sampling (#505)

examples/pytorch/sampling/README.md

+# Stochastic Training for Graph Convolutional Networks
+
+* Paper: [Control Variate](https://arxiv.org/abs/1710.10568)
+* Paper: [Skip Connection](https://arxiv.org/abs/1809.05343)
+* Author's code: [https://github.com/thu-ml/stochastic_gcn](https://github.com/thu-ml/stochastic_gcn)
+
+Dependencies
+------------
+- PyTorch 0.4.1+
+- requests
+
+``bash
+pip install torch requests
+``
+
+### Neighbor Sampling & Skip Connection
+cora: test accuracy ~83% with --num-neighbors 2, ~84% by training on the full graph
+```
+python gcn_ns_sc.py --dataset cora --self-loop --num-neighbors 2 --batch-size 1000000 --test-batch-size 1000000 --gpu 0
+```
+
+citeseer: test accuracy ~69% with --num-neighbors 2, ~70% by training on the full graph
+```
+python gcn_ns_sc.py --dataset citeseer --self-loop --num-neighbors 2 --batch-size 1000000 --test-batch-size 1000000 --gpu 0
+```
+
+pubmed: test accuracy ~76% with --num-neighbors 3, ~77% by training on the full graph
+```
+python gcn_ns_sc.py --dataset pubmed --self-loop --num-neighbors 3 --batch-size 1000000 --test-batch-size 1000000 --gpu 0
+```
+
+reddit: test accuracy ~91% with --num-neighbors 2 and --batch-size 1000, ~93% by training on the full graph
+```
+python gcn_ns_sc.py --dataset reddit-self-loop --num-neighbors 2 --batch-size 1000 --test-batch-size 500000 --n-hidden 64
+```
+
+### Control Variate & Skip Connection
+cora: test accuracy ~84% with --num-neighbors 1, ~84% by training on the full graph
+```
+python gcn_cv_sc.py --dataset cora --self-loop --num-neighbors 1 --batch-size 1000000 --test-batch-size 1000000 --gpu 0
+```
+
+citeseer: test accuracy ~69% with --num-neighbors 1, ~70% by training on the full graph
+```
+python gcn_cv_sc.py --dataset citeseer --self-loop --num-neighbors 1 --batch-size 1000000 --test-batch-size 1000000 --gpu 0
+```
+
+pubmed: test accuracy ~77% with --num-neighbors 1, ~77% by training on the full graph
+```
+python gcn_cv_sc.py --dataset pubmed --self-loop --num-neighbors 1 --batch-size 1000000 --test-batch-size 1000000 --gpu 0
+```
+
+reddit: test accuracy ~93% with --num-neighbors 1 and --batch-size 1000, ~93% by training on the full graph
+```
+python gcn_cv_sc.py --dataset reddit-self-loop --num-neighbors 1 --batch-size 1000 --test-batch-size 500000 --n-hidden 64
+```
+

examples/pytorch/sampling/gcn_cv_sc.py

+import argparse, time, math
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import dgl
+import dgl.function as fn
+from dgl import DGLGraph
+from dgl.data import register_data_args, load_data
+
+
+class NodeUpdate(nn.Module):
+    def __init__(self, layer_id, in_feats, out_feats, dropout, activation=None, test=False, concat=False):
+        super(NodeUpdate, self).__init__()
+        self.layer_id = layer_id
+        self.linear = nn.Linear(in_feats, out_feats)
+        self.dropout = None
+        if dropout != 0:
+            self.dropout = nn.Dropout(p=dropout)
+        self.activation = activation
+        self.concat = concat
+        self.test = test
+
+    def forward(self, node):
+        h = node.data['h']
+        if self.test:
+            norm = node.data['norm']
+            h = h * norm
+        else:
+            agg_history_str = 'agg_h_{}'.format(self.layer_id-1)
+            agg_history = node.data[agg_history_str]
+            # control variate
+            h = h + agg_history
+            if self.dropout:
+                h = self.dropout(h)
+        h = self.linear(h)
+        if self.concat:
+            h = torch.cat((h, self.activation(h)), dim=1)
+        elif self.activation:
+            h = self.activation(h)
+        return {'activation': h}
+
+
+class GCNSampling(nn.Module):
+    def __init__(self,
+                 in_feats,
+                 n_hidden,
+                 n_classes,
+                 n_layers,
+                 activation,
+                 dropout):
