def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
"""The `model_fn` for TPUEstimator."""
output_weights = tf.compat.v1.get_variable(
"output_weights", [4, 4],
initializer=tf.compat.v1.truncated_normal_initializer(stddev=0.02)
)
loss = tf.reduce_sum(output_weights)
tvars = tf.compat.v1.trainable_variables()
initialized_variable_names = {}
scaffold_fn = None
log_var_assignments(tvars, initialized_variable_names)
TPUEstimatorSpec = tf.compat.v1.estimator.tpu.TPUEstimatorSpec
output_spec = None
if mode == tf.estimator.ModeKeys.TRAIN:
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:
output_spec = NotImplemented
else:
output_spec = tf.compat.v1.estimator.tpu.TPUEstimatorSpec(
mode=mode,
predictions={},
scaffold_fn=scaffold_fn)
return output_spec
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))
label_ids = features["label_ids"]
label_ids = tf.reshape(label_ids, [-1])
if "is_real_example" in features:
is_real_example = tf.cast(features["is_real_example"], dtype=tf.float32)
else:
is_real_example = tf.ones(tf.shape(label_ids), dtype=tf.float32)
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
model = model_class(
config=model_config,
is_training=is_training,
use_one_hot_embeddings=train_config.use_one_hot_embeddings,
features=features,
)
logits = model.get_logits()
loss = model.get_loss(label_ids)
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
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 = (classification_metric_fn, [
logits, label_ids, is_real_example
])
output_spec = TPUEstimatorSpec(mode=mode, loss=loss, eval_metrics=eval_metrics, scaffold_fn=scaffold_fn)
else:
predictions = {
"label_ids": label_ids,
"logits": logits,
}
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
tf_logging.info("model_fn_sero_classification")
"""The `model_fn` for TPUEstimator."""
log_features(features)
input_ids = features["input_ids"]
input_mask = features["input_mask"]
segment_ids = features["segment_ids"]
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
# Updated
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,
)
pooled_output = model.get_pooled_output()
if is_training:
pooled_output = dropout(pooled_output, 0.1)
logits = get_prediction_structure(modeling_opt, pooled_output)
loss = 0
tvars = tf.compat.v1.trainable_variables()
assignment_fn = 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)
predictions = None
if modeling_opt == "multi_label_hinge":
predictions = {
"input_ids":input_ids,
"logits":logits,
}
else:
predictions = {
"input_ids": input_ids,
"logits": logits,
}
useful_inputs = ["data_id", "input_ids2", "data_ids"]
for input_name in useful_inputs:
if input_name in features:
predictions[input_name] = features[input_name]
output_spec = rank_predict_estimator_spec(logits, mode, scaffold_fn, predictions)
return output_spec
def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
tf_logging.info("model_fn_sero_ranking_predict")
"""The `model_fn` for TPUEstimator."""
log_features(features)
input_ids = features["input_ids"]
input_mask = features["input_mask"]
segment_ids = features["segment_ids"]
batch_size, _ = get_shape_list(input_mask)
use_context = tf.ones([batch_size, 1], tf.int32)
stacked_input_ids, stacked_input_mask, stacked_segment_ids, \
= split_and_append_sep(input_ids, input_mask, segment_ids,
config.total_sequence_length, config.window_size, CLS_ID, EOW_ID)
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
# Updated
with tf.compat.v1.variable_scope("sero"):
model = model_class(config, is_training,
train_config.use_one_hot_embeddings)
model.network_stacked(stacked_input_ids, stacked_input_mask,
stacked_segment_ids, use_context)
pooled_output = model.get_pooled_output()
logits = get_prediction_structure(config.loss, pooled_output)
tvars = tf.compat.v1.trainable_variables()
assignment_fn = assignment_map.assignment_map_v2_to_v2
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)
output_spec = rank_predict_estimator_spec(logits, mode, 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 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"]
if "next_sentence_labels" in features:
next_sentence_labels = features["next_sentence_labels"]
else:
next_sentence_labels = get_dummy_next_sentence_labels(input_ids)
if mode == tf.estimator.ModeKeys.PREDICT:
tf.random.set_seed(0)
seed = 0
print("Seed as zero")
else:
seed = None
tf_logging.info("Doing dynamic masking (random)")
special_tokens = [LABEL_UNK, LABEL_0, LABEL_1, LABEL_2]
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,
special_tokens)
masked_input_ids, masked_lm_positions_label, masked_label_ids_label, is_test_inst \
= get_label_indices(masked_input_ids)
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
model = model_class(
config=model_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_fn(
model_config, model.get_sequence_output(),
model.get_embedding_table(), masked_lm_positions, masked_lm_ids,
masked_lm_weights)
with tf.compat.v1.variable_scope("label_token"):
(masked_lm_loss_label, masked_lm_example_loss_label,
masked_lm_log_probs_label) = get_masked_lm_output_fn(
model_config, model.get_sequence_output(),
model.get_embedding_table(), masked_lm_positions_label,
masked_label_ids_label, is_test_inst)
(next_sentence_loss, next_sentence_example_loss,
next_sentence_log_probs) = get_next_sentence_output(
model_config, model.get_pooled_output(), next_sentence_labels)
total_loss = masked_lm_loss + masked_lm_loss_label * model_config.ratio
