  Recent work has shown how to train Convolutional Neural Networks (CNNs)
rapidly on large image datasets, then transfer the knowledge gained from these
models to a variety of tasks. Following [Radford 2017], in this work, we
demonstrate similar scalability and transfer for Recurrent Neural Networks
(RNNs) for Natural Language tasks. By utilizing mixed precision arithmetic and
a 32k batch size distributed across 128 NVIDIA Tesla V100 GPUs, we are able to
train a character-level 4096-dimension multiplicative LSTM (mLSTM) for
unsupervised text reconstruction over 3 epochs of the 40 GB Amazon Reviews
dataset in four hours. This runtime compares favorably with previous work
taking one month to train the same size and configuration for one epoch over
the same dataset. Converging large batch RNN models can be challenging. Recent
work has suggested scaling the learning rate as a function of batch size, but
we find that simply scaling the learning rate as a function of batch size leads
either to significantly worse convergence or immediate divergence for this
problem. We provide a learning rate schedule that allows our model to converge
with a 32k batch size. Since our model converges over the Amazon Reviews
dataset in hours, and our compute requirement of 128 Tesla V100 GPUs, while
substantial, is commercially available, this work opens up large scale
unsupervised NLP training to most commercial applications and deep learning
researchers. A model can be trained over most public or private text datasets
overnight.
