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run_pretraining.py
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# coding=utf-8
"""Run (MLM + NSP + RUR + ISS + PCD + MSUR + SND) pre-training for MPC-BERT-2.0."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from time import time
import tensorflow as tf
import modeling_speaker as modeling
import optimization
flags = tf.compat.v1.flags
FLAGS = flags.FLAGS
# Required parameters
flags.DEFINE_string("task_name", 'MPC-BERT Pre-training',
"The name of the task to train.")
flags.DEFINE_string("input_file", './data/ijcai2019/pretraining_data.tfrecord',
"The input data dir. Should contain the .tsv files (or other data files) for the task.")
flags.DEFINE_string("output_dir", './uncased_L-12_H-768_A-12_pretrained',
"The output directory where the model checkpoints will be written.")
flags.DEFINE_string("bert_config_file", 'uncased_L-12_H-768_A-12/bert_config.json',
"The config json file corresponding to the pre-trained BERT model. "
"This specifies the model architecture.")
flags.DEFINE_string("vocab_file", 'uncased_L-12_H-768_A-12/vocab.txt',
"The vocabulary file that the BERT model was trained on.")
flags.DEFINE_string("init_checkpoint", 'uncased_L-12_H-768_A-12/bert_model.ckpt',
"Initial checkpoint (usually from a pre-trained BERT model).")
flags.DEFINE_bool("do_train", True,
"Whether to run training.")
# flags.DEFINE_bool("do_eval", True,
# "Whether to run eval on the dev set.")
flags.DEFINE_integer("mid_save_step", 20000,
"Epoch is so long, mid_save_step 15000 is roughly 3 hours")
flags.DEFINE_integer("max_seq_length", 230,
"The maximum total input sequence length after WordPiece tokenization. "
"Sequences longer than this will be truncated, and sequences shorter "
"than this will be padded. Must match data generation.")
flags.DEFINE_integer("max_utr_length", 30,
"Maximum single utterance length.")
flags.DEFINE_integer("max_utr_num", 7,
"Maximum utterance number.")
flags.DEFINE_integer("max_predictions_per_seq", 25,
"Maximum number of masked LM predictions per sequence. "
"Must match data generation.")
flags.DEFINE_integer("max_predictions_per_seq_ar", 4,
"Maximum number of Reply-to Utterance Recognition (RUR) predictions per sequence.")
flags.DEFINE_integer("max_predictions_per_seq_sr", 2,
"Maximum number of Identical Speaker Searching (ISS) predictions per sequence.")
flags.DEFINE_integer("max_predictions_per_seq_cd", 2,
"Maximum number of Pointer Consistency Distinction (PCD) predictions per sequence.")
flags.DEFINE_integer("train_batch_size", 16,
"Total batch size for training.")
# flags.DEFINE_integer("eval_batch_size", 8,
# "Total batch size for eval.")
flags.DEFINE_float("learning_rate", 5e-5,
"The initial learning rate for Adam.")
flags.DEFINE_float("warmup_proportion", 0.1,
"Number of warmup steps.")
flags.DEFINE_integer("num_train_epochs", 10,
"num_train_epochs.")
def print_configuration_op(FLAGS):
print('My Configurations:')
for name, value in FLAGS.__flags.items():
value = value.value
if type(value) == float:
print(' %s:\t %f' % (name, value))
elif type(value) == int:
print(' %s:\t %d' % (name, value))
elif type(value) == str:
print(' %s:\t %s' % (name, value))
elif type(value) == bool:
print(' %s:\t %s' % (name, value))
else:
print('%s:\t %s' % (name, value))
print('End of configuration')
def count_data_size(file_name):
sample_nums = 0
for record in tf.compat.v1.python_io.tf_record_iterator(file_name):
sample_nums += 1
return sample_nums
def parse_exmp(serial_exmp):
input_data = tf.compat.v1.parse_single_example(serial_exmp,
features={
"input_ids_mlm_nsp":
tf.compat.v1.FixedLenFeature([FLAGS.max_seq_length],
tf.int64),
"input_mask_mlm_nsp":
tf.compat.v1.FixedLenFeature([FLAGS.max_seq_length],
tf.int64),
"segment_ids_mlm_nsp":
tf.compat.v1.FixedLenFeature([FLAGS.max_seq_length],
tf.int64),
"speaker_ids_mlm_nsp":
tf.compat.v1.FixedLenFeature([FLAGS.max_seq_length],
tf.int64),
"next_sentence_labels":
tf.compat.v1.FixedLenFeature([1], tf.int64),
"masked_lm_positions":
tf.compat.v1.FixedLenFeature([FLAGS.max_predictions_per_seq],
tf.int64),
"masked_lm_ids":
tf.compat.v1.FixedLenFeature([FLAGS.max_predictions_per_seq],
tf.int64),
"masked_lm_weights":
tf.compat.v1.FixedLenFeature([FLAGS.max_predictions_per_seq],
tf.float32),
"cls_positions":
tf.compat.v1.FixedLenFeature([FLAGS.max_utr_num], tf.int64),
"input_ids_ar_msr_pcd":
tf.compat.v1.FixedLenFeature([FLAGS.max_seq_length],
tf.int64),
"input_mask_ar_msr_pcd":
tf.compat.v1.FixedLenFeature([FLAGS.max_seq_length],
tf.int64),
"segment_ids_ar_msr_pcd":
tf.compat.v1.FixedLenFeature([FLAGS.max_seq_length],
tf.int64),
"speaker_ids_ar_msr_pcd":
tf.compat.v1.FixedLenFeature([FLAGS.max_seq_length],
tf.int64),
"adr_recog_positions":
tf.compat.v1.FixedLenFeature(
[FLAGS.max_predictions_per_seq_ar], tf.int64),
"adr_recog_labels":
tf.compat.v1.FixedLenFeature(
[FLAGS.max_predictions_per_seq_ar], tf.int64),
"adr_recog_weights":
tf.compat.v1.FixedLenFeature(
[FLAGS.max_predictions_per_seq_ar], tf.float32),
"masked_sr_positions":
tf.compat.v1.FixedLenFeature(
[FLAGS.max_predictions_per_seq_sr], tf.int64),
"masked_sr_labels":
tf.compat.v1.FixedLenFeature(