tvars = tf.compat.v1.trainable_variables()
initialized_variable_names, initialized_variable_names2, init_fn\
= align_checkpoint_for_lm(tvars,
train_config.checkpoint_type,
train_config.init_checkpoint,
train_config.second_init_checkpoint,
)
scaffold_fn = get_tpu_scaffold_or_init(init_fn, train_config.use_tpu)
log_var_assignments(tvars, initialized_variable_names,
initialized_variable_names2)
output_spec = None
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,
training_hooks=[OomReportingHook()],
scaffold_fn=scaffold_fn)
elif mode == tf.estimator.ModeKeys.EVAL:
eval_metrics = (metric_fn, [
masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids,
masked_lm_weights, masked_lm_example_loss_label,
masked_lm_log_probs_label, masked_label_ids_label, is_test_inst
])
output_spec = tf.compat.v1.estimator.tpu.TPUEstimatorSpec(
mode=mode,
loss=total_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,
"masked_lm_example_loss_label": masked_lm_example_loss_label,
"masked_lm_log_probs_label": masked_lm_log_probs_label,
"masked_label_ids_label": masked_label_ids_label,
"is_test_inst": is_test_inst,
}
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
"""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"]
next_sentence_labels = get_dummy_next_sentence_labels(input_ids)
batch_size, seq_length = get_batch_and_seq_length(input_ids, 2)
n_trial = seq_length - 20
masked_input_ids, masked_lm_positions, masked_lm_ids, masked_lm_weights \
= one_by_one_masking(input_ids, input_mask, MASK_ID, n_trial)
num_classes = train_config.num_classes
n_repeat = num_classes * n_trial
# [ num_classes * n_trial * batch_size, seq_length]
repeat_masked_input_ids = tf.tile(masked_input_ids, [num_classes, 1])
repeat_input_mask = tf.tile(input_mask, [n_repeat, 1])
repeat_segment_ids = tf.tile(segment_ids, [n_repeat, 1])
masked_lm_positions = tf.tile(masked_lm_positions, [num_classes, 1])
masked_lm_ids = tf.tile(masked_lm_ids, [num_classes, 1])
masked_lm_weights = tf.tile(masked_lm_weights, [num_classes, 1])
next_sentence_labels = tf.tile(next_sentence_labels, [n_repeat, 1])
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
virtual_labels_ids = tf.tile(tf.expand_dims(tf.range(num_classes), 0),
[1, batch_size * n_trial])
virtual_labels_ids = tf.reshape(virtual_labels_ids, [-1, 1])
print("repeat_masked_input_ids", repeat_masked_input_ids.shape)
print("repeat_input_mask", repeat_input_mask.shape)
print("virtual_labels_ids", virtual_labels_ids.shape)
model = BertModelWithLabelInner(
config=model_config,
is_training=is_training,
input_ids=repeat_masked_input_ids,
input_mask=repeat_input_mask,
token_type_ids=repeat_segment_ids,
use_one_hot_embeddings=train_config.use_one_hot_embeddings,
label_ids=virtual_labels_ids,
)
(masked_lm_loss, masked_lm_example_loss,
masked_lm_log_probs) = get_masked_lm_output_fn(
model_config, model.get_sequence_output(),
model.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(
model_config, model.get_pooled_output(), next_sentence_labels)
total_loss = masked_lm_loss
# loss = -log(prob)
# TODO compare log prob of each label
per_case_loss = tf.reshape(masked_lm_example_loss,
[num_classes, -1, batch_size])
per_label_loss = tf.reduce_sum(per_case_loss, axis=1)
bias = tf.zeros([3, 1])
per_label_score = tf.transpose(-per_label_loss + bias, [1, 0])
tvars = tf.compat.v1.trainable_variables()
initialized_variable_names, initialized_variable_names2, init_fn\
= align_checkpoint_for_lm(tvars,
train_config.checkpoint_type,
train_config.init_checkpoint,
train_config.second_init_checkpoint,
)
scaffold_fn = get_tpu_scaffold_or_init(init_fn, train_config.use_tpu)
log_var_assignments(tvars, initialized_variable_names,
initialized_variable_names2)
output_spec = None
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,
training_hooks=[OomReportingHook()],
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 = tf.compat.v1.estimator.tpu.TPUEstimatorSpec(
mode=mode,
loss=total_loss,
eval_metrics=eval_metrics,
scaffold_fn=scaffold_fn)
else:
predictions = {"input_ids": input_ids, "logits": per_label_score}
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)
input_ids = features["input_ids"]
input_mask = features["input_mask"]
segment_ids = features["segment_ids"]
if mode == tf.estimator.ModeKeys.PREDICT:
tf.random.set_seed(0)
seed = 0
else:
seed = None
tf_logging.info("Doing dynamic masking (random)")
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)
tf_logging.info("Using masked_input_ids")
model = APR(
masked_input_ids,
input_mask,
segment_ids,
is_training,
train_config.use_one_hot_embeddings,
bert_config,
ssdr_config,
dict_run_config.def_per_batch,
dict_run_config.inner_batch_size,
dict_run_config.max_def_length,
# MainTransformer,
# SecondTransformerEmbeddingLess,
)
masked_lm_loss, masked_lm_example_loss, masked_lm_log_probs \
= get_masked_lm_output(bert_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 = dict_model_fn.get_bert_assignment_map_for_dict
initialized_variable_names, init_fn = align_checkpoint_twice(
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:
if ssdr_config.compare_attrib_value_safe("use_two_lr", True):
tf_logging.info("Using two lr for each parts")
train_op = create_optimizer_with_separate_lr(
loss, train_config)
else:
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,
training_hooks=[OomReportingHook()],
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."""
