Hugging Face's logo

Transformers documentation

使用 torch.compile() 优化推理

You are viewing v4.45.2 version. A newer version v4.47.1 is available.
Hugging Face's logo
Join the Hugging Face community

and get access to the augmented documentation experience

to get started

使用 torch.compile() 优化推理

本指南旨在为使用torch.compile()在🤗 Transformers中的计算机视觉模型中引入的推理速度提升提供一个基准。

torch.compile 的优势

根据模型和GPU的不同，torch.compile()在推理过程中可以提高多达30%的速度。要使用torch.compile()，只需安装2.0及以上版本的torch即可。

编译模型需要时间，因此如果您只需要编译一次模型而不是每次推理都编译，那么它非常有用。 要编译您选择的任何计算机视觉模型，请按照以下方式调用torch.compile()：

from transformers import AutoModelForImageClassification

model = AutoModelForImageClassification.from_pretrained(MODEL_ID).to("cuda")
+ model = torch.compile(model)

compile() 提供了多种编译模式，它们在编译时间和推理开销上有所不同。max-autotune 比 reduce-overhead 需要更长的时间，但会得到更快的推理速度。默认模式在编译时最快，但在推理时间上与 reduce-overhead 相比效率较低。在本指南中，我们使用了默认模式。您可以在这里了解更多信息。

我们在 PyTorch 2.0.1 版本上使用不同的计算机视觉模型、任务、硬件类型和数据批量大小对 torch.compile 进行了基准测试。

基准测试代码

以下是每个任务的基准测试代码。我们在推理之前”预热“GPU，并取300次推理的平均值，每次使用相同的图像。

使用 ViT 进行图像分类

import torch
from PIL import Image
import requests
import numpy as np
from transformers import AutoImageProcessor, AutoModelForImageClassification

url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)

processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224")
model = AutoModelForImageClassification.from_pretrained("google/vit-base-patch16-224").to("cuda")
model = torch.compile(model)

processed_input = processor(image, return_tensors='pt').to(device="cuda")

with torch.no_grad():
    _ = model(**processed_input)

使用 DETR 进行目标检测

from transformers import AutoImageProcessor, AutoModelForObjectDetection

processor = AutoImageProcessor.from_pretrained("facebook/detr-resnet-50")
model = AutoModelForObjectDetection.from_pretrained("facebook/detr-resnet-50").to("cuda")
model = torch.compile(model)

texts = ["a photo of a cat", "a photo of a dog"]
inputs = processor(text=texts, images=image, return_tensors="pt").to("cuda")

with torch.no_grad():
    _ = model(**inputs)

使用 Segformer 进行图像分割

from transformers import SegformerImageProcessor, SegformerForSemanticSegmentation

processor = SegformerImageProcessor.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512")
model = SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512").to("cuda")
model = torch.compile(model)
seg_inputs = processor(images=image, return_tensors="pt").to("cuda")

with torch.no_grad():
    _ = model(**seg_inputs)

以下是我们进行基准测试的模型列表。

图像分类

图像分割

目标检测

Duration Comparison on V100 with Batch Size of 1

Percentage Improvement on T4 with Batch Size of 4

下面可以找到每个模型使用和不使用compile()的推理时间（毫秒）。请注意，OwlViT在大批量大小下会导致内存溢出。

A100 (batch size: 1)

Task/Model torch 2.0 -
no compile		 torch 2.0 -
compile
Image Classification/ViT 9.325 7.584
Image Segmentation/Segformer 11.759 10.500
Object Detection/OwlViT 24.978 18.420
Image Classification/BeiT 11.282 8.448
Object Detection/DETR 34.619 19.040
Image Classification/ConvNeXT 10.410 10.208
Image Classification/ResNet 6.531 4.124
Image Segmentation/Mask2former 60.188 49.117
Image Segmentation/Maskformer 75.764 59.487
Image Segmentation/MobileNet 8.583 3.974
Object Detection/Resnet-101 36.276 18.197
Object Detection/Conditional-DETR 31.219 17.993

A100 (batch size: 4)

