Scalable Training of Language Models using JAX pjit and TPUv4

TL;DR

This paper discusses the development of a scalable training framework for large language models using JAX pjit and TPUv4, highlighting efficiency gains from recent software and hardware advancements.

Contribution

It presents a detailed analysis of challenges, design choices, and efficiency improvements in training large language models with new hardware and software tools.

Findings

Significant efficiency improvements with TPUv4 and JAX pjit

Identification of key challenges in scalable training

Quantitative analysis of hardware/software impact

Abstract

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.