  Recently the LARS and LAMB optimizers have been proposed for training neural
networks faster using large batch sizes. LARS and LAMB add layer-wise
normalization to the update rules of Heavy-ball momentum and Adam,
respectively, and have become popular in prominent benchmarks and deep learning
libraries. However, without fair comparisons to standard optimizers, it remains
an open question whether LARS and LAMB have any benefit over traditional,
generic algorithms. In this work we demonstrate that standard optimization
algorithms such as Nesterov momentum and Adam can match or exceed the results
of LARS and LAMB at large batch sizes. Our results establish new, stronger
baselines for future comparisons at these batch sizes and shed light on the
difficulties of comparing optimizers for neural network training more
generally.
