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    • ReZero is All You Need Fast Convergence at Large Depth
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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. Normalize_And_Residual

Query-Key Normalization for Transformers

PreviousTransformers without Tears: Improving the Normalization of Self-AttentionNextUnderstanding the difficulty of training transformers

Last updated 2 years ago

论文地址：

整体思路以及计算方式

Attention中计算Softmax之前需要先除以d\sqrt dd​，其原因是为了缩小极值的影响（避免出现One-hot情形），这篇文章是对这点改进：

q^i=qi∥qi∥k^i=ki∥ki∥\begin{aligned} \hat{\mathbf q}_i& =\frac{\mathbf q_i}{\|\mathbf q_i \|}\\ \hat{\mathbf k}_i& =\frac{\mathbf k_i}{\|\mathbf k_i \|} \end{aligned}q^​i​k^i​​=∥qi​∥qi​​=∥ki​∥ki​​​

最后的计算方式为：

Softmax(g×(q^i⊤k^j))\mathrm{Softmax}(g\times (\hat{\mathbf q}_i^{\top} \hat{\mathbf k}_j))Softmax(g×(q^​i⊤​k^j​))

其中ggg为可学习的参数，初始化为：

g0=log⁡2(L2−L)g_0=\log_2(L^2-L)g0​=log2​(L2−L)

其中LLL和序列长度有关（序列长度的97.5分位数），g0g_0g0​的含义为Attention Matrix独立元素的信息熵。

时间复杂度

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，在NMT中性能有提升。

细节

暂无。

简评

很合理的一个思路，可以减少Attention的工程部分，Swin-V2中也使用了这个思路。

https://aclanthology.org/2020.findings-emnlp.379/
https://github.com/CyndxAI/QKNorm