Transformer-Evolution-Paper
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      • Fourier Neural Operator for Parametric Partial Differential Equations
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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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      • Sparse Factorization of Large Square Matrices
      • Blockwise Self-Attention for Long Document Understanding
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      • Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting
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      • 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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      • Transformer Dissection: A Unified Understanding of Transformer's Attention via the Lens of Kern
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    • Accelerating Neural Transformer via an Average Attention Network
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    • 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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    • 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
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    • 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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    • Make Your Pre-trained Model Reversible: From Parameter to Memory Efficient Fine-Tuning
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    • 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
  • 细节
  • 简评
  1. MHA
  2. SparseOrLowRank

Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting

PreviousChunkFormer: Learning Long Time Series with Multi-stage Chunked TransformerNextFast Transformers with Clustered Attention

Last updated 2 years ago

论文地址：

  • https://arxiv.org/abs/1907.00235

参考资料：

  • https://zhuanlan.zhihu.com/p/391337035

整体思路以及计算方式

思路很简单，分为两点：

  • Token mix：每个token和周围几个token进行融合得到Q,K,VQ,K,VQ,K,V，这样做可以明显加速收敛；

  • Local attention：每个token只和局部token做attention，每层关注的局部位置不同，最后达到全局attention的效果；

时间复杂度

O(nlog⁡22n)O(n\log_2^2n)O(nlog22​n)

训练以及loss

不变。

代码

  • https://github.com/mlpotter/Transformer_Time_Series/blob/master/Transformer_Decoder_nologsparse.ipynb

  • https://github.com/mlpotter/Transformer_Time_Series/blob/master/causal_convolution_layer.py

实验以及适用场景

作者测试了时间序列任务，该方法可以推广到其他任务。

细节

暂无。

简评

Token mix的思路非常好，相当于强行让每个token关注周围的信息；Local attention实现较为复杂，只要理解思路即可；