Transformer-Evolution-Paper
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    • Hierarchical Transformers Are More Efficient Language Models
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    • A Simple and Effective Positional Encoding for Transformers
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    • Accelerating Neural Transformer via an Average Attention Network
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    • Why self-attention is Natural for Sequence-to-Sequence Problems? A Perspective from Symmetries
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    • Simple Hardware-Efficient Long Convolutions for Sequence Modeling
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    • Towards a General Purpose CNN for Long Range Dependencies in ND
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    • When Attention Meets Fast Recurrence: Training Language Models with Reduced Compute
    • Linear Transformers Are Secretly Fast Weight Programmers
    • Going Beyond Linear Transformers with Recurrent Fast Weight Programmers
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    • Parameter-Efficient Fine-Tuning without Introducing New Latency
    • Make Your Pre-trained Model Reversible: From Parameter to Memory Efficient Fine-Tuning
  • LLM
    • LLM Details Summary
    • What Language Model to Train if You Have One Million GPU Hours?
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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. Arch

Hierarchical Transformers Are More Efficient Language Models

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Last updated 2 years ago

论文地址：

  • https://arxiv.org/abs/2110.13711

整体思路以及计算方式

对seqlen维度利用全连接进行downsample和upsample，从而降低时间复杂度。

时间复杂度

假设downsample的比例为kkk，那么时间复杂度为O(n2dk2)O\left(\frac{n^2 d}{k^2}\right)O(k2n2d​)。

训练以及loss

不变。

代码

  • https://github.com/lucidrains/hourglass-transformer-pytorch

实验以及适用场景

作者测试了LM，以及一些图像分类分类任务，均取得不错的效果。

细节

如何在downsample和upsample的同时保证信息不会泄露(LM情形)值得仔细看源码研究。

简评

整体思路是很简单的，类似Linformer，不过可以处理LM任务，值得复现。