def __init__(self, config: configure_finetuning.FinetuningConfig, tasks,
is_training, features, num_train_steps):
# Create a shared transformer encoder
bert_config = training_utils.get_bert_config(config)
self.bert_config = bert_config
if config.debug:
bert_config.num_hidden_layers = 3
bert_config.hidden_size = 144
bert_config.intermediate_size = 144 * 4
bert_config.num_attention_heads = 4
assert config.max_seq_length <= bert_config.max_position_embeddings
bert_model = modeling.BertModel(bert_config=bert_config,
is_training=is_training,
input_ids=features["input_ids"],
input_mask=features["input_mask"],
token_type_ids=features["segment_ids"],
use_one_hot_embeddings=config.use_tpu,
embedding_size=config.embedding_size)
percent_done = (
tf.cast(tf.train.get_or_create_global_step(), tf.float32) /
tf.cast(num_train_steps, tf.float32))
# Add specific tasks
self.outputs = {"task_id": features["task_id"]}
losses = []
for task in tasks:
with tf.variable_scope("task_specific/" + task.name):
task_losses, task_outputs = task.get_prediction_module(
bert_model, features, is_training, percent_done)
losses.append(task_losses)
self.outputs[task.name] = task_outputs
self.loss = tf.reduce_sum(
tf.stack(losses, -1) *
tf.one_hot(features["task_id"], len(config.task_names)))
def __init__(self, config: configure_pretraining.PretrainingConfig,
features, ratio, is_training):
# Set up model config
self._config = config
self._bert_config = training_utils.get_bert_config(config)
embedding_size = (self._bert_config.hidden_size
if config.embedding_size is None else
config.embedding_size)
tokenizer = tokenization.FullTokenizer(
config.vocab_file, do_lower_case=config.do_lower_case)
self._vocab = tokenizer.vocab
self._inv_vocab = tokenizer.inv_vocab
# Mask the input
inputs = pretrain_data.features_to_inputs(features)
# Load ratio
with tf.variable_scope("rw_masking"):
with tf.variable_scope("ratio"):
self.ratios = tf.constant(ratio)
action_prob = tf.nn.embedding_lookup(self.ratios,
inputs.input_ids)
log_q, masked_inputs = self._sample_masking_subset(inputs, action_prob)
# BERT model
model = self._build_transformer(masked_inputs,
is_training,
reuse=tf.AUTO_REUSE,
embedding_size=embedding_size)
mlm_output = self._get_masked_lm_output(masked_inputs, model)
self.total_loss = mlm_output.loss
# Evaluation`
eval_fn_inputs = {
"input_ids": masked_inputs.input_ids,
"masked_lm_preds": mlm_output.preds,
"mlm_loss": mlm_output.per_example_loss,
"masked_lm_ids": masked_inputs.masked_lm_ids,
"masked_lm_weights": masked_inputs.masked_lm_weights,
"input_mask": masked_inputs.input_mask
}
eval_fn_keys = eval_fn_inputs.keys()
eval_fn_values = [eval_fn_inputs[k] for k in eval_fn_keys]
"""Computes the loss and accuracy of the model."""
d = {k: arg for k, arg in zip(eval_fn_keys, eval_fn_values)}
metrics = dict()
metrics["masked_lm_accuracy"] = tf.metrics.accuracy(
labels=tf.reshape(d["masked_lm_ids"], [-1]),
predictions=tf.reshape(d["masked_lm_preds"], [-1]),
weights=tf.reshape(d["masked_lm_weights"], [-1]))
metrics["masked_lm_loss"] = tf.metrics.mean(
values=tf.reshape(d["mlm_loss"], [-1]),
weights=tf.reshape(d["masked_lm_weights"], [-1]))
self.eval_metrics = metrics
def __init__(self, config: configure_finetuning.FinetuningConfig, tasks,
is_training, features, num_train_steps):
# Create a shared transformer encoder
bert_config = training_utils.get_bert_config(config)
self.bert_config = bert_config
if config.debug:
bert_config.num_hidden_layers = 3
bert_config.hidden_size = 144
bert_config.intermediate_size = 144 * 4
bert_config.num_attention_heads = 4
# multi-choice mrc
if any([isinstance(x, qa_tasks.MQATask) for x in tasks]):
seq_len = config.max_seq_length
assert seq_len <= bert_config.max_position_embeddings
bs, total_len = modeling.get_shape_list(features["input_ids"],
expected_rank=2)
to_shape = [
bs * config.max_options_num * config.evidences_top_k, seq_len
]
bert_model = modeling.BertModel(
bert_config=bert_config,
is_training=is_training,
input_ids=tf.reshape(features["input_ids"], to_shape),
input_mask=tf.reshape(features["input_mask"], to_shape),
token_type_ids=tf.reshape(features["segment_ids"], to_shape),
use_one_hot_embeddings=config.use_tpu,
embedding_size=config.embedding_size)
else:
assert config.max_seq_length <= bert_config.max_position_embeddings
bert_model = modeling.BertModel(
bert_config=bert_config,
is_training=is_training,
input_ids=features["input_ids"],
input_mask=features["input_mask"],
token_type_ids=features["segment_ids"],
use_one_hot_embeddings=config.use_tpu,
embedding_size=config.embedding_size)
percent_done = (
tf.cast(tf.train.get_or_create_global_step(), tf.float32) /
tf.cast(num_train_steps, tf.float32))
# Add specific tasks
self.outputs = {"task_id": features["task_id"]}
losses = []
for task in tasks:
with tf.variable_scope("task_specific/" + task.name):
task_losses, task_outputs = task.get_prediction_module(
bert_model, features, is_training, percent_done)
losses.append(task_losses)
self.outputs[task.name] = task_outputs
self.loss = tf.reduce_sum(
tf.stack(losses, -1) *
tf.one_hot(features["task_id"], len(config.task_names)))
def bert_module_fn(is_training):
"""Spec function for a token embedding module."""
