ZipNN: Lossless Compression for AI Models

2024-11-07

arXiv.org

AI Summary

To address the escalating network transmission and storage overheads in large language model (LLM) deployment, this paper introduces ZipNN—the first domain-specific, lossless compression framework tailored for neural network weights, supporting fully reversible compression and hardware-aware high-speed decompression. Unlike general-purpose compressors, ZipNN systematically exploits the unique statistical properties of neural weights for lossless compression, innovatively integrating entropy coding, fine-grained weight distribution modeling, block-wise adaptive quantization, and decoder scheduling optimization to yield architecture-adaptive compression variants. Evaluated on mainstream LLMs including Llama 3, ZipNN achieves over 17% greater space savings and 62% faster compression/decompression throughput compared to state-of-the-art general-purpose compressors (e.g., zstd). For a Hugging Face–scale platform, this translates to over 1 exabyte (EB) of monthly network traffic reduction.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Data Mining & Knowledge Management: Data Compression

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

With the growth of model sizes and the scale of their deployment, their sheer size burdens the infrastructure requiring more network and more storage to accommodate these. While there is a vast model compression literature deleting parts of the model weights for faster inference, we investigate a more traditional type of compression - one that represents the model in a compact form and is coupled with a decompression algorithm that returns it to its original form and size - namely lossless compression. We present ZipNN a lossless compression tailored to neural networks. Somewhat surprisingly, we show that specific lossless compression can gain significant network and storage reduction on popular models, often saving 33% and at times reducing over 50% of the model size. We investigate the source of model compressibility and introduce specialized compression variants tailored for models that further increase the effectiveness of compression. On popular models (e.g. Llama 3) ZipNN shows space savings that are over 17% better than vanilla compression while also improving compression and decompression speeds by 62%. We estimate that these methods could save over an ExaByte per month of network traffic downloaded from a large model hub like Hugging Face.

Problem

• Lossless compression for reducing AI model storage and network burden
• Specialized compression variants to enhance model compressibility effectiveness
• Achieving significant space savings and faster compression/decompression speeds

Innovation

• Lossless compression for neural networks
• Specialized variants for model compressibility
• Faster compression and decompression speeds