[NN] Add MXNet impl for TAGCN module. (#799)

* 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

* Add nn.TGConv and example

* Fix bug

* Update data in mxnet.tagcn test acc.

* Fix some comments and code

* delete useless code

* Fix namming

* Fix bug

* Fix bug

* Add test code for mxnet TAGCov

* Update some docs

* Fix some code

* Update docs dgl.nn.mxnet

* Update weight init

* Fix

docs/source/api/python/nn.mxnet.rst

@@ -16,6 +16,10 @@ dgl.nn.mxnet.conv
     :members: forward
     :show-inheritance:
 
+.. autoclass:: dgl.nn.mxnet.conv.TAGConv
+    :members: forward
+    :show-inheritance:
+
 dgl.nn.mxnet.glob
 -----------------
 

examples/mxnet/tagcn/README.md

@@ -0,0 +1,25 @@
+Topology Adaptive Graph Convolutional networks (TAGCN)
+============
+
+- Paper link: [https://arxiv.org/abs/1710.10370](https://arxiv.org/abs/1710.10370)
+
+Dependencies
+------------
+- MXNet nightly build
+- requests
+
+``bash
+pip install mxnet --pre
+pip install requests
+``
+
+Results
+-------
+Run with following (available dataset: "cora", "citeseer", "pubmed")
+```bash
+DGLBACKEND=mxnet python3 train.py --dataset cora --gpu 0 --self-loop
+```
+
+* cora: ~0.820 (paper: 0.833)
+* citeseer: ~0.702 (paper: 0.714)
+* pubmed: ～0.798 (paper: 0.811)

examples/mxnet/tagcn/tagcn.py

@@ -0,0 +1,39 @@
+"""TAGCN using DGL nn package
+
+References:
+- Topology Adaptive Graph Convolutional Networks
+- Paper: https://arxiv.org/abs/1710.10370
+"""
+import mxnet as mx
+from mxnet import gluon
+import dgl
+from dgl.nn.mxnet import TAGConv
+
+class TAGCN(gluon.Block):
+    def __init__(self,
+                 g,
+                 in_feats,
+                 n_hidden,
+                 n_classes,
+                 n_layers,
+                 activation,
+                 dropout):
+        super(TAGCN, self).__init__()
+        self.g = g
+        self.layers = gluon.nn.Sequential()
+        # input layer
+        self.layers.add(TAGConv(in_feats, n_hidden, activation=activation))
+        # hidden layers
+        for i in range(n_layers - 1):
+            self.layers.add(TAGConv(n_hidden, n_hidden, activation=activation))
+        # output layer
+        self.layers.add(TAGConv(n_hidden, n_classes)) #activation=None
+        self.dropout = gluon.nn.Dropout(rate=dropout)
+
+    def forward(self, features):
+        h = features
+        for i, layer in enumerate(self.layers):
+            if i != 0:
+                h = self.dropout(h)
+            h = layer(self.g, h)
+        return h

examples/mxnet/tagcn/train.py

@@ -0,0 +1,126 @@
+import argparse, time
+import numpy as np
+import mxnet as mx
+from mxnet import gluon
+
+from dgl import DGLGraph
+from dgl.data import register_data_args, load_data
+
+from tagcn import TAGCN
+
+def evaluate(model, features, labels, mask):
+    pred = model(features).argmax(axis=1)
+    accuracy = ((pred == labels) * mask).sum() / mask.sum().asscalar()
+    return accuracy.asscalar()
+
+def main(args):
+    # load and preprocess dataset
+    data = load_data(args)
+    features = mx.nd.array(data.features)
+    labels = mx.nd.array(data.labels)
+    train_mask = mx.nd.array(data.train_mask)
+    val_mask = mx.nd.array(data.val_mask)
+    test_mask = mx.nd.array(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().asscalar(),
+              val_mask.sum().asscalar(),
+              test_mask.sum().asscalar()))
+
+    if args.gpu < 0:
+        cuda = False
+        ctx = mx.cpu(0)
+    else:
+        cuda = True
+        ctx = mx.gpu(args.gpu)
+
+    features = features.as_in_context(ctx)
+    labels = labels.as_in_context(ctx)
+    train_mask = train_mask.as_in_context(ctx)
+    val_mask = val_mask.as_in_context(ctx)
+    test_mask = test_mask.as_in_context(ctx)
+
+    # 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)
+
+    # create TAGCN model
+    model = TAGCN(g,
+                in_feats,
+                args.n_hidden,
+                n_classes,
+                args.n_layers,
+                mx.nd.relu,
+                args.dropout)
+
+    model.initialize(ctx=ctx)
+    n_train_samples = train_mask.sum().asscalar()
+    loss_fcn = gluon.loss.SoftmaxCELoss()
+
+    # use optimizer
+    print(model.collect_params())
+    trainer = gluon.Trainer(model.collect_params(), 'adam',
+            {'learning_rate': args.lr, 'wd': args.weight_decay})
+
+    # initialize graph
+    dur = []
+    for epoch in range(args.n_epochs):
+        if epoch >= 3:
+            t0 = time.time()
+        # forward
+        with mx.autograd.record():
+            pred = model(features)
+            loss = loss_fcn(pred, labels, mx.nd.expand_dims(train_mask, 1))
+            loss = loss.sum() / n_train_samples
+
+        loss.backward()
+        trainer.step(batch_size=1)
+
+        if epoch >= 3:
+            loss.asscalar()
+            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.asscalar(), acc, n_edges / np.mean(dur) / 1000))
+
+    print()
+    acc = evaluate(model, features, labels, val_mask)
+    print("Test accuracy {:.2%}".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)

