def main(unused_argv):
del unused_argv
if FLAGS.strategy_type == "mirror":
strategy = tf.distribute.MirroredStrategy()
elif FLAGS.strategy_type == "tpu":
cluster_resolver = tpu_lib.tpu_initialize(FLAGS.tpu)
strategy = tf.distribute.experimental.TPUStrategy(cluster_resolver)
else:
raise ValueError(
"The distribution strategy type is not supported: %s" %
FLAGS.strategy_type)
if strategy:
logging.info("***** Number of cores used : %d",
strategy.num_replicas_in_sync)
train_input_fn = functools.partial(
data_utils.get_classification_input_data, FLAGS.train_batch_size,
FLAGS.seq_len, strategy, True, FLAGS.train_tfrecord_path)
test_input_fn = functools.partial(data_utils.get_classification_input_data,
FLAGS.test_batch_size, FLAGS.seq_len,
strategy, False,
FLAGS.test_tfrecord_path)
total_training_steps = FLAGS.train_steps
steps_per_loop = FLAGS.iterations
eval_steps = int(FLAGS.test_data_size / FLAGS.test_batch_size)
eval_fn = functools.partial(run_evaluation, strategy, test_input_fn,
eval_steps)
optimizer, learning_rate_fn = optimization.create_optimizer(
FLAGS.learning_rate,
total_training_steps,
FLAGS.warmup_steps,
adam_epsilon=FLAGS.adam_epsilon)
model_config = xlnet_config.XLNetConfig(FLAGS)
run_config = xlnet_config.create_run_config(True, False, FLAGS)
model_fn = functools.partial(get_classificationxlnet_model, model_config,
run_config, FLAGS.n_class, FLAGS.summary_type)
input_meta_data = {}
input_meta_data["d_model"] = FLAGS.d_model
input_meta_data["mem_len"] = FLAGS.mem_len
input_meta_data["batch_size_per_core"] = int(FLAGS.train_batch_size /
strategy.num_replicas_in_sync)
input_meta_data["n_layer"] = FLAGS.n_layer
input_meta_data["lr_layer_decay_rate"] = FLAGS.lr_layer_decay_rate
input_meta_data["n_class"] = FLAGS.n_class
training_utils.train(strategy=strategy,
model_fn=model_fn,
input_meta_data=input_meta_data,
eval_fn=eval_fn,
metric_fn=get_metric_fn,
train_input_fn=train_input_fn,
init_checkpoint=FLAGS.init_checkpoint,
init_from_transformerxl=FLAGS.init_from_transformerxl,
total_training_steps=total_training_steps,
steps_per_loop=steps_per_loop,
optimizer=optimizer,
learning_rate_fn=learning_rate_fn,
model_dir=FLAGS.model_dir,
save_steps=FLAGS.save_steps)
def main(unused_argv):
del unused_argv
strategy = distribute_utils.get_distribution_strategy(
distribution_strategy=FLAGS.strategy_type, tpu_address=FLAGS.tpu)
if strategy:
logging.info("***** Number of cores used : %d",
strategy.num_replicas_in_sync)
train_input_fn = functools.partial(
data_utils.get_classification_input_data, FLAGS.train_batch_size,
FLAGS.seq_len, strategy, True, FLAGS.train_tfrecord_path)
test_input_fn = functools.partial(data_utils.get_classification_input_data,
FLAGS.test_batch_size, FLAGS.seq_len,
strategy, False,
FLAGS.test_tfrecord_path)
total_training_steps = FLAGS.train_steps
steps_per_loop = FLAGS.iterations
eval_steps = int(FLAGS.test_data_size / FLAGS.test_batch_size)
eval_fn = functools.partial(run_evaluation, strategy, test_input_fn,
eval_steps)
optimizer, learning_rate_fn = optimization.create_optimizer(
FLAGS.learning_rate,
total_training_steps,
FLAGS.warmup_steps,
adam_epsilon=FLAGS.adam_epsilon)
model_config = xlnet_config.XLNetConfig(FLAGS)
run_config = xlnet_config.create_run_config(True, False, FLAGS)
model_fn = functools.partial(modeling.classification_model, model_config,
run_config, FLAGS.n_class, FLAGS.summary_type)
input_meta_data = {}
input_meta_data["d_model"] = FLAGS.d_model
input_meta_data["mem_len"] = FLAGS.mem_len
input_meta_data["batch_size_per_core"] = int(FLAGS.train_batch_size /
strategy.num_replicas_in_sync)
input_meta_data["n_layer"] = FLAGS.n_layer
input_meta_data["lr_layer_decay_rate"] = FLAGS.lr_layer_decay_rate
input_meta_data["n_class"] = FLAGS.n_class
training_utils.train(strategy=strategy,
model_fn=model_fn,
input_meta_data=input_meta_data,
eval_fn=eval_fn,
metric_fn=get_metric_fn,
train_input_fn=train_input_fn,
init_checkpoint=FLAGS.init_checkpoint,
init_from_transformerxl=FLAGS.init_from_transformerxl,
total_training_steps=total_training_steps,
steps_per_loop=steps_per_loop,
optimizer=optimizer,
learning_rate_fn=learning_rate_fn,
model_dir=FLAGS.model_dir,
save_steps=FLAGS.save_steps)
def main(unused_argv):
del unused_argv
num_hosts = 1
strategy = distribute_utils.get_distribution_strategy(
distribution_strategy=FLAGS.strategy_type, tpu_address=FLAGS.tpu)
if FLAGS.strategy_type == "tpu":
num_hosts = strategy.extended.num_hosts
if strategy:
logging.info("***** Number of cores used : %d",
strategy.num_replicas_in_sync)
logging.info("***** Number of hosts used : %d", num_hosts)
online_masking_config = data_utils.OnlineMaskingConfig(
sample_strategy=FLAGS.sample_strategy,
max_num_tokens=FLAGS.max_num_tokens,
min_num_tokens=FLAGS.min_num_tokens,
max_num_words=FLAGS.max_num_words,
min_num_words=FLAGS.min_num_words)
train_input_fn = functools.partial(
data_utils.get_pretrain_input_data, FLAGS.train_batch_size,
FLAGS.seq_len, strategy, FLAGS.train_tfrecord_path, FLAGS.reuse_len,
FLAGS.perm_size, FLAGS.leak_ratio, FLAGS.num_predict, FLAGS.uncased,
online_masking_config, num_hosts)
total_training_steps = FLAGS.train_steps
steps_per_loop = FLAGS.iterations
optimizer, learning_rate_fn = optimization.create_optimizer(
init_lr=FLAGS.learning_rate,
num_train_steps=total_training_steps,
num_warmup_steps=FLAGS.warmup_steps,
min_lr_ratio=FLAGS.min_lr_ratio,
adam_epsilon=FLAGS.adam_epsilon,
weight_decay_rate=FLAGS.weight_decay_rate)
model_config = xlnet_config.XLNetConfig(FLAGS)
run_config = xlnet_config.create_run_config(True, False, FLAGS)
input_meta_data = {}
input_meta_data["d_model"] = FLAGS.d_model
input_meta_data["mem_len"] = FLAGS.mem_len
input_meta_data["batch_size_per_core"] = int(FLAGS.train_batch_size /
strategy.num_replicas_in_sync)
input_meta_data["n_layer"] = FLAGS.n_layer
input_meta_data["lr_layer_decay_rate"] = FLAGS.lr_layer_decay_rate
model_fn = functools.partial(get_pretrainxlnet_model, model_config,
run_config)
model = training_utils.train(
strategy=strategy,
model_fn=model_fn,
input_meta_data=input_meta_data,
eval_fn=None,
metric_fn=None,
train_input_fn=train_input_fn,
init_checkpoint=FLAGS.init_checkpoint,
init_from_transformerxl=FLAGS.init_from_transformerxl,
total_training_steps=total_training_steps,
steps_per_loop=steps_per_loop,
optimizer=optimizer,
learning_rate_fn=learning_rate_fn,
model_dir=FLAGS.model_dir,
save_steps=FLAGS.save_steps)
