Event Details

DietCode: Automatic Optimization for Dynamic Tensor Programs

Date: 12/16/2021 2:30 pm
Open Source Lounge

Organization: University of Toronto
Speakers: Bojian Zheng, Ziheng Jiang

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.

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