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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. SparseOrLowRank

Sparse Factorization of Large Square Matrices

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Last updated 2 years ago

论文地址：

整体思路以及计算方式

利用多个稀疏矩阵近似方阵：

minimize⁡W(1),…,W(M)∥X−∏m=1MW(m)∥F2\underset{\mathbf W^{(1)}, \ldots, \mathbf W^{(M)}}{\operatorname{minimize}}\left\|\mathbf X-\prod_{m=1}^{M}\mathbf W^{(m)}\right\|_{F}^{2}W(1),…,W(M)minimize​​X−m=1∏M​W(m)​F2​

每个稀疏矩阵的pattern由Chord protocol协议人为指定。

图示：

时间复杂度

不太好计算，涉及稀疏矩阵乘法。

训练以及loss

不变。

代码

实验以及适用场景

方法是普适的，实验比较简单，效果尚可。

细节

暂无。

简评

指定稀疏形式的动机不明确，LRA代码部分值得参考。

https://github.com/RuslanKhalitov/SparseFactorization
https://arxiv.org/abs/2109.08184