The article introduces Grouped Query Attention (GQA), a variation of multi-head attention used in large language models. It explains traditional multi-head attention, multi-query attention, and the emergence of GQA, highlighting its balance between quality and speed by grouping query heads. GQA allows for efficient pre-training and has been utilized in LLM models like LLaMA-2 and Mistral7B.
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Demystifying GQA — Grouped Query Attention for Efficient LLM Pre-training
The variant of multi-head attention powering LLMs like LLaMA-2, Mistral7B, etc.
Introduction
In this article, we will explore Grouped Query Attention (GQA) and its practical applications in efficient training for large language models. GQA is a generalization of multi-head attention (MHA) and multi-query attention (MQA), offering a balanced approach between quality and speed.
Understanding Multi-Head Attention (MHA)
Multi-head attention is a critical component of Transformer models, enabling them to efficiently process and understand complex sequences in tasks like language translation, summarization, and more. It employs multiple ‘heads’ of attention layers, allowing the model to attend to information from different representation subspaces.
Challenges and Solutions
The Memory Bandwidth Challenge in Multi-Head Attention: MHA poses a significant demand on memory bandwidth, especially during decoder inference. This challenge led to the emergence of Multi-Query Attention (MQA) as a solution to mitigate the bottleneck, enhancing inference speed.
Grouped Query Attention (GQA)
GQA is a simple approach that blends elements of MHA and MQA to create a more efficient attention mechanism. By utilizing GQA, the model maintains a balance between MHA quality and MQA speed, minimizing memory bandwidth demands and making it appropriate for scaling up models.
Conclusion
GQA minimizes memory bandwidth demands by grouping query heads, providing a fair trade-off between quality and speed. It has been used in place of typical multi-head attention in recent models such as the LLaMA-2 and Mistral7B.
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