[Doc] Patch tutorial (#1380)

* patched 1_first

* done 2_basics

* done 4_batch

* done 1_gcn, 9_gat, 2_capsule

* 4_rgcn.py

* revert

* more fix

tutorials/basics/1_first.py

@@ -45,32 +45,26 @@ At the end of this tutorial, we hope you get a brief feeling of how DGL works.
 # Create the graph for Zachary's karate club as follows:
 
 import dgl
+import numpy as np
 
 def build_karate_club_graph():
-    g = dgl.DGLGraph()
-    # add 34 nodes into the graph; nodes are labeled from 0~33
-    g.add_nodes(34)
-    # all 78 edges as a list of tuples
-    edge_list = [(1, 0), (2, 0), (2, 1), (3, 0), (3, 1), (3, 2),
-        (4, 0), (5, 0), (6, 0), (6, 4), (6, 5), (7, 0), (7, 1),
-        (7, 2), (7, 3), (8, 0), (8, 2), (9, 2), (10, 0), (10, 4),
-        (10, 5), (11, 0), (12, 0), (12, 3), (13, 0), (13, 1), (13, 2),
-        (13, 3), (16, 5), (16, 6), (17, 0), (17, 1), (19, 0), (19, 1),
-        (21, 0), (21, 1), (25, 23), (25, 24), (27, 2), (27, 23),
-        (27, 24), (28, 2), (29, 23), (29, 26), (30, 1), (30, 8),
-        (31, 0), (31, 24), (31, 25), (31, 28), (32, 2), (32, 8),
-        (32, 14), (32, 15), (32, 18), (32, 20), (32, 22), (32, 23),
-        (32, 29), (32, 30), (32, 31), (33, 8), (33, 9), (33, 13),
-        (33, 14), (33, 15), (33, 18), (33, 19), (33, 20), (33, 22),
-        (33, 23), (33, 26), (33, 27), (33, 28), (33, 29), (33, 30),
-        (33, 31), (33, 32)]
-    # add edges two lists of nodes: src and dst
-    src, dst = tuple(zip(*edge_list))
-    g.add_edges(src, dst)
-    # edges are directional in DGL; make them bi-directional
-    g.add_edges(dst, src)
-
-    return g
+    # All 78 edges are stored in two numpy arrays. One for source endpoints
+    # while the other for destination endpoints.
+    src = np.array([1, 2, 2, 3, 3, 3, 4, 5, 6, 6, 6, 7, 7, 7, 7, 8, 8, 9, 10, 10,
+        10, 11, 12, 12, 13, 13, 13, 13, 16, 16, 17, 17, 19, 19, 21, 21,
+        25, 25, 27, 27, 27, 28, 29, 29, 30, 30, 31, 31, 31, 31, 32, 32,
+        32, 32, 32, 32, 32, 32, 32, 32, 32, 33, 33, 33, 33, 33, 33, 33,
+        33, 33, 33, 33, 33, 33, 33, 33, 33, 33])
+    dst = np.array([0, 0, 1, 0, 1, 2, 0, 0, 0, 4, 5, 0, 1, 2, 3, 0, 2, 2, 0, 4,
+        5, 0, 0, 3, 0, 1, 2, 3, 5, 6, 0, 1, 0, 1, 0, 1, 23, 24, 2, 23,
+        24, 2, 23, 26, 1, 8, 0, 24, 25, 28, 2, 8, 14, 15, 18, 20, 22, 23,
+        29, 30, 31, 8, 9, 13, 14, 15, 18, 19, 20, 22, 23, 26, 27, 28, 29, 30,
+        31, 32])
+    # Edges are directional in DGL; Make them bi-directional.
+    u = np.concatenate([src, dst])
+    v = np.concatenate([dst, src])
+    # Construct a DGLGraph
+    return dgl.DGLGraph((u, v))
 
