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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

Block-Recurrent Transformers

PreviousGMAT Global Memory Augmentation for TransformersNextAugmenting Self-attention with Persistent Memory

Last updated 2 years ago

论文地址：

  • https://arxiv.org/abs/2203.07852

整体思路以及计算方式

利用Memory的方式增加Transformer的性能，利用窗口的方式降低计算复杂度，对Memory部分使用递归的方式更新。

计算方式：

  • 输入：X∈Rn×d,M∈Rm×d\mathbf X\in \mathbb R^{n\times d}, \mathbf M \in \mathbb R^{m\times d}X∈Rn×d,M∈Rm×d；

    • M\mathbf MM表示Memory；

  • 输出部分更新方式：

    • X1=MHAw(X,X)∈Rn×d\mathbf X_1=\mathrm{MHA}_{w}(\mathbf X, \mathbf X)\in \mathbb R^{n\times d}X1​=MHAw​(X,X)∈Rn×d

    • Y1=MHAw(X,M)∈Rn×d\mathbf Y_1=\mathrm{MHA}_{w}(\mathbf X, \mathbf M)\in \mathbb R^{n\times d}Y1​=MHAw​(X,M)∈Rn×d

    • O=[X1,Y1]W∈Rn×d\mathbf O =[\mathbf X_1, \mathbf Y_1] \mathbf W \in \mathbb R^{n\times d}O=[X1​,Y1​]W∈Rn×d

  • Memory部分更新方式：

    • M1=MHAw(M,M)∈Rm×d\mathbf M_1=\mathrm{MHA}_{w}(\mathbf M, \mathbf M)\in \mathbb R^{m\times d}M1​=MHAw​(M,M)∈Rm×d

    • Y1=MHAw(M,X)∈Rm×d\mathbf Y_1=\mathrm{MHA}_{w}(\mathbf M, \mathbf X)\in \mathbb R^{m\times d}Y1​=MHAw​(M,X)∈Rm×d

    • O=[M1,Y1]W∈Rm×d\mathbf O =[\mathbf M_1, \mathbf Y_1] \mathbf W \in \mathbb R^{m\times d}O=[M1​,Y1​]W∈Rm×d

备注，这里省略了ffn部分，其中M\mathbf MM的ffn为rnn。

整体结构：

时间复杂度

训练以及loss

不变。

代码

  • https://github.com/dashstander/block-recurrent-transformer

实验以及适用场景

论文测试了lm，效果还不错。

细节

暂无。

简评

依然是一个空间换性能的方法。

O((n+m)wd)O((n+m)wd)O((n+m)wd)。