def vae(state, num_units, scope='vae'):
"""VAE implementation, Hard Coding.
The formula to calculate the vae loss:
vae_loss = (-0.5 * tf.reduce_sum(1.0 + vae_vb - tf.square(vae_mean) - tf.exp(vae_vb)) / batch_size) * 0.001
"""
shape = ft.get_shape_list(state, expected_rank=2)
with tf.variable_scope(scope):
vae_mean = tf.layers.dense(state,
num_units,
activation=tf.nn.tanh,
name='vae_mean',
kernel_initializer=ft.create_initializer())
vae_vb = tf.layers.dense(state,
num_units,
activation=tf.nn.tanh,
name='vae_vb',
kernel_initializer=ft.create_initializer())
eps = tf.random_normal([shape[0], num_units],
0.0,
1.0,
dtype=tf.float32)
z = vae_mean + tf.sqrt(tf.exp(vae_vb)) * eps
return z, vae_mean, vae_vb
def embedding_postprocessor(input_tensor,
use_positional_embeddings=True,
positional_embedding_name='positional_embeddings',
initializer_range=0.02,
max_positional_embeddings=512,
dropout_prob=0.1):
"""Perform positional embeddings on a word embedding tensor.
Args:
input_tensor: float Tensor of shape [batch_size, seq_length, embedding_size].
use_position_embeddings: bool. Whether to add position embeddings for the
position of each token in the sequence.
position_embedding_name: string. The name of the embedding table variable
for positional embeddings.
initializer_range: float. Range of the weight initialization.
max_position_embeddings: int. Maximum sequence length that might ever be
used with this model. This can be longer than the sequence length of
input_tensor, but cannot be shorter.
dropout_prob: float. Dropout probability applied to the final output tensor.
Returns:
float tensor with same sahpe as 'input_tensor'.
"""
input_shape = ft.get_shape_list(input_tensor, expected_rank=3)
seq_length = input_shape[1]
width = input_shape[2]
if use_positional_embeddings:
assert_op = tf.assert_less_equal(seq_length, max_positional_embeddings)
with tf.control_dependencies([assert_op]):
full_positional_embeddings = tf.get_variable(
name=positional_embedding_name,
shape=[max_positional_embeddings, width],
initializer=ft.create_initializer(
initializer_range=initializer_range))
positional_embeddings = tf.slice(
full_positional_embeddings, [0, 0],
[seq_length, -1]) # [seq_length, width]
positional_embeddings = tf.expand_dims(positional_embeddings,
[0]) # [1, seq_length, width]
output = input_tensor + positional_embeddings
output = ft.layer_norm_and_dropout(output, dropout_prob)
return output
def embedding_lookup(input_ids,
vocab_size,
embedding_size,
initializer_range,
word_embedding_name='word_embeddings',
use_one_hot_embeddings=False):
"""Looks up words embeddings for id tensor.
Args:
input_ids: int32 Tensor of shape [batch_size, seq_length] containing word ids.
vocab_size: int. Size of the embedding vocabulary.
embedding_size: int. Width of the word embeddings.
initializer_range: float. Embedding initialation range.
word_embedding_name: string. Name of the embedding table.
use_one_hot_embeddings: bool. If True. use one-hot method for word embedding.
If False, use 'tf.gather()'.
Returns:
float Tensor of shape [batch_size, seq_length, embedding_size].
"""
embedding_table = tf.get_variable(
name=word_embedding_name,
shape=[vocab_size, embedding_size],
initializer=ft.create_initializer(initializer_range=initializer_range))
if use_one_hot_embeddings:
input_shape = ft.get_shape_list(input_ids, expected_rank=2)
input_ids_squeeze = tf.reshape(input_ids, [-1])
one_hot_input_ids = tf.one_hot(input_ids_squeeze, depth=vocab_size)
output = tf.matmul(one_hot_input_ids, embedding_table)
output = tf.reshape(output, [input_shape[0], input_shape[1], -1])
else:
output = tf.nn.embedding_lookup(embedding_table, input_ids)
return output, embedding_table
def transformer_model(input_tensor,
attention_mask,
hidden_size,
num_hidden_layers,
num_attention_heads,
intermediate_size,
intermediate_act_fn,
hidden_dropout_prob,
attention_dropout_prob,
initializer_range,
do_return_all_layers=False):
"""Multi-headed, multi-layer Transformer from 'Attention is All you need'.
