10.2. Sequence to Sequence with Attention Mechanisms¶ Open the notebook in Colab

In this section, we add the attention mechanism to the sequence to sequence (seq2seq) model as introduced in Section 9.7 to explicitly aggregate states with weights. Fig. 10.2.1 shows the model architecture for encoding and decoding at the timestep $$t$$. Here, the memory of the attention layer consists of all the information that the encoder has seen—the encoder output at each timestep. During the decoding, the decoder output from the previous timestep $$t-1$$ is used as the query. The output of the attention model is viewed as the context information, and such context is concatenated with the decoder input $$D_t$$. Finally, we feed the concatenation into the decoder.

Fig. 10.2.1 The second timestep in decoding for the sequence to sequence model with attention mechanism.

To illustrate the overall architecture of seq2seq with attention model, the layer structure of its encoder and decoder is shown in Fig. 10.2.2.

Fig. 10.2.2 The layers in the sequence to sequence model with attention mechanism.

import d2l
from mxnet import np, npx
from mxnet.gluon import rnn, nn
npx.set_np()


10.2.1. Decoder¶

Since the encoder of seq2seq with attention mechanisms is the same as Seq2SeqEncoder in Section 9.7, we will just focus on the decoder. We add an MLP attention layer (MLPAttention) which has the same hidden size as the LSTM layer in the decoder. Then we initialize the state of the decoder by passing three items from the encoder:

• the encoder outputs of all timesteps: they are used as the attention layer’s memory with identical keys and values;

• the hidden state of the encoder’s final timestep: it is used as the initial decoder’s hidden state;

• the encoder valid length: so the attention layer will not consider the padding tokens with in the encoder outputs.

At each timestep of the decoding, we use the output of the decoder’s last RNN layer as the query for the attention layer. The attention model’s output is then concatenated with the input embedding vector to feed into the RNN layer. Although the RNN layer hidden state also contains history information from decoder, the attention output explicitly selects the encoder outputs based on enc_valid_len, so that the attention output suspends other irrelevant information.

Let’s implement the Seq2SeqAttentionDecoder, and see how it differs from the decoder in seq2seq from Section 9.7.2.

class Seq2SeqAttentionDecoder(d2l.Decoder):
def __init__(self, vocab_size, embed_size, num_hiddens, num_layers,
dropout=0, **kwargs):
super(Seq2SeqAttentionDecoder, self).__init__(**kwargs)
self.attention_cell = d2l.MLPAttention(num_hiddens, dropout)
self.embedding = nn.Embedding(vocab_size, embed_size)
self.rnn = rnn.LSTM(num_hiddens, num_layers, dropout=dropout)
self.dense = nn.Dense(vocab_size, flatten=False)

def init_state(self, enc_outputs, enc_valid_len, *args):
outputs, hidden_state = enc_outputs
# Transpose outputs to (batch_size, seq_len, hidden_size)
return (outputs.swapaxes(0, 1), hidden_state, enc_valid_len)

def forward(self, X, state):
enc_outputs, hidden_state, enc_valid_len = state
X = self.embedding(X).swapaxes(0, 1)
outputs = []
for x in X:
# query shape: (batch_size, 1, hidden_size)
query = np.expand_dims(hidden_state[0][-1], axis=1)
# context has same shape as query
context = self.attention_cell(
query, enc_outputs, enc_outputs, enc_valid_len)
# Concatenate on the feature dimension
x = np.concatenate((context, np.expand_dims(x, axis=1)), axis=-1)
# Reshape x to (1, batch_size, embed_size+hidden_size)
out, hidden_state = self.rnn(x.swapaxes(0, 1), hidden_state)
outputs.append(out)
outputs = self.dense(np.concatenate(outputs, axis=0))
return outputs.swapaxes(0, 1), [enc_outputs, hidden_state,
enc_valid_len]


Now we can test the seq2seq with attention model. To be consistent with the model without attention in Section 9.7, we use the same hyper-parameters for vocab_size, embed_size, num_hiddens, and num_layers. As a result, we get the same decoder output shape, but the state structure is changed.

encoder = d2l.Seq2SeqEncoder(vocab_size=10, embed_size=8,
num_hiddens=16, num_layers=2)
encoder.initialize()
decoder = Seq2SeqAttentionDecoder(vocab_size=10, embed_size=8,
num_hiddens=16, num_layers=2)
decoder.initialize()
X = np.zeros((4, 7))
state = decoder.init_state(encoder(X), None)
out, state = decoder(X, state)
out.shape, len(state), state[0].shape, len(state[1]), state[1][0].shape

((4, 7, 10), 3, (4, 7, 16), 2, (2, 4, 16))


10.2.2. Training¶

Similar to Section 9.7.4, we try a toy model by applying the same training hyperparameters and the same training loss. As we can see from the result, since the sequences in the training dataset are relative short, the additional attention layer does not lead to a significant improvement. Due to the computational overhead of both the encoder’s and the decoder’s attention layers, this model is much slower than the seq2seq model without attention.

embed_size, num_hiddens, num_layers, dropout = 32, 32, 2, 0.0
batch_size, num_steps = 64, 10
lr, num_epochs, ctx = 0.005, 200, d2l.try_gpu()

src_vocab, tgt_vocab, train_iter = d2l.load_data_nmt(batch_size, num_steps)
encoder = d2l.Seq2SeqEncoder(
len(src_vocab), embed_size, num_hiddens, num_layers, dropout)
decoder = Seq2SeqAttentionDecoder(
len(tgt_vocab), embed_size, num_hiddens, num_layers, dropout)
model = d2l.EncoderDecoder(encoder, decoder)
d2l.train_s2s_ch9(model, train_iter, lr, num_epochs, ctx)

loss 0.030, 4040 tokens/sec on gpu(0)


Last, we predict several sample examples.

for sentence in ['Go .', 'Wow !', "I'm OK .", 'I won !']:
print(sentence + ' => ' + d2l.predict_s2s_ch9(
model, sentence, src_vocab, tgt_vocab, num_steps, ctx))

Go . => va !
Wow ! => <unk> !
I'm OK . => je vais bien .
I won ! => j'ai gagné !


10.2.3. Summary¶

• The seq2seq model with attention adds an additional attention layer to the model without attention.

• The decoder of the seq2seq with attention model passes three items from the encoder: the encoder outputs of all timesteps, the hidden state of the encoder’s final timestep, and the encoder valid length.

10.2.4. Exercises¶

1. Compare Seq2SeqAttentionDecoder and Seq2seqDecoder by using the same parameters and checking their losses.

2. Can you think of any use cases where Seq2SeqAttentionDecoder will outperform Seq2seqDecoder?