input_ids = tf.placeholder(shape=[None, None],
dtype=tf.int32,
name="input_ids")
input_mask = tf.placeholder(shape=[None, None],
dtype=tf.int32,
name="input_mask")
token_type = tf.placeholder(shape=[None, None],
dtype=tf.int32,
name="segment_ids")
bert_config = training_utils.get_bert_config(config)
model = modeling.BertModel(bert_config=bert_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=token_type,
use_one_hot_embeddings=use_tpu,
embedding_size=config.embedding_size)
seq_output = model.sequence_output
pool_output = model.pooled_output
vocab_file = tf.constant(value=vocab_path,
dtype=tf.string,
name="vocab_file")
lower_case = tf.constant(do_lower_case)
tf.add_to_collection(tf.GraphKeys.ASSET_FILEPATHS, vocab_file)
input_map = {
"input_ids": input_ids,
"input_mask": input_mask,
"segment_ids": token_type
}
output_map = {
"pooled_output": pool_output,
"sequence_output": seq_output
}
output_info_map = {
"vocab_file": vocab_file,
"do_lower_case": lower_case
}
hub.add_signature(name="tokens", inputs=input_map, outputs=output_map)
hub.add_signature(name="tokenization_info",
inputs={},
outputs=output_info_map)
def __init__(self, config: configure_pretraining.PretrainingConfig,
features, is_training):
# Set up model config
self._config = config
self._bert_config = training_utils.get_bert_config(config)
if config.debug:
self._bert_config.num_hidden_layers = 3
self._bert_config.hidden_size = 144
self._bert_config.intermediate_size = 144 * 4
self._bert_config.num_attention_heads = 4
# Mask the input
masked_inputs = pretrain_helpers.mask(
config, pretrain_data.features_to_inputs(features),
config.mask_prob)
# Generator
embedding_size = (self._bert_config.hidden_size
if config.embedding_size is None else
config.embedding_size)
if config.uniform_generator:
mlm_output = self._get_masked_lm_output(masked_inputs, None)
elif config.electra_objective and config.untied_generator:
generator = self._build_transformer(
masked_inputs,
is_training,
bert_config=get_generator_config(config, self._bert_config),
embedding_size=(None if config.untied_generator_embeddings else
embedding_size),
untied_embeddings=config.untied_generator_embeddings,
name="generator")
mlm_output = self._get_masked_lm_output(masked_inputs, generator)
else:
generator = self._build_transformer(masked_inputs,
is_training,
embedding_size=embedding_size)
mlm_output = self._get_masked_lm_output(masked_inputs, generator)
fake_data = self._get_fake_data(masked_inputs, mlm_output.logits)
self.mlm_output = mlm_output
self.total_loss = config.gen_weight * mlm_output.loss
# Discriminator
disc_output = None
if config.electra_objective:
discriminator = self._build_transformer(
fake_data.inputs,
is_training,
reuse=not config.untied_generator,
embedding_size=embedding_size)
disc_output = self._get_discriminator_output(
fake_data.inputs, discriminator, fake_data.is_fake_tokens)
self.total_loss += config.disc_weight * disc_output.loss
# Evaluation
eval_fn_inputs = {
"input_ids": masked_inputs.input_ids,
"masked_lm_preds": mlm_output.preds,
"mlm_loss": mlm_output.per_example_loss,
"masked_lm_ids": masked_inputs.masked_lm_ids,
"masked_lm_weights": masked_inputs.masked_lm_weights,
"input_mask": masked_inputs.input_mask
}
if config.electra_objective:
eval_fn_inputs.update({
"disc_loss":
disc_output.per_example_loss,
"disc_labels":
disc_output.labels,
"disc_probs":
disc_output.probs,
"disc_preds":
disc_output.preds,
"sampled_tokids":
tf.argmax(fake_data.sampled_tokens, -1, output_type=tf.int32)
})
eval_fn_keys = eval_fn_inputs.keys()
eval_fn_values = [eval_fn_inputs[k] for k in eval_fn_keys]
def metric_fn(*args):
"""Computes the loss and accuracy of the model."""