[FLAGS.max_predictions_per_seq_sr], tf.int64),
"masked_sr_weights":
tf.compat.v1.FixedLenFeature(
[FLAGS.max_predictions_per_seq_sr], tf.float32),
"pointer_cd_positions_spk1":
tf.compat.v1.FixedLenFeature(
[FLAGS.max_predictions_per_seq_cd], tf.int64),
"pointer_cd_positions_adr1":
tf.compat.v1.FixedLenFeature(
[FLAGS.max_predictions_per_seq_cd], tf.int64),
"pointer_cd_positions_spk2":
tf.compat.v1.FixedLenFeature(
[FLAGS.max_predictions_per_seq_cd], tf.int64),
"pointer_cd_positions_adr2":
tf.compat.v1.FixedLenFeature(
[FLAGS.max_predictions_per_seq_cd], tf.int64),
"pointer_cd_positions_spk3":
tf.compat.v1.FixedLenFeature(
[FLAGS.max_predictions_per_seq_cd], tf.int64),
"pointer_cd_positions_adr3":
tf.compat.v1.FixedLenFeature(
[FLAGS.max_predictions_per_seq_cd], tf.int64),
"pointer_cd_weights":
tf.compat.v1.FixedLenFeature(
[FLAGS.max_predictions_per_seq_cd], tf.float32),
"input_ids_msur":
tf.compat.v1.FixedLenFeature([FLAGS.max_seq_length],
tf.int64),
"input_mask_msur":
tf.compat.v1.FixedLenFeature([FLAGS.max_seq_length],
tf.int64),
"segment_ids_msur":
tf.compat.v1.FixedLenFeature([FLAGS.max_seq_length],
tf.int64),
"speaker_ids_msur":
tf.compat.v1.FixedLenFeature([FLAGS.max_seq_length],
tf.int64),
"masked_sur_positions":
tf.compat.v1.FixedLenFeature([FLAGS.max_utr_length],
tf.int64),
"masked_sur_ids":
tf.compat.v1.FixedLenFeature([FLAGS.max_utr_length],
tf.int64),
"masked_sur_weights":
tf.compat.v1.FixedLenFeature([FLAGS.max_utr_length],
tf.float32),
"input_ids_snd":
tf.compat.v1.FixedLenFeature([FLAGS.max_seq_length],
tf.int64),
"input_mask_snd":
tf.compat.v1.FixedLenFeature([FLAGS.max_seq_length],
tf.int64),
"segment_ids_snd":
tf.compat.v1.FixedLenFeature([FLAGS.max_seq_length],
tf.int64),
"speaker_ids_snd":
tf.compat.v1.FixedLenFeature([FLAGS.max_seq_length],
tf.int64),
"next_thread_labels":
tf.compat.v1.FixedLenFeature([1], tf.int64),
}
)
# So cast all int64 to int32.
for name in list(input_data.keys()):
t = input_data[name]
if t.dtype == tf.int64:
t = tf.compat.v1.to_int32(t)
input_data[name] = t
input_ids_mlm_nsp = input_data["input_ids_mlm_nsp"]
input_mask_mlm_nsp = input_data["input_mask_mlm_nsp"]
segment_ids_mlm_nsp = input_data["segment_ids_mlm_nsp"]
speaker_ids_mlm_nsp = input_data["speaker_ids_mlm_nsp"]
next_sentence_labels = input_data["next_sentence_labels"]
masked_lm_positions = input_data["masked_lm_positions"]
masked_lm_ids = input_data["masked_lm_ids"]
masked_lm_weights = input_data["masked_lm_weights"]
cls_positions = input_data["cls_positions"]
input_ids_ar_msr_pcd = input_data["input_ids_ar_msr_pcd"]
input_mask_ar_msr_pcd = input_data["input_mask_ar_msr_pcd"]
segment_ids_ar_msr_pcd = input_data["segment_ids_ar_msr_pcd"]
speaker_ids_ar_msr_pcd = input_data["speaker_ids_ar_msr_pcd"]
adr_recog_positions = input_data["adr_recog_positions"]
adr_recog_labels = input_data["adr_recog_labels"]
adr_recog_weights = input_data["adr_recog_weights"]
masked_sr_positions = input_data["masked_sr_positions"]
masked_sr_labels = input_data["masked_sr_labels"]
masked_sr_weights = input_data["masked_sr_weights"]
pointer_cd_positions_spk1 = input_data["pointer_cd_positions_spk1"]
pointer_cd_positions_adr1 = input_data["pointer_cd_positions_adr1"]
pointer_cd_positions_spk2 = input_data["pointer_cd_positions_spk2"]
pointer_cd_positions_adr2 = input_data["pointer_cd_positions_adr2"]
pointer_cd_positions_spk3 = input_data["pointer_cd_positions_spk3"]
pointer_cd_positions_adr3 = input_data["pointer_cd_positions_adr3"]
pointer_cd_weights = input_data["pointer_cd_weights"]
input_ids_msur = input_data["input_ids_msur"]
input_mask_msur = input_data["input_mask_msur"]
segment_ids_msur = input_data["segment_ids_msur"]
speaker_ids_msur = input_data["speaker_ids_msur"]
masked_sur_positions = input_data["masked_sur_positions"]
masked_sur_ids = input_data["masked_sur_ids"]
masked_sur_weights = input_data["masked_sur_weights"]
input_ids_snd = input_data["input_ids_snd"]
input_mask_snd = input_data["input_mask_snd"]
segment_ids_snd = input_data["segment_ids_snd"]
speaker_ids_snd = input_data["speaker_ids_snd"]
next_thread_labels = input_data["next_thread_labels"]
return input_ids_mlm_nsp, input_mask_mlm_nsp, segment_ids_mlm_nsp, speaker_ids_mlm_nsp, \
next_sentence_labels, masked_lm_positions, masked_lm_ids, masked_lm_weights, \
cls_positions, input_ids_ar_msr_pcd, input_mask_ar_msr_pcd, segment_ids_ar_msr_pcd, speaker_ids_ar_msr_pcd, \
adr_recog_positions, adr_recog_labels, adr_recog_weights, masked_sr_positions, masked_sr_labels, masked_sr_weights, \
pointer_cd_positions_spk1, pointer_cd_positions_adr1, pointer_cd_positions_spk2, pointer_cd_positions_adr2, \
pointer_cd_positions_spk3, pointer_cd_positions_adr3, pointer_cd_weights, \
input_ids_msur, input_mask_msur, segment_ids_msur, speaker_ids_msur, masked_sur_positions, masked_sur_ids, masked_sur_weights, \
input_ids_snd, input_mask_snd, segment_ids_snd, speaker_ids_snd, next_thread_labels
def model_fn_builder(features, is_training, bert_config, init_checkpoint, learning_rate,
num_train_steps, num_warmup_steps, use_tpu, use_one_hot_embeddings):
"""Returns `model_fn` closure for TPUEstimator."""
