Distributed Learning with Compressed Gradient Differences

Training large machine learning models requires a distributed computing approach, with communication of the model updates being the bottleneck. For this reason, several methods based on the compression (e.g., sparsification and/or quantization) of updates were recently proposed, including QSGD (Alistarh et al., 2017), TernGrad (Wen et al., 2017), SignSGD (Bernstein et al., 2018), and DQGD (Khirirat et al., 2018). However, none of these methods are able to learn the gradients, which renders them incapable of converging to the true optimum in the batch mode, incompatible with non-smooth regularizers, and slows down their convergence. In this work we propose a new distributed learning method --- DIANA --- which resolves these issues via compression of gradient differences. We perform a theoretical analysis in the strongly convex and nonconvex settings and show that our rates are superior to existing rates. Our analysis of block-quantization and differences between $\ell_2$ and $\ell_\infty$ quantization closes the gaps in theory and practice. Finally, by applying our analysis technique to TernGrad, we establish the first convergence rate for this method.Comment: 46 page

Quality Adaptive Least Squares Trained Filters for Video Compression Artifacts Removal Using a No-reference Block Visibility Metric

Compression artifacts removal is a challenging problem because videos can be compressed at different qualities. In this paper, a least squares approach that is self-adaptive to the visual quality of the input sequence is proposed. For compression artifacts, the visual quality of an image is measured by a no-reference block visibility metric. According to the blockiness visibility of an input image, an appropriate set of filter coefficients that are trained beforehand is selected for optimally removing coding artifacts and reconstructing object details. The performance of the proposed algorithm is evaluated on a variety of sequences compressed at different qualities in comparison to several other deblocking techniques. The proposed method outperforms the others significantly both objectively and subjectively

Natural Compression for Distributed Deep Learning

Modern deep learning models are often trained in parallel over a collection of distributed machines to reduce training time. In such settings, communication of model updates among machines becomes a significant performance bottleneck and various lossy update compression techniques have been proposed to alleviate this problem. In this work, we introduce a new, simple yet theoretically and practically effective compression technique: {\em natural compression (NC)}. Our technique is applied individually to all entries of the to-be-compressed update vector and works by randomized rounding to the nearest (negative or positive) power of two, which can be computed in a "natural" way by ignoring the mantissa. We show that compared to no compression, NC increases the second moment of the compressed vector by not more than the tiny factor \nicefrac{9}{8}, which means that the effect of NC on the convergence speed of popular training algorithms, such as distributed SGD, is negligible. However, the communications savings enabled by NC are substantial, leading to {\em $3$-$4\times$ improvement in overall theoretical running time}. For applications requiring more aggressive compression, we generalize NC to {\em natural dithering}, which we prove is {\em exponentially better} than the common random dithering technique. Our compression operators can be used on their own or in combination with existing operators for a more aggressive combined effect, and offer new state-of-the-art both in theory and practice.Comment: 8 pages, 20 pages of Appendix, 6 Tables, 14 Figure