Add an RGCNConv model using the primitives in cugraph-ops
#6278
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…Gs, num_out_nodes is always num_nodes
This was referenced Jan 10, 2023
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This PR adds a GraphSAGE model `torch_geometric.nn.SAGEConvCuGraph` that leverages the optimized aggregation function in cugraph-ops, similar to #6278. Fixes rapidsai/cugraph-ops#178. --------- Co-authored-by: Matthias Fey <matthias.fey@tu-dortmund.de>
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@rusty1s I have updated RGCNConv and tested it locally. Feel free to give it a go. |
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This PR adds a GAT model `torch_geometric.nn.GATConvCuGraph` that leverages the optimized aggregation function in cugraph-ops, similar to #6278. Fixes rapidsai/cugraph-ops#182. --------- Co-authored-by: Matthias Fey <matthias.fey@tu-dortmund.de>
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This PR adds an RGCN model
torch_geometric.nn.RGCNConvCuGraphthat leverages the optimized aggregation functions in cugraph-ops.Fixes https://github.com/rapidsai/cugraph-ops/issues/180.
It requires the package
pylibcugraphops >= 23.02, which is going to be released by next February. I have tested it using the nightly build.The user interface largely resembles
RGCNConv. Our aggregation function has two modes: full-graph and message flow graph (MFG) mode, with the latter being more performant. That's why ourforwardfunction has to have extra arguments likenum_nodes,from_neighbor_samplerandmax_num_neighbors, to help convert PyG edge_index to cugraph-ops graph type and specify which mode to opt for.Here are some preliminary performance results using MAG dataset and 2-layer RGCN model, with batch_size=1024, num_neighbors=[25,20] in NeighborLoader. I set feature dim to 16 and did not use the provided paper features, as this is just a synthetic benchmark.
Right now, since cugraph-ops requires csc/csr representation of the graph, the forward time is dominated by the coo-to-csc conversion using the utility for
SparseTensor, taking 50~70% of the forward time. We can expect a higher speedup if 1). dataloader can emit csc graph and 2). dataloader can output a list of MFG, instead of a fused subgraph.