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. Memory

Augmenting Self-attention with Persistent Memory

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Last updated 2 years ago

论文地址：

  • https://arxiv.org/abs/1907.01470

整体思路以及计算方式

思路非常简单，给Transformer模块增加和输入无关的记忆模块，并且删除FFN：

计算流程：

时间复杂度

训练以及loss

不变。

代码

暂无，但是实现起来不难。

实验以及适用场景

论文测试了lm，有一定的性能提升。

细节

暂无。

简评

不错的想法，有助于大家理解FFN的作用，后续可以考虑复现。

输入：Q,K,V∈Rn×d\mathbf Q,\mathbf K, \mathbf V\in \mathbb R^{n\times d}Q,K,V∈Rn×d；

内部参数：Mk,Mv∈RN×d\mathbf M_k, \mathbf M_{v} \in \mathbb R^{N\times d}Mk​,Mv​∈RN×d；

NNN为预设的最大值；

K1=[K,Mk[:n]]∈R(n+N)×d\mathbf K_1 = [\mathbf K, \mathbf M_{k}[:n]]\in \mathbf R^{(n+N)\times d}K1​=[K,Mk​[:n]]∈R(n+N)×d

V1=[V,Mv[:n]]∈R(n+N)×d\mathbf V_1 = [\mathbf V, \mathbf M_{v}[:n]]\in \mathbf R^{(n+N)\times d}V1​=[V,Mv​[:n]]∈R(n+N)×d

输出：MHA(Q,K1,V1)∈Rn×d\mathrm{MHA}(\mathbf Q, \mathbf K_1, \mathbf V_1)\in \mathbb R^{n\times d}MHA(Q,K1​,V1​)∈Rn×d

O(n(n+N)d)O(n(n+N)d)O(n(n+N)d)。