning_ended: bool | None = None,
|
|
*,
|
|
resumable: bool = False,
|
|
message_type: str = "add_request",
|
|
) -> dict[str, Any]:
|
|
"""Build an add_request message after stage-0 preprocessing."""
|
|
effective_sampling_params_list = (
|
|
list(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
|
|
|
|
stage_type = self.stage_metadata[0].get("stage_type")
|
|
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)
|
|
|
|
# Full input processing (tokenization, multimodal, etc.)
|
|
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,
|
|
)
|
|
# 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
|
|
|
|
# Register with stage 0's output processor.
|
|
output_prompt_text = prompt_text
|
|
if output_prompt_text is None and isinstance(original_prompt, dict):
|
|
output_prompt_text = original_prompt.get("prompt")
|
|
self.output_processors[0].add_request(
|
|
request=request,
|
|
prompt=output_prompt_text,
|
|
parent_req=None,
|
|
request_index=0,
|
|
queue=None,
|
|
)
|
|
prompt = request
|
|
|
|
return {
|
|
"type": message_type,
|
|
"request_id": request_id,
|
|
"prompt": prompt,
|
|
"original_prompt": original_prompt,
|
|
"sampling_params_list": effective_sampling_params_list,
|
|
"final_stage_id": final_stage_id,
|
|
}
|
|
|
|
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 = _upgrade_to_omni_request(request, companion_prompt)
|
|
request.external_req_id = cid
|
|
|
|
self.output_processors[0].add_request(
|
|
request=request,
|
|
prompt=companion_prompt,
|
|
parent_req=None,
|
|
request_index=0,
|
|
queue=None,
|
|
)
|
|
|
|
self.request_queue.sync_q.put_nowait(
|
|
{
|
|
"type": "add_companion_request",
|
|
"companion_id": cid,
|
|
"parent_id": parent_id,
|
|
"role": ep.role,
|
|
"prompt": request,
|
|
"sampling_params_list": companion_spl,
|
|
}
|
|
)
|
|
|
|
logger.info(
|
|
"[AsyncOmniEngine] CFG expansion for req %s: %d companions",
|
|
parent_id,
|
|
len(expanded),
|
|
)
|
|
|
|
@staticmethod
|
|
def _get_default_cache_config(cache_backend: str | None) -> dict[str, Any] | None:
|
|
if cache_backend == "cache_dit":
|
|
return {
|
|
"Fn_compute_blocks": 1,
|
|
"Bn_compute_blocks": 0,
|
|
"max_warmup_steps": 4,
|
|
"residual_diff_threshold": 0.24,
|
|
"max_continuous_cached_steps": 3,
|
|
"enable_taylorseer": False,
|
|
"taylorseer_order": 1,
|
|
"scm_steps_mask_policy": None,
|
|
"scm_steps_policy": "dynamic",
|
|
}
|
|
if cache_backend == "tea_cache":
|
|
return {
|
|
"rel_l1_thresh": 0.2,
|
|
}
|
|
return None
|
|
|
|
@staticmethod
|
|
def _normalize_cache_config(cache_backend: str | None, cache_config: Any | None) -> Any | None:
|
|
if isinstance(cache_config, str):
|
|
try:
|
|
cache_config = json.loads(cache_config)
|
|
except json.JSONDecodeError:
|
|
logger.warning("Invalid cache_config JSON, using defaults.")
