Transformer-Evolution-Paper
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    • Neural Machine Translation in Linear Time
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    • LLM Details Summary
    • What Language Model to Train if You Have One Million GPU Hours?
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  • 整体思路以及计算方式
  • 代码
  • 简评
  1. CrossAttention

Neural Machine Translation in Linear Time

PreviousCrossAttentionNextInference

Last updated 2 years ago

论文地址：

整体思路以及计算方式

对这篇主要感兴趣的点是利用非CrossAttention的方式做多输入的交互，即Encoder-Decoder Attention的部分是否有替代方案。

先简叙下问题描述，给定输入X∈Rn×d\mathbf X\in \mathbb R^{n\times d}X∈Rn×d，输出Y∈Rm×d\mathbf Y\in \mathbb R^{m\times d}Y∈Rm×d，我们希望构造X→Y\mathbf X \to \mathbf YX→Y的映射，这里作者采用的方案如下，首先将输入输出padding到相同长度：

X→X1∈Rl×d,Y→Y1∈Rl×d,l≥n,m\mathbf X \to \mathbf X_1 \in \mathbb R^{l\times d},\mathbf Y\to \mathbf Y_1 \in \mathbb R^{l\times d}, l \ge n, mX→X1​∈Rl×d,Y→Y1​∈Rl×d,l≥n,m

然后作者希望建立X1→Y1\mathbf X_1 \to \mathbf Y_1 X1​→Y1​的映射关系，这里用代码说明可能更直观：

x_pad, y_pad = data
x1 = encoder(x_pad)
y1 = decoder(x1)
loss = criterion(y1, y_pad)

代码

简评

总感觉这个思路不太对，有以下几点：

  • 强行对齐有点过于粗暴，感觉没有抓住核心问题；

  • decoder端的每次输出之和encoder部分有关，但是和之前时刻无关，感觉不太合理；

https://arxiv.org/abs/1610.10099
https://github.com/dhpollack/bytenet.pytorch