@inproceedings{yang-etal-2024-pyramidinfer,
title = "{P}yramid{I}nfer: Pyramid {KV} Cache Compression for High-throughput {LLM} Inference",
author = "Yang, Dongjie and
Han, Xiaodong and
Gao, Yan and
Hu, Yao and
Zhang, Shilin and
Zhao, Hai",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.195",
pages = "3258--3270",
abstract = "Large Language Models (LLMs) have shown remarkable comprehension abilities but face challenges in GPU memory usage during inference, hindering their scalability for real-time applications like chatbots. To accelerate inference, we store computed keys and values (KV cache) in the GPU memory. Existing methods study the KV cache compression to reduce memory by pruning the pre-computed KV cache. However, they neglect the inter-layer dependency between layers and huge memory consumption in pre-computation. To explore these deficiencies, we find that the number of crucial keys and values that influence future generations decreases layer by layer and we can extract them by the consistency in attention weights. Based on the findings, we propose PyramidInfer, a method that compresses the KV cache by layer-wise retaining crucial context. PyramidInfer saves significant memory by computing fewer keys and values without sacrificing performance. Experimental results show PyramidInfer improves 2.2x throughput compared to Accelerate with over 54{\%} GPU memory reduction in KV cache.",
}
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%0 Conference Proceedings
%T PyramidInfer: Pyramid KV Cache Compression for High-throughput LLM Inference
%A Yang, Dongjie
%A Han, Xiaodong
%A Gao, Yan
%A Hu, Yao
%A Zhang, Shilin
%A Zhao, Hai
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F yang-etal-2024-pyramidinfer
%X Large Language Models (LLMs) have shown remarkable comprehension abilities but face challenges in GPU memory usage during inference, hindering their scalability for real-time applications like chatbots. To accelerate inference, we store computed keys and values (KV cache) in the GPU memory. Existing methods study the KV cache compression to reduce memory by pruning the pre-computed KV cache. However, they neglect the inter-layer dependency between layers and huge memory consumption in pre-computation. To explore these deficiencies, we find that the number of crucial keys and values that influence future generations decreases layer by layer and we can extract them by the consistency in attention weights. Based on the findings, we propose PyramidInfer, a method that compresses the KV cache by layer-wise retaining crucial context. PyramidInfer saves significant memory by computing fewer keys and values without sacrificing performance. Experimental results show PyramidInfer improves 2.2x throughput compared to Accelerate with over 54% GPU memory reduction in KV cache.
%U https://aclanthology.org/2024.findings-acl.195
%P 3258-3270
Markdown (Informal)
[PyramidInfer: Pyramid KV Cache Compression for High-throughput LLM Inference](https://aclanthology.org/2024.findings-acl.195) (Yang et al., Findings 2024)
ACL