Transformer-Evolution-Paper
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      • Explicit Sparse Transformer: Concentrated Attention Through Explicit Selection
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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
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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
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    • Parameter-Efficient Fine-Tuning without Introducing New Latency
    • 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

Memory-efficient Transformers via Top-k Attention

PreviousLuna: Linear Unified Nested AttentionNextSeparable Self-attention for Mobile Vision Transformers

Last updated 2 years ago

论文地址：

整体思路以及计算方式

利用两点来减少计算：

  • 利用top-k取每行attention score的前kkk个最大值；

  • 通过chunk的方式减少内存的锋值；

时间复杂度

时间复杂度为O(nkd)O(nkd)O(nkd)，内存峰值为O(knd)O(knd)O(knd)。

训练以及loss

不变。

代码

实验以及适用场景

适用于所有场景，效果尚可。

细节

暂无。

简评

算是一个工程优化，不过简单优雅。

https://arxiv.org/abs/2106.06899
https://github.com/ag1988/top_k_attention