File size: 8,444 Bytes
eea3ce3 |
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 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 |
import tensorflow as tf
from tensorflow import keras
from keras import layers
class TransformerEncoder(layers.Layer):
"""
The TransformerEncoder class is a custom Keras layer that implements a
single transformer encoder block. The transformer encoder block consists
of a multi-head self-attention layer followed by a feedforward neural
network with a residual connection and layer normalization applied at
the input and output of each sub-layer.
The class takes in the following arguments:
embed_dim: an integer specifying the dimensionality of the embedding space.
dense_dim: an integer specifying the number of units in the feedforward neural network.
num_heads: an integer specifying the number of attention heads to use.
The call method is the main computation performed by the layer. It takes
in an input tensor and an optional mask tensor indicating which inputs to
consider in the attention calculation. It returns the output tensor of the
transformer encoder block.
The get_config method returns a dictionary of configuration information for
the layer, including the embed_dim, num_heads, and dense_dim parameters.
"""
def __init__(self, embed_dim, dense_dim, num_heads, **kwargs):
super().__init__(**kwargs)
self.embed_dim = embed_dim
self.dense_dim = dense_dim
self.num_heads = num_heads
self.attention = layers.MultiHeadAttention(
num_heads=num_heads, key_dim=embed_dim)
self.dense_proj = keras.Sequential(
[layers.Dense(dense_dim, activation="relu"),
layers.Dense(embed_dim),]
)
self.layernorm_1 = layers.LayerNormalization()
self.layernorm_2 = layers.LayerNormalization()
def call(self, inputs, mask=None):
if mask is not None:
mask = mask[:, tf.newaxis, :]
attention_output = self.attention(
inputs, inputs, attention_mask=mask)
proj_input = self.layernorm_1(inputs + attention_output)
proj_output = self.dense_proj(proj_input)
return self.layernorm_2(proj_input + proj_output)
def get_config(self):
config = super().get_config()
config.update({
"embed_dim": self.embed_dim,
"num_heads": self.num_heads,
"dense_dim": self.dense_dim,
})
return config
class TransformerDecoder(layers.Layer):
"""
A Transformer decoder layer that attends over the input
sequence and the encoder outputs.
Args:
embed_dim (int): Dimension of the input embeddings.
dense_dim (int): Dimension of the dense layer in the feedforward sublayer.
num_heads (int): Number of attention heads in each multi-head attention layer.
Attributes:
attention_1 (MultiHeadAttention): First multi-head attention layer.
attention_2 (MultiHeadAttention): Second multi-head attention layer.
dense_proj (Sequential): Feedforward sublayer consisting of two dense layers.
layernorm_1 (LayerNormalization): Layer normalization layer
after the first attention layer.
layernorm_2 (LayerNormalization): Layer normalization layer
after the second attention layer.
layernorm_3 (LayerNormalization): Layer normalization layer
after the feedforward sublayer.
supports_masking (bool): Whether the layer supports masking.
Methods:
get_config(): Returns a dictionary with the configuration of the layer.
get_causal_attention_mask(inputs): Returns a 3D tensor with a
causal mask for the given input sequence.
call(inputs, encoder_outputs, mask=None): Computes the output of
the layer for the given inputs and encoder outputs.
"""
def __init__(self, embed_dim, dense_dim, num_heads, **kwargs):
super().__init__(**kwargs)
self.embed_dim = embed_dim
self.dense_dim = dense_dim
self.num_heads = num_heads
self.attention_1 = layers.MultiHeadAttention(
num_heads=num_heads, key_dim=embed_dim)
self.attention_2 = layers.MultiHeadAttention(
num_heads=num_heads, key_dim=embed_dim)
self.dense_proj = keras.Sequential(
[layers.Dense(dense_dim, activation="relu"),
layers.Dense(embed_dim),]
)
self.layernorm_1 = layers.LayerNormalization()
self.layernorm_2 = layers.LayerNormalization()
self.layernorm_3 = layers.LayerNormalization()
self.supports_masking = True
def get_config(self):
config = super().get_config()
config.update({
"embed_dim": self.embed_dim,
"num_heads": self.num_heads,
"dense_dim": self.dense_dim,
})
return config
def get_causal_attention_mask(self, inputs):
input_shape = tf.shape(inputs)
batch_size, sequence_length = input_shape[0], input_shape[1]
i = tf.range(sequence_length)[:, tf.newaxis]
j = tf.range(sequence_length)
mask = tf.cast(i >= j, dtype="int32")
mask = tf.reshape(mask, (1, input_shape[1], input_shape[1]))
mult = tf.concat(
[tf.expand_dims(batch_size, -1),
tf.constant([1, 1], dtype=tf.int32)], axis=0)
return tf.tile(mask, mult)
def call(self, inputs, encoder_outputs, mask=None):
causal_mask = self.get_causal_attention_mask(inputs)
if mask is not None:
padding_mask = tf.cast(
mask[:, tf.newaxis, :], dtype="int32")
padding_mask = tf.minimum(padding_mask, causal_mask)
attention_output_1 = self.attention_1(
query=inputs,
value=inputs,
key=inputs,
attention_mask=causal_mask)
attention_output_1 = self.layernorm_1(inputs + attention_output_1)
attention_output_2 = self.attention_2(
query=attention_output_1,
value=encoder_outputs,
key=encoder_outputs,
attention_mask=padding_mask,
)
attention_output_2 = self.layernorm_2(
attention_output_1 + attention_output_2)
proj_output = self.dense_proj(attention_output_2)
return self.layernorm_3(attention_output_2 + proj_output)
class PositionalEmbedding(layers.Layer):
"""
The PositionalEmbedding layer class is used to create an embedding layer that
combines both token embeddings and positional embeddings for input sequences.
The class takes in the following arguments:
sequence_length: An integer representing the maximum length of the input sequence.
input_dim: An integer representing the size of the input vocabulary.
output_dim: An integer representing the size of the embedding vectors.
The call(self, inputs) method that takes input tensor as an argument and
returns the embedded tensor after adding the token embeddings and positional
embeddings. It also computes the positions for the input sequence.
The compute_mask(self, inputs, mask=None) method that returns a mask tensor
computed based on the input tensor.
The get_config(self): Method that returns a dictionary containing the configuration
of the layer.
"""
def __init__(self, sequence_length, input_dim, output_dim, **kwargs):
super().__init__(**kwargs)
self.token_embeddings = layers.Embedding(
input_dim=input_dim, output_dim=output_dim)
self.position_embeddings = layers.Embedding(
input_dim=sequence_length, output_dim=output_dim)
self.sequence_length = sequence_length
self.input_dim = input_dim
self.output_dim = output_dim
def call(self, inputs):
length = tf.shape(inputs)[-1]
positions = tf.range(start=0, limit=length, delta=1)
embedded_tokens = self.token_embeddings(inputs)
embedded_positions = self.position_embeddings(positions)
return embedded_tokens + embedded_positions
def compute_mask(self, inputs, mask=None):
return tf.math.not_equal(inputs, 0)
def get_config(self):
config = super(PositionalEmbedding, self).get_config()
config.update({
"output_dim": self.output_dim,
"sequence_length": self.sequence_length,
"input_dim": self.input_dim,
})
return config |