Beyond Uniform Query Distribution: Key-Driven Grouped Query Attention

TL;DR

This paper introduces advanced variants of Grouped Query Attention (GQA) for Transformers, leveraging key head norms for adaptive grouping, which improves accuracy and reduces memory for long-sequence tasks, demonstrated on vision datasets.

Contribution

The paper proposes Key-Distributed GQA and Dynamic Key-Distributed GQA, enhancing GQA with norm-based adaptive query grouping, and introduces Perturbed GQA to add variability, advancing efficient attention mechanisms.

Findings

DGQA improves accuracy by up to 8% on ViT-L.

Norm-based grouping enhances model performance.

Adaptive grouping reduces memory without sacrificing accuracy.

Abstract

The Transformer architecture has revolutionized deep learning through its Self-Attention mechanism, which effectively captures contextual information. However, the memory footprint of Self-Attention presents significant challenges for long-sequence tasks. Grouped Query Attention (GQA) addresses this issue by grouping queries and mean-pooling the corresponding key-value heads - reducing the number of overall parameters and memory requirements in a flexible manner without adversely compromising model accuracy. In this work, we introduce enhancements to GQA, focusing on two novel approaches that deviate from the static nature of grouping: Key-Distributed GQA (KDGQA) and Dynamic Key-Distributed GQA (DGQA), which leverage information from the norms of the key heads to inform query allocation. Specifically, KDGQA looks at the ratios of the norms of the key heads during each forward pass, while DGQA examines the ratios of the norms as they evolve through training. Additionally, we present Perturbed GQA (PGQA) as a case-study, which introduces variability in (static) group formation via subtracting noise from the attention maps. Our experiments with up-trained Vision Transformers, for Image Classification on datasets such as CIFAR-10, CIFAR-100, Food101, and Tiny ImageNet, demonstrate the promise of these variants in improving upon the original GQA through more informed and adaptive grouping mechanisms: specifically ViT-L experiences accuracy gains of up to 8% when utilizing DGQA in comparison to GQA and other variants. We further analyze the impact of the number of Key-Value Heads on performance, underscoring the importance of utilizing query-key affinities. Code is available on GitHub.