Layer-wise Quantization for Quantized Optimistic Dual Averaging
Abstract
Modern deep neural networks exhibit heterogeneity across numerous layers of various types such as residuals, multi-head attention, etc., due to varying structures (dimensions, activation functions, etc.), distinct representation characteristics, which impact predictions. We develop a general layer-wise quantization framework with tight variance and code-length bounds, adapting to the heterogeneities over the course of training. We then apply a new layer-wise quantization technique within distributed variational inequalities (VIs), proposing a novel Quantized Optimistic Dual Averaging (QODA) algorithm with adaptive learning rates, which achieves competitive convergence rates for monotone VIs. We empirically show that QODA achieves up to a speedup over the baselines in end-to-end training time for training Wasserstein GAN on GPUs.
Cite
@article{arxiv.2505.14371,
title = {Layer-wise Quantization for Quantized Optimistic Dual Averaging},
author = {Anh Duc Nguyen and Ilia Markov and Frank Zhengqing Wu and Ali Ramezani-Kebrya and Kimon Antonakopoulos and Dan Alistarh and Volkan Cevher},
journal= {arXiv preprint arXiv:2505.14371},
year = {2025}
}
Comments
Accepted at the International Conference on Machine Learning (ICML 2025)