input_ids_mlm_nsp, input_mask_mlm_nsp, segment_ids_mlm_nsp, speaker_ids_mlm_nsp, \
next_sentence_labels, masked_lm_positions, masked_lm_ids, masked_lm_weights, \
cls_positions, input_ids_ar_msr_pcd, input_mask_ar_msr_pcd, segment_ids_ar_msr_pcd, speaker_ids_ar_msr_pcd, \
adr_recog_positions, adr_recog_labels, adr_recog_weights, masked_sr_positions, masked_sr_labels, masked_sr_weights, \
pointer_cd_positions_spk1, pointer_cd_positions_adr1, pointer_cd_positions_spk2, pointer_cd_positions_adr2, \
pointer_cd_positions_spk3, pointer_cd_positions_adr3, pointer_cd_weights, \
input_ids_msur, input_mask_msur, segment_ids_msur, speaker_ids_msur, masked_sur_positions, masked_sur_ids, masked_sur_weights, \
input_ids_snd, input_mask_snd, segment_ids_snd, speaker_ids_snd, next_thread_labels = features
model_mlm_nsp = modeling.BertModel(
config=bert_config,
is_training=is_training,
input_ids=input_ids_mlm_nsp,
input_mask=input_mask_mlm_nsp,
token_type_ids=segment_ids_mlm_nsp,
speaker_ids=speaker_ids_mlm_nsp,
use_one_hot_embeddings=use_one_hot_embeddings,
scope="bert",
scope_reuse=False)
(masked_lm_loss, masked_lm_example_loss, masked_lm_log_probs) = get_masked_lm_output(
bert_config, model_mlm_nsp.get_sequence_output(), model_mlm_nsp.get_embedding_table(), masked_lm_positions,
masked_lm_ids, masked_lm_weights)
(next_sentence_loss, next_sentence_example_loss, next_sentence_log_probs) = get_next_sentence_output(
bert_config, model_mlm_nsp.get_pooled_output(), next_sentence_labels)
model_ar_msr_pcd = modeling.BertModel(
config=bert_config,
is_training=is_training,
input_ids=input_ids_ar_msr_pcd,
input_mask=input_mask_ar_msr_pcd,
token_type_ids=segment_ids_ar_msr_pcd,
speaker_ids=speaker_ids_ar_msr_pcd,
use_one_hot_embeddings=use_one_hot_embeddings,
scope="bert",
scope_reuse=True)
(adr_recog_loss, adr_recog_example_loss, adr_recog_log_probs) = get_replyto_utterance_recognition_output(
bert_config, model_ar_msr_pcd.get_sequence_output(), cls_positions, adr_recog_positions, adr_recog_labels,
adr_recog_weights)
(masked_sr_loss, masked_sr_example_loss, masked_sr_log_probs) = get_identical_speaker_searching_output(
bert_config, model_ar_msr_pcd.get_sequence_output(), cls_positions, masked_sr_positions, masked_sr_labels,
masked_sr_weights)
(pointer_cd_loss, pointer_cd_example_loss, pointer_cd_log_probs) = get_pointer_consistency_distinction_output(
bert_config, model_ar_msr_pcd.get_sequence_output(), cls_positions, pointer_cd_positions_spk1,
pointer_cd_positions_adr1,
pointer_cd_positions_spk2, pointer_cd_positions_adr2, pointer_cd_positions_spk3, pointer_cd_positions_adr3,
pointer_cd_weights)
model_msur = modeling.BertModel(
config=bert_config,
is_training=is_training,
input_ids=input_ids_msur,
input_mask=input_mask_msur,
token_type_ids=segment_ids_msur,
speaker_ids=speaker_ids_msur,
use_one_hot_embeddings=use_one_hot_embeddings,
scope="bert",
scope_reuse=True)
(masked_sur_loss, masked_sur_example_loss, masked_sur_log_probs) = get_masked_shared_utterance_restoration_output(
bert_config, model_msur.get_sequence_output(), model_msur.get_embedding_table(), masked_sur_positions,
masked_sur_ids, masked_sur_weights)
model_snd = modeling.BertModel(
config=bert_config,
is_training=is_training,
input_ids=input_ids_snd,
input_mask=input_mask_snd,
token_type_ids=segment_ids_snd,
speaker_ids=speaker_ids_snd,
use_one_hot_embeddings=use_one_hot_embeddings,
scope="bert",
scope_reuse=True)
(shared_nd_loss, shared_nd_example_loss, shared_nd_log_probs) = get_shared_node_detection_output(
bert_config, model_snd.get_pooled_output(), next_thread_labels)
total_loss = masked_lm_loss + next_sentence_loss + \
adr_recog_loss + masked_sr_loss + pointer_cd_loss + \
masked_sur_loss + shared_nd_loss
tvars = tf.compat.v1.trainable_variables()
if init_checkpoint:
(assignment_map, initialized_variable_names) = modeling.get_assignment_map_from_checkpoint(tvars,
init_checkpoint)
tf.compat.v1.train.init_from_checkpoint(init_checkpoint, assignment_map)
tf.compat.v1.logging.info("**** Trainable Variables ****")
for var in tvars:
init_string = ""
if var.name in initialized_variable_names:
init_string = ", *INIT_FROM_CKPT*"
tf.compat.v1.logging.info(" name = %s, shape = %s%s", var.name, var.shape, init_string)
train_op = optimization.create_optimizer(total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)
metrics = metric_fn(masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids, masked_lm_weights,
next_sentence_example_loss, next_sentence_log_probs, next_sentence_labels,
adr_recog_example_loss, adr_recog_log_probs, adr_recog_labels, adr_recog_weights,
masked_sr_example_loss, masked_sr_log_probs, masked_sr_labels, masked_sr_weights,
pointer_cd_example_loss, pointer_cd_log_probs, pointer_cd_weights,
masked_sur_example_loss, masked_sur_log_probs, masked_sur_ids, masked_sur_weights,
shared_nd_example_loss, shared_nd_log_probs, next_thread_labels)
return train_op, total_loss, metrics, input_ids_mlm_nsp
def get_masked_lm_output(bert_config, input_tensor, output_weights, positions, label_ids, label_weights):
"""Get loss and log probs for the masked LM."""
