  The scale of deep learning nowadays calls for efficient distributed training
algorithms. Decentralized momentum SGD (DmSGD), in which each node averages
only with its neighbors, is more communication efficient than vanilla Parallel
momentum SGD that incurs global average across all computing nodes. On the
other hand, the large-batch training has been demonstrated critical to achieve
runtime speedup. This motivates us to investigate how DmSGD performs in the
large-batch scenario.
  In this work, we find the momentum term can amplify the inconsistency bias in
DmSGD. Such bias becomes more evident as batch-size grows large and hence
results in severe performance degradation. We next propose DecentLaM, a novel
decentralized large-batch momentum SGD to remove the momentum-incurred bias.
The convergence rate for both non-convex and strongly-convex scenarios is
established. Our theoretical results justify the superiority of DecentLaM to
DmSGD especially in the large-batch scenario. Experimental results on a variety
of computer vision tasks and models demonstrate that DecentLaM promises both
efficient and high-quality training.
