Bojian Zheng, Ziheng Jiang, Cody Hao Yu, Haichen Shen, Joshua Fromm, Yizhi Liu, Yida Wang, Luis Ceze, Tianqi Chen, Gennady Pekhimenko
Achieving high performance for compute-intensive operators in machine learning (ML) workloads is a crucial but challenging task. Many ML and system practitioners rely on vendor libraries or auto-schedulers to do the job. While the former requires large engineering efforts, the latter only supports static-shape workloads in existing works. It is difficult, if not impractical, to apply existing auto-schedulers directly to dynamic-shape workloads, as this leads to extremely long auto-scheduling time.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.We evaluate DietCode using state-of-the-art machine learning workloads on a modern GPU. Our evaluation shows that DietCode has the following key strengths when auto-scheduling an entire model end-to-end: (1) reduces the auto-scheduling time by up to 5.88x less than the state-of-the-art auto-scheduler on the uniformly sampled dynamic shapes (94.1x estimated if all possible shapes are included), (2) improves performance by up to 69.5% better than the auto-scheduler and 18.6% better than the vendor library. All these advantages make DietCode an efficient and practical solution for dynamic-shape workloads.