Layer-wise Quantization for Quantized Optimistic Dual Averaging

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.

Authors

• Anh Duc Nguyen
• Ilia Markov
• Frank Zhengqing Wu
• Ali Ramezani-Kebrya
• Kimon Antonakopoulos
• Dan Alistarh
• Volkan Cevher

Keywords

• Adaptive Compression
• Layer-wise Compression
• Optimistic Dual Averaging
• Distributed Variational Inequality