# Optimize inference using torch.compile()

This guide aims to provide a benchmark on the inference speed-ups introduced with [`torch.compile()`](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) for [computer vision models in 🤗 Transformers](https://huggingface.co/models?pipeline_tag=image-classification&library=transformers&sort=trending).

## 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](https://pytorch.org/get-started/pytorch-2.0/#user-experience).

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

thon 

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

thon 

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

thon 

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](https://huggingface.co/google/vit-base-patch16-224)

- [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k)

- [facebook/convnext-large-224](https://huggingface.co/facebook/convnext-large-224)

- [microsoft/resnet-50](https://huggingface.co/)

**Image Segmentation** 

- [nvidia/segformer-b0-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512)

- [facebook/mask2former-swin-tiny-coco-panoptic](https://huggingface.co/facebook/mask2former-swin-tiny-coco-panoptic)

- [facebook/maskformer-swin-base-ade](https://huggingface.co/facebook/maskformer-swin-base-ade)

- [google/deeplabv3_mobilenet_v2_1.0_513](https://huggingface.co/google/deeplabv3_mobilenet_v2_1.0_513)

**Object Detection** 

- [google/owlvit-base-patch32](https://huggingface.co/google/owlvit-base-patch32)

- [facebook/detr-resnet-101](https://huggingface.co/facebook/detr-resnet-101)

- [microsoft/conditional-detr-resnet-50](https://huggingface.co/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. 

![Duration Comparison on V100 with Batch Size of 1](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/torch_compile/v100_1_duration.png)

![Percentage Improvement on T4 with Batch Size of 4](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/torch_compile/T4_4_percentage.png)

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](https://download.pytorch.org/whl/nightly/cu118)) 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 |