Bump torch-geometric from 2.3.0 to 2.6.1

[dependabot]

Bumps torch-geometric from 2.3.0 to 2.6.1.

Release notes

Sourced from torch-geometric's releases.

PyG 2.6.1: Bugfixes

PyG 2.6.1 includes a bugfix in the WebQSDataset.

Bug Fixes

• Fixed a bug in the WebQSDataset dataset where empty edges were not treated gracefully (#9665)

Full Changelog: pyg-team/pytorch_geometric@2.6.0...2.6.1

PyG 2.6.0

We are excited to announce the release of PyG 2.6 🎉🎉🎉

• Highlights
• Breaking Changes
• Features
• Bugfixes
• Changes
• Full Changelog

PyG 2.6 is the culmination of work from 59 contributors who have worked on features and bug-fixes for a total of over 238 commits since torch-geometric==2.5.0.

Highlights

PyTorch 2.4 Support

PyG 2.6 is fully compatible with PyTorch 2.4, and supports the following combinations:

PyTorch 2.2	cpu	cu118	cu121	cu124
Linux	✅	✅	✅	✅
macOS	✅
Windows	✅	✅	✅	✅

You can still install PyG 2.6 with an older PyTorch release up to PyTorch 1.13 in case you are not eager to update your PyTorch version.

GNNs+LLMs

In order to facilitate further research on combining GNNs with LLMs, PyG 2.6 introduces

• a new sub-package torch_geometric.nn.nlp with fast access to SentenceTransformer models and LLMs
• a new model GRetriever that is able to co-train LLAMA2 with GAT for answering questions based on knowledge graph information
• a new example folder examples/llm that shows how to utilize these models in practice

Index Tensor Representation

Similar to the EdgeIndex class introduced in PyG 2.5, torch-geometric==2.6.0 introduces the Index class for efficient storage of 1D indices. While Index sub-classes a general torch.Tensor, it can hold additional (meta)data, i.e.:

• dim_size: The size of the underlying sparse vector, i.e. the size of a dimension that can be indexed via Index. By default, it is inferred as dim_size=index.max() + 1
• is_sorted: Whether indices are sorted in ascending order.

... (truncated)

Changelog

Sourced from torch-geometric's changelog.

Changelog

All notable changes to this project will be documented in this file. The format is based on Keep a Changelog.

[2.7.0] - 2025-MM-DD

Added

• Improving documentation for cuGraph (#10083)
• Added HashTensor (#10072)
• Added SGFormer model and example (#9904)
• Added AveragePopularity metric for link prediction (#10022)
• Added Personalization metric for link prediction (#10015)
• Added HitRatio metric for link prediction (#10013)
• Added Data Splitting Tutorial (#8366)
• Added Diversity metric for link prediction (#10009)
• Added Coverage metric for link prediction (#10006)
• Added Graph Transformer Tutorial (#8144)
• Consolidate Cugraph examples into ogbn_train_cugraph.py and ogbn_train_cugraph_multigpu.py for ogbn-arxiv, ogbn-products and ogbn-papers100M (#9953)
• Added InstructMol dataset (#9975)
• Added support for weighted LinkPredRecall metric (#9947)
• Added support for weighted LinkPredNDCG metric (#9945)
• Added LinkPredMetricCollection (#9941)
• Added various GRetriever architecture benchmarking examples (#9666)
• Added profiler.nvtxit with some examples (#9666)
• Added loader.RagQueryLoader with Remote Backend Example (#9666)
• Added data.LargeGraphIndexer (#9666)
• Added GIT-Mol (#9730)
• Added comment in g_retriever.py pointing to Neo4j Graph DB integration demo (#9748)
• Added MoleculeGPT example (#9710)
• Added nn.models.GLEM (#9662)
• Added TAGDataset (#9662)
• Added support for fast Delaunay() triangulation via the torch_delaunay package (#9748)
• Added PyTorch 2.5 support (#9779, #9779)
• Support 3D tetrahedral mesh elements of shape [4, num_faces] in the FaceToEdge transformation (#9776)
• Added the use_pcst option to WebQSPDataset (#9722)
• Allowed users to pass edge_weight to GraphUNet models (#9737)
• Consolidated examples/ogbn_{papers_100m,products_gat,products_sage}.py into examples/ogbn_train.py (#9467)
• Add ComplexWebQuestions (CWQ) dataset (#9950)

Changed

• Updated cuGraph examples to use buffered sampling which keeps data in memory and is significantly faster than the deprecated buffered sampling (#10079)
• Updated Dockerfile to use latest from NVIDIA (#9794)
• Dropped Python 3.8 support (#9696)
• Added a check that confirms that custom edge types of NumNeighbors actually exist in the graph (#9807)
• Automatic num_params in LLM + update GRetriever default llm (#9938)

Deprecated

... (truncated)

Commits

• 7ca40d6 Fix empty dataframe handling in WebQSPDataset
• 12421c2 Add G-retriever (GNN+LLM) example (#9167)
• bfc6d1a Add WebQSPDataset (#9481)
• 6d9e850 Add nn.models.GRetriever (#9480)
• df3df3b Added the ClusterPooling layer (#9627)
• 241a8c3 Mean reciprocal rank metric (#9632)
• 3f4f1a0 examples/multi_gpu/papers100m_gcn.py Low CPU RAM Warning (#9628)
• 85bd33d Fix autograd for torch_coo_tensor (#9626)
• 09f84a1 Fix nightly tests (#9625)
• d2b175a Bug fix in OPFDataset parsing (#9623)
• Additional commits viewable in compare view

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[dependabot]

Superseded by #12.