GammaGL: A Multi-Backend Library for Graph Neural Networks

Graph Neural Networks (GNNs) have shown their superiority in modeling graph-structured data, and gained much attention over the last five years. Though traditional deep learning frameworks such as TensorFlow and PyTorch provide convenient tools for implementing neural network algorithms, they do not support the key operations of GNNs well, e.g., the message passing computation based on sparse matrices. To address this issue, GNN libraries such as PyG are proposed by introducing rich Application Programming Interfaces (APIs) specialized for GNNs. However, most current GNN libraries only support a specific deep learning framework as the backend, e.g., PyG is tied up with PyTorch. In practice, users usually need to combine GNNs with other neural network components, which may come from their co-workers or open-source codes with different deep-learning backends. Consequently, users have to be familiar with various GNN libraries, and rewrite their GNNs with corresponding APIs. To provide a more convenient user experience, we present Gamma Graph Library (GammaGL), a GNN library that supports multiple deep learning frameworks as backends. GammaGL uses a framework-agnostic design that allows users to easily switch between deep learning backends on top of existing components with a single line of code change. Following the tensor-centric design idea, GammaGL splits the graph data into several key tensors, and abstracts GNN computational processes (such as message passing and graph mini-batch operations) into a few key functions. We develop many efficient operators in GammaGL for acceleration. So far, GammaGL has provided more than 40 GNN examples that can be applied to a variety of downstream tasks. GammaGL also provides tools for heterogeneous graph neural networks and recommendations to facilitate research in related fields. We present the performance of models implemented by GammaGL and the time consumption of our optimized operators to show the efficiency. Our library is available at https://github.com/BUPTGAMMA/ GammaGL.