|
|
cache_config = None
|
|
if cache_config is None and cache_backend not in (None, "", "none"):
|
|
cache_config = AsyncOmniEngine._get_default_cache_config(cache_backend)
|
|
return cache_config
|
|
|
|
@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)
|
|
|
|
# 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")
|
|
tensor_parallel_size = normalized_kwargs.get("tensor_parallel_size") or 1
|
|
cfg_parallel_size = normalized_kwargs.get("cfg_parallel_size") or 1
|
|
vae_patch_parallel_size = normalized_kwargs.get("vae_patch_parallel_size") or 1
|
|
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=1,
|
|
data_parallel_size=1,
|
|
tensor_parallel_size=tensor_parallel_size,
|
|
sequence_parallel_size=sequence_parallel_size,
|
|
ulysses_degree=ulysses_degree,
|
|
ring_degree=ring_degree,
|
|
ulysses_mode=ulysses_mode,
|
|
cfg_parallel_size=cfg_parallel_size,
|
|
vae_patch_parallel_size=vae_patch_parallel_size,
|
|
use_hsdp=use_hsdp,
|
|
hsdp_shard_size=hsdp_shard_size,
|
|
hsdp_replicate_size=hsdp_replicate_size,
|
|
)
|
|
|
|
num_devices = max(1, int(parallel_config.world_size))
|
|
devices = ",".join(str(i) for i in range(num_devices))
|
|
|
|
default_stage_cfg = [
|
|
{
|
|
"stage_id": 0,
|
|
"stage_type": "diffusion",
|
|
"runtime": {
|
|
"process": True,
|
|
"devices": devices,
|
|
},
|
|
"engine_args": {
|
|
"max_num_seqs": 1,
|
|
"parallel_config": parallel_config,
|
|
"model_class_name": kwargs.get("model_class_name", None),
|
|
"step_execution": kwargs.get("step_execution", False),
|
|
"vae_use_slicing": kwargs.get("vae_use_slicing", False),
|
|
"vae_use_tiling": kwargs.get("vae_use_tiling", False),
|
|
"cache_backend": cache_backend,
|
|
"cache_config": cache_config,
|
|
"enable_cache_dit_summary": kwargs.get("enable_cache_dit_summary", False),
|
|
"enable_cpu_offload": kwargs.get("enable_cpu_offload", False),
|
|
"enable_layerwise_offload": kwargs.get("enable_layerwise_offload", False),
|
|
"enforce_eager": kwargs.get("enforce_eager", False),
|
|
"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),
|
|
"enable_sleep_mode": kwargs.get("enable_sleep_mode", False),
|
|
"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),
|
|
"enable_diffusion_pipeline_profiler": kwargs.get("enable_diffusion_pipeline_profiler", False),
|
|
**(
|
|
{
|
|
"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 {}
|
|
),
|
|
},
|
|
"final_output": True,
|
|
"final_output_type": "image",
|
|
}
|
|
]
|
|
default_stage_cfg[0]["engine_args"]["model_stage"] = "diffusion"
|
|
return default_stage_cfg
|
|
|
|
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)
|
|
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."
|
|
)
|
|
|
|
# Use the legacy config loading path (load_and_resolve_stage_configs).
|
|
# StageConfigFactory wiring will be done in config refactor [2/N].
|
|
config_path, stage_configs = load_and_resolve_stage_configs(
|
|
model,
|
|
stage_configs_path,
|
|
kwargs,
|
|
default_stage_cfg_factory=lambda: self._create_default_diffusion_stage_cfg(kwargs),
|
|
)
|
|
|
|
# Inject diffusion LoRA-related knobs from kwargs if not present in the stage config.
|
|
for cfg in stage_configs:
|
|
try:
|
|
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({})
|
|
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
|
|
quantization_config = kwargs.get("quantization_config")
|
|
if quantization_config is not None:
|
|
if (
|
|
not hasattr(cfg.engine_args, "quantization_config")
|
|
or cfg.engine_args.quantization_config is None
|
|
):
|
|
cfg.engine_args.quantization_config = quantization_config
|
|
except Exception as e:
|
|
logger.warning("Failed to inject LoRA config for stage: %s", e)
|
|
|
|
return config_path, stage_configs
|
|
|
|
# ==================== Public API ====================
|
|
|
|
def add_request(
|
|
self,
|
|
request_id: str,
|
|
prompt: EngineCoreRequest | PromptType,
|
|
prompt_text: str | None = None,
|
|
sampling_params_list: Sequence[Any] | None = None,
|
|
final_stage_id: int = 0,
|
|
arrival_time: float | None = None,
|
|
lora_request: Any = None,
|
|
tokenization_kwargs: dict[str, Any] | None = None,
|
|
trace_headers: Mapping[str, str] | None = None,
|
|
priority: int = 0,
|
|
data_parallel_rank: int | None = None,
|
|
reasoning_ended: bool | None = None,
|
|
*,
|
|
resumable: bool = False,
|
|
) -> None:
|
|
"""Process stage-0 input locally, then send to the Orchestrator.
|
|
|
|
Input processing and output
|
|
processor registration happen here in the caller's thread, avoiding
|
|
a queue + coroutine-switch round-trip. The Orchestrator receives a
|
|
ready-to-submit OmniEngineCoreRequest.
