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    • Accelerating Neural Transformer via an Average Attention Network
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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. Others

Accelerating Neural Transformer via an Average Attention Network

PreviousOthersNextDo Transformer Modifications Transfer Across Implementations and Applications?

Last updated 2 years ago

论文地址：

  • https://arxiv.org/abs/1805.00631

参考资料：

  • https://blog.csdn.net/wwx123521/article/details/83238989

  • http://www.xuwei.io/2019/08/09/accelerating-neural-transformer-via-an-average-attention-network/

  • https://zhuanlan.zhihu.com/p/77434191

整体思路以及计算方式

替换Deocder中self Attention为AAN，计算方式如下：

  • yj∈Rd1\mathbf y_j\in \mathbb R^{ d_1}yj​∈Rd1​

  • gj=FFN⁡(1j∑k=1jyk)∈Rd2\mathbf {g}_{j}=\operatorname{FFN}\left(\frac{1}{j} \sum_{k=1}^{j} \mathbf {y}_{k}\right)\in \mathbb R^{d_2}gj​=FFN(j1​∑k=1j​yk​)∈Rd2​

  • ij,fj=σ(W[yj;gj])∈R{i}_{j}, {f}_{j}=\sigma\left(W\left[\mathbf {y}_{j} ; \mathbf {g}_{j}\right]\right)\in \mathbb Rij​,fj​=σ(W[yj​;gj​])∈R

  • h~j=ij⊙yj+fj⊙gj∈Rd1\tilde{\mathbf {h}}_{j}={i}_{j} \odot \mathbf {y}_{j}+{f}_{j} \odot \mathbf {g}_{j} \in \mathbb R^{ d_1}h~j​=ij​⊙yj​+fj​⊙gj​∈Rd1​

  • hj=LayerNorm⁡(yj+h~j)∈Rd1\mathbf {h}_{j}=\operatorname{LayerNorm}\left(\mathbf {y}_{j}+\tilde{\mathbf {h}}_{j}\right)\in \mathbb R^{d_1}hj​=LayerNorm(yj​+h~j​)∈Rd1​

时间复杂度

循环实现的时间复杂度为O(nd1d2)O(nd_1 d_2)O(nd1​d2​)，并行实现的时间复杂度为O(n2d1+nd1d2)O(n^2d_1 + nd_1 d_2)O(n2d1​+nd1​d2​)。

训练以及loss

没有变化。

代码

  • https://github.com/bzhangGo/transformer-aan/blob/master/code/thumt/models/transformer.py

实验以及适用场景

适用于Causal Attention，可以替换LM中的Attention；论文测试了NMT实验，取得了相当的效果，但是没有速度提升。

细节

暂无。

简评

  • 本质上和Attention类似，只不过假定等权重，训练时不能提速，解码时能提速；

  • 可以在lm上测试；