🏷️sec_transformer
We have compared CNNs, RNNs, and self-attention in
:numref:subsec_cnn-rnn-self-attention.
Notably,
self-attention
enjoys both parallel computation and
the shortest maximum path length.
Therefore natually,
it is appealing to design deep architectures
by using self-attention.
Unlike earlier self-attention models
that still rely on RNNs for input representations :cite:Cheng.Dong.Lapata.2016,Lin.Feng.Santos.ea.2017,Paulus.Xiong.Socher.2017,
the Transformer model
is solely based on attention mechanisms
without any convolutional or recurrent layer :cite:Vaswani.Shazeer.Parmar.ea.2017.
Though originally proposed
for sequence to sequence learning on text data,
Transformers have been
pervasive in a wide range of
modern deep learning applications,
such as in areas of language, vision, speech, and reinforcement learning.
As an instance of the encoder-decoder
architecture,
the overall architecture of
the Transformer
is presented in :numref:fig_transformer.
As we can see,
the Transformer is composed of an encoder and a decoder.
Different from
Bahdanau attention
for sequence to sequence learning
in :numref:fig_s2s_attention_details,
the input (source) and output (target)
sequence embeddings
are added with positional encoding
before being fed into
the encoder and the decoder
that stack modules based on self-attention.
:width:500px
🏷️fig_transformer
Now we provide an overview of the
Transformer architecture in :numref:fig_transformer.
On a high level,
the Transformer encoder is a stack of multiple identical layers,
where each layer
has two sublayers (either is denoted as sec_resnet,
a residual connection is employed
around both sublayers.
In the Transformer,
for any input Ba.Kiros.Hinton.2016.
As a result, the Transformer encoder outputs a
The Transformer decoder is also a stack of multiple identical layers with residual connections and layer normalizations. Besides the two sublayers described in the encoder, the decoder inserts a third sublayer, known as the encoder-decoder attention, between these two. In the encoder-decoder attention, queries are from the outputs of the previous decoder layer, and the keys and values are from the Transformer encoder outputs. In the decoder self-attention, queries, keys, and values are all from the the outputs of the previous decoder layer. However, each position in the decoder is allowed to only attend to all positions in the decoder up to that position. This masked attention preserves the auto-regressive property, ensuring that the prediction only depends on those output tokens that have been generated.
We have already described and implemented
multi-head attention based on scaled dot-products
in :numref:sec_multihead-attention
and positional encoding in :numref:subsec_positional-encoding.
In the following,
we will implement the rest of the Transformer model.
from d2l import mxnet as d2l
import math
from mxnet import autograd, np, npx
from mxnet.gluon import nn
import pandas as pd
npx.set_np()
#@tab pytorch
from d2l import torch as d2l
import math
import pandas as pd
import torch
from torch import nn
The positionwise feed-forward network
transforms
the representation at all the sequence positions
using the same MLP.
This is why we call it positionwise.
In the implementation below,
the input X with shape
(batch size, number of time steps or sequence length in tokens, number of hidden units or feature dimension)
will be transformed by a two-layer MLP into
an output tensor of shape
(batch size, number of time steps, ffn_num_outputs).
#@save
class PositionWiseFFN(nn.Block):
def __init__(self, ffn_num_hiddens, ffn_num_outputs, **kwargs):
super(PositionWiseFFN, self).__init__(**kwargs)
self.dense1 = nn.Dense(ffn_num_hiddens, flatten=False,
activation='relu')
self.dense2 = nn.Dense(ffn_num_outputs, flatten=False)
def forward(self, X):
return self.dense2(self.dense1(X))
#@tab pytorch
#@save
class PositionWiseFFN(nn.Module):
def __init__(self, ffn_num_input, ffn_num_hiddens, ffn_num_outputs,
**kwargs):
super(PositionWiseFFN, self).__init__(**kwargs)
self.dense1 = nn.Linear(ffn_num_input, ffn_num_hiddens)
self.relu = nn.ReLU()
self.dense2 = nn.Linear(ffn_num_hiddens, ffn_num_outputs)
def forward(self, X):
return self.dense2(self.relu(self.dense1(X)))
The following example shows that the innermost dimension of a tensor changes to the number of outputs in the positionwise feed-forward network. Since the same MLP transforms at all the positions, when the inputs at all these positions are the same, their outputs are also identical.
ffn = PositionWiseFFN(4, 8)
ffn.initialize()
ffn(np.ones((2, 3, 4)))[0]
#@tab pytorch
ffn = PositionWiseFFN(4, 4, 8)
ffn.eval()
ffn(d2l.ones((2, 3, 4)))[0]
Now let us focus on
the "add & norm" component in :numref:fig_transformer.
As we described at the beginning
of this section,
this is a residual connection immediately
followed by layer normalization.
Both are key to effective deep architectures.
In :numref:sec_batch_norm,
we explained how batch normalization
recenters and rescales across the examples within
a minibatch.
Layer normalization is the same as batch normalization
except that the former
normalizes across the feature dimension.
Despite its pervasive applications
in computer vision,
batch normalization
is usually empirically
less effective than layer normalization
in natural language processing
tasks, whose inputs are often
variable-length sequences.
The following code snippet compares the normalization across different dimensions by layer normalization and batch normalization.
ln = nn.LayerNorm()
ln.initialize()
bn = nn.BatchNorm()
bn.initialize()
X = d2l.tensor([[1, 2], [2, 3]])
# Compute mean and variance from `X` in the training mode
with autograd.record():
print('layer norm:', ln(X), '\nbatch norm:', bn(X))
#@tab pytorch
ln = nn.LayerNorm(2)
bn = nn.BatchNorm1d(2)
X = d2l.tensor([[1, 2], [2, 3]], dtype=torch.float32)
# Compute mean and variance from `X` in the training mode
print('layer norm:', ln(X), '\nbatch norm:', bn(X))
Now we can implement the AddNorm class
using a residual connection followed by layer normalization.
Dropout is also applied for regularization.
#@save
class AddNorm(nn.Block):
def __init__(self, dropout, **kwargs):
super(AddNorm, self).__init__(**kwargs)
self.dropout = nn.Dropout(dropout)
self.ln = nn.LayerNorm()
def forward(self, X, Y):
return self.ln(self.dropout(Y) + X)
#@tab pytorch
#@save
class AddNorm(nn.Module):
def __init__(self, normalized_shape, dropout, **kwargs):
super(AddNorm, self).__init__(**kwargs)
self.dropout = nn.Dropout(dropout)
self.ln = nn.LayerNorm(normalized_shape)
def forward(self, X, Y):
return self.ln(self.dropout(Y) + X)
The residual connection requires that the two inputs are of the same shape so that the output tensor also has the same shape after the addition operation.
add_norm = AddNorm(0.5)
add_norm.initialize()
add_norm(d2l.ones((2, 3, 4)), d2l.ones((2, 3, 4))).shape
#@tab pytorch
add_norm = AddNorm([3, 4], 0.5) # Normalized_shape is input.size()[1:]
add_norm.eval()
add_norm(d2l.ones((2, 3, 4)), d2l.ones((2, 3, 4))).shape
With all the essential components to assemble
the Transformer encoder,
let us start by
implementing a single layer within the encoder.
The following EncoderBlock class
contains two sublayers: multi-head self-attention and positionwise feed-forward networks,
where a residual connection followed by layer normalization is employed
around both sublayers.
#@save
class EncoderBlock(nn.Block):
def __init__(self, num_hiddens, ffn_num_hiddens, num_heads, dropout,
use_bias=False, **kwargs):
super(EncoderBlock, self).__init__(**kwargs)
self.attention = d2l.MultiHeadAttention(
num_hiddens, num_heads, dropout, use_bias)
self.addnorm1 = AddNorm(dropout)
self.ffn = PositionWiseFFN(ffn_num_hiddens, num_hiddens)
self.addnorm2 = AddNorm(dropout)
def forward(self, X, valid_lens):
Y = self.addnorm1(X, self.attention(X, X, X, valid_lens))
return self.addnorm2(Y, self.ffn(Y))
#@tab pytorch
#@save
class EncoderBlock(nn.Module):
def __init__(self, key_size, query_size, value_size, num_hiddens,
norm_shape, ffn_num_input, ffn_num_hiddens, num_heads,
dropout, use_bias=False, **kwargs):
super(EncoderBlock, self).__init__(**kwargs)
self.attention = d2l.MultiHeadAttention(
key_size, query_size, value_size, num_hiddens, num_heads, dropout,
use_bias)
self.addnorm1 = AddNorm(norm_shape, dropout)
self.ffn = PositionWiseFFN(
ffn_num_input, ffn_num_hiddens, num_hiddens)
self.addnorm2 = AddNorm(norm_shape, dropout)
def forward(self, X, valid_lens):
Y = self.addnorm1(X, self.attention(X, X, X, valid_lens))
return self.addnorm2(Y, self.ffn(Y))
As we can see, any layer in the Transformer encoder does not change the shape of its input.
