We observe that the key challenge faced by existing auto-schedulers when handling a dynamic-shape workload is that they cannot construct a unified search space for all the possible shapes of the workload, because their search space is shape-dependent. To address this, we propose DietCode, a new auto-scheduler framework that efficiently supports dynamic-shape workloads by constructing a shape-generic search space and cost model. Under this construction, all shapes jointly search within the same space and update the same cost model when auto-scheduling, which is therefore more efficient compared with existing auto-schedulers.
This session is broken into two parts: a 10 minute talk followed by a 5 minute breakout session.
