On the Expressiveness of Softmax Attention: A Recurrent Neural Network
  Perspective
Since its introduction, softmax attention has become the backbone of modern
transformer architectures due to its expressiveness and scalability across a
wide range of tasks. However, the main drawback of softmax attention is the
quadratic memory requirement and computational complexity with respect to the
sequence length. By replacing the softmax nonlinearity, linear attention and
similar methods have been introduced to avoid the quadratic bottleneck of
softmax attention. Despite these linear forms of attention being derived from
the original softmax formulation, they typically lag in terms of downstream
accuracy. While strong intuition of the softmax nonlinearity on the query and
key inner product suggests that it has desirable properties compared to other
nonlinearities, the question of why this discrepancy exists still remains
unanswered. This work demonstrates that linear attention is an approximation of
softmax attention by deriving the recurrent form of softmax attention. Using
this form, each part of softmax attention can be described in the language of
recurrent neural networks (RNNs). Describing softmax attention as an RNN allows
for the ablation of the components of softmax attention to understand the
importance of each part and how they interact. In this way, our work helps
explain why softmax attention is more expressive than its counterparts.
688c37828c434640078cc3e2
Gabriel Mongaras
688c37828c434640078cc3e3
Eric C. Larson
On the Expressiveness of Softmax Attention: A Recurrent Neural Network
  Perspective
Since its introduction, softmax attention has become the backbone of modern
transformer architectures due to its expressiveness and scalability across a
wide range of tasks. However, the main drawback of softmax attention is the
quadratic memory requirement and computational complexity with respect to the
sequence length. By replacing the softmax nonlinearity, linear attention and
similar methods have been introduced to avoid the quadratic bottleneck of
softmax attention. Despite these linear forms of attention being derived from
the original softmax formulation, they typically lag in terms of downstream
accuracy. While strong intuition of the softmax nonlinearity on the query and
key inner product suggests that it has desirable properties compared to other
nonlinearities, the question of why this discrepancy exists still remains
unanswered. This work demonstrates that linear attention is an approximation of
softmax attention by deriving the recurrent form of softmax attention. Using
this form, each part of softmax attention can be described in the language of
recurrent neural networks (RNNs). Describing softmax attention as an RNN allows
for the ablation of the components of softmax attention to understand the
importance of each part and how they interact. In this way, our work helps
explain why softmax attention is more expressive than its counterparts.
Softmax attention is more expressive than linear attention due to its recurrent form, which can be analyzed using RNN components.
softmax attention transformer architectures linear attention quadratic memory requirement computational complexity sequence length recurrent form recurrent neural networks (RNNs)