Title: Reconstructing KV Caches with Cross-Layer Fusion for Enhanced Transformers
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: KV Cache, Pretraining, LLM

Abstract: Transformer decoders have achieved strong results across tasks, but the memory required for the KV cache becomes prohibitive at long sequence lengths.
Although Cross-layer KV Cache sharing (e.g., YOCO, CLA) offers a path to mitigate KV Cache bottleneck, it typically underperforms within-layer methods like GQA.
To understand the root cause, we investigate the information flow of keys and values of the top-layers.
Our preliminary reveals a clear distribution: values are predominantly derived from the bottom layer, while keys draw more information from both bottom and middle layers.
Building upon this, we propose FusedKV, whose top-layer KV caches are a learnable fusion of the most informative ones from the bottom and middle layers. 
This fusion operates directly on post-RoPE keys, preserving relative positional information without the computational cost of re-applying rotary embeddings.
To further improve efficiency, we propose FusedKV-Lite, an cross-layer sharing approach, where top-layer KV caches are directly derived from the bottom-layer values and the middle-layer keys.
Compared to FusedKV, FusedKV-Lite reduces I/O overhead at the cost of a slight increase in perplexity.
In experiments on LLMs ranging from 332M to 4B parameters, our proposed method reduce 50\% cache memory while achieving lower validation perplexity than the standard Transformer decoder, establishing it as a memory-efficient, high-performance architectural alternative. We have made our Triton implementation available.

TL;DR: This paper proposes FusedKV and FusedKV-Lite. Both methods reduce KV cache memory by up to 50% while achieving lower perplexity than standard Transformers, offering a memory-efficient, high-performance architectural alternative