# Export transformer-xl model checkpoint to be used in finetuning.
checkpoint = tf.train.Checkpoint(transformer_xl=model.transformerxl_model)
saved_path = checkpoint.save(
os.path.join(FLAGS.model_dir, "pretrained/transformer_xl.ckpt"))
logging.info(
"Exporting the transformer-xl model as a new TF checkpoint: %s",
saved_path)
def main(unused_argv):
del unused_argv
if FLAGS.strategy_type == "mirror":
strategy = tf.distribute.MirroredStrategy()
elif FLAGS.strategy_type == "tpu":
cluster_resolver = tpu_lib.tpu_initialize(FLAGS.tpu)
strategy = tf.distribute.experimental.TPUStrategy(cluster_resolver)
else:
raise ValueError(
"The distribution strategy type is not supported: %s" %
FLAGS.strategy_type)
if strategy:
logging.info("***** Number of cores used : %d",
strategy.num_replicas_in_sync)
train_input_fn = functools.partial(data_utils.get_squad_input_data,
FLAGS.train_batch_size, FLAGS.seq_len,
FLAGS.query_len, strategy, True,
FLAGS.train_tfrecord_path)
test_input_fn = functools.partial(data_utils.get_squad_input_data,
FLAGS.test_batch_size, FLAGS.seq_len,
FLAGS.query_len, strategy, False,
FLAGS.test_tfrecord_path)
total_training_steps = FLAGS.train_steps
steps_per_loop = FLAGS.iterations
eval_steps = int(FLAGS.test_data_size / FLAGS.test_batch_size)
optimizer, learning_rate_fn = optimization.create_optimizer(
FLAGS.learning_rate,
total_training_steps,
FLAGS.warmup_steps,
adam_epsilon=FLAGS.adam_epsilon)
model_config = xlnet_config.XLNetConfig(FLAGS)
run_config = xlnet_config.create_run_config(True, False, FLAGS)
input_meta_data = {}
input_meta_data["start_n_top"] = FLAGS.start_n_top
input_meta_data["end_n_top"] = FLAGS.end_n_top
input_meta_data["lr_layer_decay_rate"] = FLAGS.lr_layer_decay_rate
input_meta_data["predict_dir"] = FLAGS.predict_dir
input_meta_data["n_best_size"] = FLAGS.n_best_size
input_meta_data["max_answer_length"] = FLAGS.max_answer_length
input_meta_data["test_batch_size"] = FLAGS.test_batch_size
input_meta_data["batch_size_per_core"] = int(FLAGS.train_batch_size /
strategy.num_replicas_in_sync)
input_meta_data["mem_len"] = FLAGS.mem_len
model_fn = functools.partial(get_qaxlnet_model, model_config, run_config,
FLAGS.start_n_top, FLAGS.end_n_top)
eval_examples = squad_utils.read_squad_examples(FLAGS.predict_file,
is_training=False)
if FLAGS.test_feature_path:
logging.info("start reading pickle file...")
with tf.io.gfile.GFile(FLAGS.test_feature_path, "rb") as f:
eval_features = pickle.load(f)
logging.info("finishing reading pickle file...")
else:
sp_model = spm.SentencePieceProcessor()
sp_model.LoadFromSerializedProto(
tf.io.gfile.GFile(FLAGS.spiece_model_file, "rb").read())
spm_basename = os.path.basename(FLAGS.spiece_model_file)
eval_features = squad_utils.create_eval_data(
spm_basename, sp_model, eval_examples, FLAGS.max_seq_length,
FLAGS.max_query_length, FLAGS.doc_stride, FLAGS.uncased)
with tf.io.gfile.GFile(FLAGS.predict_file) as f:
original_data = json.load(f)["data"]
eval_fn = functools.partial(run_evaluation, strategy, test_input_fn,
eval_examples, eval_features, original_data,
eval_steps, input_meta_data)
training_utils.train(strategy=strategy,
model_fn=model_fn,
input_meta_data=input_meta_data,
eval_fn=eval_fn,
metric_fn=None,
train_input_fn=train_input_fn,
init_checkpoint=FLAGS.init_checkpoint,
init_from_transformerxl=FLAGS.init_from_transformerxl,
total_training_steps=total_training_steps,
steps_per_loop=steps_per_loop,
optimizer=optimizer,
learning_rate_fn=learning_rate_fn,
model_dir=FLAGS.model_dir,
save_steps=FLAGS.save_steps)
