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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. Memory

GMAT Global Memory Augmentation for Transformers

PreviousLaMemo Language Modeling with Look-Ahead MemoryNextBlock-Recurrent Transformers

Last updated 2 years ago

论文地址：

整体思路以及计算方式

整体思路是attention模块增加memory模块，为了验证memory模块有效性，作者使用chunk的方式计算attention（因为无法捕捉全局信息）。

整体计算方式如下：

  • 输入：X∈Rn×d\mathbf X\in \mathbb R^{n\times d}X∈Rn×d

  • memory：XM∈Rm×d\mathbf X_M\in \mathbb R^{m\times d}XM​∈Rm×d

  • 记：

    Y=[XXM]∈R(n+m)×d\mathbf Y=\left[\begin{array}{l} \mathbf X \\ \mathbf X_{M} \end{array}\right] \in \mathbb R^{(n+m)\times d}Y=[XXM​​]∈R(n+m)×d

  • for i=1,…,n1i=1,\ldots, n_1i=1,…,n1​:

    • XM=MHA(XM,Y)∈Rm×d\mathbf X_M=\mathrm{MHA}(\mathbf X_M, \mathbf Y)\in \mathbb R^{m\times d}XM​=MHA(XM​,Y)∈Rm×d

  • for i=1,…,n2i=1,\ldots,n_2i=1,…,n2​:

    • XM=MHA(XM,XM)∈Rm×d\mathbf X_M=\mathrm{MHA}(\mathbf X_M, \mathbf X_M)\in \mathbb R^{m\times d}XM​=MHA(XM​,XM​)∈Rm×d

  • for i=1,…,n3i=1,\ldots,n_3i=1,…,n3​:

    • X=MHA(X,Y)∈Rn×d\mathbf X=\mathrm{MHA}(\mathbf X, \mathbf Y)\in \mathbb R^{n\times d}X=MHA(X,Y)∈Rn×d

每个阶段的作用分别为：

  • 第一阶段：压缩信息至memory；

  • 第二阶段：编码memory信息；

  • 第三阶段：解压缩信息；

时间复杂度

O(m(m+n)d)O(m(m+n )d)O(m(m+n)d)，其中mmm是memory长度。

训练以及loss

不变。

代码

实验以及适用场景

适用于所有场景，可以带来一定提升。

细节

暂无。

简评

主要是验证这样训练设置下，memory的有效性，不过改方法应该会增加不少时间复杂度，所以是否值得有待商榷。

https://arxiv.org/abs/2006.03274
https://github.com/ag1988/gmat