[Roadmap] DGL General Roadmap

To the entire DGL community,

It has been more than two years (actually 33 months) since I clicked the Make It Public button of the repo, at which time I was not expecting more than a small codebase for playing with some fancy new models called Graph Neural Networks. Throughout the years, it is amazing to see the project growing with the area of Graph Deep Learning (which accidentally shares the same set of first letters with DGL), extending its scope from single machine to distributed training, becoming backbones of other packages and foundations of exciting new researches. But what makes me more honored is the wonderful community and contributors. It is your advises, questions, feedback, issue reports and PRs that make this project thrived. As we are heading to the third year anniversary, it is time for us to think about the next stage for DGL that is the first stable release. Of course, there are still a tons of work to be done before that happens so we would like to share the plan with everyone so you guys can chime in your thoughts.

[03/16/22] Updated the list according to the new v0.8 release.

Documentation

DGL v1.0 will provide a full-fledged set of documentation including tutorials, user guide, API reference for users from beginner level to expert. The major focus will be a set of HeteroGNN tutorials for beginners and advanced documentation for in-depth DGL developers.

• [Tutorial] Creating a heterogeneous graph dataset from CSV
• [Tutorial] Heterograph node classification
• [Tutorial] Heterograph link prediction
• [Tutorial] Heterogeneous graph node/link prediction with sampling
• [Tutorial] Writing a heterograph NN module
• [Tutorial] Distributed link prediction @ruisizhang123 #3993
• [Tutorial] Inductive Learning with DGL
• Developer guide
• General advices of using edge features
• [User guide] Implement custom graph sampler
• Clean up the “Paper Study with DGL” tutorials. Update the out-dated contents.
• [Blog] Feature attribution with Captum
• [Blog] Spatial-temporal GNN models (e.g., for traffic network)
• [Blog] GNN models on Discrete-time Dynamic Graphs / Continuous-time Dynamic Graphs. @otaviocx
• #4367

GNN models, modules, samplers and others

See the separate roadmap #3849 .

Sampling infrastructure

Besides adding more sampling algorithms, we plan to improve the sampling pipeline in terms of both system efficiency and customizability.

• [Neighbor sampling] Make MultiLayerNeighborSampler support non-uniform sampling
• [Efficiency] NodeDataLoader/EdgeDataLoader/etc. interface change proposal to enable async CPU-GPU copy
• [Efficiency] Support DGLGraph async transfer between CPU and GPU on specified stream with pinned memory
• [Efficiency] Use unified buffer to accelerate feature fetching
• Exclude edges in sample_neighbors (https://github.com/dmlc/dgl/pull/2971)
• Finalize the interface of GraphStorage and FeatureStorage (#3600 ).
• Integrate cuGraph sampling pipeline
• Integrate multi-GPU sampling (#3021 )

Core infrastructure

• Move half precision and mixed precision training out of experimental stage. Make it a default feature without the need to build from source.
• Integrate the new CUDA gSpMM kernels.
• Release dgl.sparse, a new backbone subpackage for sparse (adjacency) matrix and the related operators.
• Add type annotations to all core APIs.
• Use native PyTorch codepath for PyTorch backend (suggested by @yzh119):
  • Use PyTorch FFI system to register operators and custom data type (e.g., DGLGraph)
  • Write autograd in C++.

Distributed training infrastructure

• Support distributed graph partitioning for link prediction (e.g., training on one set of edges but testing on others)
• Change RPC backbone to use tensorpipe
• Replace the low-level communication stack with torch.distributed.
• Allow graph server to live after training processes finished. Allow new training process groups to connect to a running graph server.

Ecosystem

We want to see DGL being used by or using more and more amazing project in the ecosystem.

• cuGraph: https://github.com/rapidsai/cugraph #4166
• GNNVis: A Visual Analytics Approach for Prediction Error Diagnosis of Graph Neural Networks https://arxiv.org/abs/2011.11048. Released in https://github.com/dmlc/gnnlens2
• Tensorboard
• AArch64 wheels (#3336)

Compiler

See the separate roadmap #3850 .

DGL-Go

See the separate roadmap #3912 .

[yzh119]

Some suggestions:

1. Official support of half precision (user should find it in pip wheels, rather than compile the library by themselves).
2. For PyTorch backends, we should have better compatibility. 3. rewrite autograd in C++. 4. make DGLGraph compatible with Torchscript. 5. integration with torchscript and JIT. 6. Use PyTorch’s ffi system.
3. Source code side, we’d better add type hinting (add pypy checks if possible) so that user have better auto completion experience.
4. Expose spspmm interface to user in the form of sparse matrices instead of graphs.

[ruisizhang123]

Hi @jermainewang, thanks for the awesome roadmap. I’m currently working distributed graph training and I think I can help with the following features:

• [Tutorial] Distributed link prediction
• Fix distributed graph partitioning to train GNNs on large heterogeneous graphs.
• Support multiple distributed graphs to training and validation in link prediction tasks.

Please let me know if I could help. 😃