UNComp: Uncertainty-Aware Long-Context Compressor for Efficient Large Language Model Inference (2410.03090v1)

Abstract: Deploying LLMs is challenging due to their high memory and computational demands, especially during long-context inference. While key-value (KV) caching accelerates inference by reusing previously computed keys and values, it also introduces significant memory overhead. Existing KV cache compression methods such as eviction and merging typically compress the KV cache after it is generated and overlook the eviction of hidden states, failing to improve the speed of the prefilling stage. Additionally, applying a uniform compression rate across different attention heads can harm crucial retrieval heads in needle-in-a-haystack tasks due to excessive compression. In this paper, we propose UNComp, an uncertainty-aware compression scheme that leverages matrix entropy to estimate model uncertainty across layers and heads at the token sequence level. By grouping layers and heads based on their uncertainty, UNComp adaptively compresses both the hidden states and the KV cache. Our method achieves a 1.6x speedup in the prefilling stage and reduces the KV cache to 4.74% of its original size, resulting in a 6.4x increase in throughput and a 1.4x speedup in inference with only a 1.41% performance loss. Remarkably, in needle-in-a-haystack tasks, UNComp outperforms the full-size KV cache even when compressed to 9.38% of its original size. Our approach offers an efficient, training-free Grouped-Query Attention paradigm that can be seamlessly integrated into existing KV cache schemes.

• The paper presents an uncertainty-aware compression method that leverages matrix entropy to adaptively compress both hidden states and key-value caches.
• It achieves a 1.6× speedup in prefilling, reduces KV cache size to 4.74%, and enhances throughput by 6.4× with minimal performance loss.
• The approach refines traditional post-generation strategies by quantifying token-level uncertainty, paving the way for scalable and energy-efficient LLM inference.

Overview of "Uncertainty-Aware Long-Context Compressor for Efficient LLM Inference"

The paper presents a novel uncertainty-aware compression methodology, termed UNComp, targeting the optimization of memory and computational resource utilization during LLM inference, especially under the intricacies of long-context processing. The core innovation of UNComp revolves around leveraging matrix entropy to gauge the uncertainty embedded within the hidden states and key-value (KV) caches, facilitating a more adaptive and fine-grained compression strategy.

Context and Challenges:

LLMs have seen rapid advancements in language processing capabilities ranging from basic text generation to intricate reasoning tasks. However, these models face significant bottlenecks during long-context inference due to the substantial memory and computational requirements. Traditional approaches to mitigating these challenges involve key-value caching mechanisms, which, while alleviating computational burdens to some extent, introduce additional memory overheads. Existing KV compression strategies, such as eviction and merging, often function at a post-generation stage, disregarding the hidden states, thus failing to optimize the inference process comprehensively.

Proposed Methodology:

UNComp aims to address the inefficiencies of traditional methods by introducing an uncertainty-aware mechanism that quantifies the uncertainty across attention layers and heads using matrix entropy. This innovative approach estimates model uncertainty at the token sequence level, allowing for the compression of both hidden states and KV caches before their generation. The method unfolds in two main dimensions: inter-layer and inter-head compression, where hidden states and KV caches are grouped and compressed based on their respective uncertainties. This results in a more targeted and reduction-oriented compression strategy that avoids the pitfall of over-compression prevalent in uniform strategies across all attention heads.

Empirical Results:

The empirical evaluations highlight significant improvements in computational efficiency and memory usage without substantial performance loss. The UNComp methodology achieves a 1.6× speedup in the prefilling stage and reduces the KV cache size to a mere 4.74% of its original, yielding a 6.4× throughput enhancement and a 1.4× speedup during inference with only a 1.41% performance loss. Notably, UNComp surpasses even the full-scale KV cache performance in needle-in-a-haystack tasks when compressed to 9.38% of the original KV cache size, reinforcing its robustness in handling scenarios requiring precise information retrieval amidst large contexts.

Implications and Speculations:

The introduction of matrix entropy into adaptive compression strategies sets a notable precedent for further explorations into uncertainty-aware mechanisms. This strategy not only resonates with evolving paradigms in model compression but could fundamentally influence how future compression schemes are devised, particularly in maximizing the information retention while minimizing redundant allocations. The methodological insights offered by UNComp could pave the way for more energy-efficient and scalable deployment of LLMs across varying domains.

Concluding Remarks:

The UNComp approach presents a distinct shift from conventional compression methodologies by incorporating a quantifiable measure of uncertainty into the compression process. The empirical claims, supported by rigorous experimentation across diverse models and benchmarks, underscore the potential of uncertainty-aware compression in transforming LLM inference, particularly in long-context scenarios. Future developments could delve into further refining these strategies to accommodate even the most resource-constrained environments, potentially heralding a new generation of adaptive and efficient inference models in AI.