We present an end-to-end MLIR schedule for ML workloads and the auto-tuning thereof. The schedule targets Intel Battlemage GPUs and takes kernels all the way from Linalg-dialect ingress to LLVM IR. Alongside scheduling ops for, e.g., tiling, the schedule encodes its auto-tuning problem through tuneable ?knob? ops and encodes the many hardware-imposed constraints on parameters as ops as well. We present a harness that performs automatic optimization of such auto-tuneable schedules and show how our approach - using upstream MLIR and without user-specified tiling - can achieve performance comparable to state-of-the-art frameworks.

Speakers: Tuomas Karna (Intel), Rolf Morel (Intel)