SVDq: 1.25-bit and 410x Key Cache Compression for LLM Attention

• URL: http://arxiv.org/abs/2502.15304v1
• Date: Fri, 21 Feb 2025 08:55:21 GMT
• Title: SVDq: 1.25-bit and 410x Key Cache Compression for LLM Attention
• Authors: Hong Yankun, Li Xing, Zhen Hui-Ling, Yu Xianzhi, Liu Wulong, Yuan Mingxuan,
• Abstract summary: Three main types of KV cache compression techniques, namely sparsity, channel compression, and quantization, have been identified.<n>This study presents SVDq, a Singular Value Decomposition (SVD) - based mixed precision quantization method for K cache.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For the efficient inference of Large Language Models (LLMs), the effective compression of key-value (KV) cache is essential. Three main types of KV cache compression techniques, namely sparsity, channel compression, and quantization, have been identified. This study presents SVDq, a Singular Value Decomposition (SVD) - based mixed precision quantization method for K cache. Initially, K cache is transformed into latent channels using SVD basis representations. Since the values in latent channels decay rapidly and become negligible after only a few latent channels, our method then incorporates importance-aware quantization and compression for latent channels. This enables the effective allocation of higher precision to more significant channels. Theoretically, we prove that SVDq results in quantization errors (x0.1 or even lower) that are much lower than those of per-channel key quantization in the original space. Our findings based on RULER and LongBench benchmarks demonstrate that SVDq can achieve an equivalent key cache precision as low as 1.25-bit. When combined with key sparsity, it can reach a key compression ratio of up to 410x for attention computation, all while maintaining comparable model performance. Notably, our method is nearly lossless for LongBench datasets. This indicates that SVDq enables high-precision low-bit quantization, providing a more efficient solution for KV cache compression in LLMs.

Related papers

• Plug-and-Play 1.x-Bit KV Cache Quantization for Video Large Language Models [28.379533608574814]
  Video large language models (VideoLLMs) have demonstrated the capability to process longer video inputs and enable complex reasoning and analysis. Due to the thousands of visual tokens from the video frames, key-value ( KV) cache can significantly increase memory requirements. We introduce VidKV, a plug-and-play KV cache quantization method to compress the KV cache to lower than 2 bits.
  arXiv  Detail & Related papers  (2025-03-20T15:52:43Z)
• KVCrush: Key value cache size-reduction using similarity in head-behaviour [40.792661186062396]
  Key-value (KV) caching has emerged as a crucial optimization technique for accelerating inference in large language models (LLMs) However, the memory footprint of the KV is a huge bottleneck for model deployment directly impacting the model's batch size. We propose KVCrush which can be combined with many KV compression technologies to improve the model accuracy at a much smaller memory.
  arXiv  Detail & Related papers  (2025-02-24T02:57:51Z)
• More for Keys, Less for Values: Adaptive KV Cache Quantization [59.708443710731146]
  This paper introduces an information-aware quantization framework that adaptively compresses the key-value cache in large language models. We show that key matrices consistently exhibit higher norm values and are more sensitive to quantization than value matrices. We propose a mixed-precision quantization strategy, KV-AdaQuant, which allocates more bitwidth for keys and fewer for values.
  arXiv  Detail & Related papers  (2025-02-20T22:24:27Z)
• KVTuner: Sensitivity-Aware Layer-wise Mixed Precision KV Cache Quantization for Efficient and Nearly Lossless LLM Inference [40.97781175723418]
  KV cache quantization can improve Large Language Models inference throughput and latency in long contexts. Current methods have three unsolved issues: overlooking layer-wise sensitivity to KV cache quantization, high overhead of online fine-grained decision-making, and low flexibility to different LLMs and constraints. We propose a simple yet effective framework KVTuner to adaptively search for the optimal hardware-friendly layer-wise KV quantization precision pairs for coarse-grained KV cache.
  arXiv  Detail & Related papers  (2025-02-06T15:26:26Z)
• Cache Me If You Must: Adaptive Key-Value Quantization for Large Language Models [28.16603647353951]
  AQUA-KV is an adaptive quantization for Key-Value caches that relies on compact adapters.<n>We achieve near-lossless inference at 2-2.5 bits per value with under $1%$ relative error in perplexity and LongBench scores.
  arXiv  Detail & Related papers  (2025-01-31T18:47:42Z)
• More Tokens, Lower Precision: Towards the Optimal Token-Precision Trade-off in KV Cache Compression [71.42818367729573]
  In large language models (LLMs), the memory usage of KV cache has become a critical bottleneck during inference.<n>The mainstream KV compression methods, including KV pruning and KV quantization, primarily focus on either token or precision dimension separately.<n>In this paper, we comprehensively investigate the token-precision trade-off in KV cache compression.
  arXiv  Detail & Related papers  (2024-12-17T09:20:31Z)
• 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)
• 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)
• ZipCache: Accurate and Efficient KV Cache Quantization with Salient Token Identification [19.985314022860432]
  KV cache stores key and value states from previous tokens to avoid re-computation. KV cache compression seeks to discern the saliency of tokens, preserving vital information while aggressively compressing those of less importance. We present ZipCache, an accurate and efficient KV cache quantization method for LLMs.
  arXiv  Detail & Related papers  (2024-05-23T07:37:16Z)
• Unlocking Data-free Low-bit Quantization with Matrix Decomposition for KV Cache Compression [87.5604418100301]
  Key-value( KV) caching is an important technique to accelerate the inference of large language models. Existing methods often compromise precision or require extra data for calibration. We introduce textbfDecoQuant, a novel data-free low-bit quantization technique based on tensor decomposition methods.
  arXiv  Detail & Related papers  (2024-05-21T08:35:10Z)
• KV Cache is 1 Bit Per Channel: Efficient Large Language Model Inference with Coupled Quantization [34.824534775022144]
  We propose Coupled Quantization (CQ) as a technique for KV cache compression. CQ couples multiple key/value channels together to exploit their inter-dependency and encode the activations in a more information-efficient manner. We demonstrate that CQ can preserve model quality with KV cache quantized down to 1-bit.
  arXiv  Detail & Related papers  (2024-05-07T00:25:20Z)
• KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cache [67.9776980972508]
  We develop a tuning-free 2bit KV cache quantization algorithm named KIVI. KIVI can enable Llama, Falcon, and Mistral models to maintain almost the same quality while using $mathbf2.6times$ less peak memory.
  arXiv  Detail & Related papers  (2024-02-05T06:06:47Z)

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.