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

• URL: http://arxiv.org/abs/2311.03426v2
• Date: Wed, 13 Dec 2023 16:57:19 GMT
• Title: GQKVA: Efficient Pre-training of Transformers by Grouping Queries, Keys, and Values
• Authors: Farnoosh Javadi, Walid Ahmed, Habib Hajimolahoseini, Foozhan Ataiefard, Mohammad Hassanpour, Saina Asani, Austin Wen, Omar Mohamed Awad, Kangling Liu, Yang Liu
• Abstract summary: 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.
• Score: 3.960622297616708
• License: http://creativecommons.org/licenses/by/4.0/
• 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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