Linear Transformer Topological Masking with Graph Random Features
Isaac Reid Kumar Avinava Dubey Deepali Jain William F Whitney Amr Ahmed Joshua Ainslie Alex Bewley Mithun George Jacob Aranyak Mehta David Rendleman Connor Schenck Richard E. Turner René Wagner Adrian Weller Krzysztof Marcin Choromanski
When training transformers on graph-structured data, incorporating information about the underlying topology is crucial for good performance. Topological masking, a type of relative position encoding, achieves this by upweighting or downweighting attention depending on the relationship between the query and keys in the graph. In this paper, we propose to parameterise topological masks as a learnable function of a weighted adjacency matrix -- a novel, flexible approach which incorporates a strong structural inductive bias. By approximating this mask with graph random features (for which we prove the first known concentration bounds), we show how this can be made fully compatible with linear attention, preserving $\mathcal{O}(N)$ time and space complexity with respect to the number of input tokens. The fastest previous alternative was $\mathcal{O}(N \log N)$ and only suitable for specific graphs. Our efficient masking algorithms provide strong performance gains for image and point cloud data, including with $>30$k nodes.
transformer, linear, attention, graph, random walk, Monte Carlo, encoding, topological masking, point cloud, Performer
An efficient algorithm to incorporate structural information about the underlying graph topology into linear attention transformers