This is almost an exact implementation of the original Transformer encoder.
Args:
input_tensor: float Tensor of shape [batch_size, seq_length, hidden_size].
attention_mask: (optional) int32 Tensor of shape [batch_size, seq_length,
seq_length], with 1 for positions that can be attended to and 0 in
positions that should not be.
hidden_size: int. Hidden size of the Transformer.
num_hidden_layers: int. Number of layers (blocks) in the Transformer.
num_attention_heads: int. Number of attention heads in the Transformer.
intermediate_size: int. The size of the "intermediate" (a.k.a., feed
forward) layer.
intermediate_act_fn: function. The non-linear activation function to apply
to the output of the intermediate/feed-forward layer.
hidden_dropout_prob: float. Dropout probability for the hidden layers.
attention_probs_dropout_prob: float. Dropout probability of the attention
probabilities.
initializer_range: float. Range of the initializer (stddev of truncated
normal).
do_return_all_layers: Whether to also return all layers or just the final
layer.
Returns:
float Tensor of shape [batch_size, seq_length, hidden_size], the final
hidden layer of the Transformer.
"""
if hidden_size % num_attention_heads != 0:
raise ValueError(
'The hidden size ({}) is not a multiple of the number of attention head\
({}).'.format(hidden_size, num_attention_heads))
attention_head_size = int(hidden_size / num_attention_heads)
input_shape = ft.get_shape_list(input_tensor, expected_rank=3)
batch_size = input_shape[0]
seq_length = input_shape[1]
input_width = input_shape[2]
if input_width != hidden_size:
raise ValueError(
'The width of the input tensor ({}) != hidden size ({}).'.format(
input_width, hidden_size))
prev_output = input_tensor
all_layers_outputs = []
for layer_idx in range(num_hidden_layers):
with tf.variable_scope('layer_{}'.format(layer_idx)):
layer_input = prev_output
with tf.variable_scope('attention'):
with tf.variable_scope('self'):
# [b, s, n * a]
attention_head = attention_layer(
input_tensor=layer_input,
attention_mask=attention_mask,
num_attention_heads=num_attention_heads,
size_per_head=attention_head_size,
query_act=None,
key_act=None,
value_act=None,
attention_dropout_prob=attention_dropout_prob,
initializer_range=initializer_range,
batch_size=batch_size,
seq_length=seq_length)
with tf.variable_scope('output'):
# [b, s, h]
attention_output = tf.layers.dense(
attention_head,
hidden_size,
kernel_initializer=ft.create_initializer(
initializer_range=initializer_range))
attention_output = ft.dropout(attention_output,
hidden_dropout_prob)
attention_output = ft.layer_norm(attention_output +
layer_input)
with tf.variable_scope('intermediate'):
# [b, s, i]
intermediate_output = tf.layers.dense(
attention_head,
intermediate_size,
activation=intermediate_act_fn,
kernel_initializer=ft.create_initializer(
initializer_range=initializer_range))
with tf.variable_scope('output'):
# [b, s, h]
layer_output = tf.layers.dense(
intermediate_output,
hidden_size,
kernel_initializer=ft.create_initializer(
initializer_range=initializer_range))
layer_output = ft.dropout(layer_output, hidden_dropout_prob)
layer_output = ft.layer_norm(layer_output + attention_output)
prev_output = layer_output
all_layers_outputs.append(prev_output)
if do_return_all_layers:
return all_layers_outputs
else:
return all_layers_outputs[-1]
def attention_layer(input_tensor, attention_mask, num_attention_heads,
size_per_head, query_act, key_act, value_act,
attention_dropout_prob, initializer_range, batch_size,
seq_length):
"""Performs multi-headed attention from `from_tensor` to `to_tensor`.
This is an implementation of multi-headed attention based on "Attention
is all you Need". If `from_tensor` and `to_tensor` are the same, then
this is self-attention. Each timestep in `from_tensor` attends to the
corresponding sequence in `to_tensor`, and returns a fixed-with vector.
