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import tensorflow as tf
import tensorflow_probability as tfp
from tensorflow import keras
class TransformerEncoderLayer(keras.layers.Layer):
def __init__(self, embed_dim, hidden_dim, num_heads, dropout_rate=0.1):
super().__init__()
self.attention = keras.layers.MultiHeadAttention(
num_heads=num_heads, key_dim=embed_dim
)
self.feed_forward = keras.Sequential(
[
keras.layers.Dense(hidden_dim, activation="relu"),
keras.layers.Dense(embed_dim, activation=None)
]
)
self.layernorm1 = keras.layers.LayerNormalization()
self.layernorm2 = keras.layers.LayerNormalization()
self.dropout1 = keras.layers.Dropout(dropout_rate)
self.dropout2 = keras.layers.Dropout(dropout_rate)
def call(self, inputs, padding_mask, training=False):
attn_out = self.attention(
query=inputs,
value=inputs,
key=inputs,
attention_mask=padding_mask
)
attn_out = self.dropout1(attn_out, training=training)
x = self.layernorm1(inputs + attn_out)
ff_out = self.feed_forward(x)
ff_out = self.dropout2(ff_out, training=training)
return self.layernorm2(x + ff_out)
class TransformerEncoder(keras.Model):
def __init__(self, num_layers, seq_length, embed_dim, hidden_dim, num_heads, vocab_size,
dropout_rate=0.1):
super().__init__()
self.embedding = PositionalEmbedding(seq_length, vocab_size, embed_dim)
self.dropout = keras.layers.Dropout(dropout_rate)
self.encoder_layers = [
TransformerEncoderLayer(embed_dim, hidden_dim, num_heads, dropout_rate=dropout_rate)
for _ in range(num_layers)
]
def call(self, inputs, padding_mask, training=False):
x = self.embedding(inputs)
x = self.dropout(x, training=training)
for i in range(len(self.encoder_layers)):
x = self.encoder_layers[i](x, padding_mask, training=training)
return x
class TransformerDecoderLayer(keras.layers.Layer):
def __init__(self, embed_dim, hidden_dim, num_heads, dropout_rate=0.1):
super().__init__()
self.self_attention = keras.layers.MultiHeadAttention(
num_heads=num_heads, key_dim=embed_dim
)
self.attention = keras.layers.MultiHeadAttention(
num_heads=num_heads, key_dim=embed_dim
)
self.feed_fordward = keras.Sequential(
[
keras.layers.Dense(hidden_dim, activation="relu"),
keras.layers.Dense(embed_dim, activation=None)
]
)
self.layernorm1 = keras.layers.LayerNormalization()
self.layernorm2 = keras.layers.LayerNormalization()
self.layernorm3 = keras.layers.LayerNormalization()
self.dropout1 = keras.layers.Dropout(dropout_rate)
self.dropout2 = keras.layers.Dropout(dropout_rate)
self.dropout3 = keras.layers.Dropout(dropout_rate)
def call(self, inputs, encoder_outputs, look_ahead_mask, training=False, padding_mask=None):
self_attn_out = self.self_attention(
query=inputs,
value=inputs,
key=inputs,
attention_mask=look_ahead_mask
)
self_attn_out = self.dropout1(self_attn_out, training=training)
x = self.layernorm1(inputs + self_attn_out)
attn_out = self.attention(
query=x,
value=encoder_outputs,
key=encoder_outputs,
attention_mask=padding_mask
)
attn_out = self.dropout2(attn_out, training=training)
x = self.layernorm2(x + attn_out)
ff_out = self.feed_fordward(x)
ff_out = self.dropout3(ff_out, training=training)
return self.layernorm3(x + ff_out)
class TransformerDecoder(keras.Model):
def __init__(self, num_layers, seq_length, embed_dim, hidden_dim, num_heads, vocab_size,
dropout_rate=0.1):
super().__init__()
self.embedding = PositionalEmbedding(seq_length, vocab_size, embed_dim)
self.dropout = keras.layers.Dropout(dropout_rate)
self.decoder_layers = [
TransformerDecoderLayer(embed_dim, hidden_dim, num_heads, dropout_rate=dropout_rate)
for _ in range(num_layers)
]
def call(self, inputs, encoder_outputs, training=False, padding_mask=None):
look_ahead_mask = get_look_ahead_mask(inputs)
x = self.embedding(inputs)
x = self.dropout(x, training=training)
for i in range(len(self.decoder_layers)):
x = self.decoder_layers[i](x, encoder_outputs, look_ahead_mask,
training=training, padding_mask=padding_mask)
return x
def get_padding_mask(inputs):
mask = tf.cast(tf.math.not_equal(inputs, 0), tf.int32)
return mask[:, tf.newaxis, :]
def get_look_ahead_mask(inputs):
input_shape = tf.shape(inputs)
batch_size, seq_length = input_shape[0], input_shape[1]
n = int(seq_length * (seq_length + 1) / 2)
mask = tfp.math.fill_triangular(tf.ones((n,), dtype=tf.int32))
mask = tf.repeat(mask[tf.newaxis, :], batch_size, axis=0)
return tf.minimum(mask, get_padding_mask(inputs))
class PositionalEmbedding(keras.layers.Layer):
def __init__(self, seq_length, vocab_size, embed_dim):
super().__init__()
self.token_embeddings = keras.layers.Embedding(
input_dim=vocab_size, output_dim=embed_dim
)
self.position_embeddings = keras.layers.Embedding(
input_dim=seq_length, output_dim=embed_dim
)
def call(self, inputs):
positions = tf.range(start=0, limit=tf.shape(inputs)[-1], delta=1)
embedded_tokens = self.token_embeddings(inputs)
embedded_positions = self.position_embeddings(positions)
return embedded_tokens + embedded_positions
class Transformer(keras.Model):
def __init__(self, encoder_layers, decoder_layers, input_seq_length, target_seq_length, embed_dim,
hidden_dim, num_heads, input_vocab_size, target_vocab_size, dropout_rate=0.1):
super().__init__()
self.encoder = TransformerEncoder(encoder_layers, input_seq_length, embed_dim, hidden_dim,
num_heads, input_vocab_size, dropout_rate=dropout_rate)
self.decoder = TransformerDecoder(decoder_layers, target_seq_length, embed_dim, hidden_dim,
num_heads, target_vocab_size, dropout_rate=dropout_rate)
self.linear = keras.layers.Dense(target_vocab_size, activation=None)
def call(self, inputs, training=False):
encoder_inputs, targets = inputs
padding_mask = get_padding_mask(encoder_inputs)
encoder_outputs = self.encoder(encoder_inputs, padding_mask, training=training)
decoder_outputs = self.decoder(targets, encoder_outputs, training=training,
padding_mask=padding_mask)
return self.linear(decoder_outputs) |