Composable Function-preserving Expansions for Transformer Architectures
Andrea Gesmundo Kaitlin Maile
Training state-of-the-art neural networks requires a high cost in terms of
compute and time. Model scale is recognized to be a critical factor to achieve
and improve the state-of-the-art. Increasing the scale of a neural network
normally requires restarting from scratch by randomly initializing all the
parameters of the model, as this implies a change of architecture's parameters
that does not allow for a straightforward transfer of knowledge from smaller
size models. In this work, we propose six composable transformations to
incrementally increase the size of transformer-based neural networks while
preserving functionality, allowing to expand the capacity of the model as
needed. We provide proof of exact function preservation under minimal
initialization constraints for each transformation. The proposed methods may
enable efficient training pipelines for larger and more powerful models by
progressively expanding the architecture throughout training.