ClusterKV: Manipulating LLM KV Cache in Semantic Space for Recallable Compression (2412.03213v2)

Abstract: LLMs have been widely deployed in a variety of applications, and the context length is rapidly increasing to handle tasks such as long-document QA and complex logical reasoning. However, long context poses significant challenges for inference efficiency, including high memory costs of key-value (KV) cache and increased latency due to extensive memory accesses. Recent works have proposed compressing KV cache to approximate computation, but these methods either evict tokens permanently, never recalling them for later inference, or recall previous tokens at the granularity of pages divided by textual positions. Both approaches degrade the model accuracy and output quality. To achieve efficient and accurate recallable KV cache compression, we introduce ClusterKV, which recalls tokens at the granularity of semantic clusters. We design and implement efficient algorithms and systems for clustering, selection, indexing and caching. Experiment results show that ClusterKV attains negligible accuracy loss across various tasks with 32k context lengths, using only a 1k to 2k KV cache budget, and achieves up to a 2$\times$ speedup in latency and a 2.5$\times$ improvement in decoding throughput. Compared to SoTA recallable KV compression methods, ClusterKV demonstrates higher model accuracy and output quality, while maintaining or exceeding inference efficiency. Our code is available at https://github.com/sjtu-zhao-lab/ClusterKV.

• The paper presents a novel recallable KV cache compression strategy that clusters tokens semantically to enhance recall accuracy.
• It leverages cosine similarity to group tokens, reducing the cache size to 1k-2k tokens while achieving up to a 2× latency improvement.
• Empirical results on datasets like LongBench and TriviaQA demonstrate near full-cache accuracy with a 2.5× increase in decoding throughput.

An Expert Review of "ClusterKV: Manipulating LLM KV Cache in Semantic Space for Recallable Compression"

The paper "ClusterKV: Manipulating LLM KV Cache in Semantic Space for Recallable Compression" addresses a crucial challenge in the field of LLMs: efficient management of key-value (KV) cache during inference with long contexts. As the demand for handling tasks requiring long document analysis and complex reasoning increases, achieving efficiency in terms of both memory and computation during LLM inference becomes paramount. ClusterKV offers an innovative approach to compress the KV cache by leveraging semantic clustering, which stands distinct from existing methodologies that suffer from accuracy degradation due to permanent token eviction or inefficient page-level recall strategies.

Technical Contributions

ClusterKV proposes a novel recallable KV cache compression strategy that operates at the semantic cluster level:

1. Semantic Clustering: Tokens are grouped into clusters based on their proximity in semantic space, defined by cosine similarity of their key vectors. This approach ensures that tokens with similar attention contributions are managed collectively, allowing for more precise recall when needed.
2. Efficient Processing: The design includes optimized algorithms for clustering and selection, as well as an indexing system that allows for swift determination of the most important tokens based on the current context.
3. Performance Gains: Experimental results demonstrate that ClusterKV maintains model accuracy with less than 1k to 2k KV cache, achieving up to a 2× speedup in latency and a 2.5× enhancement in decoding throughput compared to state-of-the-art techniques like Quest and InfiniGen.

Empirical Evaluation

The paper provides a thorough evaluation on various datasets from LongBench, and a LLMing task with Llama-3.1-8B models. ClusterKV consistently outperformed existing methods in terms of recall accuracy and model adaptability in long-context scenarios.

• Recall Rate and Accuracy: ClusterKV shows superior recall of relevant tokens, translating to higher model accuracy and maintained output quality even as context length increases. This is evidenced by benchmark results on datasets like 2WikiMQA and TriviaQA where it nearly matches the performance of a full KV cache with significantly reduced storage requirements.
• Inference Efficiency: The work demonstrates clear improvements in inference speed due to efficient cache management, reducing the overhead typically introduced by high memory costs and latency associated with extensive context lengths.

Theoretical and Practical Implications

The theoretical implications of ClusterKV's design highlight a shift from traditional token-based or page-based recall methods to a more granularity-aware approach that aligns token recall with their relative importance as dictated by semantic relevance. This contributes to a nuanced understanding of how clustering in semantic space can significantly impact the performance and efficiency of LLMs in practical deployment scenarios.

On the practical side, the adoption of ClusterKV methodology can lead to tangible benefits in applications spanning from natural language processing to complex logical reasoning, where long-context understanding is necessary. Organizations aiming to deploy LLMs at scale could leverage such methods to reduce computational loads, thereby optimizing response times and resource utilization.

Future Developments

Moving forward, future research could explore adaptive clustering techniques that dynamically adjust based on evolving context within a session or user-specific interactions. Additionally, integrating ClusterKV with other architectural innovations in neural networks could further enhance the deployment of LLMs across diverse fields and applications.

In conclusion, ClusterKV provides a compelling approach to addressing the challenges of long-context inference in LLMs, emphasizing efficiency without compromising accuracy, and laying the groundwork for further exploration into semantic-based computational strategies.