X = d2l.ones((2, 100, 24))
valid_lens = d2l.tensor([3, 2])
encoder_blk = EncoderBlock(24, 48, 8, 0.5)
encoder_blk.initialize()
encoder_blk(X, valid_lens).shape
#@tab pytorch
X = d2l.ones((2, 100, 24))
valid_lens = d2l.tensor([3, 2])
encoder_blk = EncoderBlock(24, 24, 24, 24, [100, 24], 24, 48, 8, 0.5)
encoder_blk.eval()
encoder_blk(X, valid_lens).shape
In the following Transformer encoder implementation,
we stack num_layers instances of the above EncoderBlock classes.
Since we use the fixed positional encoding
whose values are always between -1 and 1,
we multiply values of the learnable input embeddings
by the square root of the embedding dimension
to rescale before summing up the input embedding and the positional encoding.
#@save
class TransformerEncoder(d2l.Encoder):
def __init__(self, vocab_size, num_hiddens, ffn_num_hiddens,
num_heads, num_layers, dropout, use_bias=False, **kwargs):
super(TransformerEncoder, self).__init__(**kwargs)
self.num_hiddens = num_hiddens
self.embedding = nn.Embedding(vocab_size, num_hiddens)
self.pos_encoding = d2l.PositionalEncoding(num_hiddens, dropout)
self.blks = nn.Sequential()
for _ in range(num_layers):
self.blks.add(
EncoderBlock(num_hiddens, ffn_num_hiddens, num_heads, dropout,
use_bias))
def forward(self, X, valid_lens, *args):
# Since positional encoding values are between -1 and 1, the embedding
# values are multiplied by the square root of the embedding dimension
# to rescale before they are summed up
X = self.pos_encoding(self.embedding(X) * math.sqrt(self.num_hiddens))
self.attention_weights = [None] * len(self.blks)
for i, blk in enumerate(self.blks):
X = blk(X, valid_lens)
self.attention_weights[
i] = blk.attention.attention.attention_weights
return X
#@tab pytorch
#@save
class TransformerEncoder(d2l.Encoder):
def __init__(self, vocab_size, key_size, query_size, value_size,
num_hiddens, norm_shape, ffn_num_input, ffn_num_hiddens,
num_heads, num_layers, dropout, use_bias=False, **kwargs):
super(TransformerEncoder, self).__init__(**kwargs)
self.num_hiddens = num_hiddens
self.embedding = nn.Embedding(vocab_size, num_hiddens)
self.pos_encoding = d2l.PositionalEncoding(num_hiddens, dropout)
self.blks = nn.Sequential()
for i in range(num_layers):
self.blks.add_module("block"+str(i),
EncoderBlock(key_size, query_size, value_size, num_hiddens,
norm_shape, ffn_num_input, ffn_num_hiddens,
num_heads, dropout, use_bias))
def forward(self, X, valid_lens, *args):
# Since positional encoding values are between -1 and 1, the embedding
# values are multiplied by the square root of the embedding dimension
# to rescale before they are summed up
X = self.pos_encoding(self.embedding(X) * math.sqrt(self.num_hiddens))
self.attention_weights = [None] * len(self.blks)
for i, blk in enumerate(self.blks):
X = blk(X, valid_lens)
self.attention_weights[
i] = blk.attention.attention.attention_weights
return X
Below we specify hyperparameters to create a two-layer Transformer encoder.
The shape of the Transformer encoder output
is (batch size, number of time steps, num_hiddens).
encoder = TransformerEncoder(200, 24, 48, 8, 2, 0.5)
encoder.initialize()
encoder(np.ones((2, 100)), valid_lens).shape
#@tab pytorch
encoder = TransformerEncoder(
200, 24, 24, 24, 24, [100, 24], 24, 48, 8, 2, 0.5)
encoder.eval()
encoder(d2l.ones((2, 100), dtype=torch.long), valid_lens).shape
As shown in :numref:fig_transformer,
the Transformer decoder
is composed of multiple identical layers.
