About Google GShard
GShard is a module developed by Google that addresses the challenges of scaling neural networks, which is crucial for improving model quality in machine learning applications. It is designed to handle vast amounts of training data and compute resources. Here are four key features of GShard:
- Lightweight Annotation APIs and XLA Compiler Extension: GShard is composed of a set of lightweight annotation APIs and an extension to the XLA compiler. This design allows for the expression of a wide range of parallel computation patterns with minimal changes to the existing model code.
- Automatic Sharding: GShard enables the scaling up of multilingual neural machine translation Transformer models with Sparsely-Gated Mixture-of-Experts beyond 600 billion parameters using automatic sharding.
- Efficient Training on Parallel Devices: GShard allows for efficient implementation on parallel devices. For instance, a model with over 600 billion parameters can be trained on 2048 TPU v3 accelerators in just 4 days.
- Superior Translation Quality: With GShard, the quality of translation from 100 languages to English is significantly improved compared to previous methods. This is achieved by leveraging the power of scaled-up models and efficient training methods.