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PyG 2.7.0
We are excited to announce the release of PyG 2.7 ๐๐๐
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Changelog
Graph Neural Network Library for PyTorch
Last updated about 1 month ago
We are excited to announce the release of PyG 2.7 ๐๐๐
PyG 2.7 is the culmination of work from 53 contributors who have worked on features and bug-fixes for a total of over 282 commits since torch-geometric==2.6.0.
PyG 2.7 is fully compatible with PyTorch 2.8 and supports the following combinations:
| PyTorch 2.8 | cpu | cu126 | cu128 | cu129 |
|--------------|-------|---------|---------|---------|
| Linux | โ
| โ
| โ
| โ
|
| Windows | โ
| โ
| โ
| โ
|
| macOS | โ
| | | |
In addition, PyG 2.7 supports two previous PyTorch minor releases, PyTorch 2.7 and 2.6:
| PyTorch 2.7 | cpu | cu118 | cu126 | cu128 |
|--------------|-------|---------|---------|---------|
| Linux | โ
| โ
| โ
| โ
|
| Windows | โ
| โ
| โ
| โ
|
| macOS | โ
| | | |
| PyTorch 2.6 | cpu | cu118 | cu124 | cu126 |
|--------------|-------|---------|---------|---------|
| Linux | โ
| โ
| โ
| โ
|
| Windows | โ
| โ
| โ
| โ
|
| macOS | โ
| | | |
torch_geometric.distributed (#10411)ogbn_train_cugraph example for distributed cuGraph (#10439)safe_onnx_export function with workarounds for onnx_ir.serde.SerdeError issues in ONNX export (#10422)torch_geometric.graphgym and torch_geometric.data.lightning when using lightning instead of pytorch-lightning (#10404, #10417))detach() warnings in example scripts involving tensor conversions (#10357)cuGraph graph objects by ensuring cudf column names are correctly specified (#10343)_recursive_config() for torch.nn.ModuleList and torch.nn.ModuleDict (#10124, #10129)k_hop_subgraph() method for directed graphs (#9756)utils.group_cat concatenating dimension (#9766)WebQSDataset.process raising exceptions (#9665)is_node_attr() and is_edge_attr() errors when cat_dim is a tuple (#9895)num_gnn_layers == 0 (#10156)TAGDataset (#9918)torch_geometric.llm and its examples (#10436)sparse_cross_entropy (#10432)connected_components() method to Data and HeterData (#10388)BidirectionalSampler, which samples both forwards and backwards on graph edges (#10126)NeighborSampler (#10126)SamplerOutput objects (#10126)SamplerOutput (#10200)Polynormer model and example (#9908)ProteinMPNN model and example (#10289)Teeth3DS dataset, an extended benchmark for intraoral 3D scan analysis (#9833)torch.device to PatchTransformerAggregation #10342torch.device to normalization layers #10341total_influence for quantifying long-range dependency (#10263)MedShapeNet Dataset (#9823)CityNetwork dataset (#10115)visualize_graph to HeteroExplanation (#10207)AttentionExplainer (#10169)PGExplainer (#10168)GNNExplainer (#10158)ARLinkPredictor for implementing Attract-Repel embeddings for link prediction (#10105)HashTensor (#10072)SGFormer model and example (#9904)AveragePopularity metric for link prediction (#10022)Personalization metric for link prediction (#10015)HitRatio metric for link prediction (#10013)Diversity metric for link prediction (#10009)Coverage metric for link prediction (#10006)ogbn_train_cugraph.py and ogbn_train_cugraph_multigpu.py for ogbn-arxiv, ogbn-products and ogbn-papers100M (#9953)InstructMol dataset (#9975)LinkPredRecall metric (#9947)LinkPredNDCG metric (#9945)LinkPredMetricCollection (#9941)GRetriever architecture benchmarking examples (#9666)profiler.nvtxit with some examples (#9666)loader.RagQueryLoader with Remote Backend Example (#9666)data.LargeGraphIndexer (#9666)GIT-Mol (#9730)g_retriever.py pointing to Neo4j Graph DB integration demo (#9748)MoleculeGPT example (#9710)nn.models.GLEM (#9662)TAGDataset (#9662)Delaunay() triangulation via the torch_delaunay package (#9748)[4, num_faces] in the FaceToEdge transformation (#9776)use_pcst option to WebQSPDataset (#9722)edge_weight to GraphUNet models (#9737)examples/ogbn_{papers_100m,products_gat,products_sage}.py into examples/ogbn_train.py (#9467)dgcnn_classification example to work with ModelNet and MedShapeNet Datasets (#9823)NumNeighbors actually exist in the graph (#9807)GRetriever default llm (#9938)np.in1d to np.isin (#10283)Full Changelog: https://github.com/pyg-team/pytorch_geometric/compare/2.6.0...2.7.0
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