def __init__(self,
xlnet_config,
is_training,
input_ids,
seg_ids,
input_mask,
mems,
perm_mask,
target,
target_mask,
target_mapping,
inp_q,
sample_weight=None,
**kwargs):
super().__init__()
run_config = XLNetRunConfig(
is_training=is_training,
bi_data=True,
use_tpu=False,
use_bfloat16=False,
dropout=(0.1 if is_training else 0.0),
dropatt=(0.1 if is_training else 0.0),
init='normal',
init_range=0.1,
init_std=0.02,
clamp_len=-1)
model = XLNetEncoder(
xlnet_config=xlnet_config,
is_training=is_training,
input_ids=input_ids,
seg_ids=seg_ids,
input_mask=input_mask,
mems=mems,
perm_mask=perm_mask,
target_mapping=target_mapping,
inp_q=inp_q,
**kwargs)
with tf.variable_scope('model', reuse=tf.AUTO_REUSE):
per_example_loss, preds = lm_loss(
hidden=model.get_sequence_output(),
target=target,
n_token=xlnet_config.n_token,
d_model=xlnet_config.d_model,
initializer=model.get_initializer(),
lookup_table=model.get_embedding_table(),
tie_weight=True,
bi_data=run_config.bi_data,
use_tpu=run_config.use_tpu)
if sample_weight is not None:
sample_weight = tf.expand_dims(
tf.cast(sample_weight, dtype=tf.float32), axis=-1)
per_example_loss *= sample_weight
self.total_loss = tf.reduce_sum(
per_example_loss * target_mask) / tf.reduce_sum(target_mask)
self.losses['PLM'] = per_example_loss * target_mask
self.preds['PLM'] = preds
self.preds['PLM_mask'] = target_mask
def __init__(self,
bert_config,
is_training,
sketchy_encoder,
intensive_encoder,
query_mask,
label_ids,
has_answer,
sample_weight=None,
scope='retro_reader',
matching_mechanism='cross-attention',
beta_1=0.5,
beta_2=0.5,
threshold=1.0,
trainable=True,
**kwargs):
super().__init__(**kwargs)
# verifier
with tf.variable_scope(scope):
# sketchy reading module
with tf.variable_scope('sketchy/prediction'):
sketchy_output = sketchy_encoder.get_pooled_output()
hidden_size = sketchy_output.shape.as_list()[-1]
output_weights = tf.get_variable(
'output_weights',
shape=[2, hidden_size],
initializer=util.create_initializer(
bert_config.initializer_range),
trainable=trainable)
output_bias = tf.get_variable(
'output_bias',
shape=[2],
initializer=tf.zeros_initializer(),
trainable=trainable)
output_layer = util.dropout(
sketchy_output, bert_config.hidden_dropout_prob \
if is_training else 0.0)
logits = tf.matmul(
output_layer, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
log_probs = tf.nn.log_softmax(logits, axis=-1)
one_hot_labels = tf.one_hot(
has_answer, depth=2, dtype=tf.float32)
per_example_loss = - tf.reduce_sum(
one_hot_labels * log_probs, axis=-1)
if sample_weight is not None:
per_example_loss = tf.cast(
sample_weight, dtype=tf.float32) * per_example_loss
self.losses['sketchy_losses'] = per_example_loss
sketchy_loss = tf.reduce_mean(per_example_loss)
score_ext = logits[:, 1] - logits[:, 0]
# intensive reading module
with tf.variable_scope('intensive'):
H = intensive_encoder.get_sequence_output()
H_Q = H * tf.cast(
tf.expand_dims(query_mask, axis=-1), tf.float32)
(batch_size, max_seq_length, hidden_size) = \
util.get_shape_list(H)
# cross-attention
if matching_mechanism == 'cross-attention':
with tf.variable_scope('cross_attention'):
attention_mask = \
self.create_attention_mask_from_input_mask(
query_mask, batch_size, max_seq_length)
(H_prime, _) = self.attention_layer(
from_tensor=H,
to_tensor=H_Q,
attention_mask=attention_mask,
num_attention_heads=\
bert_config.num_attention_heads,
size_per_head=\
hidden_size // bert_config.num_attention_heads,
attention_probs_dropout_prob=\
bert_config.hidden_dropout_prob,
initializer_range=bert_config.initializer_range,
do_return_2d_tensor=False,
batch_size=batch_size,
from_max_seq_length=max_seq_length,
to_max_seq_length=max_seq_length,
trainable=trainable)
# matching-attention
elif matching_mechanism == 'matching-attention':
with tf.variable_scope('matching_attention'):
output_weights = tf.get_variable(
'output_weights',
shape=[hidden_size, hidden_size],
initializer=util.create_initializer(
bert_config.initializer_range),
trainable=trainable)
output_bias = tf.get_variable(
'output_bias',
shape=[hidden_size],
initializer=tf.zeros_initializer(),
trainable=trainable)
trans = tf.matmul(
H_Q, tf.tile(
tf.expand_dims(output_weights, axis=0),
[batch_size, 1, 1]),
transpose_b=True)
trans = tf.nn.bias_add(trans, output_bias)
M = tf.nn.softmax(
tf.matmul(H, trans, transpose_b=True), axis=-1)
H_prime = tf.matmul(M, H_Q)
with tf.variable_scope('prediction'):
output_weights = tf.get_variable(
'output_weights',
shape=[2, hidden_size],
initializer=util.create_initializer(
bert_config.initializer_range),
trainable=trainable)
output_bias = tf.get_variable(
'output_bias',
shape=[2],
initializer=tf.zeros_initializer(),
trainable=trainable)
output_layer = util.dropout(
H_prime, bert_config.hidden_dropout_prob \
if is_training else 0.0)
output_layer = tf.reshape(
output_layer,
[batch_size * max_seq_length, hidden_size])
logits = tf.matmul(output_layer, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
logits = tf.reshape(
logits, [batch_size, max_seq_length, 2])
logits = tf.transpose(logits, [0, 2, 1])
probs = tf.nn.softmax(logits, axis=-1, name='probs')
self.probs['mrc_probs'] = probs
self.preds['mrc_preds'] = tf.argmax(logits, axis=-1)
start_one_hot_labels = tf.one_hot(
label_ids[:, 0], depth=max_seq_length,
dtype=tf.float32)
end_one_hot_labels = tf.one_hot(
label_ids[:, 1], depth=max_seq_length,
dtype=tf.float32)
start_log_probs = tf.nn.log_softmax(logits[:, 0, :], axis=-1)
end_log_probs = tf.nn.log_softmax(logits[:, 1, :], axis=-1)
per_example_loss = (
- 0.5 * tf.reduce_sum(
start_one_hot_labels * start_log_probs, axis=-1)
- 0.5 * tf.reduce_sum(
end_one_hot_labels * end_log_probs, axis=-1))
if sample_weight is not None:
per_example_loss *= sample_weight
intensive_loss = tf.reduce_mean(per_example_loss)
self.losses['intensive_losses'] = per_example_loss
score_has = tf.norm(
probs[:, 0, 1:] + probs[:, 1, 1:], np.inf, axis=-1)
score_null = probs[:, 0, 0] + probs[:, 1, 0]
score_diff = score_has - score_null
# rear verification
v = beta_1 * score_diff + beta_2 * score_ext
self.preds['verifier_preds'] = \
tf.cast(tf.greater(v, threshold), tf.int32)
self.probs['verifier_probs'] = v
self.total_loss = sketchy_loss + intensive_loss
def __init__(self,
vocab_size,
is_training,
input_ids,
input_mask,
segment_ids,
sample_weight=None,
reduced_size=64,
topic_size=1024,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
bias=0,
scope='vae',
trainable=True,
**kwargs):
super().__init__()
# freeze parameters
config = Config(vocab_size,
hidden_size=hidden_size,
num_hidden_layers=num_hidden_layers,
num_attention_heads=num_attention_heads)
if not is_training:
config.hidden_dropout_prob = 0.0
config.attention_probs_dropout_prob = 0.0
input_shape = util.get_shape_list(input_ids, expected_rank=2)
batch_size = input_shape[0]
seq_length = input_shape[1]
# Tilda embeddings for SMART algorithm
tilda_embeddings = None
use_tilda_embedding = kwargs.get('use_tilda_embedding')
if use_tilda_embedding:
with tf.variable_scope('', reuse=True):
tilda_embeddings = tf.get_variable('tilda_embeddings')
with tf.variable_scope(scope):
with tf.variable_scope('embeddings'):
(self.embedding_output, self.embedding_table) = \
self.embedding_lookup(
input_ids=input_ids,
vocab_size=config.vocab_size,
batch_size=batch_size,
max_seq_length=seq_length,
embedding_size=config.hidden_size,
initializer_range=config.initializer_range,
word_embedding_name='word_embeddings',
tilda_embeddings=tilda_embeddings,
trainable=trainable)
self.embedding_output = self.embedding_postprocessor(
input_tensor=self.embedding_output,
batch_size=batch_size,
max_seq_length=seq_length,
hidden_size=config.hidden_size,
use_token_type=True,
segment_ids=segment_ids,
token_type_vocab_size=config.type_vocab_size,
token_type_embedding_name='token_type_embeddings',
use_position_embeddings=True,
position_embedding_name='position_embeddings',
initializer_range=config.initializer_range,
max_position_embeddings=config.max_position_embeddings,
dropout_prob=config.hidden_dropout_prob,
trainable=trainable)
with tf.variable_scope('encoder'):
# stacked transformer
attention_mask = self.create_attention_mask_from_input_mask(
input_mask, batch_size, seq_length)
self.all_encoder_layers = self.transformer_model(
input_tensor=self.embedding_output,
batch_size=batch_size,
max_seq_length=seq_length,
attention_mask=attention_mask,
hidden_size=config.hidden_size,
num_hidden_layers=config.num_hidden_layers,
num_attention_heads=config.num_attention_heads,
intermediate_size=config.intermediate_size,
intermediate_act_fn=util.get_activation(config.hidden_act),
hidden_dropout_prob=config.hidden_dropout_prob,
attention_probs_dropout_prob=\
config.attention_probs_dropout_prob,
initializer_range=config.initializer_range,
trainable=trainable)
# projection
with tf.variable_scope('projection'):
transformer_output = tf.layers.dense(
self.all_encoder_layers[-1],
reduced_size,
activation=util.gelu,
kernel_initializer=tf.truncated_normal_initializer(
stddev=config.initializer_range),
trainable=trainable)
transformer_output = tf.reshape(transformer_output,
[batch_size, -1])
input_length = tf.reduce_sum(input_mask, axis=-1)
input_length = tf.cast(input_length, tf.float32)
input_length_1d = tf.reshape(input_length, [batch_size])
input_length_2d = tf.reshape(input_length, [batch_size, 1])
broadcast_mask = tf.sequence_mask(
tf.multiply(input_length_1d, reduced_size),
seq_length * reduced_size,
dtype=tf.float32)
broadcast_mask = tf.multiply(broadcast_mask,
seq_length / input_length_2d)
transformer_output *= broadcast_mask
# latent space
miu = tf.layers.dense(
transformer_output,
topic_size,
activation='tanh',
kernel_initializer=tf.truncated_normal_initializer(
stddev=config.initializer_range),
name='miu',
trainable=trainable)
sigma = tf.layers.dense(
transformer_output,
topic_size,
kernel_initializer=tf.truncated_normal_initializer(
stddev=config.initializer_range),
name='sigma',
trainable=trainable)
self.probs['miu'] = miu
self.probs['sigma'] = sigma
with tf.variable_scope('decoder'):
with tf.variable_scope('projection'):
# reparametarization
if is_training:
noise = tf.random_normal([batch_size, topic_size])
else:
noise = tf.random_uniform([batch_size, topic_size],
minval=-bias,
maxval=bias)
decoder_input = miu + tf.exp(sigma) * noise
# projection
decoder_input = tf.layers.dense(
decoder_input,
seq_length * reduced_size,
activation=util.gelu,
kernel_initializer=tf.truncated_normal_initializer(
stddev=config.initializer_range),
trainable=trainable)
intermediate_input = tf.reshape(
decoder_input, [-1, seq_length, reduced_size])
intermediate_input = util.layer_norm(intermediate_input,
trainable=trainable)
intermediate_input = util.dropout(
intermediate_input, config.hidden_dropout_prob)
# MLP
with tf.variable_scope('intermediate'):
intermediate_output = tf.layers.dense(
intermediate_input,
4 * reduced_size,
activation=util.gelu,
kernel_initializer=util.create_initializer(
config.initializer_range),
trainable=trainable)
with tf.variable_scope('output'):
decoder_output = tf.layers.dense(
intermediate_output,
config.hidden_size,
kernel_initializer=util.create_initializer(
config.initializer_range),
trainable=trainable)
decoder_output = util.layer_norm(decoder_output,
trainable=trainable)
decoder_output = util.dropout(decoder_output,
config.hidden_dropout_prob)
self.all_decoder_layers = [intermediate_output, decoder_output]
self.all_decoder_layers = [decoder_output]
# reconstruction
with tf.variable_scope('cls/predictions'):
with tf.variable_scope('transform'):
input_tensor = tf.layers.dense(
decoder_output,
units=config.hidden_size,
activation=util.get_activation(config.hidden_act),
kernel_initializer=util.create_initializer(
config.initializer_range),
trainable=trainable)
input_tensor = util.layer_norm(input_tensor,
trainable=trainable)
output_weights = self.embedding_table
output_bias = tf.get_variable('output_bias',
shape=[config.vocab_size],
initializer=tf.zeros_initializer(),
trainable=trainable)
flatten_input_tensor = tf.reshape(input_tensor,
[-1, config.hidden_size])
logits = tf.matmul(flatten_input_tensor,
output_weights,
transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
logits = tf.reshape(logits,
[batch_size, seq_length, config.vocab_size])
probs = tf.nn.softmax(logits, axis=-1, name='probs')
lm_log_probs = tf.nn.log_softmax(logits, axis=-1)
self.preds['preds'] = tf.argmax(probs, axis=-1)
one_hot_labels = tf.one_hot(input_ids,
depth=config.vocab_size,
dtype=tf.float32)
per_example_loss = -tf.reduce_sum(lm_log_probs * one_hot_labels,
axis=[-1])
if sample_weight is not None:
per_example_loss *= tf.expand_dims(sample_weight, axis=-1)
self.total_loss = (tf.reduce_mean(per_example_loss) +
tf.reduce_mean(tf.square(miu)) +
tf.reduce_mean(tf.exp(sigma) - sigma - 1))
self.losses['losses'] = per_example_loss
def __init__(self,
bert_config,
is_training,
input_ids,
input_mask,
segment_ids,
scope='bert',
drop_pooler=False,
trainable=True,
**kwargs):
bert_config = copy.deepcopy(bert_config)
if not is_training:
bert_config.hidden_dropout_prob = 0.0
bert_config.attention_probs_dropout_prob = 0.0
input_shape = util.get_shape_list(input_ids, expected_rank=2)
batch_size = input_shape[0]
max_seq_length = input_shape[1]
# Tilda embeddings for SMART algorithm
tilda_embeddings = None
use_tilda_embedding = kwargs.get('use_tilda_embedding')
if use_tilda_embedding:
with tf.variable_scope('', reuse=True):
tilda_embeddings = tf.get_variable('tilda_embeddings')
with tf.variable_scope(scope):
with tf.variable_scope('embeddings'):
(self.embedding_output, self.embedding_table) = \
self.embedding_lookup(
input_ids=input_ids,
vocab_size=bert_config.vocab_size,
batch_size=batch_size,
max_seq_length=max_seq_length,
embedding_size=bert_config.hidden_size,
initializer_range=bert_config.initializer_range,
word_embedding_name='word_embeddings',
tilda_embeddings=tilda_embeddings,
trainable=trainable)
