  One-Shot Neural Architecture Search (NAS) algorithms often rely on training a
hardware agnostic super-network for a domain specific task. Optimal
sub-networks are then extracted from the trained super-network for different
hardware platforms. However, training super-networks from scratch can be
extremely time consuming and compute intensive especially for large models that
rely on a two-stage training process of pre-training and fine-tuning. State of
the art pre-trained models are available for a wide range of tasks, but their
large sizes significantly limits their applicability on various hardware
platforms. We propose InstaTune, a method that leverages off-the-shelf
pre-trained weights for large models and generates a super-network during the
fine-tuning stage. InstaTune has multiple benefits. Firstly, since the process
happens during fine-tuning, it minimizes the overall time and compute resources
required for NAS. Secondly, the sub-networks extracted are optimized for the
target task, unlike prior work that optimizes on the pre-training objective.
Finally, InstaTune is easy to "plug and play" in existing frameworks. By using
multi-objective evolutionary search algorithms along with lightly trained
predictors, we find Pareto-optimal sub-networks that outperform their
respective baselines across different performance objectives such as accuracy
and MACs. Specifically, we demonstrate that our approach performs well across
both unimodal (ViT and BERT) and multi-modal (BEiT-3) transformer based
architectures.
