Adaptive Gradient Quantization for Data-Parallel SGD

Many communication-efficient variants of SGD use gradient quantization schemes. These schemes are often heuristic and fixed over the course of training. We empirically observe that the statistics of gradients of deep models change during the training. Motivated by this observation, we introduce two adaptive quantization schemes, ALQ and AMQ. In both schemes, processors update their compression schemes in parallel by efficiently computing sufficient statistics of a parametric distribution. We improve the validation accuracy by almost 2% on CIFAR-10 and 1% on ImageNet in challenging low-cost communication setups. Our adaptive methods are also significantly more robust to the choice of hyperparameters.

Authors

• Fartash Faghri
• Iman Tabrizian
• Ilia Markov
• Dan Alistarh Institute of Science and Technology Austria (ISTA)
• Daniel M. Roy
• Ali Ramezani-Kebrya University of Oslo Integreat - Norwegian Centre for knowledge-driven machine learning TRUST – The Norwegian Centre for Trustworthy AI

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