def init(self, config, is_training, input_ids, input_ids2, input_mask,
input_mask2, token_type_ids, segment_ids2,
use_one_hot_embeddings):
with tf.compat.v1.variable_scope(dual_model_prefix1):
model_1 = BertModel(
config=config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=token_type_ids,
use_one_hot_embeddings=use_one_hot_embeddings,
)
with tf.compat.v1.variable_scope(dual_model_prefix2):
model_2 = BertModel(
config=config,
is_training=is_training,
input_ids=input_ids2,
input_mask=input_mask2,
token_type_ids=segment_ids2,
use_one_hot_embeddings=use_one_hot_embeddings,
)
model_1_first_token = model_1.get_sequence_output()[:, 0, :]
model_2_first_token = model_2.get_sequence_output()[:, 0, :]
rep = tf.concat([model_1_first_token, model_2_first_token], axis=1)
self.sequence_output = tf.concat(
[model_1.get_sequence_output(),
model_2.get_sequence_output()],
axis=2)
dense_layer = tf.keras.layers.Dense(
config.hidden_size,
activation=tf.keras.activations.tanh,
kernel_initializer=create_initializer(config.initializer_range))
pooled_output = dense_layer(rep)
self.pooled_output = pooled_output
def __init__(self,
config,
is_training,
input_ids,
input_mask=None,
token_type_ids=None,
use_one_hot_embeddings=True,
features=None,
scope=None):
super(DualBertTwoInputWithDoubleInputLength, self).__init__()
input_ids1 = features["input_ids1"]
input_mask1 = features["input_mask1"]
segment_ids1 = features["segment_ids1"]
input_ids2 = features["input_ids2"]
input_mask2 = features["input_mask2"]
segment_ids2 = features["segment_ids2"]
with tf.compat.v1.variable_scope(dual_model_prefix1):
model_1 = BertModel(
config=config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=token_type_ids,
use_one_hot_embeddings=use_one_hot_embeddings,
)
with tf.compat.v1.variable_scope(dual_model_prefix2):
model_2 = DoubleLengthInputModel(
config,
is_training,
input_ids1,
input_mask1,
segment_ids1,
input_ids2,
input_mask2,
segment_ids2,
use_one_hot_embeddings=use_one_hot_embeddings,
)
model_1_first_token = model_1.get_sequence_output()[:, 0, :]
model_2_first_token = model_2.get_sequence_output()[:, 0, :]
rep = tf.concat([model_1_first_token, model_2_first_token], axis=1)
self.sequence_output = model_1.get_sequence_output()
dense_layer = tf.keras.layers.Dense(
config.hidden_size,
activation=tf.keras.activations.tanh,
kernel_initializer=create_initializer(config.initializer_range))
pooled_output = dense_layer(rep)
self.pooled_output = pooled_output
class ProjectedMaxPooling(BertModelInterface):
def __init__(self,
config,
is_training,
input_ids,
input_mask=None,
token_type_ids=None,
use_one_hot_embeddings=True,
scope=None):
super(ProjectedMaxPooling, self).__init__()
config = copy.deepcopy(config)
self.config = config
self.vector_size = config.vector_size
self.bert_model = BertModel(config, is_training, input_ids, input_mask,
token_type_ids, use_one_hot_embeddings,
scope)
def get_pooled_output(self):
seq_output = self.bert_model.get_sequence_output()
# projected = tf.keras.layers.Dense(self.vector_size,
# activation=tf.keras.activations.tanh,
# kernel_initializer=
# create_initializer(self.config.initializer_range))(seq_output)
projected = seq_output
pooled_output = tf.reduce_mean(projected, axis=1)
return pooled_output
class SimpleSharingModel:
def __init__(
self,
config,
use_one_hot_embeddings,
is_training,
masked_input_ids,
input_mask,
segment_ids,
nli_input_ids,
nli_input_mask,
nli_segment_ids,
):
all_input_ids = tf.concat([masked_input_ids, nli_input_ids], axis=0)
all_input_mask = tf.concat([input_mask, nli_input_mask], axis=0)
all_segment_ids = tf.concat([segment_ids, nli_segment_ids], axis=0)
self.batch_size, _ = get_shape_list2(masked_input_ids)
self.model = BertModel(config, is_training, all_input_ids,
all_input_mask, all_segment_ids,
use_one_hot_embeddings)
def lm_sequence_output(self):
return self.model.get_sequence_output()[:self.batch_size]