# Add positional embeddings and token type embeddings
# layer normalize and perform dropout.
self.embedding_output = self.embedding_postprocessor(
input_tensor=self.embedding_output,
batch_size=batch_size,
max_seq_length=max_seq_length,
hidden_size=bert_config.hidden_size,
use_token_type=True,
segment_ids=segment_ids,
token_type_vocab_size=bert_config.type_vocab_size,
token_type_embedding_name='token_type_embeddings',
use_position_embeddings=True,
position_embedding_name='position_embeddings',
initializer_range=bert_config.initializer_range,
max_position_embeddings=\
bert_config.max_position_embeddings,
dropout_prob=bert_config.hidden_dropout_prob,
trainable=trainable)
with tf.variable_scope('encoder'):
attention_mask = self.create_attention_mask_from_input_mask(
input_mask, batch_size, max_seq_length)
# stacked transformers
self.all_encoder_layers = self.transformer_model(
input_tensor=self.embedding_output,
batch_size=batch_size,
max_seq_length=max_seq_length,
attention_mask=attention_mask,
hidden_size=bert_config.hidden_size,
num_hidden_layers=bert_config.num_hidden_layers,
num_attention_heads=bert_config.num_attention_heads,
intermediate_size=bert_config.intermediate_size,
intermediate_act_fn=util.get_activation(
bert_config.hidden_act),
hidden_dropout_prob=bert_config.hidden_dropout_prob,
attention_probs_dropout_prob=\
bert_config.attention_probs_dropout_prob,
initializer_range=bert_config.initializer_range,
trainable=trainable)
self.sequence_output = self.all_encoder_layers[-1]
with tf.variable_scope('pooler'):
first_token_tensor = self.sequence_output[:, 0, :]
# trick: ignore the fully connected layer
if drop_pooler:
self.pooled_output = first_token_tensor
else:
self.pooled_output = tf.layers.dense(
first_token_tensor,
bert_config.hidden_size,
activation=tf.tanh,
kernel_initializer=util.create_initializer(
bert_config.initializer_range),
trainable=trainable)
def _build_forward(layer_input):
with tf.variable_scope('attention'):
attention_heads = []
with tf.variable_scope('self'):
(attention_head, attention_scores) = \
self.attention_layer(
from_tensor=layer_input,
to_tensor=layer_input,
attention_mask=attention_mask,
num_attention_heads=num_attention_heads,
size_per_head=attention_head_size,
attention_probs_dropout_prob=\
attention_probs_dropout_prob,
initializer_range=initializer_range,
do_return_2d_tensor=True,
batch_size=batch_size,
from_max_seq_length=max_seq_length,
to_max_seq_length=max_seq_length,
dtype=dtype,
trainable=trainable)
attention_heads.append(attention_head)
self.attention_scores.append(attention_scores)
attention_output = None
if len(attention_heads) == 1:
attention_output = attention_heads[0]
else:
attention_output = tf.concat(attention_heads,
axis=-1)
with tf.variable_scope('output'):
attention_output = tf.layers.dense(
attention_output,
hidden_size,
kernel_initializer=util.create_initializer(
initializer_range),
trainable=trainable)
attention_output = util.dropout(
attention_output, hidden_dropout_prob)
attention_output = util.layer_norm(
attention_output + layer_input,
trainable=trainable)
# The activation is only applied to the `intermediate`
# hidden layer.
with tf.variable_scope('intermediate'):
intermediate_output = tf.layers.dense(
attention_output,
intermediate_size,
activation=intermediate_act_fn,
kernel_initializer=util.create_initializer(
initializer_range),
trainable=trainable)
# Down-project back to hidden_size then add the residual.
with tf.variable_scope('output'):
layer_output = tf.layers.dense(
intermediate_output,
hidden_size,
kernel_initializer=util.create_initializer(
initializer_range),
trainable=trainable)
layer_output = util.dropout(layer_output,
hidden_dropout_prob)
layer_output = util.layer_norm(layer_output +
attention_output,
trainable=trainable)
return layer_output
def transformer_model(input_tensor,
attention_mask=None,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
intermediate_act_fn=util.gelu,
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
initializer_range=0.02,
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.
See the original paper:
https://arxiv.org/abs/1706.03762
Also see:
https://github.com/tensorflow/tensor2tensor/blob/master/
tensor2tensor/models/transformer.py
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.
Raises:
ValueError: A Tensor shape or parameter is invalid.
'''
if hidden_size % num_attention_heads != 0:
raise ValueError(
'The hidden size (%d) is not a multiple of the number of attention '
'heads (%d)' % (hidden_size, num_attention_heads))
attention_head_size = int(hidden_size / num_attention_heads)
input_shape = util.get_shape_list(input_tensor, expected_rank=3)
batch_size = input_shape[0]
seq_length = input_shape[1]
input_width = input_shape[2]
# The Transformer performs sum residuals on all layers so the input needs
# to be the same as the hidden size.
if input_width != hidden_size:
raise ValueError(
'The width of the input tensor (%d) != hidden size (%d)' %
(input_width, hidden_size))
# We keep the representation as a 2D tensor to avoid re-shaping it back and
# forth from a 3D tensor to a 2D tensor. Re-shapes are normally free on
# the GPU/CPU but may not be free on the TPU, so we want to minimize them to
# help the optimizer.
prev_output = util.reshape_to_matrix(input_tensor)
attn_maps = []
all_layer_outputs = []
for layer_idx in range(num_hidden_layers):
with tf.variable_scope('layer_%d' % layer_idx):
with tf.variable_scope('attention'):
attention_heads = []
with tf.variable_scope('self'):
attention_head, probs = attention_layer(
from_tensor=prev_output,
to_tensor=prev_output,
attention_mask=attention_mask,
num_attention_heads=num_attention_heads,
size_per_head=attention_head_size,
attention_probs_dropout_prob=
attention_probs_dropout_prob,
initializer_range=initializer_range,
do_return_2d_tensor=True,
batch_size=batch_size,
from_seq_length=seq_length,
to_seq_length=seq_length)
attention_heads.append(attention_head)
attn_maps.append(probs)
attention_output = None
if len(attention_heads) == 1:
attention_output = attention_heads[0]
else:
# In the case where we have other sequences, we just concatenate
# them to the self-attention head before the projection.
attention_output = tf.concat(attention_heads, axis=-1)
# Run a linear projection of `hidden_size` then add a residual
# with `layer_input`.
with tf.variable_scope('output'):
attention_output = tf.layers.dense(
attention_output,
hidden_size,
kernel_initializer=util.create_initializer(
initializer_range))
attention_output = util.dropout(attention_output,
hidden_dropout_prob)
attention_output = util.layer_norm(attention_output +
prev_output)
# The activation is only applied to the 'intermediate' hidden layer.
with tf.variable_scope('intermediate'):
intermediate_output = tf.layers.dense(
attention_output,
intermediate_size,
activation=intermediate_act_fn,
kernel_initializer=util.create_initializer(
initializer_range))
# Down-project back to `hidden_size` then add the residual.
with tf.variable_scope('output'):
prev_output = tf.layers.dense(
intermediate_output,
hidden_size,
kernel_initializer=util.create_initializer(
initializer_range))
prev_output = util.dropout(prev_output, hidden_dropout_prob)
prev_output = util.layer_norm(prev_output + attention_output)
all_layer_outputs.append(prev_output)
attn_maps = tf.stack(attn_maps, 0)
if do_return_all_layers:
return tf.stack([
util.reshape_from_matrix(layer, input_shape)
for layer in all_layer_outputs
], 0), attn_maps
else:
return util.reshape_from_matrix(prev_output, input_shape), attn_maps
def __init__(self,
is_training,
input_tensor,
n_wide_features,
wide_features,
label_ids,
label_size=2,
sample_weight=None,
scope='cls/seq_relationship',
hidden_dropout_prob=0.1,
initializer_range=0.02,
trainable=True,
**kwargs):
super().__init__(**kwargs)
hidden_size = input_tensor.shape.as_list()[-1]
feature_size = wide_features.shape.as_list()[-1]
with tf.variable_scope('wide'):
feature_embeddings = tf.get_variable(
name='feature_embeddings',
shape=[feature_size + 1, hidden_size],
initializer=util.create_initializer(initializer_range),
trainable=trainable)
wide_output = tf.gather(feature_embeddings,
wide_features) # [B, N, H]
with tf.variable_scope('wide_and_deep'):
deep_output = tf.expand_dims(input_tensor, -1) # [B, H, 1]
attention_scores = tf.matmul(wide_output, deep_output) # [B, N, 1]
attention_scores = tf.transpose(attention_scores,
[0, 2, 1]) # [B, 1, N]
attention_scores = tf.multiply(attention_scores,
1.0 / math.sqrt(hidden_size))
feature_mask = tf.cast(
tf.sequence_mask(n_wide_features, feature_size),
tf.float32) # [B, N]
feature_mask = tf.expand_dims(feature_mask, 1) # [B, 1, N]
attention_scores += (1.0 - feature_mask) * -10000.0
attention_matrix = tf.nn.softmax(attention_scores, axis=-1)
attention_output = tf.matmul(attention_matrix,
wide_output) # [B, 1, H]
attention_output = attention_output[:, 0, :] # [B, H]
# attention_output = util.dropout(
# attention_output, hidden_dropout_prob)
input_tensor = util.layer_norm(attention_output + input_tensor,
trainable=trainable)
with tf.variable_scope(scope):
output_weights = tf.get_variable(
'output_weights',
shape=[label_size, hidden_size],
initializer=util.create_initializer(initializer_range),
trainable=trainable)
output_bias = tf.get_variable('output_bias',
shape=[label_size],
initializer=tf.zeros_initializer(),
trainable=trainable)
output_layer = util.dropout(
input_tensor, hidden_dropout_prob if is_training else 0.0)
logits = tf.matmul(output_layer, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
self.preds['preds'] = tf.argmax(logits, axis=-1)
self.probs['probs'] = tf.nn.softmax(logits, axis=-1, name='probs')
log_probs = tf.nn.log_softmax(logits, axis=-1)
one_hot_labels = tf.one_hot(label_ids,
depth=label_size,
dtype=tf.float32)
per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs,
axis=-1)
if sample_weight is not None:
per_example_loss = tf.cast(sample_weight,
dtype=tf.float32) * per_example_loss
thresh = kwargs.get('tsa_thresh')
if thresh is not None:
assert isinstance(
thresh,
float), ('`tsa_thresh` must be a float between 0 and 1.')
uncertainty = tf.reduce_sum(self.probs['probs'] *
tf.log(self.probs['probs']),
axis=-1)
uncertainty /= tf.log(1 / label_size)
per_example_loss = tf.cast(
tf.greater(uncertainty, thresh), dtype=tf.float32) * \
per_example_loss
self.losses['losses'] = per_example_loss
self.total_loss = tf.reduce_mean(per_example_loss)
def attention_ffn_block(layer_input,
hidden_size=768,
attention_mask=None,
num_attention_heads=1,
attention_head_size=64,
attention_probs_dropout_prob=0.0,
intermediate_size=3072,
intermediate_act_fn=None,
initializer_range=0.02,
hidden_dropout_prob=0.0,
use_einsum=True):
"""A network with attention-ffn as sub-block.
