595e7c0032
Signed-off-by: Chenguang ZHENG <645327136@qq.com> Signed-off-by: yinpeiqi <yinpeiqi809@gmail.com> Co-authored-by: yinpeiqi <yinpeiqi809@gmail.com> Co-authored-by: Hongsheng Liu <liuhongsheng4@huawei.com>
573 lines
25 KiB
Python
573 lines
25 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import multiprocessing as mp
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import os
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import time
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import uuid
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from collections.abc import Sequence
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from dataclasses import asdict
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from pprint import pformat
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from typing import Any
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import msgspec
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from omegaconf import OmegaConf
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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.sampling_params import SamplingParams
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from vllm_omni.diffusion.request import OmniDiffusionRequest
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from vllm_omni.distributed.omni_connectors import (
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get_stage_connector_config,
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initialize_orchestrator_connectors,
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)
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from vllm_omni.distributed.omni_connectors.adapter import try_send_via_connector
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from vllm_omni.distributed.ray_utils.utils import (
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create_placement_group,
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get_ray_queue_class,
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try_close_ray,
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)
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from vllm_omni.entrypoints.log_utils import OrchestratorMetrics
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from vllm_omni.entrypoints.omni_stage import OmniStage
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from vllm_omni.entrypoints.stage_utils import maybe_load_from_ipc as _load
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from vllm_omni.entrypoints.utils import (
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get_final_stage_id_for_e2e,
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load_stage_configs_from_model,
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load_stage_configs_from_yaml,
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resolve_model_config_path,
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)
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from vllm_omni.outputs import OmniRequestOutput
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logger = init_logger(__name__)
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def _dummy_snapshot_download(model_id):
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return model_id
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def omni_snapshot_download(model_id) -> str:
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# TODO: this is just a workaround for quickly use modelscope, we should support
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# modelscope in weight loading feature instead of using `snapshot_download`
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if os.environ.get("VLLM_USE_MODELSCOPE", False):
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from modelscope.hub.snapshot_download import snapshot_download
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return snapshot_download(model_id)
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else:
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return _dummy_snapshot_download(model_id)
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class Omni:
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"""Unified entrypoint for both LLM and Diffusion models for better usability.
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Args:
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*args: Variable length argument list.
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- args[0]: Model name or path to load.
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**kwargs: Arbitrary keyword arguments.
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- model: Model name or path to load (if not in args).
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- stage_configs_path: Optional path to YAML file containing stage
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configurations. If None, configurations are loaded from the model.
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- log_stats: Whether to enable statistics logging
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be written to files with stage-specific suffixes.
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Example:
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>>> omni = Omni(model="Qwen/Qwen2.5-Omni-7B")
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>>> outputs = omni.generate(prompts="Hello, world!", sampling_params_list=[SamplingParams()])
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>>> print(outputs)
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"""
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def __init__(self, *args: Any, **kwargs: dict[str, Any]) -> None:
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model = args[0] if args else kwargs.get("model", "")
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assert model != "", "Null model id detected, please specify a model id."
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model = omni_snapshot_download(model)
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if args:
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args[0] = model
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elif kwargs.get("model", "") != "":
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kwargs["model"] = model
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# Stage management attributes
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self.stage_list: list[OmniStage] = []
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self._stage_in_queues: list[mp.Queue] = []
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self._stage_out_queues: list[mp.Queue] = []
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self._stages_ready: set[int] = set()
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self._ray_pg = None
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self._queue_cls = None
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self._ctx = None
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# Initialize stages - each stage will create appropriate instance based on stage_type
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# Stage workers will automatically create OmniLLM or OmniDiffusion instances
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# based on stage_type in YAML config (handled in omni_stage.py)
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logger.info(f"Initializing stages for model: {model}")
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self._initialize_stages(model, kwargs)
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def _initialize_stages(self, model: str, kwargs: dict[str, Any]) -> None:
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"""Initialize stage list management.
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Each stage will create appropriate instance (OmniLLM or OmniDiffusion)
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based on stage_type in YAML config (handled in omni_stage.py).
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"""
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init_sleep_seconds = kwargs.get("init_sleep_seconds", 20)
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shm_threshold_bytes = kwargs.get("shm_threshold_bytes", 65536)
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init_timeout = kwargs.get("init_timeout", 300)
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worker_backend = kwargs.get("worker_backend", "multi_process")
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ray_address = kwargs.get("ray_address", None)
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batch_timeout = kwargs.get("batch_timeout", 10)
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stage_configs_path = kwargs.get("stage_configs_path", None)
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log_stats = kwargs.get("log_stats", False)
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# Load stage configurations from YAML
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if stage_configs_path is None:
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self.config_path = resolve_model_config_path(model)
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self.stage_configs = load_stage_configs_from_model(model)
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if not self.stage_configs:
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# TODO: hack here, convert dtype to string to avoid non-premitive omegaconf create error.
