Adding trim_to_layer utility function#6661
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Thanks for this PR. I have a few questions:
- Can we split this PR into multiple such that we first integrate
sampled_infointo theNeighborLoaderbefore we think about integration inMessagePassing/benchmark scripts? - I am not entirely sure why we need the concept of a
HierarchicalSparseTensor. Can't we just letNeighborLoaderreturn a list of sliced edge indices/sparse tensors? - I would like to avoid adding any logic of this to
MessagePassingand its instances. IMO, any customization ofxandedge_indexshould happen outside of it. Would that be possible?
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Thanks @rusty1s for the review. Let's proceed first with pyg-lib part. |
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It's enabling the hierarchical tensor usage and significant performance improvement PyG part: pyg-team/pytorch_geometric#6661 --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: rusty1s <matthias.fey@tu-dortmund.de>
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trim_to_layer utility function
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Codecov Report
@@ Coverage Diff @@
## master #6661 +/- ##
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Coverage 91.46% 91.46%
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Files 431 432 +1
Lines 23458 23498 +40
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+ Hits 21455 21492 +37
- Misses 2003 2006 +3
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Adding hierarchical graph adjacency matrix feature to significantly improve performance Contributors: @rBenke @andreazanetti --------- Co-authored-by: Matthias Fey <matthias.fey@tu-dortmund.de>
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Adding hierarchical graph adjacency matrix feature to significantly improve performance
Contributors: @rBenke @andreazanetti