Growing a Compiler - Getting to Machine Learning from a General Purpose Compiler

The Compilers for Machine Learning workshop was recently held at CGO 2019. Since compiler techniques affect a large part of the machine learning stack, this workshop aimed to highlight research that incorporates compiler techniques and algorithms in optimizing machine learning workloads. The workshop included talks from various projects - Julia (Julia Computing), TVM (UW), Glow (Facebook), XLA (Google), nGraph (Intel), TensorRT (Nvidia), and the soon to release MLIR (Google).

Our talk introduced the abstractions in the Julia language and the kind of compiler transforms involved in implementing them. We then had a deep dive into dynamic semantics + static analysis - our JAOT (Just-Ahead-Of-Time) analysis. Building on these capabilities, the Zygote system implements automatic differentiation, effectively treating it as a compiler problem, giving us differentiable programming for free. Finally, compiler backends for GPUs and TPUs give us high performance execution. All this comes together beautifully in Neural ODEs, which we had to show off as our first slide!

Our presentation is available online. A PDF is also available in case Google Docs are blocked.