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if "dtype" in kwargs:
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kwargs["dtype"] = str(kwargs["dtype"])
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# TODO: hack, calculate devices based on parallel config.
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devices = "0"
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if "parallel_config" in kwargs:
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num_devices = kwargs["parallel_config"].world_size
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for i in range(1, num_devices):
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devices += f",{i}"
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logger.info(f"model: {model}, kwargs: {kwargs}")
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default_stage_cfg = [
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{
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"stage_id": 0,
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"stage_type": "diffusion",
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"runtime": {
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"process": True,
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"devices": devices,
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"max_batch_size": 1,
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},
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"engine_args": OmegaConf.create(kwargs),
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"final_output": True,
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"final_output_type": "image",
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}
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]
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default_stage_cfg[0]["engine_args"]["model_stage"] = "diffusion"
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self.stage_configs = OmegaConf.create(default_stage_cfg)
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else:
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self.config_path = stage_configs_path
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self.stage_configs = load_stage_configs_from_yaml(stage_configs_path)
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# Initialize connectors
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self.omni_transfer_config, self.connectors = initialize_orchestrator_connectors(
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self.config_path, worker_backend=worker_backend, shm_threshold_bytes=shm_threshold_bytes
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)
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# Initialize stats paths
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self._enable_stats: bool = bool(log_stats)
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self.worker_backend = worker_backend
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self.ray_address = ray_address
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self.batch_timeout = batch_timeout
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# Build OmniStage instances in parallel, preserve original order
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def _build_stage(idx_cfg: tuple[int, Any]) -> tuple[int, OmniStage]:
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idx, cfg = idx_cfg
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return idx, OmniStage(cfg)
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with ThreadPoolExecutor(max_workers=min(len(self.stage_configs), max(1, os.cpu_count() or 1))) as executor:
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futures = [executor.submit(_build_stage, (idx, cfg)) for idx, cfg in enumerate(self.stage_configs)]
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results: list[tuple[int, OmniStage]] = []
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for fut in as_completed(futures):
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results.append(fut.result())
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results.sort(key=lambda x: x[0])
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self.stage_list = [st for _, st in results]
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self.output_modalities = [st.final_output_type for st in self.stage_list]
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logger.debug("[Orchestrator] Loaded %d stages", len(self.stage_list))
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if self.worker_backend == "ray":
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self._queue_cls = get_ray_queue_class()
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else:
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self._ctx = mp.get_context("spawn")
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self._queue_cls = lambda: self._ctx.Queue(maxsize=0)
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self._init_sleep_seconds = max(0, int(init_sleep_seconds))
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self._shm_threshold_bytes = max(0, int(shm_threshold_bytes))
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self._start_stages(model)
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# Wait for all stages to report readiness before seeding
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self._wait_for_stages_ready(timeout=init_timeout)
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def _start_stages(self, model: str) -> None:
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"""Start all stage processes."""
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if self.worker_backend == "ray":
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# Initialize Ray Cluster
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self._ray_pg = create_placement_group(
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number_of_stages=len(self.stage_list), address=self.ray_address, strategy="PACK"
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)
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for stage_id, stage in enumerate(self.stage_list):
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in_q = self._queue_cls()
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out_q = self._queue_cls()
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self._stage_in_queues.append(in_q)
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self._stage_out_queues.append(out_q)
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stage.attach_queues(in_q, out_q)
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stage_connectors_config = get_stage_connector_config(
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self.omni_transfer_config,
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stage_id,
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)
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stage.init_stage_worker(
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model,
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shm_threshold_bytes=self._shm_threshold_bytes,
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ctx=self._ctx if self.worker_backend != "ray" else None,
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batch_timeout=self.batch_timeout,
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connectors_config=stage_connectors_config,
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worker_backend=self.worker_backend,
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ray_placement_group=self._ray_pg,
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)
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logger.debug("[Orchestrator] Stage-%s process started", stage_id)
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time.sleep(self._init_sleep_seconds)
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def _wait_for_stages_ready(self, timeout: int = 120) -> None:
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"""Wait for all stages to report readiness."""
