c708aecb18
Signed-off-by: Zhang Jian <jianmusings@gmail.com> Signed-off-by: Zhang <jianmusings@gmail.com> Signed-off-by: Zhang <zhang.jian@u.nus.edu> Signed-off-by: Zhang Jian <e0322744@u.nus.edu> Signed-off-by: 汪志鹏 <wangzhipeng628@gmail.com> Co-authored-by: Zhang Jian <e0322744@u.nus.edu> Co-authored-by: 汪志鹏 <wangzhipeng628@gmail.com>
567 lines
24 KiB
Python
567 lines
24 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from __future__ import annotations
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import inspect
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import queue
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import threading
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import time
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from collections.abc import Iterable
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from typing import Any
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import numpy as np
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import PIL.Image
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import torch
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from vllm.logger import init_logger
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from vllm.v1.engine.exceptions import EngineDeadError
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from vllm_omni.diffusion.data import (
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DiffusionOutput,
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DiffusionRequestAbortedError,
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OmniDiffusionConfig,
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)
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from vllm_omni.diffusion.executor.abstract import DiffusionExecutor
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from vllm_omni.diffusion.registry import (
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DiffusionModelRegistry,
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get_diffusion_post_process_func,
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get_diffusion_pre_process_func,
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)
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from vllm_omni.diffusion.request import OmniDiffusionRequest
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from vllm_omni.diffusion.sched import RequestScheduler, SchedulerInterface, StepScheduler
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from vllm_omni.diffusion.sched.interface import DiffusionRequestStatus
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from vllm_omni.diffusion.worker.utils import RunnerOutput
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from vllm_omni.inputs.data import OmniDiffusionSamplingParams, OmniTextPrompt
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from vllm_omni.outputs import OmniRequestOutput
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logger = init_logger(__name__)
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def supports_image_input(model_class_name: str) -> bool:
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model_cls = DiffusionModelRegistry._try_load_model_cls(model_class_name)
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if model_cls is None:
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return False
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return bool(getattr(model_cls, "support_image_input", False))
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def supports_audio_input(model_class_name: str) -> bool:
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model_cls = DiffusionModelRegistry._try_load_model_cls(model_class_name)
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if model_cls is None:
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return False
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return bool(getattr(model_cls, "support_audio_input", False))
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def image_color_format(model_class_name: str) -> str:
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model_cls = DiffusionModelRegistry._try_load_model_cls(model_class_name)
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return getattr(model_cls, "color_format", "RGB")
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def supports_audio_output(model_class_name: str) -> bool:
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model_cls = DiffusionModelRegistry._try_load_model_cls(model_class_name)
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if model_cls is None:
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return False
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return bool(getattr(model_cls, "support_audio_output", False))
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class DiffusionEngine:
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"""The diffusion engine for vLLM-Omni diffusion models."""
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def __init__(
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self,
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od_config: OmniDiffusionConfig,
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scheduler: SchedulerInterface | None = None,
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):
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"""Initialize the diffusion engine.
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Args:
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config: The configuration for the diffusion engine.
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"""
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self.od_config = od_config
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self.post_process_func = get_diffusion_post_process_func(od_config)
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self.pre_process_func = get_diffusion_pre_process_func(od_config)
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# Cache whether the model-specific postprocess accepts request-level
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# sampling params so step() can support both legacy and extended hooks.
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self._post_process_accepts_sampling_params = bool(
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self.post_process_func is not None
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and "sampling_params" in inspect.signature(self.post_process_func).parameters
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)
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executor_class = DiffusionExecutor.get_class(od_config)
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self.executor = executor_class(od_config)
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self.step_execution = bool(getattr(od_config, "step_execution", False))
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self.scheduler: SchedulerInterface = scheduler or (
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StepScheduler() if self.step_execution else RequestScheduler()
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)
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self.scheduler.initialize(od_config)
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self._rpc_lock = threading.RLock()
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self.abort_queue: queue.Queue[str] = queue.Queue()
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self.execute_fn = self.executor.execute_step if self.step_execution else self.executor.execute_request
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try:
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self._dummy_run()
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except Exception as e:
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logger.error(f"Dummy run failed: {e}")
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self.close()
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raise e
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def step(self, request: OmniDiffusionRequest) -> list[OmniRequestOutput]:
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diffusion_engine_start_time = time.perf_counter()
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# Apply pre-processing if available
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preprocess_time = 0.0
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if self.pre_process_func is not None:
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preprocess_start_time = time.perf_counter()
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request = self.pre_process_func(request)
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preprocess_time = time.perf_counter() - preprocess_start_time
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logger.info(f"Pre-processing completed in {preprocess_time:.4f} seconds")
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exec_start_time = time.perf_counter()
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output = self.add_req_and_wait_for_response(request)
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exec_total_time = time.perf_counter() - exec_start_time
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if output.aborted:
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raise DiffusionRequestAbortedError(output.abort_message or "Diffusion request aborted.")
