Scalify: scale propagation for efficient low-precision LLM training

TL;DR

Scalify introduces a formalized scale propagation method for low-precision training of large language models, enabling efficient float8 and float16 computations with minimal accuracy loss.

Contribution

It provides a unified, end-to-end framework for tensor scaling in low-precision LLM training, simplifying adoption and improving efficiency.

Findings

Supports out-of-the-box float8 matrix multiplication and gradients

Enables float16 optimizer state storage

Open-sourced JAX implementation available

Abstract

Low-precision formats such as float8 have been introduced in machine learning accelerated hardware to improve computational efficiency for large language models training and inference. Nevertheless, adoption by the ML community has been slowed down by the complex, and sometimes brittle, techniques required to match higher precision training accuracy. In this work, we present Scalify, a end-to-end scale propagation paradigm for computational graphs, generalizing and formalizing existing tensor scaling methods. Experiment results show that Scalify supports out-of-the-box float8 matrix multiplication and gradients representation, as well as float16 optimizer state storage. Our JAX implementation of Scalify is open-sourced at https://github.com/graphcore-research/jax-scalify