[NN] Add TAGCN nn.module and example (#788)

* upd

* fig edgebatch edges

* add test

* trigger

* Update README.md for pytorch PinSage example.

Add noting that the PinSage model example under
example/pytorch/recommendation only work with Python 3.6+
as its dataset loader depends on stanfordnlp package
which work only with Python 3.6+.

* Provid a frame agnostic API to test nn modules on both CPU and CUDA side.

1. make dgl.nn.xxx frame agnostic
2. make test.backend include dgl.nn modules
3. modify test_edge_softmax of test/mxnet/test_nn.py and
    test/pytorch/test_nn.py work on both CPU and GPU

* Fix style

* Delete unused code

* Make agnostic test only related to tests/backend

1. clear all agnostic related code in dgl.nn
2. make test_graph_conv agnostic to cpu/gpu

* Fix code style

* fix

* doc

* Make all test code under tests.mxnet/pytorch.test_nn.py
work on both CPU and GPU.

* Fix syntex

* Remove rand

* Add TAGCN nn.module and example

* Now tagcn can run on CPU.

* Add unitest for TGConv

* Fix style

* For pubmed dataset, using --lr=0.005 can achieve better acc

* Fix style

* Fix some descriptions

* trigger

* Fix doc

examples/pytorch/tagcn/README.md

+Topology Adaptive Graph Convolutional networks (TAGCN)
+============
+
+- Paper link: [https://arxiv.org/abs/1710.10370](https://arxiv.org/abs/1710.10370)
+
+Dependencies
+------------
+- PyTorch 0.4.1+
+- requests
+
+``bash
+pip install torch requests
+``
+
+Results
+-------
+Run with following (available dataset: "cora", "citeseer", "pubmed")
+```bash
+python3 train.py --dataset cora --gpu 0 --self-loop
+```
+
+* cora: ~0.812 (0.804-0.823) (paper: 0.833)
+* citeseer: ~0.715 (paper: 0.714)
+* pubmed: ～0.794 (paper: 0.811)
\ No newline at end of file

examples/pytorch/tagcn/tagcn.py

+"""GCN using DGL nn package
+
+References:
+- Semi-Supervised Classification with Graph Convolutional Networks
+- Paper: https://arxiv.org/abs/1609.02907
+- Code: https://github.com/tkipf/gcn
+"""
+import torch
+import torch.nn as nn
+from dgl.nn.pytorch.conv import TGConv
+
+class TAGCN(nn.Module):
+    def __init__(self,
+                 g,
+                 in_feats,
+                 n_hidden,
+                 n_classes,
+                 n_layers,
+                 activation,
+                 dropout):
+        super(TAGCN, self).__init__()
+        self.g = g
+        self.layers = nn.ModuleList()
+        # input layer
+        self.layers.append(TGConv(in_feats, n_hidden, activation=activation))
+        # hidden layers
+        for i in range(n_layers - 1):
+            self.layers.append(TGConv(n_hidden, n_hidden, activation=activation))
+        # output layer
+        self.layers.append(TGConv(n_hidden, n_classes)) #activation=None
+        self.dropout = nn.Dropout(p=dropout)
+
+    def forward(self, features):
+        h = features
+        for i, layer in enumerate(self.layers):
+            if i != 0:
+                h = self.dropout(h)
+            h = layer(h, self.g)
+        return h

