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modules.py
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# -*- coding: utf-8 -*-
#/usr/bin/python2
'''
June 2017 by kyubyong park.
https://www.github.com/kyubyong/transformer
'''
from __future__ import print_function
import tensorflow as tf
import numpy as np
def positional_encoding(dim, sentence_length, dtype=tf.float32):
encoded_vec = np.array([pos/np.power(10000, 2*i/dim) for pos in range(sentence_length) for i in range(dim)])
encoded_vec[::2] = np.sin(encoded_vec[::2])
encoded_vec[1::2] = np.cos(encoded_vec[1::2])
return tf.convert_to_tensor(encoded_vec.reshape([sentence_length, dim]), dtype=dtype)
def normalize(inputs,
epsilon = 1e-8,
scope="ln",
reuse=None):
'''Applies layer normalization.
Args:
inputs: A tensor with 2 or more dimensions, where the first dimension has
`batch_size`.
epsilon: A floating number. A very small number for preventing ZeroDivision Error.
scope: Optional scope for `variable_scope`.
reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Returns:
A tensor with the same shape and data dtype as `inputs`.
'''
with tf.variable_scope(scope, reuse=reuse):
inputs_shape = inputs.get_shape()
params_shape = inputs_shape[-1:]
mean, variance = tf.nn.moments(inputs, [-1], keep_dims=True)
beta= tf.Variable(tf.zeros(params_shape))
gamma = tf.Variable(tf.ones(params_shape))
normalized = (inputs - mean) / ( (variance + epsilon) ** (.5) )
outputs = gamma * normalized + beta
return outputs
def embedding(inputs,
vocab_size,
num_units,
zero_pad=True,
scale=True,
l2_reg=0.0,
scope="embedding",
with_t=False,
reuse=None):
'''Embeds a given tensor.
Args:
inputs: A `Tensor` with type `int32` or `int64` containing the ids
to be looked up in `lookup table`.
vocab_size: An int. Vocabulary size.
num_units: An int. Number of embedding hidden units.
zero_pad: A boolean. If True, all the values of the fist row (id 0)
should be constant zeros.
scale: A boolean. If True. the outputs is multiplied by sqrt num_units.
scope: Optional scope for `variable_scope`.
reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Returns:
A `Tensor` with one more rank than inputs's. The last dimensionality
should be `num_units`.
For example,
```
import tensorflow as tf
inputs = tf.to_int32(tf.reshape(tf.range(2*3), (2, 3)))
outputs = embedding(inputs, 6, 2, zero_pad=True)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
print sess.run(outputs)
>>
[[[ 0. 0. ]
[ 0.09754146 0.67385566]
[ 0.37864095 -0.35689294]]
[[-1.01329422 -1.09939694]
[ 0.7521342 0.38203377]
[-0.04973143 -0.06210355]]]
```
```
import tensorflow as tf
inputs = tf.to_int32(tf.reshape(tf.range(2*3), (2, 3)))
outputs = embedding(inputs, 6, 2, zero_pad=False)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
print sess.run(outputs)
>>
[[[-0.19172323 -0.39159766]
[-0.43212751 -0.66207761]
[ 1.03452027 -0.26704335]]
[[-0.11634696 -0.35983452]
[ 0.50208133 0.53509563]
[ 1.22204471 -0.96587461]]]
```
'''
with tf.variable_scope(scope, reuse=reuse):
lookup_table = tf.get_variable('lookup_table',
dtype=tf.float32,
shape=[vocab_size, num_units],
#initializer=tf.contrib.layers.xavier_initializer(),
regularizer=tf.keras.regularizers.l2(l=l2_reg)
)
if zero_pad:
lookup_table = tf.concat((tf.zeros(shape=[1, num_units]),
lookup_table[1:, :]), 0)
outputs = tf.nn.embedding_lookup(lookup_table, inputs)
if scale:
outputs = outputs * (num_units ** 0.5)
if with_t: return outputs,lookup_table
else: return outputs
def multihead_attention(queries,
keys,
time_matrix_K,
time_matrix_V,
absolute_pos_K,
absolute_pos_V,
num_units=None,
num_heads=8,
dropout_rate=0,
is_training=True,
causality=False,
scope="multihead_attention",
reuse=None,
with_qk=False,
):
'''Applies multihead attention.
Args:
queries: A 3d tensor with shape of [N, T_q, C_q].
keys: A 3d tensor with shape of [N, T_k, C_k].
num_units: A scalar. Attention size.
dropout_rate: A floating point number.
is_training: Boolean. Controller of mechanism for dropout.
causality: Boolean. If true, units that reference the future are masked.
num_heads: An int. Number of heads.
scope: Optional scope for `variable_scope`.
reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Returns
A 3d tensor with shape of (N, T_q, C)
'''
with tf.variable_scope(scope, reuse=reuse):
# Set the fall back option for num_units
if num_units is None:
num_units = queries.get_shape().as_list[-1]
Q = tf.layers.dense(queries, num_units, activation=None) # (N, T_q, C)
K = tf.layers.dense(keys, num_units, activation=None) # (N, T_k, C)
V = tf.layers.dense(keys, num_units, activation=None) # (N, T_k, C)
# Split and concat
Q_ = tf.concat(tf.split(Q, num_heads, axis=2), axis=0) # (h*N, T_q, C/h)
K_ = tf.concat(tf.split(K, num_heads, axis=2), axis=0) # (h*N, T_k, C/h)
V_ = tf.concat(tf.split(V, num_heads, axis=2), axis=0) # (h*N, T_k, C/h)
time_matrix_K_ = tf.concat(tf.split(time_matrix_K, num_heads, axis=3), axis=0)
time_matrix_V_ = tf.concat(tf.split(time_matrix_V, num_heads, axis=3), axis=0)
absolute_pos_K_ = tf.concat(tf.split(absolute_pos_K, num_heads, axis=2), axis=0)
absolute_pos_V_ = tf.concat(tf.split(absolute_pos_V, num_heads, axis=2), axis=0)
# Multiplication
outputs = tf.matmul(Q_, tf.transpose(K_, [0, 2, 1])) # (h*N, T_q, T_k)
outputs_pos = tf.matmul(Q_, tf.transpose(absolute_pos_K_, [0,2,1]))
outputs_time = tf.squeeze(tf.matmul(time_matrix_K_, tf.expand_dims(Q_,axis=3)))
outputs = outputs + outputs_time + outputs_pos
# Scale
outputs = outputs / (K_.get_shape().as_list()[-1] ** 0.5)
# Key Masking
key_masks = tf.sign(tf.reduce_sum(tf.abs(keys), axis=-1)) # (N, T_k)
key_masks = tf.tile(key_masks, [num_heads, 1]) # (h*N, T_k)
key_masks = tf.tile(tf.expand_dims(key_masks, 1), [1, tf.shape(queries)[1], 1]) # (h*N, T_q, T_k)
paddings = tf.ones_like(outputs)*(-2**32+1)
outputs = tf.where(tf.equal(key_masks, 0), paddings, outputs) # (h*N, T_q, T_k)
# Causality = Future blinding
if causality:
diag_vals = tf.ones_like(outputs[0, :, :]) # (T_q, T_k)
tril = tf.linalg.LinearOperatorLowerTriangular(diag_vals).to_dense() # (T_q, T_k)
masks = tf.tile(tf.expand_dims(tril, 0), [tf.shape(outputs)[0], 1, 1]) # (h*N, T_q, T_k)
paddings = tf.ones_like(masks)*(-2**32+1)
outputs = tf.where(tf.equal(masks, 0), paddings, outputs) # (h*N, T_q, T_k)
# Activation
outputs = tf.nn.softmax(outputs)
# Query Masking
query_masks = tf.sign(tf.reduce_sum(tf.abs(queries), axis=-1)) # (N, T_q)
query_masks = tf.tile(query_masks, [num_heads, 1]) # (h*N, T_q)
query_masks = tf.tile(tf.expand_dims(query_masks, -1), [1, 1, tf.shape(keys)[1]]) # (h*N, T_q, T_k)
outputs *= query_masks # broadcasting. (N, T_q, C)
# Dropouts
outputs = tf.layers.dropout(outputs, rate=dropout_rate, training=tf.convert_to_tensor(is_training))
# Weighted sum
outputs_value = tf.matmul(outputs, V_) # ( h*N, T_q, C/h)
outputs_pos_value = tf.matmul(outputs, absolute_pos_V_)
output_time_value = tf.reshape(tf.matmul(tf.expand_dims(outputs, axis=2), time_matrix_V_), [tf.shape(outputs_pos)[0], tf.shape(outputs_pos)[1], num_units])
outputs = outputs_value + output_time_value + outputs_pos_value
# Restore shape
outputs = tf.concat(tf.split(outputs, num_heads, axis=0), axis=2 ) # (N, T_q, C)
# Residual connection
outputs += queries
# Normalize
#outputs = normalize(outputs) # (N, T_q, C)
if with_qk: return Q,K
else: return outputs
def feedforward(inputs,
num_units=[2048, 512],
scope="multihead_attention",
dropout_rate=0.2,
is_training=True,
reuse=None):
'''Point-wise feed forward net.
Args:
inputs: A 3d tensor with shape of [N, T, C].
num_units: A list of two integers.
scope: Optional scope for `variable_scope`.
reuse: Boolean, whether to reuse the weights of a previous layer
by the same name.
Returns:
A 3d tensor with the same shape and dtype as inputs
'''
with tf.variable_scope(scope, reuse=reuse):
# Inner layer
params = {"inputs": inputs, "filters": num_units[0], "kernel_size": 1,
"activation": tf.nn.relu, "use_bias": True}
outputs = tf.layers.conv1d(**params)
outputs = tf.layers.dropout(outputs, rate=dropout_rate, training=tf.convert_to_tensor(is_training))
# Readout layer
params = {"inputs": outputs, "filters": num_units[1], "kernel_size": 1,
"activation": None, "use_bias": True}
outputs = tf.layers.conv1d(**params)
outputs = tf.layers.dropout(outputs, rate=dropout_rate, training=tf.convert_to_tensor(is_training))
# Residual connection
outputs += inputs
# Normalize
#outputs = normalize(outputs)
return outputs