Beyond KV Caching: Shared Attention for Efficient LLMs

• URL: http://arxiv.org/abs/2407.12866v1
• Date: Sat, 13 Jul 2024 07:23:07 GMT
• Title: Beyond KV Caching: Shared Attention for Efficient LLMs
• Authors: Bingli Liao, Danilo Vasconcellos Vargas,
• Abstract summary: This paper introduces a novel Shared Attention (SA) mechanism to enhance the efficiency of large language models (LLMs) Our approach utilizes the isotropic tendencies of attention distributions observed in advanced LLMs post-pretraining to reduce both the computational flops and the size of the KV cache required during inference. Our findings suggest that SA not only conserves computational resources but also maintains robust model performance, thereby facilitating the deployment of more efficient LLMs in resource-constrained environments.
• Score: 5.801044612920816
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The efficiency of large language models (LLMs) remains a critical challenge, particularly in contexts where computational resources are limited. Traditional attention mechanisms in these models, while powerful, require significant computational and memory resources due to the necessity of recalculating and storing attention weights across different layers. This paper introduces a novel Shared Attention (SA) mechanism, designed to enhance the efficiency of LLMs by directly sharing computed attention weights across multiple layers. Unlike previous methods that focus on sharing intermediate Key-Value (KV) caches, our approach utilizes the isotropic tendencies of attention distributions observed in advanced LLMs post-pretraining to reduce both the computational flops and the size of the KV cache required during inference. We empirically demonstrate that implementing SA across various LLMs results in minimal accuracy loss on standard benchmarks. Our findings suggest that SA not only conserves computational resources but also maintains robust model performance, thereby facilitating the deployment of more efficient LLMs in resource-constrained environments.

Related papers

• A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs [74.35290684163718]
  A primary challenge in large language model (LLM) development is their onerous pre-training cost. This paper explores a promising paradigm to improve LLM pre-training efficiency and quality by leveraging a small language model (SLM)
  arXiv  Detail & Related papers  (2024-10-24T14:31:52Z)
• Beyond Linear Approximations: A Novel Pruning Approach for Attention Matrix [17.086679273053853]
  Large Language Models (LLMs) have shown immense potential in enhancing various aspects of our daily lives. Their growing capabilities come at the cost of extremely large model sizes, making deployment on edge devices challenging. This paper introduces a novel approach to LLM weight pruning that directly optimize for approximating the attention matrix.
  arXiv  Detail & Related papers  (2024-10-15T04:35:56Z)
• Reprogramming Foundational Large Language Models(LLMs) for Enterprise Adoption for Spatio-Temporal Forecasting Applications: Unveiling a New Era in Copilot-Guided Cross-Modal Time Series Representation Learning [0.0]
  patio-temporal forecasting plays a crucial role in various sectors such as transportation systems, logistics, and supply chain management. We introduce a hybrid approach that combines the strengths of open-source large and small-scale language models (LLMs and LMs) with traditional forecasting methods.
  arXiv  Detail & Related papers  (2024-08-26T16:11:53Z)
• FactorLLM: Factorizing Knowledge via Mixture of Experts for Large Language Models [50.331708897857574]
  We introduce FactorLLM, a novel approach that decomposes well-trained dense FFNs into sparse sub-networks without requiring any further modifications. FactorLLM achieves comparable performance to the source model securing up to 85% model performance while obtaining over a 30% increase in inference speed.
  arXiv  Detail & Related papers  (2024-08-15T16:45:16Z)
• 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)
• Mamba-PTQ: Outlier Channels in Recurrent Large Language Models [49.1574468325115]
  We show that Mamba models exhibit the same pattern of outlier channels observed in attention-based LLMs. We show that the reason for the difficulty of quantizing SSMs is caused by activation outliers, similar to those observed in transformer-based LLMs.
  arXiv  Detail & Related papers  (2024-07-17T08:21:06Z)
• Effectively Compress KV Heads for LLM [28.0801697946958]
  We propose a novel approach for compressing Key-Value ( KV) caches. Our method can compress half or even three-quarters of KV heads while maintaining performance comparable to the original LLMs.
  arXiv  Detail & Related papers  (2024-06-11T08:37:33Z)
• Attention is Naturally Sparse with Gaussian Distributed Input [8.602260591839318]
  This study presents a rigorous theoretical analysis of the sparsity in attention scores within Large Language Models (LLMs) Our main contribution lies in providing a detailed theoretical examination of how sparsity manifests in attention mechanisms, offering insights into the potential trade-offs between computational savings and model effectiveness.
  arXiv  Detail & Related papers  (2024-04-03T12:37:34Z)
• Extending Token Computation for LLM Reasoning [5.801044612920816]
  Large Language Models (LLMs) are pivotal in advancing natural language processing. LLMs often struggle with complex reasoning tasks due to inefficient attention distributions. We introduce a novel method for extending computed tokens in the Chain-of-Thought process, utilizing attention mechanism optimization.
  arXiv  Detail & Related papers  (2024-03-22T03:23:58Z)
• MobiLlama: Towards Accurate and Lightweight Fully Transparent GPT [87.4910758026772]
  "Bigger the better" has been the predominant trend in recent Large Language Models (LLMs) development. This paper explores the "less is more" paradigm by addressing the challenge of designing accurate yet efficient Small Language Models (SLMs) for resource constrained devices.
  arXiv  Detail & Related papers  (2024-02-26T18:59:03Z)
• WKVQuant: Quantizing Weight and Key/Value Cache for Large Language Models Gains More [55.0856305773081]
  Large Language Models (LLMs) face significant deployment challenges due to their substantial memory requirements and the computational demands of auto-regressive text generation process. This paper addresses these challenges by focusing on the quantization of LLMs, a technique that reduces memory consumption by converting model parameters and activations into low-bit integers.
  arXiv  Detail & Related papers  (2024-02-19T11:33:21Z)

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.