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Skip-Attention: Improving Vision Transformers by Paying Less Attention

This is the official code for ICLR 2024 paper: https://arxiv.org/abs/2301.02240

Abstract

This work aims to improve the efficiency of vision transformers (ViT). While ViTs use computationally expensive self-attention operations in every layer, we identify that these operations are highly correlated across layers – a key redundancy that causes unnecessary computations. Based on this observation, we propose SKIPAT, a method to reuse self-attention computation from preceding layers to approximate attention at one or more subsequent layers. To ensure that reusing self-attention blocks across layers does not degrade the performance, we introduce a simple parametric function, which outperforms the baseline transformer’s performance while running computationally faster. We show the effectiveness of our method in image classification and self-supervised learning on ImageNet-1K, semantic segmentation on ADE20K, image denoising on SIDD, and video denoising on DAVIS. We achieve improved throughput at the same-or-higher accuracy levels in all these tasks.

SkipAt Architecture

The code and pretrained models in this repository allow you to replicate our results on SkipAt for Vision Transformers (ViT). It provides evaluation for image classification task on ImageNet, making it up to 25% faster than baseline ViT. For using it for other tasks such as unsupervised segmentation check section 5.

  1. Repo Structure
  2. Setup
  3. Evaluating SkipAt
  4. Dataset
  5. Other Tasks
  6. Citation

Repo Structure

/
|-- README.md               # This file
|-- requirements.txt        # Python dependencies
|-- validate.py             # Script to evaluate SkipAt
|-- utils.py                # Utils to evaluate SkipAt
|-- model/              
    |-- __init__.py
    |-- vit_skipat.py       # SkipAt model definition
    |-- uformer_utils.py    # Utils for SkipAt
|-- assets/                 # Resources for this README.md

Setup

This code is experimented using torch==1.10.1 and timm==0.5.4.
First, clone the repo:

git clone https://github.com/Qualcomm-AI-research/skip-attention.git
cd skip-attention

Then, create the environment by one of following setups:

Using conda

The following assumes conda is installed on your machine:

conda create -n skipat python=3.8
conda activate skipat
pip install -r requirements.txt

Verify your installation by running: python3 validate.py --help".

Using venv

The following assumes python 3.8 is installed on your machine:

python3.8 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Verify your installation by running: python3 validate.py --help".

Using docker

The following assumes docker v20.10 is installed on your machine:

docker run -it --rm --gpus 1 --shm-size 1G -v <..:..> python:3.8 bash
# Where locations for the code, dataset and checkpoints are mounted using `-v`.
cd <root folder of your SkipAt git clone>
python3 -m pip install -r requirements.txt

Verify your installation by running: python3 validate.py --help".

Evaluating SkipAt

This code is verified to work with python 3.8 on Ubuntu 20.04, with Intel x86_64 CPU and an Nvidia GeForce RTX 2080 Ti GPU, using CUDA version 10.2 and the python dependencies specified in requirements.txt.
Command format to evaluate our model is the same as timm's validate command:

python3 validate.py <IMAGENET_ROOT> --checkpoint <CHECKPOINTS_ROOT>/<checkpoint>.t7 --model <MODEL_TYPE> --num_classes 1000

Where <IMAGENET_ROOT> is the path to where ImageNet is stored (see section dataset), <CHECKPOINTS_ROOT> is the path to where model checkpoints are downloaded, and <MODEL_TYPE> specifies the model to use.
For evaluation, the user can compare ViT baseline to SkipAt for tiny, and small models, as specified in the following table.

Name <MODEL_TYPE> Top-1 Acc Throughput Checkpoints
ViT-T baseline vit_tiny_patch16_224 72.8 5.8 Link
ViT-T SkipAt skipat_tiny_patch16_224 73.3 6.9 Link
ViT-S baseline vit_small_patch16_224 79.8 3.2 Link
ViT-S SkipAt skipat_small_patch16_224 80.0 3.8 Link

*throughput (image/sec) is measured using a batch size of 1024 on a single NVIDIA A100 GPU, averaged over the validation set of ImageNet-1

Dataset

This repo assumes ImageNet dataset is already downloaded in <IMAGENET_ROOT>. Otherwise download it from https://www.image-net.org/download.php The data root should look like

/
|-- <IMAGENET_ROOT>/              
    |-- train
    |-- val

Other Tasks

SkipAt is a new model definition and can easily be used in different training paradigms and tasks including self-supervised learning and semantic segmentation. It achieves superior accuracy vs. efficiency tradeoff compared to the baseline transformer on a wide variety of tasks, datasets and model sizes.

SkipAt Tasks

Citation

@article{venkataramanan2023skip,
  title={Skip-attention: Improving vision transformers by paying less attention},
  author={Venkataramanan, Shashanka and Ghodrati, Amir and Asano, Yuki M and Porikli, Fatih and Habibian, Amirhossein},
  journal={arXiv preprint arXiv:2301.02240},
  year={2023}
}