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Language Models as Zero-shot Lossless Gradient Compressors: Towards General Neural Parameter Prior Models

Hui-Po Wang, Mario Fritz

TL;DR

This work investigates using pre-trained large language models as zero-shot priors for neural gradient distributions to achieve lossless gradient compression. The authors introduce LM-GC, which serializes gradients into grouped hexadecimal text that LLMs can process, then applies arithmetic coding with token probabilities predicted by frozen LLMs to achieve high compression efficiency. Experiments across ConvNet, VGG-16, ResNet-18, and ViT on MNIST, CIFAR-10, and TinyImageNet show that LM-GC outperforms traditional lossless codecs by 10–17.2% and that serialization quality greatly influences performance; LM-GC also complements lossy methods like quantization and sparsification. The work highlights the potential of LLMs as priors for gradient modeling in distributed learning while acknowledging throughput bottlenecks and outlining avenues for optimization and broader impact considerations.

Abstract

Despite the widespread use of statistical prior models in various fields, such models for neural network gradients have long been overlooked. The inherent challenge stems from their high-dimensional structures and complex interdependencies, which complicate effective modeling. In this work, we demonstrate the potential of large language models (LLMs) to act as gradient priors in a zero-shot setting. We examine the property by considering lossless gradient compression -- a critical application in distributed learning -- that depends heavily on precise probability modeling. To achieve this, we introduce LM-GC, a novel method that integrates LLMs with arithmetic coding. Our technique converts plain gradients into text-like formats, enhancing token efficiency by up to 38 times compared to their plain representations. We ensure that this data conversion maintains a close alignment with the structure of plain gradients and the symbols commonly recognized by LLMs. Our experiments indicate that LM-GC surpasses existing state-of-the-art lossless compression methods, improving compression rates by 10% up to 17.2% across various datasets and architectures. Additionally, our approach shows promising compatibility with lossy compression techniques such as quantization and sparsification. These findings highlight the significant potential of LLMs as a model for effectively handling gradients. Code is available at https://github.com/hui-po-wang/LM-GC.

Language Models as Zero-shot Lossless Gradient Compressors: Towards General Neural Parameter Prior Models

TL;DR

This work investigates using pre-trained large language models as zero-shot priors for neural gradient distributions to achieve lossless gradient compression. The authors introduce LM-GC, which serializes gradients into grouped hexadecimal text that LLMs can process, then applies arithmetic coding with token probabilities predicted by frozen LLMs to achieve high compression efficiency. Experiments across ConvNet, VGG-16, ResNet-18, and ViT on MNIST, CIFAR-10, and TinyImageNet show that LM-GC outperforms traditional lossless codecs by 10–17.2% and that serialization quality greatly influences performance; LM-GC also complements lossy methods like quantization and sparsification. The work highlights the potential of LLMs as priors for gradient modeling in distributed learning while acknowledging throughput bottlenecks and outlining avenues for optimization and broader impact considerations.

Abstract

Despite the widespread use of statistical prior models in various fields, such models for neural network gradients have long been overlooked. The inherent challenge stems from their high-dimensional structures and complex interdependencies, which complicate effective modeling. In this work, we demonstrate the potential of large language models (LLMs) to act as gradient priors in a zero-shot setting. We examine the property by considering lossless gradient compression -- a critical application in distributed learning -- that depends heavily on precise probability modeling. To achieve this, we introduce LM-GC, a novel method that integrates LLMs with arithmetic coding. Our technique converts plain gradients into text-like formats, enhancing token efficiency by up to 38 times compared to their plain representations. We ensure that this data conversion maintains a close alignment with the structure of plain gradients and the symbols commonly recognized by LLMs. Our experiments indicate that LM-GC surpasses existing state-of-the-art lossless compression methods, improving compression rates by 10% up to 17.2% across various datasets and architectures. Additionally, our approach shows promising compatibility with lossy compression techniques such as quantization and sparsification. These findings highlight the significant potential of LLMs as a model for effectively handling gradients. Code is available at https://github.com/hui-po-wang/LM-GC.
Paper Structure (12 sections, 4 equations, 4 figures, 4 tables)

This paper contains 12 sections, 4 equations, 4 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related work
  3. Background
  4. LM-GC
  5. Experiments
  6. Setup
  7. Compression effectiveness
  8. Ablation study
  9. Compatibility
  10. Discussion and Limitation
  11. Conclusion
  12. Run Length Encoding

Figures (4)

  • Figure 1: Overview of LM-GC. Our method initially converts every 4 bits into hexadecimal numbers and groups them with separators in between, e.g., commas in the figure. The grouped text is then input to a pre-trained, frozen tokenizer and LLM to produce the probability of each token. These probabilities are used for arithmetic encoding, where a line segment between 0 and 1 is repeatedly split according to the token probability until reaching a predefined maximum length. Any number from that region (e.g., the midpoint) can accurately represent the original data. We provide an example of how arithmetic coding works in Sec. \ref{['sec:background']}.
  • Figure 2: Compression rates of LLAMA 2-7B using context window sizes of 256, 512, 1024, 2048, and 4096. The compression rates improve as the context window increases.
  • Figure 3: Ablation study on numbers of grouped bytes. We report the compression rates and the number of tokens yielded by different serializations. The settings that closely obey the data format perform better. However, smaller numbers yield higher computation overhead.
  • Figure 4: Compatibility analysis with sparsification (left) and quantization (right).