X-RLflow: Graph Reinforcement Learning for Neural Network Subgraphs Transformation
This addresses the inefficiency of greedy search in optimizing neural network computation graphs, offering a novel solution for deep learning practitioners.
The paper tackles the tensor graph superoptimisation problem by using reinforcement learning to perform neural network subgraph transformations, achieving up to 40% performance improvement over state-of-the-art systems on transformer-style architectures.
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.