Towards Quantized Model Parallelism for Graph-Augmented MLPs Based on Gradient-Free ADMM Framework

TL;DR

This paper introduces a novel parallel graph deep learning framework using ADMM that enables model parallelism for GA-MLP models, reducing communication costs and improving efficiency while maintaining convergence and performance.

Contribution

It proposes the pdADMM-G framework and its quantized version, pdADMM-G-Q, for efficient model parallelism in GA-MLP models, addressing communication and efficiency challenges.

Findings

Achieves convergence with sublinear rate $o(1/k)$.

Demonstrates significant speedup and improved accuracy over state-of-the-art methods.

Reduces communication overheads by up to 45% without performance loss.

Abstract

While Graph Neural Networks (GNNs) are popular in the deep learning community, they suffer from several challenges including over-smoothing, over-squashing, and gradient vanishing. Recently, a series of models have attempted to relieve these issues by first augmenting the node features and then imposing node-wise functions based on Multi-Layer Perceptron (MLP), which are widely referred to as GA-MLP models. However, while GA-MLP models enjoy deeper architectures for better accuracy, their efficiency largely deteriorates. Moreover, popular acceleration techniques such as stochastic-version or data-parallelism cannot be effectively applied due to the dependency among samples (i.e., nodes) in graphs. To address these issues, in this paper, instead of data parallelism, we propose a parallel graph deep learning Alternating Direction Method of Multipliers (pdADMM-G) framework to achieve model parallelism: parameters in each layer of GA-MLP models can be updated in parallel. The extended pdADMM-G-Q algorithm reduces communication costs by introducing the quantization technique. Theoretical convergence to a (quantized) stationary point of the pdADMM-G algorithm and the pdADMM-G-Q algorithm is provided with a sublinear convergence rate $o(1/k)$, where $k$ is the number of iterations. Extensive experiments demonstrate the convergence of two proposed algorithms. Moreover, they lead to a more massive speedup and better performance than all state-of-the-art comparison methods on nine benchmark datasets. Last but not least, the proposed pdADMM-G-Q algorithm reduces communication overheads by up to $45\%$ without loss of performance. Our code is available at \url{https://github.com/xianggebenben/pdADMM-G}.