Bump torch-geometric from 2.3.0 to 2.7.0

[dependabot]

Bumps torch-geometric from 2.3.0 to 2.7.0.

Release notes

Sourced from torch-geometric's releases.

PyG 2.7.0

We are excited to announce the release of PyG 2.7 🎉🎉🎉

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

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.

Highlights

PyTorch 2.8 Support

PyG 2.7 is fully compatible with PyTorch 2.8 and supports the following combinations:

PyTorch 2.8	cpu	cu126	cu128	cu129
Linux	✅	✅	✅	✅
Windows	✅	✅	✅	✅
macOS	✅

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	✅	✅	✅	✅
Windows	✅	✅	✅	✅
macOS	✅

PyTorch 2.6	cpu	cu118	cu124	cu126
Linux	✅	✅	✅	✅
Windows	✅	✅	✅	✅
macOS	✅

Breaking Changes

• Dropped support for Python 3.9 (#10461)
• Dropped support for PyTorch 1.11 - 2.5 (#10500, #10248, #10247)

Deprecation

• Deprecated torch_geometric.distributed (#10411)

Bugfixes

... (truncated)

Changelog

Sourced from torch-geometric's changelog.

[2.7.0] - 2025-10-14

Fixed

• Fixed ogbn_train_cugraph example for distributed cuGraph (#10439)
• Added safe_onnx_export function with workarounds for onnx_ir.serde.SerdeError issues in ONNX export (#10422)
• Fixed importing PyTorch Lightning in torch_geometric.graphgym and torch_geometric.data.lightning when using lightning instead of pytorch-lightning (#10404, #10417))
• Fixed detach() warnings in example scripts involving tensor conversions (#10357)
• Fixed non-tuple indexing to resolve PyTorch deprecation warning (#10389)

Added

• Added llm generated explanations to TAGDataset (#9918)
• Added torch_geometric.llm and its examples (#10436)
• Added support for negative weights in sparse_cross_entropy (#10432)
• Added connected_components() method to Data and HeterData (#10388)
• Added LPFormer Graph Transformer for Link Prediction (#9956)
• Added BidirectionalSampler, which samples both forwards and backwards on graph edges (#10126)
• Enable Sampling both forwards and reverse edges on NeighborSampler (#10126)
• Added ability to merge together SamplerOutput objects (#10126)
• Added ability to get global row and col ids from SamplerOutput (#10200)
• Added PyTorch 2.8 support (#10403)
• Added Polynormer model and example (#9908)
• Added ProteinMPNN model and example (#10289)
• Added the Teeth3DS dataset, an extended benchmark for intraoral 3D scan analysis (#9833)
• Added torch.device to PatchTransformerAggregation #10342
• Added torch.device to normalization layers #10341
• Added total_influence for quantifying long-range dependency (#10263)
• Added MedShapeNet Dataset (#9823)
• Added RelBench example (#10230)
• Added CityNetwork dataset (#10115)
• Added visualize_graph to HeteroExplanation (#10207)
• Added PyTorch 2.6 support (#10170)
• Added support for heterogenous graphs in AttentionExplainer (#10169)
• Added support for heterogenous graphs in PGExplainer (#10168)
• Added support for heterogenous graphs in GNNExplainer (#10158)
• Added Graph Positional and Structural Encoder (GPSE) and example (#9018) (#10118)
• Added attract-repel link prediction example (#10107)
• Added ARLinkPredictor for implementing Attract-Repel embeddings for link prediction (#10105)
• 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)

... (truncated)

Commits

• 76ff9c2 Prepare for PyG 2.7 release (#10501)
• 87a9c07 ci: Drop support for PyTorch 1.13-2.5 (#10500)
• b2c3941 Fix docs for sentence transformer (#10495)
• 62d0db3 add no grad to retrieval SentenceTransformer calls (#10494)
• 5e8f244 Fix test_train to not rely on 'Sanity Checking' stdout in multi-GPU runs (#...
• 1252027 Bump astral-sh/setup-uv from 6 to 7 in /.github/actions/setup (#10490)
• 973447c [pre-commit.ci] pre-commit suggestions (#10488)
• c5126ac Update CODEOWNERS for CuGraph-Ops -> Ting Yu (#10420)
• e656302 Put LLM CI back (#10476)
• ea4c9d4 Resolve indexing issue when loading medshapenet dataset. (#10472)
• Additional commits viewable in compare view

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

---

Dependabot commands and options

You can trigger Dependabot actions by commenting on this PR:

• @dependabot rebase will rebase this PR
• @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
• @dependabot merge will merge this PR after your CI passes on it
• @dependabot squash and merge will squash and merge this PR after your CI passes on it
• @dependabot cancel merge will cancel a previously requested merge and block automerging
• @dependabot reopen will reopen this PR if it is closed
• @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
• @dependabot show <dependency name> ignore conditions will show all of the ignore conditions of the specified dependency
• @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
• @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
• @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)