examples/pytorch/tagcn/train.py

+import argparse, time
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from dgl import DGLGraph
+from dgl.data import register_data_args, load_data
+
+from tagcn import TAGCN
+
+def evaluate(model, features, labels, mask):
+    model.eval()
+    with torch.no_grad():
+        logits = model(features)
+        logits = logits[mask]
+        labels = labels[mask]
+        _, indices = torch.max(logits, dim=1)
+        correct = torch.sum(indices == labels)
+        return correct.item() * 1.0 / len(labels)
+
+def main(args):
+    # load and preprocess dataset
+    data = load_data(args)
+    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()
+    print("""----Data statistics------'
+      #Edges %d
+      #Classes %d
+      #Train samples %d
+      #Val samples %d
+      #Test samples %d""" %
+          (n_edges, n_classes,
+              train_mask.sum().item(),
+              val_mask.sum().item(),
+              test_mask.sum().item()))
+
+    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()
+
+    # graph preprocess and calculate normalization factor
+    g = data.graph
+    # add self loop
+    if args.self_loop:
+            g.remove_edges_from(g.selfloop_edges())
+            g.add_edges_from(zip(g.nodes(), g.nodes()))
+    g = DGLGraph(g)
+    n_edges = g.number_of_edges()
+
+    # create TAGCN model
+    model = TAGCN(g,
+                in_feats,
+                args.n_hidden,
+                n_classes,
+                args.n_layers,
+                F.relu,
+                args.dropout)
+
+    if cuda:
+        model.cuda()
+    loss_fcn = torch.nn.CrossEntropyLoss()
+
+    # 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):
+        model.train()
+        if epoch >= 3:
+            t0 = time.time()
+        # forward
+        logits = model(features)
+        loss = loss_fcn(logits[train_mask], labels[train_mask])
+
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+        if epoch >= 3:
+            dur.append(time.time() - t0)
+
+        acc = evaluate(model, features, labels, val_mask)
+        print("Epoch {:05d} | Time(s) {:.4f} | Loss {:.4f} | Accuracy {:.4f} | "
+              "ETputs(KTEPS) {:.2f}". format(epoch, np.mean(dur), loss.item(),
+                                             acc, n_edges / np.mean(dur) / 1000))
+
+    print()
+    acc = evaluate(model, features, labels, test_mask)
+    print("Test Accuracy {:.4f}".format(acc))
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='TAGCN')
+    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=1e-2,
+            help="learning rate")
+    parser.add_argument("--n-epochs", type=int, default=200,
+            help="number of training epochs")
+    parser.add_argument("--n-hidden", type=int, default=16,
+            help="number of hidden tagcn units")
+    parser.add_argument("--n-layers", type=int, default=1,
+            help="number of hidden tagcn layers")
+    parser.add_argument("--weight-decay", type=float, default=5e-4,
+            help="Weight for L2 loss")
+    parser.add_argument("--self-loop", action='store_true',
+            help="graph self-loop (default=False)")
+    parser.set_defaults(self_loop=False)
+    args = parser.parse_args()
+    print(args)
+
+    main(args)

python/dgl/nn/pytorch/conv.py

@@ -7,7 +7,7 @@ from torch.nn import init
 from . import utils
 from ... import function as fn
 
-__all__ = ['GraphConv', 'RelGraphConv']
+__all__ = ['GraphConv', 'TGConv', 'RelGraphConv']
 
 class GraphConv(nn.Module):
     r"""Apply graph convolution over an input signal.
@@ -150,6 +150,95 @@ class GraphConv(nn.Module):
             summary += ', activation={_activation}'
         return summary.format(**self.__dict__)
 