def get_embedding_table(self):
return self.model.get_embedding_table()
def get_tt_feature(self):
return self.model.get_pooled_output()[self.batch_size:]
class AddLayerSharingModel:
def __init__(
self,
config,
use_one_hot_embeddings,
is_training,
masked_input_ids,
input_mask,
segment_ids,
tt_input_ids,
tt_input_mask,
tt_segment_ids,
):
all_input_ids = tf.concat([masked_input_ids, tt_input_ids], axis=0)
all_input_mask = tf.concat([input_mask, tt_input_mask], axis=0)
all_segment_ids = tf.concat([segment_ids, tt_segment_ids], axis=0)
self.config = config
self.lm_batch_size, _ = get_shape_list2(masked_input_ids)
self.model = BertModel(config, is_training, all_input_ids,
all_input_mask, all_segment_ids,
use_one_hot_embeddings)
initializer = base.create_initializer(config.initializer_range)
self.tt_layer = ForwardLayer(config, initializer)
self.tt_input_mask = tt_input_mask
seq_output = self.model.get_sequence_output()[self.lm_batch_size:]
tt_batch_size, seq_length = get_shape_list2(tt_input_ids)
tt_attention_mask = create_attention_mask_from_input_mask2(
seq_output, self.tt_input_mask)
print('tt_attention_mask', tt_attention_mask.shape)
print("seq_output", seq_output.shape)
seq_output = self.tt_layer.apply_3d(seq_output, tt_batch_size,
seq_length, tt_attention_mask)
self.tt_feature = mimic_pooling(seq_output, self.config.hidden_size,
self.config.initializer_range)
def lm_sequence_output(self):
return self.model.get_sequence_output()[:self.lm_batch_size]
def get_embedding_table(self):
return self.model.get_embedding_table()
def get_tt_feature(self):
return self.tt_feature
def tlm2_raw_prob(bert_config, use_one_hot_embeddings, input_ids, input_mask, segment_ids):
encode_model = BertModel(
config=bert_config,
is_training=False,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=use_one_hot_embeddings,
)
loss_model = IndependentLossModel(bert_config)
loss_model.build_predictions(encode_model.get_sequence_output())
output = -(loss_model.prob1 - loss_model.prob2)
return output, loss_model.prob1, loss_model.prob2
def tlm_prefer_hard(bert_config, use_one_hot_embeddings, features):
input_ids = features["input_ids"]
input_mask = features["input_mask"]
segment_ids = features["segment_ids"]
encode_model = BertModel(
config=bert_config,
is_training=False,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=use_one_hot_embeddings,
)
loss_model = IndependentLossModel(bert_config)
loss_model.build_predictions(encode_model.get_sequence_output())
# if score is higher, it is more often sampled
output = -loss_model.prob1
return output
def __init__(self,
sero_config,
config,
is_training,
input_ids,
input_mask=None,
token_type_ids=None,
use_one_hot_embeddings=True,
scope=None):
super(DualSeroBertModel, self).__init__()
with tf.compat.v1.variable_scope(dual_model_prefix1):
model_1 = BertModel(
config=config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=token_type_ids,
use_one_hot_embeddings=use_one_hot_embeddings,
)
with tf.compat.v1.variable_scope(dual_model_prefix2):
with tf.compat.v1.variable_scope("sero"):
model = SeroEpsilon(sero_config, is_training,
use_one_hot_embeddings)
batch_size, _ = get_shape_list(input_mask)
use_context = tf.ones([batch_size, 1], tf.int32)
input_ids = tf.expand_dims(input_ids, 1)
input_mask = tf.expand_dims(input_mask, 1)
segment_ids = tf.expand_dims(token_type_ids, 1)
sequence_output2 = model.network_stacked(
input_ids, input_mask, segment_ids, use_context)
model_1_first_token = model_1.get_sequence_output()[:, 0, :]
model_2_first_token = sequence_output2[:, 0, :]
rep = tf.concat([model_1_first_token, model_2_first_token], axis=1)
dense_layer = tf.keras.layers.Dense(
config.hidden_size,
activation=tf.keras.activations.tanh,
kernel_initializer=create_initializer(config.initializer_range))
pooled_output = dense_layer(rep)
self.pooled_output = pooled_output
def tlm2(bert_config, use_one_hot_embeddings, features):
