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| """Defining common flags used across all BERT models/applications."""
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|
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| from absl import flags
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| import tensorflow as tf, tf_keras
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|
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| from official.utils import hyperparams_flags
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| from official.utils.flags import core as flags_core
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| def define_common_bert_flags():
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| """Define common flags for BERT tasks."""
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| flags_core.define_base(
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| data_dir=False,
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| model_dir=True,
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| clean=False,
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| train_epochs=False,
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| epochs_between_evals=False,
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| stop_threshold=False,
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| batch_size=False,
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| num_gpu=True,
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| export_dir=False,
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| distribution_strategy=True,
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| run_eagerly=True)
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| flags_core.define_distribution()
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| flags.DEFINE_string('bert_config_file', None,
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| 'Bert configuration file to define core bert layers.')
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| flags.DEFINE_string(
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| 'model_export_path', None,
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| 'Path to the directory, where trainined model will be '
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| 'exported.')
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| flags.DEFINE_string('tpu', '', 'TPU address to connect to.')
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| flags.DEFINE_string(
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| 'init_checkpoint', None,
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| 'Initial checkpoint (usually from a pre-trained BERT model).')
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| flags.DEFINE_integer('num_train_epochs', 3,
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| 'Total number of training epochs to perform.')
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| flags.DEFINE_integer(
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| 'steps_per_loop', None,
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| 'Number of steps per graph-mode loop. Only training step '
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| 'happens inside the loop. Callbacks will not be called '
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| 'inside. If not set the value will be configured depending on the '
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| 'devices available.')
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| flags.DEFINE_float('learning_rate', 5e-5,
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| 'The initial learning rate for Adam.')
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| flags.DEFINE_float('end_lr', 0.0,
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| 'The end learning rate for learning rate decay.')
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| flags.DEFINE_string('optimizer_type', 'adamw',
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| 'The type of optimizer to use for training (adamw|lamb)')
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| flags.DEFINE_boolean(
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| 'scale_loss', False,
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| 'Whether to divide the loss by number of replica inside the per-replica '
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| 'loss function.')
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| flags.DEFINE_boolean(
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| 'use_keras_compile_fit', False,
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| 'If True, uses Keras compile/fit() API for training logic. Otherwise '
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| 'use custom training loop.')
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| flags.DEFINE_string(
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| 'hub_module_url', None, 'TF-Hub path/url to Bert module. '
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| 'If specified, init_checkpoint flag should not be used.')
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| flags.DEFINE_bool('hub_module_trainable', True,
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| 'True to make keras layers in the hub module trainable.')
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| flags.DEFINE_string(
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| 'sub_model_export_name', None,
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| 'If set, `sub_model` checkpoints are exported into '
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| 'FLAGS.model_dir/FLAGS.sub_model_export_name.')
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| flags.DEFINE_bool('explicit_allreduce', False,
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| 'True to use explicit allreduce instead of the implicit '
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| 'allreduce in optimizer.apply_gradients(). If fp16 mixed '
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| 'precision training is used, this also enables allreduce '
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| 'gradients in fp16.')
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| flags.DEFINE_integer('allreduce_bytes_per_pack', 0,
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| 'Number of bytes of a gradient pack for allreduce. '
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| 'Should be positive integer, if set to 0, all '
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| 'gradients are in one pack. Breaking gradient into '
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| 'packs could enable overlap between allreduce and '
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| 'backprop computation. This flag only takes effect '
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| 'when explicit_allreduce is set to True.')
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|
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| flags_core.define_log_steps()
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| flags_core.define_performance(
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| num_parallel_calls=False,
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| inter_op=False,
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| intra_op=False,
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| synthetic_data=False,
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| max_train_steps=False,
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| dtype=True,
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| loss_scale=True,
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| all_reduce_alg=True,
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| num_packs=False,
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| tf_gpu_thread_mode=True,
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| datasets_num_private_threads=True,
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| enable_xla=True,
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| fp16_implementation=True,
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| )
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|
| hyperparams_flags.define_gin_flags()
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|
|
| def dtype():
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| return flags_core.get_tf_dtype(flags.FLAGS)
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|
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|
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| def use_float16():
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| return flags_core.get_tf_dtype(flags.FLAGS) == tf.float16
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|
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|
|
| def get_loss_scale():
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| return flags_core.get_loss_scale(flags.FLAGS, default_for_fp16='dynamic')
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|
|
