Transformer-Evolution-Paper
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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

LongT5: Efficient Text-To-Text Transformer for Long Sequences

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Last updated 2 years ago

论文地址：

整体思路以及计算方式

利用Local Attention + Global Attention减少运算量：

Local Attention：

每个token只和附近2r+12r+12r+1个token交互。

Global Attention：

将nnn个token按每组lll个划分，组内token取均值，这样一共得到n/ln/ln/l个token，每个token和这n/ln/ln/l个token交互。

时间复杂度

时间复杂度为O(n(2r+1)d+n2/ld)O(n(2r+1)d + n^2 /l d)O(n(2r+1)d+n2/ld)。

训练以及loss

不变。

代码

实验以及适用场景

论文测试了encoder-decoder结构。

细节

因为global token是通过求均值得到的，所以单向模型时实现起来较为复杂。

简评

很简单并且优雅的方式，可以考虑复现。

https://github.com/google-research/longt5
https://arxiv.org/abs/2112.07916