Task/Model torch 2.0 -
no compile		 torch 2.0 -
compile
Image Classification/ViT 14.832 14.499
Image Segmentation/Segformer 18.838 16.476
Image Classification/BeiT 13.205 13.048
Object Detection/DETR 48.657 32.418
Image Classification/ConvNeXT 22.940 21.631
Image Classification/ResNet 6.657 4.268
Image Segmentation/Mask2former 74.277 61.781
Image Segmentation/Maskformer 180.700 159.116
Image Segmentation/MobileNet 14.174 8.515
Object Detection/Resnet-101 68.101 44.998
Object Detection/Conditional-DETR 56.470 35.552

A100 (batch size: 16)

Task/Model torch 2.0 -
no compile		 torch 2.0 -
compile
Image Classification/ViT 40.944 40.010
Image Segmentation/Segformer 37.005 31.144
Image Classification/BeiT 41.854 41.048
Object Detection/DETR 164.382 161.902
Image Classification/ConvNeXT 82.258 75.561
Image Classification/ResNet 7.018 5.024
Image Segmentation/Mask2former 178.945 154.814
Image Segmentation/Maskformer 638.570 579.826
Image Segmentation/MobileNet 51.693 30.310
Object Detection/Resnet-101 232.887 155.021
Object Detection/Conditional-DETR 180.491 124.032

V100 (batch size: 1)

Task/Model torch 2.0 -
no compile		 torch 2.0 -
compile
Image Classification/ViT 10.495 6.00
Image Segmentation/Segformer 13.321 5.862
Object Detection/OwlViT 25.769 22.395
Image Classification/BeiT 11.347 7.234
Object Detection/DETR 33.951 19.388
Image Classification/ConvNeXT 11.623 10.412
Image Classification/ResNet 6.484 3.820
Image Segmentation/Mask2former 64.640 49.873
Image Segmentation/Maskformer 95.532 72.207
Image Segmentation/MobileNet 9.217 4.753
Object Detection/Resnet-101 52.818 28.367
Object Detection/Conditional-DETR 39.512 20.816

V100 (batch size: 4)

Task/Model torch 2.0 -
no compile		 torch 2.0 -
compile
Image Classification/ViT 15.181 14.501
Image Segmentation/Segformer 16.787 16.188
Image Classification/BeiT 15.171 14.753
Object Detection/DETR 88.529 64.195
Image Classification/ConvNeXT 29.574 27.085
Image Classification/ResNet 6.109 4.731
Image Segmentation/Mask2former 90.402 76.926
Image Segmentation/Maskformer 234.261 205.456
Image Segmentation/MobileNet 24.623 14.816
Object Detection/Resnet-101 134.672 101.304
Object Detection/Conditional-DETR 97.464 69.739

V100 (batch size: 16)

Task/Model torch 2.0 -
no compile		 torch 2.0 -
compile
Image Classification/ViT 52.209 51.633
Image Segmentation/Segformer 61.013 55.499
Image Classification/BeiT 53.938 53.581
Object Detection/DETR OOM OOM
Image Classification/ConvNeXT 109.682 100.771
Image Classification/ResNet 14.857 12.089
Image Segmentation/Mask2former 249.605 222.801
Image Segmentation/Maskformer 831.142 743.645
Image Segmentation/MobileNet 93.129 55.365
Object Detection/Resnet-101 482.425 361.843
Object Detection/Conditional-DETR 344.661 255.298

T4 (batch size: 1)

Task/Model torch 2.0 -
no compile		 torch 2.0 -
compile
Image Classification/ViT 16.520 15.786
Image Segmentation/Segformer 16.116 14.205
Object Detection/OwlViT 53.634 51.105
Image Classification/BeiT 16.464 15.710
Object Detection/DETR 73.100 53.99
Image Classification/ConvNeXT 32.932 30.845
Image Classification/ResNet 6.031 4.321
Image Segmentation/Mask2former 79.192 66.815
Image Segmentation/Maskformer 200.026 188.268
Image Segmentation/MobileNet 18.908 11.997
Object Detection/Resnet-101 106.622 82.566
Object Detection/Conditional-DETR 77.594 56.984

T4 (batch size: 4)

