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Stochastic gradient descent (SGD) is an inherently sequential trainingalgorithm--computing the gradient at batch $i$ depends on the model parameterslearned from batch $i-1$. Prior approaches that break this dependence do nothonor them (e.g., sum the gradients for each batch, which is not whatsequential SGD would do) and thus potentially suffer from poor convergence.This paper introduces a novel method to combine gradients called Adasum (foradaptive sum) that converges faster than prior work. Adasum is easy toimplement, almost as efficient as simply summing gradients, and is integratedinto the open-source toolkit Horovod.  This paper first provides a formal justification for Adasum and thenempirically demonstrates Adasum is more accurate than prior gradientaccumulation methods. It then introduces a series of case-studies to showAdasum works with multiple frameworks, (TensorFlow and PyTorch), scalesmultiple optimizers (Momentum-SGD, Adam, and LAMB) to larger batch-sizes whilestill giving good downstream accuracy. Finally, it proves that Adasumconverges.  To summarize, Adasum scales Momentum-SGD on the MLPerf Resnet50 benchmark to64K examples before communication (no MLPerf v0.5 entry converged with morethan 16K), the Adam optimizer to 64K examples before communication onBERT-LARGE (prior work showed Adam stopped scaling at 16K), and the LAMBoptimizer to 128K before communication on BERT-LARGE (prior work used 64K), allwhile maintaining downstream accuracy metrics. Finally, if a user does not needto scale, we show LAMB with Adasum on BERT-LARGE converges in 30% fewer stepsthan the baseline.

Scaling Distributed Training with Adaptive Summation