Scalify: scale propagation for efficient low-precision LLM training
Authors: name: Paul Balança, name: Sam Hosegood, name: Carlo Luschi, name: Andrew Fitzgibbon

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