Note

Click here to download the full example code

# Transformer Tutorial¶

**Author**: Zihao Ye, Jinjing Zhou, Qipeng Guo, Quan Gan, Zheng Zhang

The Transformer model, as a replacement of CNN/RNN architecture for sequence modeling, was introduced in Google’s paper: Attention is All You Need. It improved the state of the art for machine translation as well as natural language inference task (GPT). Recent work on pre-training Transformer with large scale corpus (BERT) supports that it is capable of learning high quality semantic representation.

The interesting part of Transformer is its extensive employment of attention. The classic use of attention comes from machine translation model, where the output token attends to all input tokens.

Transformer additionally applies *self-attention* in both decoder and
encoder. This process forces words relate to each other to combine
together, irrespective of their positions in the sequence. This is
different from RNN-based model, where words (in the source sentence) are
combined along the chain, which is thought to be too constrained.

## Attention Layer of Transformer¶

In Attention Layer of Transformer, for each node the module learns to assign weights on its in-coming edges. For node pair \((i, j)\) (from \(i\) to \(j\)) with node \(x_i, x_j \in \mathbb{R}^n\), the score of their connection is defined as follows:

where \(W_q, W_k, W_v \in \mathbb{R}^{n\times d_k}\) map the representations \(x\) to “query”, “key”, and “value” space respectively.

There are other possibilities to implement the score function. The dot product measures the similarity of a given query \(q_j\) and a key \(k_i\): if \(j\) needs the information stored in \(i\), the query vector at position \(j\) (\(q_j\)) is supposed to be close to key vector at position \(i\) (\(k_i\)).

The score is then used to compute the sum of the incoming values, normalized over the weights of edges, stored in \(\textrm{wv}\). Then we apply an affine layer to \(\textrm{wv}\) to get the output \(o\):

### Multi-Head Attention Layer¶

In Transformer, attention is *multi-headed*. A head is very much like a
channel in a convolutional network. The Multi-Head Attention consist of
multiple attention heads, in which each head refers to a single
attention module. \(\textrm{wv}^{(i)}\) for all the heads are
concatenated and mapped to output \(o\) with an affine layer:

The code below wraps necessary components for Multi-Head Attention, and provide two interfaces:

`get`

maps state ‘x’, to query, key and value, which is required by following steps(`propagate_attention`

).`get_o`

maps the updated value after attention to the output \(o\) for post-processing.

```
class MultiHeadAttention(nn.Module):
"Multi-Head Attention"
def __init__(self, h, dim_model):
"h: number of heads; dim_model: hidden dimension"
super(MultiHeadAttention, self).__init__()
self.d_k = dim_model // h
self.h = h
# W_q, W_k, W_v, W_o
self.linears = clones(nn.Linear(dim_model, dim_model), 4)
def get(self, x, fields='qkv'):
"Return a dict of queries / keys / values."
batch_size = x.shape[0]
ret = {}
if 'q' in fields:
ret['q'] = self.linears[0](x).view(batch_size, self.h, self.d_k)
if 'k' in fields:
ret['k'] = self.linears[1](x).view(batch_size, self.h, self.d_k)
if 'v' in fields:
ret['v'] = self.linears[2](x).view(batch_size, self.h, self.d_k)
return ret
def get_o(self, x):
"get output of the multi-head attention"
batch_size = x.shape[0]
return self.linears[3](x.view(batch_size, -1))
```

In this tutorial, we show a simplified version of the implementation in order to highlight the most important design points (for instance we only show single-head attention); the complete code can be found here. The overall structure is similar to the one from The Annotated Transformer.

## How DGL Implements Transformer with a Graph Neural Network¶

We offer a different perspective of Transformer by treating the attention as edges in a graph and adopt message passing on the edges to induce the appropriate processing.

### Graph Structure¶

We construct the graph by mapping tokens of the source and target sentence to nodes. The complete Transformer graph is made up of three subgraphs:

**Source language graph**. This is a complete graph, each
token \(s_i\) can attend to any other token \(s_j\) (including
self-loops).
**Target language graph**. The graph is
half-complete, in that \(t_i\) attends only to \(t_j\) if
\(i > j\) (an output token can not depend on future words).
**Cross-language graph**. This is a bi-partitie graph, where there is
an edge from every source token \(s_i\) to every target token
\(t_j\), meaning every target token can attend on source tokens.

