Transformer-Evolution-Paper
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      • On Learning the Transformer Kernel
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      • Explicit Sparse Transformer: Concentrated Attention Through Explicit Selection
      • Scatterbrain: Unifying Sparse and Low-rank Attention Approximation
      • Sparse Factorization of Large Square Matrices
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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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      • 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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    • Transformer with a Mixture of Gaussian Keys
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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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    • When Attention Meets Fast Recurrence: Training Language Models with Reduced Compute
    • Linear Transformers Are Secretly Fast Weight Programmers
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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

Explicit Sparse Transformer: Concentrated Attention Through Explicit Selection

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Last updated 2 years ago

论文地址：

整体思路以及计算方式

对Score Matrix S=QK⊤\mathbf S=\mathbf Q \mathbf K^{\top}S=QK⊤增加阈值操作，小于阈值直接设置为−∞-\infty−∞，达到稀疏的效果。

时间复杂度

朴素的实现依然是O(n2d)O(n^2d)O(n2d)，利用cuda可能可以提速。

训练以及loss

不变。

代码

实验以及适用场景

该方法适用于所有场景。

细节

朴素的实现应该无法提速。

简评

暂无。

https://arxiv.org/abs/1912.11637
https://github.com/lancopku/Explicit-Sparse-Transformer