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    <abstract>Multi-query attention (MQA), which only uses a single key-value head, drastically speeds up decoder inference. However, MQA can lead to quality degradation, and moreover it may not be desirable to train a separate model just for faster inference. We (1) propose a recipe for uptraining existing multi-head language model checkpoints into models with MQA using 5% of original pre-training compute, and (2) introduce grouped-query attention (GQA), a generalization of multi-query attention which uses an intermediate (more than one, less than number of query heads) number of key-value heads. We show that uptrained GQA achieves quality close to multi-head attention with comparable speed to MQA.</abstract>
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%0 Conference Proceedings
%T GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints
%A Ainslie, Joshua
%A Lee-Thorp, James
%A de Jong, Michiel
%A Zemlyanskiy, Yury
%A Lebron, Federico
%A Sanghai, Sumit
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F ainslie-etal-2023-gqa
%X Multi-query attention (MQA), which only uses a single key-value head, drastically speeds up decoder inference. However, MQA can lead to quality degradation, and moreover it may not be desirable to train a separate model just for faster inference. We (1) propose a recipe for uptraining existing multi-head language model checkpoints into models with MQA using 5% of original pre-training compute, and (2) introduce grouped-query attention (GQA), a generalization of multi-query attention which uses an intermediate (more than one, less than number of query heads) number of key-value heads. We show that uptrained GQA achieves quality close to multi-head attention with comparable speed to MQA.
%R 10.18653/v1/2023.emnlp-main.298
%U https://aclanthology.org/2023.emnlp-main.298
%U https://doi.org/10.18653/v1/2023.emnlp-main.298
%P 4895-4901

Markdown (Informal)

[GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints](https://aclanthology.org/2023.emnlp-main.298) (Ainslie et al., EMNLP 2023)

• GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints (Ainslie et al., EMNLP 2023)

ACL

• Joshua Ainslie, James Lee-Thorp, Michiel de Jong, Yury Zemlyanskiy, Federico Lebron, and Sumit Sanghai. 2023. GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 4895–4901, Singapore. Association for Computational Linguistics.