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On this page
  • 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. FFN

GLU Variants Improve Transformer

PreviousTransformer Feed-Forward Layers Are Key-Value MemoriesNextSimple Recurrence Improves Masked Language Models

Last updated 2 years ago

论文地址：

整体思路以及计算方式

FFN的替代品，首先是GLU模块：

GLU(x,W,V,b,c)=f(xW+b)⊙(xV+c)\mathrm{GLU}(\mathbf x, \mathbf W, \mathbf V, \mathbf b, \mathbf c)=f(\mathbf x\mathbf W+\mathbf b) \odot (\mathbf x \mathbf V+\mathbf c)GLU(x,W,V,b,c)=f(xW+b)⊙(xV+c)

其中fff是任意激活函数。

定义新的FFN模块：

FFNGLU(x,W,V,W2)=(f(xW)⊙xV)W2\mathrm{FFN}_{\mathrm{GLU}}\left(\mathbf x, \mathbf W, \mathbf V, \mathbf W_{2}\right)=(f(\mathbf x \mathbf W) \odot \mathbf x \mathbf V)\mathbf W_{2}FFNGLU​(x,W,V,W2​)=(f(xW)⊙xV)W2​

时间复杂度

假设：

  • x∈Rn×d1\mathbf x\in \mathbb R^{n\times d_1}x∈Rn×d1​

  • W,V∈Rd1×d2,W2∈Rd2×d1\mathbf W,\mathbf V\in \mathbb R^{d_1\times d_2},\mathbf W_2\in \mathbb R^{d_2\times d_1}W,V∈Rd1​×d2​,W2​∈Rd2​×d1​

所以时间复杂度为：

O(nd1d2)O(nd_1d_2)O(nd1​d2​)

这里引入的参数数量为3d1d23d_1d_23d1​d2​，传统FFN的参数数量为8d128d_1^28d12​，要对标参数数量，取

d2=83d1=23×4d1d_2 =\frac{8}{3}d_1 =\frac{2}{3} \times 4d_1d2​=38​d1​=32​×4d1​

训练以及loss

不变。

代码

无，很简单，直接实现即可。

实验以及适用场景

由于是FFN的替代，所以适用于所有场景；作者测试了GLUE任务，效果相当不错。

细节

暂无。

简评

总结：

  • FFN的简单改进，效果不错，速度如何需要进行测试；

  • 值得复现；

https://arxiv.org/abs/2002.05202