The full picture looks like this:

We pre-build the graphs in dataset preparation stage.

### Message Passing¶

Once we defined the graph structure, we can move on to defining the computation for message passing.

Assuming that we have already computed all the queries \(q_i\), keys \(k_i\) and values \(v_i\). For each node \(i\) (no matter whether it is a source token or target token), we can decompose the attention computation into two steps:

**Message computation:**Compute attention score \(\mathrm{score}_{ij}\) between \(i\) and all nodes \(j\) to be attended over, by taking the scaled-dot product between \(q_i\) and \(k_j\). The message sent from \(j\) to \(i\) will consist of the score \(\mathrm{score}_{ij}\) and the value \(v_j\).**Message aggregation:**Aggregate the values \(v_j\) from all \(j\) according to the scores \(\mathrm{score}_{ij}\).

#### Naive Implementation¶

##### Message Computation¶

Compute `score`

and send source node’s `v`

to destination’s mailbox

```
def message_func(edges):
return {'score': ((edges.src['k'] * edges.dst['q'])
.sum(-1, keepdim=True)),
'v': edges.src['v']}
```

##### Message Aggregation¶

Normalize over all in-edges and weighted sum to get output

```
import torch as th
import torch.nn.functional as F
def reduce_func(nodes, d_k=64):
v = nodes.mailbox['v']
att = F.softmax(nodes.mailbox['score'] / th.sqrt(d_k), 1)
return {'dx': (att * v).sum(1)}
```

##### Execute on specific edges¶

```
import functools.partial as partial
def naive_propagate_attention(self, g, eids):
g.send_and_recv(eids, message_func, partial(reduce_func, d_k=self.d_k))
```

#### Speeding up with built-in functions¶

To speed up the message passing process, we utilize DGL’s builtin function, including:

`fn.src_mul_egdes(src_field, edges_field, out_field)`

multiplies source’s attribute and edges attribute, and send the result to the destination node’s mailbox keyed by`out_field`

.`fn.copy_edge(edges_field, out_field)`

copies edge’s attribute to destination node’s mailbox.`fn.sum(edges_field, out_field)`

sums up edge’s attribute and sends aggregation to destination node’s mailbox.

Here we assemble those built-in function into `propagate_attention`

,
which is also the main graph operation function in our final
implementation. To accelerate, we break the `softmax`

operation into
the following steps. Recall that for each head there are two phases:

Compute attention score by multiply src node’s

`k`

and dst node’s`q`

`g.apply_edges(src_dot_dst('k', 'q', 'score'), eids)`

Scaled Softmax over all dst nodes’ in-coming edges

Step 1: Exponentialize score with scale normalize constant

`g.apply_edges(scaled_exp('score', np.sqrt(self.d_k)))`

\[\textrm{score}_{ij}\leftarrow\exp{\left(\frac{\textrm{score}_{ij}}{ \sqrt{d_k}}\right)}\]

Step 2: Get the “values” on associated nodes weighted by “scores” on in-coming edges of each node; get the sum of “scores” on in-coming edges of each node for normalization. Note that here \(\textrm{wv}\) is not normalized.

`msg: fn.src_mul_edge('v', 'score', 'v'), reduce: fn.sum('v', 'wv')`

\[\textrm{wv}_j=\sum_{i=1}^{N} \textrm{score}_{ij} \cdot v_i\]`msg: fn.copy_edge('score', 'score'), reduce: fn.sum('score', 'z')`

\[\textrm{z}_j=\sum_{i=1}^{N} \textrm{score}_{ij}\]

The normalization of \(\textrm{wv}\) is left to post processing.

```
def src_dot_dst(src_field, dst_field, out_field):
def func(edges):
return {out_field: (edges.src[src_field] * edges.dst[dst_field]).sum(-1, keepdim=True)}
return func
def scaled_exp(field, scale_constant):
def func(edges):
# clamp for softmax numerical stability
return {field: th.exp((edges.data[field] / scale_constant).clamp(-5, 5))}
return func
def propagate_attention(self, g, eids):
# Compute attention score
g.apply_edges(src_dot_dst('k', 'q', 'score'), eids)
g.apply_edges(scaled_exp('score', np.sqrt(self.d_k)))
# Update node state
g.send_and_recv(eids,
[fn.src_mul_edge('v', 'score', 'v'), fn.copy_edge('score', 'score')],
[fn.sum('v', 'wv'), fn.sum('score', 'z')])
```

### Preprocessing and Postprocessing¶

In Transformer, data needs to be pre- and post-processed before and
after the `propagate_attention`

function.