d = {k: arg for k, arg in zip(eval_fn_keys, args)}
metrics = dict()
metrics["masked_lm_accuracy"] = tf.metrics.accuracy(
labels=tf.reshape(d["masked_lm_ids"], [-1]),
predictions=tf.reshape(d["masked_lm_preds"], [-1]),
weights=tf.reshape(d["masked_lm_weights"], [-1]))
metrics["masked_lm_loss"] = tf.metrics.mean(
values=tf.reshape(d["mlm_loss"], [-1]),
weights=tf.reshape(d["masked_lm_weights"], [-1]))
if config.electra_objective:
metrics["sampled_masked_lm_accuracy"] = tf.metrics.accuracy(
labels=tf.reshape(d["masked_lm_ids"], [-1]),
predictions=tf.reshape(d["sampled_tokids"], [-1]),
weights=tf.reshape(d["masked_lm_weights"], [-1]))
if config.disc_weight > 0:
metrics["disc_loss"] = tf.metrics.mean(d["disc_loss"])
metrics["disc_auc"] = tf.metrics.auc(
d["disc_labels"] * d["input_mask"],
d["disc_probs"] * tf.cast(d["input_mask"], tf.float32))
metrics["disc_accuracy"] = tf.metrics.accuracy(
labels=d["disc_labels"],
predictions=d["disc_preds"],
weights=d["input_mask"])
metrics["disc_precision"] = tf.metrics.accuracy(
labels=d["disc_labels"],
predictions=d["disc_preds"],
weights=d["disc_preds"] * d["input_mask"])
metrics["disc_recall"] = tf.metrics.accuracy(
labels=d["disc_labels"],
predictions=d["disc_preds"],
weights=d["disc_labels"] * d["input_mask"])
return metrics
self.eval_metrics = (metric_fn, eval_fn_values)
def __init__(self, config: configure_pretraining.PretrainingConfig,
features, is_training):
# Set up model config
self._config = config
self._bert_config = training_utils.get_bert_config(config)
# if config.debug:
# self._bert_config.num_hidden_layers = 3
# self._bert_config.hidden_size = 144
# self._bert_config.intermediate_size = 144 * 4
# self._bert_config.num_attention_heads = 4
embedding_size = (self._bert_config.hidden_size
if config.embedding_size is None else
config.embedding_size)
# Mask the input
inputs = pretrain_data.features_to_inputs(features)
proposal_distribution = 1.0
if config.masking_strategy == pretrain_helpers.ENTROPY_STRATEGY:
old_model = self._build_transformer(inputs,
is_training,
embedding_size=embedding_size)
entropy_output = self._get_entropy_output(inputs, old_model)
proposal_distribution = entropy_output.entropy
masked_inputs = pretrain_helpers.mask(
config,
pretrain_data.features_to_inputs(features),
config.mask_prob,
proposal_distribution=proposal_distribution)
# BERT model
model = self._build_transformer(masked_inputs,
is_training,
reuse=tf.AUTO_REUSE,
embedding_size=embedding_size)
mlm_output = self._get_masked_lm_output(masked_inputs, model)
self.total_loss = mlm_output.loss
# Evaluation
eval_fn_inputs = {
"input_ids": masked_inputs.input_ids,
"masked_lm_preds": mlm_output.preds,
"mlm_loss": mlm_output.per_example_loss,
"masked_lm_ids": masked_inputs.masked_lm_ids,
"masked_lm_weights": masked_inputs.masked_lm_weights,
"input_mask": masked_inputs.input_mask
}
eval_fn_keys = eval_fn_inputs.keys()
eval_fn_values = [eval_fn_inputs[k] for k in eval_fn_keys]
"""Computes the loss and accuracy of the model."""
d = {k: arg for k, arg in zip(eval_fn_keys, eval_fn_values)}
metrics = dict()
metrics["masked_lm_accuracy"] = tf.metrics.accuracy(
labels=tf.reshape(d["masked_lm_ids"], [-1]),
predictions=tf.reshape(d["masked_lm_preds"], [-1]),
weights=tf.reshape(d["masked_lm_weights"], [-1]))
metrics["masked_lm_loss"] = tf.metrics.mean(
values=tf.reshape(d["mlm_loss"], [-1]),
weights=tf.reshape(d["masked_lm_weights"], [-1]))
self.eval_metrics = metrics
def __init__(self, config: configure_pretraining.PretrainingConfig,
features, is_training):
# Set up model config
self._config = config
self._bert_config = training_utils.get_bert_config(config)
self._teacher_config = training_utils.get_teacher_config(config)
embedding_size = (self._bert_config.hidden_size
if config.embedding_size is None else
config.embedding_size)
tokenizer = tokenization.FullTokenizer(
config.vocab_file, do_lower_case=config.do_lower_case)
self._vocab = tokenizer.vocab
self._inv_vocab = tokenizer.inv_vocab
# Mask the input
inputs = pretrain_data.features_to_inputs(features)
old_model = self._build_transformer(inputs,
is_training,
embedding_size=embedding_size)
input_states = old_model.get_sequence_output()
input_states = tf.stop_gradient(input_states)
teacher_output = self._build_teacher(input_states,
inputs,
is_training,
embedding_size=embedding_size)
# calculate the proposal distribution
action_prob = teacher_output.action_probs #pi(x_i)
coin_toss = tf.random.uniform([])
log_q, masked_inputs = self._sample_masking_subset(inputs, action_prob)
if config.masking_strategy == pretrain_helpers.MIX_ADV_STRATEGY:
random_masked_input = pretrain_helpers.mask(
config, pretrain_data.features_to_inputs(features),
config.mask_prob)
B, L = modeling.get_shape_list(inputs.input_ids)