 ###############################################################################
 # Print out the number of nodes and edges in our newly constructed graph:
@@ -95,27 +89,28 @@ nx.draw(nx_G, pos, with_labels=True, node_color=[[.7, .7, .7]])
 # Step 2: Assign features to nodes or edges
 # --------------------------------------------
 # Graph neural networks associate features with nodes and edges for training.
-# For our classification example, we assign each node an input feature as a one-hot vector:
-# node :math:`v_i`'s feature vector is :math:`[0,\ldots,1,\dots,0]`,
-# where the :math:`i^{th}` position is one.
-#
+# For our classification example, since there is no input feature, we assign each node
+# with a learnable embedding vector.
+
 # In DGL, you can add features for all nodes at once, using a feature tensor that
-# batches node features along the first dimension. The code below adds the one-hot
-# feature for all nodes:
+# batches node features along the first dimension. The code below adds the learnable
+# embeddings for all nodes:
 
 import torch
+import torch.nn as nn
+import torch.nn.functional as F
 
-G.ndata['feat'] = torch.eye(34)
-
+embed = nn.Embedding(34, 5)  # 34 nodes with embedding dim equal to 5
+G.ndata['feat'] = embed.weight
 
 ###############################################################################
 # Print out the node features to verify:
 
 # print out node 2's input feature
-print(G.nodes[2].data['feat'])
+print(G.ndata['feat'][2])
 
 # print out node 10 and 11's input features
-print(G.nodes[[10, 11]].data['feat'])
+print(G.ndata['feat'][[10, 11]])
 
 ###############################################################################
 # Step 3: Define a Graph Convolutional Network (GCN)
@@ -139,74 +134,41 @@ print(G.nodes[[10, 11]].data['feat'])
 #    :alt: mailbox
 #    :align: center
 #
-# Now, we show that the GCN layer can be easily implemented in DGL.
-
-import torch.nn as nn
-import torch.nn.functional as F
-
-# Define the message and reduce function
-# NOTE: We ignore the GCN's normalization constant c_ij for this tutorial.
-def gcn_message(edges):
-    # The argument is a batch of edges.
-    # This computes a (batch of) message called 'msg' using the source node's feature 'h'.
-    return {'msg' : edges.src['h']}
+# In DGL, we provide implementations of popular Graph Neural Network layers under
+# the `dgl.<backend>.nn` subpackage. The :class:`~dgl.nn.pytorch.GraphConv` module
+# implements one Graph Convolutional layer.
 
-def gcn_reduce(nodes):
-    # The argument is a batch of nodes.
-    # This computes the new 'h' features by summing received 'msg' in each node's mailbox.
-    return {'h' : torch.sum(nodes.mailbox['msg'], dim=1)}
-
-# Define the GCNLayer module
-class GCNLayer(nn.Module):
-    def __init__(self, in_feats, out_feats):
-        super(GCNLayer, self).__init__()
-        self.linear = nn.Linear(in_feats, out_feats)
-
-    def forward(self, g, inputs):
-        # g is the graph and the inputs is the input node features
-        # first set the node features
-        g.ndata['h'] = inputs
-        # trigger message passing on all edges 
-        g.send(g.edges(), gcn_message)
-        # trigger aggregation at all nodes
-        g.recv(g.nodes(), gcn_reduce)
-        # get the result node features
-        h = g.ndata.pop('h')
-        # perform linear transformation
-        return self.linear(h)
+from dgl.nn.pytorch import GraphConv
 
 ###############################################################################
-# In general, the nodes send information computed via the *message functions*,
-# and aggregate incoming information with the *reduce functions*.
-#
 # Define a deeper GCN model that contains two GCN layers:
 
-# Define a 2-layer GCN model
 class GCN(nn.Module):
     def __init__(self, in_feats, hidden_size, num_classes):
         super(GCN, self).__init__()
-        self.gcn1 = GCNLayer(in_feats, hidden_size)
-        self.gcn2 = GCNLayer(hidden_size, num_classes)
+        self.conv1 = GraphConv(in_feats, hidden_size)
+        self.conv2 = GraphConv(hidden_size, num_classes)
 
     def forward(self, g, inputs):
-        h = self.gcn1(g, inputs)
+        h = self.conv1(g, inputs)
         h = torch.relu(h)
-        h = self.gcn2(g, h)
+        h = self.conv2(g, h)
         return h
-# The first layer transforms input features of size of 34 to a hidden size of 5.
+
+# The first layer transforms input features of size of 5 to a hidden size of 5.
 # The second layer transforms the hidden layer and produces output features of
 # size 2, corresponding to the two groups of the karate club.
-net = GCN(34, 5, 2)
+net = GCN(5, 5, 2)
 
 ###############################################################################
 # Step 4: Data preparation and initialization
 # -------------------------------------------
 #
-# We use one-hot vectors to initialize the node features. Since this is a
+# We use learnable embeddings to initialize the node features. Since this is a
 # semi-supervised setting, only the instructor (node 0) and the club president
 # (node 33) are assigned labels. The implementation is available as follow.
 
