Heterodata to Seq#518
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yliu2-sc wants to merge 7 commits intoyliu2/positional_encoding_transformfrom
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svij-sc,
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zfan3-sc
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yliu2-sc
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zfan3-sc
reviewed
Mar 12, 2026
gigl/transforms/graph_transformer.py
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| def __init__( | ||
| self, | ||
| batch_size: int, |
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Does this transform require all batches to share the same batch size? For test batches, since we want to include all test samples, the last batch can contain less samples, will it still work?
zfan3-sc
reviewed
Mar 17, 2026
| reachable.indices(), | ||
| torch.ones(reachable._nnz(), device=device, dtype=torch.float), | ||
| size=(batch_size, num_nodes), | ||
| ).coalesce() |
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It seems it does no harm if we skip the coalesce here? For positional encoding, if the user only plans to use hop distances w.r.t anchors, they can grab that information here, and do not need to run the hop distance transform that produces n x n sparse matrices on the whole graph.
zfan3-sc
reviewed
Mar 17, 2026
| max_seq_len: Maximum sequence length (neighbors beyond this are truncated). | ||
| anchor_node_type: The node type of anchor nodes. | ||
| feature_dim: Output feature dimension. If None, inferred from data. | ||
| If provided and different from input, features are projected. |
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I don't see the projection logic?
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Move to combine changes with #537 |
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Scope of work done
Where is the documentation for this feature?: N/A
Did you add automated tests or write a test plan?
Updated Changelog.md? NO
Ready for code review?: NO