input_tensor = gather_indexes(input_tensor, positions) # [batch_size*max_predictions_per_seq, dim]
with tf.compat.v1.variable_scope("cls/predictions"):
# We apply one more non-linear transformation before the output layer.
with tf.compat.v1.variable_scope("transform"):
input_tensor = tf.compat.v1.layers.dense(
input_tensor,
units=bert_config.hidden_size,
activation=modeling.get_activation(bert_config.hidden_act),
kernel_initializer=modeling.create_initializer(
bert_config.initializer_range))
input_tensor = modeling.layer_norm(input_tensor)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
output_bias = tf.compat.v1.get_variable(
"output_bias",
shape=[bert_config.vocab_size],
initializer=tf.zeros_initializer())
logits = tf.compat.v1.matmul(input_tensor, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
log_probs = tf.nn.log_softmax(logits, axis=-1) # [batch_size*max_predictions_per_seq, vocab_size]
label_ids = tf.reshape(label_ids, [-1])
label_weights = tf.reshape(label_weights, [-1])
one_hot_labels = tf.one_hot(label_ids, depth=bert_config.vocab_size, dtype=tf.float32)
# The `label_weights` tensor has a value of 1.0 for every real prediction and 0.0 for the padding predictions.
per_example_loss = -tf.reduce_sum(log_probs * one_hot_labels,
axis=[-1]) # [batch_size*max_predictions_per_seq, ]
numerator = tf.reduce_sum(label_weights * per_example_loss) # [1, ]
denominator = tf.reduce_sum(label_weights) + 1e-5
loss = numerator / denominator
return loss, per_example_loss, log_probs
def get_next_sentence_output(bert_config, input_tensor, labels):
"""Get loss and log probs for the next sentence prediction."""
# Simple binary classification. Note that 0 is "next sentence" and 1 is
# "random sentence". This weight matrix is not used after pre-training.
with tf.compat.v1.variable_scope("cls/seq_relationship"):
output_weights = tf.compat.v1.get_variable(
"output_weights",
shape=[2, bert_config.hidden_size],
initializer=modeling.create_initializer(bert_config.initializer_range))
output_bias = tf.compat.v1.get_variable(
"output_bias", shape=[2], initializer=tf.zeros_initializer())
logits = tf.compat.v1.matmul(input_tensor, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
log_probs = tf.nn.log_softmax(logits, axis=-1) # [batch_size, 2]
labels = tf.reshape(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) # [batch_size, ]
loss = tf.reduce_mean(per_example_loss) # [1, ]
return loss, per_example_loss, log_probs
def get_replyto_utterance_recognition_output(bert_config, input_tensor, cls_positions, adr_recog_positions,
adr_recog_labels, adr_recog_weights):
"""Get loss and log probs for the Reply-to Utterance Recognition (RUR)."""
# input_tensor: [batch_size, max_sequence_len, dim]
# cls_positions: [batch_size, max_utr_num]
# adr_recog_positions: [batch_size, max_predictions_per_seq_ar]
# adr_recog_labels: [batch_size, max_predictions_per_seq_ar]
# adr_recog_weights: [batch_size, max_predictions_per_seq_ar]
cls_shape = modeling.get_shape_list(cls_positions, expected_rank=2)
max_utr_num = cls_shape[1]
input_tensor = gather_indexes(input_tensor, cls_positions) # [batch_size*max_utr_num, dim]
input_shape = modeling.get_shape_list(input_tensor, expected_rank=2)
width = input_shape[1]
with tf.compat.v1.variable_scope("cls/addressee_recognize"):
# We apply one more non-linear transformation before the output layer.
with tf.compat.v1.variable_scope("transform"):
input_tensor = tf.compat.v1.layers.dense(
input_tensor,
units=bert_config.hidden_size,
activation=modeling.get_activation(bert_config.hidden_act),
kernel_initializer=modeling.create_initializer(
bert_config.initializer_range))
input_tensor = modeling.layer_norm(input_tensor) # [batch_size*max_utr_num, dim]
input_tensor = tf.compat.v1.reshape(input_tensor, [-1, max_utr_num, width]) # [batch_size, max_utr_num, dim]
output_weights = tf.compat.v1.get_variable(
"output_weights",
shape=[width, width],
initializer=modeling.create_initializer(bert_config.initializer_range))
logits = tf.compat.v1.matmul(tf.einsum('aij,jk->aik', input_tensor, output_weights),
input_tensor, transpose_b=True) # [batch_size, max_utr_num, max_utr_num]
# mask = [[0. 0. 0. 0. 0.]
# [1. 0. 0. 0. 0.]
# [1. 1. 0. 0. 0.]
# [1. 1. 1. 0. 0.]
# [1. 1. 1. 1. 0.]]
mask = tf.compat.v1.matrix_band_part(tf.ones((max_utr_num, max_utr_num)), -1,
0) - tf.compat.v1.matrix_band_part(
tf.ones((max_utr_num, max_utr_num)), 0, 0)
logits = logits * mask + -1e9 * (1 - mask) # [batch_size, max_utr_num, max_utr_num]
logits = gather_indexes(logits, adr_recog_positions) # [batch_size*max_predictions_per_seq_ar, max_utr_num]
log_probs = tf.nn.log_softmax(logits, axis=-1) # [batch_size*max_predictions_per_seq_ar, max_utr_num]
label_ids = tf.reshape(adr_recog_labels, [-1]) # [batch_size*max_predictions_per_seq_ar, ]
label_weights = tf.reshape(adr_recog_weights, [-1]) # [batch_size*max_predictions_per_seq_ar, ]
one_hot_labels = tf.one_hot(label_ids, depth=max_utr_num,
dtype=tf.float32) # [batch_size*max_predictions_per_seq_ar, max_utr_num]
# The `label_weights` tensor has a value of 1.0 for every real prediction and 0.0 for the padding predictions.
per_example_loss = - tf.reduce_sum(log_probs * one_hot_labels,
axis=[-1]) # [batch_size*max_predictions_per_seq_ar, ]
numerator = tf.reduce_sum(label_weights * per_example_loss) # [1, ]
denominator = tf.reduce_sum(label_weights) + 1e-5 # [1, ]
loss = numerator / denominator
return loss, per_example_loss, log_probs
def get_identical_speaker_searching_output(bert_config, input_tensor, cls_positions, masked_sr_positions,
masked_sr_labels, masked_sr_weights):
"""Get loss and log probs for the Identical Speaker Searching (ISS)."""
