Transformer-Evolution-Paper
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      • Fourier Neural Operator for Parametric Partial Differential Equations
      • Global Filter Networks for Image Classification
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      • FNet: Mixing Tokens with Fourier Transforms
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      • CrossFormer: A Versatile Vision Transformer Hinging on Cross-scale Attention
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      • On Learning the Transformer Kernel
      • Momentum Transformer: Closing the Performance Gap Between Self-attention and Its Linearization
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      • Explicit Sparse Transformer: Concentrated Attention Through Explicit Selection
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      • Sparse Factorization of Large Square Matrices
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      • Separable Self-attention for Mobile Vision Transformers
      • Simple Local Attentions Remain Competitive for Long-Context Tasks
      • You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling
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      • Synthesizer: Rethinking Self-Attention in Transformer Models
      • Transformer Dissection: A Unified Understanding of Transformer's Attention via the Lens of Kern
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      • Ripple Attention for Visual Perception with Sub-quadratic Complexity
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      • Swin Transformer V2: Scaling Up Capacity and Resolution
      • Skip-Attention: Improving Vision Transformers by Paying Less Attention
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    • ReZero is All You Need Fast Convergence at Large Depth
    • Batch Normalization Biases Residual Blocks Towards the Identity Function in Deep Networks
    • Improving Deep Transformer with Depth-Scaled Initialization and Merged Attention
    • RealFormer Transformer Likes Residual Attention
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    • A Simple and Effective Positional Encoding for Transformers
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    • Translational Equivariance in Kernelizable Attention
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    • Transformer with a Mixture of Gaussian Keys
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    • Accelerating Neural Transformer via an Average Attention Network
    • Do Transformer Modifications Transfer Across Implementations and Applications?
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    • Do Transformer Modifications Transfer Across Implementations and Applications?
    • Why self-attention is Natural for Sequence-to-Sequence Problems? A Perspective from Symmetries
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    • Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks
    • Parallelizing Legendre Memory Unit Training
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    • Simple Hardware-Efficient Long Convolutions for Sequence Modeling
    • Time-aware large kernel convolutions
    • Resurrecting Recurrent Neural Networks for Long Sequences
    • CKConv: Continuous Kernel Convolution For Sequential Data
    • FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes
    • Towards a General Purpose CNN for Long Range Dependencies in ND
  • Rnn
    • When Attention Meets Fast Recurrence: Training Language Models with Reduced Compute
    • Linear Transformers Are Secretly Fast Weight Programmers
    • Going Beyond Linear Transformers with Recurrent Fast Weight Programmers
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    • Neural Machine Translation in Linear Time
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      • Structured Prompting: Scaling In-Context Learning to 1,000 Examples
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  • Peft
    • Parameter-Efficient Fine-Tuning without Introducing New Latency
    • Make Your Pre-trained Model Reversible: From Parameter to Memory Efficient Fine-Tuning
  • LLM
    • LLM Details Summary
    • What Language Model to Train if You Have One Million GPU Hours?
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  • 整体思路以及计算方式
  • 时间复杂度
  • 训练以及loss
  • 代码
  • 实验以及适用场景
  • 简评
  1. MHA
  2. SparseOrLowRank

Separable Self-attention for Mobile Vision Transformers

PreviousMemory-efficient Transformers via Top-k AttentionNextSimple Local Attentions Remain Competitive for Long-Context Tasks

Last updated 2 years ago

论文地址：

整体思路以及计算方式

提出了一种Attention的计算方式，主要思想是将QQQ压缩为一个向量，这里带来的问题是，KKK交互的token变成了一个，所以应该会带来一些性能损失，计算公式如下：

  • 输入：X∈Rn×dX\in \mathbb R^{n\times d}X∈Rn×d

  • cs=Softmax(XW1)∈Rn×1c_s = \mathrm{Softmax}(X W_1) \in \mathbb R^{n\times 1}cs​=Softmax(XW1​)∈Rn×1

  • Xk=XWk∈Rn×d,Xv=XWv∈Rn×dX_k = XW_k \in \mathbb R^{n\times d}, X_v=XW_v \in \mathbb R^{n\times d}Xk​=XWk​∈Rn×d,Xv​=XWv​∈Rn×d

  • cv=cs⊤Xk∈R1×dc_v= c_s^{\top} X_k \in \mathbb R^{1\times d}cv​=cs⊤​Xk​∈R1×d

  • O1=cv⊙ReLU(Xv)∈Rn×dO_1=c_v \odot \mathrm{ReLU}(X_v)\in \mathbb R^{n\times d}O1​=cv​⊙ReLU(Xv​)∈Rn×d

  • O2=O1Wo∈Rn×dO_2=O_1 W_o \in \mathbb R^{n\times d}O2​=O1​Wo​∈Rn×d

时间复杂度

O(nd2)O(nd^2)O(nd2)。

训练以及loss

不变。

代码

实验以及适用场景

只适用于Encoder（csc_scs​的计算），性能尚可，作者这里考虑的主要是效率，从效率角度来说却是不错。

简评

思路总体来说是很简单的，可以考虑适配到NLP任务中。

https://arxiv.org/abs/2206.02680
https://github.com/apple/ml-cvnets