+        super(GCNSampling, self).__init__()
+        self.n_layers = n_layers
+        self.dropout = None
+        if dropout != 0:
+            self.dropout = nn.Dropout(p=dropout)
+        self.activation = activation
+        # input layer
+        self.linear = nn.Linear(in_feats, n_hidden)
+        self.layers = nn.ModuleList()
+        # hidden layers
+        for i in range(1, n_layers):
+            skip_start = (i == n_layers-1)
+            self.layers.append(NodeUpdate(i, n_hidden, n_hidden, dropout, activation, concat=skip_start))
+        # output layer
+        self.layers.append(NodeUpdate(n_layers, 2*n_hidden, n_classes, dropout))
+
+    def forward(self, nf):
+        h = nf.layers[0].data['preprocess']
+        if self.dropout:
+            h = self.dropout(h)
+        h = self.linear(h)
+
+        skip_start = (0 == self.n_layers-1)
+        if skip_start:
+            h = torch.cat((h, self.activation(h)), dim=1)
+        else:
+            h = self.activation(h)
+
+        for i, layer in enumerate(self.layers):
+            new_history = h.clone().detach()
+            history_str = 'h_{}'.format(i)
+            history = nf.layers[i].data[history_str]
+            h = h - history
+
+            nf.layers[i].data['h'] = h
+            nf.block_compute(i,
+                             fn.copy_src(src='h', out='m'),
+                             lambda node : {'h': node.mailbox['m'].mean(dim=1)},
+                             layer)
+            h = nf.layers[i+1].data.pop('activation')
+            # update history
+            if i < nf.num_layers-1:
+                nf.layers[i].data[history_str] = new_history
+
+        return h
+
+
+class GCNInfer(nn.Module):
+    def __init__(self,
+                 in_feats,
+                 n_hidden,
+                 n_classes,
+                 n_layers,
+                 activation):
+        super(GCNInfer, self).__init__()
+        self.n_layers = n_layers
+        self.activation = activation
+        # input layer
+        self.linear = nn.Linear(in_feats, n_hidden)
+        self.layers = nn.ModuleList()
+        # hidden layers
+        for i in range(1, n_layers):
+            skip_start = (i == n_layers-1)
+            self.layers.append(NodeUpdate(i, n_hidden, n_hidden, 0, activation, True, concat=skip_start))
+        # output layer
+        self.layers.append(NodeUpdate(n_layers, 2*n_hidden, n_classes, 0, None, True))
+
+    def forward(self, nf):
+        h = nf.layers[0].data['preprocess']
+        h = self.linear(h)
+
+        skip_start = (0 == self.n_layers-1)
+        if skip_start:
+            h = torch.cat((h, self.activation(h)), dim=1)
+        else:
+            h = self.activation(h)
+
+        for i, layer in enumerate(self.layers):
+            nf.layers[i].data['h'] = h
+            nf.block_compute(i,
+                             fn.copy_src(src='h', out='m'),
+                             fn.sum(msg='m', out='h'),
+                             layer)
+            h = nf.layers[i+1].data.pop('activation')
+
+        return h
+
+
+def main(args):
+    # load and preprocess dataset
+    data = load_data(args)
+
+    if args.self_loop and not args.dataset.startswith('reddit'):
+        data.graph.add_edges_from([(i,i) for i in range(len(data.graph))])
+
+    train_nid = np.nonzero(data.train_mask)[0].astype(np.int64)
+    test_nid = np.nonzero(data.test_mask)[0].astype(np.int64)
+
+    features = torch.FloatTensor(data.features)
+    labels = torch.LongTensor(data.labels)
+    train_mask = torch.ByteTensor(data.train_mask)
+    val_mask = torch.ByteTensor(data.val_mask)
+    test_mask = torch.ByteTensor(data.test_mask)
+    in_feats = features.shape[1]
+    n_classes = data.num_labels
+    n_edges = data.graph.number_of_edges()
+
+    n_train_samples = train_mask.sum().item()
+    n_val_samples = val_mask.sum().item()
+    n_test_samples = test_mask.sum().item()