Args:
layer_input: float Tensor of shape [batch_size, from_seq_length,
from_width].
hidden_size: (optional) int, size of hidden layer.
attention_mask: (optional) int32 Tensor of shape [batch_size, 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.
attention_head_size: int. Size of attention head.
attention_probs_dropout_prob: float. dropout probability for attention_layer
intermediate_size: int. Size of intermediate hidden layer.
intermediate_act_fn: (optional) Activation function for the intermediate
layer.
initializer_range: float. Range of the weight initializer.
hidden_dropout_prob: (optional) float. Dropout probability of the hidden
layer.
use_einsum: bool. Whether to use einsum or reshape+matmul for dense layers
Returns:
layer output
"""
with tf.variable_scope("attention_1"):
with tf.variable_scope("self"):
attention_output = attention_layer(
from_tensor=layer_input,
to_tensor=layer_input,
attention_mask=attention_mask,
num_attention_heads=num_attention_heads,
attention_probs_dropout_prob=attention_probs_dropout_prob,
initializer_range=initializer_range,
use_einsum=use_einsum)
# Run a linear projection of `hidden_size` then add a residual
# with `layer_input`.
with tf.variable_scope("output"):
attention_output = dense_layer_3d_proj(
attention_output,
hidden_size,
attention_head_size,
util.create_initializer(initializer_range),
None,
use_einsum=use_einsum,
name="dense")
attention_output = util.dropout(attention_output,
hidden_dropout_prob)
attention_output = util.layer_norm(attention_output + layer_input)
with tf.variable_scope("ffn_1"):
with tf.variable_scope("intermediate"):
intermediate_output = dense_layer_2d(
attention_output,
intermediate_size,
util.create_initializer(initializer_range),
intermediate_act_fn,
use_einsum=use_einsum,
num_attention_heads=num_attention_heads,
name="dense")
with tf.variable_scope("output"):
ffn_output = dense_layer_2d(
intermediate_output,
hidden_size,
util.create_initializer(initializer_range),
None,
use_einsum=use_einsum,
num_attention_heads=num_attention_heads,
name="dense")
ffn_output = util.dropout(ffn_output, hidden_dropout_prob)
ffn_output = util.layer_norm(ffn_output + attention_output)
return ffn_output
def transformer_model(input_tensor,
attention_mask=None,
hidden_size=768,
num_hidden_layers=12,
num_hidden_groups=12,
num_attention_heads=12,
intermediate_size=3072,
inner_group_num=1,
intermediate_act_fn="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
initializer_range=0.02,
do_return_all_layers=False,
use_einsum=True,
trainable=True):
"""Multi-headed, multi-layer Transformer from "Attention is All You Need".
This is almost an exact implementation of the original Transformer encoder.
See the original paper:
https://arxiv.org/abs/1706.03762
Also see:
https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/models/transformer.py
Args:
input_tensor: float Tensor of shape [batch_size, seq_length, hidden_size].
attention_mask: (optional) int32 Tensor of shape [batch_size, 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_hidden_groups: int. Number of group for the hidden layers, parameters
in the same group are shared.
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.
inner_group_num: int, number of inner repetition of attention and ffn.
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.
use_einsum: bool. Whether to use einsum or reshape+matmul for dense layers
Returns:
float Tensor of shape [batch_size, seq_length, hidden_size], the final
hidden layer of the Transformer.
Raises:
ValueError: A Tensor shape or parameter is invalid.
"""
if hidden_size % num_attention_heads != 0:
raise ValueError(
"The hidden size (%d) is not a multiple of the number of attention "
"heads (%d)" % (hidden_size, num_attention_heads))
attention_head_size = hidden_size // num_attention_heads
input_shape = util.get_shape_list(input_tensor, expected_rank=3)
input_width = input_shape[2]
all_layer_outputs = []
if input_width != hidden_size:
prev_output = dense_layer_2d(
input_tensor,
hidden_size,
util.create_initializer(initializer_range),
None,
use_einsum=use_einsum,
name="embedding_hidden_mapping_in")
else:
prev_output = input_tensor
with tf.variable_scope("transformer", reuse=tf.AUTO_REUSE):
for layer_idx in range(num_hidden_layers):
group_idx = int(layer_idx / num_hidden_layers * num_hidden_groups)
with tf.variable_scope("group_%d" % group_idx):
with tf.name_scope("layer_%d" % layer_idx):
layer_output = prev_output
for inner_group_idx in range(inner_group_num):
with tf.variable_scope("inner_group_%d" %
inner_group_idx):
layer_output = attention_ffn_block(
layer_input=layer_output,
hidden_size=hidden_size,
attention_mask=attention_mask,
num_attention_heads=num_attention_heads,
attention_head_size=attention_head_size,
attention_probs_dropout_prob=
attention_probs_dropout_prob,
intermediate_size=intermediate_size,
intermediate_act_fn=intermediate_act_fn,
initializer_range=initializer_range,
hidden_dropout_prob=hidden_dropout_prob,
use_einsum=use_einsum)
prev_output = layer_output
all_layer_outputs.append(layer_output)
if do_return_all_layers:
return all_layer_outputs
else:
return all_layer_outputs[-1]
def transformer_xl(inp_k,
n_token,
n_layer,
d_model,
n_head,
d_head,
d_inner,
dropout,
dropatt,
attn_type,
bi_data,
initializer,
is_training,
mem_len=None,
inp_q=None,
mems=None,
same_length=False,
clamp_len=-1,
untie_r=False,
use_tpu=True,
input_mask=None,
perm_mask=None,
seg_id=None,
reuse_len=None,
ff_activation='relu',
target_mapping=None,
use_bfloat16=False,
scope='transformer',
tilda_embeddings=None,
**kwargs):
'''
Defines a Transformer-XL computation graph with additional
support for XLNet.
Args:
inp_k: int32 Tensor in shape [len, bsz], the input token IDs.
seg_id: int32 Tensor in shape [len, bsz], the input segment IDs.
input_mask: float32 Tensor in shape [len, bsz], the input mask.
0 for real tokens and 1 for padding.
mems: a list of float32 Tensors in shape [mem_len, bsz, d_model], memory
from previous batches. The length of the list equals n_layer.
If None, no memory is used.
perm_mask: float32 Tensor in shape [len, len, bsz].
If perm_mask[i, j, k] = 0, i attend to j in batch k;
if perm_mask[i, j, k] = 1, i does not attend to j in batch k.
If None, each position attends to all the others.
target_mapping: float32 Tensor in shape [num_predict, len, bsz].
If target_mapping[i, j, k] = 1, the i-th predict in batch k is
on the j-th token.
Only used during pretraining for partial prediction.
Set to None during finetuning.
inp_q: float32 Tensor in shape [len, bsz].
1 for tokens with losses and 0 for tokens without losses.
Only used during pretraining for two-stream attention.
Set to None during finetuning.
n_layer: int, the number of layers.
d_model: int, the hidden size.
n_head: int, the number of attention heads.
d_head: int, the dimension size of each attention head.
d_inner: int, the hidden size in feed-forward layers.
ff_activation: str, 'relu' or 'gelu'.
untie_r: bool, whether to untie the biases in attention.
n_token: int, the vocab size.
is_training: bool, whether in training mode.
use_tpu: bool, whether TPUs are used.
use_bfloat16: bool, use bfloat16 instead of float32.
dropout: float, dropout rate.
dropatt: float, dropout rate on attention probabilities.
init: str, the initialization scheme, either 'normal' or 'uniform'.
init_range: float, initialize the parameters with a uniform distribution
in [-init_range, init_range]. Only effective when init='uniform'.
init_std: float, initialize the parameters with a normal distribution
with mean 0 and stddev init_std. Only effective when init='normal'.
mem_len: int, the number of tokens to cache.
reuse_len: int, the number of tokens in the currect batch to be cached
and reused in the future.
bi_data: bool, whether to use bidirectional input pipeline.
Usually set to True during pretraining and False during finetuning.
clamp_len: int, clamp all relative distances larger than clamp_len.
-1 means no clamping.
same_length: bool, whether to use the same attention length for each token.
summary_type: str, 'last', 'first', 'mean', or 'attn'. The method
to pool the input to get a vector representation.
initializer: A tf initializer.
scope: scope name for the computation graph.
'''
tf_float = tf.bfloat16 if use_bfloat16 else tf.float32
new_mems = []
with tf.variable_scope(scope):
if untie_r:
r_w_bias = tf.get_variable('r_w_bias', [n_layer, n_head, d_head],
dtype=tf_float,
initializer=initializer)
r_r_bias = tf.get_variable('r_r_bias', [n_layer, n_head, d_head],
dtype=tf_float,
initializer=initializer)
else:
r_w_bias = tf.get_variable('r_w_bias', [n_head, d_head],
dtype=tf_float,
initializer=initializer)
r_r_bias = tf.get_variable('r_r_bias', [n_head, d_head],
dtype=tf_float,
initializer=initializer)
bsz = tf.shape(inp_k)[1]
qlen = tf.shape(inp_k)[0]
mlen = tf.shape(mems[0])[0] if mems is not None else 0
klen = mlen + qlen
##### Attention mask
# causal attention mask
if attn_type == 'uni':
attn_mask = _create_mask(qlen, mlen, tf_float, same_length)
attn_mask = attn_mask[:, :, None, None]
elif attn_type == 'bi':
attn_mask = None
else:
raise ValueError('Unsupported attention type: %s' % attn_type)
# data mask: input mask & perm mask
if input_mask is not None and perm_mask is not None:
data_mask = input_mask[None] + perm_mask
elif input_mask is not None and perm_mask is None:
data_mask = input_mask[None]
elif input_mask is None and perm_mask is not None:
data_mask = perm_mask
else:
data_mask = None
if data_mask is not None:
# all mems can be attended to
mems_mask = tf.zeros([tf.shape(data_mask)[0], mlen, bsz],
dtype=tf_float)
data_mask = tf.cast(data_mask, dtype=tf.float32)
data_mask = tf.concat([mems_mask, data_mask], 1)
if attn_mask is None:
attn_mask = data_mask[:, :, :, None]
else:
attn_mask += data_mask[:, :, :, None]
if attn_mask is not None:
attn_mask = tf.cast(attn_mask > 0, dtype=tf_float)
if attn_mask is not None:
non_tgt_mask = -tf.eye(qlen, dtype=tf_float)
non_tgt_mask = tf.concat(
[tf.zeros([qlen, mlen], dtype=tf_float), non_tgt_mask],
axis=-1)
non_tgt_mask = tf.cast(
(attn_mask + non_tgt_mask[:, :, None, None]) > 0,
dtype=tf_float)
else:
non_tgt_mask = None
##### Word embedding
word_emb_k, lookup_table = embedding_lookup(
x=inp_k,
n_token=n_token,
d_embed=d_model,
initializer=initializer,
use_tpu=use_tpu,
dtype=tf_float,
scope='word_embedding',
tilda_embeddings=tilda_embeddings)
if inp_q is not None:
with tf.variable_scope('mask_emb'):
mask_emb = tf.get_variable('mask_emb', [1, 1, d_model],
dtype=tf_float)
if target_mapping is not None:
word_emb_q = tf.tile(mask_emb,
[tf.shape(target_mapping)[0], bsz, 1])
else:
inp_q_ext = inp_q[:, :, None]
word_emb_q = \
inp_q_ext * mask_emb + (1 - inp_q_ext) * word_emb_k
output_h = tf.layers.dropout(word_emb_k, dropout, training=is_training)
if inp_q is not None:
output_g = tf.layers.dropout(word_emb_q,
dropout,
training=is_training)
##### Segment embedding
if seg_id is not None:
if untie_r:
r_s_bias = tf.get_variable('r_s_bias',
[n_layer, n_head, d_head],
dtype=tf_float,
initializer=initializer)
else:
# default case (tie)
r_s_bias = tf.get_variable('r_s_bias', [n_head, d_head],
dtype=tf_float,
initializer=initializer)
seg_embed = tf.get_variable('seg_embed',
[n_layer, 2, n_head, d_head],
dtype=tf_float,
initializer=initializer)
# Convert `seg_id` to one-hot `seg_mat`
mem_pad = tf.zeros([mlen, bsz], dtype=tf.int32)
cat_ids = tf.concat([mem_pad, seg_id], 0)
# `1` indicates not in the same segment [qlen x klen x bsz]
seg_mat = tf.cast(
tf.logical_not(tf.equal(seg_id[:, None], cat_ids[None, :])),
tf.int32)
seg_mat = tf.one_hot(seg_mat, 2, dtype=tf_float)
else:
seg_mat = None
##### Positional encoding
pos_emb = relative_positional_encoding(qlen,
klen,
d_model,
clamp_len,
attn_type,
bi_data,
bsz=bsz,
dtype=tf_float)
pos_emb = tf.layers.dropout(pos_emb, dropout, training=is_training)
##### Attention layers
if mems is None:
mems = [None] * n_layer
for i in range(n_layer):
# cache new mems
new_mems.append(_cache_mem(output_h, mems[i], mem_len, reuse_len))
# segment bias
if seg_id is None:
r_s_bias_i = None
seg_embed_i = None
else:
r_s_bias_i = r_s_bias if not untie_r else r_s_bias[i]
seg_embed_i = seg_embed[i]
with tf.variable_scope('layer_{}'.format(i)):
if inp_q is not None:
output_h, output_g = two_stream_rel_attn(
h=output_h,
g=output_g,
r=pos_emb,
r_w_bias=r_w_bias if not untie_r else r_w_bias[i],
r_r_bias=r_r_bias if not untie_r else r_r_bias[i],
seg_mat=seg_mat,
r_s_bias=r_s_bias_i,
seg_embed=seg_embed_i,
attn_mask_h=non_tgt_mask,
attn_mask_g=attn_mask,
mems=mems[i],
target_mapping=target_mapping,
d_model=d_model,
n_head=n_head,
d_head=d_head,
dropout=dropout,
dropatt=dropatt,
is_training=is_training,
kernel_initializer=initializer)
reuse = True
else:
reuse = False
output_h = rel_multihead_attn(
h=output_h,
r=pos_emb,
r_w_bias=r_w_bias if not untie_r else r_w_bias[i],
r_r_bias=r_r_bias if not untie_r else r_r_bias[i],
seg_mat=seg_mat,
r_s_bias=r_s_bias_i,
seg_embed=seg_embed_i,
attn_mask=non_tgt_mask,
mems=mems[i],
d_model=d_model,
n_head=n_head,
d_head=d_head,
dropout=dropout,
dropatt=dropatt,
is_training=is_training,
kernel_initializer=initializer,
reuse=reuse)
if inp_q is not None:
output_g = positionwise_ffn(inp=output_g,
d_model=d_model,
d_inner=d_inner,
dropout=dropout,
kernel_initializer=initializer,
activation_type=ff_activation,
is_training=is_training)
output_h = positionwise_ffn(inp=output_h,
d_model=d_model,
d_inner=d_inner,
dropout=dropout,
kernel_initializer=initializer,
activation_type=ff_activation,
is_training=is_training,
reuse=reuse)
if inp_q is not None:
output = tf.layers.dropout(output_g, dropout, training=is_training)
else:
output = tf.layers.dropout(output_h, dropout, training=is_training)
return output, new_mems, lookup_table
def __init__(self,
albert_config,
is_training,
input_ids,
input_mask=None,
segment_ids=None,
scope='bert',
drop_pooler=False,
trainable=True,
**kwargs):
"""Constructor for AlbertModel.