tf_logging.info("*** Features ***")
for name in sorted(features.keys()):
tf_logging.info(" name = %s, shape = %s" %
(name, features[name].shape))
q_input_ids_1 = features["q_input_ids_1"]
q_input_mask_1 = features["q_input_mask_1"]
d_input_ids_1 = features["d_input_ids_1"]
d_input_mask_1 = features["d_input_mask_1"]
q_input_ids_2 = features["q_input_ids_2"]
q_input_mask_2 = features["q_input_mask_2"]
d_input_ids_2 = features["d_input_ids_2"]
d_input_mask_2 = features["d_input_mask_2"]
q_input_ids = tf.stack([q_input_ids_1, q_input_ids_2], axis=0)
q_input_mask = tf.stack([q_input_mask_1, q_input_mask_2], axis=0)
q_segment_ids = tf.zeros_like(q_input_ids, tf.int32)
d_input_ids = tf.stack([d_input_ids_1, d_input_ids_2], axis=0)
d_input_mask = tf.stack([d_input_mask_1, d_input_mask_2], axis=0)
d_segment_ids = tf.zeros_like(d_input_ids, tf.int32)
label_ids = features["label_ids"]
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
if "is_real_example" in features:
is_real_example = tf.cast(features["is_real_example"],
dtype=tf.float32)
else:
is_real_example = tf.ones(tf.shape(label_ids), dtype=tf.float32)
with tf.compat.v1.variable_scope("query"):
model_q = model_class(
config=model_config,
is_training=is_training,
input_ids=q_input_ids,
input_mask=q_input_mask,
token_type_ids=q_segment_ids,
use_one_hot_embeddings=train_config.use_one_hot_embeddings,
)
with tf.compat.v1.variable_scope("document"):
model_d = model_class(
config=model_config,
is_training=is_training,
input_ids=d_input_ids,
input_mask=d_input_mask,
token_type_ids=d_segment_ids,
use_one_hot_embeddings=train_config.use_one_hot_embeddings,
)
pooled_q = model_q.get_pooled_output()
pooled_d = model_d.get_pooled_output()
logits = tf.matmul(pooled_q, pooled_d, transpose_b=True)
y = tf.cast(label_ids, tf.float32) * 2 - 1
losses = tf.maximum(1.0 - logits * y, 0)
loss = tf.reduce_mean(losses)
pred = tf.cast(logits > 0, tf.int32)
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
output_spec = None
if mode == tf.estimator.ModeKeys.TRAIN:
if "simple_optimizer" in special_flags:
tf_logging.info("using simple optimizer")
train_op = create_simple_optimizer(loss,
train_config.learning_rate,
train_config.use_tpu)
else:
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 = (classification_metric_fn,
[pred, label_ids, is_real_example])
output_spec = TPUEstimatorSpec(mode=mode,
loss=loss,
eval_metrics=eval_metrics,
scaffold_fn=scaffold_fn)
else:
predictions = {
"q_input_ids": q_input_ids,
"d_input_ids": d_input_ids,
"score": logits
}
useful_inputs = ["data_id", "input_ids2", "data_ids"]
for input_name in useful_inputs:
if input_name in features:
predictions[input_name] = features[input_name]
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."""
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"]
if mode == tf.estimator.ModeKeys.PREDICT:
label_ids = tf.ones([input_ids.shape[0]], dtype=tf.int32)
else:
label_ids = features["label_ids"]
label_ids = tf.reshape(label_ids, [-1])
if "is_real_example" in features:
is_real_example = tf.cast(features["is_real_example"],
dtype=tf.float32)
else:
is_real_example = tf.ones(tf.shape(label_ids), dtype=tf.float32)
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
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=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,
)
pooled = model_1.get_pooled_output()
if is_training:
pooled = dropout(pooled, 0.1)
logits = tf.keras.layers.Dense(train_config.num_classes,
name="cls_dense")(pooled)
with tf.compat.v1.variable_scope(dual_model_prefix2):
model_2 = BertModel(
config=model_config,
is_training=is_training,
input_ids=input_ids2,
input_mask=input_mask2,
token_type_ids=segment_ids2,
use_one_hot_embeddings=train_config.use_one_hot_embeddings,
)
pooled = model_2.get_pooled_output()
if is_training:
pooled = dropout(pooled, 0.1)
conf_probs = tf.keras.layers.Dense(
train_config.num_classes,
name="cls_dense",
activation=tf.keras.activations.softmax)(pooled)
confidence = conf_probs[:, 1]
confidence_loss = 1 - confidence
cls_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=label_ids)
k = model_config.k
alpha = model_config.alpha
loss_arr = cls_loss * confidence + confidence_loss * k
loss_arr = apply_weighted_loss(loss_arr, label_ids, alpha)
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(log_probs, label, is_real_example, confidence):
r = classification_metric_fn(log_probs, label, is_real_example)
r['confidence'] = tf.compat.v1.metrics.mean(confidence)
return r
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,
[logits, label_ids, is_real_example, confidence])
output_spec = TPUEstimatorSpec(mode=mode,
loss=loss,
eval_metrics=eval_metrics,
scaffold_fn=scaffold_fn)
else:
predictions = {
"input_ids": input_ids,
"logits": logits,
"confidence": confidence,
}
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
tf_logging.info("model_fn_nli_lm")
"""The `model_fn` for TPUEstimator."""
log_features(features)
input_ids = features["input_ids"] # [batch_size, seq_length]
input_mask = features["input_mask"]
segment_ids = features["segment_ids"]
batch_size, _ = get_shape_list2(input_ids)
if "nli_input_ids" in features:
nli_input_ids = features[
"nli_input_ids"] # [batch_size, seq_length]
nli_input_mask = features["nli_input_mask"]
nli_segment_ids = features["nli_segment_ids"]
else:
nli_input_ids = input_ids
nli_input_mask = input_mask
nli_segment_ids = segment_ids
features["label_ids"] = tf.ones([batch_size], tf.int32)
if mode == tf.estimator.ModeKeys.PREDICT:
tf.random.set_seed(0)
seed = 0
else:
seed = None
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
tf_logging.info("Doing dynamic masking (random)")
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)
sharing_model = sharing_model_factory(
config, train_config.use_one_hot_embeddings, is_training,
masked_input_ids, input_mask, segment_ids, nli_input_ids,
nli_input_mask, nli_segment_ids)
sequence_output_lm = sharing_model.lm_sequence_output()
nli_feature = sharing_model.get_tt_feature()
masked_lm_loss, masked_lm_example_loss, masked_lm_log_probs \
= get_masked_lm_output(config, sequence_output_lm, sharing_model.get_embedding_table(),
masked_lm_positions, masked_lm_ids, masked_lm_weights)
masked_lm_log_probs = tf.reshape(masked_lm_log_probs, [batch_size, -1])
top_guess = masked_lm_log_probs
task = Classification(3, features, nli_feature, is_training)
nli_loss = task.loss
overlap_score = shared_gradient_fine_grained(
masked_lm_example_loss, task.logits,
train_config.max_predictions_per_seq)
loss = combine_loss_fn(masked_lm_loss, nli_loss)
tvars = tf.compat.v1.trainable_variables()
assignment_fn = 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:
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,
"masked_lm_log_probs": masked_lm_log_probs,
"overlap_score": overlap_score,
"top_guess": top_guess,
}
output_spec = 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."""
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"]
label_ids = tf.reshape(label_ids, [-1])
if "is_real_example" in features:
is_real_example = tf.cast(features["is_real_example"], dtype=tf.float32)
else:
is_real_example = tf.ones(tf.shape(label_ids), dtype=tf.float32)
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
model = model_class(
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,
features=features,
)
probs = model.get_prob()
logits = probs
epsilon = 1e-12
# probs_ad = tf.clip_by_value(probs, epsilon, 1.0 - epsilon)
# logits1 = tf.math.log(probs_ad)
# logits0 = tf.zeros_like(logits1)
# logits = tf.stack([logits0, logits1], axis=1)
# prob2 = tf.nn.softmax(logits, axis=1)
# prob_err = prob2[:, 1] - probs
y_true = tf.cast(label_ids, tf.float32)
loss_arr = tf.keras.losses.BinaryCrossentropy()(y_true, probs)
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
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 = (classification_metric_fn, [
logits, 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,
"label_ids": label_ids,
"logits": logits,
"score1": model.score1,
"score2": model.score2,
# "prob_err": prob_err,
}
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
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
"""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))
vectors = features["vectors"] # [batch_size, max_unit, num_hidden]
valid_mask = features["valid_mask"]
label_ids = features["label_ids"]
vectors = tf.reshape(vectors, [
-1, model_config.num_window, model_config.max_sequence,
model_config.hidden_size
])
valid_mask = tf.reshape(
valid_mask,
[-1, model_config.num_window, model_config.max_sequence])
label_ids = tf.reshape(label_ids, [-1])
if "is_real_example" in features:
is_real_example = tf.cast(features["is_real_example"],
dtype=tf.float32)
else:
is_real_example = tf.ones(tf.shape(label_ids), dtype=tf.float32)
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
model = MultiEvidenceCombiner(config=model_config,
is_training=is_training,
vectors=vectors,
valid_mask=valid_mask,
scope=None)
pooled = model.pooled_output
if is_training:
pooled = dropout(pooled, 0.1)
logits = tf.keras.layers.Dense(config.num_classes,
name="cls_dense")(pooled)
loss_arr = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=label_ids)
if "bias_loss" in special_flags:
tf_logging.info("Using special_flags : bias_loss")
loss_arr = reweight_zero(label_ids, loss_arr)
loss = tf.reduce_mean(input_tensor=loss_arr)
tvars = tf.compat.v1.trainable_variables()
initialized_variable_names = {}
scaffold_fn = None
if config.init_checkpoint:
initialized_variable_names, init_fn = get_init_fn(config, tvars)
scaffold_fn = get_tpu_scaffold_or_init(init_fn, config.use_tpu)
log_var_assignments(tvars, initialized_variable_names)
TPUEstimatorSpec = tf.compat.v1.estimator.tpu.TPUEstimatorSpec
output_spec = None
if mode == tf.estimator.ModeKeys.TRAIN:
if "simple_optimizer" in special_flags:
tf_logging.info("using simple optimizer")
train_op = create_simple_optimizer(loss, config.learning_rate,
config.use_tpu)
else:
if "ask_tvar" in special_flags:
tvars = model.get_trainable_vars()
else:
tvars = None
train_op = optimization.create_optimizer_from_config(
loss, 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 = (classification_metric_fn,
[logits, label_ids, is_real_example])
output_spec = TPUEstimatorSpec(mode=model,
loss=loss,
eval_metrics=eval_metrics,
scaffold_fn=scaffold_fn)
else:
predictions = {"logits": logits, "label_ids": label_ids}
if override_prediction_fn is not None:
predictions = override_prediction_fn(predictions, model)
useful_inputs = ["data_id", "input_ids2"]
for input_name in useful_inputs:
if input_name in features:
predictions[input_name] = features[input_name]
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."""
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"]
label_ids = tf.reshape(label_ids, [-1])
if "is_real_example" in features:
is_real_example = tf.cast(features["is_real_example"],
dtype=tf.float32)
else:
is_real_example = tf.ones(tf.shape(label_ids), dtype=tf.float32)
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
if "feed_features" in special_flags:
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,
features=features,
)
else:
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,
)
if "new_pooling" in special_flags:
pooled = mimic_pooling(model.get_sequence_output(),
bert_config.hidden_size,
bert_config.initializer_range)
else:
pooled = model.get_pooled_output()
if train_config.checkpoint_type != "bert_nli" and train_config.use_old_logits:
tf_logging.info("Use old version of logistic regression")
logits = tf.keras.layers.Dense(train_config.num_classes,
name="cls_dense")(pooled)
else:
tf_logging.info("Use fixed version of logistic regression")
output_weights = tf.compat.v1.get_variable(
"output_weights", [3, bert_config.hidden_size],
initializer=tf.compat.v1.truncated_normal_initializer(
stddev=0.02))
output_bias = tf.compat.v1.get_variable(
"output_bias", [3],
initializer=tf.compat.v1.zeros_initializer())
if is_training:
pooled = dropout(pooled, 0.1)
logits = tf.matmul(pooled, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
loss_arr = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=label_ids)
if "bias_loss" in special_flags:
tf_logging.info("Using special_flags : bias_loss")
loss_arr = reweight_zero(label_ids, loss_arr)
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
output_spec = None
if mode == tf.estimator.ModeKeys.TRAIN:
if "simple_optimizer" in special_flags:
tf_logging.info("using simple optimizer")
train_op = create_simple_optimizer(loss,
train_config.learning_rate,
train_config.use_tpu)
else:
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 = (classification_metric_fn,
[logits, label_ids, is_real_example])
output_spec = TPUEstimatorSpec(mode=model,
loss=loss,
eval_metrics=eval_metrics,
scaffold_fn=scaffold_fn)
else:
probs = tf.nn.softmax(logits, axis=-1)
gradient_list = tf.gradients(probs[:, 1], model.embedding_output)
print(len(gradient_list))
gradient = gradient_list[0]
print(gradient.shape)
gradient = tf.reduce_sum(gradient, axis=2)
predictions = {
"input_ids": input_ids,
"gradient": gradient,
"labels": label_ids,
"logits": logits
}
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."""