-inputs = torch.eye(34)
+inputs = embed.weight
 labeled_nodes = torch.tensor([0, 33])  # only the instructor and the president nodes are labeled
 labels = torch.tensor([0, 1])  # their labels are different
 
@@ -216,10 +178,11 @@ labels = torch.tensor([0, 1])  # their labels are different
 # The training loop is exactly the same as other PyTorch models.
 # We (1) create an optimizer, (2) feed the inputs to the model,
 # (3) calculate the loss and (4) use autograd to optimize the model.
+import itertools
 
-optimizer = torch.optim.Adam(net.parameters(), lr=0.01)
+optimizer = torch.optim.Adam(itertools.chain(net.parameters(), embed.parameters()), lr=0.01)
 all_logits = []
-for epoch in range(30):
+for epoch in range(50):
     logits = net(G, inputs)
     # we save the logits for visualization later
     all_logits.append(logits.detach())

tutorials/basics/2_basics.py

@@ -33,18 +33,50 @@ plt.show()
 
 
 ###############################################################################
-# The examples here show the same graph, except that :class:`DGLGraph` is always directional.
+# There are many ways to construct a :class:`DGLGraph`. Below are the allowed
+# data types ordered by our recommendataion.
 #
-# You can also create a graph by calling the DGL interface.
+# * A pair of arrays ``(u, v)`` storing the source and destination nodes respectively.
+#   They can be numpy arrays or tensor objects from the backend framework.
+# * ``scipy`` sparse matrix representing the adjacency matrix of the graph to be
+#   constructed.
+# * ``networkx`` graph object.
+# * A list of edges in the form of integer pairs.
 #
-# In the next example, you build a star graph. :class:`DGLGraph` nodes are a consecutive range of
-# integers between 0 and :func:`number_of_nodes() <DGLGraph.number_of_nodes>`
-# and can grow by calling :func:`add_nodes <DGLGraph.add_nodes>`.
+# The examples below construct the same star graph via different methods.
+#
+# :class:`DGLGraph` nodes are a consecutive range of integers between 0 and
+# :func:`number_of_nodes() <DGLGraph.number_of_nodes>`. 
 # :class:`DGLGraph` edges are in order of their additions. Note that
-# edges are accessed in much the same way as nodes, with one extra feature: *edge broadcasting*.
+# edges are accessed in much the same way as nodes, with one extra feature:
+# *edge broadcasting*.
 
-import dgl
 import torch as th
+import numpy as np
+import scipy.sparse as spp
+
+# Create a star graph from a pair of arrays (using ``numpy.array`` works too).
+u = th.tensor([0, 0, 0, 0, 0])
+v = th.tensor([1, 2, 3, 4, 5])
+star1 = dgl.DGLGraph((u, v))
+
+# Create the same graph in one go! Essentially, if one of the arrays is a scalar,
+# the value is automatically broadcasted to match the length of the other array
+# -- a feature called *edge broadcasting*.
+start2 = dgl.DGLGraph((0, v))
+
+# Create the same graph from a scipy sparse matrix (using ``scipy.sparse.csr_matrix`` works too).
+adj = spp.coo_matrix((np.ones(len(u)), (u.numpy(), v.numpy())))
+star3 = dgl.DGLGraph(adj)
+
+# Create the same graph from a list of integer pairs.
+elist = [(0, 1), (0, 2), (0, 3), (0, 4), (0, 5)]
+star4 = dgl.DGLGraph(elist)
+
+###############################################################################
+# You can also create a graph by progressively adding more nodes and edges.
+# Although it is not as efficient as the above constructors, it is suitable
+# for applications where the graph cannot be constructed in one shot.
 
 g = dgl.DGLGraph()
 g.add_nodes(10)
@@ -63,12 +95,10 @@ g.clear(); g.add_nodes(10)
 src = th.tensor(list(range(1, 10)));
 g.add_edges(src, 0)
 
-import networkx as nx
-import matplotlib.pyplot as plt
+# Visualize the graph.
 nx.draw(g.to_networkx(), with_labels=True)
 plt.show()
 
-
 ###############################################################################
 # Assigning a feature
 # -------------------
@@ -89,19 +119,14 @@ import torch as th
 x = th.randn(10, 3)
 g.ndata['x'] = x
 
