  Deploying various deep learning (DL) models efficiently has boosted the
research on DL compilers. The difficulty of generating optimized tensor codes
drives DL compiler to ask for the auto-tuning approaches, and the increasing
demands require increasing auto-tuning efficiency and quality. Currently, the
DL compilers partition the input DL models into several subgraphs and leverage
the auto-tuning to find the optimal tensor codes of these subgraphs. However,
existing auto-tuning approaches usually regard subgraphs as individual ones and
overlook the similarities across them, and thus fail to exploit better tensor
codes under limited time budgets. We propose FamilySeer, an auto-tuning
framework for DL compilers that can generate better tensor codes even with
limited time budgets. FamilySeer exploits the similarities and differences
among subgraphs can organize them into subgraph families, where the tuning of
one subgraph can also improve other subgraphs within the same family. The cost
model of each family gets more purified training samples generated by the
family and becomes more accurate so that the costly measurements on real
hardware can be replaced with the lightweight estimation through cost model.
Our experiments show that FamilySeer can generate model codes with the same
code performance more efficiently than state-of-the-art auto-tuning frameworks.