# input_tensor: [batch_size, max_sequence_len, dim]
# cls_positions: [batch_size, max_utr_num]
# masked_sr_positions: [batch_size, max_predictions_per_seq_sr]
# masked_sr_labels: [batch_size, max_predictions_per_seq_sr]
# masked_sr_weights: [batch_size, max_predictions_per_seq_sr]
cls_shape = modeling.get_shape_list(cls_positions, expected_rank=2)
max_utr_num = cls_shape[1]
input_tensor = gather_indexes(input_tensor, cls_positions) # [batch_size*max_utr_num, dim]
input_shape = modeling.get_shape_list(input_tensor, expected_rank=2)
width = input_shape[1]
with tf.compat.v1.variable_scope("cls/speaker_restore"):
# We apply one more non-linear transformation before the output layer.
with tf.compat.v1.variable_scope("transform"):
input_tensor = tf.compat.v1.layers.dense(
input_tensor,
units=bert_config.hidden_size,
activation=modeling.get_activation(bert_config.hidden_act),
kernel_initializer=modeling.create_initializer(
bert_config.initializer_range))
input_tensor = modeling.layer_norm(input_tensor) # [batch_size*max_utr_num, dim]
input_tensor = tf.reshape(input_tensor, [-1, max_utr_num, width]) # [batch_size, max_utr_num, dim]
output_weights = tf.compat.v1.get_variable(
"output_weights",
shape=[width, width],
initializer=modeling.create_initializer(bert_config.initializer_range))
logits = tf.compat.v1.matmul(tf.einsum('aij,jk->aik', input_tensor, output_weights),
input_tensor, transpose_b=True) # [batch_size, max_utr_num, max_utr_num]
# mask = [[0. 0. 0. 0. 0.]
# [1. 0. 0. 0. 0.]
# [1. 1. 0. 0. 0.]
# [1. 1. 1. 0. 0.]
# [1. 1. 1. 1. 0.]]
mask = tf.compat.v1.matrix_band_part(tf.ones((max_utr_num, max_utr_num)), -1,
0) - tf.compat.v1.matrix_band_part(
tf.ones((max_utr_num, max_utr_num)), 0, 0)
logits = logits * mask + -1e9 * (1 - mask) # [batch_size, max_utr_num, max_utr_num]
logits = gather_indexes(logits, masked_sr_positions) # [batch_size*max_predictions_per_seq_sr, max_utr_num]
log_probs = tf.nn.log_softmax(logits, axis=-1) # [batch_size*max_predictions_per_seq_sr, max_utr_num]
label_ids = tf.reshape(masked_sr_labels, [-1]) # [batch_size*max_predictions_per_seq_sr, ]
label_weights = tf.reshape(masked_sr_weights, [-1]) # [batch_size*max_predictions_per_seq_sr, ]
one_hot_labels = tf.one_hot(label_ids, depth=max_utr_num,
dtype=tf.float32) # [batch_size*max_predictions_per_seq_sr, max_utr_num]
# The `label_weights` tensor has a value of 1.0 for every real prediction and 0.0 for the padding predictions.
per_example_loss = - tf.reduce_sum(log_probs * one_hot_labels,
axis=[-1]) # [batch_size*max_predictions_per_seq_sr, ]
numerator = tf.reduce_sum(label_weights * per_example_loss) # [1, ]
denominator = tf.reduce_sum(label_weights) + 1e-5 # [1, ]
loss = numerator / denominator
return loss, per_example_loss, log_probs
def get_pointer_consistency_distinction_output(bert_config, input_tensor, cls_positions, pointer_cd_positions_spk1,
pointer_cd_positions_adr1,
pointer_cd_positions_spk2, pointer_cd_positions_adr2,
pointer_cd_positions_spk3, pointer_cd_positions_adr3,
pointer_cd_weights):
"""Get loss and log probs for the Pointer Consistency Distinction (PCD)."""
# input_tensor: [batch_size, max_sequence_len, dim]
# cls_positions: [batch_size, max_utr_num]
# pointer_cd_positions_spk1: [batch_size, max_predictions_per_seq_cd]
cls_shape = modeling.get_shape_list(cls_positions, expected_rank=2)
max_utr_num = cls_shape[1]
positions_shape = modeling.get_shape_list(pointer_cd_positions_spk1, expected_rank=2)
max_predictions_per_seq_cd = positions_shape[1]
input_tensor = gather_indexes(input_tensor, cls_positions) # [batch_size*max_utr_num, dim]
input_shape = modeling.get_shape_list(input_tensor, expected_rank=2)
width = input_shape[1]
with tf.compat.v1.variable_scope("cls/pointer_consistency_distinct"):
# We apply one more non-linear transformation before the output layer.
# This matrix is not used after pre-training.
with tf.compat.v1.variable_scope("transform"):
input_tensor = tf.compat.v1.layers.dense(
input_tensor,
units=bert_config.hidden_size,
activation=modeling.get_activation(bert_config.hidden_act),
kernel_initializer=modeling.create_initializer(
bert_config.initializer_range))
input_tensor = modeling.layer_norm(input_tensor) # [batch_size*max_utr_num, dim]
input_tensor = tf.reshape(input_tensor, [-1, max_utr_num, width]) # [batch_size, max_utr_num, dim]
spk1 = gather_indexes(input_tensor, pointer_cd_positions_spk1) # [batch_size*max_predictions_per_seq_cd, dim]
adr1 = gather_indexes(input_tensor, pointer_cd_positions_adr1) # [batch_size*max_predictions_per_seq_cd, dim]
spk2 = gather_indexes(input_tensor, pointer_cd_positions_spk2) # [batch_size*max_predictions_per_seq_cd, dim]
adr2 = gather_indexes(input_tensor, pointer_cd_positions_adr2) # [batch_size*max_predictions_per_seq_cd, dim]
spk3 = gather_indexes(input_tensor, pointer_cd_positions_spk3) # [batch_size*max_predictions_per_seq_cd, dim]
adr3 = gather_indexes(input_tensor, pointer_cd_positions_adr3) # [batch_size*max_predictions_per_seq_cd, dim]
# [multiply, minus] + FFNN
W_aggre = tf.compat.v1.get_variable(
"W_aggre",
shape=[width * 2, width],
initializer=modeling.create_initializer(bert_config.initializer_range))
b_aggre = tf.compat.v1.get_variable(
"b_aggre",
shape=[width, ],
initializer=modeling.create_initializer(bert_config.initializer_range))
pointer1 = tf.concat(values=[tf.multiply(spk1, adr1), spk1 - adr1], axis=-1)
pointer1 = tf.nn.relu(tf.matmul(pointer1, W_aggre) + b_aggre)
pointer2 = tf.concat(values=[tf.multiply(spk2, adr2), spk2 - adr2], axis=-1)
pointer2 = tf.nn.relu(tf.matmul(pointer2, W_aggre) + b_aggre)
pointer3 = tf.concat(values=[tf.multiply(spk3, adr3), spk3 - adr3], axis=-1)
pointer3 = tf.nn.relu(tf.matmul(pointer3, W_aggre) + b_aggre)
output_weights = tf.compat.v1.get_variable(
"output_weights",
shape=[width, width],
initializer=modeling.create_initializer(bert_config.initializer_range))
logits12 = tf.reduce_sum(tf.multiply(tf.matmul(pointer1, output_weights), pointer2),
axis=[-1]) # [batch_size*max_predictions_per_seq_cd, ]
log_probs12 = tf.sigmoid(logits12, name="logits12")
logits13 = tf.reduce_sum(tf.multiply(tf.matmul(pointer1, output_weights), pointer3),
axis=[-1]) # [batch_size*max_predictions_per_seq_cd, ]
log_probs13 = tf.sigmoid(logits13, name="logits13")
logits23 = tf.reduce_sum(tf.multiply(tf.matmul(pointer2, output_weights), pointer3),
axis=[-1]) # [batch_size*max_predictions_per_seq_cd, ]
log_probs23 = tf.sigmoid(logits23, name="logits23")
label_weights = tf.reshape(pointer_cd_weights, [-1]) # [batch_size*max_predictions_per_seq_cd, ]