-
 ###############################################################################
-# :func:`ndata <DGLGraph.ndata>` is a syntax sugar to access the state of all nodes. 
-# States are stored
-# in a container ``data`` that hosts a user-defined dictionary.
-
-print(g.ndata['x'] == g.nodes[:].data['x'])
-
-# Access node set with integer, list, or integer tensor
-g.nodes[0].data['x'] = th.zeros(1, 3)
-g.nodes[[0, 1, 2]].data['x'] = th.zeros(3, 3)
-g.nodes[th.tensor([0, 1, 2])].data['x'] = th.zeros(3, 3)
+# :func:`ndata <DGLGraph.ndata>` is a syntax sugar to access the feature
+# data of all nodes. To get the features of some particular nodes, slice out
+# the corresponding rows.
 
+g.ndata['x'][0] = th.zeros(1, 3)
+g.ndata['x'][[0, 1, 2]] = th.zeros(3, 3)
+g.ndata['x'][th.tensor([0, 1, 2])] = th.randn((3, 3))
 
 ###############################################################################
 # Assigning edge features is similar to that of node features,
@@ -110,14 +135,15 @@ g.nodes[th.tensor([0, 1, 2])].data['x'] = th.zeros(3, 3)
 g.edata['w'] = th.randn(9, 2)
 
 # Access edge set with IDs in integer, list, or integer tensor
-g.edges[1].data['w'] = th.randn(1, 2)
-g.edges[[0, 1, 2]].data['w'] = th.zeros(3, 2)
-g.edges[th.tensor([0, 1, 2])].data['w'] = th.zeros(3, 2)
-
-# You can also access the edges by giving endpoints
-g.edges[1, 0].data['w'] = th.ones(1, 2)                 # edge 1 -> 0
-g.edges[[1, 2, 3], [0, 0, 0]].data['w'] = th.ones(3, 2) # edges [1, 2, 3] -> 0
+g.edata['w'][1] = th.randn(1, 2)
+g.edata['w'][[0, 1, 2]] = th.zeros(3, 2)
+g.edata['w'][th.tensor([0, 1, 2])] = th.zeros(3, 2)
 
+# You can get the edge ids by giving endpoints, which are useful for accessing the features.
+g.edata['w'][g.edge_id(1, 0)] = th.ones(1, 2)                   # edge 1 -> 0
+g.edata['w'][g.edge_ids([1, 2, 3], [0, 0, 0])] = th.ones(3, 2)  # edges [1, 2, 3] -> 0
+# Use edge broadcasting whenever applicable.
+g.edata['w'][g.edge_ids([1, 2, 3], 0)] = th.ones(3, 2)          # edges [1, 2, 3] -> 0
 
 ###############################################################################
 # After assignments, each node or edge field will be associated with a scheme
@@ -170,7 +196,6 @@ print(g_multi.edata['w'])
 #    * Updating a feature of different schemes raises the risk of error on individual nodes (or
 #      node subset).
 
-
 ###############################################################################
 # Next steps
 # ----------

tutorials/basics/4_batch.py

@@ -72,6 +72,7 @@ plt.show()
 # list of graph and label pairs.
 
 import dgl
+import torch
 
 def collate(samples):
     # The input `samples` is a list of pairs
@@ -99,57 +100,9 @@ def collate(samples):
 # be called readout or aggregation. Finally, the graph 
 # representations are fed into a classifier :math:`g` to predict the graph labels.
 #
-# Graph convolution
-# -----------------
-# The graph convolution operation is basically the same as that for graph convolutional network (GCN). To learn more, 
-# see the GCN `tutorial <https://docs.dgl.ai/tutorials/models/1_gnn/1_gcn.html>`_). The only difference is
-# that we replace :math:`h_{v}^{(l+1)} = \text{ReLU}\left(b^{(l)}+\sum_{u\in\mathcal{N}(v)}h_{u}^{(l)}W^{(l)}\right)` 
-# by
-# :math:`h_{v}^{(l+1)} = \text{ReLU}\left(b^{(l)}+\frac{1}{|\mathcal{N}(v)|}\sum_{u\in\mathcal{N}(v)}h_{u}^{(l)}W^{(l)}\right)`
-# 
-# The replacement of summation by average is to balance nodes with different
-# degrees. This gives a better performance for this experiment.
-#
-# The self edges added in the dataset initialization allows you to
-# include the original node feature :math:`h_{v}^{(l)}` when taking the average.
-
-import dgl.function as fn
-import torch
-import torch.nn as nn
-
-
-# Sends a message of node feature h.
-msg = fn.copy_src(src='h', out='m')
+# Graph convolution layer can be found in the ``dgl.nn.<backend>`` submodule.
 
-def reduce(nodes):
-    """Take an average over all neighbor node features hu and use it to
-    overwrite the original node feature."""
-    accum = torch.mean(nodes.mailbox['m'], 1)
-    return {'h': accum}
-
-class NodeApplyModule(nn.Module):
-    """Update the node feature hv with ReLU(Whv+b)."""
-    def __init__(self, in_feats, out_feats, activation):
-        super(NodeApplyModule, self).__init__()
-        self.linear = nn.Linear(in_feats, out_feats)
-        self.activation = activation
-
-    def forward(self, node):
-        h = self.linear(node.data['h'])
-        h = self.activation(h)
-        return {'h' : h}
-
-class GCN(nn.Module):
-    def __init__(self, in_feats, out_feats, activation):
-        super(GCN, self).__init__()
-        self.apply_mod = NodeApplyModule(in_feats, out_feats, activation)
-
-    def forward(self, g, feature):
-        # Initialize the node features with h.
-        g.ndata['h'] = feature
-        g.update_all(msg, reduce)
-        g.apply_nodes(func=self.apply_mod)
-        return g.ndata.pop('h')
+from dgl.nn.pytorch import GraphConv
 
 ###############################################################################
 # Readout and classification
@@ -166,25 +119,25 @@ class GCN(nn.Module):
 # graphs with variable size. You then feed the graph representations into a
 # classifier with one linear layer to obtain pre-softmax logits.
 
+import torch.nn as nn
 import torch.nn.functional as F
 
-
 class Classifier(nn.Module):
     def __init__(self, in_dim, hidden_dim, n_classes):
         super(Classifier, self).__init__()
-
-        self.layers = nn.ModuleList([
-            GCN(in_dim, hidden_dim, F.relu),
-            GCN(hidden_dim, hidden_dim, F.relu)])
+        self.conv1 = GraphConv(in_dim, hidden_dim)
+        self.conv2 = GraphConv(hidden_dim, hidden_dim)
         self.classify = nn.Linear(hidden_dim, n_classes)
 
     def forward(self, g):
-        # For undirected graphs, in_degree is the same as
-        # out_degree.
+        # Use node degree as the initial node feature. For undirected graphs, the in-degree
+        # is the same as the out_degree.
         h = g.in_degrees().view(-1, 1).float()
-        for conv in self.layers:
-            h = conv(g, h)
+        # Perform graph convolution and activation function.
+        h = F.relu(self.conv1(g, h))
+        h = F.relu(self.conv2(g, h))
         g.ndata['h'] = h
+        # Calculate graph representation by averaging all the node representations.
         hg = dgl.mean_nodes(g, 'h')
         return self.classify(hg)
 

tutorials/models/1_gnn/1_gcn.py

@@ -9,9 +9,14 @@ Yu Gai, Quan Gan, Zheng Zhang
 
 This is a gentle introduction of using DGL to implement Graph Convolutional
 Networks (Kipf & Welling et al., `Semi-Supervised Classification with Graph
-Convolutional Networks <https://arxiv.org/pdf/1609.02907.pdf>`_). We build upon
-the :doc:`earlier tutorial <../../basics/3_pagerank>` on DGLGraph and demonstrate
-how DGL combines graph with deep neural network and learn structural representations.
+Convolutional Networks <https://arxiv.org/pdf/1609.02907.pdf>`_). We explain
+what is under the hood of the :class:`~dgl.nn.pytorch.GraphConv` module.
+The reader is expected to learn how to define a new GNN layer using DGL's
+message passing APIs.
+
+We build upon the :doc:`earlier tutorial <../../basics/3_pagerank>` on DGLGraph
+and demonstrate how DGL combines graph with deep neural network and learn
+structural representations.
 """
 
 ###############################################################################
@@ -28,8 +33,8 @@ how DGL combines graph with deep neural network and learn structural representat
 # representation :math:`\hat{h}_{u}` with a linear projection followed by a
 # non-linearity: :math:`h_{u} = f(W_{u} \hat{h}_u)`.
 # 
-# We will implement step 1 with DGL message passing, and step 2 with the
-# ``apply_nodes`` method, whose node UDF will be a PyTorch ``nn.Module``.
+# We will implement step 1 with DGL message passing, and step 2 by
+# PyTorch ``nn.Module``.
 # 
 # GCN implementation with DGL
 # ``````````````````````````````````````````
@@ -48,35 +53,23 @@ gcn_msg = fn.copy_src(src='h', out='m')
 gcn_reduce = fn.sum(msg='m', out='h')
 
 ###############################################################################
-# We then define the node UDF for ``apply_nodes``, which is a fully-connected layer:
+# We then proceed to define the GCNLayer module. A GCNLayer essentially performs
+# message passing on all the nodes then applies a fully-connected layer.
 
-class NodeApplyModule(nn.Module):
-    def __init__(self, in_feats, out_feats, activation):
-        super(NodeApplyModule, self).__init__()
+class GCNLayer(nn.Module):
+    def __init__(self, in_feats, out_feats):
+        super(GCNLayer, self).__init__()
         self.linear = nn.Linear(in_feats, out_feats)
-        self.activation = activation
-
-    def forward(self, node):
-        h = self.linear(node.data['h'])
-        if self.activation is not None:
-            h = self.activation(h)
-        return {'h' : h}
-
-###############################################################################
-# We then proceed to define the GCN module. A GCN layer essentially performs
-# message passing on all the nodes then applies the `NodeApplyModule`. Note
-# that we omitted the dropout in the paper for simplicity.
-
-class GCN(nn.Module):
-    def __init__(self, in_feats, out_feats, activation):
-        super(GCN, self).__init__()
-        self.apply_mod = NodeApplyModule(in_feats, out_feats, activation)
 
     def forward(self, g, feature):
+        # Creating a local scope so that all the stored ndata and edata
+        # (such as the `'h'` ndata below) are automatically popped out
+        # when the scope exits.
+        with g.local_scope():
             g.ndata['h'] = feature
             g.update_all(gcn_msg, gcn_reduce)
-        g.apply_nodes(func=self.apply_mod)
-        return g.ndata.pop('h')
+            h = g.ndata['h']
+            return self.linear(h)
 
 ###############################################################################
 # The forward function is essentially the same as any other commonly seen NNs
@@ -84,17 +77,17 @@ class GCN(nn.Module):
 # let's define a simple neural network consisting of two GCN layers. Suppose we
 # are training the classifier for the cora dataset (the input feature size is
 # 1433 and the number of classes is 7). The last GCN layer computes node embeddings,
-# so the last layer in general doesn't apply activation.
+# so the last layer in general does not apply activation.
 
 class Net(nn.Module):
     def __init__(self):
         super(Net, self).__init__()
-        self.gcn1 = GCN(1433, 16, F.relu)
-        self.gcn2 = GCN(16, 7, None)
+        self.layer1 = GCNLayer(1433, 16)
+        self.layer2 = GCNLayer(16, 7)
     
     def forward(self, g, features):
-        x = self.gcn1(g, features)
-        x = self.gcn2(g, x)
+        x = F.relu(self.layer1(g, features))
+        x = self.layer2(g, x)
         return x
 net = Net()
 print(net)
@@ -110,11 +103,7 @@ def load_cora_data():
     labels = th.LongTensor(data.labels)
     train_mask = th.BoolTensor(data.train_mask)
     test_mask = th.BoolTensor(data.test_mask)
-    g = data.graph
-    # add self loop
-    g.remove_edges_from(nx.selfloop_edges(g))
-    g = DGLGraph(g)
-    g.add_edges(g.nodes(), g.nodes())
+    g = DGLGraph(data.graph)
     return g, features, labels, train_mask, test_mask
 
 ###############################################################################
@@ -137,7 +126,7 @@ def evaluate(model, g, features, labels, mask):
 import time
 import numpy as np
 g, features, labels, train_mask, test_mask = load_cora_data()
-optimizer = th.optim.Adam(net.parameters(), lr=1e-3)
+optimizer = th.optim.Adam(net.parameters(), lr=1e-2)
 dur = []
 for epoch in range(50):
     if epoch >=3:

tutorials/models/1_gnn/4_rgcn.py

@@ -298,9 +298,7 @@ lr = 0.01 # learning rate
 l2norm = 0 # L2 norm coefficient
 
 # create graph
-g = DGLGraph()
-g.add_nodes(num_nodes)
-g.add_edges(data.edge_src, data.edge_dst)
+g = DGLGraph((data.edge_src, data.edge_dst))
 g.edata.update({'rel_type': edge_type, 'norm': edge_norm})
 
 # create model

tutorials/models/1_gnn/9_gat.py

@@ -94,6 +94,15 @@ structure-free normalization, in the style of attention.
 # GAT in DGL
 # ----------
 #
+# DGL provides an off-the-shelf implementation of the GAT layer under the ``dgl.nn.<backend>``
+# subpackage. Simply import the ``GATConv`` as the follows.
+
+from dgl.nn.pytorch import GATConv
+
+###############################################################
+# Readers can skip the following step-by-step explanation of the implementation and
+# jump to the `Put everything together`_ for training and visualization results.
+#
 # To begin, you can get an overall impression about how a ``GATLayer`` module is
 # implemented in DGL. In this section, the four equations above are broken down 
 # one at a time.
@@ -277,11 +286,7 @@ def load_cora_data():
     features = torch.FloatTensor(data.features)
     labels = torch.LongTensor(data.labels)
     mask = torch.BoolTensor(data.train_mask)
-    g = data.graph
-    # add self loop
-    g.remove_edges_from(nx.selfloop_edges(g))
-    g = DGLGraph(g)
-    g.add_edges(g.nodes(), g.nodes())
+    g = DGLGraph(data.graph)
     return g, features, labels, mask
 
 ##############################################################################

tutorials/models/4_old_wines/2_capsule.py

@@ -68,18 +68,11 @@ import dgl
 
 
 def init_graph(in_nodes, out_nodes, f_size):
-    g = dgl.DGLGraph()
-    all_nodes = in_nodes + out_nodes
-    g.add_nodes(all_nodes)
-
-    in_indx = list(range(in_nodes))
-    out_indx = list(range(in_nodes, in_nodes + out_nodes))
-    # add edges use edge broadcasting
-    for u in in_indx:
-        g.add_edges(u, out_indx)
-
+    u = np.repeat(np.arange(in_nodes), out_nodes)
+    v = np.tile(np.arange(in_nodes, in_nodes + out_nodes), in_nodes)
+    g = dgl.DGLGraph((u, v))
     # init states
-    g.ndata['v'] = th.zeros(all_nodes, f_size)
+    g.ndata['v'] = th.zeros(in_nodes + out_nodes, f_size)
     g.edata['b'] = th.zeros(in_nodes * out_nodes, 1)
     return g
 
@@ -113,6 +106,8 @@ def init_graph(in_nodes, out_nodes, f_size):
 #    -  The scalar product :math:`\hat{u}_{j|i}\cdot v_j` can be considered as how well capsule :math:`i` agrees with :math:`j`. It is used to update
 #       :math:`b_{ij}=b_{ij}+\hat{u}_{j|i}\cdot v_j`
 
+import dgl.function as fn
+
 class DGLRoutingLayer(nn.Module):
     def __init__(self, in_nodes, out_nodes, f_size):
         super(DGLRoutingLayer, self).__init__()
@@ -125,24 +120,14 @@ class DGLRoutingLayer(nn.Module):
     def forward(self, u_hat, routing_num=1):
         self.g.edata['u_hat'] = u_hat
 
-        # step 2 (line 5)
-        def cap_message(edges):
-            return {'m': edges.data['c'] * edges.data['u_hat']}
-
-        self.g.register_message_func(cap_message)
-
-        def cap_reduce(nodes):
-            return {'s': th.sum(nodes.mailbox['m'], dim=1)}
-
-        self.g.register_reduce_func(cap_reduce)
-
         for r in range(routing_num):
             # step 1 (line 4): normalize over out edges
             edges_b = self.g.edata['b'].view(self.in_nodes, self.out_nodes)
             self.g.edata['c'] = F.softmax(edges_b, dim=1).view(-1, 1)
+            self.g.edata['c u_hat'] = self.g.edata['c'] * self.g.edata['u_hat']
 
             # Execute step 1 & 2
-            self.g.update_all()
+            self.g.update_all(fn.copy_e('c u_hat', 'm'), fn.sum('m', 's'))
 
             # step 3 (line 6)
             self.g.nodes[self.out_indx].data['v'] = self.squash(self.g.nodes[self.out_indx].data['s'], dim=1)