hsliuustc0106
e1c4b6153a
Per review, the warmup recipe is a property of the model's request geometry, not of distributed layerwise offload: rename dlo_dummy_run_recipe to dummy_run_recipe and drop the DLO gate in _make_dummy_request. A model whose geometry needs recipe-specific sampling arguments warms up at startup whatever features are enabled — under DLO the generation also primes the component cache-retention budget; without it, the same warmup covers any other first-request cost. Validated both ways on L20X: DLO boot unchanged (releases stay in the startup window) and a non-DLO MiniMax-H3 boot now runs the warmup and serves normally. Signed-off-by: hsliuustc0106 <liuhongsheng4@huawei.com>
78 lines
3.3 KiB
Python
78 lines
3.3 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
|
|
from __future__ import annotations
|
|
|
|
import inspect
|
|
from typing import Any
|
|
|
|
from vllm_omni.diffusion.data import OmniDiffusionConfig
|
|
from vllm_omni.diffusion.model_metadata import get_diffusion_model_metadata
|
|
from vllm_omni.diffusion.models.diffusers_adapter.pipeline_utils import resolve_diffusers_pipeline_class
|
|
from vllm_omni.diffusion.registry import DiffusionModelRegistry
|
|
|
|
|
|
def supports_multimodal_input(od_config: OmniDiffusionConfig) -> tuple[bool, bool]:
|
|
if (
|
|
od_config.diffusion_load_format == "diffusers"
|
|
and (pipe_cls := resolve_diffusers_pipeline_class(od_config)) is not None
|
|
):
|
|
signature = inspect.signature(pipe_cls.__call__)
|
|
support_image_input = "image" in signature.parameters
|
|
support_audio_input = (
|
|
"audio" in signature.parameters or "audio_latents" in signature.parameters
|
|
) # ref. LTX-2 format
|
|
return support_image_input, support_audio_input
|
|
|
|
supports_image_input = False
|
|
supports_audio_input = False
|
|
|
|
model_cls = DiffusionModelRegistry._try_load_model_cls(od_config.model_class_name)
|
|
if model_cls is not None:
|
|
supports_image_input = bool(getattr(model_cls, "support_image_input", False))
|
|
supports_audio_input = bool(getattr(model_cls, "support_audio_input", False))
|
|
return supports_image_input, supports_audio_input
|
|
|
|
|
|
def image_color_format(model_class_name: str) -> str:
|
|
model_cls = DiffusionModelRegistry._try_load_model_cls(model_class_name)
|
|
return getattr(model_cls, "color_format", "RGB")
|
|
|
|
|
|
def supports_audio_output(model_class_name: str) -> bool:
|
|
model_cls = DiffusionModelRegistry._try_load_model_cls(model_class_name)
|
|
if model_cls is None:
|
|
return False
|
|
return bool(getattr(model_cls, "support_audio_output", False))
|
|
|
|
|
|
def get_dummy_run_num_frames(model_class_name: str, supports_audio_input: bool) -> int:
|
|
"""Get num_frames for the dummy warmup run. Returns 0 to skip warmup."""
|
|
|
|
model_cls = DiffusionModelRegistry._try_load_model_cls(model_class_name)
|
|
if model_cls is not None and hasattr(model_cls, "dummy_run_num_frames"):
|
|
return int(getattr(model_cls, "dummy_run_num_frames"))
|
|
return 2 if supports_audio_input or supports_audio_output(model_class_name) else 1
|
|
|
|
|
|
def get_dummy_run_recipe(model_class_name: str) -> dict[str, Any] | None:
|
|
"""Get the model-declared startup-warmup recipe, or None.
|
|
|
|
Models that opt out of the generic dummy run (``dummy_run_num_frames = 0``)
|
|
because their request geometry needs model-specific sampling arguments
|
|
(task, duration, aspect ratio, ...) declare a recipe instead. The recipe
|
|
is feature-neutral: the engine runs it at startup whatever features are
|
|
enabled — under distributed layerwise offload the generation also primes
|
|
the component allocator-cache retention policy's observed-peak budget.
|
|
"""
|
|
|
|
model_cls = DiffusionModelRegistry._try_load_model_cls(model_class_name)
|
|
recipe = getattr(model_cls, "dummy_run_recipe", None) if model_cls is not None else None
|
|
return dict(recipe) if recipe else None
|
|
|
|
|
|
def get_dummy_run_num_image_inputs(model_class_name: str) -> int:
|
|
"""Return the maximum advertised image-input count for profiling."""
|
|
|
|
return get_diffusion_model_metadata(model_class_name).max_multimodal_image_inputs or 1
|