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if output.error:
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raise RuntimeError(output.error)
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logger.info("Generation completed successfully.")
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if output.output is None:
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logger.warning("Output is None, returning empty OmniRequestOutput")
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return [
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OmniRequestOutput.from_diffusion(
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request_id=request.request_ids[i] if i < len(request.request_ids) else "",
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images=[],
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prompt=prompt,
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metrics={},
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latents=None,
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)
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for i, prompt in enumerate(request.prompts)
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]
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# When CPU offload is enabled, move output to CPU before
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# post-processing to avoid device OOM — model weights may still
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# reside on the device and leave no headroom for intermediates.
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output_data = output.output
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if (
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self.od_config.enable_cpu_offload
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and isinstance(output_data, torch.Tensor)
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and output_data.device.type != "cpu"
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):
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output_data = output_data.cpu()
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postprocess_start_time = time.perf_counter()
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if self.post_process_func is not None:
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# Some video pipelines need request-level controls during
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# postprocess (for example worker-side frame interpolation).
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if self._post_process_accepts_sampling_params:
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outputs = self.post_process_func(output_data, sampling_params=request.sampling_params)
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else:
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outputs = self.post_process_func(output_data)
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else:
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outputs = output_data
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audio_payload = None
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custom_output = output.custom_output or {}
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model_audio_sample_rate = None
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model_fps = None
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if isinstance(outputs, dict):
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audio_payload = outputs.get("audio")
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custom_output.update(outputs.get("custom_output") or {})
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model_audio_sample_rate = outputs.get("audio_sample_rate")
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model_fps = outputs.get("fps")
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outputs = outputs.get("video", outputs)
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postprocess_time = time.perf_counter() - postprocess_start_time
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logger.info(f"Post-processing completed in {postprocess_time:.4f} seconds")
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step_total_ms = (time.perf_counter() - diffusion_engine_start_time) * 1000
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logger.info(
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"DiffusionEngine.step breakdown: preprocess=%.2f ms, "
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"add_req_and_wait=%.2f ms, postprocess=%.2f ms, total=%.2f ms",
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preprocess_time * 1000,
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exec_total_time * 1000,
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postprocess_time * 1000,
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step_total_ms,
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)
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# Convert to OmniRequestOutput format
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# Ensure outputs is a list
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if not isinstance(outputs, list):
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outputs = [outputs] if outputs is not None else []
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metrics = {
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"preprocess_time_ms": preprocess_time * 1000,
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"diffusion_engine_exec_time_ms": exec_total_time * 1000,
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"diffusion_engine_total_time_ms": step_total_ms,
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"image_num": int(request.sampling_params.num_outputs_per_prompt),
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"resolution": int(request.sampling_params.resolution),
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"postprocess_time_ms": postprocess_time * 1000,
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}
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# Handle single request or multiple requests
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is_audio_output = supports_audio_output(self.od_config.model_class_name)
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if is_audio_output and model_audio_sample_rate is None:
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model_cls = DiffusionModelRegistry._try_load_model_cls(self.od_config.model_class_name)
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model_audio_sample_rate = getattr(model_cls, "audio_sample_rate", None)
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def _audio_mm(payload: Any) -> dict[str, Any]:
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mm: dict[str, Any] = {"audio": payload}
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if model_audio_sample_rate is not None:
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mm["audio_sample_rate"] = model_audio_sample_rate
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return mm
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if len(request.prompts) == 1:
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# Single request: return single OmniRequestOutput
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prompt = request.prompts[0]
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request_id = request.request_ids[0] if request.request_ids else ""
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if is_audio_output:
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request_audio_payload = outputs[0] if len(outputs) == 1 else outputs
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return [
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OmniRequestOutput.from_diffusion(
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request_id=request_id,
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images=[],
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prompt=prompt,
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metrics=metrics,
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latents=output.trajectory_latents,
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trajectory_latents=output.trajectory_latents,
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trajectory_timesteps=output.trajectory_timesteps,
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trajectory_log_probs=output.trajectory_log_probs,
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trajectory_decoded=output.trajectory_decoded,
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multimodal_output=_audio_mm(request_audio_payload),
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final_output_type="audio",
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stage_durations=output.stage_durations,