Args:
albert_config: `AlbertConfig` instance.
is_training: bool. true for training model, false for eval model.
Controls whether dropout will be applied.
input_ids: int32 Tensor of shape [batch_size, seq_length].
input_mask: (optional) int32 Tensor of shape [batch_size, seq_length].
segment_ids: (optional) int32 Tensor of shape [batch_size, seq_length].
use_einsum: (optional) bool. Whether to use einsum or reshape+matmul for
dense layers
scope: (optional) variable scope. Defaults to "bert".
Raises:
ValueError: The config is invalid or one of the input tensor shapes
is invalid.
"""
albert_config = copy.deepcopy(albert_config)
if not is_training:
albert_config.hidden_dropout_prob = 0.0
albert_config.attention_probs_dropout_prob = 0.0
input_shape = util.get_shape_list(input_ids, expected_rank=2)
batch_size = input_shape[0]
seq_length = input_shape[1]
if input_mask is None:
input_mask = tf.ones(shape=[batch_size, seq_length],
dtype=tf.int32)
if segment_ids is None:
segment_ids = tf.zeros(shape=[batch_size, seq_length],
dtype=tf.int32)
# Tilda embeddings for SMART algorithm
tilda_embeddings = None
use_tilda_embedding = kwargs.get('use_tilda_embedding')
if use_tilda_embedding:
with tf.variable_scope('', reuse=True):
tilda_embeddings = tf.get_variable('tilda_embeddings')
with tf.variable_scope(scope):
with tf.variable_scope("embeddings"):
# Perform embedding lookup on the word ids.
(self.word_embedding_output,
self.output_embedding_table) = embedding_lookup(
input_ids=input_ids,
vocab_size=albert_config.vocab_size,
embedding_size=albert_config.embedding_size,
initializer_range=albert_config.initializer_range,
word_embedding_name="word_embeddings",
tilda_embeddings=tilda_embeddings,
trainable=trainable)
# Add positional embeddings and token type embeddings, then layer
# normalize and perform dropout.
self.embedding_output = embedding_postprocessor(
input_tensor=self.word_embedding_output,
use_token_type=True,
segment_ids=segment_ids,
token_type_vocab_size=albert_config.type_vocab_size,
token_type_embedding_name="token_type_embeddings",
use_position_embeddings=True,
position_embedding_name="position_embeddings",
initializer_range=albert_config.initializer_range,
max_position_embeddings=albert_config.
max_position_embeddings,
dropout_prob=albert_config.hidden_dropout_prob,
trainable=trainable)
with tf.variable_scope("encoder"):
# Run the stacked transformer.
# `sequence_output` shape = [batch_size, seq_length, hidden_size].
self.all_encoder_layers = transformer_model(
input_tensor=self.embedding_output,
attention_mask=input_mask,
hidden_size=albert_config.hidden_size,
num_hidden_layers=albert_config.num_hidden_layers,
num_hidden_groups=albert_config.num_hidden_groups,
num_attention_heads=albert_config.num_attention_heads,
intermediate_size=albert_config.intermediate_size,
inner_group_num=albert_config.inner_group_num,
intermediate_act_fn=util.get_activation(
albert_config.hidden_act),
hidden_dropout_prob=albert_config.hidden_dropout_prob,
attention_probs_dropout_prob=albert_config.
attention_probs_dropout_prob,
initializer_range=albert_config.initializer_range,
do_return_all_layers=True,
use_einsum=False,
trainable=trainable)
self.sequence_output = self.all_encoder_layers[-1]
# The "pooler" converts the encoded sequence tensor of shape
# [batch_size, seq_length, hidden_size] to a tensor of shape
# [batch_size, hidden_size]. This is necessary for segment-level
# (or segment-pair-level) classification tasks where we need a fixed
# dimensional representation of the segment.
with tf.variable_scope("pooler"):
# We "pool" the model by simply taking the hidden state corresponding
# to the first token. We assume that this has been pre-trained
first_token_tensor = tf.squeeze(self.sequence_output[:,
0:1, :],
axis=1)
# trick: ignore the fully connected layer
if drop_pooler:
self.pooled_output = first_token_tensor
else:
self.pooled_output = tf.layers.dense(
first_token_tensor,
albert_config.hidden_size,
activation=tf.tanh,
kernel_initializer=util.create_initializer(
albert_config.initializer_range),
trainable=trainable)
def two_stream_rel_attn(h,
g,
r,
mems,
r_w_bias,
r_r_bias,
seg_mat,
r_s_bias,
seg_embed,
attn_mask_h,
attn_mask_g,
target_mapping,
d_model,
n_head,
d_head,
dropout,
dropatt,
is_training,
kernel_initializer,
scope='rel_attn'):
'''Two-stream attention with relative positional encoding.'''
scale = 1 / (d_head**0.5)
with tf.variable_scope(scope, reuse=False):
# content based attention score
if mems is not None and mems.shape.ndims > 1:
cat = tf.concat([mems, h], 0)
else:
cat = h
# content-based key head
k_head_h = head_projection(cat, d_model, n_head, d_head,
kernel_initializer, 'k')
# content-based value head
v_head_h = head_projection(cat, d_model, n_head, d_head,
kernel_initializer, 'v')
# position-based key head
k_head_r = head_projection(r, d_model, n_head, d_head,
kernel_initializer, 'r')
##### h-stream
# content-stream query head
q_head_h = head_projection(h, d_model, n_head, d_head,
kernel_initializer, 'q')
# core attention ops
attn_vec_h = rel_attn_core(q_head_h, k_head_h, v_head_h, k_head_r,
seg_embed, seg_mat, r_w_bias, r_r_bias,
r_s_bias, attn_mask_h, dropatt, is_training,
scale)
# post processing
output_h = post_attention(h, attn_vec_h, d_model, n_head, d_head,
dropout, is_training, kernel_initializer)
with tf.variable_scope(scope, reuse=True):
##### g-stream
# query-stream query head
q_head_g = head_projection(g, d_model, n_head, d_head,
kernel_initializer, 'q')
# core attention ops
if target_mapping is not None:
q_head_g = tf.einsum('mbnd,mlb->lbnd', q_head_g, target_mapping)
attn_vec_g = rel_attn_core(q_head_g, k_head_h, v_head_h, k_head_r,
seg_embed, seg_mat, r_w_bias, r_r_bias,
r_s_bias, attn_mask_g, dropatt,
is_training, scale)
attn_vec_g = tf.einsum('lbnd,mlb->mbnd', attn_vec_g,
target_mapping)
else:
attn_vec_g = rel_attn_core(q_head_g, k_head_h, v_head_h, k_head_r,
seg_embed, seg_mat, r_w_bias, r_r_bias,
r_s_bias, attn_mask_g, dropatt,
is_training, scale)
# post processing
output_g = post_attention(g, attn_vec_g, d_model, n_head, d_head,
dropout, is_training, kernel_initializer)
return output_h, output_g
def __init__(self,
xlnet_config,
is_training,
input_ids,
seg_ids,
input_mask,
mems=None,
perm_mask=None,
target_mapping=None,
inp_q=None,
**kwargs):
'''
Args:
xlnet_config: XLNetConfig.
is_training: bool, whether is training or not.
input_ids: int32 Tensor in shape [len, bsz], the input token IDs.
seg_ids: int32 Tensor in shape [len, bsz], the input segment IDs.
input_mask: float32 Tensor in shape [len, bsz], the input mask.
0 for real tokens and 1 for padding.
mems: a list of float32 Tensors in shape [mem_len, bsz, d_model],
memory from previous batches. The length of the list equals
n_layer. If None, no memory is used.
perm_mask: float32 Tensor in shape [len, len, bsz].
If perm_mask[i, j, k] = 0, i attend to j in batch k;
if perm_mask[i, j, k] = 1, i does not attend to j in batch k.
If None, each position attends to all the others.
target_mapping: float32 Tensor in shape [num_predict, len, bsz].
If target_mapping[i, j, k] = 1, the i-th predict in batch k is
on the j-th token.
Only used during pretraining for partial prediction.
Set to None during finetuning.
inp_q: float32 Tensor in shape [len, bsz].
1 for tokens with losses and 0 for tokens without losses.
Only used during pretraining for two-stream attention.
Set to None during finetuning.
'''
run_config = XLNetRunConfig(is_training=is_training,
bi_data=False,
use_tpu=False,
use_bfloat16=False,
dropout=(0.1 if is_training else 0.0),
dropatt=(0.1 if is_training else 0.0),
init='normal',
init_range=0.1,
init_std=0.02,
clamp_len=-1)
initializer = _get_initializer(run_config)
# Tilda embeddings for SMART algorithm
tilda_embeddings = None
use_tilda_embedding = kwargs.get('use_tilda_embedding')
if use_tilda_embedding:
with tf.variable_scope('', reuse=True):
tilda_embeddings = tf.get_variable('tilda_embeddings')
tfm_args = dict(n_token=xlnet_config.n_token,
initializer=initializer,
attn_type='bi',
n_layer=xlnet_config.n_layer,
d_model=xlnet_config.d_model,
n_head=xlnet_config.n_head,
d_head=xlnet_config.d_head,
d_inner=xlnet_config.d_inner,
ff_activation=xlnet_config.ff_activation,
untie_r=xlnet_config.untie_r,
is_training=run_config.is_training,
use_bfloat16=run_config.use_bfloat16,
use_tpu=run_config.use_tpu,
dropout=run_config.dropout,
dropatt=run_config.dropatt,
mem_len=run_config.mem_len,
reuse_len=run_config.reuse_len,
bi_data=run_config.bi_data,
clamp_len=run_config.clamp_len,
same_length=run_config.same_length)
input_args = dict(inp_k=input_ids,
seg_id=seg_ids,
input_mask=input_mask,
mems=mems,
perm_mask=perm_mask,
target_mapping=target_mapping,
inp_q=inp_q,
tilda_embeddings=tilda_embeddings)
tfm_args.update(input_args)
with tf.variable_scope('model', reuse=tf.AUTO_REUSE):
(self.output, self.new_mems, self.lookup_table) = \
transformer_xl(**tfm_args)
self.input_mask = input_mask
self.initializer = initializer
self.xlnet_config = xlnet_config
self.run_config = run_config
def summarize_sequence(summary_type,
hidden,
d_model,
n_head,
d_head,
dropout,
dropatt,
input_mask,
is_training,
initializer,
scope=None,
reuse=None,
use_proj=True):
'''
Different classification tasks may not may not share the same parameters
to summarize the sequence features.
