Accelerating Training with Neuron Interaction and Nowcasting Networks

Boris Knyazev, Abhinav Moudgil, Guillaume Lajoie, Eugene Belilovsky, Simon Lacoste-Julien

Keywords: accelerated optimization, parameter prediction, graphs, transformers, learning to optimize

TL;DR: We present a meta-model predicting future parameters along the SGD/Adam-optimization trajectories using graphs of neurons and graph neural networks to speed up training of neural networks.

Abstract: Neural network training can be accelerated when a learnable update rule is used in lieu of classic adaptive optimizers (e.g. Adam). However, learnable update rules can be costly and unstable to train and use. Recently, Jang et al. (2023) proposed a simpler approach to accelerate training based on weight nowcaster networks (WNNs). In their approach, Adam is used for most of the optimization steps and periodically, only every few steps, a WNN nowcasts (predicts near future) parameters. We improve WNNs by proposing neuron interaction and nowcasting (NiNo) networks. In contrast to WNNs, NiNo leverages neuron connectivity and graph neural networks to more accurately nowcast parameters. We further show that in some networks, such as Transformers, modeling neuron connectivity accurately is challenging. We address this and other limitations, which allows NiNo to accelerate Adam training by up to 50% in vision and language tasks.

Primary Area: learning on graphs and other geometries & topologies

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Submission Number: 3202