Each layer is implemented in the following
DecoderBlock class,
which contains three sublayers:
decoder self-attention,
encoder-decoder attention,
and positionwise feed-forward networks.
These sublayers employ
a residual connection around them
followed by layer normalization.
As we described earlier in this section,
in the masked multi-head decoder self-attention
(the first sublayer),
queries, keys, and values
all come from the outputs of the previous decoder layer.
When training sequence-to-sequence models,
tokens at all the positions (time steps)
of the output sequence
are known.
However,
during prediction
the output sequence is generated token by token;
thus,
at any decoder time step
only the generated tokens
can be used in the decoder self-attention.
To preserve auto-regression in the decoder,
its masked self-attention
specifies dec_valid_lens so that
any query
only attends to
all positions in the decoder
up to the query position.
class DecoderBlock(nn.Block):
# The `i`-th block in the decoder
def __init__(self, num_hiddens, ffn_num_hiddens, num_heads,
dropout, i, **kwargs):
super(DecoderBlock, self).__init__(**kwargs)
self.i = i
self.attention1 = d2l.MultiHeadAttention(num_hiddens, num_heads,
dropout)
self.addnorm1 = AddNorm(dropout)
self.attention2 = d2l.MultiHeadAttention(num_hiddens, num_heads,
dropout)
self.addnorm2 = AddNorm(dropout)
self.ffn = PositionWiseFFN(ffn_num_hiddens, num_hiddens)
self.addnorm3 = AddNorm(dropout)
def forward(self, X, state):
enc_outputs, enc_valid_lens = state[0], state[1]
# During training, all the tokens of any output sequence are processed
# at the same time, so `state[2][self.i]` is `None` as initialized.
# When decoding any output sequence token by token during prediction,
# `state[2][self.i]` contains representations of the decoded output at
# the `i`-th block up to the current time step
if state[2][self.i] is None:
key_values = X
else:
key_values = np.concatenate((state[2][self.i], X), axis=1)
state[2][self.i] = key_values
if autograd.is_training():
batch_size, num_steps, _ = X.shape
# Shape of `dec_valid_lens`: (`batch_size`, `num_steps`), where
# every row is [1, 2, ..., `num_steps`]
dec_valid_lens = np.tile(np.arange(1, num_steps + 1, ctx=X.ctx),
(batch_size, 1))
else:
dec_valid_lens = None
# Self-attention
X2 = self.attention1(X, key_values, key_values, dec_valid_lens)
Y = self.addnorm1(X, X2)
# Encoder-decoder attention. Shape of `enc_outputs`:
# (`batch_size`, `num_steps`, `num_hiddens`)
Y2 = self.attention2(Y, enc_outputs, enc_outputs, enc_valid_lens)
Z = self.addnorm2(Y, Y2)
return self.addnorm3(Z, self.ffn(Z)), state
#@tab pytorch
class DecoderBlock(nn.Module):
# The `i`-th block in the decoder
def __init__(self, key_size, query_size, value_size, num_hiddens,
norm_shape, ffn_num_input, ffn_num_hiddens, num_heads,
dropout, i, **kwargs):
super(DecoderBlock, self).__init__(**kwargs)
self.i = i
self.attention1 = d2l.MultiHeadAttention(
key_size, query_size, value_size, num_hiddens, num_heads, dropout)
self.addnorm1 = AddNorm(norm_shape, dropout)
self.attention2 = d2l.MultiHeadAttention(
key_size, query_size, value_size, num_hiddens, num_heads, dropout)
self.addnorm2 = AddNorm(norm_shape, dropout)
self.ffn = PositionWiseFFN(ffn_num_input, ffn_num_hiddens,
num_hiddens)
self.addnorm3 = AddNorm(norm_shape, dropout)
def forward(self, X, state):
enc_outputs, enc_valid_lens = state[0], state[1]