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deadline = time.time() + max(0, int(timeout))
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num_stages = len(self.stage_list)
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while len(self._stages_ready) < num_stages and time.time() < deadline:
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progressed = False
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for stage_id, stage in enumerate(self.stage_list):
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if stage_id in self._stages_ready:
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continue
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result = stage.try_collect()
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if result is None:
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continue
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progressed = True
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if result.get("type") == "stage_ready":
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self._stages_ready.add(stage_id)
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logger.info("[Orchestrator] Stage-%s reported ready", stage_id)
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else:
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# No user data should arrive before seeding; ignore other messages
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pass
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if not progressed:
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time.sleep(0.01)
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if len(self._stages_ready) < num_stages:
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not_ready = sorted(set(range(num_stages)) - set(self._stages_ready))
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logger.warning(
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"[Orchestrator] Initialization timeout: only %s/%s stages are ready; not ready: %s",
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len(self._stages_ready),
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num_stages,
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not_ready,
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)
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# Provide actionable suggestions before shutdown
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try:
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suggestions = [
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"Verify GPU/device assignment in config (runtime.devices) is correct.",
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"Check GPU/host memory availability; reduce model or batch size if needed.",
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"Check model weights path and network reachability (if loading remotely).",
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"Increase initialization wait time (init_sleep_seconds or call-site timeout).",
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]
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logger.error(
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"[Orchestrator] Stage initialization failed, shutting down. Suggestions:\n- %s",
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"\n- ".join(suggestions),
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)
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except Exception:
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# Best-effort logging of suggestions
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logger.error(
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"[Orchestrator] Stage initialization failed and an error occurred while logging suggestions",
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)
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elif len(self._stages_ready) == num_stages:
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logger.info("[Orchestrator] All stages initialized successfully")
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def generate(self, *args: Any, **kwargs: dict[str, Any]) -> list[OmniRequestOutput]:
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"""Generate outputs for the given prompts.
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Orchestrates the multi-stage pipeline based on YAML configuration.
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Each stage will use OmniLLM or OmniDiffusion based on stage_type.
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Args:
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*args: Variable length argument list.
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- args[0]: Input prompts for generation.
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- args[1]: Optional list of per-stage parameters.
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**kwargs: Arbitrary keyword arguments.
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- prompt: Input prompts for generation (if not in args).
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- sampling_params_list: Optional list of per-stage parameters (if not in args).
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Returns:
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List of OmniRequestOutput objects, one for each input prompt.
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Each output contains the stage_id, final_output_type, and
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the request_output from the final stage.
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Raises:
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ValueError: If sampling_params_list is None or has incorrect length.
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"""
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prompts = args[0] if args else kwargs.get("prompts")
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sampling_params_list = args[1] if len(args) > 1 else kwargs.get("sampling_params_list")
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if prompts is None:
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if kwargs.get("prompt") is None:
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raise ValueError("prompts is required for generation")
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prompts = kwargs.get("prompt")
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if sampling_params_list is None:
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omni_params_kwargs = {k: v for k, v in kwargs.items() if k not in ["prompts", "sampling_params_list"]}
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per_stage_params: list[Any] = []
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for stage in self.stage_list:
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stage_type = getattr(stage, "stage_type", "llm")
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default_dict = msgspec.to_builtins(getattr(stage, "default_sampling_params", {}))
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# Merge user-provided kwargs
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merged = {**default_dict, **omni_params_kwargs}
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if stage_type == "diffusion":
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# Diffusion only needs to keep diff params, will be used via OmniDiffusionRequest
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per_stage_params.append(merged)
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else:
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# LLM directly constructs SamplingParams
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per_stage_params.append(SamplingParams(**merged))
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sampling_params_list = per_stage_params
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return self._run_generation(prompts, sampling_params_list)
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def _run_generation(
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self,
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prompts: PromptType | Sequence[PromptType] | OmniDiffusionRequest | Sequence[OmniDiffusionRequest],
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sampling_params_list: Any | Sequence[Any] | None = None,
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) -> list[OmniRequestOutput]:
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"""Run generation through all stages in the pipeline."""
