  Deep Learning (DL) compilers are widely adopted to optimize advanced DL
models for efficient deployment on diverse hardware. Their quality has profound
effect on the quality of compiled DL models. A recent bug study shows that the
optimization of high-level intermediate representation (IR) is the most
error-prone compilation stage. Bugs in this stage are accountable for 44.92% of
the whole collected ones. However, existing testing techniques do not consider
high-level optimization related features (e.g. high-level IR), and are
therefore weak in exposing bugs at this stage. To bridge this gap, we propose
HirGen, an automated testing technique that aims to effectively expose coding
mistakes in the optimization of high-level IR. The design of HirGen includes 1)
three coverage criteria to generate diverse and valid computational graphs; 2)
full use of high-level IRs language features to generate diverse IRs; 3) three
test oracles inspired from both differential testing and metamorphic testing.
HirGen has successfully detected 21 bugs that occur at TVM, with 17 bugs
confirmed and 12 fixed. Further, we construct four baselines using the
state-of-the-art DL compiler fuzzers that can cover the high-level optimization
stage. Our experiment results show that HirGen can detect 10 crashes and
inconsistencies that cannot be detected by the baselines in 48 hours. We
further validate the usefulness of our proposed coverage criteria and test
oracles in evaluation.
