#
Optimize inference using torch.compile()

This guide aims to provide a benchmark on the inference speed-ups introduced with `torch.compile()` for computer vision models in 🤗 Transformers.

##
Benefits of torch.compile

Depending on the model and the GPU, `torch.compile()` yields up to 30% speed-up during inference. To use `torch.compile()`, simply install any version of `torch` above 2.0.

Compiling a model takes time, so it’s useful if you are compiling the model only once instead of every time you infer. To compile any computer vision model of your choice, call `torch.compile()` on the model as shown below:

    from transformers import AutoModelForImageClassification

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

`compile()` comes with multiple modes for compiling, which essentially differ in compilation time and inference overhead. `max-autotune` takes longer than `reduce-overhead` but results in faster inference. Default mode is fastest for compilation but is not as efficient compared to `reduce-overhead` for inference time. In this guide, we used the default mode. You can learn more about it here.

We benchmarked `torch.compile` with different computer vision models, tasks, types of hardware, and batch sizes on `torch` version 2.0.1.

##
Benchmarking code

Below you can find the benchmarking code for each task. We warm up the GPU before inference and take the mean time of 300 inferences, using the same image each time.

###
Image Classification with ViT

    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)

####
Object Detection with DETR

    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)

####
Image Segmentation with Segformer

    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)

Below you can find the list of the models we benchmarked.

Image Classification

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

Image Segmentation

- 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

Object Detection

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

Below you can find visualization of inference durations with and without `torch.compile()` and percentage improvements for each model in different hardware and batch sizes.

Below you can find inference durations in milliseconds for each model with and without `compile()`. Note that OwlViT results in OOM in larger batch sizes.

###
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

We also benchmarked on PyTorch nightly (2.1.0dev, find the wheel here) and observed improvement in latency both for uncompiled and compiled models.

###
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

##
Reduce Overhead

We benchmarked `reduce-overhead` compilation mode for A100 and T4 in Nightly.

###
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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