# SwiftFormer ### **SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications** [Abdelrahman Shaker](https://scholar.google.com/citations?hl=en&user=eEz4Wu4AAAAJ), [Muhammad Maaz](https://scholar.google.com/citations?user=vTy9Te8AAAAJ&hl=en&authuser=1&oi=sra), [Hanoona Rasheed](https://scholar.google.com/citations?user=yhDdEuEAAAAJ&hl=en&authuser=1&oi=sra), [Salman Khan](https://salman-h-khan.github.io), [Ming-Hsuan Yang](https://scholar.google.com/citations?user=p9-ohHsAAAAJ&hl=en), and [Fahad Shahbaz Khan](https://scholar.google.es/citations?user=zvaeYnUAAAAJ&hl=en) [![paper](https://img.shields.io/badge/arXiv-Paper-.svg)](arxiv_link) ## :rocket: News * **(Mar 27, 2023):** Classification training and evaluation codes along with pre-trained models are released.


Comparison of our SwiftFormer Models with state-of-the-art on ImgeNet-1K. The latency is measured on iPhone 14 Neural Engine (iOS 16).


Comparison with different self-attention modules. (a) is a typical self-attention. (b) is the transpose self-attention, where the self-attention operation is applied across channel feature dimensions (d×d) instead of the spatial dimension (n×n). (c) is the separable self-attention of MobileViT-v2, it uses element-wise operations to compute the context vector from the interactions of Q and K matrices. Then, the context vector is multiplied by V matrix to produce the final output. (d) Our proposed efficient additive self-attention. Here, the query matrix is multiplied by learnable weights and pooled to produce global queries. Then, the matrix K is element-wise multiplied by the broadcasted global queries, resulting the global context representation.

Abstract Self-attention has become a defacto choice for capturing global context in various vision applications. However, its quadratic computational complexity with respect to image resolution limits its use in real-time applications, especially for deployment on resource-constrained mobile devices. Although hybrid approaches have been proposed to combine the advantages of convolutions and self-attention for a better speed-accuracy trade-off, the expensive matrix multiplication operations in self-attention remain a bottleneck. In this work, we introduce a novel efficient additive attention mechanism that effectively replaces the quadratic matrix multiplication operations with linear element-wise multiplications. Our design shows that the key-value interaction can be replaced with a linear layer without sacrificing any accuracy. Unlike previous state-of-the-art methods, our efficient formulation of self-attention enables its usage at all stages of the network. Using our proposed efficient additive attention, we build a series of models called "SwiftFormer" which achieves state-of-the-art performance in terms of both accuracy and mobile inference speed. Our small variant achieves 78.5% top-1 ImageNet-1K accuracy with only 0.8~ms latency on iPhone 14, which is more accurate and 2x faster compared to MobileViT-v2.

## Classification on ImageNet-1K ### Models | Model | Top-1 accuracy | #params | GMACs | Latency | Ckpt | CoreML| |:---------------|:----:|:---:|:--:|:--:|:--:|:--:| | SwiftFormer-XS | 75.7% | 3.5M | 0.4G | 0.7ms | [XS](https://drive.google.com/file/d/15Ils-U96pQePXQXx2MpmaI-yAceFAr2x/view?usp=sharing) | [XS](https://drive.google.com/file/d/1tZVxtbtAZoLLoDc5qqoUGulilksomLeK/view?usp=sharing) | | SwiftFormer-S | 78.5% | 6.1M | 1.0G | 0.8ms | [S](https://drive.google.com/file/d/1_0eWwgsejtS0bWGBQS3gwAtYjXdPRGlu/view?usp=sharing) | [S](https://drive.google.com/file/d/13EOCZmtvbMR2V6UjezSZnbBz2_-59Fva/view?usp=sharing) | | SwiftFormer-L1 | 80.9% | 12.1M | 1.6G | 1.1ms | [L1](https://drive.google.com/file/d/1jlwrwWQ0SQzDRc5adtWIwIut5d1g9EsM/view?usp=sharing) | [L1](https://drive.google.com/file/d/1c3VUsi4q7QQ2ykXVS2d4iCRL478fWF3e/view?usp=sharing) | | SwiftFormer-L3 | 83.0% | 26.5M | 4.0G | 1.9ms | [L3](https://drive.google.com/file/d/1ypBcjx04ShmPYRhhjBRubiVjbExUgSa7/view?usp=sharing) | [L3](https://drive.google.com/file/d/1svahgIjh7da781jHOHjX58mtzCzYXSsJ/view?usp=sharing) | ## Detection and Segmentation Qualitative Results



## Latency Measurement The latency reported in SwiftFormer for iPhone 14 (iOS 16) uses the benchmark tool from [XCode 14](https://developer.apple.com/videos/play/wwdc2022/10027/). ## ImageNet ### Prerequisites `conda` virtual environment is recommended. ```shell conda create --name=swiftformer python=3.9 conda activate swiftformer pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 --extra-index-url https://download.pytorch.org/whl/cu113 pip install timm ``` ### Data preparation Download and extract ImageNet train and val images from http://image-net.org. The training and validation data are expected to be in the `train` folder and `val` folder respectively: ``` |-- /path/to/imagenet/ |-- train |-- val ``` ### Single machine multi-GPU training We provide training script for all models in `dist_train.sh` using PyTorch distributed data parallel (DDP). To train SwiftFormer models on an 8-GPU machine: ``` sh dist_train.sh /path/to/imagenet 8 ``` Note: specify which model command you want to run in the script. To reproduce the results of the paper, use 16-GPU machine with batch-size of 128 or 8-GPU machine with batch size of 256. Auto Augmentation, CutMix, MixUp are disabled for SwiftFormer-XS only. ### Multi-node training On a Slurm-managed cluster, multi-node training can be launched as ``` sbatch slurm_train.sh /path/to/imagenet SwiftFormer_XS ``` Note: specify slurm specific paramters in `slurm_train.sh` script. ### Testing We provide an example test script `dist_test.sh` using PyTorch distributed data parallel (DDP). For example, to test SwiftFormer-XS on an 8-GPU machine: ``` sh dist_test.sh SwiftFormer_XS 8 weights/SwiftFormer_XS_ckpt.pth ``` ## Citation if you use our work, please consider citing us: ```BibTeX @article{Shaker2023SwiftFormer, title={SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications}, author={Shaker, Abdelrahman and Maaz, Muhammad and Rasheed, Hanoona and Khan, Salman and Yang, Ming-Hsuan and Khan, Fahad Shahbaz}, journal={arXiv preprint arXiv:X.X}, year={2023} } ``` ## Contact: If you have any question, please create an issue on this repository or contact at abdelrahman.youssief@mbzuai.ac.ae. ## Acknowledgement Our code base is based on [LeViT](https://github.com/facebookresearch/LeViT) and [EfficientFormer](https://github.com/snap-research/EfficientFormer) repositories. We thank authors for their open-source implementation. ## Our Related Works - EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications, CADL'22, ECCV. [Paper](https://arxiv.org/abs/2206.10589) | [Code](https://github.com/mmaaz60/EdgeNeXt).