Gated Slot Attention for Efficient Linear-Time Sequence Modeling
Authors: name: Yu Zhang, name: Songlin Yang, name: Ruijie Zhu, name: Yue Zhang, name: Leyang Cui, name: Yiqiao Wang, name: Bolun Wang, name: Freda Shi, name: Bailin Wang, name: Wei Bi, name: Peng Zhou, name: Guohong Fu

Linear attention Transformers and their gated variants, celebrated for
enabling parallel training and efficient recurrent inference, still fall short
in recall-intensive tasks compared to traditional Transformers and demand
significant resources for training from scratch. This paper introduces Gated
Slot Attention (GSA), which enhances Attention with Bounded-memory-Control
(ABC) by incorporating a gating mechanism inspired by Gated Linear Attention
(GLA). Essentially, GSA comprises a two-layer GLA linked via softmax, utilizing
context-aware memory reading and adaptive forgetting to improve memory capacity
while maintaining compact recurrent state size. This design greatly enhances
both training and inference efficiency through GLA's hardware-efficient
training algorithm and reduced state size. Additionally, retaining the softmax
operation is particularly beneficial in "finetuning pretrained Transformers to
RNNs" (T2R) settings, reducing the need for extensive training from scratch.
Extensive experiments confirm GSA's superior performance in scenarios requiring
in-context recall and in T2R settings.