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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. LocalGlobal

Adaptive Attention Span in Transformers

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Last updated 2 years ago

论文地址：

整体思路以及计算方式

本质上是Local Attention，即计算局部注意力，改进点是给每个头一个mask，所以各个头的侧重点不同。

计算方式：

  • 给定q,k,v∈Rn×dq, k, v\in \mathbb R^{n\times d}q,k,v∈Rn×d

  • 计算相似度str=qt⊤kr∈Rs_{tr}= q_t^{\top} k_r \in \mathbb Rstr​=qt⊤​kr​∈R

  • 计算mask：

    mz(x)=min⁡[max⁡[1R(R+z−x),0],1]m_{z}(x)=\min \left[\max \left[\frac{1}{R}(R+z-x), 0\right], 1\right]mz​(x)=min[max[R1​(R+z−x),0],1]

  • 计算局部权重：

    atr=mz(t−r)exp⁡(str)∑q=t−St−1mz(t−q)exp⁡(stq)a_{t r}=\frac{m_{z}(t-r) \exp \left(s_{t r}\right)}{\sum_{q=t-S}^{t-1} m_{z}(t-q) \exp \left(s_{t q}\right)}atr​=∑q=t−St−1​mz​(t−q)exp(stq​)mz​(t−r)exp(str​)​

  • 其余部分相同

时间复杂度

依然是标准Attention的计算方式，所以时间复杂度为O(n2d)O(n^2 d)O(n2d)。

训练以及loss

loss增加了zzz的正则项部分：

L=−log⁡P(w1,…,wT)+λM∑iziL=-\log P\left(w_{1}, \ldots, w_{T}\right)+\frac{\lambda}{M} \sum_{i} z_{i}L=−logP(w1​,…,wT​)+Mλ​i∑​zi​

代码

实验以及适用场景

Encoder和Decoder均适用；论文里测试了lm的结果，有一些提升。

细节

暂无。

简评

优点：

  • 适用于单向和双向模型；

  • 对每个head使用不同的mask，是一个不错的思路；

总结：

  • 感觉是一个不错的思路，可以尝试复现；

https://arxiv.org/abs/1905.07799
https://github.com/facebookresearch/adaptive-span