PyTorch Autograd Wrappers#

Simple Neighborhood Aggregator (SAGEConv)#

operators.agg_concat_n2n(feat, graph[, ...])

PyTorch autograd function for simple aggregation using node features in an node-to-node reduction (n2n) while concatenating the original features of output at the end (agg_concat).

Graph Attention (GATConv/GATv2Conv)#

operators.mha_gat_n2n(feat, attn_weights, graph)

PyTorch autograd function for a multi-head attention layer (GAT-like) without using cudnn (mha_gat) in a node-to-node reduction (n2n).

operators.mha_gat_v2_n2n(feat, attn_weights, ...)

PyTorch autograd function for a multi-head attention layer (GAT-like) without using cudnn (mha_gat_v2) with an activation prior to the dot product but none afterwards in a node-to-node reduction (n2n).

Heterogenous Aggregator using Basis Decomposition (RGCNConv)#

operators.agg_hg_basis_n2n_post(feat, ...[, ...])

PyTorch autograd function for node-to-node RGCN-like basis regularized aggregation, with features being transformed after (post) this aggregation.

Update Edges: Concatenation or Sum of Edge and Node Features#

operators.update_efeat_e2e(edge_feat, ...[, ...])

PyTorch autograd function for creating new edge features (update_efeat) based on either concatenating or summing edge features and the features of the corresponding source and destination node of each edge in an edge-to-edge fashion (e2e).

operators.update_efeat_e2e(edge_feat, ...[, ...])

PyTorch autograd function for creating new edge features (update_efeat) based on either concatenating or summing edge features and the features of the corresponding source and destination node of each edge in an edge-to-edge fashion (e2e).