**Preprocessing** The preprocessing function `pre_func`

first
normalizes the node representations and then map them to a set of
queries, keys and values, using self-attention as an example:

**Postprocessing** The postprocessing function `post_funcs`

completes
the whole computation correspond to one layer of the transformer: 1.
Normalize \(\textrm{wv}\) and get the output of Multi-Head Attention
Layer \(o\).

add residual connection:

Applying a two layer position-wise feed forward layer on \(x\) then add residual connection:

\[x \leftarrow x + \textrm{LayerNorm}(\textrm{FFN}(x))\]where \(\textrm{FFN}\) refers to the feed forward function.

```
class Encoder(nn.Module):
def __init__(self, layer, N):
super(Encoder, self).__init__()
self.N = N
self.layers = clones(layer, N)
self.norm = LayerNorm(layer.size)
def pre_func(self, i, fields='qkv'):
layer = self.layers[i]
def func(nodes):
x = nodes.data['x']
norm_x = layer.sublayer[0].norm(x)
return layer.self_attn.get(norm_x, fields=fields)
return func
def post_func(self, i):
layer = self.layers[i]
def func(nodes):
x, wv, z = nodes.data['x'], nodes.data['wv'], nodes.data['z']
o = layer.self_attn.get_o(wv / z)
x = x + layer.sublayer[0].dropout(o)
x = layer.sublayer[1](x, layer.feed_forward)
return {'x': x if i < self.N - 1 else self.norm(x)}
return func
class Decoder(nn.Module):
def __init__(self, layer, N):
super(Decoder, self).__init__()
self.N = N
self.layers = clones(layer, N)
self.norm = LayerNorm(layer.size)
def pre_func(self, i, fields='qkv', l=0):
layer = self.layers[i]
def func(nodes):
x = nodes.data['x']
if fields == 'kv':
norm_x = x # In enc-dec attention, x has already been normalized.
else:
norm_x = layer.sublayer[l].norm(x)
return layer.self_attn.get(norm_x, fields)
return func
def post_func(self, i, l=0):
layer = self.layers[i]
def func(nodes):
x, wv, z = nodes.data['x'], nodes.data['wv'], nodes.data['z']
o = layer.self_attn.get_o(wv / z)
x = x + layer.sublayer[l].dropout(o)
if l == 1:
x = layer.sublayer[2](x, layer.feed_forward)
return {'x': x if i < self.N - 1 else self.norm(x)}
return func
```

This completes all procedures of one layer of encoder and decoder in Transformer.

Note

The sublayer connection part is little bit different from the original paper. However our implementation is the same as The Annotated Transformer and OpenNMT.

## Main class of Transformer Graph¶

The processing flow of Transformer can be seen as a 2-stage message-passing within the complete graph (adding pre- and post- processing appropriately): 1) self-attention in encoder, 2) self-attention in decoder followed by cross-attention between encoder and decoder, as shown below.

```
class Transformer(nn.Module):
def __init__(self, encoder, decoder, src_embed, tgt_embed, pos_enc, generator, h, d_k):
super(Transformer, self).__init__()
self.encoder, self.decoder = encoder, decoder
self.src_embed, self.tgt_embed = src_embed, tgt_embed
self.pos_enc = pos_enc
self.generator = generator
self.h, self.d_k = h, d_k
def propagate_attention(self, g, eids):
# Compute attention score
g.apply_edges(src_dot_dst('k', 'q', 'score'), eids)
g.apply_edges(scaled_exp('score', np.sqrt(self.d_k)))
# Send weighted values to target nodes
g.send_and_recv(eids,
[fn.src_mul_edge('v', 'score', 'v'), fn.copy_edge('score', 'score')],
[fn.sum('v', 'wv'), fn.sum('score', 'z')])
def update_graph(self, g, eids, pre_pairs, post_pairs):
"Update the node states and edge states of the graph."
# Pre-compute queries and key-value pairs.
for pre_func, nids in pre_pairs:
g.apply_nodes(pre_func, nids)
self.propagate_attention(g, eids)
# Further calculation after attention mechanism
for post_func, nids in post_pairs:
g.apply_nodes(post_func, nids)
def forward(self, graph):
g = graph.g
nids, eids = graph.nids, graph.eids
# Word Embedding and Position Embedding
src_embed, src_pos = self.src_embed(graph.src[0]), self.pos_enc(graph.src[1])
tgt_embed, tgt_pos = self.tgt_embed(graph.tgt[0]), self.pos_enc(graph.tgt[1])
g.nodes[nids['enc']].data['x'] = self.pos_enc.dropout(src_embed + src_pos)
g.nodes[nids['dec']].data['x'] = self.pos_enc.dropout(tgt_embed + tgt_pos)
for i in range(self.encoder.N):
# Step 1: Encoder Self-attention
pre_func = self.encoder.pre_func(i, 'qkv')
post_func = self.encoder.post_func(i)
nodes, edges = nids['enc'], eids['ee']
self.update_graph(g, edges, [(pre_func, nodes)], [(post_func, nodes)])
for i in range(self.decoder.N):
# Step 2: Dncoder Self-attention
pre_func = self.decoder.pre_func(i, 'qkv')
post_func = self.decoder.post_func(i)
nodes, edges = nids['dec'], eids['dd']
self.update_graph(g, edges, [(pre_func, nodes)], [(post_func, nodes)])
# Step 3: Encoder-Decoder attention
pre_q = self.decoder.pre_func(i, 'q', 1)
pre_kv = self.decoder.pre_func(i, 'kv', 1)
post_func = self.decoder.post_func(i, 1)
nodes_e, nodes_d, edges = nids['enc'], nids['dec'], eids['ed']
self.update_graph(g, edges, [(pre_q, nodes_d), (pre_kv, nodes_e)], [(post_func, nodes_d)])
return self.generator(g.ndata['x'][nids['dec']])
```

Note

By calling `update_graph`

function, we can “DIY our own
Transformer” on any subgraphs with nearly the same code. This
flexibility enables us to discover new, sparse structures (c.f. local attention
mentioned here). Note in our
implementation we does not use mask or padding, which makes the logic
more clear and saves memory. The trade-off is that the implementation is
slower; we will improve with future DGL optimizations.

## Training¶

This tutorial does not cover several other techniques such as Label Smoothing and Noam Optimizations mentioned in the original paper. For detailed description about these modules, we recommend you to read The Annotated Transformer written by Harvard NLP team.

### Task and the Dataset¶

The Transformer is a general framework for a variety of NLP tasks. In this tutorial we only focus on the sequence to sequence learning: it’s a typical case to illustrate how it works.

As for the dataset, we provide two toy tasks: copy and sort, together with two real-world translation tasks: multi30k en-de task and wmt14 en-de task.

**copy dataset**: copy input sequences to output. (train/valid/test: 9000, 1000, 1000)**sort dataset**: sort input sequences as output. (train/valid/test: 9000, 1000, 1000)**Multi30k en-de**, translate sentences from En to De. (train/valid/test: 29000, 1000, 1000)**WMT14 en-de**, translate sentences from En to De. (Train/Valid/Test: 4500966/3000/3003)

Note

We are working on training with wmt14, which requires Multi-GPU support(this would be fixed soon).

### Graph Building¶

**Batching** Just like how we handle Tree-LSTM. We build a graph pool in
advance, including all possible combination of input lengths and output
lengths. Then for each sample in a batch, we call `dgl.batch`

to batch
graphs of their sizes together in to a single large graph.

We have wrapped the process of creating graph pool and building
BatchedGraph in `dataset.GraphPool`

and
`dataset.TranslationDataset`

.

```
graph_pool = GraphPool()
data_iter = dataset(graph_pool, mode='train', batch_size=1, devices=devices)
for graph in data_iter:
print(graph.nids['enc']) # encoder node ids
print(graph.nids['dec']) # decoder node ids
print(graph.eids['ee']) # encoder-encoder edge ids
print(graph.eids['ed']) # encoder-decoder edge ids
print(graph.eids['dd']) # decoder-decoder edge ids
print(graph.src[0]) # Input word index list
print(graph.src[1]) # Input positions
print(graph.tgt[0]) # Output word index list
print(graph.tgt[1]) # Ouptut positions
break
```