This function first projects `from_tensor` into a "query" tensor and
`to_tensor` into "key" and "value" tensors. These are (effectively) a list
of tensors of length `num_attention_heads`, where each tensor is of shape
[batch_size, seq_length, size_per_head].
Then, the query and key tensors are dot-producted and scaled. These are
softmaxed to obtain attention probabilities. The value tensors are then
interpolated by these probabilities, then concatenated back to a single
tensor and returned.
In practice, the multi-headed attention are done with transposes and
reshapes rather than actual separate tensors.
Args:
input_tensor: float Tensor of shape [batch_size, from_seq_length,
from_width].
attention_mask: (optional) int32 Tensor of shape [batch_size,
from_seq_length, to_seq_length]. The values should be 1 or 0. The
attention scores will effectively be set to -infinity for any positions in
the mask that are 0, and will be unchanged for positions that are 1.
num_attention_heads: int. Number of attention heads.
size_per_head: int. Size of each attention head.
query_act: (optional) Activation function for the query transform.
key_act: (optional) Activation function for the key transform.
value_act: (optional) Activation function for the value transform.
attention_probs_dropout_prob: (optional) float. Dropout probability of the
attention probabilities.
initializer_range: float. Range of the weight initializer.
do_return_2d_tensor: bool. If True, the output will be of shape [batch_size
* from_seq_length, num_attention_heads * size_per_head]. If False, the
output will be of shape [batch_size, from_seq_length, num_attention_heads
* size_per_head].
batch_size: (Optional) int. If the input is 2D, this might be the batch size
of the 3D version of the `from_tensor` and `to_tensor`.
seq_length: (Optional) If the input is 2D, this might be the seq length
of the 3D version of the `input_tensor`.
Returns:
float Tensor of shape [batch_size, from_seq_length,
num_attention_heads * size_per_head].
Raises:
ValueError: Any of the arguments or tensor shapes are invalid.
"""
def transpose_for_scores(input_tensor, batch_size, seq_length,
num_attention_heads, width):
output_tensor = tf.reshape(
input_tensor, [batch_size, seq_length, num_attention_heads, width])
output_tensor = tf.transpose(output_tensor, [0, 2, 1, 3])
return output_tensor
# [b, s, n * a]
query_layer = tf.layers.dense(input_tensor,
num_attention_heads * size_per_head,
activation=query_act,
name='query',
kernel_initializer=ft.create_initializer(
initializer_range=initializer_range))
key_layer = tf.layers.dense(input_tensor,
num_attention_heads * size_per_head,
activation=key_act,
name='key',
kernel_initializer=ft.create_initializer(
initializer_range=initializer_range))
value_layer = tf.layers.dense(input_tensor,
num_attention_heads * size_per_head,
activation=value_act,
name='value',
kernel_initializer=ft.create_initializer(
initializer_range=initializer_range))
# [b, n, s, a]
query_layer = transpose_for_scores(query_layer, batch_size, seq_length,
num_attention_heads, size_per_head)
key_layer = transpose_for_scores(key_layer, batch_size, seq_length,
num_attention_heads, size_per_head)
# [b, n, s, s]
attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
attention_scores = tf.multiply(attention_scores,
1.0 / math.sqrt(float(size_per_head)))
if attention_mask is not None:
# [b, 1, s, s]
attention_mask = tf.expand_dims(attention_mask, axis=[1])
adder = (1.0 - attention_mask) * -10000.0
attention_scores += adder
attention_prob = tf.nn.softmax(attention_scores)
attention_prob = ft.dropout(attention_prob, attention_dropout_prob)
# [b, n, s, a]
value_layer = transpose_for_scores(value_layer, batch_size, seq_length,
num_attention_heads, size_per_head)
# [b, n, s, a]
context_layer = tf.matmul(attention_prob, value_layer)
# [b, s, n, a]
context_layer = tf.transpose(context_layer, [0, 2, 1, 3])
# [b, s, n * a]
context_layer = tf.reshape(
context_layer,
[batch_size, seq_length, num_attention_heads * size_per_head])
return context_layer
def model_fn(features, labels, mode, params):