+
+    print("""----Data statistics------'
+      #Edges %d
+      #Classes %d
+      #Train samples %d
+      #Val samples %d
+      #Test samples %d""" %
+          (n_edges, n_classes,
+              n_train_samples,
+              n_val_samples,
+              n_test_samples))
+
+    # create GCN model
+    g = DGLGraph(data.graph, readonly=True)
+    norm = 1. / g.in_degrees().float().unsqueeze(1)
+
+    if args.gpu < 0:
+        cuda = False
+    else:
+        cuda = True
+        torch.cuda.set_device(args.gpu)
+        features = features.cuda()
+        labels = labels.cuda()
+        train_mask = train_mask.cuda()
+        val_mask = val_mask.cuda()
+        test_mask = test_mask.cuda()
+        norm = norm.cuda()
+
+    g.ndata['features'] = features
+
+    num_neighbors = args.num_neighbors
+    n_layers = args.n_layers
+
+    g.ndata['norm'] = norm
+
+    g.update_all(fn.copy_src(src='features', out='m'),
+                 fn.sum(msg='m', out='preprocess'),
+                 lambda node : {'preprocess': node.data['preprocess'] * node.data['norm']})
+
+    for i in range(n_layers):
+        g.ndata['h_{}'.format(i)] = torch.zeros(features.shape[0], args.n_hidden).to(device=features.device)
+
+    g.ndata['h_{}'.format(n_layers-1)] = torch.zeros(features.shape[0], 2*args.n_hidden).to(device=features.device)
+
+
+    model = GCNSampling(in_feats,
+                        args.n_hidden,
+                        n_classes,
+                        n_layers,
+                        F.relu,
+                        args.dropout)
+
+    loss_fcn = nn.CrossEntropyLoss()
+
+    infer_model = GCNInfer(in_feats,
+                           args.n_hidden,
+                           n_classes,
+                           n_layers,
+                           F.relu)
+
+    if cuda:
+        model.cuda()
+        infer_model.cuda()
+
+    # use optimizer
+    optimizer = torch.optim.Adam(model.parameters(),
+                                 lr=args.lr,
+                                 weight_decay=args.weight_decay)
+
+    for epoch in range(args.n_epochs):
+        for nf in dgl.contrib.sampling.NeighborSampler(g, args.batch_size,
+                                                       num_neighbors,
+                                                       neighbor_type='in',
+                                                       shuffle=True,
+                                                       num_hops=n_layers,
+                                                       seed_nodes=train_nid):
+            for i in range(n_layers):
+                agg_history_str = 'agg_h_{}'.format(i)
+                g.pull(nf.layer_parent_nid(i+1).long(), fn.copy_src(src='h_{}'.format(i), out='m'),
+                       fn.sum(msg='m', out=agg_history_str),
+                       lambda node : {agg_history_str: node.data[agg_history_str] * node.data['norm']})
+
+            node_embed_names = [['preprocess', 'h_0']]
+            for i in range(1, n_layers):
+                node_embed_names.append(['h_{}'.format(i), 'agg_h_{}'.format(i-1)])
+            node_embed_names.append(['agg_h_{}'.format(n_layers-1)])
+            nf.copy_from_parent(node_embed_names=node_embed_names)
+
+            model.train()
+            # forward
+            pred = model(nf)
+            batch_nids = nf.layer_parent_nid(-1).to(device=pred.device).long()
+            batch_labels = labels[batch_nids]
+            loss = loss_fcn(pred, batch_labels)
+
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+            node_embed_names = [['h_{}'.format(i)] for i in range(n_layers)]
+            node_embed_names.append([])
+            nf.copy_to_parent(node_embed_names=node_embed_names)
+
+
+        for infer_param, param in zip(infer_model.parameters(), model.parameters()):
+            infer_param.data.copy_(param.data)
+
+
+        num_acc = 0.
