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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
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  • 简评
  1. Pe

DeBERTa Decoding-enhanced BERT with Disentangled Attention

PreviousA Simple and Effective Positional Encoding for TransformersNextDecBERT Enhancing the Language Understanding of BERT with Causal Attention Masks

Last updated 2 years ago

论文地址：

整体思路以及计算方式

传统的Attention计算，Q,K\mathbf Q,\mathbf KQ,K可以拆成context和pos部分：

Qc=HWq,cKc=HWk,cQr=PWq,rKr=PWk,r\begin{aligned} \mathbf Q_{c}&=\mathbf H \mathbf W_{q, c}\\ \mathbf K_{c}&=\mathbf H\mathbf W_{k, c}\\ \mathbf Q_{r}&=\mathbf P\mathbf W_{q, r}\\ \mathbf K_{r}&=\mathbf P\mathbf W_{k, r} \end{aligned}Qc​Kc​Qr​Kr​​=HWq,c​=HWk,c​=PWq,r​=PWk,r​​

所以Attention Score的计算可以拆成4项：

A~i,j=QicKjc⊤+QicKjr⊤+KjcQjr⊤+KirQir⊤\begin{aligned} \tilde{\mathbf A}_{i, j}=\mathbf Q_{i}^{c}\mathbf K_{j}^{c \top}+\mathbf Q_{i}^{c} \mathbf K_{j}^{r\top}+\mathbf K_{j}^{c}\mathbf Q_{j}^{r \top} +\mathbf K_{i}^{r}\mathbf Q_{i}^{r \top} \end{aligned}A~i,j​=Qic​Kjc⊤​+Qic​Kjr⊤​+Kjc​Qjr⊤​+Kir​Qir⊤​​

DeBERTa的计算方式是将上式修改为：

A~i,j=QicKjc⊤⏟(a) content-to-content +QicKδ(i,j)r⊤⏟(b) content-to-position +KjcQδ(j,i)r⊤⏟(c) position-to-content \tilde{\mathbf A}_{i, j}=\underbrace{\mathbf Q_{i}^{c} \mathbf K_{j}^{c \top}}_{\text {(a) content-to-content }}+\underbrace{\mathbf Q_{i}^{c}\mathbf K_{\delta(i, j)}^{r{\top}}}_{\text {(b) content-to-position }}+\underbrace{\mathbf K_{j}^{c}\mathbf Q_{\delta(j, i)}^{r{\top}}}_{\text {(c) position-to-content }}A~i,j​=(a) content-to-content Qic​Kjc⊤​​​+(b) content-to-position Qic​Kδ(i,j)r⊤​​​+(c) position-to-content Kjc​Qδ(j,i)r⊤​​​

其中：

δ(i,j)={0 for i−j≤−k2k−1 for i−j≥ki−j+k others. \delta(i, j)=\left\{\begin{array}{rcl} 0 & \text { for } & i-j \le-k \\ 2 k-1 & \text { for } & i-j \ge k \\ i-j+k & \text { others. } & \end{array}\right.δ(i,j)=⎩⎨⎧​02k−1i−j+k​ for  for  others. ​i−j≤−ki−j≥k​

即在一定范围内由相对位置确定，该范围外为固定值。

时间复杂度

Attention Matrix的时间复杂度由n2dn^2dn2d增加为3n2d3n^2d3n2d，其余部分不变。

训练以及loss

不变。

代码

实验以及适用场景

适用于所有场景，论文主要测试了在BERT中的效果。

细节

暂无。

简评

性能很好，但是无法适用于Linear Attention，所以暂时不考虑复现。

https://arxiv.org/abs/2006.03654
https://github.com/microsoft/DeBERTa