# The `label_weights` tensor has a value of 1.0 for every real prediction and 0.0 for the padding predictions.
delta = 0.4
per_example_loss = tf.maximum(0.0, delta - log_probs12 + log_probs13)
numerator = tf.reduce_sum(label_weights * per_example_loss) # [1, ]
denominator = tf.reduce_sum(label_weights) + 1e-5 # [1, ]
loss = numerator / denominator
return loss, per_example_loss, log_probs12 - log_probs13
def get_masked_shared_utterance_restoration_output(bert_config, input_tensor, output_weights, positions, label_ids,
label_weights):
"""Get loss and log probs for the Masked Shared Utterance Restoration (MSUR)."""
input_tensor = gather_indexes(input_tensor, positions) # [batch_size*max_utr_length, dim]
with tf.compat.v1.variable_scope("cls/shared_utterance_restore"):
# We apply one more non-linear transformation before the output layer.
with tf.compat.v1.variable_scope("transform"):
input_tensor = tf.compat.v1.layers.dense(
input_tensor,
units=bert_config.hidden_size,
activation=modeling.get_activation(bert_config.hidden_act),
kernel_initializer=modeling.create_initializer(
bert_config.initializer_range))
input_tensor = modeling.layer_norm(input_tensor)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
output_bias = tf.compat.v1.get_variable(
"output_bias",
shape=[bert_config.vocab_size],
initializer=tf.zeros_initializer())
logits = tf.matmul(input_tensor, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
log_probs = tf.nn.log_softmax(logits, axis=-1) # [batch_size*max_utr_length, vocab_size]
label_ids = tf.reshape(label_ids, [-1])
label_weights = tf.reshape(label_weights, [-1])
one_hot_labels = tf.one_hot(label_ids, depth=bert_config.vocab_size, dtype=tf.float32)
# The `label_weights` tensor has a value of 1.0 for every real prediction and 0.0 for the padding predictions.
per_example_loss = -tf.reduce_sum(log_probs * one_hot_labels,
axis=[-1]) # [batch_size*max_predictions_per_seq, ]
numerator = tf.reduce_sum(label_weights * per_example_loss) # [1, ]
denominator = tf.reduce_sum(label_weights) + 1e-5
loss = numerator / denominator
return loss, per_example_loss, log_probs
def get_shared_node_detection_output(bert_config, input_tensor, labels):
with tf.compat.v1.variable_scope("cls/shared_node_detect"):
output_weights = tf.compat.v1.get_variable(
"output_weights",
shape=[2, bert_config.hidden_size],
initializer=modeling.create_initializer(bert_config.initializer_range))
output_bias = tf.compat.v1.get_variable(
"output_bias", shape=[2], initializer=tf.zeros_initializer())
logits = tf.matmul(input_tensor, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
log_probs = tf.nn.log_softmax(logits, axis=-1) # [batch_size, 2]
labels = tf.reshape(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) # [batch_size, ]
loss = tf.reduce_mean(per_example_loss) # [1, ]
return loss, per_example_loss, log_probs
def gather_indexes(sequence_tensor, positions):
"""Gathers the vectors at the specific positions over a minibatch."""
# sequence_tensor = [batch_size, seq_length, width]
# positions = [batch_size, max_predictions_per_seq]
sequence_shape = modeling.get_shape_list(sequence_tensor, expected_rank=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]) # [batch_size, 1]
flat_positions = tf.reshape(positions + flat_offsets, [-1]) # [batch_size*max_predictions_per_seq, ]
flat_sequence_tensor = tf.reshape(sequence_tensor, [batch_size * seq_length, width])
output_tensor = tf.gather(flat_sequence_tensor, flat_positions) # [batch_size*max_predictions_per_seq, width]
return output_tensor
def metric_fn(masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids, masked_lm_weights,
next_sentence_example_loss, next_sentence_log_probs, next_sentence_labels,
adr_recog_example_loss, adr_recog_log_probs, adr_recog_labels, adr_recog_weights,
masked_sr_example_loss, masked_sr_log_probs, masked_sr_labels, masked_sr_weights,
pointer_cd_example_loss, pointer_cd_log_probs, pointer_cd_weights,
masked_sur_example_loss, masked_sur_log_probs, masked_sur_ids, masked_sur_weights,
shared_nd_example_loss, shared_nd_log_probs, next_thread_labels):
"""Computes the loss and accuracy of the model."""
