Transformer-Evolution-Paper
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    • A survey on recently proposed activation functions for Deep Learning
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    • ReZero is All You Need Fast Convergence at Large Depth
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    • A Simple and Effective Positional Encoding for Transformers
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    • Transformer with a Mixture of Gaussian Keys
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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
  • LongConv
    • Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks
    • Parallelizing Legendre Memory Unit Training
    • Simplified State Space Layers for Sequence Modeling
    • Pretraining Without Attention
    • What Makes Convolutional Models Great on Long Sequence Modeling?
    • Hungry Hungry Hippos: Towards Language Modeling with State Space Models
    • Hyena Hierarchy: Towards Larger Convolutional Language Models
    • RWKV
    • Simple Hardware-Efficient Long Convolutions for Sequence Modeling
    • Time-aware large kernel convolutions
    • Resurrecting Recurrent Neural Networks for Long Sequences
    • CKConv: Continuous Kernel Convolution For Sequential Data
    • FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes
    • Towards a General Purpose CNN for Long Range Dependencies in ND
  • Rnn
    • 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
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      • Parallel Context Windows for Large Language Models
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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. LongConv

Simple Hardware-Efficient Long Convolutions for Sequence Modeling

PreviousRWKVNextTime-aware large kernel convolutions

Last updated 2 years ago

论文地址：

整体思路以及计算方式

第一部分和Tnn类似，不过Toeplitz matrix的系数直接初始化为独立的(2n−1)×d(2n-1)\times d(2n−1)×d个独立的参数，然后利用如下操作进行平滑：

Kk←(2p+1)−1∑j=12p+1Kk+j−pK←sign⁡(K)⊙max⁡(∣K∣−λ,0)\begin{aligned} & \mathbf{K}_k \leftarrow(2 p+1)^{-1} \sum_{j=1}^{2 p+1} \mathbf{K}_{k+j-p} \\ & \mathbf{K} \leftarrow \operatorname{sign}(\mathbf{K}) \odot \max (|\mathbf{K}|-\lambda, 0)\end{aligned}​Kk​←(2p+1)−1j=1∑2p+1​Kk+j−p​K←sign(K)⊙max(∣K∣−λ,0)​

第二部分主要是一个快速的FFT算法，这部分可以学习一下。

代码

简评

这篇主要推的应该是一个高效的kernel，关于模型部分，主要说明Toeplitz matrix的系数需要平滑，而这点可以用网络拟合参数得到。

https://arxiv.org/abs/2302.06646
https://github.com/HazyResearch/safari