def define_common_bert_flags():
"""Define common flags for BERT tasks."""
flags_core.define_base(
data_dir=False,
model_dir=True,
clean=False,
train_epochs=False,
epochs_between_evals=False,
stop_threshold=False,
batch_size=False,
num_gpu=True,
export_dir=False,
distribution_strategy=True,
run_eagerly=True)
flags_core.define_distribution()
flags.DEFINE_string('bert_config_file', None,
'Bert configuration file to define core bert layers.')
flags.DEFINE_string(
'model_export_path', None,
'Path to the directory, where trainined model will be '
'exported.')
flags.DEFINE_string('tpu', '', 'TPU address to connect to.')
flags.DEFINE_string(
'init_checkpoint', None,
'Initial checkpoint (usually from a pre-trained BERT model).')
flags.DEFINE_integer('num_train_epochs', 3,
'Total number of training epochs to perform.')
flags.DEFINE_integer(
'steps_per_loop', None,
'Number of steps per graph-mode loop. Only training step '
'happens inside the loop. Callbacks will not be called '
'inside. If not set the value will be configured depending on the '
'devices available.')
flags.DEFINE_float('learning_rate', 5e-5,
'The initial learning rate for Adam.')
flags.DEFINE_float('end_lr', 0.0,
'The end learning rate for learning rate decay.')
flags.DEFINE_string('optimizer_type', 'adamw',
'The type of optimizer to use for training (adamw|lamb)')
flags.DEFINE_boolean(
'scale_loss', False,
'Whether to divide the loss by number of replica inside the per-replica '
'loss function.')
flags.DEFINE_boolean(
'use_keras_compile_fit', False,
'If True, uses Keras compile/fit() API for training logic. Otherwise '
'use custom training loop.')
flags.DEFINE_string(
'hub_module_url', None, 'TF-Hub path/url to Bert module. '
'If specified, init_checkpoint flag should not be used.')
flags.DEFINE_bool('hub_module_trainable', True,
'True to make keras layers in the hub module trainable.')
flags.DEFINE_string(
'sub_model_export_name', None,
'If set, `sub_model` checkpoints are exported into '
'FLAGS.model_dir/FLAGS.sub_model_export_name.')
flags.DEFINE_bool('explicit_allreduce', False,
'True to use explicit allreduce instead of the implicit '
'allreduce in optimizer.apply_gradients(). If fp16 mixed '
'precision training is used, this also enables allreduce '
'gradients in fp16.')
flags.DEFINE_integer('allreduce_bytes_per_pack', 0,
'Number of bytes of a gradient pack for allreduce. '
'Should be positive integer, if set to 0, all '
'gradients are in one pack. Breaking gradient into '
'packs could enable overlap between allreduce and '
'backprop computation. This flag only takes effect '
'when explicit_allreduce is set to True.')
flags_core.define_log_steps()
# Adds flags for mixed precision and multi-worker training.
flags_core.define_performance(
num_parallel_calls=False,
inter_op=False,
intra_op=False,
synthetic_data=False,
max_train_steps=False,
dtype=True,
loss_scale=True,
all_reduce_alg=True,
num_packs=False,
tf_gpu_thread_mode=True,
datasets_num_private_threads=True,
enable_xla=True,
fp16_implementation=True,
)
# Adds gin configuration flags.
hyperparams_flags.define_gin_flags()
def define_common_bert_flags():
"""Define common flags for BERT tasks."""
flags_core.define_base(
data_dir=False,
model_dir=True,
clean=False,
train_epochs=False,
epochs_between_evals=False,
stop_threshold=False,
batch_size=False,
num_gpu=True,
export_dir=False,
distribution_strategy=True,
run_eagerly=True)
flags_core.define_distribution()
flags.DEFINE_string('bert_config_file', None,
'Bert configuration file to define core bert layers.')
flags.DEFINE_string(
'model_export_path', None,
'Path to the directory, where trainined model will be '
'exported.')
flags.DEFINE_string('tpu', '', 'TPU address to connect to.')
flags.DEFINE_string(
'init_checkpoint', None,
'Initial checkpoint (usually from a pre-trained BERT model).')
flags.DEFINE_integer('num_train_epochs', 3,
'Total number of training epochs to perform.')
flags.DEFINE_integer(
'steps_per_loop', 1,
'Number of steps per graph-mode loop. Only training step '
'happens inside the loop. Callbacks will not be called '
'inside.')
flags.DEFINE_float('learning_rate', 5e-5,
'The initial learning rate for Adam.')
flags.DEFINE_float('end_lr', 0.0,
'The end learning rate for learning rate decay.')
flags.DEFINE_string('optimizer_type', 'adamw',
'The type of optimizer to use for training (adamw|lamb)')
flags.DEFINE_boolean(
'scale_loss', False,
'Whether to divide the loss by number of replica inside the per-replica '
'loss function.')
flags.DEFINE_boolean(
'use_keras_compile_fit', False,
'If True, uses Keras compile/fit() API for training logic. Otherwise '
'use custom training loop.')
flags.DEFINE_string(
'hub_module_url', None, 'TF-Hub path/url to Bert module. '
'If specified, init_checkpoint flag should not be used.')
flags.DEFINE_bool('hub_module_trainable', True,
'True to make keras layers in the hub module trainable.')
flags_core.define_log_steps()
# Adds flags for mixed precision and multi-worker training.
flags_core.define_performance(
num_parallel_calls=False,
inter_op=False,
intra_op=False,
synthetic_data=False,
max_train_steps=False,
dtype=True,
dynamic_loss_scale=True,
loss_scale=True,
all_reduce_alg=True,
num_packs=False,
tf_gpu_thread_mode=True,
datasets_num_private_threads=True,
enable_xla=True,
fp16_implementation=True,
)
# Adds gin configuration flags.
hyperparams_flags.define_gin_flags()