  Domain-specific optimizing compilers have demonstrated significant
performance and portability benefits, but require programs to be represented in
their specialized IRs. Existing frontends to these compilers suffer from the
"language subset problem" where some host language features are unsupported in
the subset of the user's program that interacts with the domain-specific
compiler. By contrast, define-by-run ML frameworks-colloquially called "eager"
mode-are popular due to their ease of use and expressivity, where the full
power of the host programming language can be used. LazyTensor is a technique
to target domain specific compilers without sacrificing define-by-run
ergonomics. Initially developed to support PyTorch on Cloud TPUs, the
technique, along with a substantially shared implementation, has been used by
Swift for TensorFlow across CPUs, GPUs, and TPUs, demonstrating the generality
of the approach across (1) Tensor implementations, (2) hardware accelerators,
and (3) programming languages.