input_ids = features["input_ids"]
input_mask = features["input_mask"]
segment_ids = features["segment_ids"]
hp = hyperparams.HPBert()
voca_size = 30522
sequence_shape = bert_common.get_shape_list2(input_ids)
encode_model = BertModel(
config=bert_config,
is_training=False,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=use_one_hot_embeddings,
)
loss_model = IndependentLossModel(bert_config)
loss_model.build_predictions(encode_model.get_sequence_output())
output = -(loss_model.prob1 - loss_model.prob2)
return output
class transformer_nli:
def __init__(self, hp, voca_size, method, is_training=True):
config = BertConfig(
vocab_size=voca_size,
hidden_size=hp.hidden_units,
num_hidden_layers=hp.num_blocks,
num_attention_heads=hp.num_heads,
intermediate_size=hp.intermediate_size,
type_vocab_size=hp.type_vocab_size,
)
seq_length = hp.seq_max
use_tpu = False
task = Classification(data_generator.NLI.nli_info.num_classes)
input_ids = placeholder(tf.int64, [None, seq_length])
input_mask = placeholder(tf.int64, [None, seq_length])
segment_ids = placeholder(tf.int64, [None, seq_length])
label_ids = placeholder(tf.int64, [None])
self.x_list = [input_ids, input_mask, segment_ids]
self.y = label_ids
use_one_hot_embeddings = use_tpu
self.model = BertModel(config=config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=use_one_hot_embeddings)
pred, loss = task.predict(self.model.get_sequence_output(), label_ids,
True)
self.logits = task.logits
self.sout = tf.nn.softmax(self.logits)
self.pred = pred
self.loss = loss
self.acc = task.acc
def __init__(self,
config,
is_training,
input_ids,
input_mask=None,
token_type_ids=None,
use_one_hot_embeddings=True,
features=None,
scope=None):
super(TripleBertMasking, self).__init__()
input_ids2 = features["input_ids2"]
input_mask2 = features["input_mask2"]
segment_ids2 = features["segment_ids2"]
input_ids3 = features["input_ids3"]
input_mask3 = features["input_mask3"]
segment_ids3 = features["segment_ids3"]
with tf.compat.v1.variable_scope(triple_model_prefix1):
model_1 = BertModel(
config=config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=token_type_ids,
use_one_hot_embeddings=use_one_hot_embeddings,
)
with tf.compat.v1.variable_scope(triple_model_prefix2):
model_2 = BertModel(
config=config,
is_training=is_training,
input_ids=input_ids2,
input_mask=input_mask2,
token_type_ids=segment_ids2,
use_one_hot_embeddings=use_one_hot_embeddings,
)
with tf.compat.v1.variable_scope(triple_model_prefix3):
model_3 = BertModel(
config=config,
is_training=is_training,
input_ids=input_ids3,
input_mask=input_mask3,
token_type_ids=segment_ids3,
use_one_hot_embeddings=use_one_hot_embeddings,
)
model_1_first_token = model_1.get_sequence_output()[:, 0, :]
model_2_first_token = model_2.get_sequence_output()[:, 0, :]
pooled3 = model_3.get_pooled_output()
probs3 = tf.keras.layers.Dense(2,
activation=tf.keras.activations.softmax,
kernel_initializer=create_initializer(
config.initializer_range))(pooled3)
mask_scalar = probs3[:, 1:2]
self.rel_score = mask_scalar
model_2_first_token = mask_scalar * model_2_first_token
rep = tf.concat([model_1_first_token, model_2_first_token], axis=1)
self.sequence_output = tf.concat(
[model_1.get_sequence_output(),
model_2.get_sequence_output()],
axis=2)
dense_layer = tf.keras.layers.Dense(
config.hidden_size,
activation=tf.keras.activations.tanh,
kernel_initializer=create_initializer(config.initializer_range))
pooled_output = dense_layer(rep)
self.pooled_output = pooled_output
def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
log_features(features)
input_ids = features["input_ids"]
input_mask = features["input_mask"]
segment_ids = features["segment_ids"]
next_sentence_labels = features["next_sentence_labels"]
masked_input_ids, masked_lm_positions, masked_lm_ids, masked_lm_weights \
= random_masking(input_ids, input_mask, train_config.max_predictions_per_seq, MASK_ID)
