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arXiv · 1905.02494

Reinforced Genetic Algorithm Learning for Optimizing Computation Graphs

Aditya Paliwal·Felix Gimeno·Vinod Nair·Yujia Li·Miles Lubin·Pushmeet Kohli·Oriol Vinyals
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Abstract

We present a deep reinforcement learning approach to minimizing the execution cost of neural network computation graphs in an optimizing compiler. Unlike earlier learning-based works that require training the optimizer on the same graph to be optimized, we propose a learning approach that trains an optimizer offline and then generalizes to previously unseen graphs without further training. This allows our approach to produce high-quality execution decisions on real-world TensorFlow graphs in seconds instead of hours. We consider two optimization tasks for computation graphs: minimizing running time and peak memory usage. In comparison to an extensive set of baselines, our approach achieves significant improvements over classical and other learning-based methods on these two tasks.

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Aditya Paliwal, Felix Gimeno, Vinod Nair, Yujia Li, Miles Lubin, Pushmeet Kohli, Oriol Vinyals. 2019-05-07. Reinforced Genetic Algorithm Learning for Optimizing Computation Graphs. https://arxiv.org/abs/1905.02494

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