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logger.debug("[Orchestrator] generate() called")
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if sampling_params_list is None:
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raise ValueError("sampling_params_list is required for pipelined generation")
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# Normalize sampling_params_list to a list
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if not isinstance(sampling_params_list, (list, tuple)):
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sampling_params_list = [sampling_params_list]
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else:
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sampling_params_list = list(sampling_params_list)
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if len(sampling_params_list) != len(self.stage_list):
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raise ValueError(f"Expected {len(self.stage_list)} sampling params, got {len(sampling_params_list)}")
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# Normalize prompts to a list for per-request iteration
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if not isinstance(prompts, (list, tuple)):
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request_prompts: list[PromptType] = [prompts]
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else:
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request_prompts = list(prompts)
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final_outputs: list[OmniRequestOutput] = []
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# Orchestrator keeps stage objects for input derivation
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num_stages = len(self.stage_list)
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# Generate globally unique request IDs and map them to original prompts
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request_ids: list[str] = [f"{i}_{uuid.uuid4()}" for i in range(len(request_prompts))]
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request_id_to_prompt: dict[str, PromptType] = {rid: p for rid, p in zip(request_ids, request_prompts)}
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# Track per-request start time for end-to-end timing
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_req_start_ts: dict[str, float] = {}
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_wall_start_ts: float = time.time()
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# Determine the final stage for E2E stats (highest stage_id with final_output=True; fallback to last stage)
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final_stage_id_to_prompt: dict[str, int] = {}
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for rid, prompt in request_id_to_prompt.items():
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if isinstance(prompt, dict):
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prompt_modalities = prompt.get("modalities", None)
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else:
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prompt_modalities = None
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final_stage_id_for_e2e = get_final_stage_id_for_e2e(
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prompt_modalities, self.output_modalities, self.stage_list
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)
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final_stage_id_to_prompt[rid] = final_stage_id_for_e2e
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# Metrics/aggregation helper
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metrics = OrchestratorMetrics(
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num_stages,
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self._enable_stats,
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_wall_start_ts,
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)
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# Seed stage-0 queue with all requests
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logger.debug("[Orchestrator] Seeding %d requests into stage-0", len(request_prompts))
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# Mark first input time for stage-0
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metrics.stage_first_ts[0] = metrics.stage_first_ts[0] or time.time()
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for req_id, prompt in request_id_to_prompt.items():
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sp0 = sampling_params_list[0] # type: ignore[index]
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task = {
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"request_id": req_id,
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"engine_inputs": prompt,
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"sampling_params": sp0,
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}
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self.stage_list[0].submit(task)
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_req_start_ts[req_id] = time.time()
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logger.debug("[Orchestrator] Enqueued request %s to stage-0", req_id)
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# For each stage, forward results to next stage; collect finals at the end
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# We pipeline by continually polling output queues in stage order
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remaining_by_stage: list[int] = [len(request_prompts)] + [0] * (num_stages - 1)
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completed_requests = 0
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total_requests = len(request_prompts)
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logger.debug(
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"[Orchestrator] Entering scheduling loop: total_requests=%d, stages=%d",
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total_requests,
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num_stages,
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)
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while completed_requests < total_requests:
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made_progress = False
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for stage_id, stage in enumerate(self.stage_list):
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result = stage.try_collect()
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if result is None:
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continue
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made_progress = True
