UNComp: Uncertainty-Aware Long-Context Compressor for Efficient Large Language Model Inference

• URL: http://arxiv.org/abs/2410.03090v1
• Date: Fri, 4 Oct 2024 02:32:36 GMT
• Title: UNComp: Uncertainty-Aware Long-Context Compressor for Efficient Large Language Model Inference
• Authors: Jing Xiong, Jianghan Shen, Fanghua Ye, Chaofan Tao, Zhongwei Wan, Jianqiao Lu, Xun Wu, Chuanyang Zheng, Zhijiang Guo, Lingpeng Kong, Ngai Wong,
• Abstract summary: UNComp is an uncertainty-aware compression scheme that 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. Remarkably, in needle-in-a-haystack tasks, UNComp outperforms the full-size KV cache even when compressed to 9.38% of its original size.
• Score: 38.11539884622708
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Deploying large language models (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.

Related papers

• Not All Heads Matter: A Head-Level KV Cache Compression Method with Integrated Retrieval and Reasoning [19.942402563256962]
  Key-Value (KV) caching is a common technique to enhance the computational efficiency of Large Language Models (LLMs) We propose HeadKV, a head-level KV cache compression method, and HeadKV-R2, which leverages a novel contextual reasoning ability estimation for compression. 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.
  arXiv  Detail & Related papers  (2024-10-25T02:22:00Z)
• KVSharer: Efficient Inference via Layer-Wise Dissimilar KV Cache Sharing [58.29726147780976]
  We propose a plug-and-play method called textit KVSharer, which shares the KV cache between layers to achieve layer-wise compression. Experiments show that textit KVSharer can reduce KV cache computation by 30%, thereby lowering memory consumption. We verify that textit KVSharer is compatible with existing intra-layer KV cache compression methods, and combining both can further save memory.
  arXiv  Detail & Related papers  (2024-10-24T08:06:41Z)
• Lossless KV Cache Compression to 2% [22.98828332096935]
  This work introduces a novel architecture, Cross-Layer Latent Attention (CLLA), aimed at compressing the KV cache to less than 2% of its original size. CLLA integrates attention head/dimension reduction, layer sharing, and quantization techniques, into a cohesive framework.
  arXiv  Detail & Related papers  (2024-10-20T02:17:35Z)
• LoRC: Low-Rank Compression for LLMs KV Cache with a Progressive Compression Strategy [59.1298692559785]
  Key-Value ( KV) cache is crucial component in serving transformer-based autoregressive large language models (LLMs) Existing approaches to mitigate this issue include: (1) efficient attention variants integrated in upcycling stages; (2) KV cache compression at test time; and (3) KV cache compression at test time. We propose a low-rank approximation of KV weight matrices, allowing plug-in integration with existing transformer-based LLMs without model retraining. Our method is designed to function without model tuning in upcycling stages or task-specific profiling in test stages.
  arXiv  Detail & Related papers  (2024-10-04T03:10:53Z)
• KV-Compress: Paged KV-Cache Compression with Variable Compression Rates per Attention Head [0.8158530638728501]
  We introduce KV-Compress, a novel compression method that evicts contiguous KV blocks within a PagedAttention framework. Our method achieves state-of-the-art performance on LongBench for both Mistral-7B-Instruct-v0.2 and Llama-3.1-8B-Instruct while lowering the total number of compressed KVs by 4x. Evaluations on Llama-3.1-8B-Instruct and Llama-3.1-70B-Instruct-FP8 achieve compression rates up to 8x with negligible impact on performance, and up to 64x while retaining over 90% of full-cache performance
  arXiv  Detail & Related papers  (2024-09-30T19:09:13Z)
• CSKV: Training-Efficient Channel Shrinking for KV Cache in Long-Context Scenarios [13.144156413032896]
  We introduce CSKV, a training-efficient Channel Shrinking technique for KV cache compression. We show that CSKV can reduce the memory overhead of the KV cache by 80% while maintaining the model's long-context capability. Our method can be seamlessly combined with quantization to further reduce the memory overhead, achieving a compression ratio of up to 95%.
  arXiv  Detail & Related papers  (2024-09-16T17:36:50Z)
• ThinK: Thinner Key Cache by Query-Driven Pruning [63.13363917871414]
  Large Language Models (LLMs) have revolutionized the field of natural language processing, achieving unprecedented performance across a variety of applications. This paper focuses on the long-context scenario, addressing the inefficiencies in KV cache memory consumption during inference. We propose ThinK, a novel query-dependent KV cache pruning method designed to minimize attention weight loss while selectively pruning the least significant channels.
  arXiv  Detail & Related papers  (2024-07-30T17:59:08Z)
• PyramidKV: Dynamic KV Cache Compression based on Pyramidal Information Funneling [53.08975547824068]
  We investigate whether attention-based information flow inside large language models (LLMs) is aggregated through noticeable patterns for long context processing. Our observations reveal that LLMs aggregate information through Pyramidal Information Funneling where attention is scattering widely in lower layers. Motivated by these insights, we developed Pyramid KV, a novel and effective KV cache compression method.
  arXiv  Detail & Related papers  (2024-06-04T07:51:30Z)
• MiniCache: KV Cache Compression in Depth Dimension for Large Language Models [48.03117580340151]
  Key-Value ( KV) cache stores key-value states of previously generated tokens. The size of the KV cache grows linearly with sequence length, posing challenges for applications requiring long context input and extensive sequence generation. We present a simple yet effective approach, called MiniCache, to compress the KV cache across layers from a novel depth perspective.
  arXiv  Detail & Related papers  (2024-05-23T09:43:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.