N = config.max_predictions_per_seq
strategy_prob = tf.random.uniform([B])
strategy_prob = tf.expand_dims(
tf.cast(tf.greater(strategy_prob, 0.5), tf.int32), 1)
l_strategy_prob = tf.tile(strategy_prob, [1, L])
n_strategy_prob = tf.tile(strategy_prob, [1, N])
mix_input_ids = masked_inputs.input_ids * l_strategy_prob + random_masked_input.input_ids * (
1 - l_strategy_prob)
mix_masked_lm_positions = masked_inputs.masked_lm_positions * n_strategy_prob + random_masked_input.masked_lm_positions * (
1 - n_strategy_prob)
mix_masked_lm_ids = masked_inputs.masked_lm_ids * n_strategy_prob + random_masked_input.masked_lm_ids * (
1 - n_strategy_prob)
n_strategy_prob = tf.cast(n_strategy_prob, tf.float32)
mix_masked_lm_weights = masked_inputs.masked_lm_weights * n_strategy_prob + random_masked_input.masked_lm_weights * (
1 - n_strategy_prob)
mix_masked_inputs = pretrain_data.get_updated_inputs(
inputs,
input_ids=tf.stop_gradient(mix_input_ids),
masked_lm_positions=mix_masked_lm_positions,
masked_lm_ids=mix_masked_lm_ids,
masked_lm_weights=mix_masked_lm_weights,
tag_ids=inputs.tag_ids)
masked_inputs = mix_masked_inputs
# BERT model
model = self._build_transformer(masked_inputs,
is_training,
reuse=tf.AUTO_REUSE,
embedding_size=embedding_size)
mlm_output = self._get_masked_lm_output(masked_inputs, model)
self.total_loss = mlm_output.loss
# Teacher reward is the -log p(x_S|x;B)
reward = tf.stop_gradient(
tf.reduce_mean(mlm_output.per_example_loss, 1))
self._baseline = tf.reduce_mean(reward, -1)
self._std = tf.math.reduce_std(reward, -1)
# Calculate teacher loss
def compute_teacher_loss(log_q, reward, baseline, std):
advantage = tf.abs((reward - baseline) / std)
advantage = tf.stop_gradient(advantage)
log_q = tf.Print(log_q, [log_q], "log_q: ")
teacher_loss = tf.reduce_mean(-log_q * advantage)
return teacher_loss
teacher_loss = tf.cond(
coin_toss < 0.1, lambda: compute_teacher_loss(
log_q, reward, self._baseline, self._std),
lambda: tf.constant(0.0))
self.total_loss = mlm_output.loss + teacher_loss
self.teacher_loss = teacher_loss
self.mlm_loss = mlm_output.loss
# Evaluation`
eval_fn_inputs = {
"input_ids": masked_inputs.input_ids,
"masked_lm_preds": mlm_output.preds,
"mlm_loss": mlm_output.per_example_loss,
"masked_lm_ids": masked_inputs.masked_lm_ids,
"masked_lm_weights": masked_inputs.masked_lm_weights,
"input_mask": masked_inputs.input_mask
}
eval_fn_keys = eval_fn_inputs.keys()
eval_fn_values = [eval_fn_inputs[k] for k in eval_fn_keys]
"""Computes the loss and accuracy of the model."""
d = {k: arg for k, arg in zip(eval_fn_keys, eval_fn_values)}
metrics = dict()
metrics["masked_lm_accuracy"] = tf.metrics.accuracy(
labels=tf.reshape(d["masked_lm_ids"], [-1]),
predictions=tf.reshape(d["masked_lm_preds"], [-1]),
weights=tf.reshape(d["masked_lm_weights"], [-1]))
metrics["masked_lm_loss"] = tf.metrics.mean(
values=tf.reshape(d["mlm_loss"], [-1]),
weights=tf.reshape(d["masked_lm_weights"], [-1]))
self.eval_metrics = metrics
def __init__(self, config: PretrainingConfig, features, is_training,
init_checkpoint):
# Set up model config
self._config = config
self._bert_config = training_utils.get_bert_config(config)
if config.debug:
self._bert_config.num_hidden_layers = 3
self._bert_config.hidden_size = 144
self._bert_config.intermediate_size = 144 * 4
self._bert_config.num_attention_heads = 4
compute_type = modeling.infer_dtype(config.use_fp16)
custom_getter = modeling.get_custom_getter(compute_type)
with tf.variable_scope(tf.get_variable_scope(),
custom_getter=custom_getter):
# Mask the input
masked_inputs = pretrain_helpers.mask(
config, pretrain_data.features_to_inputs(features),
config.mask_prob)
# Generator
embedding_size = self._bert_config.hidden_size if config.embedding_size is None else config.embedding_size
if config.uniform_generator:
mlm_output = self._get_masked_lm_output(masked_inputs, None)
elif config.electra_objective and config.untied_generator:
generator = self._build_transformer(
name="generator",
inputs=masked_inputs,
is_training=is_training,
use_fp16=config.use_fp16,
bert_config=get_generator_config(config,
self._bert_config),
embedding_size=None
if config.untied_generator_embeddings else embedding_size,
untied_embeddings=config.untied_generator_embeddings)
mlm_output = self._get_masked_lm_output(
masked_inputs, generator)
else:
generator = self._build_transformer(
name="electra",
inputs=masked_inputs,
is_training=is_training,
use_fp16=config.use_fp16,
embedding_size=embedding_size)
mlm_output = self._get_masked_lm_output(
masked_inputs, generator)
fake_data = self._get_fake_data(masked_inputs, mlm_output.logits)
self.mlm_output = mlm_output
self.total_loss = config.gen_weight * mlm_output.loss
utils.log("Generator is built!")
