def test_load_dataset_not_same_masks(self):
max_seq_length = 128
batch_size = 2
input_path_1 = os.path.join(self.get_temp_dir(), 'train_3.tf_record')
_create_fake_dataset(input_path_1,
seq_length=60,
num_masked_tokens=20,
max_seq_length=max_seq_length,
num_examples=batch_size)
input_path_2 = os.path.join(self.get_temp_dir(), 'train_4.tf_record')
_create_fake_dataset(input_path_2,
seq_length=60,
num_masked_tokens=15,
max_seq_length=max_seq_length,
num_examples=batch_size)
input_paths = ','.join([input_path_1, input_path_2])
data_config = pretrain_dynamic_dataloader.BertPretrainDataConfig(
is_training=False,
input_path=input_paths,
seq_bucket_lengths=[64, 128],
use_position_id=True,
global_batch_size=batch_size * 2)
dataset = pretrain_dynamic_dataloader.PretrainingDynamicDataLoader(
data_config).load()
dataset_it = iter(dataset)
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
'.*Number of non padded mask tokens.*'):
next(dataset_it)
def test_load_dataset(self):
tf.random.set_seed(0)
max_seq_length = 128
batch_size = 2
input_path_1 = os.path.join(self.get_temp_dir(), 'train_1.tf_record')
_create_fake_dataset(input_path_1,
seq_length=60,
num_masked_tokens=20,
max_seq_length=max_seq_length,
num_examples=batch_size)
input_path_2 = os.path.join(self.get_temp_dir(), 'train_2.tf_record')
_create_fake_dataset(input_path_2,
seq_length=100,
num_masked_tokens=70,
max_seq_length=max_seq_length,
num_examples=batch_size)
input_paths = ','.join([input_path_1, input_path_2])
data_config = pretrain_dynamic_dataloader.BertPretrainDataConfig(
is_training=False,
input_path=input_paths,
seq_bucket_lengths=[64, 128],
use_position_id=True,
global_batch_size=batch_size,
deterministic=True)
dataset = pretrain_dynamic_dataloader.PretrainingDynamicDataLoader(
data_config).load()
dataset_it = iter(dataset)
features = next(dataset_it)
self.assertCountEqual([
'input_word_ids',
'input_mask',
'input_type_ids',
'next_sentence_labels',
'masked_lm_positions',
'masked_lm_ids',
'masked_lm_weights',
'position_ids',
], features.keys())
# Sequence length dimension should be bucketized and pad to 64.
self.assertEqual(features['input_word_ids'].shape, (batch_size, 64))
self.assertEqual(features['input_mask'].shape, (batch_size, 64))
self.assertEqual(features['input_type_ids'].shape, (batch_size, 64))
self.assertEqual(features['position_ids'].shape, (batch_size, 64))
self.assertEqual(features['masked_lm_positions'].shape,
(batch_size, 20))
features = next(dataset_it)
self.assertEqual(features['input_word_ids'].shape, (batch_size, 128))
self.assertEqual(features['input_mask'].shape, (batch_size, 128))
self.assertEqual(features['input_type_ids'].shape, (batch_size, 128))
self.assertEqual(features['position_ids'].shape, (batch_size, 128))
self.assertEqual(features['masked_lm_positions'].shape,
(batch_size, 70))
def test_distribution_strategy(self, distribution_strategy):
max_seq_length = 128
batch_size = 8
input_path = os.path.join(self.get_temp_dir(), 'train.tf_record')
_create_fake_dataset(input_path,
seq_length=60,
num_masked_tokens=20,
max_seq_length=max_seq_length,
num_examples=batch_size)
data_config = pretrain_dynamic_dataloader.BertPretrainDataConfig(
is_training=False,
input_path=input_path,
seq_bucket_lengths=[64, 128],
global_batch_size=batch_size)
dataloader = pretrain_dynamic_dataloader.PretrainingDynamicDataLoader(
data_config)
distributed_ds = orbit.utils.make_distributed_dataset(
distribution_strategy, dataloader.load)
train_iter = iter(distributed_ds)
with distribution_strategy.scope():
config = masked_lm.MaskedLMConfig(
init_checkpoint=self.get_temp_dir(),
model=bert.PretrainerConfig(
encoders.EncoderConfig(bert=encoders.BertEncoderConfig(
vocab_size=30522, num_layers=1)),
cls_heads=[
bert.ClsHeadConfig(inner_dim=10,
num_classes=2,
name='next_sentence')
]),
train_data=data_config)
task = masked_lm.MaskedLMTask(config)
model = task.build_model()
metrics = task.build_metrics()
@tf.function
def step_fn(features):
return task.validation_step(features, model, metrics=metrics)
distributed_outputs = distribution_strategy.run(
step_fn, args=(next(train_iter), ))
local_results = tf.nest.map_structure(
distribution_strategy.experimental_local_results,
distributed_outputs)
logging.info('Dynamic padding: local_results= %s', str(local_results))
dynamic_metrics = {}
for metric in metrics:
dynamic_metrics[metric.name] = metric.result()
data_config = pretrain_dataloader.BertPretrainDataConfig(
is_training=False,
input_path=input_path,
seq_length=max_seq_length,
max_predictions_per_seq=20,
global_batch_size=batch_size)
dataloader = pretrain_dataloader.BertPretrainDataLoader(data_config)
distributed_ds = orbit.utils.make_distributed_dataset(
distribution_strategy, dataloader.load)
train_iter = iter(distributed_ds)
with distribution_strategy.scope():
metrics = task.build_metrics()
@tf.function
def step_fn_b(features):
return task.validation_step(features, model, metrics=metrics)
distributed_outputs = distribution_strategy.run(
step_fn_b, args=(next(train_iter), ))
local_results = tf.nest.map_structure(
distribution_strategy.experimental_local_results,
distributed_outputs)
logging.info('Static padding: local_results= %s', str(local_results))
static_metrics = {}
for metric in metrics:
static_metrics[metric.name] = metric.result()
for key in static_metrics:
# We need to investigate the differences on losses.
if key != 'next_sentence_loss':
self.assertEqual(dynamic_metrics[key], static_metrics[key])