Not All Heads Matter: A Head-Level KV Cache Compression Method with Integrated Retrieval and Reasoning

Key-Value (KV) caching is a common technique to enhance the computational efficiency of Large Language Models (LLMs), but its memory overhead grows rapidly with input length. Prior work has shown that not all tokens are equally important for text generation, proposing layer-level KV cache compression to selectively retain key information. Recognizing the distinct roles of attention heads in generation, we propose HeadKV, a head-level KV cache compression method, and HeadKV-R2, which leverages a novel contextual reasoning ability estimation for compression. Our approach operates at the level of individual heads, estimating their importance for contextual QA tasks that require both retrieval and reasoning capabilities. Extensive experiments across diverse benchmarks (LongBench, LooGLE), model architectures (e.g., Llama-3-8B-Instruct, Mistral-7B-Instruct), and long-context abilities tests demonstrate that our head-level KV cache compression significantly outperforms strong baselines, particularly in low-resource settings (KV size = 64 & 128). Notably, our method retains just 1.5% of the KV cache while achieving 97% of the performance of the full KV cache on the contextual question answering benchmark. Modern Large Language Models (LLMs) increasingly support extremely long inputs: GPT-4 (Achiam et al., 2023), Llama-3 (Dubey et al., 2024), and Qwen-2 (Yang et al., 2024) handle up to 128K tokens, while Claude (Anthropic, 2024) supports up to 1 million tokens. These extended capacities improve performance on tasks like dialogue generation (Li et al., 2024a; Yi et al., 2024), question answering (Ho et al., 2020; Xu et al., 2023), and summarization (Xiao & Carenini, 2019; Koh et al., 2022). As input lengths increase, memory usage and latency grow significantly due to self-attention in transformers.