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peak_memory_mb=output.peak_memory_mb,
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),
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]
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else:
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mm_output = {}
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if audio_payload is not None:
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mm_output["audio"] = audio_payload
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if model_audio_sample_rate is not None:
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mm_output["audio_sample_rate"] = model_audio_sample_rate
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if model_fps is not None:
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mm_output["fps"] = model_fps
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return [
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OmniRequestOutput.from_diffusion(
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request_id=request_id,
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images=outputs,
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prompt=prompt,
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metrics=metrics,
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latents=output.trajectory_latents,
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trajectory_latents=output.trajectory_latents,
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trajectory_timesteps=output.trajectory_timesteps,
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trajectory_log_probs=output.trajectory_log_probs,
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trajectory_decoded=output.trajectory_decoded,
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custom_output=custom_output,
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multimodal_output=mm_output,
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stage_durations=output.stage_durations,
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peak_memory_mb=output.peak_memory_mb,
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),
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]
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else:
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# Multiple requests: return list of OmniRequestOutput
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# Split images based on num_outputs_per_prompt for each request
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results = []
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output_idx = 0
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for i, prompt in enumerate(request.prompts):
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request_id = request.request_ids[i] if i < len(request.request_ids) else ""
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# Get images for this request
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num_outputs = request.sampling_params.num_outputs_per_prompt
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start_idx = output_idx
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end_idx = start_idx + num_outputs
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request_outputs = outputs[start_idx:end_idx] if output_idx < len(outputs) else []
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output_idx = end_idx
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if is_audio_output:
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request_audio_payload = request_outputs[0] if len(request_outputs) == 1 else request_outputs
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results.append(
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OmniRequestOutput.from_diffusion(
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request_id=request_id,
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images=[],
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prompt=prompt,
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metrics=metrics,
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latents=output.trajectory_latents,
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trajectory_latents=output.trajectory_latents,
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trajectory_timesteps=output.trajectory_timesteps,
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trajectory_log_probs=output.trajectory_log_probs,
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trajectory_decoded=output.trajectory_decoded,
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multimodal_output=_audio_mm(request_audio_payload),
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final_output_type="audio",
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stage_durations=output.stage_durations,
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peak_memory_mb=output.peak_memory_mb,
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),
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)
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else:
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mm_output = {}
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if audio_payload is not None:
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sliced_audio = audio_payload
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if isinstance(audio_payload, (list, tuple)):
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sliced_audio = audio_payload[start_idx:end_idx]
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if len(sliced_audio) == 1:
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sliced_audio = sliced_audio[0]
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elif hasattr(audio_payload, "shape") and getattr(audio_payload, "shape", None) is not None:
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if len(audio_payload.shape) > 0 and audio_payload.shape[0] >= end_idx:
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sliced_audio = audio_payload[start_idx:end_idx]
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if num_outputs == 1:
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sliced_audio = sliced_audio[0]
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mm_output["audio"] = sliced_audio
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if model_audio_sample_rate is not None:
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mm_output["audio_sample_rate"] = model_audio_sample_rate
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if model_fps is not None:
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mm_output["fps"] = model_fps
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results.append(
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OmniRequestOutput.from_diffusion(
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request_id=request_id,
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images=request_outputs,
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prompt=prompt,
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metrics=metrics,
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latents=output.trajectory_latents,
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trajectory_latents=output.trajectory_latents,
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trajectory_timesteps=output.trajectory_timesteps,
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trajectory_log_probs=output.trajectory_log_probs,
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trajectory_decoded=output.trajectory_decoded,
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custom_output=custom_output,
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multimodal_output=mm_output,
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stage_durations=output.stage_durations,
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peak_memory_mb=output.peak_memory_mb,
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),
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)
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return results
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@staticmethod
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def make_engine(
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config: OmniDiffusionConfig,
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scheduler: SchedulerInterface | None = None,
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) -> DiffusionEngine:
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"""Factory method to create a DiffusionEngine instance.
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Args:
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config: The configuration for the diffusion engine.
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Returns:
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An instance of DiffusionEngine.
