Repository: dmlc/dgl

Issue:
Sparse Ops: Hetero-RGCN example



Discussion:
Depending tasks:
- [ ] #4416 
- [ ] #4415 

Resolving patch:
diff --git a/examples/pytorch/mock_sparse/rgcn-hetero/entity_classify.py b/examples/pytorch/mock_sparse/rgcn-hetero/entity_classify.py
new file mode 100644
--- /dev/null
+++ b/examples/pytorch/mock_sparse/rgcn-hetero/entity_classify.py
@@ -0,0 +1,162 @@
+"""Modeling Relational Data with Graph Convolutional Networks
+Paper: https://arxiv.org/abs/1703.06103
+Reference Code: https://github.com/tkipf/relational-gcn
+"""
+import argparse
+import numpy as np
+import torch as th
+import torch.nn as nn
+import torch.nn.functional as F
+
+from dgl.data.rdf import AIFBDataset, MUTAGDataset, BGSDataset, AMDataset
+
+from dgl.mock_sparse import create_from_coo
+
+
+class RelGraphConvLayer(nn.Module):
+    def __init__(self, in_feat, out_feat, rel_names):
+        super(RelGraphConvLayer, self).__init__()
+        self.in_feat = in_feat
+        self.out_feat = out_feat
+        self.rel_names = rel_names
+        self.weight = {
+            rel: nn.Parameter(th.Tensor(in_feat, out_feat))
+            for rel in self.rel_names
+        }
+        for w in self.weight.values():
+            nn.init.xavier_uniform_(w, gain=nn.init.calculate_gain("relu"))
+
+    def forward(self, adjs, x_dict):
+        h_dict = {ntype: 0 for ntype in x_dict.keys()}
+        for stype, etype, dtype in adjs.keys():
+            h = x_dict[stype] @ self.weight[etype]  # dense mm
+            h_dict[dtype] += adjs[(stype, etype, dtype)] @ h
+        h_dict = {ntype: th.relu(h) for ntype, h in h_dict.items()}
+        return h_dict
+
+
+class EntityClassify(nn.Module):
+    def __init__(self, adjs, ntype_to_num_nodes, h_dim, out_dim):
+        super(EntityClassify, self).__init__()
+        self.adjs = adjs
+        self.h_dim = h_dim
+        self.out_dim = out_dim
+        self.rel_names = list(set(etype for _, etype, _ in adjs.keys()))
+
+        self.embeds = nn.ParameterDict()
+        for ntype, num_nodes in ntype_to_num_nodes.items():
+            embed = nn.Parameter(th.Tensor(num_nodes, h_dim))
+            nn.init.xavier_uniform_(embed, gain=nn.init.calculate_gain("relu"))
+            self.embeds[ntype] = embed
+
+        self.layers = nn.ModuleList()
+        self.layers.append(
+            RelGraphConvLayer(self.h_dim, self.h_dim, self.rel_names)
+        )
+        self.layers.append(
+            RelGraphConvLayer(self.h_dim, self.out_dim, self.rel_names)
+        )
+
+    def forward(self):
+        h = self.embeds
+        for layer in self.layers:
+            h = layer(self.adjs, h)
+        return h
+
+
+def main(args):
+    # load graph data
+    if args.dataset == "aifb":
+        dataset = AIFBDataset()
+    elif args.dataset == "mutag":
+        dataset = MUTAGDataset()
+    elif args.dataset == "bgs":
+        dataset = BGSDataset()
+    elif args.dataset == "am":
+        dataset = AMDataset()
+    else:
+        raise ValueError()
+
+    g = dataset[0]
+    category = dataset.predict_category
+    num_classes = dataset.num_classes
+    train_mask = g.nodes[category].data.pop("train_mask")
+    test_mask = g.nodes[category].data.pop("test_mask")
+    train_idx = th.nonzero(train_mask, as_tuple=False).squeeze()
+    test_idx = th.nonzero(test_mask, as_tuple=False).squeeze()
+    labels = g.nodes[category].data.pop("labels")
+    val_idx = train_idx
+
+    def create_adjs(g):
+        adjs = {}
+        for rel in g.canonical_etypes:
+            stype, _, dtype = rel
+            row, col = g.edges(etype=rel)
+            adjs[rel] = create_from_coo(
+                col,
+                row,
+                shape=(g.number_of_nodes(dtype), g.number_of_nodes(stype)),
+            )
+        return adjs
+
+    # Dict: canonical_etype -> SparseMatrix
+    adjs = create_adjs(g)
+    ntype_to_num_nodes = {ntype: g.number_of_nodes(ntype) for ntype in g.ntypes}
+    model = EntityClassify(adjs, ntype_to_num_nodes, 16, num_classes)
+
+    optimizer = th.optim.Adam(model.parameters(), lr=1e-2)
+
+    print("start training...")
+    model.train()
+    for epoch in range(50):
+        optimizer.zero_grad()
+        logits = model()[category]
+        loss = F.cross_entropy(logits[train_idx], labels[train_idx])
+        loss.backward()
+        optimizer.step()
+
+        train_acc = th.sum(
+            logits[train_idx].argmax(dim=1) == labels[train_idx]
+        ).item() / len(train_idx)
+        val_loss = F.cross_entropy(logits[val_idx], labels[val_idx])
+        val_acc = th.sum(
+            logits[val_idx].argmax(dim=1) == labels[val_idx]
+        ).item() / len(val_idx)
+        print(
+            "Epoch {:05d} | Train Acc: {:.4f} | Train Loss: {:.4f} | Valid Acc: {:.4f} | Valid loss: {:.4f}".format(
+                epoch,
+                train_acc,
+                loss.item(),
+                val_acc,
+                val_loss.item(),
+            )
+        )
+    print()
+
+    model.eval()
+    logits = model.forward()[category]
+    test_loss = F.cross_entropy(logits[test_idx], labels[test_idx])
+    test_acc = th.sum(
+        logits[test_idx].argmax(dim=1) == labels[test_idx]
+    ).item() / len(test_idx)
+    print(
+        "Test Acc: {:.4f} | Test loss: {:.4f}".format(
+            test_acc, test_loss.item()
+        )
+    )
+    print()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="RGCN")
+    parser.add_argument(
+        "-d",
+        "--dataset",
+        type=str,
+        required=True,
+        help="Dataset name ('aifb', 'mutag', 'bgs', 'am').",
+    )
+
+    args = parser.parse_args()
+    print(args)
+    main(args)


Test patch: