Note

Click here to download the full example code

# Tree LSTM DGL Tutorial¶

**Author**: Zihao Ye, Qipeng Guo, Minjie Wang, Jake Zhao, Zheng Zhang

Tree-LSTM structure was first introduced by Kai et. al in an ACL 2015 paper: Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks. The core idea is to introduce syntactic information for language tasks by extending the chain-structured LSTM to a tree-structured LSTM. The Dependency Tree/Constituency Tree techniques were leveraged to obtain a ‘’latent tree’‘.

One, if not all, difficulty of training Tree-LSTMs is batching — a standard technique in machine learning to accelerate optimization. However, since trees generally have different shapes by nature, parallization becomes non trivial. DGL offers an alternative: to pool all the trees into one single graph then induce the message passing over them guided by the structure of each tree.

## The task and the dataset¶

In this tutorial, we will use Tree-LSTMs for sentiment analysis.
We have wrapped the
Stanford Sentiment Treebank in
`dgl.data`

. The dataset provides a fine-grained tree level sentiment
annotation: 5 classes(very negative, negative, neutral, positive, and
very positive) that indicates the sentiment in current subtree. Non-leaf
nodes in constituency tree does not contain words, we use a special
`PAD_WORD`

token to denote them, during the training/inferencing,
their embeddings would be masked to all-zero.

The figure displays one sample of the SST dataset, which is a constituency parse tree with their nodes labeled with sentiment. To speed up things, let’s build a tiny set with 5 sentences and take a look at the first one:

```
import dgl
from dgl.data.tree import SST
from dgl.data import SSTBatch
# Each sample in the dataset is a constituency tree. The leaf nodes
# represent words. The word is a int value stored in the "x" field.
# The non-leaf nodes has a special word PAD_WORD. The sentiment
# label is stored in the "y" feature field.
trainset = SST(mode='tiny') # the "tiny" set has only 5 trees
tiny_sst = trainset.trees
num_vocabs = trainset.num_vocabs
num_classes = trainset.num_classes
vocab = trainset.vocab # vocabulary dict: key -> id
inv_vocab = {v: k for k, v in vocab.items()} # inverted vocabulary dict: id -> word
a_tree = tiny_sst[0]
for token in a_tree.ndata['x'].tolist():
if token != trainset.PAD_WORD:
print(inv_vocab[token], end=" ")
```

Out:

```
Preprocessing...
Dataset creation finished. #Trees: 5
the rock is destined to be the 21st century 's new `` conan '' and that he 's going to make a splash even greater than arnold schwarzenegger , jean-claud van damme or steven segal .
```

## Step 1: batching¶

The first step is to throw all the trees into one graph, using
the `batch()`

API.

```
import networkx as nx
import matplotlib.pyplot as plt
graph = dgl.batch(tiny_sst)
def plot_tree(g):
# this plot requires pygraphviz package
pos = nx.nx_agraph.graphviz_layout(g, prog='dot')
nx.draw(g, pos, with_labels=False, node_size=10,
node_color=[[.5, .5, .5]], arrowsize=4)
plt.show()
plot_tree(graph.to_networkx())
```

You can read more about the definition of `batch()`

(by clicking the API), or can skip ahead to the next step:

Note

**Definition**: a `BatchedDGLGraph`

is a
`DGLGraph`

that unions a list of `DGLGraph`

s.

The union includes all the nodes, edges, and their features. The order of nodes, edges and features are preserved.

- Given that we have \(V_i\) nodes for graph \(\mathcal{G}_i\), the node ID \(j\) in graph \(\mathcal{G}_i\) correspond to node ID \(j + \sum_{k=1}^{i-1} V_k\) in the batched graph.
- Therefore, performing feature transformation and message passing on
`BatchedDGLGraph`

is equivalent to doing those on all`DGLGraph`

constituents in parallel.

Duplicate references to the same graph are treated as deep copies; the nodes, edges, and features are duplicated, and mutation on one reference does not affect the other.

Currently,

`BatchedDGLGraph`

is immutable in graph structure (i.e. one can’t add nodes and edges to it). We need to support mutable batched graphs in (far) future.The

`BatchedDGLGraph`

keeps track of the meta information of the constituents so it can be`unbatch()`

ed to list of`DGLGraph`

s.

For more details about the `BatchedDGLGraph`

module in DGL, you can click the class name.

## Step 2: Tree-LSTM Cell with message-passing APIs¶

The authors proposed two types of Tree LSTM: Child-Sum
Tree-LSTMs, and \(N\)-ary Tree-LSTMs. In this tutorial we focus
on applying *Binary* Tree-LSTM to binarized constituency trees(this
application is also known as *Constituency Tree-LSTM*). We use PyTorch
as our backend framework to set up the network.

In N-ary Tree LSTM, each unit at node \(j\) maintains a hidden representation \(h_j\) and a memory cell \(c_j\). The unit \(j\) takes the input vector \(x_j\) and the hidden representations of the their child units: \(h_{jl}, 1\leq l\leq N\) as input, then update its new hidden representation \(h_j\) and memory cell \(c_j\) by:

It can be decomposed into three phases: `message_func`

,
`reduce_func`

and `apply_node_func`

.

Note

`apply_node_func`

is a new node UDF we have not introduced before. In
`apply_node_func`

, user specifies what to do with node features,
without considering edge features and messages. In Tree-LSTM case,
`apply_node_func`

is a must, since there exists (leaf) nodes with
\(0\) incoming edges, which would not be updated via
`reduce_func`

.

```
import torch as th
import torch.nn as nn
class TreeLSTMCell(nn.Module):
def __init__(self, x_size, h_size):
super(TreeLSTMCell, self).__init__()
self.W_iou = nn.Linear(x_size, 3 * h_size, bias=False)
self.U_iou = nn.Linear(2 * h_size, 3 * h_size, bias=False)
self.b_iou = nn.Parameter(th.zeros(1, 3 * h_size))
self.U_f = nn.Linear(2 * h_size, 2 * h_size)
def message_func(self, edges):
return {'h': edges.src['h'], 'c': edges.src['c']}
def reduce_func(self, nodes):
# concatenate h_jl for equation (1), (2), (3), (4)
h_cat = nodes.mailbox['h'].view(nodes.mailbox['h'].size(0), -1)
# equation (2)
f = th.sigmoid(self.U_f(h_cat)).view(*nodes.mailbox['h'].size())
# second term of equation (5)
c = th.sum(f * nodes.mailbox['c'], 1)
return {'iou': self.U_iou(h_cat), 'c': c}
def apply_node_func(self, nodes):
# equation (1), (3), (4)
iou = nodes.data['iou'] + self.b_iou
i, o, u = th.chunk(iou, 3, 1)
i, o, u = th.sigmoid(i), th.sigmoid(o), th.tanh(u)
# equation (5)
c = i * u + nodes.data['c']
# equation (6)
h = o * th.tanh(c)
return {'h' : h, 'c' : c}
```

## Step 3: define traversal¶

After defining the message passing functions, we then need to induce the
right order to trigger them. This is a significant departure from models
such as GCN, where all nodes are pulling messages from upstream ones
*simultaneously*.

In the case of Tree-LSTM, messages start from leaves of the tree, and propagate/processed upwards until they reach the roots. A visualization is as follows:

DGL defines a generator to perform the topological sort, each item is a tensor recording the nodes from bottom level to the roots. One can appreciate the degree of parallelism by inspecting the difference of the followings:

```
print('Traversing one tree:')
print(dgl.topological_nodes_generator(a_tree))
print('Traversing many trees at the same time:')
print(dgl.topological_nodes_generator(graph))
```

Out:

```
Traversing one tree:
(tensor([ 2, 3, 6, 8, 13, 15, 17, 19, 22, 23, 25, 27, 28, 29, 30, 32, 34, 36,
38, 40, 43, 46, 47, 49, 50, 52, 58, 59, 60, 62, 64, 65, 66, 68, 69, 70]), tensor([ 1, 21, 26, 45, 48, 57, 63, 67]), tensor([24, 44, 56, 61]), tensor([20, 42, 55]), tensor([18, 54]), tensor([16, 53]), tensor([14, 51]), tensor([12, 41]), tensor([11, 39]), tensor([10, 37]), tensor([35]), tensor([33]), tensor([31]), tensor([9]), tensor([7]), tensor([5]), tensor([4]), tensor([0]))
Traversing many trees at the same time:
(tensor([ 2, 3, 6, 8, 13, 15, 17, 19, 22, 23, 25, 27, 28, 29,
30, 32, 34, 36, 38, 40, 43, 46, 47, 49, 50, 52, 58, 59,
60, 62, 64, 65, 66, 68, 69, 70, 74, 76, 78, 79, 82, 83,
85, 88, 90, 92, 93, 95, 96, 100, 102, 103, 105, 109, 110, 112,
113, 117, 118, 119, 121, 125, 127, 129, 130, 132, 133, 135, 138, 140,
141, 142, 143, 150, 152, 153, 155, 158, 159, 161, 162, 164, 168, 170,
171, 174, 175, 178, 179, 182, 184, 185, 187, 189, 190, 191, 192, 195,
197, 198, 200, 202, 205, 208, 210, 212, 213, 214, 216, 218, 219, 220,
223, 225, 227, 229, 230, 232, 235, 237, 240, 242, 244, 246, 248, 249,
251, 253, 255, 256, 257, 259, 262, 263, 267, 269, 270, 271, 272]), tensor([ 1, 21, 26, 45, 48, 57, 63, 67, 77, 81, 91, 94, 101, 108,
111, 116, 128, 131, 139, 151, 157, 160, 169, 173, 177, 183, 188, 196,
211, 217, 228, 247, 254, 261, 268]), tensor([ 24, 44, 56, 61, 75, 89, 99, 107, 115, 126, 137, 149, 156, 167,
181, 186, 194, 209, 215, 226, 245, 252, 266]), tensor([ 20, 42, 55, 73, 87, 124, 136, 154, 180, 207, 224, 243, 250, 265]), tensor([ 18, 54, 86, 123, 134, 148, 176, 206, 222, 241, 264]), tensor([ 16, 53, 84, 122, 172, 204, 239, 260]), tensor([ 14, 51, 80, 120, 166, 203, 238, 258]), tensor([ 12, 41, 72, 114, 165, 201, 236]), tensor([ 11, 39, 106, 163, 199, 234]), tensor([ 10, 37, 104, 147, 193, 233]), tensor([ 35, 98, 146, 231]), tensor([ 33, 97, 145, 221]), tensor([ 31, 71, 144]), tensor([9]), tensor([7]), tensor([5]), tensor([4]), tensor([0]))
```

We then call `prop_nodes()`

to trigger the message passing:

```
import dgl.function as fn
import torch as th
graph.ndata['a'] = th.ones(graph.number_of_nodes(), 1)
graph.register_message_func(fn.copy_src('a', 'a'))
graph.register_reduce_func(fn.sum('a', 'a'))
traversal_order = dgl.topological_nodes_generator(graph)
graph.prop_nodes(traversal_order)
# the following is a syntax sugar that does the same
# dgl.prop_nodes_topo(graph)
```

Note

Before we call `prop_nodes()`

, we must specify a
message_func and reduce_func in advance, here we use built-in
copy-from-source and sum function as our message function and reduce
function for demonstration.

## Putting it together¶

Here is the complete code that specifies the `Tree-LSTM`

class:

```
class TreeLSTM(nn.Module):
def __init__(self,
num_vocabs,
x_size,
h_size,
num_classes,
dropout,
pretrained_emb=None):
super(TreeLSTM, self).__init__()
self.x_size = x_size
self.embedding = nn.Embedding(num_vocabs, x_size)
if pretrained_emb is not None:
print('Using glove')
self.embedding.weight.data.copy_(pretrained_emb)
self.embedding.weight.requires_grad = True
self.dropout = nn.Dropout(dropout)
self.linear = nn.Linear(h_size, num_classes)
self.cell = TreeLSTMCell(x_size, h_size)
def forward(self, batch, h, c):
"""Compute tree-lstm prediction given a batch.
Parameters
----------
batch : dgl.data.SSTBatch
The data batch.
h : Tensor
Initial hidden state.
c : Tensor
Initial cell state.
Returns
-------
logits : Tensor
The prediction of each node.
"""
g = batch.graph
g.register_message_func(self.cell.message_func)
g.register_reduce_func(self.cell.reduce_func)
g.register_apply_node_func(self.cell.apply_node_func)
# feed embedding
embeds = self.embedding(batch.wordid * batch.mask)
g.ndata['iou'] = self.cell.W_iou(self.dropout(embeds)) * batch.mask.float().unsqueeze(-1)
g.ndata['h'] = h
g.ndata['c'] = c
# propagate
dgl.prop_nodes_topo(g)
# compute logits
h = self.dropout(g.ndata.pop('h'))
logits = self.linear(h)
return logits
```

## Main Loop¶

Finally, we could write a training paradigm in PyTorch:

```
from torch.utils.data import DataLoader
import torch.nn.functional as F
device = th.device('cpu')
# hyper parameters
x_size = 256
h_size = 256
dropout = 0.5
lr = 0.05
weight_decay = 1e-4
epochs = 10
# create the model
model = TreeLSTM(trainset.num_vocabs,
x_size,
h_size,
trainset.num_classes,
dropout)
print(model)
# create the optimizer
optimizer = th.optim.Adagrad(model.parameters(),
lr=lr,
weight_decay=weight_decay)
def batcher(dev):
def batcher_dev(batch):
batch_trees = dgl.batch(batch)
return SSTBatch(graph=batch_trees,
mask=batch_trees.ndata['mask'].to(device),
wordid=batch_trees.ndata['x'].to(device),
label=batch_trees.ndata['y'].to(device))
return batcher_dev
train_loader = DataLoader(dataset=tiny_sst,
batch_size=5,
collate_fn=batcher(device),
shuffle=False,
num_workers=0)
# training loop
for epoch in range(epochs):
for step, batch in enumerate(train_loader):
g = batch.graph
n = g.number_of_nodes()
h = th.zeros((n, h_size))
c = th.zeros((n, h_size))
logits = model(batch, h, c)
logp = F.log_softmax(logits, 1)
loss = F.nll_loss(logp, batch.label, reduction='sum')
optimizer.zero_grad()
loss.backward()
optimizer.step()
pred = th.argmax(logits, 1)
acc = float(th.sum(th.eq(batch.label, pred))) / len(batch.label)
print("Epoch {:05d} | Step {:05d} | Loss {:.4f} | Acc {:.4f} |".format(
epoch, step, loss.item(), acc))
```

Out:

```
TreeLSTM(
(embedding): Embedding(19536, 256)
(dropout): Dropout(p=0.5)
(linear): Linear(in_features=256, out_features=5, bias=True)
(cell): TreeLSTMCell(
(W_iou): Linear(in_features=256, out_features=768, bias=False)
(U_iou): Linear(in_features=512, out_features=768, bias=False)
(U_f): Linear(in_features=512, out_features=512, bias=True)
)
)
Epoch 00000 | Step 00000 | Loss 439.2055 | Acc 0.1978 |
Epoch 00001 | Step 00000 | Loss 274.8585 | Acc 0.7399 |
Epoch 00002 | Step 00000 | Loss 1291.8838 | Acc 0.2637 |
Epoch 00003 | Step 00000 | Loss 320.1866 | Acc 0.5788 |
Epoch 00004 | Step 00000 | Loss 161.6583 | Acc 0.8571 |
Epoch 00005 | Step 00000 | Loss 159.1942 | Acc 0.8132 |
Epoch 00006 | Step 00000 | Loss 223.6723 | Acc 0.8425 |
Epoch 00007 | Step 00000 | Loss 113.4838 | Acc 0.8755 |
Epoch 00008 | Step 00000 | Loss 89.6812 | Acc 0.9011 |
Epoch 00009 | Step 00000 | Loss 73.2779 | Acc 0.9267 |
```

To train the model on full dataset with different settings(CPU/GPU, etc.), please refer to our repo’s example. Besides, we also provide an implementation of the Child-Sum Tree LSTM.

**Total running time of the script:** ( 0 minutes 1.242 seconds)