Transformer-Evolution-Paper
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      • Fourier Neural Operator for Parametric Partial Differential Equations
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      • A Dot Product Attention Free Transformer
      • On Learning the Transformer Kernel
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      • Synthesizer: Rethinking Self-Attention in Transformer Models
      • Transformer Dissection: A Unified Understanding of Transformer's Attention via the Lens of Kern
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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
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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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    • FlexConv: Continuous Kernel Convolutions with Differentiable Kernel Sizes
    • 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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    • 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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  • A Dot Product Attention Free Transformer
  • 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

A Dot Product Attention Free Transformer

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Last updated 2 years ago

A Dot Product Attention Free Transformer

论文地址：

整体思路以及计算方式

利用点乘的方式计算Attention：

  • 输入：Q,K,V∈Rn×d,p1,p2∈Rn×d1\mathbf Q, \mathbf K, \mathbf V\in \mathbb R^{n\times d}, \mathbf p_1,\mathbf p_2\in \mathbb R^{n\times d_1}Q,K,V∈Rn×d,p1​,p2​∈Rn×d1​

  • 计算W=p1p2⊤∈Rn×n\mathbf W= \mathbf p_1\mathbf p_2 ^\top \in \mathbb R^{n\times n}W=p1​p2⊤​∈Rn×n

  • 输出：

    oi=σq(qi)⊙∑j=1nexp⁡(kj+wi,j)⊙vj∑j=1nexp⁡(kj+wi,j)\mathbf o_{i}=\sigma_{q}\left(\mathbf q_{i}\right) \odot \frac {\sum_{j=1}^{n} \exp \left(\mathbf k_{j}+w_{i,j}\right) \odot \mathbf v_{j}} {\sum_{j=1}^{n} \exp \left(\mathbf k_{j}+w_{i,j}\right)}oi​=σq​(qi​)⊙∑j=1n​exp(kj​+wi,j​)∑j=1n​exp(kj​+wi,j​)⊙vj​​

时间复杂度

O(d2n+n2d1)O(d^2n + n^2d_1)O(d2n+n2d1​)

训练以及loss

不变。

代码

暂无，但是论文里有伪代码。

实验以及适用场景

Encoder和Decoder情形都进行了实验，总体来说效果还不错。

细节

因为没有计算Attention matrix，所以token之间的交互是通过W\mathbf WW矩阵。

简评

挺好的一个思路，可以考虑复现。

https://openreview.net/forum?id=JVR4JswsEM