# Discriminator
self.disc_output = None
if config.electra_objective:
discriminator = self._build_transformer(
name="electra",
inputs=fake_data.inputs,
is_training=is_training,
use_fp16=config.use_fp16,
embedding_size=embedding_size)
utils.log("Discriminator is built!")
self.disc_output = self._get_discriminator_output(
inputs=fake_data.inputs,
discriminator=discriminator,
labels=fake_data.is_fake_tokens)
self.total_loss += config.disc_weight * self.disc_output.loss
if init_checkpoint and hvd.rank() == 0:
print("Loading checkpoint", init_checkpoint)
assignment_map, _ = modeling.get_assignment_map_from_checkpoint(
tvars=tf.trainable_variables(),
init_checkpoint=init_checkpoint,
prefix="")
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
# Evaluation
eval_fn_inputs = {
"input_ids": masked_inputs.input_ids,
"masked_lm_preds": mlm_output.preds,
"mlm_loss": mlm_output.per_example_loss,
"masked_lm_ids": masked_inputs.masked_lm_ids,
"masked_lm_weights": masked_inputs.masked_lm_weights,
"input_mask": masked_inputs.input_mask
}
if config.electra_objective:
eval_fn_inputs.update({
"disc_loss":
self.disc_output.per_example_loss,
"disc_labels":
self.disc_output.labels,
"disc_probs":
self.disc_output.probs,
"disc_preds":
self.disc_output.preds,
"sampled_tokids":
tf.argmax(fake_data.sampled_tokens, -1, output_type=tf.int32)
})
eval_fn_keys = eval_fn_inputs.keys()
eval_fn_values = [eval_fn_inputs[k] for k in eval_fn_keys]
def metric_fn(*args):
"""Computes the loss and accuracy of the model."""
d = dict(zip(eval_fn_keys, args))
metrics = dict()
metrics["masked_lm_accuracy"] = tf.metrics.accuracy(
labels=tf.reshape(d["masked_lm_ids"], [-1]),
predictions=tf.reshape(d["masked_lm_preds"], [-1]),
weights=tf.reshape(d["masked_lm_weights"], [-1]))
metrics["masked_lm_loss"] = tf.metrics.mean(
values=tf.reshape(d["mlm_loss"], [-1]),
weights=tf.reshape(d["masked_lm_weights"], [-1]))
if config.electra_objective:
metrics["sampled_masked_lm_accuracy"] = tf.metrics.accuracy(
labels=tf.reshape(d["masked_lm_ids"], [-1]),
predictions=tf.reshape(d["sampled_tokids"], [-1]),
weights=tf.reshape(d["masked_lm_weights"], [-1]))
if config.disc_weight > 0:
metrics["disc_loss"] = tf.metrics.mean(d["disc_loss"])
metrics["disc_auc"] = tf.metrics.auc(
d["disc_labels"] * d["input_mask"],
d["disc_probs"] * tf.cast(d["input_mask"], tf.float32))
metrics["disc_accuracy"] = tf.metrics.accuracy(
labels=d["disc_labels"],
predictions=d["disc_preds"],
weights=d["input_mask"])
metrics["disc_precision"] = tf.metrics.accuracy(
labels=d["disc_labels"],
predictions=d["disc_preds"],
weights=d["disc_preds"] * d["input_mask"])
metrics["disc_recall"] = tf.metrics.accuracy(
labels=d["disc_labels"],
predictions=d["disc_preds"],
weights=d["disc_labels"] * d["input_mask"])
return metrics
self.eval_metrics = (metric_fn, eval_fn_values)
def main():
tf.set_random_seed(1234)
np.random.seed(0)
batch_size = 1
tf_datatype = tf.int32
np_datatype = np.int32
iterations = 10
features_ph = {}
features_ph["input_ids"] = tf.placeholder(dtype=tf_datatype,
shape=[batch_size, 128],
name="input_ids")
features_ph["input_mask"] = tf.placeholder(dtype=tf_datatype,
shape=[batch_size, 128],
name="input_mask")
features_ph["token_type_ids"] = tf.placeholder(dtype=tf_datatype,
shape=[batch_size, 128],
name="token_type_ids")
features_data = {}
features_data["input_ids"] = np.random.rand(batch_size,
128).astype(np_datatype)
features_data["input_mask"] = np.random.rand(batch_size,
128).astype(np_datatype)
features_data["token_type_ids"] = np.random.rand(
batch_size, 128).astype(np_datatype)
features_feed_dict = {
features_ph[key]: features_data[key]
for key in features_ph
}
finetuning_config = configure_finetuning.FinetuningConfig("ConvBert", "./")
bert_config = training_utils.get_bert_config(finetuning_config)
bert_model = modeling.BertModel(
bert_config=bert_config,
is_training=False,
input_ids=features_ph["input_ids"],
input_mask=features_ph["input_mask"],
token_type_ids=features_ph["token_type_ids"])
#outputs_names = "discriminator_predictions/Sigmoid:0,discriminator_predictions/truediv:0,discriminator_predictions/Cast_2:0,discriminator_predictions/truediv_1:0"
graph_outputs = bert_model.get_sequence_output()
outputs_names = graph_outputs.name
print("graph output: ", graph_outputs)
run_op_list = []
outputs_names_with_port = outputs_names.split(",")
outputs_names_without_port = [ name.split(":")[0] for name in outputs_names_with_port ]
for index in range(len(outputs_names_without_port)):
run_op_list.append(outputs_names_without_port[index])
inputs_names_with_port = [features_ph[key].name for key in features_ph]
# define saver
#saver = tf.train.Saver(var_list=tf.trainable_variables())
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
for i in range(iterations):
sess.run(run_op_list, feed_dict=features_feed_dict)
tf_time_sum = 0
a = datetime.now()
for i in range(iterations):
tf_result = sess.run(run_op_list, feed_dict=features_feed_dict)
b = datetime.now()
tf_time_sum = (b - a).total_seconds()
tf_time = "[INFO] TF execution time: " + str(
tf_time_sum * 1000 / iterations) + " ms"
# tf_result = tf_result.flatten()
frozen_graph = tf.graph_util.convert_variables_to_constants(
sess, sess.graph_def, outputs_names_without_port)
# frozen_graph = tf.graph_util.remove_training_nodes(frozen_graph)
# save frozen model
with open("ConvBert.pb", "wb") as ofile:
ofile.write(frozen_graph.SerializeToString())
# tf.reset_default_graph()
# tf.import_graph_def(frozen_graph, name='')
# #with tf.Session(config=config) as sess:
# sess = tf.Session(config=config)
# graph_def = tf_optimize(inputs_names_with_port, outputs_names_without_port,
# sess.graph_def, True)
# with open("ConvBert_optimized_model.pb", "wb") as ofile:
# ofile.write(graph_def.SerializeToString())
onnx_model_file = "ConvBert.onnx"
command = "python3 -m tf2onnx.convert --input ConvBert.pb --output %s --fold_const --opset 12 --verbose" % onnx_model_file
command += " --inputs "
for name in inputs_names_with_port:
command += "%s," % name
command = command[:-1] + " --outputs "
for name in outputs_names_with_port:
command += "%s," % name
command = command[:-1]
os.system(command)
print(command)
#exit(0)
command = "trtexec - -onnx = ConvBert.onnx - -verbose"
os.system(command)
print(command)
def __init__(self, config: configure_pretraining.PretrainingConfig,
features, is_training):
# Set up model config
self._config = config
self._bert_config = training_utils.get_bert_config(config)
if config.debug:
self._bert_config.num_hidden_layers = 3
self._bert_config.hidden_size = 144
self._bert_config.intermediate_size = 144 * 4
self._bert_config.num_attention_heads = 4
# Mask the input
# masked_inputs = pretrain_helpers.mask(
# config, pretrain_data.features_to_inputs(features), config.mask_prob)
masked_inputs = Inputs(
tf.constant(
[[
101,
2151,
11385,
2052,
2031,
103,
4235,
15484,
2011,
2796,
4153,
14731,
1999,
103,
2733,
2144,
1996,
4946,
5419,
1012,
1523,
103,
2031,
2116,
103,
3001,
4082,
1999,
1996,
103,
1010,
2021,
2498,
2001,
3856,
2039,
1010,
103,
4373,
5902,
19219,
11961,
17357,
103,
1010,
2708,
1997,
3095,
1997,
2634,
1521,
103,
1998,
23093,
2015,
1998,
19332,
8237,
3094,
1010,
2409,
26665,
1012,
1523,
2009,
2003,
2825,
2008,
1996,
103,
7217,
2015,
103,
7237,
2125,
2004,
2057,
5452,
2006,
2019,
2004,
1011,
3223,
3978,
1012,
2061,
3383,
3905,
103,
2015,
2020,
4082,
1010,
2029,
2089,
2025,
2031,
1996,
3223,
2846,
2000,
11487,
1037,
3462,
2012,
2019,
7998,
1997,
103,
1010,
2199,
2519,
1012,
1524,
102,
103,
2796,
4153,
2038,
2019,
2779,
5995,
1997,
2062,
2084,
2260,
1010,
102,
]],
dtype=tf.int32,
),
tf.constant(
[[
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
]],
dtype=tf.int32,
),
tf.constant(
[[
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
1,
]],
dtype=tf.int32,
),
tf.constant([[
37, 69, 5, 106, 13, 115, 51, 43, 72, 116, 21, 21, 88, 24, 13,
29, 93, 69, 108
]],
dtype=tf.int32),
tf.constant(
[[
1524,
2510,
2042,
7998,
1996,
1996,
1055,
4886,
2020,
2796,
2057,
2057,
7217,
7217,
1996,
2181,
2029,
2510,
3486,
]],
dtype=tf.int32,
),
tf.constant(
[[
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0
]],
dtype=tf.float32,
),
)
self.masked_inputs = masked_inputs
# Generator
embedding_size = self._bert_config.hidden_size if config.embedding_size is None else config.embedding_size
if config.uniform_generator:
mlm_output = self._get_masked_lm_output(masked_inputs, None)
elif config.electra_objective and config.untied_generator:
generator = self._build_transformer(
masked_inputs,
is_training,
bert_config=get_generator_config(config, self._bert_config),
embedding_size=(None if config.untied_generator_embeddings else
embedding_size),
untied_embeddings=config.untied_generator_embeddings,
name="generator",
)
mlm_output, self.relevant_hidden = self._get_masked_lm_output(
masked_inputs, generator)
else:
generator = self._build_transformer(masked_inputs,
is_training,
embedding_size=embedding_size)
mlm_output = self._get_masked_lm_output(masked_inputs, generator)
fake_data = self._get_fake_data(masked_inputs, mlm_output.logits)
FakedData = collections.namedtuple(
"FakedData", ["inputs", "is_fake_tokens"]) # , "sampled_tokens"])
fake_data = FakedData(
Inputs(
tf.constant([[
101, 2151, 11385, 2052, 2031, 20464, 4235, 15484, 2011,
2796, 4153, 14731, 1999, 4952, 2733, 2144, 1996, 4946,
5419, 1012, 1523, 8045, 2031, 2116, 7367, 3001, 4082, 1999,
1996, 22366, 1010, 2021, 2498, 2001, 3856, 2039, 1010,
1422, 4373, 5902, 19219, 11961, 17357, 16374, 1010, 2708,
1997, 3095, 1997, 2634, 1521, 21904, 1998, 23093, 2015,
1998, 19332, 8237, 3094, 1010, 2409, 26665, 1012, 1523,
2009, 2003, 2825, 2008, 1996, 15778, 7217, 2015, 12767,
7237, 2125, 2004, 2057, 5452, 2006, 2019, 2004, 1011, 3223,
3978, 1012, 2061, 3383, 3905, 4852, 2015, 2020, 4082, 1010,
20229, 2089, 2025, 2031, 1996, 3223, 2846, 2000, 11487,
1037, 3462, 2012, 2019, 21157, 1997, 431, 1010, 2199, 2519,
1012, 1524, 102, 16353, 7069, 4153, 2038, 2019, 2779, 5995,
1997, 2062, 2084, 2260, 1010, 102
]],
dtype=tf.int32),
tf.constant([[
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1
]],
dtype=tf.int32),
tf.constant([[
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1
]],
dtype=tf.int32),
tf.constant([[
37, 69, 5, 106, 13, 115, 51, 43, 72, 116, 21, 21, 88, 24,
13, 29, 93, 69, 108
]],
dtype=tf.int32),
tf.constant([[
1524, 2510, 2042, 7998, 1996, 1996, 1055, 4886, 2020, 2796,
2057, 2057, 7217, 7217, 1996, 2181, 2029, 2510, 3486
]],
dtype=tf.int32),
tf.constant([[
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1.
]])),
tf.constant([[
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0
]],
dtype=tf.int32))
print("FAKE DATA", fake_data)
self.token_embeddings = tf.identity(generator.token_embeddings)
self.all_embeddings = tf.identity(generator.all_embeddings)
# self.attention_mask = tf.identity(generator.attention_mask)
self.generator_embedding_output = tf.identity(
generator.get_embedding_output())
self.generator_all_encoder_layers = tf.identity(
generator.get_all_encoder_layers())
self.sequence_output = tf.identity(generator.get_sequence_output())
self.pooled_output = tf.identity(generator.get_pooled_output())
self.mlm_output = mlm_output
self.total_loss = config.gen_weight * mlm_output.loss
# Discriminator
disc_output = None
if config.electra_objective:
discriminator = self._build_transformer(
fake_data.inputs,
is_training,
reuse=not config.untied_generator,
embedding_size=embedding_size)
disc_output = self._get_discriminator_output(
fake_data.inputs, discriminator, fake_data.is_fake_tokens)
self.total_loss += config.disc_weight * disc_output.loss
self.discriminator_embedding_output = tf.identity(
discriminator.get_embedding_output())
self.discriminator_all_encoder_layers = tf.identity(
discriminator.get_all_encoder_layers())
self.discriminator_output = disc_output
def __init__(self, config: configure_pretraining.PretrainingConfig,
features, is_training):
# Set up model config
self._config = config
self._bert_config = training_utils.get_bert_config(config)
if config.debug:
self._bert_config.num_hidden_layers = 3
self._bert_config.hidden_size = 144
self._bert_config.intermediate_size = 144 * 4
self._bert_config.num_attention_heads = 4
# Mask the input
unmasked_inputs = pretrain_data.features_to_inputs(features)
masked_inputs = pretrain_helpers.mask(config, unmasked_inputs,
config.mask_prob)
# Generator
embedding_size = (self._bert_config.hidden_size
if config.embedding_size is None else
config.embedding_size)
cloze_output = None
if config.uniform_generator:
# simple generator sampling fakes uniformly at random
mlm_output = self._get_masked_lm_output(masked_inputs, None)
elif ((config.electra_objective or config.electric_objective)
and config.untied_generator):
generator_config = get_generator_config(config, self._bert_config)
if config.two_tower_generator:
# two-tower cloze model generator used for electric
generator = TwoTowerClozeTransformer(config, generator_config,
unmasked_inputs,
is_training,
embedding_size)
cloze_output = self._get_cloze_outputs(unmasked_inputs,
generator)
mlm_output = get_softmax_output(
pretrain_helpers.gather_positions(
cloze_output.logits,
masked_inputs.masked_lm_positions),
masked_inputs.masked_lm_ids,
masked_inputs.masked_lm_weights,
self._bert_config.vocab_size)
else:
# small masked language model generator
generator = build_transformer(
config,
masked_inputs,
is_training,
generator_config,
embedding_size=(None if config.untied_generator_embeddings
else embedding_size),
untied_embeddings=config.untied_generator_embeddings,
scope="generator")
mlm_output = self._get_masked_lm_output(
masked_inputs, generator)
else:
# full-sized masked language model generator if using BERT objective or if
# the generator and discriminator have tied weights
generator = build_transformer(config,
masked_inputs,
is_training,
self._bert_config,
embedding_size=embedding_size)
mlm_output = self._get_masked_lm_output(masked_inputs, generator)
fake_data = self._get_fake_data(masked_inputs, mlm_output.logits)
self.mlm_output = mlm_output
self.total_loss = config.gen_weight * (cloze_output.loss
if config.two_tower_generator
else mlm_output.loss)
# Discriminator
disc_output = None
if config.electra_objective or config.electric_objective:
discriminator = build_transformer(
config,
fake_data.inputs,
is_training,
self._bert_config,
reuse=not config.untied_generator,
embedding_size=embedding_size)
disc_output = self._get_discriminator_output(
fake_data.inputs, discriminator, fake_data.is_fake_tokens,
cloze_output)
self.total_loss += config.disc_weight * disc_output.loss
# Evaluation
eval_fn_inputs = {
"input_ids": masked_inputs.input_ids,
"masked_lm_preds": mlm_output.preds,
"mlm_loss": mlm_output.per_example_loss,
"masked_lm_ids": masked_inputs.masked_lm_ids,
"masked_lm_weights": masked_inputs.masked_lm_weights,
"input_mask": masked_inputs.input_mask
}
if config.electra_objective or config.electric_objective:
eval_fn_inputs.update({
"disc_loss":
disc_output.per_example_loss,
"disc_labels":
disc_output.labels,
"disc_probs":
disc_output.probs,
"disc_preds":
disc_output.preds,
"sampled_tokids":
tf.argmax(fake_data.sampled_tokens, -1, output_type=tf.int32)
})
eval_fn_keys = eval_fn_inputs.keys()
eval_fn_values = [eval_fn_inputs[k] for k in eval_fn_keys]
def metric_fn(*args):
"""Computes the loss and accuracy of the model."""
d = {k: arg for k, arg in zip(eval_fn_keys, args)}
metrics = dict()
metrics["masked_lm_accuracy"] = tf.metrics.accuracy(
labels=tf.reshape(d["masked_lm_ids"], [-1]),
predictions=tf.reshape(d["masked_lm_preds"], [-1]),
weights=tf.reshape(d["masked_lm_weights"], [-1]))
metrics["masked_lm_loss"] = tf.metrics.mean(
values=tf.reshape(d["mlm_loss"], [-1]),
weights=tf.reshape(d["masked_lm_weights"], [-1]))
if config.electra_objective or config.electric_objective:
metrics["sampled_masked_lm_accuracy"] = tf.metrics.accuracy(
labels=tf.reshape(d["masked_lm_ids"], [-1]),
predictions=tf.reshape(d["sampled_tokids"], [-1]),
weights=tf.reshape(d["masked_lm_weights"], [-1]))
if config.disc_weight > 0:
metrics["disc_loss"] = tf.metrics.mean(d["disc_loss"])
metrics["disc_auc"] = tf.metrics.auc(
d["disc_labels"] * d["input_mask"],
d["disc_probs"] * tf.cast(d["input_mask"], tf.float32))
metrics["disc_accuracy"] = tf.metrics.accuracy(
labels=d["disc_labels"],
predictions=d["disc_preds"],
weights=d["input_mask"])
metrics["disc_precision"] = tf.metrics.accuracy(
labels=d["disc_labels"],
predictions=d["disc_preds"],
weights=d["disc_preds"] * d["input_mask"])
metrics["disc_recall"] = tf.metrics.accuracy(
labels=d["disc_labels"],
predictions=d["disc_preds"],
weights=d["disc_labels"] * d["input_mask"])
return metrics
self.eval_metrics = (metric_fn, eval_fn_values)
