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Update transformer.md #1365

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83 changes: 40 additions & 43 deletions chapter_attention-mechanisms/transformer.md
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Original file line number Diff line number Diff line change
Expand Up @@ -297,12 +297,12 @@ class EncoderBlock(nn.Block):
#@save
class EncoderBlock(nn.Module):
"""Transformer编码器块"""
def __init__(self, key_size, query_size, value_size, num_hiddens,
def __init__(self, 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,
num_hiddens, num_heads, dropout,
use_bias)
self.addnorm1 = AddNorm(norm_shape, dropout)
self.ffn = PositionWiseFFN(
Expand All @@ -319,10 +319,10 @@ class EncoderBlock(nn.Module):
#@save
class EncoderBlock(tf.keras.layers.Layer):
"""Transformer编码器块"""
def __init__(self, key_size, query_size, value_size, num_hiddens,
def __init__(self, num_hiddens,
norm_shape, ffn_num_hiddens, num_heads, dropout, bias=False, **kwargs):
super().__init__(**kwargs)
self.attention = d2l.MultiHeadAttention(key_size, query_size, value_size, num_hiddens,
self.attention = d2l.MultiHeadAttention( num_hiddens,
num_heads, dropout, bias)
self.addnorm1 = AddNorm(norm_shape, dropout)
self.ffn = PositionWiseFFN(ffn_num_hiddens, num_hiddens)
Expand All @@ -338,12 +338,12 @@ class EncoderBlock(tf.keras.layers.Layer):
#@save
class EncoderBlock(nn.Layer):
"""transformer编码器块"""
def __init__(self, key_size, query_size, value_size, num_hiddens,
def __init__(self, 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,
num_hiddens, num_heads, dropout,
use_bias)
self.addnorm1 = AddNorm(norm_shape, dropout)
self.ffn = PositionWiseFFN(
Expand All @@ -369,7 +369,7 @@ encoder_blk(X, valid_lens).shape
#@tab pytorch, paddle
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 = EncoderBlock( 24, [100, 24], 24, 48, 8, 0.5)
encoder_blk.eval()
encoder_blk(X, valid_lens).shape
```
Expand All @@ -379,7 +379,7 @@ encoder_blk(X, valid_lens).shape
X = tf.ones((2, 100, 24))
valid_lens = tf.constant([3, 2])
norm_shape = [i for i in range(len(X.shape))][1:]
encoder_blk = EncoderBlock(24, 24, 24, 24, norm_shape, 48, 8, 0.5)
encoder_blk = EncoderBlock( 24, norm_shape, 48, 8, 0.5)
encoder_blk(X, valid_lens, training=False).shape
```

Expand Down Expand Up @@ -419,7 +419,7 @@ class TransformerEncoder(d2l.Encoder):
#@save
class TransformerEncoder(d2l.Encoder):
"""Transformer编码器"""
def __init__(self, vocab_size, key_size, query_size, value_size,
def __init__(self, vocab_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)
Expand All @@ -429,7 +429,7 @@ class TransformerEncoder(d2l.Encoder):
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,
EncoderBlock(num_hiddens,
norm_shape, ffn_num_input, ffn_num_hiddens,
num_heads, dropout, use_bias))

Expand All @@ -451,15 +451,15 @@ class TransformerEncoder(d2l.Encoder):
#@save
class TransformerEncoder(d2l.Encoder):
"""Transformer编码器"""
def __init__(self, vocab_size, key_size, query_size, value_size,
def __init__(self, vocab_size,
num_hiddens, norm_shape, ffn_num_hiddens, num_heads,
num_layers, dropout, bias=False, **kwargs):
super().__init__(**kwargs)
self.num_hiddens = num_hiddens
self.embedding = tf.keras.layers.Embedding(vocab_size, num_hiddens)
self.pos_encoding = d2l.PositionalEncoding(num_hiddens, dropout)
self.blks = [EncoderBlock(
key_size, query_size, value_size, num_hiddens, norm_shape,
num_hiddens, norm_shape,
ffn_num_hiddens, num_heads, dropout, bias) for _ in range(
num_layers)]