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
prefix1 = "MaybeBERT"
prefix2 = "MaybeBFN"
with tf.compat.v1.variable_scope(prefix1):
model1 = BertModel(
config=bert_config,
is_training=is_training,
input_ids=masked_input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=train_config.use_one_hot_embeddings,
)
(masked_lm_loss, masked_lm_example_loss1,
masked_lm_log_probs1) = get_masked_lm_output(
bert_config, model1.get_sequence_output(),
model1.get_embedding_table(), masked_lm_positions,
masked_lm_ids, masked_lm_weights)
masked_lm_example_loss1 = tf.reshape(masked_lm_example_loss1,
masked_lm_ids.shape)
with tf.compat.v1.variable_scope(prefix2):
model2 = BertModel(
config=bert_config,
is_training=is_training,
input_ids=masked_input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=train_config.use_one_hot_embeddings,
)
(masked_lm_loss, masked_lm_example_loss2,
masked_lm_log_probs2) = get_masked_lm_output(
bert_config, model2.get_sequence_output(),
model2.get_embedding_table(), masked_lm_positions,
masked_lm_ids, masked_lm_weights)
print(model2.get_sequence_output().shape)
masked_lm_example_loss2 = tf.reshape(masked_lm_example_loss2,
masked_lm_ids.shape)
model = model_class(
config=bert_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=train_config.use_one_hot_embeddings,
)
loss_model = IndependentLossModel(bert_config)
loss_model.train_modeling(model.get_sequence_output(),
masked_lm_positions, masked_lm_weights,
tf.stop_gradient(masked_lm_example_loss1),
tf.stop_gradient(masked_lm_example_loss2))
total_loss = loss_model.total_loss
loss1 = loss_model.loss1
loss2 = loss_model.loss2
per_example_loss1 = loss_model.per_example_loss1
per_example_loss2 = loss_model.per_example_loss2
losses1 = tf.reduce_sum(per_example_loss1, axis=1)
losses2 = tf.reduce_sum(per_example_loss2, axis=1)
prob1 = loss_model.prob1
prob2 = loss_model.prob2
checkpoint2_1, checkpoint2_2 = train_config.second_init_checkpoint.split(
",")
tvars = tf.compat.v1.trainable_variables()
initialized_variable_names_1, init_fn_1 = get_init_fn_for_two_checkpoints(
train_config, tvars, checkpoint2_1, prefix1, checkpoint2_2,
prefix2)
assignment_fn = get_bert_assignment_map
assignment_map2, initialized_variable_names_2 = assignment_fn(
tvars, train_config.init_checkpoint)
initialized_variable_names = {}
initialized_variable_names.update(initialized_variable_names_1)
initialized_variable_names.update(initialized_variable_names_2)
def init_fn():
init_fn_1()
tf.compat.v1.train.init_from_checkpoint(
train_config.init_checkpoint, assignment_map2)
scaffold_fn = get_tpu_scaffold_or_init(init_fn, train_config.use_tpu)
log_var_assignments(tvars, initialized_variable_names)
if mode == tf.estimator.ModeKeys.TRAIN:
train_op = optimization.create_optimizer_from_config(
total_loss, train_config)
output_spec = tf.compat.v1.estimator.tpu.TPUEstimatorSpec(
mode=mode,
loss=total_loss,
train_op=train_op,
scaffold_fn=scaffold_fn)
elif mode == tf.estimator.ModeKeys.EVAL:
def metric_fn(per_example_loss1, per_example_loss2):
loss1 = tf.compat.v1.metrics.mean(values=per_example_loss1)
loss2 = tf.compat.v1.metrics.mean(values=per_example_loss2)
return {
"loss1": loss1,
"loss2": loss2,
}
eval_metrics = (metric_fn, [losses1, losses2])
output_spec = tf.compat.v1.estimator.tpu.TPUEstimatorSpec(
mode=mode,
loss=total_loss,
eval_metrics=eval_metrics,
scaffold_fn=scaffold_fn)
else:
predictions = {
"prob1": prob1,
"prob2": prob2,
"per_example_loss1": per_example_loss1,
"per_example_loss2": per_example_loss2,
"input_ids": input_ids,
"masked_lm_positions": masked_lm_positions,
}
output_spec = tf.compat.v1.estimator.tpu.TPUEstimatorSpec(
mode=mode,
loss=total_loss,
predictions=predictions,
scaffold_fn=scaffold_fn)
return output_spec
def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
tf_logging.info("model_fn_apr_lm")
"""The `model_fn` for TPUEstimator."""