+
+        for nf in dgl.contrib.sampling.NeighborSampler(g, args.test_batch_size,
+                                                       g.number_of_nodes(),
+                                                       neighbor_type='in',
+                                                       num_hops=n_layers,
+                                                       seed_nodes=test_nid):
+            node_embed_names = [['preprocess']]
+            for i in range(n_layers):
+                node_embed_names.append(['norm'])
+            nf.copy_from_parent(node_embed_names=node_embed_names)
+
+            infer_model.eval()
+            with torch.no_grad():
+                pred = infer_model(nf)
+                batch_nids = nf.layer_parent_nid(-1).to(device=pred.device).long()
+                batch_labels = labels[batch_nids]
+                num_acc += (pred.argmax(dim=1) == batch_labels).sum().cpu().item()
+
+        print("Test Accuracy {:.4f}". format(num_acc/n_test_samples))
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='GCN')
+    register_data_args(parser)
+    parser.add_argument("--dropout", type=float, default=0.5,
+            help="dropout probability")
+    parser.add_argument("--gpu", type=int, default=-1,
+            help="gpu")
+    parser.add_argument("--lr", type=float, default=3e-2,
+            help="learning rate")
+    parser.add_argument("--n-epochs", type=int, default=200,
+            help="number of training epochs")
+    parser.add_argument("--batch-size", type=int, default=1000,
+            help="train batch size")
+    parser.add_argument("--test-batch-size", type=int, default=1000,
+            help="test batch size")
+    parser.add_argument("--num-neighbors", type=int, default=2,
+            help="number of neighbors to be sampled")
+    parser.add_argument("--n-hidden", type=int, default=16,
+            help="number of hidden gcn units")
+    parser.add_argument("--n-layers", type=int, default=1,
+            help="number of hidden gcn layers")
+    parser.add_argument("--self-loop", action='store_true',
+            help="graph self-loop (default=False)")
+    parser.add_argument("--weight-decay", type=float, default=5e-4,
+            help="Weight for L2 loss")
+    args = parser.parse_args()
+
+    print(args)
+
+    main(args)
+
+

examples/pytorch/sampling/gcn_ns_sc.py

+import argparse, time, math
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from functools import partial
+import dgl
+import dgl.function as fn
+from dgl import DGLGraph
+from dgl.data import register_data_args, load_data
+
+
+class NodeUpdate(nn.Module):
+    def __init__(self, in_feats, out_feats, activation=None, test=False, concat=False):
+        super(NodeUpdate, self).__init__()
+        self.linear = nn.Linear(in_feats, out_feats)
+        self.activation = activation
+        self.concat = concat
+        self.test = test
+
+    def forward(self, node):
+        h = node.data['h']
+        if self.test:
+            h = h * node.data['norm']
+        h = self.linear(h)
+        # skip connection
+        if self.concat:
+            h = torch.cat((h, self.activation(h)), dim=1)
+        elif self.activation:
+            h = self.activation(h)
+        return {'activation': h}
+
+
+class GCNSampling(nn.Module):
+    def __init__(self,
+                 in_feats,
+                 n_hidden,
+                 n_classes,
+                 n_layers,
+                 activation,
+                 dropout):
+        super(GCNSampling, self).__init__()
+        self.n_layers = n_layers
+        if dropout != 0:
+            self.dropout = nn.Dropout(p=dropout)
+        else:
+            self.dropout = None
+        self.layers = nn.ModuleList()
+        # input layer
+        skip_start = (0 == n_layers-1)
+        self.layers.append(NodeUpdate(in_feats, n_hidden, activation, concat=skip_start))
