Inefficiency of K-FAC for Large Batch Size Training
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In stochastic optimization, large batch training can leverage parallel resources to produce faster wall-clock training times per epoch. However, for both training loss and testing error, recent results analyzing large batch Stochastic Gradient Descent (SGD) have found sharp diminishing returns beyond a certain critical batch size. In the hopes of addressing this, the Kronecker-Factored Approximate Curvature (K-FAC) method has been hypothesized to allow for greater scalability to large batch sizes for non-convex machine learning problems, as well as greater robustness to variation in hyperparameters. Here, we perform a detailed empirical analysis of these two hypotheses, evaluating performance in terms of both wall-clock time and aggregate computational cost. Our main results are twofold: first, we find that K-FAC does not exhibit improved large-batch scalability behavior, as compared to SGD; and second, we find that K-FAC, in addition to requiring more hyperparameters to tune, suffers from the same hyperparameter sensitivity patterns as SGD. We discuss extensive results using residual networks on CIFAR-10, as well as more general implications of our findings.
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