# adr_recog_example_loss: [batch_size*max_predictions_per_seq_ar, ]
# adr_recog_log_probs: [batch_size*max_predictions_per_seq_ar, max_utr_num]
# adr_recog_labels: [batch_size, max_predictions_per_seq_ar]
# adr_recog_weights: [batch_size, max_predictions_per_seq_ar]
# masked_sr_example_loss: [batch_size*max_utr_num, ]
# masked_sr_log_probs: [batch_size*max_utr_num, max_utr_num]
# masked_sr_labels: [batch_size, max_utr_num]
# masked_sr_weights: [batch_size, max_utr_num]
# pointer_cd_example_loss: [batch_size*max_predictions_per_seq_cd, ]
# pointer_cd_log_probs: [batch_size*max_predictions_per_seq_cd, ]
# pointer_cd_weights: [batch_size, max_predictions_per_seq_cd]
# masked_sur_example_loss: [batch_size*max_utr_length, ]
# masked_sur_log_probs: [batch_size*max_utr_length, vocab_size]
# masked_sur_labels: [batch_size, max_utr_length]
# masked_sur_weights: [batch_size, max_utr_length]
# shared_nd_example_loss: [batch_size, ]
# shared_nd_log_probs: [batch_size, 2]
# next_thread_labels: [batch_size, ]
masked_lm_predictions = tf.compat.v1.argmax(masked_lm_log_probs, axis=-1,
output_type=tf.int32) # [batch_size*max_predictions_per_seq, ]
masked_lm_ids = tf.reshape(masked_lm_ids, [-1])
masked_lm_weights = tf.reshape(masked_lm_weights, [-1])
masked_lm_accuracy = tf.compat.v1.metrics.accuracy(
labels=masked_lm_ids,
predictions=masked_lm_predictions,
weights=masked_lm_weights)
masked_lm_mean_loss = tf.compat.v1.metrics.mean(
values=masked_lm_example_loss,
weights=masked_lm_weights)
next_sentence_predictions = tf.argmax(next_sentence_log_probs, axis=-1, output_type=tf.int32) # [batch_size, ]
next_sentence_accuracy = tf.compat.v1.metrics.accuracy(
labels=next_sentence_labels,
predictions=next_sentence_predictions)
next_sentence_mean_loss = tf.compat.v1.metrics.mean(
values=next_sentence_example_loss)
adr_recog_predictions = tf.compat.v1.argmax(adr_recog_log_probs, axis=-1,
output_type=tf.int32) # [batch_size*max_predictions_per_seq_ar, ]
adr_recog_labels = tf.reshape(adr_recog_labels, [-1]) # [batch_size*max_predictions_per_seq_ar, ]
adr_recog_weights = tf.reshape(adr_recog_weights, [-1]) # [batch_size*max_predictions_per_seq_ar, ]
adr_recog_accuracy = tf.compat.v1.metrics.accuracy(
labels=adr_recog_labels,
predictions=adr_recog_predictions,
weights=adr_recog_weights)
adr_recog_mean_loss = tf.compat.v1.metrics.mean(
values=adr_recog_example_loss,
weights=adr_recog_weights)
masked_sr_predictions = tf.argmax(masked_sr_log_probs, axis=-1,
output_type=tf.int32) # [batch_size*max_predictions_per_seq_sr, ]
masked_sr_labels = tf.reshape(masked_sr_labels, [-1]) # [batch_size*max_predictions_per_seq_sr, ]
masked_sr_weights = tf.reshape(masked_sr_weights, [-1]) # [batch_size*max_predictions_per_seq_sr, ]
masked_sr_accuracy = tf.compat.v1.metrics.accuracy(
labels=masked_sr_labels,
predictions=masked_sr_predictions,
weights=masked_sr_weights)
masked_sr_mean_loss = tf.compat.v1.metrics.mean(
values=masked_sr_example_loss,
weights=masked_sr_weights)
pointer_cd_weights = tf.reshape(pointer_cd_weights, [-1]) # [batch_size*max_predictions_per_seq_cd, ]
pointer_cd_mean_simi = tf.compat.v1.metrics.mean(
values=pointer_cd_log_probs,
weights=pointer_cd_weights)
pointer_cd_mean_loss = tf.compat.v1.metrics.mean(
values=pointer_cd_example_loss,
weights=pointer_cd_weights)
masked_sur_predictions = tf.argmax(masked_sur_log_probs, axis=-1,
output_type=tf.int32) # [batch_size*max_utr_length, ]
masked_sur_ids = tf.reshape(masked_sur_ids, [-1]) # [batch_size*max_utr_length, ]
masked_sur_weights = tf.reshape(masked_sur_weights, [-1]) # [batch_size*max_utr_length, ]
masked_sur_accuracy = tf.compat.v1.metrics.accuracy(
labels=masked_sur_ids,
predictions=masked_sur_predictions,
weights=masked_sur_weights)
masked_sur_mean_loss = tf.compat.v1.metrics.mean(
values=masked_sur_example_loss,
weights=masked_sur_weights)
shared_nd_predictions = tf.argmax(shared_nd_log_probs, axis=-1, output_type=tf.int32) # [batch_size, ]
shared_nd_accuracy = tf.compat.v1.metrics.accuracy(
labels=next_thread_labels,
predictions=shared_nd_predictions)
shared_nd_mean_loss = tf.compat.v1.metrics.mean(
values=shared_nd_example_loss)
return {
"masked_lm_accuracy": masked_lm_accuracy,
"masked_lm_loss": masked_lm_mean_loss,
"next_sentence_accuracy": next_sentence_accuracy,
"next_sentence_loss": next_sentence_mean_loss,
"adr_recog_accuracy": adr_recog_accuracy,
"adr_recog_loss": adr_recog_mean_loss,
"masked_sr_accuracy": masked_sr_accuracy,
"masked_sr_loss": masked_sr_mean_loss,
"pointer_cd_simi": pointer_cd_mean_simi,
"pointer_cd_loss": pointer_cd_mean_loss,
"masked_sur_accuracy": masked_sur_accuracy,
"masked_sur_loss": masked_sur_mean_loss,
"shared_nd_accuracy": shared_nd_accuracy,
"shared_nd_loss": shared_nd_mean_loss
}
def run_epoch(epoch, sess, saver, output_dir, epoch_save_step, mid_save_step,
input_ids, eval_metrics, total_loss, train_op, eval_op):
total_sample = 0
# accumulate_loss = 0
step = 0
t0 = time()
tf.compat.v1.logging.info("*** Start epoch {} training ***".format(epoch))
try:
while True:
step += 1
_input_ids, batch_metrics, batch_loss, _, _ = sess.run(
[input_ids, eval_metrics, total_loss, train_op, eval_op])
masked_lm_accuracy, masked_lm_loss, next_sentence_accuracy, next_sentence_loss, \
