Scalable Training of Language Models using JAX pjit and TPUv4
This addresses the challenge of distributed training for large language models, but it appears incremental as it builds on existing software and hardware advancements.
The authors tackled the problem of efficiently training large language models by developing a scalable framework using JAX pjit and TPUv4, resulting in quantitative efficiency improvements.
Modern large language models require distributed training strategies due to their size. The challenges of efficiently and robustly training them are met with rapid developments on both software and hardware frontiers. In this technical report, we explore challenges and design decisions associated with developing a scalable training framework, and present a quantitative analysis of efficiency improvements coming from adopting new software and hardware solutions.