# During training, all the tokens of any output sequence are processed
# at the same time, so `state[2][self.i]` is `None` as initialized.
# When decoding any output sequence token by token during prediction,
# `state[2][self.i]` contains representations of the decoded output at
# the `i`-th block up to the current time step
if state[2][self.i] is None:
key_values = X
else:
key_values = torch.cat((state[2][self.i], X), axis=1)
state[2][self.i] = key_values
if self.training:
batch_size, num_steps, _ = X.shape
# Shape of `dec_valid_lens`: (`batch_size`, `num_steps`), where
# every row is [1, 2, ..., `num_steps`]
dec_valid_lens = torch.arange(
1, num_steps + 1, device=X.device).repeat(batch_size, 1)
else:
dec_valid_lens = None
# Self-attention
X2 = self.attention1(X, key_values, key_values, dec_valid_lens)
Y = self.addnorm1(X, X2)
# Encoder-decoder attention. Shape of `enc_outputs`:
# (`batch_size`, `num_steps`, `num_hiddens`)
Y2 = self.attention2(Y, enc_outputs, enc_outputs, enc_valid_lens)
Z = self.addnorm2(Y, Y2)
return self.addnorm3(Z, self.ffn(Z)), state
To facilitate scaled dot-product operations
in the encoder-decoder attention
and addition operations in the residual connections,
the feature dimension (num_hiddens) of the decoder is
the same as that of the encoder.
decoder_blk = DecoderBlock(24, 48, 8, 0.5, 0)
decoder_blk.initialize()
X = np.ones((2, 100, 24))
state = [encoder_blk(X, valid_lens), valid_lens, [None]]
decoder_blk(X, state)[0].shape
#@tab pytorch
decoder_blk = DecoderBlock(24, 24, 24, 24, [100, 24], 24, 48, 8, 0.5, 0)
decoder_blk.eval()
X = d2l.ones((2, 100, 24))
state = [encoder_blk(X, valid_lens), valid_lens, [None]]
decoder_blk(X, state)[0].shape
Now we construct the entire Transformer decoder
composed of num_layers instances of DecoderBlock.
In the end,
a fully-connected layer computes the prediction
for all the vocab_size possible output tokens.
Both of the decoder self-attention weights
and the encoder-decoder attention weights
are stored for later visualization.
class TransformerDecoder(d2l.AttentionDecoder):
def __init__(self, vocab_size, num_hiddens, ffn_num_hiddens,
num_heads, num_layers, dropout, **kwargs):
super(TransformerDecoder, self).__init__(**kwargs)
self.num_hiddens = num_hiddens
self.num_layers = num_layers
self.embedding = nn.Embedding(vocab_size, num_hiddens)
self.pos_encoding = d2l.PositionalEncoding(num_hiddens, dropout)
self.blks = nn.Sequential()
for i in range(num_layers):
self.blks.add(
DecoderBlock(num_hiddens, ffn_num_hiddens, num_heads,
dropout, i))
self.dense = nn.Dense(vocab_size, flatten=False)
def init_state(self, enc_outputs, enc_valid_lens, *args):
return [enc_outputs, enc_valid_lens, [None] * self.num_layers]
def forward(self, X, state):
X = self.pos_encoding(self.embedding(X) * math.sqrt(self.num_hiddens))
self._attention_weights = [[None] * len(self.blks) for _ in range (2)]
for i, blk in enumerate(self.blks):
X, state = blk(X, state)
# Decoder self-attention weights
self._attention_weights[0][
i] = blk.attention1.attention.attention_weights
# Encoder-decoder attention weights
self._attention_weights[1][
i] = blk.attention2.attention.attention_weights
return self.dense(X), state
@property
def attention_weights(self):
return self._attention_weights
#@tab pytorch
class TransformerDecoder(d2l.AttentionDecoder):
def __init__(self, vocab_size, key_size, query_size, value_size,
num_hiddens, norm_shape, ffn_num_input, ffn_num_hiddens,
num_heads, num_layers, dropout, **kwargs):
super(TransformerDecoder, self).__init__(**kwargs)
self.num_hiddens = num_hiddens
self.num_layers = num_layers
self.embedding = nn.Embedding(vocab_size, num_hiddens)
self.pos_encoding = d2l.PositionalEncoding(num_hiddens, dropout)
self.blks = nn.Sequential()
for i in range(num_layers):
self.blks.add_module("block"+str(i),
DecoderBlock(key_size, query_size, value_size, num_hiddens,
norm_shape, ffn_num_input, ffn_num_hiddens,
num_heads, dropout, i))
self.dense = nn.Linear(num_hiddens, vocab_size)
def init_state(self, enc_outputs, enc_valid_lens, *args):
return [enc_outputs, enc_valid_lens, [None] * self.num_layers]
def forward(self, X, state):
X = self.pos_encoding(self.embedding(X) * math.sqrt(self.num_hiddens))
self._attention_weights = [[None] * len(self.blks) for _ in range (2)]
for i, blk in enumerate(self.blks):
X, state = blk(X, state)
# Decoder self-attention weights
self._attention_weights[0][
i] = blk.attention1.attention.attention_weights
# Encoder-decoder attention weights
self._attention_weights[1][
i] = blk.attention2.attention.attention_weights
return self.dense(X), state
@property
def attention_weights(self):
return self._attention_weights
Let us instantiate an encoder-decoder model
by following the Transformer architecture.