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"]
label_ids = tf.reshape(label_ids, [-1])
if "is_real_example" in features:
is_real_example = tf.cast(features["is_real_example"],
dtype=tf.float32)
else:
is_real_example = tf.ones(tf.shape(label_ids), dtype=tf.float32)
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
model_1 = 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,
)
pooled = model_1.get_pooled_output()
if is_training:
pooled = dropout(pooled, 0.1)
logits = tf.keras.layers.Dense(train_config.num_classes,
name="cls_dense")(pooled)
loss_arr = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=label_ids)
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
global_step = tf.compat.v1.train.get_or_create_global_step()
init_lr = train_config.learning_rate
num_warmup_steps = train_config.num_warmup_steps
num_train_steps = train_config.num_train_steps
learning_rate2_const = tf.constant(value=init_lr,
shape=[],
dtype=tf.float32)
learning_rate2_decayed = tf.compat.v1.train.polynomial_decay(
learning_rate2_const,
global_step,
num_train_steps,
end_learning_rate=0.0,
power=1.0,
cycle=False)
if num_warmup_steps:
global_steps_int = tf.cast(global_step, tf.int32)
warmup_steps_int = tf.constant(num_warmup_steps, dtype=tf.int32)
global_steps_float = tf.cast(global_steps_int, tf.float32)
warmup_steps_float = tf.cast(warmup_steps_int, tf.float32)
warmup_percent_done = global_steps_float / warmup_steps_float
warmup_learning_rate = init_lr * warmup_percent_done
is_warmup = tf.cast(global_steps_int < warmup_steps_int,
tf.float32)
learning_rate = ((1.0 - is_warmup) * learning_rate2_decayed +
is_warmup * warmup_learning_rate)
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 = (classification_metric_fn,
[logits, label_ids, is_real_example])
output_spec = TPUEstimatorSpec(mode=mode,
loss=loss,
eval_metrics=eval_metrics,
scaffold_fn=scaffold_fn)
else:
def reform_scala(t):
return tf.reshape(t, [1])
predictions = {
"input_ids": input_ids,
"label_ids": label_ids,
"logits": logits,
"learning_rate2_const": reform_scala(learning_rate2_const),
"warmup_percent_done": reform_scala(warmup_percent_done),
"warmup_learning_rate": reform_scala(warmup_learning_rate),
"learning_rate": reform_scala(learning_rate),
"learning_rate2_decayed": reform_scala(learning_rate2_decayed),
}
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
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
tf_logging.info("model_fn_nli_lm")
"""The `model_fn` for TPUEstimator."""
log_features(features)
input_ids = features["input_ids"] # [batch_size, seq_length]
input_mask = features["input_mask"]
segment_ids = features["segment_ids"]
batch_size, seq_max = get_shape_list2(input_ids)
if "nli_input_ids" in features:
nli_input_ids = features[
"nli_input_ids"] # [batch_size, seq_length]
nli_input_mask = features["nli_input_mask"]
nli_segment_ids = features["nli_segment_ids"]
else:
nli_input_ids = input_ids
nli_input_mask = input_mask
nli_segment_ids = segment_ids
features["label_ids"] = tf.ones([batch_size], tf.int32)
if mode == tf.estimator.ModeKeys.PREDICT:
tf.random.set_seed(0)
seed = 0
else:
seed = None
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
tf_logging.info("Doing dynamic masking (random)")
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)
sharing_model = sharing_model_factory(
config, train_config.use_one_hot_embeddings, is_training,
masked_input_ids, input_mask, segment_ids, nli_input_ids,
nli_input_mask, nli_segment_ids)
sequence_output_lm = sharing_model.lm_sequence_output()
nli_feature = sharing_model.get_tt_feature()
masked_lm_loss, masked_lm_example_loss, masked_lm_log_probs \
= get_masked_lm_output(config, sequence_output_lm, sharing_model.get_embedding_table(),
masked_lm_positions, masked_lm_ids, masked_lm_weights)
masked_lm_log_probs = tf.reshape(masked_lm_log_probs, [batch_size, -1])
masked_lm_per_inst_loss = tf.reshape(masked_lm_example_loss,
[batch_size, -1])
task = Classification(3, features, nli_feature, is_training)
nli_loss = task.loss
task_prob = tf.nn.softmax(task.logits, axis=-1)
arg_like = task_prob[:, 1] + task_prob[:, 2]
vars = sharing_model.model.all_layer_outputs
grads_1 = tf.gradients(ys=masked_lm_loss, xs=vars) # List[ batch_szie,
grads_2 = tf.gradients(ys=arg_like, xs=vars)
l = []
for g1, g2 in zip(grads_1, grads_2):
if g1 is not None and g2 is not None:
a = tf.reshape(g1, [batch_size * 2, seq_max, -1])[:batch_size]
a = a / masked_lm_per_inst_loss
b = tf.reshape(g2, [batch_size * 2, seq_max, -1])[batch_size:]
l.append(tf.abs(a * b))
h_overlap = tf.stack(l, axis=1)
h_overlap = tf.reduce_sum(h_overlap, axis=2)
loss = combine_loss_fn(masked_lm_loss, nli_loss)
tvars = tf.compat.v1.trainable_variables()
assignment_fn = 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:
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,
"masked_lm_log_probs": masked_lm_log_probs,
"h_overlap": h_overlap,
}
output_spec = TPUEstimatorSpec(mode=mode,
predictions=predictions,
scaffold_fn=scaffold_fn)
return output_spec
def checkpoint_init(assignment_fn, train_config):
tvars = tf.compat.v1.trainable_variables()
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)
return scaffold_fn
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."""
