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arXiv · 2208.02025

OLLIE: Derivation-based Tensor Program Optimizer

Liyan Zheng·Haojie Wang·Jidong Zhai·Muyan Hu·Zixuan Ma·Tuowei Wang·Shizhi Tang·Lei Xie·Kezhao Huang·Zhihao Jia
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Abstract

Boosting the runtime performance of deep neural networks (DNNs) is critical due to their wide adoption in real-world tasks. Existing approaches to optimizing the tensor algebra expression of a DNN only consider expressions representable by a fixed set of predefined operators, missing possible optimization opportunities between general expressions. We propose OLLIE, the first derivation-based tensor program optimizer. OLLIE optimizes tensor programs by leveraging transformations between general tensor algebra expressions, enabling a significantly larger expression search space that includes those supported by prior work as special cases. OLLIE uses a hybrid derivation-based optimizer that effectively combines explorative and guided derivations to quickly discover highly optimized expressions. Evaluation on seven DNNs shows that OLLIE can outperform existing optimizers by up to 2.73$\times$ (1.46$\times$ on average) on an A100 GPU and up to 2.68$\times$ (1.51$\times$) on a V100 GPU, respectively.

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Liyan Zheng, Haojie Wang, Jidong Zhai, Muyan Hu, Zixuan Ma, Tuowei Wang, Shizhi Tang, Lei Xie, Kezhao Huang, Zhihao Jia. 2022-08-02. OLLIE: Derivation-based Tensor Program Optimizer. https://arxiv.org/abs/2208.02025

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