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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. MHA
  2. RightProduct

UFO-ViT: High Performance Linear Vision Transformer without Softmax

PreviousLinear Complexity Randomized Self-attention MechanismNextXCiT: Cross-Covariance Image Transformers

Last updated 2 years ago

论文地址：

整体思路以及计算方式

依然是利用了linear attention的方式，只不过这里Q,KQ,KQ,K没有过特征变换ϕ\phiϕ，计算方式如下：

  • 定义：XN⁡(x):=γx∑i=0h∥x∥2\operatorname{XN}(x):=\frac{\gamma x}{\sqrt{\sum_{i=0}^{h}\|x\|^{2}}}XN(x):=∑i=0h​∥x∥2​γx​

  • Q,K,V=XWQ,XWK,XWV∈Rn×dQ,K, V = XW_Q, XW_K, XW_V \in \mathbb R^{n\times d}Q,K,V=XWQ​,XWK​,XWV​∈Rn×d

  • Y1=XNaxis=1(Q)Y_1 = \mathrm{XN}_{\mathrm{axis=1}}(Q)Y1​=XNaxis=1​(Q)

  • Y2=XNaxis=0(K⊤V)Y_2=\mathrm{XN}_{\mathrm{axis=0}}(K^{\top} V)Y2​=XNaxis=0​(K⊤V)

  • O=Y1Y2O=Y_1 Y_2O=Y1​Y2​

时间复杂度

线性时间复杂度，依然是O(nd2)O(nd^2)O(nd2)。

训练以及loss

不变。

代码

非官方实现：

实验以及适用场景

适用于Encoder，Decoder，效果还行，不过感觉可能是由于在Attention和FFN之间加了卷积层的原因。

细节

暂无。

简评

个人感觉如果不加上卷积层，效果不会太好。

https://arxiv.org/abs/2109.14382
https://github.com/xmu-xiaoma666/External-Attention-pytorch/blob/master/model/attention/UFOAttention.py