chore: import upstream snapshot with attribution

examples/sparse/gat.py

@@ -0,0 +1,141 @@
+"""
+[Graph Attention Networks]
+(https://arxiv.org/abs/1710.10903)
+"""
+
+import dgl.sparse as dglsp
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from dgl.data import CoraGraphDataset
+from torch.optim import Adam
+
+
+class GATConv(nn.Module):
+    def __init__(self, in_size, out_size, num_heads, dropout):
+        super().__init__()
+
+        self.out_size = out_size
+        self.num_heads = num_heads
+
+        self.dropout = nn.Dropout(dropout)
+        self.W = nn.Linear(in_size, out_size * num_heads)
+        self.a_l = nn.Parameter(torch.zeros(1, out_size, num_heads))
+        self.a_r = nn.Parameter(torch.zeros(1, out_size, num_heads))
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        gain = nn.init.calculate_gain("relu")
+        nn.init.xavier_normal_(self.W.weight, gain=gain)
+        nn.init.xavier_normal_(self.a_l, gain=gain)
+        nn.init.xavier_normal_(self.a_r, gain=gain)
+
+    ###########################################################################
+    # (HIGHLIGHT) Take the advantage of DGL sparse APIs to implement
+    # multihead attention.
+    ###########################################################################
+    def forward(self, A_hat, Z):
+        Z = self.dropout(Z)
+        Z = self.W(Z).view(Z.shape[0], self.out_size, self.num_heads)
+
+        # a^T [Wh_i || Wh_j] = a_l Wh_i + a_r Wh_j
+        e_l = (Z * self.a_l).sum(dim=1)
+        e_r = (Z * self.a_r).sum(dim=1)
+        e = e_l[A_hat.row] + e_r[A_hat.col]
+
+        a = F.leaky_relu(e)
+        A_atten = dglsp.val_like(A_hat, a).softmax()
+        a_drop = self.dropout(A_atten.val)
+        A_atten = dglsp.val_like(A_atten, a_drop)
+        return dglsp.bspmm(A_atten, Z)
+
+
+class GAT(nn.Module):
+    def __init__(
+        self, in_size, out_size, hidden_size=8, num_heads=8, dropout=0.6
+    ):
+        super().__init__()
+
+        self.in_conv = GATConv(
+            in_size, hidden_size, num_heads=num_heads, dropout=dropout
+        )
+        self.out_conv = GATConv(
+            hidden_size * num_heads, out_size, num_heads=1, dropout=dropout
+        )
+
+    def forward(self, A_hat, X):
+        # Flatten the head and feature dimension.
+        Z = F.elu(self.in_conv(A_hat, X)).flatten(1)
+        # Average over the head dimension.
+        Z = self.out_conv(A_hat, Z).mean(-1)
+        return Z
+
+
+def evaluate(g, pred):
+    label = g.ndata["label"]
+    val_mask = g.ndata["val_mask"]
+    test_mask = g.ndata["test_mask"]
+
+    # Compute accuracy on validation/test set.
+    val_acc = (pred[val_mask] == label[val_mask]).float().mean()
+    test_acc = (pred[test_mask] == label[test_mask]).float().mean()
+    return val_acc, test_acc
+
+
+def train(model, g, A_hat, X):
+    label = g.ndata["label"]
+    train_mask = g.ndata["train_mask"]
+    optimizer = Adam(model.parameters(), lr=1e-2, weight_decay=5e-4)
+
+    for epoch in range(50):
+        # Forward.
+        model.train()
+        logits = model(A_hat, X)
+
+        # Compute loss with nodes in training set.
+        loss = F.cross_entropy(logits[train_mask], label[train_mask])
+
+        # Backward.
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+        # Compute prediction.
+        model.eval()
+        logits = model(A_hat, X)
+        pred = logits.argmax(dim=1)
+
+        # Evaluate the prediction.
+        val_acc, test_acc = evaluate(g, pred)
+        print(
+            f"In epoch {epoch}, loss: {loss:.3f}, val acc: {val_acc:.3f}, test"
+            f" acc: {test_acc:.3f}"
+        )
+
+
+if __name__ == "__main__":
+    # If CUDA is available, use GPU to accelerate the training, use CPU
+    # otherwise.
+    dev = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+    # Load graph from the existing dataset.
+    dataset = CoraGraphDataset()
+    g = dataset[0].to(dev)
+
+    # Create the sparse adjacency matrix A.
+    indices = torch.stack(g.edges())
+    N = g.num_nodes()
+    A = dglsp.spmatrix(indices, shape=(N, N))
+
+    # Add self-loops.
+    I = dglsp.identity(A.shape, device=dev)
+    A_hat = A + I
+
+    # Create GAT model.
+    X = g.ndata["feat"]
+    in_size = X.shape[1]
+    out_size = dataset.num_classes
+    model = GAT(in_size, out_size).to(dev)
+
+    # Kick off training.
+    train(model, g, A_hat, X)