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"""
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return DiffusionEngine(config, scheduler=scheduler)
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def add_req_and_wait_for_response(self, request: OmniDiffusionRequest) -> DiffusionOutput:
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with self._rpc_lock:
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target_sched_req_id = self.scheduler.add_request(request)
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# keep scheduling and executing until the target request is finished
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while True:
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self._process_aborts_queue()
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sched_output = self.scheduler.schedule()
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if sched_output.is_empty:
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if target_sched_req_id in sched_output.finished_req_ids:
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return self._finalize_finished_request(target_sched_req_id)
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if not self.scheduler.has_requests():
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raise RuntimeError("Diffusion scheduler has no runnable requests.")
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continue
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# NOTE: add_req_and_wait_for_response() is synchronous, and
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# the scheduler currently enforces _max_batch_size = 1 (see
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# vllm_omni/diffusion/sched/base_scheduler.py), so we directly
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# take the single scheduled request here.
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sched_req_id = sched_output.scheduled_req_ids[0]
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try:
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runner_output = self.execute_fn(sched_output)
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except EngineDeadError:
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raise
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except Exception as exc:
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logger.error("Execution failed for diffusion request %s", sched_req_id, exc_info=True)
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runner_output = RunnerOutput(
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req_id=sched_req_id,
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step_index=None,
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finished=True,
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result=DiffusionOutput(error=str(exc)),
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)
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self._process_aborts_queue()
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finished_req_ids = self.scheduler.update_from_output(sched_output, runner_output)
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if target_sched_req_id in finished_req_ids:
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return self._finalize_finished_request(
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target_sched_req_id,
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runner_output=runner_output,
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missing_result_error="Diffusion execution finished without a final output.",
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)
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def profile(self, is_start: bool = True, profile_prefix: str | None = None) -> None:
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"""Start or stop profiling on all diffusion workers.
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Args:
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is_start: True to start profiling, False to stop.
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profile_prefix: Optional prefix for trace filename.
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"""
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if is_start:
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if profile_prefix is None:
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profile_prefix = f"diffusion_{int(time.time())}"
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logger.info(f"Starting diffusion profiling with prefix: {profile_prefix}")
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else:
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logger.info("Stopping diffusion profiling...")
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try:
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self.collective_rpc(method="profile", args=(is_start, profile_prefix))
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except Exception as e:
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action = "start" if is_start else "stop"
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logger.error(f"Failed to {action} profiling on workers", exc_info=True)
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if is_start:
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raise RuntimeError(f"Could not {action} profiler: {e}") from e
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def _dummy_run(self):
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"""A dummy run to warm up the model."""
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num_inference_steps = 1
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height = 512
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width = 512
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if supports_image_input(self.od_config.model_class_name):
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# Provide a dummy image input if the model supports it
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color_format = image_color_format(self.od_config.model_class_name)
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dummy_image = PIL.Image.new(color_format, (width, height))
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else:
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dummy_image = None
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if supports_audio_input(self.od_config.model_class_name):
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audio_sr = 16000
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dummy_audio = np.random.randn(audio_sr * 2).astype(np.float32)
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else:
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dummy_audio = None
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prompt: OmniTextPrompt = {
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"prompt": "dummy run",
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"multi_modal_data": {"image": dummy_image, "audio": dummy_audio},
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}
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req = OmniDiffusionRequest(
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prompts=[prompt],
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request_ids=["dummy_req_id"],
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sampling_params=OmniDiffusionSamplingParams(
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height=height,
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width=width,
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num_inference_steps=num_inference_steps,
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# Keep warmup path minimal and robust across text encoders.
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# Some models may fail when warmup implicitly triggers
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# classifier-free guidance with an empty negative prompt.
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guidance_scale=0.0,
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num_outputs_per_prompt=1,
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# Disable CFG for warmup to avoid triggering CFG parallel
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# validation when cfg_parallel_size > 1.
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extra_args={"cfg_text_scale": 1.0, "cfg_img_scale": 1.0},
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),
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)
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logger.info("dummy run to warm up the model")
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request = self.pre_process_func(req) if self.pre_process_func is not None else req
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output = self.add_req_and_wait_for_response(request)
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if output.error:
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raise RuntimeError(f"Dummy run failed: {output.error}")
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def collective_rpc(
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self,
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method: str,
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|
timeout: float | None = None,
|
|
args: tuple = (),
|
|
kwargs: dict | None = None,
|
|
unique_reply_rank: int | None = None,
|
|
) -> Any:
|
|
"""Call a method on worker processes and get results immediately.
|
|
|
|
Args:
|
|
method: The method name (str) to execute on workers
|
|
timeout: Optional timeout in seconds
|
|
args: Positional arguments for the method
|
|
kwargs: Keyword arguments for the method
|
|
unique_reply_rank: If set, only get reply from this rank
|
|
|
|
Returns:
|
|
Single result if unique_reply_rank is provided, otherwise list of results
|
|
"""
|
|
assert isinstance(method, str), "Only string method names are supported for now"
|
|
|
|
deadline = None if timeout is None else time.monotonic() + timeout
|
|
acquired = False
|
|
try:
|
|
if deadline is None:
|
|
self._rpc_lock.acquire()
|
|
acquired = True
|
|
else:
|
|
lock_timeout = max(0, deadline - time.monotonic())
|
|
acquired = self._rpc_lock.acquire(timeout=lock_timeout)
|
|
if not acquired:
|
|
raise TimeoutError(f"RPC call to {method} timed out waiting for engine lock.")
|
|
|
|
rpc_timeout = None if deadline is None else max(0, deadline - time.monotonic())
|
|
if deadline is not None and rpc_timeout <= 0:
|
|
raise TimeoutError(f"RPC call to {method} timed out.")
|
|
|
|
return self.executor.collective_rpc(
|
|
method=method,
|
|
timeout=rpc_timeout,
|
|
args=args,
|
|
kwargs=kwargs,
|
|
unique_reply_rank=unique_reply_rank,
|
|
)
|
|
finally:
|
|
if acquired:
|
|
self._rpc_lock.release()
|
|
|
|
def close(self) -> None:
|
|
if hasattr(self, "scheduler"):
|
|
self.scheduler.close()
|
|
if hasattr(self, "executor"):
|
|
self.executor.shutdown()
|
|
|
|
def abort(self, request_id: str | Iterable[str]) -> None:
|
|
request_ids = [request_id] if isinstance(request_id, str) else list(request_id)
|
|
for req_id in request_ids:
|
|
self.abort_queue.put(req_id)
|
|
|
|
def _process_aborts_queue(self) -> None:
|
|
if self.abort_queue.empty():
|
|
return
|
|
|
|
request_ids: list[str] = []
|
|
while not self.abort_queue.empty():
|
|
ids = self.abort_queue.get_nowait()
|
|
request_ids.extend((ids,) if isinstance(ids, str) else ids)
|
|
|
|
self._abort_requests(request_ids)
|
|
|
|
def _abort_requests(self, request_ids: str | Iterable[str]) -> None:
|
|
request_ids = [request_ids] if isinstance(request_ids, str) else list(request_ids)
|
|
|
|
sched_req_ids: list[str] = []
|
|
for request_id in dict.fromkeys(request_ids):
|
|
sched_req_id = self.scheduler.get_sched_req_id(request_id)
|
|
if sched_req_id is not None:
|
|
sched_req_ids.append(sched_req_id)
|
|
|
|
for sched_req_id in dict.fromkeys(sched_req_ids):
|
|
if self.scheduler.get_request_state(sched_req_id) is not None:
|
|
self.scheduler.finish_requests(sched_req_id, DiffusionRequestStatus.FINISHED_ABORTED)
|
|
|
|
def _finalize_finished_request(
|
|
self,
|
|
sched_req_id: str,
|
|
runner_output: RunnerOutput | None = None,
|
|
missing_result_error: str = "Diffusion scheduler finished target request without execution output.",
|
|
) -> DiffusionOutput:
|
|
state = self.scheduler.get_request_state(sched_req_id)
|
|
popped_state = self.scheduler.pop_request_state(sched_req_id)
|
|
state = state or popped_state
|
|
|
|
if state is None:
|
|
raise RuntimeError(f"Diffusion scheduler lost state for request {sched_req_id}.")
|
|
|
|
if state.status == DiffusionRequestStatus.FINISHED_ABORTED:
|
|
request_id = state.req.request_ids[0] if state.req.request_ids else sched_req_id
|
|
return DiffusionOutput(
|
|
aborted=True,
|
|
abort_message=f"Request {request_id} aborted.",
|
|
)
|
|
|
|
if runner_output is not None and runner_output.result is not None:
|
|
return runner_output.result
|
|
|
|
return DiffusionOutput(error=missing_result_error)
|