The LLM Surgeon: Data-Driven Compression of Large Language Models

Core Concepts

Data-driven compression framework for large language models.

Abstract

Introduction to the challenges of deploying large language models due to their size. Exploration of data-driven compression as an alternative to training smaller models. Framework for unstructured, semi-structured, and structured pruning of LLMs. Comparison with existing methods like Optimal Brain Damage and Optimal Brain Surgeon. Multi-shot pruning schedule and interleaved low-rank first-order corrections. Results showing state-of-the-art performance in structured, semi-structured, and unstructured compression.

Stats

Structured compression (rows and columns) Unstructured compression (matrix elements) 1.3b 2.7b 6.7b

Quotes

"The superior performance of LLM Surgeon is achieved by scaling up the block-diagonal Kronecker-factorized approximations to the empirical Fisher from Eigendamage to LLMs." "Our method gives the first practically usable results for structured pruning of LLMs – they can be pruned by up to 30% with minor performance degradation."

Key Insights Distilled From

The LLM Surgeon

by Tycho F.A. v... at arxiv.org 03-22-2024

https://arxiv.org/pdf/2312.17244.pdf

Deeper Inquiries

How does the multi-shot pruning schedule impact the final compression performance

The multi-shot pruning schedule plays a crucial role in improving the final compression performance of large language models (LLMs). By pruning in multiple shots, the method allows for more accurate approximations of the loss landscape curvature. This approach helps mitigate issues related to local optima and unreliable Taylor expansions by updating weights iteratively. As a result, each shot refines the weight updates based on new estimates of curvature, leading to better overall compression performance. Additionally, using a linear sparsity schedule at each shot ensures that the model gradually reaches the target sparsity level without compromising performance.

What are the implications of considering weight correlations in updates for model compression

Considering weight correlations in updates for model compression has significant implications for achieving optimal pruning results. By accounting for correlations between weights during pruning, LLM Surgeon can derive joint weight updates that capture more nuanced relationships within the network structure. This approach enables more efficient removal of redundant parameters while preserving important connections between weights. Ultimately, incorporating weight correlations leads to improved accuracy in determining which weights to prune and how to update remaining weights effectively.

How can the LLM Surgeon framework be applied to other types of neural networks beyond language models

The LLM Surgeon framework can be applied beyond language models to other types of neural networks with similar architectures and characteristics. The key principles of data-driven compression through structured, semi-structured, and unstructured pruning remain applicable across various network designs. By adapting the methodology to different network structures and tasks, researchers can leverage LLM Surgeon's capabilities for optimizing model size while maintaining or even enhancing performance levels. This versatility makes it a valuable tool for compressing deep learning models across diverse domains such as computer vision, reinforcement learning, and natural language processing among others.