DGL v0.3 Release Note

V0.3 release includes many crucial updates:

• Fused message passing kernels that greatly boost the training of GNNs on large graphs. Please refer to our blogpost for more details.
• Demostration of how to train GNNs on giant graphs by graph sampling.
• New models and NN modules.
• Many other bugfixes and other enhancement.

As a result, please be aware of the following changes:

Installation

Previous installation methods with pip and conda, i.e.:

pip install dgl
conda install -c dglteam dgl

now only install CPU builds (works for Linux/MacOS/Windows).

July 2nd update

We found that the Windows build of DGL v0.3 on PyPI is currently inconsistent with the 0.3.x branch. Windows pip users, please install it with:

pip install https://s3.us-east-2.amazonaws.com/dgl.ai/wheels/dgl-0.3-cp35-cp35m-win_amd64.whl
pip install https://s3.us-east-2.amazonaws.com/dgl.ai/wheels/dgl-0.3-cp36-cp36m-win_amd64.whl
pip install https://s3.us-east-2.amazonaws.com/dgl.ai/wheels/dgl-0.3-cp37-cp37m-win_amd64.whl

Installing CUDA builds with pip

Pip users could install the DGL CUDA builds with the following:

pip install <package-url>

where <package-url> is one of the following:

Installing CUDA builds with conda

Conda users could install the CUDA builds with

conda install -c dglteam dgl-cuda9.0   # For CUDA 9.0
conda install -c dglteam dgl-cuda10.0  # For CUDA 10.0

DGL currently support CUDA 9.0 (dgl-cuda9.0) and 10.0 (dgl-cuda10.0). To find your CUDA version, use nvcc --version. To install from source, checkout our installation guide.

New built-in message and reduce functions

We have expanded the list of built-in message and reduce functions to cover more use cases. Previously, DGL only has copy_src, copy_edge, src_mul_edge. With the v0.3 release, we support more combinations. Here is a demonstration of some of the new builtin functions.

import dgl
import dgl.function as fn
import torch as th
g = ... # create a DGLGraph
g.ndata['h'] = th.randn((g.number_of_nodes(), 10)) # each node has feature size 10
g.edata['w'] = th.randn((g.number_of_edges(), 1))  # each edge has feature size 1
# collect features from source nodes and aggregate them in destination nodes
g.update_all(fn.copy_u('h', 'm'), fn.sum('m', 'h_sum'))
# multiply source node features with edge weights and aggregate them in destination nodes
g.update_all(fn.u_mul_e('h', 'w', 'm'), fn.max('m', 'h_max'))
# compute edge embedding by multiplying source and destination node embeddings
g.apply_edges(fn.u_mul_v('h', 'h', 'w_new'))

As you can see, the syntax is quite straight-forward. u_mul_e means multiplying the source node data with the edge data; u_mul_v means multiplying the source node data with the destination node data, and so on and so forth. Each builtin combination will be mapped to a CPU/CUDA kernel and broadcasting and gradient computation are also supported. Checkout our document for more details.

Tutorials for training on giant graphs

Two new tutorials are now live:

• Train GNNs by neighbor sampling and its variants (link).
• Scale the sampler-trainer architecture to giant graphs using distributed graph store (link).

We also provide scripts on how to setup such distributed setting (link).

Enhancement and bugfix

• NN modules
  • dgl.nn.[mxnet|pytorch].edge_softmax now directly returns the normalized scores on edges.
  • Fix a memory leak bug when graph is passed as the input.
• Graph
  • DGLGraph now supports direct conversion from scipy csr matrix rather than conversion to coo matrix first.
  • Readonly graph can now be batched via dgl.batch.
  • DGLGraph now supports node/edge removal via DGLGraph.remove_nodes and DGLGraph.remove_edges (doc).
  • A new API DGLGraph.to(device) that can move all node/edge data to the given device.
  • A new API dgl.to_simple that can convert a graph to a simple graph with no multi-edges.
  • A new API dgl.to_bidirected that can convert a graph to a bidirectional graph.
  • A new API dgl.contrib.sampling.random_walk that can generate random walks from a graph.
  • Allow DGLGraph to be constructed from another DGLGraph.
• New model examples
  • APPNP
  • GIN
  • PinSage (slow version)
  • DGI
• Bugfix
  • Fix a bug where numpy integer is passed in as the argument.
  • Fix a bug when constructing from a networkx graph that has no edge.
  • Fix a bug in nodeflow where id is not correctly converted sometimes.
  • Fix a bug in MiniGC dataset where the number of nodes is not consistent.
  • Fix a bug in RGCN example when bfs_level=0.
  • Fix a bug where DLContext is not correctly exposed in CFFI.
  • Fix a crash during Cython build.
  • Fix a bug in send when the given message function is a builtin.