Output:

```
tensor([0, 1, 2, 3, 4, 5, 6, 7, 8], device='cuda:0')
tensor([ 9, 10, 11, 12, 13, 14, 15, 16, 17, 18], device='cuda:0')
tensor([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,
72, 73, 74, 75, 76, 77, 78, 79, 80], device='cuda:0')
tensor([ 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94,
95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108,
109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122,
123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136,
137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150,
151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164,
165, 166, 167, 168, 169, 170], device='cuda:0')
tensor([171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184,
185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198,
199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212,
213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225],
device='cuda:0')
tensor([28, 25, 7, 26, 6, 4, 5, 9, 18], device='cuda:0')
tensor([0, 1, 2, 3, 4, 5, 6, 7, 8], device='cuda:0')
tensor([ 0, 28, 25, 7, 26, 6, 4, 5, 9, 18], device='cuda:0')
tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], device='cuda:0')
```

## Put it all together¶

We train a one-head transformer with one layer, 128 dimension on copy task. Other parameters are set to default.

Note that we do not involve inference module in this tutorial (which requires beam search), please refer to the Github Repo for full implementation.

```
from tqdm import tqdm
import torch as th
import numpy as np
from loss import LabelSmoothing, SimpleLossCompute
from modules import make_model
from optims import NoamOpt
from dgl.contrib.transformer import get_dataset, GraphPool
def run_epoch(data_iter, model, loss_compute, is_train=True):
for i, g in tqdm(enumerate(data_iter)):
with th.set_grad_enabled(is_train):
output = model(g)
loss = loss_compute(output, g.tgt_y, g.n_tokens)
print('average loss: {}'.format(loss_compute.avg_loss))
print('accuracy: {}'.format(loss_compute.accuracy))
N = 1
batch_size = 128
devices = ['cuda' if th.cuda.is_available() else 'cpu']
dataset = get_dataset("copy")
V = dataset.vocab_size
criterion = LabelSmoothing(V, padding_idx=dataset.pad_id, smoothing=0.1)
dim_model = 128
# Create model
model = make_model(V, V, N=N, dim_model=128, dim_ff=128, h=1)
# Sharing weights between Encoder & Decoder
model.src_embed.lut.weight = model.tgt_embed.lut.weight
model.generator.proj.weight = model.tgt_embed.lut.weight
model, criterion = model.to(devices[0]), criterion.to(devices[0])
model_opt = NoamOpt(dim_model, 1, 400,
th.optim.Adam(model.parameters(), lr=1e-3, betas=(0.9, 0.98), eps=1e-9))
loss_compute = SimpleLossCompute
att_maps = []
for epoch in range(4):
train_iter = dataset(graph_pool, mode='train', batch_size=batch_size, devices=devices)
valid_iter = dataset(graph_pool, mode='valid', batch_size=batch_size, devices=devices)
print('Epoch: {} Training...'.format(epoch))
model.train(True)
run_epoch(train_iter, model,
loss_compute(criterion, model_opt), is_train=True)
print('Epoch: {} Evaluating...'.format(epoch))
model.att_weight_map = None
model.eval()
run_epoch(valid_iter, model,
loss_compute(criterion, None), is_train=False)
att_maps.append(model.att_weight_map)
```

## Visualization¶

After training, we can visualize the attention our Transformer generates on copy task:

```
src_seq = dataset.get_seq_by_id(VIZ_IDX, mode='valid', field='src')
tgt_seq = dataset.get_seq_by_id(VIZ_IDX, mode='valid', field='tgt')[:-1]
# visualize head 0 of encoder-decoder attention
att_animation(att_maps, 'e2d', src_seq, tgt_seq, 0)
```

from the figure we see the decoder nodes gradually learns to attend to corresponding nodes in input sequence, which is the expected behavior.

### Multi-Head Attention¶

Besides the attention of a one-head attention trained on toy task. We also visualize the attention scores of Encoder’s Self Attention, Decoder’s Self Attention and the Encoder-Decoder attention of an one-Layer Transformer network trained on multi-30k dataset.

