forked from LongxingTan/Time-series-prediction
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transformer.py
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transformer.py
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# -*- coding: utf-8 -*-
# @author: Longxing Tan, tanlongxing888@163.com
# @date: 2020-01
# paper:
# other implementations: https://github.com/maxjcohen/transformer
import tensorflow as tf
from deepts.layers.attention_layer import *
params={
'n_layers':6,
'attention_hidden_size':64*8,
'num_heads':8,
'ffn_hidden_size':64*8,
'ffn_filter_size':64*8,
'attention_dropout':0.1,
'relu_dropout':0.1,
'layer_postprocess_dropout':0.1,
}
class Transformer(object):
def __init__(self, custom_model_params):
params.update(custom_model_params)
self.params=params
self.embedding_layer=EmbeddingLayer(embedding_size=self.params['attention_hidden_size'])
self.encoder_stack=EncoderStack(self.params)
self.decoder_stack=DecoderStack(self.params)
self.projection = tf.keras.layers.Dense(units=1)
def get_config(self):
return {}
def __call__(self, x, predict_seq_length, training):
assert isinstance(x, tuple), "please input both of inputs and targets"
inputs,targets=x
self.position_encoding_layer = PositionEncoding(max_len=inputs.get_shape().as_list()[1])
self.position_encoding_layer_2 = PositionEncoding(max_len=predict_seq_length)
if training:
src_mask = self.get_src_mask(inputs) # => batch_size * sequence_length
src_mask = self.get_src_mask_bias(src_mask) # => batch_size * 1 * 1 * input_sequence_length
memory=self.encoder(encoder_inputs=inputs,mask=src_mask,training=training)
decoder_output = self.decoder(targets,memory,src_mask,training=training,predict_seq_length=predict_seq_length)
outputs=self.projection(decoder_output)
return outputs
else:
src_mask = self.get_src_mask(x) # => batch_size * sequence_length
src_mask = self.get_src_mask_bias(src_mask) # => batch_size * 1 * 1 * input_sequence_length
memory = self.encoder(encoder_inputs=x, mask=src_mask, training=training)
decoder_inputs = tf.ones((x.shape[0], 1, 1), tf.int32)
for _ in range(predict_seq_length):
decoder_inputs_update=self.decoder(decoder_inputs,memory,src_mask,training)
decoder_inputs=tf.concat([decoder_inputs,decoder_inputs_update],axis=1)
def encoder(self,encoder_inputs, mask,training):
'''
:param inputs: sequence_inputs, batch_size * sequence_length * feature_dim
:param training:
:return:
'''
with tf.name_scope("encoder"):
src=self.embedding_layer(encoder_inputs) # batch_size * sequence_length * embedding_size
src+=self.position_encoding_layer(src)
if training:
src=tf.nn.dropout(src,rate=0.01) # batch_size * sequence_length * attention_hidden_size
return self.encoder_stack(src,mask,training)
def decoder(self,targets,memory,src_mask,training,predict_seq_length):
with tf.name_scope("shift_targets"):
# Shift targets to the right, and remove the last element
decoder_inputs = tf.pad(targets, [[0, 0], [1, 0], [0, 0]])[:, :-1, :]
tgt_mask=self.get_tgt_mask_bias(predict_seq_length)
tgt=self.embedding_layer(decoder_inputs)
with tf.name_scope("add_pos_encoding"):
pos_encoding = self.position_encoding_layer_2(tgt)
tgt += pos_encoding
if training:
tgt = tf.nn.dropout(tgt, rate=self.params["layer_postprocess_dropout"])
with tf.name_scope('decoder'):
logits=self.decoder_stack(tgt,memory,src_mask,tgt_mask,training) #Todo:mask
return logits
def predict(self):
pass
def get_src_mask(self,x,pad=0):
src_mask = tf.reduce_all(tf.math.equal(x, pad),axis=-1)
return src_mask
def get_src_mask_bias(self,mask):
attention_bias = tf.cast(mask, tf.float32)
