Pytorch_geometric: [Roadmap] PyTorch Geometric 1.6.1, 1.6.2 and 1.7.0

[rusty1s]

[Roadmap] PyTorch Geometric 1.6.1 and 1.7.0

1.6.1

• [x] [WikiCSDataset](https://pytorch-geometric.readthedocs.io/en/latest/modules/datasets.html#torch_geometric.datasets.WikiCS)
• [x] DeepGCN
  
  
  
  • [x] [GENConv](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.GENConv)
  
  
  • [x] [DeepGCNLayer](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.models.DeepGCNLayer)
  
  
  • [x] [ogn-proteins example](https://github.com/rusty1s/pytorch_geometric/blob/master/examples/ogbn_proteins_deepgcn.py)
  
  
• [x] Fix torch-sparse reductions with dim=0
• [x] Fix torch-sparse suppression of warnings
• [x] PairNorm (https://github.com/rusty1s/pytorch_geometric/pull/1448)
• [x] LayerNorm (https://github.com/rusty1s/pytorch_geometric/pull/1471)
• [x] Fix jittable bug when installing PyG via pip
• [x] PyTorch 1.6.0 support
• [x] Test GNNExplainer with more models

1.6.2

• [x] Fix wrong URLs in datasets
• [x] WebKB datasets from the Geom-GCN paper
• [x] GCN2Conv from the Simple and Deep Graph Convolutional Networks paper
• [x] TransformerConv from Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classification
• [x] Unsupervised GraphSAGE example (https://github.com/rusty1s/pytorch_geometric/pull/1514) [[Example](https://github.com/rusty1s/pytorch_geometric/blob/master/examples/graph_sage_unsup.py)]
• [x] DistributedDataParallel support:
  
  
  
  • [x] Large-scale node classification (https://github.com/rusty1s/pytorch_geometric/pull/1453)
  
  
  • [x] Batching of graphs (https://github.com/rusty1s/pytorch_geometric/issues/1417#issuecomment-664119294) [Example]
  
  
• [x] Node2Vec implementation with p != 1 and q != 1
• Faster CPU implementations in torch-scatter and torch-sparse via multi-threading:
  
  
  
  • [x] spmm
  
  
• [x] Add edge weights to the documentation of GNN formulations, see https://github.com/rusty1s/pytorch_geometric/issues/1449
• [x] Clean up documentation via autosummary (https://github.com/rusty1s/pytorch_geometric/pull/1791)
• [x] follow_batch argument in DataParallel

1.6.3.

• [x] WikipediaNetwork and Actor datasets from the Geom-GCN paper
• [x] torch_geometric.utils.homophily_ratio

1.7.0

• [x] Deep Graph Infomax Inductive Reddit Example (https://github.com/rusty1s/pytorch_geometric/pull/1859) (thanks to @harrygcoppock)
• [x] TGN (Temporal Graph Network) (https://github.com/rusty1s/pytorch_geometric/pull/1519) (thanks to @emalgorithm)
• [x] Weisfeiler-Lehman kernel (thanks to @chrsmrrs)
• [x] Label Propagation
• [x] Correct&Smooth
• [x] PyTorch Lightning Example (GIN + GraphSAGE)
• [x] FiLMConv
• [ ] NARS
• [ ] spspmm backward implementation
• [ ] Multi-dimensional indexing of SparseTensor, e.g., adj[torch.tensor([0, 1, 2]), torch.tensor([0, 1, 2])]
• [ ] Conda installation
• [ ] Allow edge types for any message passing GNN (https://github.com/rusty1s/pytorch_geometric/issues/1670)
• [ ] cupgraph conversions
• [ ] Make all CPP sampling ops respect torch.manual_seed
• [ ] random_walk with custom transition probabilities
• [ ] torch.half support
• [ ] Fix HyperGraphConv https://github.com/rusty1s/pytorch_geometric/issues/1801
• [ ] [Digraph Inception Convolutional Networks](https://proceedings.neurips.cc/paper/2020/file/cffb6e2288a630c2a787a64ccc67097c-Paper.pdf) (https://github.com/rusty1s/pytorch_geometric/issues/1847)
• Faster CPU implementations in torch-scatter and torch-sparse via multi-threading:
  
  
  
  • [x] fps
  
  
  • [ ] spspmm
  
  
  • [ ] scatter
  
  
  • [ ] segment
  
  
• [x] scatter_mul implementation
• [ ] NeighborSampler with GCNConv example
• TrimNet (TrimNet: learning molecular representation from triplet messages for biomedicine)

[sbonner0]

I am not sure if this is helpful, or indeed even the correct place for this - please let me know if not and I can remove it. However I thought it might be useful to note that I have been using pytorch geometric with pytorch 1.6.0 over the past few days and haven't noticed any issues. My exact versions of the relevant packages are:

torch                      1.6.0rc7

torch-cluster              1.5.6

torch-geometric            1.6.0

torch-scatter              2.0.5

torch-sparse               0.6.6

torch-spline-conv          1.2.0

I can run the test suite if it would be helpful?

[rusty1s]

Thanks for letting me know. I also encountered no major issues while upgrading. However, I still need to provide updated wheels for the latest PyTorch release.

[yangky11]

Hi @rusty1s ,

I tried to install PyTorch Geometric with PyTorch 1.6.0 by following the instructions here. However, I cannot find the updated wheels for torch-sparse and torch-cluster. May I know if they will become available in the near future? Thanks!

[rusty1s]

Sure, they should be available next week :)

[aluo-x]

Just built sparse/scatter/cluster/spline-conv/geometric with PyTorch master, CUDA 11, cudnn8, GCC 9.3, MAGMA 2.5.2. Encountered no issues either compiling or running.

[lgray]

Just to add more to this, I've been using torch 1.6 throughout its development period, including the RCs. Never any problems.

[GTaherzadeh]

Hi there,

I sm trying to read my data using TORCH_GEOMETRIC.IO.TU code. My data type is exactly similar to the enzymes dataset. However, the program stops with the Illegal instruction (core dumped) error at this line "node_slice = torch.cumsum(torch.from_numpy(np.bincount(batch)), 0)" in the split function. It seems that the problem is with the dimension but I can't figure out what the solution is. Could you please help with this.

Please note, I installed the package exactly as it is mentioned in the document.

Thanks

[rusty1s]

Not sure if that's the best place to discuss this issue :)

You can create a separate issue for that. If you also provide your raw data, I can help you identify the issue.

[GTaherzadeh]

Hi Matthias,

Thanks for your quick reply. I have created a new issue here:
https://github.com/rusty1s/pytorch_geometric/issues/1601

[jannessm]

Hi Matthias,
maybe it would be worth to replace network-x by cugraph, when gpu is supported.

I just have heard about it and thought, that it would make sense to give you a hint, if you havent heard about it before.

Regrads.

[EDIT:]
I have just seen, that nx is just used in datasets and examples. Therefore, it seems not to be that util. So just in case you need algorithms from network-x, it could be worth to have a look at cugraph. Or for numpy, have a look at cupy ;).

[levulinh]

Please make sure you have the SuperpixelMnist dataset fixed in the next updates, thank you.

[rusty1s]

cugraph conversions is a good idea, I added it to the roadmap. The SuperpixelDataset is already fixed in master.

[rusty1s]

Just released PyTorch Geometric 1.6.2!

Thanks to many wonderful contributors :) Please let me know if you encounter any issues.