log_features(features)
raw_input_ids = features["input_ids"] # [batch_size, seq_length]
raw_input_mask = features["input_mask"]
raw_segment_ids = features["segment_ids"]
word_tokens = features["word"]
word_input_mask = tf.cast(tf.not_equal(word_tokens, 0), tf.int32)
word_segment_ids = tf.ones_like(word_tokens, tf.int32)
if mode == tf.estimator.ModeKeys.PREDICT:
tf.random.set_seed(0)
seed = 0
else:
seed = None
input_ids = tf.concat([word_tokens, raw_input_ids], axis=1)
input_mask = tf.concat([word_input_mask, raw_input_mask], axis=1)
segment_ids = tf.concat([word_segment_ids, raw_segment_ids], axis=1)
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
tf_logging.info("Using masked_input_ids")
masked_input_ids, masked_lm_positions, masked_lm_ids, masked_lm_weights \
= random_masking(input_ids, input_mask, train_config.max_predictions_per_seq, MASK_ID, seed)
model = BertModel(
config=config,
is_training=is_training,
input_ids=masked_input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=train_config.use_one_hot_embeddings,
)
(masked_lm_loss, masked_lm_example_loss,
masked_lm_log_probs) = get_masked_lm_output(
config, model.get_sequence_output(), model.get_embedding_table(),
masked_lm_positions, masked_lm_ids, masked_lm_weights)
loss = masked_lm_loss
tvars = tf.compat.v1.trainable_variables()
assignment_fn = tlm.training.assignment_map.get_bert_assignment_map
initialized_variable_names, init_fn = get_init_fn(
tvars, train_config.init_checkpoint, assignment_fn)
scaffold_fn = get_tpu_scaffold_or_init(init_fn, train_config.use_tpu)
log_var_assignments(tvars, initialized_variable_names)
TPUEstimatorSpec = tf.compat.v1.estimator.tpu.TPUEstimatorSpec
if mode == tf.estimator.ModeKeys.TRAIN:
tf_logging.info("Using single lr ")
train_op = optimization.create_optimizer_from_config(
loss, train_config)
output_spec = TPUEstimatorSpec(mode=mode,
loss=loss,
train_op=train_op,
scaffold_fn=scaffold_fn)
elif mode == tf.estimator.ModeKeys.EVAL:
eval_metrics = (metric_fn_lm, [
masked_lm_example_loss,
masked_lm_log_probs,
masked_lm_ids,
masked_lm_weights,
])
output_spec = TPUEstimatorSpec(mode=mode,
loss=loss,
eval_metrics=eval_metrics,
scaffold_fn=scaffold_fn)
else:
predictions = {
"input_ids": input_ids,
"masked_input_ids": masked_input_ids,
"masked_lm_ids": masked_lm_ids,
"masked_lm_example_loss": masked_lm_example_loss,
"masked_lm_positions": masked_lm_positions
}
output_spec = TPUEstimatorSpec(mode=mode,
loss=loss,
predictions=predictions,
scaffold_fn=scaffold_fn)
return output_spec
def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
"""The `model_fn` for TPUEstimator."""
logging.info("*** Features ***")
for name in sorted(features.keys()):
logging.info(" name = %s, shape = %s" %
(name, features[name].shape))
input_ids = features["input_ids"]
input_mask = features["input_mask"]
segment_ids = features["segment_ids"]
label_ids = features["label_ids"]
batch_size, seq_len = get_shape_list2(input_ids)
n_trial = 5
logging.info("Doing All Masking")
new_input_ids, new_segment_ids, new_input_mask, indice, length_arr = \
candidate_gen(input_ids, input_mask, segment_ids, n_trial)
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
prefix_cls = "classification"
prefix_explain = "explain"
all_input_ids = tf.concat([input_ids, new_input_ids], axis=0)
all_segment_ids = tf.concat([segment_ids, new_segment_ids], axis=0)
all_input_mask = tf.concat([input_mask, new_input_mask], axis=0)
with tf.compat.v1.variable_scope(prefix_cls):
model = BertModel(
config=bert_config,
is_training=is_training,
input_ids=all_input_ids,
input_mask=all_input_mask,
token_type_ids=all_segment_ids,
use_one_hot_embeddings=train_config.use_one_hot_embeddings,
)
output_weights = tf.compat.v1.get_variable(
"output_weights",
[train_config.num_classes, bert_config.hidden_size],
initializer=tf.compat.v1.truncated_normal_initializer(
stddev=0.02))
output_bias = tf.compat.v1.get_variable(
"output_bias", [train_config.num_classes],
initializer=tf.compat.v1.zeros_initializer())
pooled = model.get_pooled_output()
raw_logits = tf.matmul(pooled, output_weights, transpose_b=True)
logits = tf.stop_gradient(raw_logits)
cls_logits = tf.nn.bias_add(logits, output_bias)
cls_probs = tf.nn.softmax(cls_logits)