# features name and shape
for name in sorted(features.keys()):
tf.logging.info(' name = {}, shape = {}'.format(name, features[name].shape))
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
# get data
input_data = features['input_data']
input_mask = features['input_mask']
if mode == tf.estimator.ModeKeys.TRAIN:
sentiment_labels = features['sentiment_labels']
sentiment_mask_indices = features['sentiment_mask_indices']
true_length_from_data = features['true_length']
# build model
model = BertEncoder(
config=cg.BertEncoderConfig,
is_training=is_training,
input_ids=input_data,
input_mask=input_mask)
tvars = tf.trainable_variables()
initialized_variable_names = {}
if init_checkpoint:
(assignment_map, initialized_variable_names
) = get_assignment_map_from_checkpoint(tvars, init_checkpoint)
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
tf.logging.info("**** Trainable Variables ****")
for var in tvars:
init_string = ""
if var.name in initialized_variable_names:
init_string = ", *INIT_FROM_CKPT*"
tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape,
init_string)
# [cls] output -> [b, h]
cls_output = model.get_cls_output()
# sequence_output -> [b, s, h], do not contain [CLS], because the mask indices do not shift
sequence_output = model.get_sequence_output()[:, 1:, :]
# project the hidden size to the num_classes
with tf.variable_scope('final_output'):
# [b, num_classes]
output_logits = tf.layers.dense(
cls_output,
cg.BertEncoderConfig.num_classes,
name='final_output',
kernel_initializer=ft.create_initializer(initializer_range=cg.BertEncoderConfig.initializer_range))
if mode == tf.estimator.ModeKeys.PREDICT:
output_softmax = tf.nn.softmax(output_logits, axis=-1)
output_result = tf.argmax(output_softmax, axis=-1)
predictions = {'predict': output_result}
output_spec = tf.estimator.EstimatorSpec(mode, predictions=predictions)
else:
if mode == tf.estimator.ModeKeys.TRAIN:
# masked_output -> [b * x, h]
masked_output = gather_indexs(sequence_output, sentiment_mask_indices)
# get output for word polarity prediction
with tf.variable_scope('sentiment_project'):
# [b * x, 2]
output_sentiment = tf.layers.dense(
masked_output,
2,
name='final_output',
kernel_initializer=ft.create_initializer(initializer_range=cg.BertEncoderConfig.initializer_range))
# output_sentiment_probs = tf.nn.softmax(output_sentiment, axis=-1)
batch_size = tf.cast(ft.get_shape_list(labels, expected_rank=1)[0], dtype=tf.float32)
# cross-entropy loss
cls_loss = tf.reduce_sum(tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=labels,
logits=output_logits)) / batch_size
# mse loss
# # Regression Model
true_sequence = get_true_sequence(true_length_from_data)
# mse_loss = calculate_mse_loss(
# output_sentiment, sentiment_labels, true_sequence)
# # Classification Model
true_label_flatten = tf.reshape(sentiment_labels, [-1])
mse_loss = tf.reduce_sum(tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=true_label_flatten,
logits=output_sentiment) * true_sequence) / tf.reduce_sum(true_sequence)
loss = cls_loss + mse_loss
# loss = cls_loss
learning_rate = tf.train.polynomial_decay(cg.learning_rate,
tf.train.get_or_create_global_step(),
cg.train_steps,
end_learning_rate=cg.lr_limit,
power=1.0,
cycle=False)
lr = tf.maximum(tf.constant(cg.lr_limit), learning_rate)
optimizer = tf.train.AdamOptimizer(lr, name='optimizer')
tvars = tf.trainable_variables()
gradients = tf.gradients(loss, tvars, colocate_gradients_with_ops=cg.colocate_gradients_with_ops)
clipped_gradients, _ = tf.clip_by_global_norm(gradients, 5.0)
train_op = optimizer.apply_gradients(zip(clipped_gradients, tvars), global_step=tf.train.get_global_step())
current_steps = tf.train.get_or_create_global_step()
logging_hook = tf.train.LoggingTensorHook(
{'step' : current_steps, 'loss' : loss, 'cls_loss' : cls_loss, 'mse_loss': mse_loss, 'lr' : lr},
every_n_iter=cg.print_info_interval)
output_spec = tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op, training_hooks=[logging_hook])
elif mode == tf.estimator.ModeKeys.EVAL:
# TODO
raise NotImplementedError
return output_spec