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req_id = result.get("request_id")
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if "error" in result:
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logger.error(
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"Stage %s error on request %s: %s",
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stage_id,
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req_id,
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result["error"],
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)
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continue
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if result.get("type") == "stage_ready":
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# Only happens when stage is initialized slower than expected,
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# so we wait for a short time and try again
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time.sleep(0.05)
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continue
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engine_outputs = _load(result, obj_key="engine_outputs", shm_key="engine_outputs_shm")
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# Mark last output time for this stage whenever we receive outputs
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metrics.stage_last_ts[stage_id] = max(metrics.stage_last_ts[stage_id] or 0.0, time.time())
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try:
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_m = asdict(result.get("metrics"))
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if _m is not None:
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metrics.on_stage_metrics(stage_id, req_id, _m)
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except Exception as e:
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logger.exception(
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"[Orchestrator] Failed to process metrics for stage %s, req %s: %s",
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stage_id,
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req_id,
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e,
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)
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logger.debug(
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"[Orchestrator] Stage-%s completed request %s; forwarding or finalizing",
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stage_id,
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req_id,
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)
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stage.set_engine_outputs(engine_outputs)
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if getattr(stage, "final_output", False):
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final_outputs.append(
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OmniRequestOutput(
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stage_id=stage_id,
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|
final_output_type=stage.final_output_type, # type: ignore[attr-defined]
|
|
request_output=engine_outputs,
|
|
)
|
|
)
|
|
logger.debug(
|
|
"[Orchestrator] Request %s finalized at stage-%s",
|
|
req_id,
|
|
stage_id,
|
|
)
|
|
|
|
# End-to-end timing and time-per-token for final output
|
|
# (only once per request at the designated final stage)
|
|
try:
|
|
rid_key = str(req_id)
|
|
if stage_id == final_stage_id_to_prompt[req_id] and rid_key not in metrics.e2e_done:
|
|
metrics.on_finalize_request(
|
|
stage_id,
|
|
req_id,
|
|
engine_outputs,
|
|
_req_start_ts.get(req_id, _wall_start_ts),
|
|
)
|
|
except Exception as e:
|
|
logger.exception(
|
|
"[Orchestrator] Finalize request handling error for req %s at stage %s: %s",
|
|
req_id,
|
|
stage_id,
|
|
e,
|
|
)
|
|
|
|
next_stage_id = stage_id + 1
|
|
if next_stage_id <= final_stage_id_to_prompt[req_id]:
|
|
next_stage: OmniStage = self.stage_list[next_stage_id]
|
|
try:
|
|
next_inputs = next_stage.process_engine_inputs(self.stage_list, [request_id_to_prompt[req_id]])
|
|
except Exception as e:
|
|
logger.exception(
|
|
"[Orchestrator] Process engine inputs error for req %s at stage %s: %s",
|
|
req_id,
|
|
next_stage_id,
|
|
e,
|
|
)
|
|
continue
|
|
sp_next = sampling_params_list[next_stage_id] # type: ignore[index]
|
|
|
|
# Check if we have a connector for this edge
|
|
connector_key = (str(stage_id), str(next_stage_id))
|
|
connector = self.connectors.get(connector_key)
|
|
sent_via_connector = False
|
|
if connector:
|
|
sent_via_connector = try_send_via_connector(
|
|
connector=connector,
|
|
stage_id=stage_id,
|
|
next_stage_id=next_stage_id,
|
|
req_id=req_id,
|
|
next_inputs=next_inputs,
|
|
sampling_params=sp_next,
|
|
original_prompt=request_id_to_prompt[req_id],
|
|
next_stage_queue_submit_fn=self.stage_list[next_stage_id].submit,
|
|
metrics=metrics,
|
|
)
|
|
|
|
if not sent_via_connector:
|
|
raise RuntimeError(
|
|
f"[Orchestrator] Failed to send request {req_id} to stage-{next_stage_id} via connector. "
|
|
"Configure a connector for this edge or inspect connector logs for details."
|
|
)
|
|
logger.debug(
|
|
"[Orchestrator] Forwarded request %s to stage-%s",
|
|
req_id,
|
|
next_stage_id,
|
|
)
|
|
remaining_by_stage[next_stage_id] += 1
|
|
else:
|
|
completed_requests += 1
|
|
logger.debug(
|
|
"[Orchestrator] Request %s fully completed (%d/%d)",
|
|
req_id,
|
|
completed_requests,
|
|
total_requests,
|
|
)
|
|
|
|
if not made_progress:
|
|
time.sleep(0.005)
|
|
logger.debug("[Orchestrator] All requests completed")
|
|
|
|
# Summarize and print stats
|
|
try:
|
|
summary = metrics.build_and_log_summary(final_stage_id_to_prompt)
|
|
logger.info("[Summary] %s", pformat(summary, sort_dicts=False))
|
|
except Exception as e:
|
|
logger.exception("[Orchestrator] Failed to build/log summary: %s", e)
|
|
|
|
return final_outputs
|
|
|
|
def close(self) -> None:
|
|
"""Close all stage processes and clean up resources."""
|
|
# Close stages if they exist (for LLM models)
|
|
if self.stage_list:
|
|
for q in self._stage_in_queues:
|
|
try:
|
|
q.put_nowait(None)
|
|
except Exception as e:
|
|
logger.warning(
|
|
"[Orchestrator] Failed to send shutdown signal to stage input queue: %s",
|
|
e,
|
|
)
|
|
for stage in self.stage_list:
|
|
try:
|
|
stage.stop_stage_worker()
|
|
except Exception as e:
|
|
logger.warning("[Orchestrator] Failed to stop stage worker: %s", e)
|
|
|
|
try_close_ray(self._ray_pg)
|
|
|
|
def __del__(self): # pragma: no cover - best effort cleanup
|
|
try:
|
|
self.close()
|
|
except Exception:
|
|
logger.debug("[Orchestrator] __del__ close() raised", exc_info=True)
|