orig_probs = cls_probs[:batch_size]
new_probs = tf.reshape(cls_probs[batch_size:],
[batch_size, n_trial, -1])
best_run, informative = get_informative(new_probs, orig_probs)
# informative.shape= [batch_size, num_clases]
best_del_idx, best_del_len = select_best(best_run, indice,
length_arr)
signal_label = get_mask(best_del_idx, best_del_len, seq_len)
with tf.compat.v1.variable_scope(prefix_explain):
model = BertModel(
config=bert_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=train_config.use_one_hot_embeddings,
)
seq = model.get_sequence_output()
output_weights = tf.compat.v1.get_variable(
"output_weights",
[train_config.num_classes, bert_config.hidden_size],
initializer=tf.compat.v1.truncated_normal_initializer(
stddev=0.02))
output_bias = tf.compat.v1.get_variable(
"output_bias", [train_config.num_classes],
initializer=tf.compat.v1.zeros_initializer())
logits = tf.matmul(seq, output_weights, transpose_b=True)
ex_logits = tf.nn.bias_add(
logits, output_bias) # [batch, seq_len, num_class]
ex_logits_flat = tf.reshape(tf.transpose(ex_logits, [0, 2, 1]),
[-1, seq_len])
signal_label_flat = tf.cast(tf.reshape(signal_label, [-1, seq_len]),
tf.float32)
losses_per_clas_flat = correlation_coefficient_loss(
signal_label_flat, ex_logits_flat) # [batch_size, num_class]
losses_per_clas = tf.reshape(losses_per_clas_flat, [batch_size, -1])
losses_per_clas = losses_per_clas * tf.cast(informative, tf.float32)
losses = tf.reduce_mean(losses_per_clas, axis=1)
loss = tf.reduce_mean(losses)
tvars = tf.compat.v1.trainable_variables()
scaffold_fn = None
initialized_variable_names, init_fn = get_init_fn_for_two_checkpoints(
train_config, tvars, train_config.init_checkpoint, prefix_explain,
train_config.second_init_checkpoint, prefix_cls)
if train_config.use_tpu:
def tpu_scaffold():
init_fn()
return tf.compat.v1.train.Scaffold()
scaffold_fn = tpu_scaffold
else:
init_fn()
log_var_assignments(tvars, initialized_variable_names)
output_spec = None
TPUEstimatorSpec = tf.compat.v1.estimator.tpu.TPUEstimatorSpec
if mode == tf.estimator.ModeKeys.TRAIN:
train_op = optimization.create_optimizer_from_config(
loss, train_config)
output_spec = TPUEstimatorSpec(mode=mode,
loss=loss,
train_op=train_op,
scaffold_fn=scaffold_fn)
elif mode == tf.estimator.ModeKeys.PREDICT:
predictions = {
"input_ids": input_ids,
"ex_logits": ex_logits,
"logits": logits,
}
output_spec = tf.compat.v1.estimator.tpu.TPUEstimatorSpec(
mode=mode,
loss=None,
predictions=predictions,
scaffold_fn=scaffold_fn)
return output_spec
def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
"""The `model_fn` for TPUEstimator."""
logging.info("*** Features ***")
for name in sorted(features.keys()):
logging.info(" name = %s, shape = %s" %
(name, features[name].shape))
input_ids = features["input_ids"]
input_mask = features["input_mask"]
segment_ids = features["segment_ids"]
next_sentence_labels = features["next_sentence_labels"]
n_trial = 25
logging.info("Doing All Masking")
masked_input_ids, masked_lm_positions, masked_lm_ids, masked_lm_weights \
= planned_masking(input_ids, input_mask, train_config.max_predictions_per_seq, MASK_ID, n_trial)
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
repeat_input_mask = tf.tile(input_mask, [n_trial, 1])
repeat_segment_ids = tf.tile(segment_ids, [n_trial, 1])
prefix1 = "MaybeBERT"
prefix2 = "MaybeBFN"
with tf.compat.v1.variable_scope(prefix1):
model = BertModel(
config=bert_config,
is_training=is_training,
input_ids=masked_input_ids,
input_mask=repeat_input_mask,
token_type_ids=repeat_segment_ids,
use_one_hot_embeddings=train_config.use_one_hot_embeddings,
)
(masked_lm_loss, masked_lm_example_loss1,
masked_lm_log_probs2) = get_masked_lm_output(
bert_config, model.get_sequence_output(),
model.get_embedding_table(), masked_lm_positions,
masked_lm_ids, masked_lm_weights)
with tf.compat.v1.variable_scope(prefix2):
model = BertModel(
config=bert_config,
is_training=is_training,
input_ids=masked_input_ids,
input_mask=repeat_input_mask,
token_type_ids=repeat_segment_ids,
use_one_hot_embeddings=train_config.use_one_hot_embeddings,
)
(masked_lm_loss, masked_lm_example_loss2,
masked_lm_log_probs2) = get_masked_lm_output(
bert_config, model.get_sequence_output(),
model.get_embedding_table(), masked_lm_positions,
