def __init__(self, num_classes, ssdr_config, core_model, seq_length, is_training):
super(WSSDRWrapperInterface, self).__init__()
placeholder = tf.compat.v1.placeholder
bert_config = BertConfig.from_json_file(os.path.join(data_path, "bert_config.json"))
def_max_length = FLAGS.max_def_length
loc_max_length = FLAGS.max_loc_length
tf_logging.debug("WSSDRWrapper init()")
tf_logging.debug("seq_length %d" % seq_length)
tf_logging.debug("def_max_length %d" % def_max_length)
tf_logging.debug("loc_max_length %d" % loc_max_length)
self.input_ids = placeholder(tf.int64, [None, seq_length], name="input_ids")
self.input_mask_ = placeholder(tf.int64, [None, seq_length], name="input_mask")
self.segment_ids = placeholder(tf.int64, [None, seq_length], name="segment_ids")
self.d_location_ids = placeholder(tf.int64, [None, loc_max_length], name="d_location_ids")
self.d_input_ids = placeholder(tf.int64, [None, def_max_length], name="d_input_ids")
self.d_input_mask = placeholder(tf.int64, [None, def_max_length], name="d_input_mask")
self.d_segment_ids = placeholder(tf.int64, [None, def_max_length], name="d_segment_ids")
self.ab_mapping = placeholder(tf.int64, [None, 1], name="ab_mapping")
if ssdr_config.use_ab_mapping_mask:
self.ab_mapping_mask = placeholder(tf.int64, [None, FLAGS.def_per_batch], name="ab_mapping_mask")
else:
self.ab_mapping_mask = None
# [batch,seq_len], 1 if the indices in d_locations_id
y_lookup = get_y_lookup_from_location_ids(self.d_location_ids, seq_length)
self.y_cls = placeholder(tf.int64, [None])
self.network = core_model(
config=bert_config,
ssdr_config=ssdr_config,
is_training=is_training,
input_ids=self.input_ids,
input_mask=self.input_mask_,
token_type_ids=self.segment_ids,
d_input_ids=self.d_input_ids,
d_input_mask=self.d_input_mask,
d_segment_ids=self.d_segment_ids,
d_location_ids=self.d_location_ids,
ab_mapping=self.ab_mapping,
ab_mapping_mask=self.ab_mapping_mask,
use_one_hot_embeddings=False,
)
self.cls_logits = keras.layers.Dense(num_classes)(self.network.get_pooled_output())
self.cls_loss_arr = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=self.cls_logits,
labels=self.y_cls)
self.cls_loss = tf.reduce_mean(self.cls_loss_arr)
self.lookup_logits = keras.layers.Dense(2)(self.network.get_sequence_output())
self.lookup_p_at_1 = tf_module.p_at_1(self.lookup_logits[:,:, 1], y_lookup)
self.lookup_loss_arr = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=self.lookup_logits,
labels=y_lookup)
self.y_lookup = y_lookup
self.lookup_loss_per_example = tf.reduce_sum(self.lookup_loss_arr, axis=-1)
self.lookup_loss = tf.reduce_mean(self.lookup_loss_per_example)
self.acc = tf_module.accuracy(self.cls_logits, self.y_cls)
def main(_):
bert_config = BertConfig.from_json_file(FLAGS.bert_config_file)
train_config = TrainConfigEx.from_flags(FLAGS)
input_files = get_input_files_from_flags(FLAGS)
input_fn = input_fn_builder(input_files, FLAGS, False)
model_fn = model_fn_lm(bert_config, train_config, BertModel)
return run_estimator(model_fn, input_fn)
def run_classification_w_second_input():
input_files = get_input_files_from_flags(FLAGS)
bert_config = BertConfig.from_json_file(FLAGS.bert_config_file)
train_config = TrainConfigEx.from_flags(FLAGS)
show_input_files(input_files)
model_fn = model_fn_classification(
bert_config,
train_config,
)
input_fn = input_fn_builder_use_second_input(FLAGS)
if FLAGS.do_predict:
tf_logging.addFilter(MuteEnqueueFilter())
result = run_estimator(model_fn, input_fn)
return result
def __init__(self, num_classes, seq_length, is_training):
super(DictReaderWrapper, self).__init__()
placeholder = tf.compat.v1.placeholder
bert_config = BertConfig.from_json_file(os.path.join(data_path, "bert_config.json"))
def_max_length = FLAGS.max_def_length
