Deep Graph Neural Networks (GNNs) show promising performance on a range of
graph tasks, yet at present are costly to run and lack many of the
optimisations applied to DNNs. We show, for the first time, how to
systematically quantise GNNs with minimal or no loss in performance using
Network Architecture Search (NAS). We define the possible quantisation search
space of GNNs. The proposed novel NAS mechanism, named Low Precision Graph NAS
(LPGNAS), constrains both architecture and quantisation choices to be
differentiable. LPGNAS learns the optimal architecture coupled with the best
quantisation strategy for different components in the GNN automatically using
back-propagation in a single search round. On eight different datasets, solving
the task of classifying unseen nodes in a graph, LPGNAS generates quantised
models with significant reductions in both model and buffer sizes but with
similar accuracy to manually designed networks and other NAS results. In
particular, on the Pubmed dataset, LPGNAS shows a better size-accuracy Pareto
frontier compared to seven other manual and searched baselines, offering a 2.3
times reduction in model size but a 0.4% increase in accuracy when compared to
the best NAS competitor. Finally, from our collected quantisation statistics on
a wide range of datasets, we suggest a W4A8 (4-bit weights, 8-bit activations)
quantisation strategy might be the bottleneck for naive GNN quantisations.

本研究使用 Low Precision Graph NAS (LPGNAS) 方法进行 Deep Graph Neural Networks 的定点化，并通过 8 个不同的数据集分类实现了模型和缓冲区大小的显著降低，同时保持了精度。