masked_lm_ids, masked_lm_weights)
n_mask = train_config.max_predictions_per_seq
def reform(t):
t = tf.reshape(t, [n_trial, -1, n_mask])
t = tf.transpose(t, [1, 0, 2])
return t
grouped_positions = reform(masked_lm_positions)
grouped_loss1 = reform(masked_lm_example_loss1)
grouped_loss2 = reform(masked_lm_example_loss2)
tvars = tf.compat.v1.trainable_variables()
scaffold_fn = None
initialized_variable_names, init_fn = get_init_fn_for_two_checkpoints(
train_config, tvars, train_config.init_checkpoint, prefix1,
train_config.second_init_checkpoint, prefix2)
if train_config.use_tpu:
def tpu_scaffold():
init_fn()
return tf.compat.v1.train.Scaffold()
scaffold_fn = tpu_scaffold
else:
init_fn()
log_var_assignments(tvars, initialized_variable_names)
output_spec = None
if mode == tf.estimator.ModeKeys.PREDICT:
predictions = {
"input_ids": input_ids,
"input_mask": input_mask,
"segment_ids": segment_ids,
"grouped_positions": grouped_positions,
"grouped_loss1": grouped_loss1,
"grouped_loss2": grouped_loss2,
}
output_spec = tf.compat.v1.estimator.tpu.TPUEstimatorSpec(
mode=mode,
loss=None,
predictions=predictions,
scaffold_fn=scaffold_fn)
return output_spec
def __init__(self,
config,
is_training,
input_ids,
input_mask=None,
token_type_ids=None,
use_one_hot_embeddings=True,
features=None,
scope=None):
super(DualBertTwoInputModelEx, self).__init__()
input_ids2 = features["input_ids2"]
input_mask2 = features["input_mask2"]
segment_ids2 = features["segment_ids2"]
modeling_option = config.model_option
with tf.compat.v1.variable_scope(dual_model_prefix1):
model_1 = BertModel(
config=config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=token_type_ids,
use_one_hot_embeddings=use_one_hot_embeddings,
)
with tf.compat.v1.variable_scope(dual_model_prefix2):
model_2 = BertModel(
config=config,
is_training=is_training,
input_ids=input_ids2,
input_mask=input_mask2,
token_type_ids=segment_ids2,
use_one_hot_embeddings=use_one_hot_embeddings,
)
model_1_first_token = model_1.get_sequence_output()[:, 0, :]
model_2_first_token = model_2.get_sequence_output()[:, 0, :]
print('model_2_first_token', model_2_first_token)
mask_scalar = {
"0": 0.,
"1": 1.,
"random": tf.random.uniform(shape=[], minval=0., maxval=1.)
}[modeling_option]
print("Mask_scalar:", mask_scalar)
model_2_first_token = mask_scalar * model_2_first_token
print('model_2_first_token', model_2_first_token)
rep = tf.concat([model_1_first_token, model_2_first_token], axis=1)
self.sequence_output = tf.concat(
[model_1.get_sequence_output(),
model_2.get_sequence_output()],
axis=2)
dense_layer = tf.keras.layers.Dense(
config.hidden_size,
activation=tf.keras.activations.tanh,
kernel_initializer=create_initializer(config.initializer_range))
pooled_output = dense_layer(rep)
self.pooled_output = pooled_output
def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
"""The `model_fn` for TPUEstimator."""
tf_logging.info("*** Features ***")
for name in sorted(features.keys()):
tf_logging.info(" name = %s, shape = %s" %
(name, features[name].shape))
input_ids = features["input_ids"]
input_mask = features["input_mask"]
segment_ids = features["segment_ids"]
label_ids = features["label_ids"]
input_shape = get_shape_list2(input_ids)
batch_size, seq_length = input_shape
if "is_real_example" in features:
is_real_example = tf.cast(features["is_real_example"],
dtype=tf.float32)
else:
is_real_example = tf.ones([batch_size, 1], dtype=tf.float32)
label_ids = tf.reshape(
label_ids, [batch_size, seq_length, train_config.num_classes])
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
model = BertModel(
config=model_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=train_config.use_one_hot_embeddings,
)
seq_out = model.get_sequence_output()
if is_training:
seq_out = dropout(seq_out, 0.1)
logits = tf.keras.layers.Dense(train_config.num_classes,
name="cls_dense")(seq_out)
probs = tf.math.sigmoid(logits)
eps = 1e-10
label_logs = tf.math.log(label_ids + eps)
#scale = model_config.scale
#label_ids = scale * label_ids
is_valid_mask = tf.cast(segment_ids, tf.float32)
#loss_arr = tf.keras.losses.MAE(y_true=label_ids, y_pred=probs)
loss_arr = tf.keras.losses.MAE(y_true=label_logs, y_pred=logits)
loss_arr = loss_arr * is_valid_mask
