Transformer-Evolution-Paper
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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
  • 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. Arch

Supplementary Material Implementation and Experiments for GAU-based Model

PreviousArchNextMetaFormer is Actually What You Need for Vision

Last updated 2 years ago

论文地址：

整体思路以及计算方式

一篇对苏神提出方法的总结，利用2层GAU代替Transformer：

计算方式如下：

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

  • 线性变换：Z=XWZ∈Rn×d1,U=XWU∈Rn×d1,V=XWV∈Rn×d2\mathbf Z=\mathbf X\mathbf W_Z\in \mathbb R^{n\times d_1},\mathbf U=\mathbf X\mathbf W_U \in \mathbb R^{n\times d_1},\mathbf V=\mathbf X\mathbf W_V \in \mathbb R^{n\times d_2}Z=XWZ​∈Rn×d1​,U=XWU​∈Rn×d1​,V=XWV​∈Rn×d2​

  • 计算Q,K\mathbf Q ,\mathbf KQ,K：Q=Z+bQ+RoPE∈Rn×d2,K=Z+bK+RoPE∈Rn×d2\mathbf Q=\mathbf Z + \mathbf b_Q + \mathrm{RoPE}\in \mathbb R^{n\times d_2},\mathbf K =\mathbf Z+\mathbf b_K + \mathrm{RoPE}\in \mathbb R^{n\times d_2}Q=Z+bQ​+RoPE∈Rn×d2​,K=Z+bK​+RoPE∈Rn×d2​

  • Attention Matrix：A=Softmax(log⁡512nd2QK⊤)∈Rn×n\mathbf A=\mathrm{Softmax}\left(\frac{\log_{512}n}{\sqrt{d_2}}\mathbf Q \mathbf K^{\top}\right)\in \mathbb R^{n\times n}A=Softmax(d2​​log512​n​QK⊤)∈Rn×n

  • 输出：O=[U⊙(AV)]WO∈Rn×d\mathbf O=[\mathbf U\odot (\mathbf A \mathbf V)]\mathbf W_O\in \mathbb R^{n\times d}O=[U⊙(AV)]WO​∈Rn×d

时间复杂度

依然是O(n2)O(n^2)O(n2)，但是实际中比Transformer快。

训练以及loss

不变。

代码

暂无。

实验以及适用场景

适用于所有场景，论文测了CLUE benchmark，效果还不错。

细节

暂无，基本包含全部细节。

简评

算是一个不错的方法， 把苏神提出的方法实现并改进，值得复现。

https://arxiv.org/abs/2205.05842