  Key-value (KV) caching plays an essential role in accelerating decoding for
transformer-based autoregressive large language models (LLMs). However, the
amount of memory required to store the KV cache can become prohibitive at long
sequence lengths and large batch sizes. Since the invention of the transformer,
two of the most effective interventions discovered for reducing the size of the
KV cache have been Multi-Query Attention (MQA) and its generalization,
Grouped-Query Attention (GQA). MQA and GQA both modify the design of the
attention block so that multiple query heads can share a single key/value head,
reducing the number of distinct key/value heads by a large factor while only
minimally degrading accuracy. In this paper, we show that it is possible to
take Multi-Query Attention a step further by also sharing key and value heads
between adjacent layers, yielding a new attention design we call Cross-Layer
Attention (CLA). With CLA, we find that it is possible to reduce the size of
the KV cache by another 2x while maintaining nearly the same accuracy as
unmodified MQA. In experiments training 1B- and 3B-parameter models from
scratch, we demonstrate that CLA provides a Pareto improvement over the
memory/accuracy tradeoffs which are possible with traditional MQA, enabling
inference with longer sequence lengths and larger batch sizes than would
otherwise be possible