loss = tf.reduce_mean(input_tensor=loss_arr)
tvars = tf.compat.v1.trainable_variables()
initialized_variable_names = {}
scaffold_fn = None
if train_config.init_checkpoint:
initialized_variable_names, init_fn = get_init_fn(
train_config, tvars)
scaffold_fn = get_tpu_scaffold_or_init(init_fn,
train_config.use_tpu)
log_var_assignments(tvars, initialized_variable_names)
TPUEstimatorSpec = tf.compat.v1.estimator.tpu.TPUEstimatorSpec
def metric_fn(probs, label, is_real_example):
cut = math.exp(-10)
pred_binary = probs > cut
label_binary_all = label > cut
pred_binary = pred_binary[:, :, 0]
label_binary_1 = label_binary_all[:, :, 1]
label_binary_0 = label_binary_all[:, :, 0]
precision = tf.compat.v1.metrics.precision(predictions=pred_binary,
labels=label_binary_0)
recall = tf.compat.v1.metrics.recall(predictions=pred_binary,
labels=label_binary_0)
true_rate_1 = tf.compat.v1.metrics.mean(label_binary_1)
true_rate_0 = tf.compat.v1.metrics.mean(label_binary_0)
mae = tf.compat.v1.metrics.mean_absolute_error(
labels=label, predictions=probs, weights=is_real_example)
return {
"mae": mae,
"precision": precision,
"recall": recall,
"true_rate_1": true_rate_1,
"true_rate_0": true_rate_0,
}
output_spec = None
if mode == tf.estimator.ModeKeys.TRAIN:
tvars = None
train_op = optimization.create_optimizer_from_config(
loss, train_config, tvars)
output_spec = TPUEstimatorSpec(mode=mode,
loss=loss,
train_op=train_op,
scaffold_fn=scaffold_fn)
elif mode == tf.estimator.ModeKeys.EVAL:
eval_metrics = (metric_fn, [probs, label_ids, is_real_example])
output_spec = TPUEstimatorSpec(mode=mode,
loss=loss,
eval_metrics=eval_metrics,
scaffold_fn=scaffold_fn)
else:
predictions = {
"input_ids": input_ids,
"logits": logits,
"label_ids": label_ids,
}
if "data_id" in features:
predictions['data_id'] = features['data_id']
output_spec = tf.compat.v1.estimator.tpu.TPUEstimatorSpec(
mode=mode, predictions=predictions, scaffold_fn=scaffold_fn)
return output_spec
def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
"""The `model_fn` for TPUEstimator."""
logging.info("*** Features ***")
for name in sorted(features.keys()):
logging.info(" name = %s, shape = %s" % (name, features[name].shape))
input_ids = features["input_ids"]
input_mask = features["input_mask"]
segment_ids = features["segment_ids"]
next_sentence_labels = features["next_sentence_labels"]
seed = 0
threshold = 1e-2
logging.info("Doing All Masking")
masked_input_ids, masked_lm_positions, masked_lm_ids, masked_lm_weights \
= random_masking(input_ids, input_mask, train_config.max_predictions_per_seq, MASK_ID, seed)
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
prefix1 = "MaybeBERT"
prefix2 = "MaybeNLI"
with tf.compat.v1.variable_scope(prefix1):
model = BertModel(
config=bert_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=train_config.use_one_hot_embeddings,
)
(masked_lm_loss,
masked_lm_example_loss1, masked_lm_log_probs2) = get_masked_lm_output(
bert_config, model.get_sequence_output(), model.get_embedding_table(),
masked_lm_positions, masked_lm_ids, masked_lm_weights)
all_layers1 = model.get_all_encoder_layers()
with tf.compat.v1.variable_scope(prefix2):
model = BertModel(
config=bert_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=train_config.use_one_hot_embeddings,
)
all_layers2 = model.get_all_encoder_layers()
preserved_infos = []
for a_layer, b_layer in zip(all_layers1, all_layers2):
layer_diff = a_layer - b_layer
is_preserved = tf.less(tf.abs(layer_diff), threshold)
preserved_infos.append(is_preserved)
t = tf.cast(preserved_infos[1], dtype=tf.int32) #[batch_size, seq_len, dims]
layer_1_count = tf.reduce_sum(t, axis=2)
tvars = tf.compat.v1.trainable_variables()
initialized_variable_names, init_fn = get_init_fn_for_two_checkpoints(train_config,
tvars,
train_config.init_checkpoint,
prefix1,
train_config.second_init_checkpoint,
prefix2)
scaffold_fn = get_tpu_scaffold_or_init(init_fn, train_config.use_tpu)
log_var_assignments(tvars, initialized_variable_names)
output_spec = None
if mode == tf.estimator.ModeKeys.PREDICT:
predictions = {
"input_ids": input_ids,
"layer_count": layer_1_count
}
output_spec = tf.compat.v1.estimator.tpu.TPUEstimatorSpec(
mode=mode,
loss=None,
predictions=predictions,
scaffold_fn=scaffold_fn)
return output_spec