# What does this PR do?

[Sorry for the spams, GitHub had some issues]

Same as #24075, but that PR got freezed after I force pushed (after rebase), and my changes to address the comments were not able to appear)

(amy have already approved #24075)

----

Byebye!

EOL of python 3.7 is `2023/06/27`.

https://endoflife.date/python
⚠️ Time to say goodbye to py37. # What does this PR do?

[Sorry for the spams, GitHub had some issues]

Same as #24075, but that PR got freezed after I force pushed (after rebase), and my changes to address the comments were not able to appear)

(amy have already approved #24075)

----

Byebye!

EOL of python 3.7 is `2023/06/27`.

https://endoflife.date/python
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# SwiftFormer

## Overview

The SwiftFormer model was proposed in [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) by Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan.

The SwiftFormer paper introduces a novel efficient additive attention mechanism that effectively replaces the quadratic matrix multiplication operations in the self-attention computation with linear element-wise multiplications. A series of models called 'SwiftFormer' is built based on this, which achieves state-of-the-art performance in terms of both accuracy and mobile inference speed. Even their small variant achieves 78.5% top-1 ImageNet1K accuracy with only 0.8 ms latency on iPhone 14, which is more accurate and 2× faster compared to MobileViT-v2.

The abstract from the paper is the following:

*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.*

Tips:
    - One can use the [`ViTImageProcessor`] API to prepare images for the model.


This model was contributed by [shehan97](https://huggingface.co/shehan97).
The original code can be found [here](https://github.com/Amshaker/SwiftFormer).


## SwiftFormerConfig

[[autodoc]] SwiftFormerConfig

## SwiftFormerModel

[[autodoc]] SwiftFormerModel
    - forward

## SwiftFormerForImageClassification

[[autodoc]] SwiftFormerForImageClassification
    - forward

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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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# SwiftFormer

## Overview

The SwiftFormer model was proposed in [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) by Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan.

The SwiftFormer paper introduces a novel efficient additive attention mechanism that effectively replaces the quadratic matrix multiplication operations in the self-attention computation with linear element-wise multiplications. A series of models called 'SwiftFormer' is built based on this, which achieves state-of-the-art performance in terms of both accuracy and mobile inference speed. Even their small variant achieves 78.5% top-1 ImageNet1K accuracy with only 0.8 ms latency on iPhone 14, which is more accurate and 2× faster compared to MobileViT-v2.

The abstract from the paper is the following:

*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.*

Tips:
    - One can use the [`ViTImageProcessor`] API to prepare images for the model.


This model was contributed by [shehan97](https://huggingface.co/shehan97).
The original code can be found [here](https://github.com/Amshaker/SwiftFormer).


## SwiftFormerConfig

[[autodoc]] SwiftFormerConfig

## SwiftFormerModel

[[autodoc]] SwiftFormerModel
    - forward

## SwiftFormerForImageClassification

[[autodoc]] SwiftFormerForImageClassification
    - forward
