[Sparse] Add a LADIES example (dmlc#6560)

examples/sparse/sampling/ladies.py

@@ -0,0 +1,256 @@
+"""
+This script demonstrates how to use dgl sparse library to sample on graph and 
+train model. It trains and tests a LADIES model using the sparse power and 
+sp_broadcast_v operators to sample submatrix from the whole matrix.
+
+This flowchart describes the main functional sequence of the provided example.
+main
+│
+├───> Load and preprocess full dataset
+│
+├───> Instantiate LADIES model
+│
+├───> train
+│     │
+│     └───> Training loop
+│           │
+│           ├───> Sample submatrix
+│           │
+│           └───> LADIES.forward
+└───> test
+      │
+      ├───> Sample submatrix
+      │
+      └───> Evaluate the model
+"""
+import argparse
+
+import dgl.sparse as dglsp
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchmetrics.functional as MF
+from dgl.data import AsNodePredDataset
+from dgl.sparse import sp_broadcast_v
+from ogb.nodeproppred import DglNodePropPredDataset
+
+
+class SAGEConv(nn.Module):
+    r"""LADIES layer from `Layer-Dependent Importance Sampling
+    for Training Deep and Large Graph Convolutional Networks
+    <https://arxiv.org/abs/1911.07323.pdf>`__"""
+
+    def __init__(
+        self,
+        in_size,
+        out_size,
+    ):
+        super(SAGEConv, self).__init__()
+        self._in_src_feats, self._in_dst_feats = in_size, in_size
+        self._out_size = out_size
+
+        self.fc_neigh = nn.Linear(self._in_src_feats, out_size, bias=False)
+        self.fc_self = nn.Linear(self._in_dst_feats, out_size, bias=True)
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        gain = nn.init.calculate_gain("relu")
+        nn.init.xavier_uniform_(self.fc_self.weight, gain=gain)
+        nn.init.xavier_uniform_(self.fc_neigh.weight, gain=gain)
+
+    def forward(self, A, feat):
+        feat_src = feat
+        feat_dst = feat[: A.shape[1]]
+
+        # Aggregator type: mean.
+        srcdata = self.fc_neigh(feat_src)
+        # Divided by degree.
+        D_hat = dglsp.diag(A.sum(0)) ** -1
+        A_div = A @ D_hat
+        # Conv neighbors.
+        dstdata = A_div.T @ srcdata
+
+        rst = self.fc_self(feat_dst) + dstdata
+        return rst
+
+
+class LADIES(nn.Module):
+    def __init__(self, in_size, hid_size, out_size):
+        super().__init__()
+        self.layers = nn.ModuleList()
+        # Three-layer LADIES.
+        self.layers.append(SAGEConv(in_size, hid_size))
+        self.layers.append(SAGEConv(hid_size, hid_size))
+        self.layers.append(SAGEConv(hid_size, out_size))
+
+        self.dropout = nn.Dropout(0.5)
+        self.hid_size = hid_size
+        self.out_size = out_size
+
+    def forward(self, sampled_matrices, x):
+        hidden_x = x
+        for layer_idx, (layer, sampled_matrix) in enumerate(
+            zip(self.layers, sampled_matrices)
+        ):
+            hidden_x = layer(sampled_matrix, hidden_x)
+            if layer_idx != len(self.layers) - 1:
+                hidden_x = F.relu(hidden_x)
+                hidden_x = self.dropout(hidden_x)
+        return hidden_x
+
+
+def multilayer_sample(A, fanouts, seeds, ndata):
+    sampled_matrices = []
+    src = seeds
+
+    #########################################################################
+    # (HIGHLIGHT) Using the sparse sample operator to preform LADIES sampling
+    # algorithm from the neighboring nodes of the seeds nodes.
+    # The sparse sp_power operator is applied to compute sample probability,
+    # and sp_broadcast_v is then employed to normalize weight by performing
+    # division operations on column.
+    #########################################################################
+
+    for fanout in fanouts:
+        # Sample neighbors.
+        sub_A = A.index_select(1, src)
+        # Compute probability weight.
+        row_probs = (sub_A**2).sum(1)
+        row_probs = row_probs / row_probs.sum(0)
+        # Layer-wise sample nodes.
+        row_ids = torch.multinomial(row_probs, fanout, replacement=False)
+        # Add self-loop.
+        row_ids = torch.cat((row_ids, src), 0).unique()
+        sampled_matrix = sub_A.index_select(0, row_ids)
+        # Normalize edge weights.
+        div_matirx = sp_broadcast_v(
+            sampled_matrix, row_probs[row_ids].reshape(-1, 1), "truediv"
+        )
+        div_matirx = sp_broadcast_v(div_matirx, div_matirx.sum(0), "truediv")
+
+        # Save the sampled matrix.