If shared, one can keep the `scope` to the default value `None`.
Otherwise, one should specify a different `scope` for each task.
'''
with tf.variable_scope(scope, 'sequnece_summary', reuse=reuse):
if summary_type == 'last':
summary = hidden[-1]
elif summary_type == 'first':
summary = hidden[0]
elif summary_type == 'mean':
summary = tf.reduce_mean(hidden, axis=0)
elif summary_type == 'attn':
bsz = tf.shape(hidden)[1]
summary_bias = tf.get_variable('summary_bias', [d_model],
dtype=hidden.dtype,
initializer=initializer)
summary_bias = tf.tile(summary_bias[None, None], [1, bsz, 1])
if input_mask is not None:
input_mask = input_mask[None, :, :, None]
summary = multihead_attn(summary_bias,
hidden,
hidden,
input_mask,
d_model,
n_head,
d_head,
dropout,
dropatt,
is_training,
initializer,
residual=False)
summary = summary[0]
else:
raise ValueError('Unsupported summary type %s' % summary_type)
# use another projection as in BERT
if use_proj:
summary = tf.layers.dense(summary,
d_model,
activation=tf.tanh,
kernel_initializer=initializer,
name='summary')
# dropout
summary = tf.layers.dropout(summary,
dropout,
training=is_training,
name='dropout')
return summary
def _cls_self_attention_paper(self,
prev_output,
batch_size,
max_seq_length,
label_size,
attention_mask=None,
cls_hidden_size=128,
cls_num_attention_heads=2,
attention_probs_dropout_prob=0.1,
initializer_range=0.02,
dtype=tf.float32,
trainable=True):
if cls_hidden_size % cls_num_attention_heads != 0:
raise ValueError(
'`cls_hidden_size` (%d) is not a multiple of the number of '
'`cls_num_attention_heads` (%d)' %
(cls_hidden_size, cls_num_attention_heads))
cls_attention_head_size = int(cls_hidden_size /
cls_num_attention_heads)
with tf.variable_scope('project'):
attention_input = tf.layers.dense(
prev_output,
cls_hidden_size,
activation='tanh',
kernel_initializer=util.create_initializer(initializer_range),
trainable=trainable)
with tf.variable_scope('attention'):
attention_heads = []
with tf.variable_scope('self'):
(attention_head, _) = self.attention_layer(
from_tensor=attention_input,
to_tensor=attention_input,
attention_mask=attention_mask,
num_attention_heads=cls_num_attention_heads,
size_per_head=cls_attention_head_size,
attention_probs_dropout_prob=attention_probs_dropout_prob,
initializer_range=initializer_range,
do_return_2d_tensor=False,
batch_size=batch_size,
from_max_seq_length=max_seq_length,
to_max_seq_length=max_seq_length,
dtype=dtype,
trainable=trainable)
attention_heads.append(attention_head)
attention_output = None
if len(attention_heads) == 1:
attention_output = attention_heads[0]
else:
attention_output = tf.concat(attention_heads, axis=-1)
with tf.variable_scope('intermediate'):
intermediate_output = tf.layers.dense(
attention_output[:, 0, :],
cls_hidden_size,
activation='tanh',
kernel_initializer=util.create_initializer(initializer_range),
trainable=trainable)
with tf.variable_scope('output'):
cls_output = tf.layers.dense(
intermediate_output,
label_size,
kernel_initializer=util.create_initializer(initializer_range),
trainable=trainable)
return cls_output
def __init__(self,
bert_config,
is_training,
dilated_ids,
label_ids,
max_seq_length,
spad_id=1,
loop=3,
sample_weight=None,
scope='dilated',
use_tilda_embedding=False,
**kwargs):
super().__init__()
dilated_mask = tf.cast(tf.not_equal(dilated_ids, 0), tf.float32)
shape = util.get_shape_list(dilated_ids, expected_rank=2)
batch_size = shape[0]
dilated_seq_length = shape[1]
# Tilda embeddings for SMART algorithm
tilda_embeddings = None
if use_tilda_embedding:
with tf.variable_scope('', reuse=True):
tilda_embeddings = tf.get_variable('tilda_embeddings')
with tf.variable_scope(scope):
# forward once
if is_training:
logits = self._bert_forward(bert_config,
dilated_ids,
dilated_mask,
batch_size,
dilated_seq_length,
tilda_embeddings=tilda_embeddings)
self.preds['LM'] = tf.argmax(logits, axis=-1)
# LM loss
log_probs = tf.nn.log_softmax(logits, axis=-1)
one_hot_labels = tf.one_hot(label_ids,
depth=bert_config.vocab_size)
per_token_loss = -tf.reduce_sum(one_hot_labels * log_probs,
axis=-1)
input_length = tf.reduce_sum(dilated_mask, axis=-1) * 2
label_mask = tf.sequence_mask(input_length,
max_seq_length * 2,
dtype=tf.float32)
per_example_loss = \
tf.reduce_sum(per_token_loss * label_mask, axis=-1) / \
tf.reduce_sum(label_mask, axis=-1)
if sample_weight is not None:
per_example_loss *= tf.expand_dims(sample_weight, axis=-1)
self.total_loss = tf.reduce_mean(per_example_loss)
self.losses['LM'] = per_example_loss
# forward loop
else:
def _forward(dilated_ids, dilated_mask):
logits = self._bert_forward(
bert_config,
dilated_ids,
dilated_mask,
batch_size,
dilated_seq_length,
tilda_embeddings=tilda_embeddings)
output_ids = tf.argmax(logits, axis=-1)
output_ids = tf.cast(output_ids, dtype=tf.int32)
# special padding (using `spad` token)
equal_zero = tf.cast(tf.equal(output_ids, 0), tf.int32)
equal_zero = tf.reduce_sum(equal_zero, axis=-1)
right_pad = spad_id * tf.sequence_mask(
equal_zero, dilated_seq_length, dtype=tf.int32)
paded = tf.concat([output_ids, right_pad], axis=-1)
# extract ids of length `max_seq_length`
flattened_padded = tf.reshape(paded, [-1])
is_valid = tf.cast(tf.greater(flattened_padded, 0),
dtype=tf.int32)
flattened_valid = tf.boolean_mask(flattened_padded,
is_valid)
valid = tf.reshape(flattened_valid,
[batch_size, dilated_seq_length])
cutted_valid = valid[:, :max_seq_length]
# replace `spad` token with `pad`
non_spad_mask = tf.cast(tf.not_equal(
cutted_valid, spad_id),
dtype=tf.int32)
output_ids = cutted_valid * non_spad_mask
output_length = tf.reduce_sum(non_spad_mask, axis=-1)
# dilate
reshaped_ids = tf.reshape(output_ids,
[batch_size, max_seq_length, 1])
reshaped_mask = tf.reshape(
tf.sequence_mask(output_length,
max_seq_length,
dtype=tf.int32),
[batch_size, max_seq_length, 1])
concat_ids = tf.concat(
[reshaped_ids,
tf.zeros_like(reshaped_ids)], axis=-1)
concat_mask = tf.concat([
reshaped_mask,
tf.zeros_like(reshaped_mask, dtype=tf.int32)
],
axis=-1)
dilated_ids = tf.reshape(concat_ids,
[batch_size, max_seq_length * 2])
dilated_mask = tf.reshape(concat_mask,
[batch_size, max_seq_length * 2])
return dilated_ids, dilated_mask
for _ in range(loop):
dilated_ids, dilated_mask = _forward(
dilated_ids, dilated_mask)
self.preds['LM'] = dilated_ids
def __init__(self,
bert_config,
is_training,
input_ids,
input_mask=None,
token_type_ids=None,
use_one_hot_embeddings=True,
scope=None,
embedding_size=None,
input_embeddings=None,
input_reprs=None,
update_embeddings=True,
untied_embeddings=False):
'''Constructor for BertModel.
Args:
bert_config: `BertConfig` instance.
is_training: bool. true for training model, false for eval model.
Controls whether dropout will be applied.
input_ids: int32 Tensor of shape [batch_size, seq_length].
input_mask: (optional) int32 Tensor of shape [batch_size,
seq_length].
token_type_ids: (optional) int32 Tensor of shape [batch_size,
seq_length].
use_one_hot_embeddings: (optional) bool. Whether to use one-hot word
embeddings or tf.embedding_lookup() for the word embeddings. On
the TPU, it is much faster if this is True, on the CPU or GPU,
it is faster if this is False.
scope: (optional) variable scope. Defaults to 'electra'.
Raises:
ValueError: The config is invalid or one of the input tensor shapes
is invalid.
'''
bert_config = copy.deepcopy(bert_config)
if not is_training:
bert_config.hidden_dropout_prob = 0.0
bert_config.attention_probs_dropout_prob = 0.0
input_shape = util.get_shape_list(token_type_ids, expected_rank=2)
batch_size = input_shape[0]
seq_length = input_shape[1]
if input_mask is None:
input_mask = tf.ones(shape=[batch_size, seq_length],
dtype=tf.int32)
assert token_type_ids is not None
if input_reprs is None:
with tf.variable_scope(
((scope if untied_embeddings else 'electra') + '/embeddings'),
reuse=tf.AUTO_REUSE):
# Perform embedding lookup on the word ids
if embedding_size is None:
embedding_size = bert_config.hidden_size
(token_embeddings, self.embedding_table) = \
embedding_lookup(
input_ids=input_ids,
vocab_size=bert_config.vocab_size,
embedding_size=embedding_size,
initializer_range=bert_config.initializer_range,
word_embedding_name='word_embeddings',
use_one_hot_embeddings=use_one_hot_embeddings)
with tf.variable_scope(
((scope if untied_embeddings else 'electra') + '/embeddings'),
reuse=tf.AUTO_REUSE):
# Add positional embeddings and token type embeddings, then
# layer normalize and perform dropout.
self.embedding_output = embedding_postprocessor(
input_tensor=token_embeddings,
use_token_type=True,
token_type_ids=token_type_ids,
token_type_vocab_size=bert_config.type_vocab_size,
token_type_embedding_name='token_type_embeddings',
use_position_embeddings=True,
position_embedding_name='position_embeddings',
initializer_range=bert_config.initializer_range,
max_position_embeddings=\
bert_config.max_position_embeddings,
dropout_prob=bert_config.hidden_dropout_prob)
else:
self.embedding_output = input_reprs
if not update_embeddings:
self.embedding_output = tf.stop_gradient(self.embedding_output)
with tf.variable_scope(scope, default_name='electra'):
if self.embedding_output.shape[-1] != bert_config.hidden_size:
self.embedding_output = tf.layers.dense(
self.embedding_output,
bert_config.hidden_size,
name='embeddings_project')
with tf.variable_scope('encoder'):
# This converts a 2D mask of shape [batch_size, seq_length]
# to a 3D mask of shape [batch_size, seq_length, seq_length]
# which is used for the attention scores.
attention_mask = create_attention_mask_from_input_mask(
token_type_ids, input_mask)
# Run the stacked transformer. Output shapes
# attn_maps:
# [n_layers, batch_size, n_heads, seq_length, seq_length]
(self.all_layer_outputs, self.attn_maps) = transformer_model(
input_tensor=self.embedding_output,
attention_mask=attention_mask,
hidden_size=bert_config.hidden_size,
num_hidden_layers=bert_config.num_hidden_layers,
num_attention_heads=bert_config.num_attention_heads,
intermediate_size=bert_config.intermediate_size,
intermediate_act_fn=util.get_activation(
bert_config.hidden_act),
hidden_dropout_prob=bert_config.hidden_dropout_prob,
attention_probs_dropout_prob=bert_config.