Task/Model torch 2.0 -
no compile		 torch 2.0 -
compile
Image Classification/ViT 43.653 43.626
Image Segmentation/Segformer 45.327 42.445
Image Classification/BeiT 52.007 51.354
Object Detection/DETR 277.850 268.003
Image Classification/ConvNeXT 119.259 105.580
Image Classification/ResNet 13.039 11.388
Image Segmentation/Mask2former 201.540 184.670
Image Segmentation/Maskformer 764.052 711.280
Image Segmentation/MobileNet 74.289 48.677
Object Detection/Resnet-101 421.859 357.614
Object Detection/Conditional-DETR 289.002 226.945

T4 (batch size: 16)

Task/Model torch 2.0 -
no compile		 torch 2.0 -
compile
Image Classification/ViT 163.914 160.907
Image Segmentation/Segformer 192.412 163.620
Image Classification/BeiT 188.978 187.976
Object Detection/DETR OOM OOM
Image Classification/ConvNeXT 422.886 388.078
Image Classification/ResNet 44.114 37.604
Image Segmentation/Mask2former 756.337 695.291
Image Segmentation/Maskformer 2842.940 2656.88
Image Segmentation/MobileNet 299.003 201.942
Object Detection/Resnet-101 1619.505 1262.758
Object Detection/Conditional-DETR 1137.513 897.390

PyTorch Nightly

我们还在 PyTorch Nightly 版本（2.1.0dev）上进行了基准测试，可以在这里找到 Nightly 版本的安装包，并观察到了未编译和编译模型的延迟性能改善。

A100

Task/Model Batch Size torch 2.0 - no compile torch 2.0 -
compile
Image Classification/BeiT Unbatched 12.462 6.954
Image Classification/BeiT 4 14.109 12.851
Image Classification/BeiT 16 42.179 42.147
Object Detection/DETR Unbatched 30.484 15.221
Object Detection/DETR 4 46.816 30.942
Object Detection/DETR 16 163.749 163.706

T4

Task/Model Batch Size torch 2.0 -
no compile		 torch 2.0 -
compile
Image Classification/BeiT Unbatched 14.408 14.052
Image Classification/BeiT 4 47.381 46.604
Image Classification/BeiT 16 42.179 42.147
Object Detection/DETR Unbatched 68.382 53.481
Object Detection/DETR 4 269.615 204.785
Object Detection/DETR 16 OOM OOM

V100

Task/Model Batch Size torch 2.0 -
no compile		 torch 2.0 -
compile
Image Classification/BeiT Unbatched 13.477 7.926
Image Classification/BeiT 4 15.103 14.378
Image Classification/BeiT 16 52.517 51.691
Object Detection/DETR Unbatched 28.706 19.077
Object Detection/DETR 4 88.402 62.949
Object Detection/DETR 16 OOM OOM

降低开销

我们在 PyTorch Nightly 版本中为 A100 和 T4 进行了 reduce-overhead 编译模式的性能基准测试。

A100

Task/Model Batch Size torch 2.0 -
no compile		 torch 2.0 -
compile
Image Classification/ConvNeXT Unbatched 11.758 7.335
Image Classification/ConvNeXT 4 23.171 21.490
Image Classification/ResNet Unbatched 7.435 3.801
Image Classification/ResNet 4 7.261 2.187
Object Detection/Conditional-DETR Unbatched 32.823 11.627
Object Detection/Conditional-DETR 4 50.622 33.831
Image Segmentation/MobileNet Unbatched 9.869 4.244
Image Segmentation/MobileNet 4 14.385 7.946

T4

Task/Model Batch Size torch 2.0 -
no compile		 torch 2.0 -
compile
Image Classification/ConvNeXT Unbatched 32.137 31.84
Image Classification/ConvNeXT 4 120.944 110.209
Image Classification/ResNet Unbatched 9.761 7.698
Image Classification/ResNet 4 15.215 13.871
Object Detection/Conditional-DETR Unbatched 72.150 57.660
Object Detection/Conditional-DETR 4 301.494 247.543
Image Segmentation/MobileNet Unbatched 22.266 19.339
Image Segmentation/MobileNet 4 78.311 50.983
< > Update on GitHub

←TensorFlow模型的XLA集成 如何为 🤗 Transformers 做贡献？→