Expand All @@ -482,7 +482,7 @@ class TransformerEncoder(d2l.Encoder):
#@save
class TransformerEncoder(d2l.Encoder):
"""transformer编码器"""
def __init__(self, vocab_size, key_size, query_size, value_size,
def __init__(self, vocab_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)
Expand All @@ -492,7 +492,7 @@ class TransformerEncoder(d2l.Encoder):
self.blks = nn.Sequential()
for i in range(num_layers):
self.blks.add_sublayer(str(i),
EncoderBlock(key_size, query_size, value_size, num_hiddens,
EncoderBlock(num_hiddens,
norm_shape, ffn_num_input, ffn_num_hiddens,
num_heads, dropout, use_bias))

Expand Down Expand Up @@ -521,21 +521,21 @@ encoder(np.ones((2, 100)), valid_lens).shape
```{.python .input}
#@tab pytorch
encoder = TransformerEncoder(
200, 24, 24, 24, 24, [100, 24], 24, 48, 8, 2, 0.5)
200, 24, [100, 24], 24, 48, 8, 2, 0.5)
encoder.eval()
encoder(d2l.ones((2, 100), dtype=torch.long), valid_lens).shape
```

```{.python .input}
#@tab tensorflow
encoder = TransformerEncoder(200, 24, 24, 24, 24, [1, 2], 48, 8, 2, 0.5)
encoder = TransformerEncoder(200, 24, [1, 2], 48, 8, 2, 0.5)
encoder(tf.ones((2, 100)), valid_lens, training=False).shape
```

```{.python .input}
#@tab paddle
encoder = TransformerEncoder(
200, 24, 24, 24, 24, [100, 24], 24, 48, 8, 2, 0.5)
200, 24, [100, 24], 24, 48, 8, 2, 0.5)
encoder.eval()
encoder(d2l.ones((2, 100), dtype=paddle.int64), valid_lens).shape
```
Expand Down Expand Up @@ -597,16 +597,16 @@ class DecoderBlock(nn.Block):
#@tab pytorch
class DecoderBlock(nn.Module):
"""解码器中第i个块"""
def __init__(self, key_size, query_size, value_size, num_hiddens,
def __init__(self, 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)
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)
num_hiddens, num_heads, dropout)
self.addnorm2 = AddNorm(norm_shape, dropout)
self.ffn = PositionWiseFFN(ffn_num_input, ffn_num_hiddens,
num_hiddens)
Expand Down Expand Up @@ -646,13 +646,13 @@ class DecoderBlock(nn.Module):
#@tab tensorflow
class DecoderBlock(tf.keras.layers.Layer):
"""解码器中第i个块"""
def __init__(self, key_size, query_size, value_size, num_hiddens,
def __init__(self, num_hiddens,
norm_shape, ffn_num_hiddens, num_heads, dropout, i, **kwargs):
super().__init__(**kwargs)
self.i = i
self.attention1 = d2l.MultiHeadAttention(key_size, query_size, value_size, num_hiddens, num_heads, dropout)
self.attention1 = d2l.MultiHeadAttention(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.attention2 = d2l.MultiHeadAttention(num_hiddens, num_heads, dropout)
self.addnorm2 = AddNorm(norm_shape, dropout)
self.ffn = PositionWiseFFN(ffn_num_hiddens, num_hiddens)
self.addnorm3 = AddNorm(norm_shape, dropout)
Expand Down Expand Up @@ -692,16 +692,16 @@ class DecoderBlock(tf.keras.layers.Layer):
#@tab paddle
class DecoderBlock(nn.Layer):
"""解码器中第i个块"""
def __init__(self, key_size, query_size, value_size, num_hiddens,
def __init__(self, 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)
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)
num_hiddens, num_heads, dropout)
self.addnorm2 = AddNorm(norm_shape, dropout)
self.ffn = PositionWiseFFN(ffn_num_input, ffn_num_hiddens,
num_hiddens)
Expand Down Expand Up @@ -749,7 +749,7 @@ decoder_blk(X, state)[0].shape

```{.python .input}
#@tab pytorch, paddle
decoder_blk = DecoderBlock(24, 24, 24, 24, [100, 24], 24, 48, 8, 0.5, 0)
decoder_blk = DecoderBlock(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]]
Expand All @@ -758,7 +758,7 @@ decoder_blk(X, state)[0].shape