attention_probs_dropout_prob,
initializer_range=bert_config.initializer_range,
do_return_all_layers=True)
self.sequence_output = self.all_layer_outputs[-1]
self.pooled_output = self.sequence_output[:, 0]
def _lm_forward(self,
is_training,
input_tensor,
input_mask,
label_ids,
bert_config,
batch_size,
max_seq_length,
prob,
scope,
name,
sample_weight=None,
hidden_dropout_prob=0.1,
initializer_range=0.02):
with tf.variable_scope(scope):
with tf.variable_scope('verifier'):
logits = tf.layers.dense(
input_tensor,
2,
kernel_initializer=util.create_initializer(
bert_config.initializer_range),
trainable=True)
verifier_label_ids = tf.cast(tf.greater(label_ids, 0),
tf.int32)
# loss
log_probs = tf.nn.log_softmax(logits, axis=-1)
one_hot_labels = tf.one_hot(verifier_label_ids, depth=2)
per_token_loss = -tf.reduce_sum(one_hot_labels * log_probs,
axis=-1)
input_mask = tf.cast(input_mask, tf.float32)
per_token_loss *= input_mask / tf.reduce_sum(
input_mask, keepdims=True, axis=-1)
per_example_loss = tf.reduce_sum(per_token_loss, axis=-1)
if sample_weight is not None:
per_example_loss *= tf.expand_dims(sample_weight, axis=-1)
if prob != 0:
self.total_loss += tf.reduce_mean(per_example_loss)
verifier_loss = per_example_loss
verifier_preds = tf.argmax(logits, axis=-1)
with tf.variable_scope('prediction'):
with tf.variable_scope('intermediate'):
logits = tf.layers.dense(
input_tensor,
bert_config.hidden_size * 4,
kernel_initializer=util.create_initializer(
bert_config.initializer_range),
activation=util.gelu,
trainable=True)
with tf.variable_scope('output'):
logits = tf.layers.dense(
logits,
bert_config.hidden_size,
kernel_initializer=util.create_initializer(
bert_config.initializer_range),
trainable=True)
flattened = tf.reshape(
logits,
[batch_size * max_seq_length, bert_config.hidden_size])
logits = tf.matmul(flattened,
self.embedding_table,
transpose_b=True)
logits = tf.reshape(
logits, [-1, max_seq_length, bert_config.vocab_size])
# loss
log_probs = tf.nn.log_softmax(logits, axis=-1)
one_hot_labels = tf.one_hot(label_ids,
depth=bert_config.vocab_size)
per_token_loss = -tf.reduce_sum(one_hot_labels * log_probs,
axis=-1)
input_mask *= tf.cast(verifier_preds, tf.float32)
per_token_loss *= input_mask / (
tf.reduce_sum(input_mask, keepdims=True, axis=-1) + 1e-6)
per_example_loss = tf.reduce_sum(per_token_loss, axis=-1)
if sample_weight is not None:
per_example_loss *= tf.expand_dims(sample_weight, axis=-1)
if prob != 0:
self.total_loss += tf.reduce_mean(per_example_loss)
self.losses[name + '_loss'] = verifier_loss
self.preds[name + '_preds'] = \
tf.argmax(logits, axis=-1) * verifier_preds
def _get_generator_output(self, inputs, sample_weight, generator):
'''Masked language modeling softmax layer.'''
def gather_indexes(sequence_tensor, positions):
sequence_shape = util.get_shape_list(sequence_tensor, 3)
batch_size = sequence_shape[0]
seq_length = sequence_shape[1]
width = sequence_shape[2]
flat_offsets = tf.reshape(
tf.range(0, batch_size, dtype=tf.int32) * seq_length, [-1, 1])
flat_positions = tf.reshape(positions + flat_offsets, [-1])
flat_sequence_tensor = tf.reshape(sequence_tensor,
[batch_size * seq_length, width])
output_tensor = tf.gather(flat_sequence_tensor, flat_positions)
return output_tensor
input_tensor = gather_indexes(generator.get_sequence_output(),
inputs.masked_lm_positions)
with tf.variable_scope('generator_predictions'):
input_tensor = tf.layers.dense(
input_tensor,
units=self.config.embedding_size,
activation=util.get_activation(self.bert_config.hidden_act),
kernel_initializer=util.create_initializer(
self.bert_config.initializer_range))
input_tensor = util.layer_norm(input_tensor)
output_bias = tf.get_variable('output_bias',
shape=[self.bert_config.vocab_size],
initializer=tf.zeros_initializer())
logits = tf.matmul(input_tensor,
generator.get_embedding_table(),
transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
probs = tf.nn.softmax(logits, axis=-1, name='MLM_probs')
preds = tf.argmax(logits, axis=-1)
log_probs = tf.nn.log_softmax(logits, axis=-1)
label_ids = tf.reshape(inputs.masked_lm_ids, [-1])
masked_lm_weights = inputs.masked_lm_weights
if sample_weight is not None:
sample_weight = tf.expand_dims(tf.cast(sample_weight,
dtype=tf.float32),
axis=-1)
masked_lm_weights *= sample_weight
label_weights = tf.reshape(masked_lm_weights, [-1])
one_hot_labels = tf.one_hot(label_ids,
depth=self.bert_config.vocab_size,
dtype=tf.float32)
per_example_loss = -tf.reduce_sum(log_probs * one_hot_labels,
axis=[-1])
per_example_loss = label_weights * per_example_loss
numerator = tf.reduce_sum(per_example_loss)
denominator = tf.reduce_sum(label_weights) + 1e-6
loss = numerator / denominator
MLMOutput = collections.namedtuple(
'MLMOutput',
['logits', 'probs', 'loss', 'per_example_loss', 'preds'])
return MLMOutput(logits=logits,
probs=probs,
per_example_loss=per_example_loss,
loss=loss,
preds=preds)
def __init__(self,
bert_config,
is_training,
input_ids,
add_label_ids,
del_label_ids,
sample_weight=None,
add_prob=0,
del_prob=0,
scope='bert',
use_tilda_embedding=False,
**kwargs):
super().__init__()
input_mask = tf.cast(tf.not_equal(input_ids, 0), tf.float32)
shape = util.get_shape_list(input_ids, expected_rank=2)
batch_size = shape[0]
max_seq_length = shape[1]
if not is_training:
bert_config.hidden_dropout_prob = 0.0
bert_config.attention_probs_dropout_prob = 0.0
# Tilda embeddings for SMART algorithm
tilda_embeddings = None
if use_tilda_embedding:
with tf.variable_scope('', reuse=True):
tilda_embeddings = tf.get_variable('tilda_embeddings')
with tf.variable_scope(scope):
# forward once
hidden = self._bert_forward(bert_config,
input_ids,
input_mask,
batch_size,
max_seq_length,
tilda_embeddings=tilda_embeddings)
# additional_position_embeddings = tf.get_variable(
# name='position_embeddings',
# shape=[bert_config.max_position_embeddings,
# bert_config.hidden_size],
# initializer=util.create_initializer(
# bert_config.initializer_range))
# embedding_slice = tf.slice(
# additional_position_embeddings, [0, 0], [max_seq_length, -1])
# hidden += tf.reshape(
# embedding_slice,
# [1, max_seq_length, bert_config.hidden_size])
self.total_loss = 0
self._lm_forward(is_training,
input_tensor=hidden,
input_mask=input_mask,
label_ids=add_label_ids,
bert_config=bert_config,
batch_size=batch_size,
max_seq_length=max_seq_length,
prob=add_prob,
scope='cls/add',
name='add',
sample_weight=sample_weight)
self._cls_forward(is_training,
input_tensor=hidden,
input_mask=input_mask,
label_ids=del_label_ids,
bert_config=bert_config,
batch_size=batch_size,
max_seq_length=max_seq_length,
prob=del_prob,
scope='cls/del',
name='del',
sample_weight=sample_weight)
def __init__(self,
is_training,
input_tensor,
is_supervised,
is_expanded,
label_ids,
label_size=2,
sample_weight=None,
scope='cls/seq_relationship',
hidden_dropout_prob=0.1,
initializer_range=0.02,
trainable=True,
global_step=None,
num_train_steps=None,
uda_softmax_temp=-1,
uda_confidence_thresh=-1,
tsa_schedule='linear',
**kwargs):
super().__init__(**kwargs)
is_supervised = tf.cast(is_supervised, tf.float32)
is_expanded = tf.cast(is_expanded, tf.float32)
hidden_size = input_tensor.shape.as_list()[-1]
with tf.variable_scope(scope):
output_weights = tf.get_variable(
'output_weights',
shape=[label_size, hidden_size],
initializer=util.create_initializer(initializer_range),
trainable=trainable)
output_bias = tf.get_variable('output_bias',
shape=[label_size],
initializer=tf.zeros_initializer(),
trainable=trainable)
output_layer = util.dropout(
input_tensor, hidden_dropout_prob if is_training else 0.0)
logits = tf.matmul(output_layer, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
log_probs = tf.nn.log_softmax(logits, axis=-1)
with tf.variable_scope('sup_loss'):
# reshape
sup_ori_log_probs = tf.boolean_mask(log_probs,
mask=(1.0 - is_expanded),
axis=0)
sup_log_probs = tf.boolean_mask(sup_ori_log_probs,
mask=is_supervised,
axis=0)
sup_label_ids = tf.boolean_mask(label_ids,
mask=is_supervised,
axis=0)
self.preds['preds'] = tf.argmax(sup_ori_log_probs, axis=-1)
one_hot_labels = tf.one_hot(sup_label_ids,
depth=label_size,
dtype=tf.float32)
per_example_loss = -tf.reduce_sum(
one_hot_labels * sup_log_probs, axis=-1)
loss_mask = tf.ones_like(per_example_loss, dtype=tf.float32)
correct_label_probs = tf.reduce_sum(one_hot_labels *
tf.exp(sup_log_probs),
axis=-1)
if is_training and tsa_schedule:
tsa_start = 1.0 / label_size
tsa_threshold = get_tsa_threshold(tsa_schedule,
global_step,
num_train_steps,
tsa_start,
end=1)
larger_than_threshold = tf.greater(correct_label_probs,
tsa_threshold)
loss_mask = loss_mask * (
1 - tf.cast(larger_than_threshold, tf.float32))
loss_mask = tf.stop_gradient(loss_mask)
per_example_loss = per_example_loss * loss_mask
if sample_weight is not None:
sup_sample_weight = tf.boolean_mask(sample_weight,
mask=is_supervised,
axis=0)
per_example_loss *= tf.cast(sup_sample_weight,
dtype=tf.float32)
sup_loss = (tf.reduce_sum(per_example_loss) /
tf.maximum(tf.reduce_sum(loss_mask), 1))
self.losses['supervised'] = per_example_loss
with tf.variable_scope('unsup_loss'):
# reshape
ori_log_probs = tf.boolean_mask(sup_ori_log_probs,
mask=(1.0 - is_supervised),
axis=0)
aug_log_probs = tf.boolean_mask(log_probs,
mask=is_expanded,
axis=0)
sup_ori_logits = tf.boolean_mask(logits,
mask=(1.0 - is_expanded),
axis=0)
ori_logits = tf.boolean_mask(sup_ori_logits,
mask=(1.0 - is_supervised),
axis=0)
unsup_loss_mask = 1
if uda_softmax_temp != -1:
tgt_ori_log_probs = tf.nn.log_softmax(ori_logits /
uda_softmax_temp,
axis=-1)
tgt_ori_log_probs = tf.stop_gradient(tgt_ori_log_probs)
else:
tgt_ori_log_probs = tf.stop_gradient(ori_log_probs)
if uda_confidence_thresh != -1:
largest_prob = tf.reduce_max(tf.exp(ori_log_probs),
axis=-1)
unsup_loss_mask = tf.cast(
tf.greater(largest_prob, uda_confidence_thresh),
tf.float32)
unsup_loss_mask = tf.stop_gradient(unsup_loss_mask)
per_example_loss = kl_for_log_probs(
tgt_ori_log_probs, aug_log_probs) * unsup_loss_mask
if sample_weight is not None:
unsup_sample_weight = tf.boolean_mask(sample_weight,
mask=(1.0 -
is_supervised),
axis=0)
per_example_loss *= tf.cast(unsup_sample_weight,
dtype=tf.float32)
unsup_loss = tf.reduce_mean(per_example_loss)
self.losses['unsupervised'] = per_example_loss
self.total_loss = sup_loss + unsup_loss
def _bert_forward(self,
bert_config,
input_ids,
input_mask,
batch_size,
max_seq_length,
dtype=tf.float32,
trainable=True,
tilda_embeddings=None):
with tf.variable_scope('embeddings'):
(embedding_output, self.embedding_table) = self.embedding_lookup(
input_ids=input_ids,
vocab_size=bert_config.vocab_size,
batch_size=batch_size,
max_seq_length=max_seq_length,
embedding_size=bert_config.hidden_size,
initializer_range=bert_config.initializer_range,
word_embedding_name='word_embeddings',
dtype=dtype,
trainable=trainable,
tilda_embeddings=tilda_embeddings)
