Abstract: Deep learning compiler frameworks are gaining ground as a more portable
back-end for deep learning applications on increasingly diverse hardware.
However, they face the daunting challenge of matching performance offered by
hand-tuned target-specific libraries. While auto-tuning frameworks with
statistical cost models can provide dynamic and efficient code optimization,
they suffer from large space exploration and cost model training overheads.
This paper proposes MetaTune, a meta-learning based cost model that more
quickly and accurately predicts the performance of optimized codes with
pre-trained model parameters. MetaTune encodes convolution kernel codes as
structurally similar graphs to facilitate meta-learning, meta-trains a GNN
model with a very small input data set, and then predicts optimization
parameters for unseen convolution operations with varying sizes and structures
during compilation. The resulting framework with MetaTune provides 8 to 13%
better inference time on average for four CNN models with comparable or lower
optimization time while outperforming transfer learning by 10% in