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arXiv · 2505.14371

Layer-wise Quantization for Quantized Optimistic Dual Averaging

Anh Duc Nguyen·Ilia Markov·Frank Zhengqing Wu·Ali Ramezani-Kebrya·Kimon Antonakopoulos·Dan Alistarh·Volkan Cevher
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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 $150\%$ speedup over the baselines in end-to-end training time for training Wasserstein GAN on $12+$ GPUs.

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Anh Duc Nguyen, Ilia Markov, Frank Zhengqing Wu, Ali Ramezani-Kebrya, Kimon Antonakopoulos, Dan Alistarh, Volkan Cevher. 2025-05-20. Layer-wise Quantization for Quantized Optimistic Dual Averaging. https://arxiv.org/abs/2505.14371

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