Transformer-Evolution-Paper
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    • Accelerating Neural Transformer via an Average Attention Network
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    • Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks
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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
    • Parallelizing Linear Recurrent Neural Nets Over Sequence Length
    • Quasi-recurrent neural networks
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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
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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
  • 代码
  • 简评
  1. Rnn

Quasi-recurrent neural networks

PreviousParallelizing Linear Recurrent Neural Nets Over Sequence LengthNextCrossAttention

Last updated 2 years ago

论文地址：

整体思路以及计算方式

利用卷积+linear rnn的思路进行token mixing。

第一步，卷积进行local mixing：

Z=tanh⁡(Wz∗X)F=σ(Wf∗X)O=σ(Wo∗X)\begin{aligned} & \mathbf{Z}=\tanh \left(\mathbf{W}_z * \mathbf{X}\right) \\ & \mathbf{F}=\sigma\left(\mathbf{W}_f * \mathbf{X}\right) \\ & \mathbf{O}=\sigma\left(\mathbf{W}_o * \mathbf{X}\right) \end{aligned}​Z=tanh(Wz​∗X)F=σ(Wf​∗X)O=σ(Wo​∗X)​

第二步，linear rnn进行global mixing：

ht=ft⊙ht−1+(1−ft)⊙zt\mathbf{h}_t=\mathbf{f}_t \odot \mathbf{h}_{t-1}+\left(1-\mathbf{f}_t\right) \odot \mathbf{z}_tht​=ft​⊙ht−1​+(1−ft​)⊙zt​

注意这个形式展开后写成long conv的形式。

代码

简评

不错的思路，本质上还是local conv + global conv。

https://arxiv.org/abs/1611.01576
https://github.com/salesforce/pytorch-qrnn