tf_logging.info("*** Features ***")
for name in sorted(features.keys()):
tf_logging.info(" name = %s, shape = %s" %
(name, features[name].shape))
q_input_ids = features["q_input_ids"]
q_input_mask = features["q_input_mask"]
d_input_ids = features["d_input_ids"]
d_input_mask = features["d_input_mask"]
input_shape = get_shape_list(q_input_ids, expected_rank=2)
batch_size = input_shape[0]
doc_length = model_config.max_doc_length
num_docs = model_config.num_docs
d_input_ids_unpacked = tf.reshape(d_input_ids,
[-1, num_docs, doc_length])
d_input_mask_unpacked = tf.reshape(d_input_mask,
[-1, num_docs, doc_length])
d_input_ids_flat = tf.reshape(d_input_ids_unpacked, [-1, doc_length])
d_input_mask_flat = tf.reshape(d_input_mask_unpacked, [-1, doc_length])
q_segment_ids = tf.zeros_like(q_input_ids, tf.int32)
d_segment_ids = tf.zeros_like(d_input_ids_flat, tf.int32)
label_ids = features["label_ids"]
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
if "is_real_example" in features:
is_real_example = tf.cast(features["is_real_example"],
dtype=tf.float32)
else:
is_real_example = tf.ones(tf.shape(label_ids), dtype=tf.float32)
with tf.compat.v1.variable_scope(dual_model_prefix1):
q_model_config = copy.deepcopy(model_config)
q_model_config.max_seq_length = model_config.max_sent_length
model_q = model_class(
config=model_config,
is_training=is_training,
input_ids=q_input_ids,
input_mask=q_input_mask,
token_type_ids=q_segment_ids,
use_one_hot_embeddings=train_config.use_one_hot_embeddings,
)
with tf.compat.v1.variable_scope(dual_model_prefix2):
d_model_config = copy.deepcopy(model_config)
d_model_config.max_seq_length = model_config.max_doc_length
model_d = model_class(
config=model_config,
is_training=is_training,
input_ids=d_input_ids_flat,
input_mask=d_input_mask_flat,
token_type_ids=d_segment_ids,
use_one_hot_embeddings=train_config.use_one_hot_embeddings,
)
pooled_q = model_q.get_pooled_output() # [batch, vector_size]
pooled_d_flat = model_d.get_pooled_output(
) # [batch, num_window, vector_size]
pooled_d = tf.reshape(pooled_d_flat, [batch_size, num_docs, -1])
pooled_q_t = tf.expand_dims(pooled_q, 1)
pooled_d_t = tf.transpose(pooled_d, [0, 2, 1])
all_logits = tf.matmul(pooled_q_t,
pooled_d_t) # [batch, 1, num_window]
if "hinge_all" in special_flags:
apply_loss_modeing = hinge_all
elif "sigmoid_all" in special_flags:
apply_loss_modeing = sigmoid_all
else:
apply_loss_modeing = hinge_max
logits, loss = apply_loss_modeing(all_logits, label_ids)
pred = tf.cast(logits > 0, tf.int32)
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
output_spec = None
if mode == tf.estimator.ModeKeys.TRAIN:
if "simple_optimizer" in special_flags:
tf_logging.info("using simple optimizer")
train_op = create_simple_optimizer(loss,
train_config.learning_rate,
train_config.use_tpu)
else:
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 = (classification_metric_fn,
[pred, label_ids, is_real_example])
output_spec = TPUEstimatorSpec(mode=mode,
loss=loss,
eval_metrics=eval_metrics,
scaffold_fn=scaffold_fn)
else:
predictions = {
"q_input_ids": q_input_ids,
"d_input_ids": d_input_ids,
"logits": logits
}
useful_inputs = ["data_id", "input_ids2", "data_ids"]
for input_name in useful_inputs:
if input_name in features:
predictions[input_name] = features[input_name]
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."""
log_features(features)
def reform_a_input(raw_input):
return tf.reshape(raw_input,
[dict_run_config.inner_batch_size, -1])
def reform_b_input(raw_input):
return tf.reshape(raw_input, [dict_run_config.def_per_batch, -1])
input_ids = reform_a_input(features["input_ids"])
input_mask = reform_a_input(features["input_mask"])
segment_ids = reform_a_input(features["segment_ids"])
tf_logging.info("input_ids, input_mask")
# input_ids = features["input_ids"]
# input_mask = features["input_mask"]
# segment_ids = features["segment_ids"]
if mode == tf.estimator.ModeKeys.PREDICT:
tf.random.set_seed(0)
seed = 0
else:
seed = None
# tf_logging.info("Doing dynamic masking (random)")
# 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)
# if dict_run_config.prediction_op == "loss_fixed_mask" or train_config.fixed_mask:
masked_input_ids = input_ids
masked_lm_positions = reform_a_input(features["masked_lm_positions"])
masked_lm_ids = reform_a_input(features["masked_lm_ids"])
masked_lm_weights = reform_a_input(features["masked_lm_weights"])
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
if model_name == "APR":
model = APR(
masked_input_ids,
input_mask,
segment_ids,
is_training,
train_config.use_one_hot_embeddings,
bert_config,
ssdr_config,
dict_run_config.def_per_batch,
dict_run_config.inner_batch_size,
dict_run_config.max_def_length,
)
elif model_name == "BERT":
model = 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,
)
else:
assert False
masked_lm_loss, masked_lm_example_loss, masked_lm_log_probs \
= get_masked_lm_output(bert_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 = dict_model_fn.get_bert_assignment_map_for_dict
initialized_variable_names, init_fn = align_checkpoint_twice(
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:
if ssdr_config.compare_attrib_value_safe("use_two_lr", True):
tf_logging.info("Using two lr for each parts")
train_op = create_optimizer_with_separate_lr(
loss, train_config)
else:
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,
training_hooks=[OomReportingHook()],
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."""
