Tail: An Automated and Lightweight Gradient Compression Framework for Distributed Deep Learning

Jinrong Guo, Songlin Hu, Wang Wang, Chunrong Yao, Jizhong Han, Ruixuan Li, Yijun Lu

Abstract: Existing gradient compression schemes fail to automatically determine the compression ratio or are accompanied by high compression overhead. To address this, we present Tail, an automated and lightweight gradient compression framework stacked by three modules, quantization, sparsification, and encoding. Without any hand-tuned effort, quantization module automatically adjusts the compression ratio along training iterations to retain accuracy first. Then, sparsification and encoding modules are successively applied to the quantized gradient to further improve compression ratio. Moreover, Tail reduces the compression overhead by approximate computing in the automated decision-making process. Experiments validate that Tail can reduce communication traffic by an order of magnitude while retaining or even improving model accuracy.