+        sampled_matrices.insert(0, div_matirx)
+        src = row_ids
+
+    x = ndata["feat"][src]
+    y = ndata["label"][seeds]
+    return sampled_matrices, x, y
+
+
+def evaluate(model, A, dataloader, ndata, num_classes):
+    model.eval()
+    ys = []
+    y_hats = []
+    fanouts = [4000, 4000, 4000]
+    for seeds in dataloader:
+        with torch.no_grad():
+            sampled_matrices, x, y = multilayer_sample(A, fanouts, seeds, ndata)
+            ys.append(y)
+            y_hats.append(model(sampled_matrices, x))
+
+    return MF.accuracy(
+        torch.cat(y_hats),
+        torch.cat(ys),
+        task="multiclass",
+        num_classes=num_classes,
+    )
+
+
+def validate(device, A, ndata, dataset, model, batch_size):
+    inf_id = dataset.test_idx.to(device)
+    inf_dataloader = torch.utils.data.DataLoader(inf_id, batch_size=batch_size)
+    acc = evaluate(model, A, inf_dataloader, ndata, dataset.num_classes)
+    return acc
+
+
+def train(device, A, ndata, dataset, model):
+    # Create sampler & dataloader.
+    train_idx = dataset.train_idx.to(device)
+    val_idx = dataset.val_idx.to(device)
+
+    train_dataloader = torch.utils.data.DataLoader(
+        train_idx, batch_size=1024, shuffle=True
+    )
+    val_dataloader = torch.utils.data.DataLoader(val_idx, batch_size=1024)
+
+    optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, weight_decay=5e-4)
+
+    fanouts = [4000, 4000, 4000]
+    for epoch in range(20):
+        model.train()
+        total_loss = 0
+        for it, seeds in enumerate(train_dataloader):
+            sampled_matrices, x, y = multilayer_sample(A, fanouts, seeds, ndata)
+            y_hat = model(sampled_matrices, x)
+            loss = F.cross_entropy(y_hat, y)
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+            total_loss += loss.item()
+
+        acc = evaluate(model, A, val_dataloader, ndata, dataset.num_classes)
+        print(
+            "Epoch {:05d} | Loss {:.4f} | Accuracy {:.4f} ".format(
+                epoch, total_loss / (it + 1), acc.item()
+            )
+        )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="LADIESConv")
+    parser.add_argument(
+        "--mode",
+        default="gpu",
+        choices=["cpu", "gpu"],
+        help="Training mode. 'cpu' for CPU training, 'gpu' for GPU training.",
+    )
+    args = parser.parse_args()
+    if not torch.cuda.is_available():
+        args.mode = "cpu"
+    print(f"Training in {args.mode} mode.")
+
+    #####################################################################
+    # (HIGHLIGHT) This example implements a LADIES algorithm by sparse
+    # operators, which involves sampling a subgraph from a full graph and
+    # conducting training.
+    #
+    # First, the whole graph is loaded onto the CPU or GPU and transformed
+    # to sparse matrix. To obtain the training subgraph, it samples three
+    # submatrices by seed nodes, which contains their layer-wise sampled
+    # 1-hop, 2-hop, and 3-hop neighbors. Then, the features of the
+    # subgraph are input to the network for training.
+    #####################################################################
+
+    # Load and preprocess dataset.
+    print("Loading data")
+    device = torch.device("cpu" if args.mode == "cpu" else "cuda")
+    dataset = AsNodePredDataset(DglNodePropPredDataset("ogbn-products"))
+    g = dataset[0]
+
+    # Create LADIES model.
+    in_size = g.ndata["feat"].shape[1]
+    out_size = dataset.num_classes
+    model = LADIES(in_size, 256, out_size).to(device)
+
+    # Create sparse.
+    indices = torch.stack(g.edges())
+    N = g.num_nodes()
+    A = dglsp.spmatrix(indices, shape=(N, N)).coalesce()
+    I = dglsp.identity(A.shape)
+
+    # Initialize laplacian matrix.
+    A_hat = A + I
+    D_hat = dglsp.diag(A_hat.sum(1)) ** -0.5
+    A_norm = D_hat @ A_hat @ D_hat
+    A_norm = A_norm.to(device)
+    g = g.to(device)
+
+    # Model training.
+    print("Training...")
+    train(device, A_norm, g.ndata, dataset, model)
+
+    # Test the model.
+    print("Testing...")
+    acc = validate(device, A_norm, g.ndata, dataset, model, batch_size=2048)
+    print(f"Test accuracy {acc:.4f}")