+        # hidden layers
+        for i in range(1, n_layers):
+            skip_start = (i == n_layers-1)
+            self.layers.append(NodeUpdate(n_hidden, n_hidden, activation, concat=skip_start))
+        # output layer
+        self.layers.append(NodeUpdate(2*n_hidden, n_classes))
+
+    def forward(self, nf):
+        nf.layers[0].data['activation'] = nf.layers[0].data['features']
+
+        for i, layer in enumerate(self.layers):
+            h = nf.layers[i].data.pop('activation')
+            if self.dropout:
+                h = self.dropout(h)
+            nf.layers[i].data['h'] = h
+            nf.block_compute(i,
+                             fn.copy_src(src='h', out='m'),
+                             lambda node : {'h': node.mailbox['m'].mean(dim=1)},
+                             layer)
+
+        h = nf.layers[-1].data.pop('activation')
+        return h
+
+
+class GCNInfer(nn.Module):
+    def __init__(self,
+                 in_feats,
+                 n_hidden,
+                 n_classes,
+                 n_layers,
+                 activation):
+        super(GCNInfer, self).__init__()
+        self.n_layers = n_layers
+        self.layers = nn.ModuleList()
+        # input layer
+        skip_start = (0 == n_layers-1)
+        self.layers.append(NodeUpdate(in_feats, n_hidden, activation, test=True, concat=skip_start))
+        # hidden layers
+        for i in range(1, n_layers):
+            skip_start = (i == n_layers-1)
+            self.layers.append(NodeUpdate(n_hidden, n_hidden, activation, test=True, concat=skip_start))
+        # output layer
+        self.layers.append(NodeUpdate(2*n_hidden, n_classes, test=True))
+
+    def forward(self, nf):
+        nf.layers[0].data['activation'] = nf.layers[0].data['features']
+
+        for i, layer in enumerate(self.layers):
+            h = nf.layers[i].data.pop('activation')
+            nf.layers[i].data['h'] = h
+            nf.block_compute(i,
+                             fn.copy_src(src='h', out='m'),
+                             fn.sum(msg='m', out='h'),
+                             layer)
+
+        h = nf.layers[-1].data.pop('activation')
+        return h
+
+
+def main(args):
+    # load and preprocess dataset
+    data = load_data(args)
+
+    if args.self_loop and not args.dataset.startswith('reddit'):
+        data.graph.add_edges_from([(i,i) for i in range(len(data.graph))])
+
+    train_nid = np.nonzero(data.train_mask)[0].astype(np.int64)
+    test_nid = np.nonzero(data.test_mask)[0].astype(np.int64)
+
+    features = torch.FloatTensor(data.features)
+    labels = torch.LongTensor(data.labels)
+    train_mask = torch.ByteTensor(data.train_mask)
+    val_mask = torch.ByteTensor(data.val_mask)
+    test_mask = torch.ByteTensor(data.test_mask)
+    in_feats = features.shape[1]
+    n_classes = data.num_labels
+    n_edges = data.graph.number_of_edges()
+
+    n_train_samples = train_mask.sum().item()
+    n_val_samples = val_mask.sum().item()
+    n_test_samples = test_mask.sum().item()
+
+    print("""----Data statistics------'
+      #Edges %d
+      #Classes %d
+      #Train samples %d
+      #Val samples %d
+      #Test samples %d""" %
+          (n_edges, n_classes,
+              n_train_samples,
+              n_val_samples,
+              n_test_samples))
+
+    # create GCN model
+    g = DGLGraph(data.graph, readonly=True)
+    norm = 1. / g.in_degrees().float().unsqueeze(1)
+
+    if args.gpu < 0:
+        cuda = False
+    else:
+        cuda = True
+        torch.cuda.set_device(args.gpu)
+        features = features.cuda()
+        labels = labels.cuda()
+        train_mask = train_mask.cuda()
+        val_mask = val_mask.cuda()
+        test_mask = test_mask.cuda()
+        norm = norm.cuda()
+
+    g.ndata['features'] = features
+
+    num_neighbors = args.num_neighbors
+
+    g.ndata['norm'] = norm
+
+    model = GCNSampling(in_feats,