```{.python .input}
#@tab tensorflow
decoder_blk = DecoderBlock(24, 24, 24, 24, [1, 2], 48, 8, 0.5, 0)
decoder_blk = DecoderBlock(24, [1, 2], 48, 8, 0.5, 0)
X = tf.ones((2, 100, 24))
state = [encoder_blk(X, valid_lens), valid_lens, [None]]
decoder_blk(X, state, training=False)[0].shape
Expand Down Expand Up @@ -806,7 +806,7 @@ class TransformerDecoder(d2l.AttentionDecoder):
```{.python .input}
#@tab pytorch
class TransformerDecoder(d2l.AttentionDecoder):
def __init__(self, vocab_size, key_size, query_size, value_size,
def __init__(self, vocab_size,
num_hiddens, norm_shape, ffn_num_input, ffn_num_hiddens,
num_heads, num_layers, dropout, **kwargs):
super(TransformerDecoder, self).__init__(**kwargs)
Expand All @@ -817,7 +817,7 @@ class TransformerDecoder(d2l.AttentionDecoder):
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,
DecoderBlock(num_hiddens,
norm_shape, ffn_num_input, ffn_num_hiddens,
num_heads, dropout, i))
self.dense = nn.Linear(num_hiddens, vocab_size)
Expand Down Expand Up @@ -846,14 +846,14 @@ class TransformerDecoder(d2l.AttentionDecoder):
```{.python .input}
#@tab tensorflow
class TransformerDecoder(d2l.AttentionDecoder):
def __init__(self, vocab_size, key_size, query_size, value_size,
def __init__(self, vocab_size,
num_hiddens, norm_shape, ffn_num_hidens, num_heads, num_layers, dropout, **kwargs):
super().__init__(**kwargs)
self.num_hiddens = num_hiddens
self.num_layers = num_layers
self.embedding = tf.keras.layers.Embedding(vocab_size, num_hiddens)
self.pos_encoding = d2l.PositionalEncoding(num_hiddens, dropout)
self.blks = [DecoderBlock(key_size, query_size, value_size, num_hiddens, norm_shape,
self.blks = [DecoderBlock(num_hiddens, norm_shape,
ffn_num_hiddens, num_heads, dropout, i) for i in range(num_layers)]
self.dense = tf.keras.layers.Dense(vocab_size)

Expand All @@ -879,7 +879,7 @@ class TransformerDecoder(d2l.AttentionDecoder):
```{.python .input}
#@tab paddle
class TransformerDecoder(d2l.AttentionDecoder):
def __init__(self, vocab_size, key_size, query_size, value_size,
def __init__(self, vocab_size,
num_hiddens, norm_shape, ffn_num_input, ffn_num_hiddens,
num_heads, num_layers, dropout, **kwargs):
super(TransformerDecoder, self).__init__(**kwargs)
Expand All @@ -890,7 +890,7 @@ class TransformerDecoder(d2l.AttentionDecoder):
self.blks = nn.Sequential()
for i in range(num_layers):
self.blks.add_sublayer(str(i),
DecoderBlock(key_size, query_size, value_size, num_hiddens,
DecoderBlock(num_hiddens,
norm_shape, ffn_num_input, ffn_num_hiddens,
num_heads, dropout, i))
self.dense = nn.Linear(num_hiddens, vocab_size)
Expand Down Expand Up @@ -942,17 +942,16 @@ d2l.train_seq2seq(net, train_iter, lr, num_epochs, tgt_vocab, device)
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,
len(src_vocab), 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,
len(tgt_vocab), num_hiddens,
norm_shape, ffn_num_input, ffn_num_hiddens, num_heads,
num_layers, dropout)
net = d2l.EncoderDecoder(encoder, decoder)
Expand All @@ -964,15 +963,14 @@ d2l.train_seq2seq(net, train_iter, lr, num_epochs, tgt_vocab, device)
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
key_size, query_size, value_size = 32, 32, 32
norm_shape = [2]

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,
len(src_vocab), num_hiddens, norm_shape,
ffn_num_hiddens, num_heads, num_layers, dropout)
decoder = TransformerDecoder(
len(tgt_vocab), key_size, query_size, value_size, num_hiddens, norm_shape,
len(tgt_vocab), num_hiddens, norm_shape,
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)
Expand All @@ -983,17 +981,16 @@ d2l.train_seq2seq(net, train_iter, lr, num_epochs, tgt_vocab, device)
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,
len(src_vocab), 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,
len(tgt_vocab), num_hiddens,
norm_shape, ffn_num_input, ffn_num_hiddens, num_heads,
num_layers, dropout)
net = d2l.EncoderDecoder(encoder, decoder)
Expand Down
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