GQKVA: Efficient Pre-training of Transformers by Grouping Queries, Keys, and Values

Farnoosh Javadi; Walid Ahmed; Habib Hajimolahoseini; Foozhan Ataiefard; Mohammad Hassanpour; Saina Asani; Austin Wen; Omar Mohamed Awad; Kangling Liu; Yang Liu

arXiv · 2311.03426

GQKVA: Efficient Pre-training of Transformers by Grouping Queries, Keys, and Values

Farnoosh Javadi·Walid Ahmed·Habib Hajimolahoseini·Foozhan Ataiefard·Mohammad Hassanpour·Saina Asani·Austin Wen·Omar Mohamed Awad·Kangling Liu·Yang Liu

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Abstract

Massive transformer-based models face several challenges, including slow and computationally intensive pre-training and over-parametrization. This paper addresses these challenges by proposing a versatile method called GQKVA, which generalizes query, key, and value grouping techniques. GQKVA is designed to speed up transformer pre-training while reducing the model size. Our experiments with various GQKVA variants highlight a clear trade-off between performance and model size, allowing for customized choices based on resource and time limitations. Our findings also indicate that the conventional multi-head attention approach is not always the best choice, as there are lighter and faster alternatives available. We tested our method on ViT, which achieved an approximate 0.3% increase in accuracy while reducing the model size by about 4% in the task of image classification. Additionally, our most aggressive model reduction experiment resulted in a reduction of approximately 15% in model size, with only around a 1% drop in accuracy.

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Farnoosh Javadi, Walid Ahmed, Habib Hajimolahoseini, Foozhan Ataiefard, Mohammad Hassanpour, Saina Asani, Austin Wen, Omar Mohamed Awad, Kangling Liu, Yang Liu. 2023-12-13. GQKVA: Efficient Pre-training of Transformers by Grouping Queries, Keys, and Values. https://arxiv.org/abs/2311.03426

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