Improving Transformers with Probabilistic Attention Keys

TL;DR

This paper introduces Transformer-MGK, a novel architecture that replaces redundant attention heads with Gaussian mixture models, leading to faster, more efficient transformers with comparable or improved accuracy.

Contribution

The paper proposes Transformer-MGK, a new transformer design using Gaussian mixture keys to reduce redundancy and improve efficiency without sacrificing performance.

Findings

Transformer-MGK achieves comparable or better accuracy than baseline transformers.

It accelerates training and inference while reducing parameters and FLOPs.

Effective on language modeling and long sequence tasks.

Abstract

Multi-head attention is a driving force behind state-of-the-art transformers, which achieve remarkable performance across a variety of natural language processing (NLP) and computer vision tasks. It has been observed that for many applications, those attention heads learn redundant embedding, and most of them can be removed without degrading the performance of the model. Inspired by this observation, we propose Transformer with a Mixture of Gaussian Keys (Transformer-MGK), a novel transformer architecture that replaces redundant heads in transformers with a mixture of keys at each head. These mixtures of keys follow a Gaussian mixture model and allow each attention head to focus on different parts of the input sequence efficiently. Compared to its conventional transformer counterpart, Transformer-MGK accelerates training and inference, has fewer parameters, and requires fewer FLOPs to compute while achieving comparable or better accuracy across tasks. Transformer-MGK can also be easily extended to use with linear attention. We empirically demonstrate the advantage of Transformer-MGK in a range of practical applications, including language modeling and tasks that involve very long sequences. On the Wikitext-103 and Long Range Arena benchmark, Transformer-MGKs with 4 heads attain comparable or better performance to the baseline transformers with 8 heads.