+                        args.n_hidden,
+                        n_classes,
+                        args.n_layers,
+                        F.relu,
+                        args.dropout)
+
+    if cuda:
+        model.cuda()
+
+    loss_fcn = nn.CrossEntropyLoss()
+
+    infer_model = GCNInfer(in_feats,
+                           args.n_hidden,
+                           n_classes,
+                           args.n_layers,
+                           F.relu)
+
+    if cuda:
+        infer_model.cuda()
+
+    # use optimizer
+    optimizer = torch.optim.Adam(model.parameters(),
+                                 lr=args.lr,
+                                 weight_decay=args.weight_decay)
+
+    # initialize graph
+    dur = []
+    for epoch in range(args.n_epochs):
+        for nf in dgl.contrib.sampling.NeighborSampler(g, args.batch_size,
+                                                       args.num_neighbors,
+                                                       neighbor_type='in',
+                                                       shuffle=True,
+                                                       num_hops=args.n_layers+1,
+                                                       seed_nodes=train_nid):
+            nf.copy_from_parent()
+            model.train()
+            # forward
+            pred = model(nf)
+            batch_nids = nf.layer_parent_nid(-1).to(device=pred.device, dtype=torch.long)
+            batch_labels = labels[batch_nids]
+            loss = loss_fcn(pred, batch_labels)
+
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+        for infer_param, param in zip(infer_model.parameters(), model.parameters()):
+            infer_param.data.copy_(param.data)
+
+        num_acc = 0.
+
+        for nf in dgl.contrib.sampling.NeighborSampler(g, args.test_batch_size,
+                                                       g.number_of_nodes(),
+                                                       neighbor_type='in',
+                                                       num_hops=args.n_layers+1,
+                                                       seed_nodes=test_nid):
+            nf.copy_from_parent()
+            infer_model.eval()
+            with torch.no_grad():
+                pred = infer_model(nf)
+                batch_nids = nf.layer_parent_nid(-1).to(device=pred.device, dtype=torch.long)
+                batch_labels = labels[batch_nids]
+                num_acc += (pred.argmax(dim=1) == batch_labels).sum().cpu().item()
+
+        print("Test Accuracy {:.4f}". format(num_acc/n_test_samples))
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='GCN')
+    register_data_args(parser)
+    parser.add_argument("--dropout", type=float, default=0.5,
+            help="dropout probability")
+    parser.add_argument("--gpu", type=int, default=-1,
+            help="gpu")
+    parser.add_argument("--lr", type=float, default=3e-2,
+            help="learning rate")
+    parser.add_argument("--n-epochs", type=int, default=200,
+            help="number of training epochs")
+    parser.add_argument("--batch-size", type=int, default=1000,
+            help="batch size")
+    parser.add_argument("--test-batch-size", type=int, default=1000,
+            help="test batch size")
+    parser.add_argument("--num-neighbors", type=int, default=3,
+            help="number of neighbors to be sampled")
+    parser.add_argument("--n-hidden", type=int, default=16,
+            help="number of hidden gcn units")
+    parser.add_argument("--n-layers", type=int, default=1,
+            help="number of hidden gcn layers")
+    parser.add_argument("--self-loop", action='store_true',
+            help="graph self-loop (default=False)")
+    parser.add_argument("--weight-decay", type=float, default=5e-4,
+            help="Weight for L2 loss")
+    args = parser.parse_args()
+
+    print(args)
+
+    main(args)
+
+