Here we specify that
both the Transformer encoder and the Transformer decoder
have 2 layers using 4-head attention.
Similar to :numref:sec_seq2seq_training,
we train the Transformer model
for sequence to sequence learning on the English-French machine translation dataset.
num_hiddens, num_layers, dropout, batch_size, num_steps = 32, 2, 0.1, 64, 10
lr, num_epochs, device = 0.005, 200, d2l.try_gpu()
ffn_num_hiddens, num_heads = 64, 4
train_iter, src_vocab, tgt_vocab = d2l.load_data_nmt(batch_size, num_steps)
encoder = TransformerEncoder(
len(src_vocab), num_hiddens, ffn_num_hiddens, num_heads, num_layers,
dropout)
decoder = TransformerDecoder(
len(tgt_vocab), num_hiddens, ffn_num_hiddens, num_heads, num_layers,
dropout)
net = d2l.EncoderDecoder(encoder, decoder)
d2l.train_seq2seq(net, train_iter, lr, num_epochs, tgt_vocab, device)
#@tab pytorch
num_hiddens, num_layers, dropout, batch_size, num_steps = 32, 2, 0.1, 64, 10
lr, num_epochs, device = 0.005, 200, d2l.try_gpu()
ffn_num_input, ffn_num_hiddens, num_heads = 32, 64, 4
key_size, query_size, value_size = 32, 32, 32
norm_shape = [32]
train_iter, src_vocab, tgt_vocab = d2l.load_data_nmt(batch_size, num_steps)
encoder = TransformerEncoder(
len(src_vocab), key_size, query_size, value_size, num_hiddens,
norm_shape, ffn_num_input, ffn_num_hiddens, num_heads,
num_layers, dropout)
decoder = TransformerDecoder(
len(tgt_vocab), key_size, query_size, value_size, num_hiddens,
norm_shape, ffn_num_input, ffn_num_hiddens, num_heads,
num_layers, dropout)
net = d2l.EncoderDecoder(encoder, decoder)
d2l.train_seq2seq(net, train_iter, lr, num_epochs, tgt_vocab, device)
After training, we use the Transformer model to translate a few English sentences into French and compute their BLEU scores.
#@tab all
engs = ['go .', "i lost .", 'he\'s calm .', 'i\'m home .']
fras = ['va !', 'j\'ai perdu .', 'il est calme .', 'je suis chez moi .']
for eng, fra in zip(engs, fras):
translation, dec_attention_weight_seq = d2l.predict_seq2seq(
net, eng, src_vocab, tgt_vocab, num_steps, device, True)
print(f'{eng} => {translation}, ',
f'bleu {d2l.bleu(translation, fra, k=2):.3f}')
Let us visualize the Transformer attention weights when translating the last English sentence into French.
The shape of the encoder self-attention weights
is (number of encoder layers, number of attention heads, num_steps or number of queries, num_steps or number of key-value pairs).
