  Neural network scaling has been critical for improving the model quality in
many real-world machine learning applications with vast amounts of training
data and compute. Although this trend of scaling is affirmed to be a sure-fire
approach for better model quality, there are challenges on the path such as the
computation cost, ease of programming, and efficient implementation on parallel
devices. GShard is a module composed of a set of lightweight annotation APIs
and an extension to the XLA compiler. It provides an elegant way to express a
wide range of parallel computation patterns with minimal changes to the
existing model code. GShard enabled us to scale up multilingual neural machine
translation Transformer model with Sparsely-Gated Mixture-of-Experts beyond 600
billion parameters using automatic sharding. We demonstrate that such a giant
model can efficiently be trained on 2048 TPU v3 accelerators in 4 days to
achieve far superior quality for translation from 100 languages to English
compared to the prior art.
