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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

- google/vit-base-patch16-224
- microsoft/beit-base-patch16-224-pt22k-ft22k
- facebook/convnext-large-224
- microsoft/resnet-50

图像分割

- nvidia/segformer-b0-finetuned-ade-512-512
- facebook/mask2former-swin-tiny-coco-panoptic
- facebook/maskformer-swin-base-ade
- google/deeplabv3_mobilenet_v2_1.0_513

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。

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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