adr_recog_accuracy, adr_recog_loss, masked_sr_accuracy, masked_sr_loss, \
pointer_cd_simi, pointer_cd_loss, masked_sur_accuracy, masked_sur_loss, \
shared_nd_accuracy, shared_nd_loss = batch_metrics
batch_sample = len(_input_ids)
total_sample += batch_sample
# accumulate_loss += batch_loss * batch_sample
# print
print_every_step = 200
if step % print_every_step == 0:
tf.compat.v1.logging.info("Step: {}, Loss: {:.4f}, Sample: {}, Time (min): {:.2f}".format(
step, batch_loss, total_sample, (time() - t0) / 60))
tf.compat.v1.logging.info(
'MLM_accuracy: {:.6f}, MLM_loss: {:.6f}, NSP_accuracy: {:.6f}, NSP_loss: {:.6f}, '
'RUR_accuracy: {:.6f}, RUR_loss: {:.6f}, ISS_accuracy: {:.6f}, ISS_loss: {:.6f}, '
'PCD_similarity: {:.6f}, PCD_loss: {:.6f}, MSUR_accuracy: {:.6f}, MSUR_loss: {:.6f}, '
'SND_accuracy: {:.6f}, SND_loss: {:.6f}'.format(
masked_lm_accuracy, masked_lm_loss, next_sentence_accuracy, next_sentence_loss,
adr_recog_accuracy, adr_recog_loss, masked_sr_accuracy, masked_sr_loss,
pointer_cd_simi, pointer_cd_loss, masked_sur_accuracy, masked_sur_loss,
shared_nd_accuracy, shared_nd_loss))
if (step % mid_save_step == 0) or (step % epoch_save_step == 0):
# c_time = str(int(time()))
save_path = os.path.join(output_dir, 'pretrained_bert_model_epoch_{}_step_{}'.format(epoch, step))
if not os.path.exists(save_path):
os.makedirs(save_path)
saver.save(sess, os.path.join(save_path, 'bert_model_epoch_{}_step_{}.ckpt'.format(epoch, step)),
global_step=step)
tf.compat.v1.logging.info('========== Save model at epoch: {}, step: {} =========='.format(epoch, step))
tf.compat.v1.logging.info("Step: {}, Loss: {:.4f}, Sample: {}, Time (min): {:.2f}".format(
step, batch_loss, total_sample, (time() - t0) / 60))
tf.compat.v1.logging.info(
'MLM_accuracy: {:.6f}, MLM_loss: {:.6f}, NSP_accuracy: {:.6f}, NSP_loss: {:.6f}, '
'RUR_accuracy: {:.6f}, RUR_loss: {:.6f}, ISS_accuracy: {:.6f}, ISS_loss: {:.6f}, '
'PCD_similarity: {:.6f}, PCD_loss: {:.6f}, MSUR_accuracy: {:.6f}, MSUR_loss: {:.6f}, '
'SND_accuracy: {:.6f}, SND_loss: {:.6f}'.format(
masked_lm_accuracy, masked_lm_loss, next_sentence_accuracy, next_sentence_loss,
adr_recog_accuracy, adr_recog_loss, masked_sr_accuracy, masked_sr_loss,
pointer_cd_simi, pointer_cd_loss, masked_sur_accuracy, masked_sur_loss,
shared_nd_accuracy, shared_nd_loss))
except tf.errors.OutOfRangeError:
tf.compat.v1.logging.info('*** Epoch {} is finished ***'.format(epoch))
pass
def main(_):
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
print_configuration_op(FLAGS)
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
if not os.path.exists(FLAGS.output_dir):
os.makedirs(FLAGS.output_dir)
train_data_size = count_data_size(FLAGS.input_file)
tf.compat.v1.logging.info('*** train data size: {} ***'.format(train_data_size))
num_train_steps = train_data_size // FLAGS.train_batch_size * FLAGS.num_train_epochs
num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)
epoch_save_step = train_data_size // FLAGS.train_batch_size
buffer_size = 1000
tf.compat.v1.disable_eager_execution()
filenames = tf.compat.v1.placeholder(tf.string, shape=[None])
dataset = tf.compat.v1.data.TFRecordDataset(filenames)
dataset = dataset.map(parse_exmp) # Parse the record into tensors.
dataset = dataset.repeat(1)
dataset = dataset.shuffle(buffer_size)
dataset = dataset.batch(FLAGS.train_batch_size)
iterator = dataset.make_initializable_iterator()
input_ids_mlm_nsp, input_mask_mlm_nsp, segment_ids_mlm_nsp, speaker_ids_mlm_nsp, \
next_sentence_labels, masked_lm_positions, masked_lm_ids, masked_lm_weights, \
cls_positions, input_ids_ar_msr_pcd, input_mask_ar_msr_pcd, segment_ids_ar_msr_pcd, speaker_ids_ar_msr_pcd, \
adr_recog_positions, adr_recog_labels, adr_recog_weights, masked_sr_positions, masked_sr_labels, masked_sr_weights, \
pointer_cd_positions_spk1, pointer_cd_positions_adr1, pointer_cd_positions_spk2, pointer_cd_positions_adr2, \
pointer_cd_positions_spk3, pointer_cd_positions_adr3, pointer_cd_weights, \
input_ids_msur, input_mask_msur, segment_ids_msur, speaker_ids_msur, masked_sur_positions, masked_sur_ids, masked_sur_weights, \
input_ids_snd, input_mask_snd, segment_ids_snd, speaker_ids_snd, next_thread_labels = iterator.get_next()
features = [input_ids_mlm_nsp, input_mask_mlm_nsp, segment_ids_mlm_nsp, speaker_ids_mlm_nsp, \
next_sentence_labels, masked_lm_positions, masked_lm_ids, masked_lm_weights, \
cls_positions, input_ids_ar_msr_pcd, input_mask_ar_msr_pcd, segment_ids_ar_msr_pcd,
speaker_ids_ar_msr_pcd, \
adr_recog_positions, adr_recog_labels, adr_recog_weights, masked_sr_positions, masked_sr_labels,
masked_sr_weights, \
pointer_cd_positions_spk1, pointer_cd_positions_adr1, pointer_cd_positions_spk2,
pointer_cd_positions_adr2, \
pointer_cd_positions_spk3, pointer_cd_positions_adr3, pointer_cd_weights, \
input_ids_msur, input_mask_msur, segment_ids_msur, speaker_ids_msur, masked_sur_positions,
masked_sur_ids, masked_sur_weights, \
input_ids_snd, input_mask_snd, segment_ids_snd, speaker_ids_snd, next_thread_labels]
train_op, total_loss, metrics, input_ids = model_fn_builder(
features=features,
is_training=True,
bert_config=bert_config,
init_checkpoint=FLAGS.init_checkpoint,
learning_rate=FLAGS.learning_rate,
num_train_steps=num_train_steps,