+class TGConv(nn.Module):
+    r"""Apply Topology Adaptive Graph Convolutional Network
+
+    .. math::
+        \mathbf{X}^{\prime} = \sum_{k=0}^K \mathbf{D}^{-1/2} \mathbf{A}
+        \mathbf{D}^{-1/2}\mathbf{X} \mathbf{\Theta}_{k},
+
+    where :math:`\mathbf{A}` denotes the adjacency matrix and
+    :math:`D_{ii} = \sum_{j=0} A_{ij}` its diagonal degree matrix.
+
+    Parameters
+    ----------
+    in_feats : int
+        Number of input features.
+    out_feats : int
+        Number of output features.
+    k: int, optional
+        Number of hops :math: `k`. (default: 3)
+    bias: bool, optional
+        If True, adds a learnable bias to the output. Default: ``True``.
+    activation: callable activation function/layer or None, optional
+        If not None, applies an activation function to the updated node features.
+        Default: ``None``.
+
+    Attributes
+    ----------
+    lin : torch.Module
+        The learnable linear module.
+    """
+    def __init__(self,
+                 in_feats,
+                 out_feats,
+                 k=2,
+                 bias=True,
+                 activation=None):
+        super(TGConv, self).__init__()
+        self._in_feats = in_feats
+        self._out_feats = out_feats
+        self._k = k
+        self._activation = activation
+        self.lin = nn.Linear(in_feats * (self._k + 1), out_feats, bias=bias)
+
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        """Reinitialize learnable parameters."""
+        self.lin.reset_parameters()
+
+    def forward(self, feat, graph):
+        r"""Compute graph convolution
+
+        Parameters
+        ----------
+        feat : torch.Tensor
+            The input feature
+        graph : DGLGraph
+            The graph.
+
+        Returns
+        -------
+        torch.Tensor
+            The output feature
+        """
+        graph = graph.local_var()
+
+        norm = th.pow(graph.in_degrees().float(), -0.5)
+        shp = norm.shape + (1,) * (feat.dim() - 1)
+        norm = th.reshape(norm, shp).to(feat.device)
+
+        #D-1/2 A D -1/2 X
+        fstack = [feat]
+        for _ in range(self._k):
+
+            rst = fstack[-1] * norm
+            graph.ndata['h'] = rst
+
+            graph.update_all(fn.copy_src(src='h', out='m'),
+                             fn.sum(msg='m', out='h'))
+            rst = graph.ndata['h']
+            rst = rst * norm
+            fstack.append(rst)
+
+        rst = self.lin(th.cat(fstack, dim=-1))
+
+        if self._activation is not None:
+            rst = self._activation(rst)
+
+        return rst
+
 class RelGraphConv(nn.Module):
     r"""Relational graph convolution layer.
 

tests/pytorch/test_nn.py

@@ -69,6 +69,54 @@ def test_graph_conv():
     new_weight = conv.weight.data
     assert not F.allclose(old_weight, new_weight)
 
+def _S2AXWb(A, N, X, W, b):
+    X1 = X * N
+    X1 = th.matmul(A, X1.view(X1.shape[0], -1))
+    X1 = X1 * N
+    X2 = X1 * N
+    X2 = th.matmul(A, X2.view(X2.shape[0], -1))
+    X2 = X2 * N
+    X = th.cat([X, X1, X2], dim=-1)
+    Y = th.matmul(X, W.rot90())
+
+    return Y + b
+
+def test_tgconv():
+    g = dgl.DGLGraph(nx.path_graph(3))
+    ctx = F.ctx()
+    adj = g.adjacency_matrix(ctx=ctx)
+    norm = th.pow(g.in_degrees().float(), -0.5)
+
+    conv = nn.TGConv(5, 2, bias=True)
+    if F.gpu_ctx():
+        conv.cuda()
+    print(conv)
+
+    # test#1: basic
+    h0 = F.ones((3, 5))
+    h1 = conv(h0, g)
+    assert len(g.ndata) == 0
+    assert len(g.edata) == 0
+    shp = norm.shape + (1,) * (h0.dim() - 1)
+    norm = th.reshape(norm, shp).to(ctx)
+
+    assert F.allclose(h1, _S2AXWb(adj, norm, h0, conv.lin.weight, conv.lin.bias))
+
+    conv = nn.TGConv(5, 2)
+    if F.gpu_ctx():
+        conv.cuda()
+    # test#2: basic
+    h0 = F.ones((3, 5))
+    h1 = conv(h0, g)
+    assert len(g.ndata) == 0
+    assert len(g.edata) == 0
+
+    # test rest_parameters
+    old_weight = deepcopy(conv.lin.weight.data)
+    conv.reset_parameters()
+    new_weight = conv.lin.weight.data
+    assert not F.allclose(old_weight, new_weight)
+
 def test_set2set():
     g = dgl.DGLGraph(nx.path_graph(10))