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

#ai #technology #switchtransformer

Scale is the next frontier for AI. Google Brain uses sparsity and hard routing to massively increase a model's parameters, while keeping the FLOPs per forward pass constant. The Switch Transformer compares favorably to its dense counterparts in terms of speed and sample efficiency and breaks the next magic number: One Trillion Parameters.

OUTLINE: 0:00 - Intro & Overview 4:30 - Performance Gains from Scale 8:30 - Switch Transformer Architecture 17:00 - Model-, Data- and Expert-Parallelism 25:30 - Experimental Results 29:00 - Stabilizing Training 32:20 - Distillation into Dense Models 33:30 - Final Comments

Paper: https://arxiv.org/abs/2101.03961 Codebase T5: https://github.com/google-research/text-to-text-transfer-transformer

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.

Authors: William Fedus, Barret Zoph, Noam Shazeer

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