Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity

TL;DR

This paper introduces the Switch Transformer, a simplified and efficient sparse model that enables training trillion-parameter language models with reduced communication costs, improved stability, and faster training speeds, advancing large-scale NLP capabilities.

Contribution

The paper presents a simplified routing algorithm for MoE models, training techniques for stability, and demonstrates training trillion-parameter models efficiently with lower precision formats.

Findings

Up to 7x faster pre-training with T5-based models.

Successful training of trillion-parameter models on large datasets.

Improved multilingual performance across 101 languages.

Abstract

In deep learning, models typically reuse the same parameters for all inputs. Mixture of Experts (MoE) defies this and instead selects different parameters for each incoming example. The result is a sparsely-activated model -- with outrageous numbers of parameters -- but a constant computational cost. However, despite several notable successes of MoE, widespread adoption has been hindered by complexity, communication costs and training instability -- we address these with the Switch Transformer. We simplify the MoE routing algorithm and design intuitive improved models with reduced communication and computational costs. Our proposed training techniques help wrangle the instabilities and we show large sparse models may be trained, for the first time, with lower precision (bfloat16) formats. We design models based off T5-Base and T5-Large to obtain up to 7x increases in pre-training speed with the same computational resources. These improvements extend into multilingual settings where we measure gains over the mT5-Base version across all 101 languages. Finally, we advance the current scale of language models by pre-training up to trillion parameter models on the "Colossal Clean Crawled Corpus" and achieve a 4x speedup over the T5-XXL model.