# Add positional embeddings and token type embeddings
# layer normalize and perform dropout.
embedding_output = self.embedding_postprocessor(
input_tensor=embedding_output,
batch_size=batch_size,
max_seq_length=max_seq_length,
hidden_size=bert_config.hidden_size,
use_token_type=False,
use_position_embeddings=True,
position_embedding_name='position_embeddings',
initializer_range=bert_config.initializer_range,
max_position_embeddings=\
bert_config.max_position_embeddings,
dropout_prob=bert_config.hidden_dropout_prob,
dtype=dtype,
trainable=trainable)
with tf.variable_scope('encoder'):
attention_mask = self.create_attention_mask_from_input_mask(
input_mask, batch_size, max_seq_length, dtype=dtype)
# stacked transformers
all_encoder_layers = self.transformer_model(
input_tensor=embedding_output,
batch_size=batch_size,
max_seq_length=max_seq_length,
attention_mask=attention_mask,
hidden_size=bert_config.hidden_size,
num_hidden_layers=bert_config.num_hidden_layers,
num_attention_heads=bert_config.num_attention_heads,
intermediate_size=bert_config.intermediate_size,
intermediate_act_fn=util.get_activation(
bert_config.hidden_act),
hidden_dropout_prob=bert_config.hidden_dropout_prob,
attention_probs_dropout_prob=\
bert_config.attention_probs_dropout_prob,
initializer_range=bert_config.initializer_range,
dtype=dtype,
trainable=trainable)
return all_encoder_layers[-1]
def transformer_model(self,
input_tensor,
batch_size,
max_seq_length,
attention_mask=None,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
intermediate_act_fn=util.gelu,
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
initializer_range=0.02,
dtype=tf.float32,
trainable=True):
if hidden_size % num_attention_heads != 0:
raise ValueError(
'The hidden size (%d) is not a multiple of the number '
'of attention heads (%d)' % (hidden_size, num_attention_heads))
attention_head_size = int(hidden_size / num_attention_heads)
prev_output = util.reshape_to_matrix(input_tensor)
self.attention_scores = []
all_layer_outputs = []
for layer_idx in range(num_hidden_layers):
with tf.variable_scope('layer_%d' % layer_idx):
layer_input = prev_output
def _build_forward(layer_input):
with tf.variable_scope('attention'):
attention_heads = []
with tf.variable_scope('self'):
(attention_head, attention_scores) = \
self.attention_layer(
from_tensor=layer_input,
to_tensor=layer_input,
attention_mask=attention_mask,
num_attention_heads=num_attention_heads,
size_per_head=attention_head_size,
attention_probs_dropout_prob=\
attention_probs_dropout_prob,
initializer_range=initializer_range,
do_return_2d_tensor=True,
batch_size=batch_size,
from_max_seq_length=max_seq_length,
to_max_seq_length=max_seq_length,
dtype=dtype,
trainable=trainable)
attention_heads.append(attention_head)
self.attention_scores.append(attention_scores)
attention_output = None
if len(attention_heads) == 1:
attention_output = attention_heads[0]
else:
attention_output = tf.concat(attention_heads,
axis=-1)
with tf.variable_scope('output'):
attention_output = tf.layers.dense(
attention_output,
hidden_size,
kernel_initializer=util.create_initializer(
initializer_range),
trainable=trainable)
attention_output = util.dropout(
attention_output, hidden_dropout_prob)
attention_output = util.layer_norm(
attention_output + layer_input,
trainable=trainable)
# The activation is only applied to the `intermediate`
# hidden layer.
with tf.variable_scope('intermediate'):
intermediate_output = tf.layers.dense(
attention_output,
intermediate_size,
activation=intermediate_act_fn,
kernel_initializer=util.create_initializer(
initializer_range),
trainable=trainable)
# Down-project back to hidden_size then add the residual.
with tf.variable_scope('output'):
layer_output = tf.layers.dense(
intermediate_output,
hidden_size,
kernel_initializer=util.create_initializer(
initializer_range),
trainable=trainable)
layer_output = util.dropout(layer_output,
hidden_dropout_prob)
layer_output = util.layer_norm(layer_output +
attention_output,
trainable=trainable)
return layer_output
layer_output = _build_forward(layer_input)
prev_output = layer_output
all_layer_outputs.append(layer_output)
original_shape = [batch_size * max_seq_length, hidden_size]
input_shape = [batch_size, max_seq_length, hidden_size]
final_all_layer_outputs = []
for layer_output in all_layer_outputs:
final_output = util.reshape_from_matrix(
layer_output, input_shape, original_shape=original_shape)
final_all_layer_outputs.append(final_output)
return final_all_layer_outputs
def layer_norm(input_tensor,
center=True,
scale=True,
activation_fn=None,
variables_collections=None,
outputs_collections=None,
begin_norm_axis=-1,
begin_params_axis=-1,
trainable=True):
''' Runs layer normalization on the last dimension of the tensor.
Args:
input_tensor: A tensor having rank `R`. The normalization is performed
over axes `begin_norm_axis ... R - 1` and centering and scaling
parameters are calculated over `begin_params_axis ... R - 1`.
center: If True, add offset of `beta` to normalized tensor. If False,
`beta` is ignored.
scale: If True, multiply by `gamma`. If False, `gamma` is not used.
When the next layer is linear (also e.g. `nn.relu`), this can be
disabled since the scaling can be done by the next layer.
activation_fn: Activation function, default set to None to skip it and
maintain a linear activation.
variables_collections: Optional collections for the variables.
outputs_collections: Collections to add the outputs.
trainable: If `True` also add variables to the graph collection
`GraphKeys.TRAINABLE_VARIABLES` (see tf.Variable).
begin_norm_axis: The first normalization dimension: normalization will
be performed along dimensions `begin_norm_axis : rank(input_tensor)`
begin_params_axis: The first parameter (beta, gamma) dimension: scale
and centering parameters will have dimensions
`begin_params_axis : rank(input_tensor)` and will be broadcast with
the normalized inputs accordingly.
scope: Optional scope for `variable_scope`.
Returns:
A `Tensor` representing the output of the operation, having the same
shape and dtype as `input_tensor`.
Raises:
ValueError: If the rank of `input_tensor` is not known at graph build
time, or if `input_tensor.shape[begin_params_axis:]` is not fully
defined at graph build time.
'''
with tf.variable_scope('LayerNorm'):
inputs_shape = input_tensor.shape
inputs_rank = inputs_shape.ndims
if inputs_rank is None:
raise ValueError('Inputs %s has undefined rank.'
% input_tensor.name)
dtype = input_tensor.dtype.base_dtype
if begin_norm_axis < 0:
begin_norm_axis = inputs_rank + begin_norm_axis
if begin_params_axis >= inputs_rank or begin_norm_axis >= inputs_rank:
raise ValueError(
'begin_params_axis (%d) and begin_norm_axis (%d) '
'must be < rank(inputs) (%d)'
% (begin_params_axis, begin_norm_axis, inputs_rank))
params_shape = inputs_shape[begin_params_axis:]
if not params_shape.is_fully_defined():
raise ValueError(
'Inputs %s: shape(inputs)[%s:] is not fully defined: %s' %
(input_tensor.name, begin_params_axis, inputs_shape))
# Allocate parameters for the beta and gamma of the normalization.
beta, gamma = None, None
if center:
beta = tf.get_variable(
'beta', shape=params_shape,
dtype=dtype,
initializer=tf.zeros_initializer(),
trainable=trainable)
if scale:
gamma = tf.get_variable(
'gamma',
shape=params_shape,
dtype=dtype,
initializer=tf.ones_initializer(),
trainable=trainable)
# By default, compute the moments across all the dimensions except the
# one with index 0.
norm_axes = list(range(begin_norm_axis, inputs_rank))
mean, variance = tf.nn.moments(input_tensor, norm_axes, keep_dims=True)
# Compute layer normalization using the batch_normalization function.
# Note that epsilon must be increased for float16 due to the limited
# representable range.
variance_epsilon = 1e-12 if dtype != tf.float16 else 1e-3
outputs = tf.nn.batch_normalization(
input_tensor, mean, variance,
offset=beta, scale=gamma,
variance_epsilon=variance_epsilon)
outputs.set_shape(inputs_shape)
if activation_fn is not None:
outputs = activation_fn(outputs)
return outputs
def __init__(self,
bert_config,
is_training,
encoder,
masked_lm_positions,
masked_lm_ids,
masked_lm_weights,
next_sentence_labels,
sample_weight=None,
scope_lm='cls/predictions',
scope_cls='cls/seq_relationship',
trainable=True,
use_nsp_loss=True,
**kwargs):
super(BERTDecoder, self).__init__(**kwargs)
def gather_indexes(sequence_tensor, positions):
sequence_shape = util.get_shape_list(sequence_tensor, 3)
batch_size = sequence_shape[0]
seq_length = sequence_shape[1]
width = sequence_shape[2]
flat_offsets = tf.reshape(
tf.range(0, batch_size, dtype=tf.int32) * seq_length, [-1, 1])
flat_positions = tf.reshape(positions + flat_offsets, [-1])
flat_sequence_tensor = tf.reshape(sequence_tensor,
[batch_size * seq_length, width])
output_tensor = tf.gather(flat_sequence_tensor, flat_positions)
return output_tensor
scalar_losses = []
# masked language modeling
input_tensor = gather_indexes(encoder.get_sequence_output(),
masked_lm_positions)
with tf.variable_scope(scope_lm):
with tf.variable_scope('transform'):
input_tensor = tf.layers.dense(
input_tensor,
units=bert_config.hidden_size,
activation=util.get_activation(bert_config.hidden_act),
kernel_initializer=util.create_initializer(
bert_config.initializer_range))
input_tensor = util.layer_norm(input_tensor)
output_bias = tf.get_variable('output_bias',
shape=[bert_config.vocab_size],
initializer=tf.zeros_initializer(),
trainable=trainable)
logits = tf.matmul(input_tensor,
encoder.get_embedding_table(),
transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
probs = tf.nn.softmax(logits, axis=-1, name='MLM_probs')
log_probs = tf.nn.log_softmax(logits, axis=-1)
label_ids = tf.reshape(masked_lm_ids, [-1])
if sample_weight is not None:
sample_weight = tf.expand_dims(tf.cast(sample_weight,
dtype=tf.float32),
axis=-1)
masked_lm_weights *= sample_weight
label_weights = tf.reshape(masked_lm_weights, [-1])
one_hot_labels = tf.one_hot(label_ids,
depth=bert_config.vocab_size,
dtype=tf.float32)
per_example_loss = -tf.reduce_sum(log_probs * one_hot_labels,
axis=[-1])
per_example_loss = label_weights * per_example_loss
numerator = tf.reduce_sum(per_example_loss)
denominator = tf.reduce_sum(label_weights) + 1e-5
loss = numerator / denominator
scalar_losses.append(loss)
self.losses['MLM_losses'] = per_example_loss
self.preds['MLM_preds'] = tf.argmax(probs, axis=-1)
# next sentence prediction
with tf.variable_scope(scope_cls):
output_weights = tf.get_variable(
'output_weights',
shape=[2, bert_config.hidden_size],
initializer=util.create_initializer(
bert_config.initializer_range),
trainable=trainable)
output_bias = tf.get_variable('output_bias',
shape=[2],
initializer=tf.zeros_initializer(),
trainable=trainable)
logits = tf.matmul(encoder.get_pooled_output(),
output_weights,
transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
probs = tf.nn.softmax(logits, axis=-1, name='probs')
log_probs = tf.nn.log_softmax(logits, axis=-1)
labels = tf.reshape(next_sentence_labels, [-1])
one_hot_labels = tf.one_hot(labels, depth=2, dtype=tf.float32)
per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs,
axis=-1)
if sample_weight is not None:
per_example_loss = (tf.cast(sample_weight, dtype=tf.float32) *
per_example_loss)
loss = tf.reduce_mean(per_example_loss)
if use_nsp_loss:
scalar_losses.append(loss)
self.losses['NSP_losses'] = per_example_loss
self.probs['NSP_probs'] = probs
self.preds['NSP_preds'] = tf.argmax(probs, axis=-1)
self.total_loss = tf.add_n(scalar_losses)
def dynamic_transformer_model(self,
is_training,
input_tensor,
input_mask,
batch_size,
max_seq_length,
label_size,
attention_mask=None,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
intermediate_act_fn=util.gelu,
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
initializer_range=0.02,
dtype=tf.float32,
cls_model='self-attention',
cls_hidden_size=128,
cls_num_attention_heads=2,
speed=0.1,
ignore_cls=None):
if hidden_size % num_attention_heads != 0:
raise ValueError(
'The hidden size (%d) is not a multiple of the number of '
'attention heads (%d)' % (hidden_size, num_attention_heads))
attention_head_size = int(hidden_size / num_attention_heads)
keep_cls = list(range(num_hidden_layers + 1))
keep_cls = [
cls_idx for cls_idx in keep_cls if cls_idx not in ignore_cls
]
all_layer_outputs = []
all_layer_cls_outputs = collections.OrderedDict()
prev_output = input_tensor
prev_mask = input_mask
for layer_idx in range(num_hidden_layers):
with tf.variable_scope('layer_%d' % layer_idx):
# build child classifier
if is_training or layer_idx not in ignore_cls:
with tf.variable_scope('distill'):
# FCN + Self_Attention + FCN + FCN
if cls_model == 'self-attention-paper':
cls_output = self._cls_self_attention_paper(
prev_output,
batch_size,
max_seq_length,
label_size,
attention_mask=attention_mask,
cls_hidden_size=cls_hidden_size,
cls_num_attention_heads=\
cls_num_attention_heads,
attention_probs_dropout_prob=\
attention_probs_dropout_prob,
initializer_range=initializer_range,
dtype=tf.float32,
trainable=True)
# Self_Attention + FCN
elif cls_model == 'self-attention':
cls_output = self._cls_self_attention(
prev_output,
batch_size,
max_seq_length,
label_size,
attention_mask=attention_mask,
cls_hidden_size=cls_hidden_size,
cls_num_attention_heads=\
cls_num_attention_heads,
attention_probs_dropout_prob=\
attention_probs_dropout_prob,
initializer_range=initializer_range,
dtype=tf.float32,
trainable=True)
# FCN
elif cls_model == 'fcn':
cls_output = self._cls_fcn(
prev_output,
label_size,
hidden_size=hidden_size,
initializer_range=initializer_range,
dtype=tf.float32,
trainable=True)
else:
raise ValueError(
'Invalid `cls_model = %s`. Pick one from '
'`self-attention-paper`, `self-attention` '
'and `fcn`' % cls_model)
# distill core
layer_cls_output = tf.nn.softmax(cls_output,
axis=-1,
name='cls_%d' %
layer_idx)
uncertainty = tf.reduce_sum(layer_cls_output *
tf.log(layer_cls_output),
axis=-1)
uncertainty /= tf.log(1 / label_size)
# branching only in inference
if not is_training:
# last output
if layer_idx == keep_cls[-1]:
all_layer_outputs.append(prev_output)
all_layer_cls_outputs[layer_idx] = layer_cls_output
return (all_layer_outputs, all_layer_cls_outputs)
mask = tf.less(uncertainty, speed)
unfinished_mask = \
(tf.ones_like(mask, dtype=dtype) -
tf.cast(mask, dtype=dtype))
prev_output = tf.boolean_mask(prev_output,
mask=unfinished_mask,
axis=0)
prev_mask = tf.boolean_mask(prev_mask,
mask=unfinished_mask,
axis=0)
all_layer_cls_outputs[layer_idx] = layer_cls_output
# new attention mask
input_shape = util.get_shape_list(prev_output)
batch_size = input_shape[0]
max_seq_length = input_shape[1]
attention_mask = \
self.create_attention_mask_from_input_mask(
prev_mask, batch_size, max_seq_length, dtype=dtype)
# originial stream
with tf.variable_scope('attention'):
attention_heads = []
with tf.variable_scope('self'):
(attention_head, _) = self.attention_layer(
from_tensor=prev_output,
to_tensor=prev_output,
attention_mask=attention_mask,
num_attention_heads=num_attention_heads,
size_per_head=attention_head_size,
attention_probs_dropout_prob=\
attention_probs_dropout_prob,
initializer_range=initializer_range,
do_return_2d_tensor=False,
batch_size=batch_size,
from_max_seq_length=max_seq_length,
to_max_seq_length=max_seq_length,
dtype=dtype,
trainable=False)
attention_heads.append(attention_head)
attention_output = None
if len(attention_heads) == 1:
attention_output = attention_heads[0]
else:
attention_output = tf.concat(attention_heads, axis=-1)
with tf.variable_scope('output'):
attention_output = tf.layers.dense(
attention_output,
hidden_size,
kernel_initializer=util.create_initializer(
initializer_range),
trainable=False)
attention_output = util.dropout(
attention_output, hidden_dropout_prob)
attention_output = util.layer_norm(attention_output +
prev_output,
trainable=False)
