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  • 整体思路以及计算方式
  • 代码
  • 简评
  1. Arch

General-purpose, long-context autoregressive modeling with Perceiver AR

PreviousPerceiver General Perception with Iterative AttentionNextHierarchical Transformers Are More Efficient Language Models

Last updated 2 years ago

论文地址：

整体思路以及计算方式

基本上同Perceiver，将模型拓展为可以处理单向数据，唯一的区别是将输入拆分为：

  • X=[X1,X2]\mathbf X=[\mathbf X_1,\mathbf X_2]X=[X1​,X2​]

    • X∈Rn×d\mathbf X \in \mathbb R^{n\times d}X∈Rn×d

    • X1∈R(n−m)×d\mathbf X_1\in \mathbb R^{(n-m)\times d}X1​∈R(n−m)×d

    • X2∈Rm×d\mathbf X_2\in \mathbb R^{m\times d}X2​∈Rm×d

  • Y1=MHA(X2,X1)∈Rm×d\mathbf Y_1= \mathrm{MHA}(\mathbf X_2, \mathbf X_1)\in \mathbb R^{m\times d}Y1​=MHA(X2​,X1​)∈Rm×d（with mask）

  • O=MHA(Y1,Y1)∈Rm×d\mathbf O=\mathrm{MHA}(\mathbf Y_1,\mathbf Y_1)\in \mathbb R^{m\times d}O=MHA(Y1​,Y1​)∈Rm×d（with mask）

其余部分同Perceiver。

代码

简评

是否可以将该方法推广为一种预训练方式？

https://arxiv.org/abs/2202.07765
https://github.com/lucidrains/perceiver-ar-pytorch