From the visualization we observe the diversity of different heads (which is what we expected: different heads learn different relations between word pairs):

**Encoder Self-Attention****Encoder-Decoder Attention**Most words in target sequence attend on their related words in source sequence, for example: when generating “See” (in De), several heads attend on “lake”; when generating “Eisfischerhütte”, several heads attend on “ice”.**Decoder Self-Attention**Most words attend on their previous few words.

## Adaptive Universal Transformer¶

A recent paper by Google: Universal
Transformer is an example to
show how `update_graph`

adapts to more complex updating rules.

The Universal Transformer was proposed to address the problem that vanilla Transformer is not computationally universal by introducing recurrence in Transformer:

- The basic idea of Universal Transformer is to repeatedly revise its representations of all symbols in the sequence with each recurrent step by applying a Transformer layer on the representations.
- Compared to vanilla Transformer, Universal Transformer shares weights among its layers, and it does not fix the recurrence time (which means the number of layers in Transformer).

A further optimization employs an adaptive computation time
(ACT) mechanism to allow the
model to dynamically adjust the number of times the representation of
each position in a sequence is revised (refereed to as **step**
hereinafter). This model is also known as the Adaptive Universal
Transformer(referred to as AUT hereinafter).

In AUT, we maintain an “active nodes” list. In each step \(t\), we compute a halting probability: \(h (0<h<1)\) for all nodes in this list by:

then dynamically decide which nodes are still active. A node is halted at time \(T\) if and only if \(\sum_{t=1}^{T-1} h_t < 1 - \varepsilon \leq \sum_{t=1}^{T}h_t\). Halted nodes are removed from the list. The procedure proceeds until the list is empty or a pre-defined maximum step is reached. From DGL’s perspective, this means that the “active” graph becomes sparser over time.

The final state of a node \(s_i\) is a weighted average of \(x_i^t\) by \(h_i^t\):

In DGL, the algorithm is easy to implement, we just need to call
`update_graph`

on nodes that are still active and edges associated
with this nodes. The following code shows the Universal Transformer
class in DGL:

```
class UTransformer(nn.Module):
"Universal Transformer(https://arxiv.org/pdf/1807.03819.pdf) with ACT(https://arxiv.org/pdf/1603.08983.pdf)."
MAX_DEPTH = 8
thres = 0.99
act_loss_weight = 0.01
def __init__(self, encoder, decoder, src_embed, tgt_embed, pos_enc, time_enc, generator, h, d_k):
super(UTransformer, self).__init__()
self.encoder, self.decoder = encoder, decoder
self.src_embed, self.tgt_embed = src_embed, tgt_embed
self.pos_enc, self.time_enc = pos_enc, time_enc
self.halt_enc = HaltingUnit(h * d_k)
self.halt_dec = HaltingUnit(h * d_k)
self.generator = generator
self.h, self.d_k = h, d_k
def step_forward(self, nodes):
# add positional encoding and time encoding, increment step by one
x = nodes.data['x']
step = nodes.data['step']
pos = nodes.data['pos']
return {'x': self.pos_enc.dropout(x + self.pos_enc(pos.view(-1)) + self.time_enc(step.view(-1))),
'step': step + 1}
def halt_and_accum(self, name, end=False):
"field: 'enc' or 'dec'"
halt = self.halt_enc if name == 'enc' else self.halt_dec
thres = self.thres
def func(nodes):
p = halt(nodes.data['x'])
sum_p = nodes.data['sum_p'] + p
active = (sum_p < thres) & (1 - end)
_continue = active.float()
r = nodes.data['r'] * (1 - _continue) + (1 - sum_p) * _continue
s = nodes.data['s'] + ((1 - _continue) * r + _continue * p) * nodes.data['x']
return {'p': p, 'sum_p': sum_p, 'r': r, 's': s, 'active': active}
return func
def propagate_attention(self, g, eids):
# Compute attention score
g.apply_edges(src_dot_dst('k', 'q', 'score'), eids)
g.apply_edges(scaled_exp('score', np.sqrt(self.d_k)), eids)
# Send weighted values to target nodes
g.send_and_recv(eids,
[fn.src_mul_edge('v', 'score', 'v'), fn.copy_edge('score', 'score')],
[fn.sum('v', 'wv'), fn.sum('score', 'z')])
def update_graph(self, g, eids, pre_pairs, post_pairs):
"Update the node states and edge states of the graph."
# Pre-compute queries and key-value pairs.
for pre_func, nids in pre_pairs:
g.apply_nodes(pre_func, nids)
self.propagate_attention(g, eids)
# Further calculation after attention mechanism
for post_func, nids in post_pairs:
g.apply_nodes(post_func, nids)
def forward(self, graph):
g = graph.g
N, E = graph.n_nodes, graph.n_edges
nids, eids = graph.nids, graph.eids
# embed & pos
g.nodes[nids['enc']].data['x'] = self.src_embed(graph.src[0])
g.nodes[nids['dec']].data['x'] = self.tgt_embed(graph.tgt[0])
g.nodes[nids['enc']].data['pos'] = graph.src[1]
g.nodes[nids['dec']].data['pos'] = graph.tgt[1]
# init step
device = next(self.parameters()).device
g.ndata['s'] = th.zeros(N, self.h * self.d_k, dtype=th.float, device=device) # accumulated state
g.ndata['p'] = th.zeros(N, 1, dtype=th.float, device=device) # halting prob
g.ndata['r'] = th.ones(N, 1, dtype=th.float, device=device) # remainder
g.ndata['sum_p'] = th.zeros(N, 1, dtype=th.float, device=device) # sum of pondering values
g.ndata['step'] = th.zeros(N, 1, dtype=th.long, device=device) # step
g.ndata['active'] = th.ones(N, 1, dtype=th.uint8, device=device) # active
for step in range(self.MAX_DEPTH):
pre_func = self.encoder.pre_func('qkv')
post_func = self.encoder.post_func()
nodes = g.filter_nodes(lambda v: v.data['active'].view(-1), nids['enc'])
if len(nodes) == 0: break
edges = g.filter_edges(lambda e: e.dst['active'].view(-1), eids['ee'])
end = step == self.MAX_DEPTH - 1
self.update_graph(g, edges,
[(self.step_forward, nodes), (pre_func, nodes)],
[(post_func, nodes), (self.halt_and_accum('enc', end), nodes)])
g.nodes[nids['enc']].data['x'] = self.encoder.norm(g.nodes[nids['enc']].data['s'])
for step in range(self.MAX_DEPTH):
pre_func = self.decoder.pre_func('qkv')
post_func = self.decoder.post_func()
nodes = g.filter_nodes(lambda v: v.data['active'].view(-1), nids['dec'])
if len(nodes) == 0: break
edges = g.filter_edges(lambda e: e.dst['active'].view(-1), eids['dd'])
self.update_graph(g, edges,
[(self.step_forward, nodes), (pre_func, nodes)],
[(post_func, nodes)])
pre_q = self.decoder.pre_func('q', 1)
pre_kv = self.decoder.pre_func('kv', 1)
post_func = self.decoder.post_func(1)
nodes_e = nids['enc']
edges = g.filter_edges(lambda e: e.dst['active'].view(-1), eids['ed'])
end = step == self.MAX_DEPTH - 1
self.update_graph(g, edges,
[(pre_q, nodes), (pre_kv, nodes_e)],
[(post_func, nodes), (self.halt_and_accum('dec', end), nodes)])
g.nodes[nids['dec']].data['x'] = self.decoder.norm(g.nodes[nids['dec']].data['s'])
act_loss = th.mean(g.ndata['r']) # ACT loss
return self.generator(g.ndata['x'][nids['dec']]), act_loss * self.act_loss_weight
```

Here we call `filter_nodes`

and `filter_edge`

to find nodes/edges
that are still active:

Note

`filter_nodes()`

takes a predicate and a node ID list/tensor as input, then returns a tensor of node IDs that satisfy the given predicate.`filter_edges()`

takes a predicate and an edge ID list/tensor as input, then returns a tensor of edge IDs that satisfy the given predicate.

for the full implementation, please refer to our Github Repo.

The figure below shows the effect of Adaptive Computational Time(different positions of a sentence were revised different times):

We also visualize the dynamics of step distribution on nodes during the training of AUT on sort task(reach 99.7% accuracy), which demonstrates how AUT learns to reduce recurrence steps during training.

Note

We apologize that this notebook itself is not runnable due to many dependencies,
please download the 7_transformer.py,
and copy the python script to directory `examples/pytorch/transformer`

then run `python 7_transformer.py`

to see how it works.

**Total running time of the script:** ( 0 minutes 0.076 seconds)