Transformer-Evolution-Paper
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    • A survey on recently proposed activation functions for Deep Learning
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      • Fourier Neural Operator for Parametric Partial Differential Equations
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      • CrossFormer: A Versatile Vision Transformer Hinging on Cross-scale Attention
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      • On Learning the Transformer Kernel
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      • Explicit Sparse Transformer: Concentrated Attention Through Explicit Selection
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      • 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
      • Combiner Full Attention Transformer with Sparse Computation Cost
      • 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
  • Normalize_And_Residual
    • 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
    • On Layer Normalizations and Residual Connections in Transformers
    • Transformers without Tears: Improving the Normalization of Self-Attention
    • Query-Key Normalization for Transformers
    • Understanding the difficulty of training transformers
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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
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    • Pretraining Without Attention
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    • Simple Hardware-Efficient Long Convolutions for Sequence Modeling
    • Time-aware large kernel convolutions
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    • 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
    • Parallelizing Linear Recurrent Neural Nets Over Sequence Length
    • Quasi-recurrent neural networks
  • CrossAttention
    • Neural Machine Translation in Linear Time
  • Inference
    • Extrapolation
      • Parallel Context Windows for Large Language Models
      • Structured Prompting: Scaling In-Context Learning to 1,000 Examples
      • Naive Bayes-based Context Extension
  • 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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On this page
  • 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
  • 细节
  • 简评
  1. Normalize_And_Residual

RealFormer Transformer Likes Residual Attention

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Last updated 2 years ago

论文地址：

整体思路以及计算方式

对残差部分进行了修改，将前一层的Attention Score传到下一层：

  • O=Softmax(QK⊤+S)V\mathbf O=\mathrm{Softmax}(\mathbf Q\mathbf K^{\top} + \mathbf S)\mathbf VO=Softmax(QK⊤+S)V

  • S=QK⊤+S\mathbf S=\mathbf Q\mathbf K^{\top} +\mathbf SS=QK⊤+S

时间复杂度

依然是O(n2d)O(n^2d)O(n2d)，但是系数上有差别，总体是增加了。

训练以及loss

不变。

代码

实验以及适用场景

作者在Bert上测试了性能，比较了Post-LN, Pre-LN以及RealFormer(论文提出的方法)的性能，总体来说，RealFormer的性能更好。

细节

暂无。

简评

感觉速度上会慢一点，性能提升不算很明显。

https://arxiv.org/abs/2012.11747
https://github.com/google-research/google-research/tree/master/realformer