In the realm of language models and attention mechanisms, a drive to bolster the efficiency and enhance the performance of large language models has been undertaken. A key development is the introduction of multi-query attention (MQA), a method that promises quicker results. However, its effectiveness is tempered by a potential drop in quality and training instability.
The process of conversion from multi-head to multi-query attention entails taking key and value projection matrices from all heads and pooling them into a single head. The challenges here are twofold: first, ensuring a balance between speed and quality and second, accomplishing it in a cost-effective manner.
This paper presents two key contributions geared towards boosting the effectiveness of large language models during inference. The first reveals that language model checkpoints using multi-head attention (MHA) can be uptrained to incorporate multi-query attention (MQA) using a small fraction of the original training compute. This is a cost-effective way of achieving both quick multi-query functionality and high-quality MHA checkpoints.
The second contribution proposes grouped-query attention (GQA) as an intermediary between multi-head and multi-query attention, using single key and value heads for each subgroup of query heads. Grouped-query attention strikes a balance between multi-head and multi-query attention, maintaining quality comparable to the former while operating nearly as fast as the latter.
In conclusion, this paper seeks to enhance the performance of language models in processing large amounts of information while keeping memory usage low. This is especially important with longer sequences that pose challenges in quality assessment.
Limitations in the testing methodology lead to uncertainties regarding our choices’ accuracy. Comparisons with models trained from scratch were not conducted, limiting our understanding of relative performance. However, in focusing on models engaged in both reading and generating information, it suggests the grouped-query attention approach may prove more beneficial than other techniques like MQA, particularly for models dedicated exclusively to information generation.
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