Communication-Efficient Distributed Sgd With Sketching

Large-scale distributed training of neural networks is often limited by network bandwidth, wherein the communication time overwhelms the local computation time. Motivated by the success of sketching methods in sub-linear/streaming algorithms, we introduce SKETCHED-SGD(4), an algorithm for carrying out distributed SGD by communicating sketches instead of full gradients. We show that SKETCHED-SGD has favorable convergence rates on several classes of functions. When considering all communication - both of gradients and of updated model weights - SKETCHED-SGD reduces the amount of communication required compared to other gradient compression methods from O(d) or O(W) to O(log d), where d is the number of model parameters and W is the number of workers participating in training. We run experiments on a transformer model, an LSTM, and a residual network, demonstrating up to a 40x reduction in total communication cost with no loss in final model performance. We also show experimentally that SKETCHED-SGD scales to at least 256 workers without increasing communication cost or degrading model performance.