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"]
instance_id = features["instance_id"]
next_sentence_labels = get_dummy_next_sentence_labels(input_ids)
tf_logging.info("Doing dynamic masking (random)")
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)
model = model_class(
config=model_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(
model_config, model.get_sequence_output(),
model.get_embedding_table(), masked_lm_positions, masked_lm_ids,
masked_lm_weights)
total_loss = masked_lm_loss
tvars = tf.compat.v1.trainable_variables()
use_multiple_checkpoint = is_multiple_checkpoint(
train_config.checkpoint_type)
initialized_variable_names, initialized_variable_names2, init_fn\
= align_checkpoint_for_lm(tvars,
train_config.checkpoint_type,
train_config.init_checkpoint,
train_config.second_init_checkpoint,
use_multiple_checkpoint)
scaffold_fn = get_tpu_scaffold_or_init(init_fn, train_config.use_tpu)
log_var_assignments(tvars, initialized_variable_names,
initialized_variable_names2)
output_spec = None
if mode == tf.estimator.ModeKeys.PREDICT:
predictions = {
"input_ids": input_ids,
"masked_lm_example_loss": masked_lm_example_loss,
"instance_id": instance_id
}
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
"""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"]
if mode == tf.estimator.ModeKeys.PREDICT:
label_ids = tf.ones([input_ids.shape[0]], dtype=tf.int32)
else:
label_ids = features["label_ids"]
label_ids = tf.reshape(label_ids, [-1])
if "is_real_example" in features:
is_real_example = tf.cast(features["is_real_example"],
dtype=tf.float32)
else:
is_real_example = tf.ones(tf.shape(label_ids), dtype=tf.float32)
domain_ids = features["domain_ids"]
domain_ids = tf.reshape(domain_ids, [-1])
is_valid_label = features["is_valid_label"]
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
model_1 = 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,
)
pooled = model_1.get_pooled_output()
if is_training:
pooled = dropout(pooled, 0.1)
logits = tf.keras.layers.Dense(train_config.num_classes,
name="cls_dense")(pooled)
pred_losses = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=label_ids)
num_domain = 2
pooled_for_domain = grad_reverse(pooled)
domain_logits = tf.keras.layers.Dense(
num_domain, name="domain_dense")(pooled_for_domain)
domain_losses = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=domain_logits, labels=domain_ids)
pred_loss = tf.reduce_mean(pred_losses *
tf.cast(is_valid_label, tf.float32))
domain_loss = tf.reduce_mean(domain_losses)
tf.compat.v1.summary.scalar('domain_loss', domain_loss)
tf.compat.v1.summary.scalar('pred_loss', pred_loss)
alpha = model_config.alpha
loss = pred_loss + alpha * domain_loss
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
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 = (classification_metric_fn,
[logits, 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,
}
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."""
log_features(features)
input_ids = features["input_ids"]
input_mask = features["input_mask"]
segment_ids = features["segment_ids"]
#
#
# d_input_ids, d_input_mask, d_segment_ids, d_location_ids, ab_mapping, ab_mapping_mask \
# = get_dummy_apr_input(input_ids, input_mask,
# dict_run_config.def_per_batch,
# dict_run_config.inner_batch_size,
# ssdr_config.max_loc_length,
# dict_run_config.max_def_length)
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
model = APR(input_ids, input_mask, segment_ids, is_training,
train_config.use_one_hot_embeddings, bert_config,
ssdr_config, dict_run_config.def_per_batch,
dict_run_config.inner_batch_size,
dict_run_config.max_def_length)
#
# model = model_class(
# config=bert_config,
# ssdr_config=ssdr_config,
# is_training=is_training,
# input_ids=input_ids,
# input_mask=input_mask,
# token_type_ids=segment_ids,
# d_input_ids=d_input_ids,
# d_input_mask=d_input_mask,
# d_segment_ids=d_segment_ids,
# d_location_ids=d_location_ids,
# ab_mapping=ab_mapping,
# ab_mapping_mask=ab_mapping_mask,
# use_one_hot_embeddings=train_config.use_one_hot_embeddings,
# )
task = Classification(3, features, model.get_pooled_output(),
is_training)
loss = task.loss
tvars = tf.compat.v1.trainable_variables()
assignment_fn = tlm.training.assignment_map.get_assignment_map_as_is
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)
output_spec = None
TPUEstimatorSpec = tf.compat.v1.estimator.tpu.TPUEstimatorSpec
if mode == tf.estimator.ModeKeys.TRAIN:
if ssdr_config.compare_attrib_value_safe("use_two_lr", True):
tf_logging.info("Using two lr for each parts")
train_op = create_optimizer_with_separate_lr(
loss, train_config)
else:
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:
output_spec = TPUEstimatorSpec(mode=model,
loss=loss,
eval_metrics=task.eval_metrics(),
scaffold_fn=scaffold_fn)
elif mode == tf.estimator.ModeKeys.PREDICT:
output_spec = TPUEstimatorSpec(mode=model,
loss=loss,
predictions={"loss": task.loss_arr},
scaffold_fn=scaffold_fn)
return output_spec
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."""
