Scalable and Practical Natural Gradient for Large-Scale Deep Learning
This addresses the scalability issue in distributed deep learning for practitioners, offering a principled solution to maintain performance with large batches, though it appears incremental as it builds on natural gradient methods.
The paper tackles the problem of poor generalization in large-scale distributed deep learning due to large mini-batch sizes by proposing Scalable and Practical Natural Gradient Descent (SP-NGD), which achieves similar generalization to first-order methods with accelerated convergence, demonstrated by reaching 75.4% top-1 validation accuracy on ImageNet in 5.5 minutes with a mini-batch size of 32,768.
Large-scale distributed training of deep neural networks results in models with worse generalization performance as a result of the increase in the effective mini-batch size. Previous approaches attempt to address this problem by varying the learning rate and batch size over epochs and layers, or ad hoc modifications of batch normalization. We propose Scalable and Practical Natural Gradient Descent (SP-NGD), a principled approach for training models that allows them to attain similar generalization performance to models trained with first-order optimization methods, but with accelerated convergence. Furthermore, SP-NGD scales to large mini-batch sizes with a negligible computational overhead as compared to first-order methods. We evaluated SP-NGD on a benchmark task where highly optimized first-order methods are available as references: training a ResNet-50 model for image classification on ImageNet. We demonstrate convergence to a top-1 validation accuracy of 75.4% in 5.5 minutes using a mini-batch size of 32,768 with 1,024 GPUs, as well as an accuracy of 74.9% with an extremely large mini-batch size of 131,072 in 873 steps of SP-NGD.