|
|
"""
|
|
msg = self._build_add_request_message(
|
|
request_id=request_id,
|
|
prompt=prompt,
|
|
prompt_text=prompt_text,
|
|
sampling_params_list=sampling_params_list,
|
|
final_stage_id=final_stage_id,
|
|
arrival_time=arrival_time,
|
|
lora_request=lora_request,
|
|
tokenization_kwargs=tokenization_kwargs,
|
|
trace_headers=trace_headers,
|
|
priority=priority,
|
|
data_parallel_rank=data_parallel_rank,
|
|
reasoning_ended=reasoning_ended,
|
|
resumable=resumable,
|
|
)
|
|
if self.request_queue is None:
|
|
raise RuntimeError("request_queue is not initialized")
|
|
self.request_queue.sync_q.put_nowait(msg)
|
|
|
|
# CFG companion expansion: create and enqueue companion requests
|
|
# so the AR stage also generates their KV caches.
|
|
if self.prompt_expand_func is not None and final_stage_id > 0:
|
|
original_prompt = msg.get("original_prompt", prompt)
|
|
effective_spl = msg.get("sampling_params_list", [])
|
|
stage0_params = effective_spl[0] if effective_spl else None
|
|
if stage0_params is not None:
|
|
self._enqueue_cfg_companions(request_id, original_prompt, stage0_params, effective_spl)
|
|
|
|
async def add_request_async(
|
|
self,
|
|
request_id: str,
|
|
prompt: EngineCoreRequest | PromptType,
|
|
prompt_text: str | None = None,
|
|
sampling_params_list: Sequence[Any] | None = None,
|
|
final_stage_id: int = 0,
|
|
arrival_time: float | None = None,
|
|
lora_request: Any = None,
|
|
tokenization_kwargs: dict[str, Any] | None = None,
|
|
trace_headers: Mapping[str, str] | None = None,
|
|
priority: int = 0,
|
|
data_parallel_rank: int | None = None,
|
|
reasoning_ended: bool | None = None,
|
|
*,
|
|
resumable: bool = False,
|
|
) -> None:
|
|
"""Async add_request API."""
|
|
self.add_request(
|
|
request_id=request_id,
|
|
prompt=prompt,
|
|
prompt_text=prompt_text,
|
|
sampling_params_list=sampling_params_list,
|
|
final_stage_id=final_stage_id,
|
|
arrival_time=arrival_time,
|
|
lora_request=lora_request,
|
|
tokenization_kwargs=tokenization_kwargs,
|
|
trace_headers=trace_headers,
|
|
priority=priority,
|
|
data_parallel_rank=data_parallel_rank,
|
|
reasoning_ended=reasoning_ended,
|
|
resumable=resumable,
|
|
)
|
|
|
|
def add_streaming_update(
|
|
self,
|
|
request_id: str,
|
|
prompt: EngineCoreRequest | PromptType,
|
|
prompt_text: str | None = None,
|
|
sampling_params_list: Sequence[Any] | None = None,
|
|
final_stage_id: int = 0,
|
|
arrival_time: float | None = None,
|
|
*,
|
|
resumable: bool = True,
|
|
) -> None:
|
|
"""Send an incremental streaming update for an existing request."""
|
|
msg = self._build_add_request_message(
|
|
request_id=request_id,
|
|
prompt=prompt,
|
|
prompt_text=prompt_text,
|
|
sampling_params_list=sampling_params_list,
|
|
final_stage_id=final_stage_id,
|
|
arrival_time=arrival_time,
|
|
resumable=resumable,
|
|
message_type="streaming_update",
|
|
)
|
|
if self.request_queue is None:
|
|
raise RuntimeError("request_queue is not initialized")
|
|
self.request_queue.sync_q.put_nowait(msg)
|
|
|
|
async def add_streaming_update_async(
|
|
self,
|
|
request_id: str,
|
|
prompt: EngineCoreRequest | PromptType,
|
|
prompt_text: str | None = None,
|
|
sampling_params_list: Sequence[Any] | None = None,
|
|
final_stage_id: int = 0,
|
|
arrival_time: float | None = None,
|
|
*,
|
|
resumable: bool = True,
|
|
) -> None:
|
|
"""Async wrapper for add_streaming_update()."""
|
|
self.add_streaming_update(
|
|
request_id=request_id,
|
|
prompt=prompt,
|
|
prompt_text=prompt_text,
|
|
sampling_params_list=sampling_params_list,
|
|
final_stage_id=final_stage_id,
|
|
arrival_time=arrival_time,
|
|
resumable=resumable,
|
|
)
|
|
|
|
def try_get_output(self, timeout: float = 0.001) -> dict[str, Any] | None:
|
|
"""Read one output message from the Orchestrator output queue."""
|
|
if self.output_queue is None:
|
|
return None
|
|
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) -> dict[str, Any] | None:
|
|
"""Async read from the Orchestrator output queue."""