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"]
if mode == tf.estimator.ModeKeys.PREDICT:
label_ids = tf.ones([input_ids.shape[0]], dtype=tf.float32)
else:
label_ids = features["label_ids"]
label_ids = tf.reshape(label_ids, [-1])
if "is_real_example" in features:
is_real_example = tf.cast(features["is_real_example"], dtype=tf.float32)
else:
is_real_example = tf.ones(tf.shape(label_ids), dtype=tf.float32)
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,
)
pooled = model.get_pooled_output()
if is_training:
pooled = dropout(pooled, 0.1)
logits = tf.keras.layers.Dense(train_config.num_classes, name="cls_dense")(pooled)
scale = model_config.scale
label_ids = scale * label_ids
weight = tf.abs(label_ids)
loss_arr = tf.keras.losses.MAE(y_true=label_ids, y_pred=logits)
loss_arr = loss_arr * weight
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(logits, label, is_real_example):
mae = tf.compat.v1.metrics.mean_absolute_error(
labels=label, predictions=logits, weights=is_real_example)
return {
"mae": mae
}
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, [
logits, 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,
}
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
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"]
if "next_sentence_labels" in features:
next_sentence_labels = features["next_sentence_labels"]
else:
next_sentence_labels = get_dummy_next_sentence_labels(input_ids)
tlm_prefix = "target_task"
with tf.compat.v1.variable_scope(tlm_prefix):
priority_score = tf.stop_gradient(priority_model(features))
priority_score = priority_score * target_model_config.amp
masked_input_ids, masked_lm_positions, masked_lm_ids, masked_lm_weights\
= biased_masking(input_ids,
input_mask,
priority_score,
target_model_config.alpha,
train_config.max_predictions_per_seq,
MASK_ID)
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
model = model_class(
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_loss,
masked_lm_log_probs) = get_masked_lm_output(
bert_config, model.get_sequence_output(),
model.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.get_pooled_output(), next_sentence_labels)
total_loss = masked_lm_loss + next_sentence_loss
all_vars = tf.compat.v1.all_variables()
tf_logging.info("We assume priority model is from v2")
if train_config.checkpoint_type == "v2":
assignment_map, initialized_variable_names = assignment_map_v2_to_v2(
all_vars, train_config.init_checkpoint)
assignment_map2, initialized_variable_names2 = get_assignment_map_remap_from_v2(
all_vars, tlm_prefix, train_config.second_init_checkpoint)
else:
assignment_map, assignment_map2, initialized_variable_names \
= get_tlm_assignment_map_v2(all_vars,
tlm_prefix,
train_config.init_checkpoint,
train_config.second_init_checkpoint)
initialized_variable_names2 = None
def init_fn():
if train_config.init_checkpoint:
tf.compat.v1.train.init_from_checkpoint(
train_config.init_checkpoint, assignment_map)
if train_config.second_init_checkpoint:
tf.compat.v1.train.init_from_checkpoint(
train_config.second_init_checkpoint, assignment_map2)
scaffold_fn = get_tpu_scaffold_or_init(init_fn, train_config.use_tpu)
tvars = [v for v in all_vars if not v.name.startswith(tlm_prefix)]
log_var_assignments(tvars, initialized_variable_names,
initialized_variable_names2)
output_spec = None
if mode == tf.estimator.ModeKeys.TRAIN:
train_op = optimization.create_optimizer_from_config(
total_loss, train_config, tvars)
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:
eval_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
])
output_spec = tf.compat.v1.estimator.tpu.TPUEstimatorSpec(
mode=mode,
loss=total_loss,
eval_metrics=eval_metrics,
scaffold_fn=scaffold_fn)
else:
predictions = {
"input_ids": input_ids,
"masked_input_ids": masked_input_ids,
"priority_score": priority_score,
"lm_loss1": features["loss1"],
"lm_loss2": features["loss2"],
}
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
"""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))
query = features["query"]
doc = features["doc"]
doc_mask = features["doc_mask"]
data_ids = features["data_id"]
segment_len = max_seq_length - query_len - 3
step_size = model_config.step_size
input_ids, input_mask, segment_ids, n_segments = \
iterate_over(query, doc, doc_mask, total_doc_len, segment_len, step_size)
if mode == tf.estimator.ModeKeys.PREDICT:
label_ids = tf.ones([input_ids.shape[0]], dtype=tf.int32)
else:
label_ids = features["label_ids"]
label_ids = tf.reshape(label_ids, [-1])
if "is_real_example" in features:
is_real_example = tf.cast(features["is_real_example"],
dtype=tf.float32)
else:
is_real_example = tf.ones(tf.shape(label_ids), dtype=tf.float32)
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
if "feed_features" in special_flags:
model = model_class(
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,
features=features,
)
else:
model = model_class(
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,
)
if "new_pooling" in special_flags:
pooled = mimic_pooling(model.get_sequence_output(),
model_config.hidden_size,
model_config.initializer_range)
else:
pooled = model.get_pooled_output()
if train_config.checkpoint_type != "bert_nli" and train_config.use_old_logits:
tf_logging.info("Use old version of logistic regression")
if is_training:
pooled = dropout(pooled, 0.1)
logits = tf.keras.layers.Dense(train_config.num_classes,
name="cls_dense")(pooled)
else:
tf_logging.info("Use fixed version of logistic regression")
output_weights = tf.compat.v1.get_variable(
"output_weights",
[train_config.num_classes, model_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())
if is_training:
pooled = dropout(pooled, 0.1)
logits = tf.matmul(pooled, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
loss_arr = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits, labels=label_ids)
if "bias_loss" in special_flags:
tf_logging.info("Using special_flags : bias_loss")
loss_arr = reweight_zero(label_ids, loss_arr)
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
output_spec = None
if mode == tf.estimator.ModeKeys.TRAIN:
if "simple_optimizer" in special_flags:
tf_logging.info("using simple optimizer")
train_op = create_simple_optimizer(loss,
train_config.learning_rate,
train_config.use_tpu)
else:
if "ask_tvar" in special_flags:
tvars = model.get_trainable_vars()
else:
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 = (classification_metric_fn,
[logits, label_ids, is_real_example])
output_spec = TPUEstimatorSpec(mode=model,
loss=loss,
eval_metrics=eval_metrics,
scaffold_fn=scaffold_fn)
else:
predictions = {
"logits": logits,
"doc": doc,
"data_ids": data_ids,
}
useful_inputs = ["data_id", "input_ids2", "data_ids"]
for input_name in useful_inputs:
if input_name in features:
predictions[input_name] = features[input_name]
if override_prediction_fn is not None:
predictions = override_prediction_fn(predictions, model)
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
