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使用 torch.compile() 优化推理

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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

google/owlvit-base-patch32
facebook/detr-resnet-101
microsoft/conditional-detr-resnet-50

下面是使用和不使用torch.compile()的推理持续时间可视化，以及每个模型在不同硬件和数据批量大小下的改进百分比。

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

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