|
|
if self.output_queue is None:
|
|
return None
|
|
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) -> dict[str, Any]:
|
|
"""Get cached metadata for a stage."""
|
|
return self.stage_metadata[stage_id]
|
|
|
|
def abort(self, request_ids: list[str]) -> None:
|
|
"""Send abort message to the Orchestrator."""
|
|
if self.request_queue is None:
|
|
raise RuntimeError("request_queue is not initialized")
|
|
self.request_queue.sync_q.put_nowait(
|
|
{
|
|
"type": "abort",
|
|
"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.
|
|
"""
|
|
if self.request_queue is None:
|
|
raise RuntimeError("request_queue is not initialized")
|
|
if self.rpc_output_queue is None:
|
|
raise RuntimeError("rpc_output_queue is not initialized")
|
|
|
|
rpc_id = uuid.uuid4().hex
|
|
msg = {
|
|
"type": "collective_rpc",
|
|
"rpc_id": rpc_id,
|
|
"method": method,
|
|
"args": tuple(args),
|
|
"kwargs": kwargs or {},
|
|
"stage_ids": stage_ids,
|
|
}
|
|
|
|
with self._rpc_lock:
|
|
self.request_queue.sync_q.put_nowait(msg)
|
|
deadline = None if timeout is None else time.monotonic() + timeout
|
|
|
|
while True:
|
|
remaining = None if deadline is None else max(0.0, deadline - time.monotonic())
|
|
try:
|
|
result_msg = self.rpc_output_queue.sync_q.get(timeout=remaining)
|
|
except queue.Empty as exc:
|
|
raise TimeoutError(f"collective_rpc timed out after {timeout} seconds") from exc
|
|
|
|
if result_msg.get("type") == "error":
|
|
raise RuntimeError(result_msg.get("error", "Orchestrator returned an error message"))
|
|
|
|
if result_msg.get("type") != "collective_rpc_result":
|
|
logger.warning(
|
|
"[AsyncOmniEngine] Dropping unexpected rpc queue message type=%s",
|
|
result_msg.get("type"),
|
|
)
|
|
continue
|
|
|
|
if result_msg.get("rpc_id") != rpc_id:
|
|
logger.warning(
|
|
"[AsyncOmniEngine] Dropping mismatched rpc result rpc_id=%s expected=%s",
|
|
result_msg.get("rpc_id"),
|
|
rpc_id,
|
|
)
|
|
continue
|
|
|
|
return list(result_msg.get("results", []))
|
|
|
|
async def collective_rpc_async(
|
|
self,
|
|
method: str,
|
|
timeout: float | None = None,
|
|
args: tuple[Any, ...] = (),
|
|
kwargs: dict[str, Any] | None = None,
|
|
stage_ids: list[int] | None = None,
|
|
) -> list[Any]:
|
|
"""Async wrapper around collective_rpc()."""
|
|
loop = asyncio.get_running_loop()
|
|
return await loop.run_in_executor(
|
|
None,
|
|
lambda: self.collective_rpc(
|
|
method=method,
|
|
timeout=timeout,
|
|
args=args,
|
|
kwargs=kwargs,
|
|
stage_ids=stage_ids,
|
|
),
|
|
)
|
|
|
|
def is_alive(self) -> bool:
|
|
"""Whether the orchestrator thread is alive."""
|
|
return bool(self.orchestrator_thread.is_alive())
|
|
|
|
def shutdown(self) -> None:
|
|
"""Send shutdown message and wait for the Orchestrator thread to exit."""
|
|
if getattr(self, "_shutdown_called", False):
|
|
return
|
|
self._shutdown_called = True
|
|
finalizer = getattr(self, "_weak_finalizer", None)
|
|
if finalizer is not None and finalizer.alive:
|
|
finalizer.detach()
|
|
|
|
logger.info("[AsyncOmniEngine] Shutting down Orchestrator")
|
|
try:
|
|
if self.request_queue is not None:
|
|
self.request_queue.sync_q.put_nowait({"type": "shutdown"})
|
|
except Exception:
|
|
pass
|
|
if self.is_alive():
|
|
self.orchestrator_thread.join(timeout=10)
|
|
if self.orchestrator_thread.is_alive():
|
|
logger.warning("[AsyncOmniEngine] Orchestrator thread did not exit in time")
|
|
|
|
for q in (self.request_queue, self.output_queue, self.rpc_output_queue):
|
|
if q is None:
|
|
continue
|
|
try:
|
|
q.close()
|
|
except Exception:
|
|
pass
|