X-RLflow: Graph Reinforcement Learning for Neural Network Subgraphs Transformation

• URL: http://arxiv.org/abs/2304.14698v1
• Date: Fri, 28 Apr 2023 09:06:18 GMT
• Title: X-RLflow: Graph Reinforcement Learning for Neural Network Subgraphs Transformation
• Authors: Guoliang He, Sean Parker, Eiko Yoneki
• Abstract summary: graph superoptimisation systems perform a sequence of subgraph substitution to neural networks to find the optimal computation graph structure. We show that our approach can outperform state-of-the-art superoptimisation systems over a range of deep learning models and achieve by up to 40% on those that are based on transformer-style architectures.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tensor graph superoptimisation systems perform a sequence of subgraph substitution to neural networks, to find the optimal computation graph structure. Such a graph transformation process naturally falls into the framework of sequential decision-making, and existing systems typically employ a greedy search approach, which cannot explore the whole search space as it cannot tolerate a temporary loss of performance. In this paper, we address the tensor graph superoptimisation problem by exploring an alternative search approach, reinforcement learning (RL). Our proposed approach, X-RLflow, can learn to perform neural network dataflow graph rewriting, which substitutes a subgraph one at a time. X-RLflow is based on a model-free RL agent that uses a graph neural network (GNN) to encode the target computation graph and outputs a transformed computation graph iteratively. We show that our approach can outperform state-of-the-art superoptimisation systems over a range of deep learning models and achieve by up to 40% on those that are based on transformer-style architectures.

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