examples/pytorch/tagcn/tagcn.py

@@ -1,9 +1,8 @@
-"""GCN using DGL nn package
+"""TAGCN 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
+- Topology Adaptive Graph Convolutional Networks
+- Paper: https://arxiv.org/abs/1710.10370
 """
 import torch
 import torch.nn as nn

python/dgl/nn/mxnet/conv.py

@@ -9,7 +9,7 @@
 from . import utils
 from ... import function as fn
 
-__all__ = ['GraphConv', 'RelGraphConv']
+__all__ = ['GraphConv', 'TAGConv', 'RelGraphConv']
 
 class GraphConv(gluon.Block):
     r"""Apply graph convolution over an input signal.
@@ -74,7 +74,7 @@ def __init__(self,
 
         with self.name_scope():
             self.weight = self.params.get('weight', shape=(in_feats, out_feats),
-                                          init=mx.init.Xavier())
+                                          init=mx.init.Xavier(magnitude=math.sqrt(2.0)))
             if bias:
                 self.bias = self.params.get('bias', shape=(out_feats,),
                                             init=mx.init.Zero())
@@ -108,7 +108,7 @@ def forward(self, graph, feat):
         graph = graph.local_var()
         if self._norm:
             degs = graph.in_degrees().astype('float32')
-            norm = mx.nd.power(degs, -0.5)
+            norm = mx.nd.power(mx.nd.clip(degs, a_min=1, a_max=float("inf")), -0.5)
             shp = norm.shape + (1,) * (feat.ndim - 1)
             norm = norm.reshape(shp).as_in_context(feat.context)
             feat = feat * norm
@@ -147,6 +147,101 @@ def __repr__(self):
         summary += '\n)'
         return summary
 
+class TAGConv(gluon.Block):
+    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: 2)
+    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 : mxnet.gluon.parameter.Parameter
+        The learnable weight tensor.
+    bias : mxnet.gluon.parameter.Parameter
+        The learnable bias tensor.
+    """
+    def __init__(self,
+                 in_feats,
+                 out_feats,
+                 k=2,
+                 bias=True,
+                 activation=None):
+        super(TAGConv, self).__init__()
+        self.out_feats = out_feats
+        self.k = k
+        self.bias = bias
+        self.activation = activation
+        self.in_feats = in_feats
+
+        self.lin = self.params.get(
+            'weight', shape=(self.in_feats * (self.k + 1), self.out_feats),
+            init=mx.init.Xavier(magnitude=math.sqrt(2.0)))
+        if self.bias:
+            self.h_bias = self.params.get('bias', shape=(out_feats,),
+                                          init=mx.init.Zero())
+
+    def forward(self, graph, feat):
+        r"""Compute graph convolution
+
+        Parameters
+        ----------
+        graph : DGLGraph
+            The graph.
+        feat : mxnet.NDArray
+            The input feature of shape :math:`(N, D_{in})` where :math:`D_{in}`
+            is size of input feature, :math:`N` is the number of nodes.
+
+        Returns
+        -------
+        mxnet.NDArray
+            The output feature of shape :math:`(N, D_{out})` where :math:`D_{out}`
+            is size of output feature.
+        """
+        graph = graph.local_var()
+
+        degs = graph.in_degrees().astype('float32')
+        norm = mx.nd.power(mx.nd.clip(degs, a_min=1, a_max=float("inf")), -0.5)
+        shp = norm.shape + (1,) * (feat.ndim - 1)
+        norm = norm.reshape(shp).as_in_context(feat.context)
+
+        rst = feat
+        for _ in range(self.k):
+            rst = rst * 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
+            feat = mx.nd.concat(feat, rst, dim=-1)
+
+        rst = mx.nd.dot(feat, self.lin.data(feat.context))
+        if self.bias is not None:
+            rst = rst + self.h_bias.data(rst.context)
+
+        if self.activation is not None:
+            rst = self.activation(rst)
+
+        return rst
+
 class RelGraphConv(gluon.Block):
     r"""Relational graph convolution layer.
 

python/dgl/nn/pytorch/conv.py

@@ -171,7 +171,6 @@ def extra_repr(self):
             summary += ', activation={_activation}'
         return summary.format(**self.__dict__)
 
-
 class GATConv(nn.Module):
     r"""Apply `Graph Attention Network <https://arxiv.org/pdf/1710.10903.pdf>`__
     over an input signal.
@@ -305,7 +304,7 @@ class TAGConv(nn.Module):
     out_feats : int
         Output feature size.
     k: int, optional
-        Number of hops :math: `k`. (default: 3)
+        Number of hops :math: `k`. (default: 2)
     bias: bool, optional
         If True, adds a learnable bias to the output. Default: ``True``.
     activation: callable activation function/layer or None, optional