loc_max_length = FLAGS.max_loc_length
tf_logging.debug("DictReaderWrapper init()")
tf_logging.debug("seq_length %d" % seq_length)
tf_logging.debug("def_max_length %d" % def_max_length)
tf_logging.debug("loc_max_length %d" % loc_max_length)
self.input_ids = placeholder(tf.int64, [None, seq_length])
self.input_mask_ = placeholder(tf.int64, [None, seq_length])
self.segment_ids = placeholder(tf.int64, [None, seq_length])
self.d_input_ids = placeholder(tf.int64, [None, def_max_length])
self.d_input_mask = placeholder(tf.int64, [None, def_max_length])
self.d_location_ids = placeholder(tf.int64, [None, loc_max_length])
self.y_cls = placeholder(tf.int64, [None])
self.y_lookup = placeholder(tf.int64, [None, seq_length])
self.network = DictReaderModel(
config=bert_config,
d_config=bert_config,
is_training=is_training,
input_ids=self.input_ids,
input_mask=self.input_mask_,
d_input_ids=self.d_input_ids,
d_input_mask=self.d_input_mask,
d_location_ids=self.d_location_ids,
use_target_pos_emb=True,
token_type_ids=self.segment_ids,
use_one_hot_embeddings=False,
)
self.cls_logits = keras.layers.Dense(num_classes)(self.network.pooled_output)
self.cls_loss_arr = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=self.cls_logits,
labels=self.y_cls)
self.cls_loss = tf.reduce_mean(self.cls_loss_arr)
self.lookup_logits = keras.layers.Dense(2)(self.network.sequence_output)
self.lookup_loss_arr = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=self.lookup_logits,
labels=self.y_lookup)
self.lookup_loss_per_example = tf.reduce_mean(self.lookup_loss_arr, axis=-1)
self.lookup_loss = tf.reduce_mean(self.lookup_loss_per_example)
self.acc = tf_module.accuracy(self.cls_logits, self.y_cls)
def run_w_data_id():
input_files = get_input_files_from_flags(FLAGS)
bert_config = BertConfig.from_json_file(FLAGS.bert_config_file)
train_config = TrainConfigEx.from_flags(FLAGS)
show_input_files(input_files)
model_fn = model_fn_classification_weighted_loss(
bert_config,
train_config,
)
if FLAGS.do_predict:
tf_logging.addFilter(CounterFilter())
input_fn = input_fn_builder_classification_w_data_id(
input_files=input_files, flags=FLAGS, is_training=FLAGS.do_train)
result = run_estimator(model_fn, input_fn)
return result
def main(_):
input_files = get_input_files_from_flags(FLAGS)
bert_config = BertConfig.from_json_file(FLAGS.bert_config_file)
train_config = TrainConfigEx.from_flags(FLAGS)
show_input_files(input_files)
special_flags = FLAGS.special_flags.split(",")
model_fn = model_fn_sensitivity(
bert_config=bert_config,
train_config=train_config,
model_class=BertModel,
special_flags=special_flags,
)
if FLAGS.do_predict:
tf_logging.addFilter(MuteEnqueueFilter())
input_fn = input_fn_builder_use_second_input(FLAGS)
result = run_estimator(model_fn, input_fn)
return result
def run_w_data_id():
input_files = get_input_files_from_flags(FLAGS)
bert_config = BertConfig.from_json_file(FLAGS.bert_config_file)
train_config = TrainConfigEx.from_flags(FLAGS)
show_input_files(input_files)
special_flags = FLAGS.special_flags.split(",")
model_fn = model_fn_classification(
bert_config=bert_config,
train_config=train_config,
model_class=BertModel,
special_flags=special_flags,
)
if FLAGS.do_predict:
tf_logging.addFilter(CounterFilter())
input_fn = input_fn_builder_classification_w_data_ids_typo(
input_files=input_files, flags=FLAGS, is_training=FLAGS.do_train)
result = run_estimator(model_fn, input_fn)
return result
def main(_):
input_files = get_input_files_from_flags(FLAGS)
bert_config = BertConfig.from_json_file(FLAGS.bert_config_file)
train_config = TrainConfigEx.from_flags(FLAGS)
show_input_files(input_files)
special_flags = FLAGS.special_flags.split(",")
model_fn = model_fn_classification(
bert_config=bert_config,
train_config=train_config,
model_class=FreezeEmbedding,
special_flags=special_flags,
)
input_fn = input_fn_builder_classification_w_data_id(
input_files=input_files, flags=FLAGS, is_training=FLAGS.do_train)
result = run_estimator(model_fn, input_fn)
return result
def main(_):
input_files = get_input_files_from_flags(FLAGS)
bert_config = BertConfig.from_json_file(FLAGS.bert_config_file)
train_config = TrainConfigEx.from_flags(FLAGS)
show_input_files(input_files)
special_flags = FLAGS.special_flags.split(",")
def override_prediction_fn(predictions, model):
predictions['vector'] = model.get_output()
return predictions
model_fn = model_fn_classification(
bert_config=bert_config,
train_config=train_config,
model_class=MultiEvidenceUseFirst,
special_flags=special_flags,
override_prediction_fn=override_prediction_fn)
if FLAGS.do_predict:
tf_logging.addFilter(CounterFilter())
input_fn = input_fn_builder_use_second_input(FLAGS)
result = run_estimator(model_fn, input_fn)
return result
def __init__(self):
config = BertConfig.from_json_file(
os.path.join(data_path, "bert_config.json"))
self.attention_probs_list = []
input_ids = tf.constant([[101] + [100] * 511])
token_type_ids = tf.constant([[0] * 512])
input_mask = tf.constant([[1] * 512])
attention_mask = create_attention_mask_from_input_mask(
input_ids, input_mask)
initializer = create_initializer(config.initializer_range)
scope = None
with tf.compat.v1.variable_scope(scope, default_name="bert"):
with tf.compat.v1.variable_scope("embeddings"):
# Perform embedding lookup on the word ids.
(self.embedding_output,
self.embedding_table) = embedding_lookup(
input_ids=input_ids,
vocab_size=config.vocab_size,
embedding_size=config.hidden_size,
initializer_range=config.initializer_range,
word_embedding_name="word_embeddings",
use_one_hot_embeddings=False)
# Add positional embeddings and token type embeddings, then layer
# normalize and perform dropout.
self.embedding_output = embedding_postprocessor(
input_tensor=self.embedding_output,
use_token_type=True,
token_type_ids=token_type_ids,
token_type_vocab_size=config.type_vocab_size,
token_type_embedding_name="token_type_embeddings",
use_position_embeddings=True,
position_embedding_name="position_embeddings",
initializer_range=config.initializer_range,
max_position_embeddings=config.max_position_embeddings,
dropout_prob=config.hidden_dropout_prob)
prev_output = reshape_to_matrix(self.embedding_output)
with tf.compat.v1.variable_scope("encoder"):
for layer_idx in range(12):
with tf.compat.v1.variable_scope("layer_%d" % layer_idx):
layer_input = prev_output
with tf.compat.v1.variable_scope("attention"):
attention_heads = []
with tf.compat.v1.variable_scope("self"):
attention_head = self.attention_fn(layer_input)
attention_heads.append(attention_head)
attention_output = None
if len(attention_heads) == 1:
attention_output = attention_heads[0]
else:
# In the case where we have other sequences, we just concatenate
# them to the self-attention head before the projection.
attention_output = tf.concat(attention_heads,
axis=-1)
# Run a linear projection of `hidden_size` then add a residual
# with `layer_input`.
with tf.compat.v1.variable_scope("output"):
attention_output = dense(
hidden_size, initializer)(attention_output)
attention_output = layer_norm(
attention_output + layer_input)
# The activation is only applied to the "intermediate" hidden layer.
with tf.compat.v1.variable_scope("intermediate"):
intermediate_output = dense(
config.intermediate_size,
initializer,
activation=gelu)(attention_output)
# Down-project back to `hidden_size` then add the residual.
with tf.compat.v1.variable_scope("output"):
layer_output = dense(
hidden_size, initializer)(intermediate_output)
layer_output = layer_norm(layer_output +
attention_output)
prev_output = layer_output