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transformer.py
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'''
@Description:
@version:
@License: MIT
@Author: Wang Yao
@Date: 2020-03-23 19:42:15
@LastEditors: Wang Yao
@LastEditTime: 2020-03-27 17:50:33
'''
import os
import numpy as np
import tensorflow as tf
import tensorflow.keras.backend as K
from tensorflow.keras.callbacks import Callback
from layers import PositionEncoding
from layers import MultiHeadAttention, PositionWiseFeedForward
from layers import Add, LayerNormalization
tf.config.experimental_run_functions_eagerly(True)
class Transformer(tf.keras.layers.Layer):
def __init__(self, vocab_size, model_dim,
n_heads=8, encoder_stack=6, decoder_stack=6, feed_forward_size=2048, dropout_rate=0.1, **kwargs):
self._vocab_size = vocab_size
self._model_dim = model_dim
self._n_heads = n_heads
self._encoder_stack = encoder_stack
self._decoder_stack = decoder_stack
self._feed_forward_size = feed_forward_size
self._dropout_rate = dropout_rate
super(Transformer, self).__init__(**kwargs)
def build(self, input_shape):
self.embeddings = self.add_weight(
shape=(self._vocab_size, self._model_dim),
initializer='glorot_uniform',
trainable=True,
name="embeddings")
super(Transformer, self).build(input_shape)
def encoder(self, inputs):
if K.dtype(inputs) != 'int32':
inputs = K.cast(inputs, 'int32')
masks = K.equal(inputs, 0)
# Embeddings
embeddings = K.gather(self.embeddings, inputs)
embeddings *= self._model_dim ** 0.5 # Scale
# Position Encodings
position_encodings = PositionEncoding(self._model_dim)(embeddings)
# Embedings + Postion-encodings
encodings = embeddings + position_encodings
# Dropout
encodings = K.dropout(encodings, self._dropout_rate)
for i in range(self._encoder_stack):
# Multi-head-Attention
attention = MultiHeadAttention(
self._n_heads, self._model_dim // self._n_heads)
attention_input = [encodings, encodings, encodings, masks]
attention_out = attention(attention_input)
# Add & Norm
attention_out += encodings
attention_out = LayerNormalization()(attention_out)
# Feed-Forward
ff = PositionWiseFeedForward(
self._model_dim, self._feed_forward_size)
ff_out = ff(attention_out)
# Add & Norm
ff_out += attention_out
encodings = LayerNormalization()(ff_out)
return encodings, masks
def decoder(self, inputs):
decoder_inputs, encoder_encodings, encoder_masks = inputs
if K.dtype(decoder_inputs) != 'int32':
decoder_inputs = K.cast(decoder_inputs, 'int32')
decoder_masks = K.equal(decoder_inputs, 0)
# Embeddings
embeddings = K.gather(self.embeddings, decoder_inputs)
embeddings *= self._model_dim ** 0.5 # Scale
# Position Encodings
position_encodings = PositionEncoding(self._model_dim)(embeddings)
# Embedings + Postion-encodings
encodings = embeddings + position_encodings
# Dropout
encodings = K.dropout(encodings, self._dropout_rate)
for i in range(self._decoder_stack):
# Masked-Multi-head-Attention
masked_attention = MultiHeadAttention(
self._n_heads, self._model_dim // self._n_heads, future=True)
masked_attention_input = [encodings,
encodings, encodings, decoder_masks]
masked_attention_out = masked_attention(masked_attention_input)
# Add & Norm
masked_attention_out += encodings
masked_attention_out = LayerNormalization()(masked_attention_out)
# Multi-head-Attention
attention = MultiHeadAttention(
self._n_heads, self._model_dim // self._n_heads)
attention_input = [masked_attention_out,
encoder_encodings, encoder_encodings, encoder_masks]
attention_out = attention(attention_input)
# Add & Norm
attention_out += masked_attention_out
attention_out = LayerNormalization()(attention_out)
# Feed-Forward
ff = PositionWiseFeedForward(
self._model_dim, self._feed_forward_size)
ff_out = ff(attention_out)
# Add & Norm
ff_out += attention_out
encodings = LayerNormalization()(ff_out)
# Pre-Softmax 与 Embeddings 共享参数
linear_projection = K.dot(encodings, K.transpose(self.embeddings))
outputs = K.softmax(linear_projection)
return outputs
def call(self, inputs):
encoder_inputs, decoder_inputs = inputs
encoder_encodings, encoder_masks = self.encoder(encoder_inputs)
encoder_outputs = self.decoder(
[decoder_inputs, encoder_encodings, encoder_masks])
return encoder_outputs
def compute_output_shape(self, input_shape):
return (input_shape[0][0], input_shape[0][1], self._vocab_size)
class Noam(Callback):
def __init__(self, model_dim, step_num=0, warmup_steps=4000, verbose=False, **kwargs):
self._model_dim = model_dim
self._step_num = step_num
self._warmup_steps = warmup_steps
self.verbose = verbose
super(Noam, self).__init__(**kwargs)
def on_train_begin(self, logs=None):
logs = logs or {}
init_lr = self._model_dim ** -.5 * self._warmup_steps ** -1.5
K.set_value(self.model.optimizer.lr, init_lr)
def on_batch_end(self, epoch, logs=None):
logs = logs or {}
self._step_num += 1
lrate = self._model_dim ** -.5 * \
K.minimum(self._step_num ** -.5, self._step_num *
self._warmup_steps ** -1.5)
K.set_value(self.model.optimizer.lr, lrate)
def on_epoch_begin(self, epoch, logs=None):
if self.verbose:
lrate = K.get_value(self.model.optimizer.lr)
print(f"epoch {epoch} lr: {lrate}")
def on_epoch_end(self, epoch, logs=None):
logs = logs or {}
logs['lr'] = K.get_value(self.model.optimizer.lr)
def label_smoothing(inputs, epsilon=0.1):
output_dim = inputs.shape[-1]
smooth_label = (1 - epsilon) * inputs + (epsilon / output_dim)
return smooth_label
if __name__ == "__main__":
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input
from tensorflow.keras.utils import plot_model
vocab_size = 5000
max_seq_len = 256
model_dim = 512
encoder_inputs = Input(shape=(max_seq_len,), name='encoder_inputs')
decoder_inputs = Input(shape=(max_seq_len,), name='decoder_inputs')
outputs = Transformer(vocab_size, model_dim)(
[encoder_inputs, decoder_inputs])
model = Model(inputs=[encoder_inputs, decoder_inputs], outputs=outputs)
model.summary()
plot_model(model, 'transformer.png')