#@tab all
enc_attention_weights = d2l.reshape(
d2l.concat(net.encoder.attention_weights, 0),
(num_layers, num_heads, -1, num_steps))
enc_attention_weights.shape
In the encoder self-attention, both queries and keys come from the same input sequence. Since padding tokens do not carry meaning, with specified valid length of the input sequence, no query attends to positions of padding tokens. In the following, two layers of multi-head attention weights are presented row by row. Each head independently attends based on a separate representation subspaces of queries, keys, and values.
d2l.show_heatmaps(
enc_attention_weights, xlabel='Key positions', ylabel='Query positions',
titles=['Head %d' % i for i in range(1, 5)], figsize=(7, 3.5))
#@tab pytorch
d2l.show_heatmaps(
enc_attention_weights.cpu(), xlabel='Key positions',
ylabel='Query positions', titles=['Head %d' % i for i in range(1, 5)],
figsize=(7, 3.5))
To visualize both the decoder self-attention weights and the encoder-decoder attention weights, we need more data manipulations. For example, we fill the masked attention weights with zero. Note that the decoder self-attention weights and the encoder-decoder attention weights both have the same queries: the beginning-of-sequence token followed by the output tokens.
dec_attention_weights_2d = [d2l.tensor(head[0]).tolist()
for step in dec_attention_weight_seq
for attn in step for blk in attn for head in blk]
dec_attention_weights_filled = d2l.tensor(
pd.DataFrame(dec_attention_weights_2d).fillna(0.0).values)
dec_attention_weights = d2l.reshape(dec_attention_weights_filled,
(-1, 2, num_layers, num_heads, num_steps))
dec_self_attention_weights, dec_inter_attention_weights = \
dec_attention_weights.transpose(1, 2, 3, 0, 4)
dec_self_attention_weights.shape, dec_inter_attention_weights.shape
#@tab pytorch
dec_attention_weights_2d = [head[0].tolist()
for step in dec_attention_weight_seq
for attn in step for blk in attn for head in blk]
dec_attention_weights_filled = d2l.tensor(
pd.DataFrame(dec_attention_weights_2d).fillna(0.0).values)
dec_attention_weights = d2l.reshape(dec_attention_weights_filled,
(-1, 2, num_layers, num_heads, num_steps))
dec_self_attention_weights, dec_inter_attention_weights = \
dec_attention_weights.permute(1, 2, 3, 0, 4)
dec_self_attention_weights.shape, dec_inter_attention_weights.shape
Due to the auto-regressive property of the decoder self-attention, no query attends to key-value pairs after the query position.
#@tab all
# Plus one to include the beginning-of-sequence token
d2l.show_heatmaps(
dec_self_attention_weights[:, :, :, :len(translation.split()) + 1],
xlabel='Key positions', ylabel='Query positions',
titles=['Head %d' % i for i in range(1, 5)], figsize=(7, 3.5))
Similar to the case in the encoder self-attention, via the specified valid length of the input sequence, no query from the output sequence attends to those padding tokens from the input sequence.
#@tab all
d2l.show_heatmaps(
dec_inter_attention_weights, xlabel='Key positions',
ylabel='Query positions', titles=['Head %d' % i for i in range(1, 5)],
figsize=(7, 3.5))
Although the Transformer architecture was originally proposed for sequence-to-sequence learning, as we will discover later in the book, either the Transformer encoder or the Transformer decoder is often individually used for different deep learning tasks.
- The Transformer is an instance of the encoder-decoder architecture, though either the encoder or the decoder can be used individually in practice.
- In the Transformer, multi-head self-attention is used for representing the input sequence and the output sequence, though the decoder has to preserve the auto-regressive property via a masked version.
- Both the residual connections and the layer normalization in the Transformer are important for training a very deep model.
- The positionwise feed-forward network in the Transformer model transforms the representation at all the sequence positions using the same MLP.
- Train a deeper Transformer in the experiments. How does it affect the training speed and the translation performance?
- Is it a good idea to replace scaled dot-product attention with additive attention in the Transformer? Why?
- For language modeling, should we use the Transformer encoder, decoder, or both? How to design this method?
- What can be challenges to Transformers if input sequences are very long? Why?
- How to improve computational and memory efficiency of Transformers? Hint: you may refer to the survey paper by Tay et al. :cite:
Tay.Dehghani.Bahri.ea.2020. - How can we design Transformer-based models for image classification tasks without using CNNs? Hint: you may refer to the Vision Transformer :cite:
Dosovitskiy.Beyer.Kolesnikov.ea.2021.
:begin_tab:mxnet
Discussions
:end_tab:
:begin_tab:pytorch
Discussions
:end_tab: