This project provides a multi-dimensional parse of the Grouped Query Attention mechanism employed by Llama3:
Principle of realization::
The code comments explain in detail that GQA's design by having multiple query heads share the same set of key-value vectors significantly reduces the amount of computation compared to traditional multi-head attention. For example, the project is labeled with the weight matrix dimension change:kv_weights = model["attention.wk.weight"] # 维度降至[1024,4096]The
Engineering optimization level::
The project demonstrates how GQA has been working throughtorch.matmuland other operations to realize the computation, and suggests the user to compare the difference in memory footprint of traditional MHA. Typical code snippets are included:# GQA分组计算：4个查询头共享1组KV
group_size = 4
q_per_token_group = q_per_token.reshape(q_per_token.shape[0], -1, group_size)
Learning Advice::
It is recommended to read the corresponding code in conjunction with the paper "Llama: Open and Efficient Foundation Language Models" by adjusting thegroup_sizeParametric observation of computational performance variations for a deep understanding of the engineering value of GQA.
This answer comes from the articleDeepdive Llama3 From Scratch: Teaching You to Implement Llama3 Models From ScratchThe
