ViG

Linear-complexity Visual Sequence Learning with Gated Linear Attention

Bencheng Liao^1,2, Xinggang Wang^{2 📧}, Lianghui Zhu², Qian Zhang³, Chang Huang³,

¹ Institute of Artificial Intelligence, HUST, ² School of EIC, HUST, ³ Horizon Robotics

(^📧) corresponding author.

ArXiv Preprint (arXiv 2405.18425)

News

• June 17th, 2024: We release an initial version of ViG with code and weights.
• May 29th, 2024: We released our paper on Arxiv. Code/Models are coming soon. Please stay tuned! ☕️

Abstract

Recently, linear complexity sequence modeling networks have achieved modeling capabilities similar to Vision Transformers on a variety of computer vision tasks, while using fewer FLOPs and less memory. However, their advantage in terms of actual runtime speed is not significant. To address this issue, we introduce Gated Linear Attention (GLA) for vision, leveraging its superior hardware-awareness and efficiency. We propose direction-wise gating to capture 1D global context through bidirectional modeling and a 2D gating locality injection to adaptively inject 2D local details into 1D global context. Our hardware-aware implementation further merges forward and backward scanning into a single kernel, enhancing parallelism and reducing memory cost and latency. The proposed model, ViG, offers a favorable trade-off in accuracy, parameters, and FLOPs on ImageNet and downstream tasks, outperforming popular Transformer and CNN-based models. Notably, ViG-S matches DeiT-B's accuracy while using only 27% of the parameters and 20% of the FLOPs, running 2$\times$ faster on $224\times224$ images. At $1024\times1024$ resolution, ViG-T uses $5.2\times$ fewer FLOPs, saves 90% GPU memory, runs $4.8\times$ faster, and achieves 20.7% higher top-1 accuracy than DeiT-T. These results position ViG as an efficient and scalable solution for visual representation learning.

Quantitative analysis

Accuracy vs. Parameter

The proposed basic ViG block achieves global receptive field with linear complexity, while the CNN, vanilla softmax attention and window-attention-based blocks cannot.

Efficiency & Accuracy vs. Resolution

Getting Started

Prepare Environment

git clone https://github.com/hustvl/ViG.git
cd ViG
conda create -n vig python=3.8
conda activate vig

Install Packages

# torch
conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.8 -c pytorch -c nvidia

# requirement
pip install -r requirement.txt

# GLA
pip install triton==2.2.0
cd flash-linear-attention
python setup.py develop

Train Your ViG

For single node:

cd classification
export CONFIG=configs/vig/vig-s.yaml
python -m torch.distributed.launch --nnodes=1 --node_rank=0 --nproc_per_node=8 \
    --master_addr="127.0.0.1" --master_port=29501 main.py \
    --cfg ${CONFIG} --data-path data/IN1K/ \
    --output /path/to/output

For two nodes:

cd classification
export CONFIG=configs/vig/vig-b.yaml
export MASTER_IP=XXXXXXX
# for first node
python -m torch.distributed.launch --nnodes=2 --node_rank=0 --nproc_per_node=8 \
    --master_addr=${MASTER_IP} --master_port=29501 main.py \
    --cfg ${CONFIG} --data-path data/IN1K/ \
    --output /path/to/output --batch-size 64
# for second node, modify the node_rank arg
python -m torch.distributed.launch --nnodes=2 --node_rank=1 --nproc_per_node=8 \
    --master_addr=${MASTER_IP} --master_port=29501 main.py \
    --cfg ${CONFIG} --data-path data/IN1K/ \
    --output /path/to/output --batch-size 64

Model Weights

Non-hierarchical Architectures

Model	#param.	Top-1 Acc.	Hugginface Repo
ViG-T	6M	77.2	https://huggingface.co/hustvl/ViG/tree/main
ViG-S	23M	81.7	https://huggingface.co/hustvl/ViG/tree/main
ViG-B	89M	82.6	https://huggingface.co/hustvl/ViG/tree/main

Hierarchical Architectures

Model	#param.	Top-1 Acc.	Hugginface Repo
ViG-H-T	29M	82.8	https://huggingface.co/hustvl/ViG/tree/main
ViG-H-S	50M	83.8	https://huggingface.co/hustvl/ViG/tree/main
ViG-H-B	89M	84.2	https://huggingface.co/hustvl/ViG/tree/main

Evaluation on Provided Weights

To evaluate ViG-S on ImageNet-1K, run:

python -m torch.distributed.launch --nnodes=1 --node_rank=0 --nproc_per_node=1 \
  --master_port=29501 main.py \
  --cfg configs/vig/vig-s.yaml \
  --batch-size 128 --data-path ./data/IN1K/ \
  --output ./output/ --pretrained /path/to/ckpt \
  --eval

Acknowledgements

This code is developed on the top of Vim, VMamba, VRWKV, and FLA. Thanks for their great works.

Citation

If you find ViG is useful in your research or applications, please consider giving us a star 🌟 and citing it by the following BibTeX entry.

 @article{vig,
  title={ViG: Linear-complexity Visual Sequence Learning with Gated Linear Attention},
  author={Bencheng Liao and Xinggang Wang and Lianghui Zhu and Qian Zhang and Chang Huang},
  journal={arXiv preprint arXiv:2405.18425},
  year={2024}
}