# The activation is only applied to the `intermediate`
# hidden layer.
with tf.variable_scope('intermediate'):
intermediate_output = tf.layers.dense(
attention_output,
intermediate_size,
activation=intermediate_act_fn,
kernel_initializer=util.create_initializer(
initializer_range),
trainable=False)
# Down-project back to hidden_size then add the residual.
with tf.variable_scope('output'):
layer_output = tf.layers.dense(
intermediate_output,
hidden_size,
kernel_initializer=util.create_initializer(
initializer_range),
trainable=False)
layer_output = util.dropout(layer_output,
hidden_dropout_prob)
layer_output = util.layer_norm(layer_output +
attention_output,
trainable=False)
prev_output = layer_output
all_layer_outputs.append(layer_output)
return (all_layer_outputs, all_layer_cls_outputs)
def __init__(self,
bert_config,
is_training,
input_tensor,
sa_mask,
label_ids,
sample_weight=None,
scope='sanet',
alpha=0.5,
hidden_dropout_prob=0.1,
initializer_range=0.02,
trainable=True,
**kwargs):
super().__init__(**kwargs)
shape = util.get_shape_list(input_tensor)
batch_size = shape[0]
seq_length = shape[1]
hidden_size = shape[2]
sa_mask = tf.reshape(sa_mask, [batch_size, seq_length, seq_length])
with tf.variable_scope(scope):
with tf.variable_scope('sentence_attention'):
(sa_output, _) = self.attention_layer(
from_tensor=input_tensor,
to_tensor=input_tensor,
attention_mask=sa_mask,
num_attention_heads=bert_config.num_attention_heads,
size_per_head=\
hidden_size // bert_config.num_attention_heads,
attention_probs_dropout_prob=\
bert_config.hidden_dropout_prob,
initializer_range=bert_config.initializer_range,
do_return_2d_tensor=False,
batch_size=batch_size,
from_max_seq_length=seq_length,
to_max_seq_length=seq_length,
trainable=trainable)
with tf.variable_scope('cls/mrc'):
output_weights = tf.get_variable(
'output_weights',
shape=[2, hidden_size],
initializer=util.create_initializer(initializer_range),
trainable=trainable)
output_bias = tf.get_variable(
'output_bias',
shape=[2],
initializer=tf.zeros_initializer(),
trainable=trainable)
output_layer = alpha * sa_output + (1 - alpha) * input_tensor
output_layer = tf.reshape(output_layer, [-1, hidden_size])
logits = tf.matmul(output_layer, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
logits = tf.reshape(logits, [-1, seq_length, 2])
logits = tf.transpose(logits, [0, 2, 1])
probs = tf.nn.softmax(logits, axis=-1, name='probs')
self.probs['probs'] = probs
self.preds['preds'] = tf.argmax(logits, axis=-1)
start_one_hot_labels = tf.one_hot(label_ids[:, 0],
depth=seq_length,
dtype=tf.float32)
end_one_hot_labels = tf.one_hot(label_ids[:, 1],
depth=seq_length,
dtype=tf.float32)
start_log_probs = tf.nn.log_softmax(logits[:, 0, :], axis=-1)
end_log_probs = tf.nn.log_softmax(logits[:, 1, :], axis=-1)
per_example_loss = (
-0.5 * tf.reduce_sum(start_one_hot_labels * start_log_probs,
axis=-1) - 0.5 *
tf.reduce_sum(end_one_hot_labels * end_log_probs, axis=-1))
if sample_weight is not None:
per_example_loss *= sample_weight
self.total_loss = tf.reduce_mean(per_example_loss)
self.losses['losses'] = per_example_loss
def __init__(self,
bert_config,
is_training,
input_ids,
input_mask,
segment_ids,
sample_weight=None,
scope='bert',
dtype=tf.float32,
drop_pooler=False,
cls_model='self-attention',
label_size=2,
speed=0.1,
ignore_cls='0',
**kwargs):
super(FastBERTCLSDistillor, self).__init__()
if not ignore_cls:
ignore_cls = []
if isinstance(ignore_cls, str):
ignore_cls = ignore_cls.replace(' ', '').split(',')
ignore_cls = list(map(int, ignore_cls))
elif isinstance(ignore_cls, list):
ignore_cls = list(map(int, ignore_cls))
else:
raise ValueError(
'`ignore_cls` should be a list of child-classifier ids or '
'a string seperated with commas.')
if not speed:
raise ValueError(
'`speed` should be a float number between `0` and `1`.')
bert_config = copy.deepcopy(bert_config)
bert_config.hidden_dropout_prob = 0.0
bert_config.attention_probs_dropout_prob = 0.0
input_shape = util.get_shape_list(input_ids, expected_rank=2)
batch_size = input_shape[0]
max_seq_length = input_shape[1]
with tf.variable_scope(scope):
with tf.variable_scope('embeddings'):
(self.embedding_output, self.embedding_table) = \
self.embedding_lookup(
input_ids=input_ids,
vocab_size=bert_config.vocab_size,
batch_size=batch_size,
max_seq_length=max_seq_length,
embedding_size=bert_config.hidden_size,
initializer_range=bert_config.initializer_range,
word_embedding_name='word_embeddings',
dtype=dtype,
trainable=False,
tilda_embeddings=None)
# Add positional embeddings and token type embeddings
# layer normalize and perform dropout.
self.embedding_output = self.embedding_postprocessor(
input_tensor=self.embedding_output,
batch_size=batch_size,
max_seq_length=max_seq_length,
hidden_size=bert_config.hidden_size,
use_token_type=True,
segment_ids=segment_ids,
token_type_vocab_size=bert_config.type_vocab_size,
token_type_embedding_name='token_type_embeddings',
use_position_embeddings=True,
position_embedding_name='position_embeddings',
initializer_range=bert_config.initializer_range,
max_position_embeddings=\
bert_config.max_position_embeddings,
dropout_prob=bert_config.hidden_dropout_prob,
dtype=dtype,
trainable=False)
with tf.variable_scope('encoder'):
attention_mask = self.create_attention_mask_from_input_mask(
input_mask, batch_size, max_seq_length, dtype=dtype)
# stacked transformers
(self.all_encoder_layers, self.all_cls_layers) = \
self.dynamic_transformer_model(
is_training,
input_tensor=self.embedding_output,
input_mask=input_mask,
batch_size=batch_size,
max_seq_length=max_seq_length,
label_size=label_size,
attention_mask=attention_mask,
hidden_size=bert_config.hidden_size,
num_hidden_layers=bert_config.num_hidden_layers,
num_attention_heads=bert_config.num_attention_heads,
intermediate_size=bert_config.intermediate_size,
intermediate_act_fn=util.get_activation(
bert_config.hidden_act),
hidden_dropout_prob=bert_config.hidden_dropout_prob,
attention_probs_dropout_prob=\
bert_config.attention_probs_dropout_prob,
initializer_range=bert_config.initializer_range,
dtype=dtype,
cls_model=cls_model,
speed=speed,
ignore_cls=ignore_cls)
self.sequence_output = self.all_encoder_layers[-1]
with tf.variable_scope('pooler'):
first_token_tensor = self.sequence_output[:, 0, :]
# trick: ignore the fully connected layer
if drop_pooler:
self.pooled_output = first_token_tensor
else:
self.pooled_output = tf.layers.dense(
first_token_tensor,
bert_config.hidden_size,
activation=tf.tanh,
kernel_initializer=util.create_initializer(
bert_config.initializer_range),
trainable=False)
# teacher classifier
if bert_config.num_hidden_layers not in ignore_cls:
with tf.variable_scope('cls/seq_relationship'):
output_weights = tf.get_variable(
'output_weights',
shape=[label_size, bert_config.hidden_size],
initializer=util.create_initializer(
bert_config.initializer_range),
trainable=False)
output_bias = tf.get_variable(
'output_bias',
shape=[label_size],
initializer=tf.zeros_initializer(),
trainable=False)
logits = tf.matmul(self.pooled_output,
output_weights,
transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
probs = tf.nn.softmax(logits, axis=-1)
# distillation
if is_training:
losses = []
for cls_probs in self.all_cls_layers.values():
# KL-Divergence
per_example_loss = tf.reduce_sum(
cls_probs * (tf.log(cls_probs) - tf.log(probs)), axis=-1)
if sample_weight is not None:
per_example_loss *= tf.cast(sample_weight,
dtype=tf.float32)
loss = tf.reduce_mean(per_example_loss)
losses.append(loss)
distill_loss = tf.add_n(losses)
self.total_loss = distill_loss
self.losses['losses'] = distill_loss
else:
if bert_config.num_hidden_layers not in ignore_cls:
self.all_cls_layers[bert_config.num_hidden_layers] = probs
self.probs['probs'] = tf.concat(list(self.all_cls_layers.values()),
axis=0,
name='probs')
def mlp(x, scope, n_state, *, hparams):
with tf.variable_scope(scope):
nx = x.shape[-1].value
h = gelu(conv1d(x, 'c_fc', n_state))
h2 = conv1d(h, 'c_proj', nx)
return h2
def _build_forward(layer_input):
with tf.variable_scope('attention'):
with tf.variable_scope('self'):
layer_input *= tf.cast(tf.expand_dims(input_mask,
axis=-1),
dtype=tf.float32)
attention_layer = Attention(
hidden_size=hidden_size,
num_heads=num_attention_heads,
attention_dropout=attention_probs_dropout_prob,
kernel_transformation=\
self.kernel_transformation,
numerical_stabilizer=0.001,
causal=False,
projection_matrix_type=True \
if bool(self.nb_random_features) else None,
nb_random_features=self.nb_random_features)
attention_layer.build(layer_input.shape)
attention_output = attention_layer.call(
layer_input,
layer_input,
bias=None,
training=is_training,
cache=None,
decode_loop_step=None)
with tf.variable_scope('output'):
attention_output = tf.layers.dense(
attention_output,
hidden_size,
kernel_initializer=util.create_initializer(
initializer_range),
trainable=trainable)
attention_output = util.dropout(
attention_output, hidden_dropout_prob)
attention_output = util.layer_norm(
attention_output + layer_input,
trainable=trainable)
# The activation is only applied to the `intermediate`
# hidden layer.
with tf.variable_scope('intermediate'):
intermediate_output = tf.layers.dense(
attention_output,
intermediate_size,
activation=intermediate_act_fn,
kernel_initializer=util.create_initializer(
initializer_range),
trainable=trainable)
# Down-project back to hidden_size then add the residual.
with tf.variable_scope('output'):
layer_output = tf.layers.dense(
intermediate_output,
hidden_size,
kernel_initializer=util.create_initializer(
initializer_range),
trainable=trainable)
layer_output = util.dropout(layer_output,
hidden_dropout_prob)
layer_output = util.layer_norm(layer_output +
attention_output,
trainable=trainable)
return layer_output