attention_bias = attention_bias * tf.constant(-1e9, dtype=tf.float32)
attention_bias = tf.expand_dims(tf.expand_dims(attention_bias, 1),1) # => batch_size * 1 * 1 * input_length
return attention_bias
def get_tgt_mask_bias(self,length):
valid_locs = tf.linalg.band_part(tf.ones([length, length], dtype=tf.float32),-1, 0)
valid_locs = tf.reshape(valid_locs, [1, 1, length, length])
decoder_bias = -1e9 * (1.0 - valid_locs)
return decoder_bias
class EncoderStack(tf.keras.layers.Layer):
def __init__(self,params):
super(EncoderStack, self).__init__()
self.params=params
self.layers=[]
def build(self,input_shape):
for _ in range(self.params['n_layers']):
attention_layer=Attention(self.params['attention_hidden_size'],
self.params['num_heads'],
self.params['attention_dropout'])
feed_forward_layer=FeedForwardNetwork(self.params['ffn_hidden_size'],
self.params['ffn_filter_size'],
self.params['relu_dropout'])
post_attention_layer=SublayerConnection(attention_layer,self.params)
post_feed_forward_layer=SublayerConnection(feed_forward_layer,self.params)
self.layers.append([post_attention_layer,post_feed_forward_layer])
self.output_norm=tf.keras.layers.LayerNormalization(epsilon=1e-6, dtype="float32")
super(EncoderStack,self).build(input_shape)
def get_config(self):
return {
}
def call(self,encoder_inputs, src_mask, training):
for n, layer in enumerate(self.layers):
attention_layer = layer[0]
ffn_layer = layer[1]
with tf.name_scope('layer_{}'.format(n)):
with tf.name_scope('self_attention'):
encoder_inputs = attention_layer(encoder_inputs,encoder_inputs, src_mask, training=training)
with tf.name_scope('ffn'):
encoder_inputs = ffn_layer(encoder_inputs, training=training)
return self.output_norm(encoder_inputs)
class DecoderStack(tf.keras.layers.Layer):
def __init__(self,params):
super(DecoderStack,self).__init__()
self.params=params
self.layers=[]
def build(self,input_shape):
for _ in range(self.params['n_layers']):
self_attention_layer=Attention(self.params['attention_hidden_size'],
self.params['num_heads'],
self.params['attention_dropout'])
enc_dec_attention_layer=Attention(self.params['attention_hidden_size'],
self.params['num_heads'],
self.params['attention_dropout'])
feed_forward_layer=FeedForwardNetwork(self.params['ffn_hidden_size'],
self.params['ffn_filter_size'],
self.params['relu_dropout'])
post_self_attention_layer=SublayerConnection(self_attention_layer,self.params)
post_enc_dec_attention_layer=SublayerConnection(enc_dec_attention_layer,self.params)
post_feed_forward_layer=SublayerConnection(feed_forward_layer,self.params)
self.layers.append([post_self_attention_layer,post_enc_dec_attention_layer,post_feed_forward_layer])
self.output_norm=tf.keras.layers.LayerNormalization(epsilon=1e-6, dtype="float32")
super(DecoderStack,self).build(input_shape)
def get_config(self):
return {
'params':self.params
}
def call(self, decoder_inputs,encoder_outputs,src_mask,tgt_mask,training,cache=None,):
for n, layer in enumerate(self.layers):
self_attention_layer = layer[0]
enc_dec_attention_layer = layer[1]
ffn_layer = layer[2]
#layer_cache = cache[layer_name] if cache is not None else None
with tf.name_scope("dec_layer_{}".format(n)):
with tf.name_scope('self_attention'):
decoder_inputs = self_attention_layer(decoder_inputs,decoder_inputs,tgt_mask,training=training)
with tf.name_scope('enc_dec_attention'):
decoder_inputs = enc_dec_attention_layer(decoder_inputs,encoder_outputs,src_mask,training=training) # Todo: mask??
with tf.name_scope('ffn'):
decoder_inputs = ffn_layer(decoder_inputs,training=training)
return self.output_norm(decoder_inputs)