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[Model] Limit CPU threads for image transformations in InternVL to reduce cpu contention. (#24519)

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Signed-off-by: li-jinpeng <3332126450@qq.com>
Co-authored-by: Roger Wang <hey@rogerw.io>
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li-jinpeng 2025-09-10 16:45:44 +08:00 committed by GitHub
parent 77f62613f9
commit 267c80d31f
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1 changed files with 16 additions and 1 deletions
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17
vllm/model_executor/models/internvl.py
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@ -7,6 +7,7 @@
# Copyright (c) 2023 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
import os
from abc import ABC, abstractmethod
from collections.abc import Iterable, Mapping, Sequence
from typing import Annotated, Any, Literal, Optional, TypeVar, Union
@ -37,6 +38,7 @@ from vllm.multimodal.processing import (BaseMultiModalProcessor,
from vllm.multimodal.profiling import BaseDummyInputsBuilder
from vllm.sequence import IntermediateTensors
from vllm.transformers_utils.tokenizer import AnyTokenizer
from vllm.utils import set_default_torch_num_threads
from vllm.utils.tensor_schema import TensorSchema, TensorShape
from .interfaces import (MultiModalEmbeddings, SupportsLoRA,
@ -115,13 +117,26 @@ InternVLVideoInputs = Union[InternVLVideoPixelInputs,
# adapted from https://huggingface.co/OpenGVLab/InternVL2-1B
def build_transform(input_size: int):
MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
return T.Compose([
transform = T.Compose([
T.Lambda(lambda img: convert_image_mode(img, 'RGB')),
T.Resize((input_size, input_size),
interpolation=T.InterpolationMode.BICUBIC),
T.ToTensor(),
T.Normalize(mean=MEAN, std=STD)
])
# Image transformation operations (which include tensor computations
# on the CPU) can occupy a substantial number of CPU cores, introducing
# overhead due to CPU contention. This issue becomes particularly
# noticeable when deploying multiple vLLM instances on a single machine.
# Therefore, it is necessary to limit the number of threads allocated to
# image transformation tasks.
num_threads = int(os.environ.get("OMP_NUM_THREADS", "1"))
def apply(img):
with set_default_torch_num_threads(num_threads):
return transform(img)
return apply
# adapted from https://huggingface.co/OpenGVLab/InternVL2-1B