def create_test_trainer(self, distribution):
trainer = trainer_lib.ProgressiveTrainer(self._config,
prog_task=TestPolicy(
distribution,
self._config.task),
ckpt_dir=self.get_temp_dir())
return trainer
def create_test_trainer(self, distribution, model_dir,
change_train_dataset):
trainer = trainer_lib.ProgressiveTrainer(
self._config,
prog_task=TestPolicy(distribution, self._config.task,
change_train_dataset),
ckpt_dir=model_dir)
return trainer
def test_configure_optimizer(self, mixed_precision_dtype, loss_scale):
config = cfg.ExperimentConfig(
task=cfg.TaskConfig(model=bert.PretrainerConfig()),
runtime=cfg.RuntimeConfig(
mixed_precision_dtype=mixed_precision_dtype,
loss_scale=loss_scale),
trainer=trainer_lib.ProgressiveTrainerConfig(
export_checkpoint=True,
export_checkpoint_interval=1,
export_only_final_stage_ckpt=False))
task = TestPolicy(None, config.task)
trainer = trainer_lib.ProgressiveTrainer(config, task,
self.get_temp_dir())
if mixed_precision_dtype != 'float16':
self.assertIsInstance(trainer.optimizer, tf.keras.optimizers.SGD)
elif mixed_precision_dtype == 'float16' and loss_scale is None:
self.assertIsInstance(trainer.optimizer, tf.keras.optimizers.SGD)
metrics = trainer.train(tf.convert_to_tensor(5, dtype=tf.int32))
self.assertIn('training_loss', metrics)
def run_experiment(distribution_strategy: tf.distribute.Strategy,
task: base_task.Task,
mode: str,
params: config_definitions.ExperimentConfig,
model_dir: str,
run_post_eval: bool = False,
save_summary: bool = True) \
-> Tuple[tf.keras.Model, Mapping[str, Any]]:
"""Runs train/eval configured by the experiment params.
Args:
distribution_strategy: A distribution distribution_strategy.
task: A Task instance.
mode: A 'str', specifying the mode. Can be 'train', 'eval', 'train_and_eval'
or 'continuous_eval'.
params: ExperimentConfig instance.
model_dir: A 'str', a path to store model checkpoints and summaries.
run_post_eval: Whether to run post eval once after training, metrics logs
are returned.
save_summary: Whether to save train and validation summary.
Returns:
A 2-tuple of (model, eval_logs).
model: `tf.keras.Model` instance.
eval_logs: returns eval metrics logs when run_post_eval is set to True,
otherwise, returns {}.
"""
with distribution_strategy.scope():
logging.info('Running progressive trainer.')
trainer = prog_trainer_lib.ProgressiveTrainer(
params,
task,
ckpt_dir=model_dir,
train='train' in mode,
evaluate=('eval' in mode) or run_post_eval,
checkpoint_exporter=base_train_lib.maybe_create_best_ckpt_exporter(
params, model_dir))
if trainer.checkpoint:
checkpoint_manager = tf.train.CheckpointManager(
trainer.checkpoint,
directory=model_dir,
max_to_keep=params.trainer.max_to_keep,
step_counter=trainer.global_step,
checkpoint_interval=params.trainer.checkpoint_interval,
init_fn=trainer.initialize)
else:
checkpoint_manager = None
controller = orbit.Controller(
strategy=distribution_strategy,
trainer=trainer if 'train' in mode else None,
evaluator=trainer,
global_step=trainer.global_step,
steps_per_loop=params.trainer.steps_per_loop,
checkpoint_manager=checkpoint_manager,
summary_dir=os.path.join(model_dir, 'train') if
(save_summary) else None,
eval_summary_dir=os.path.join(model_dir, 'validation') if
(save_summary) else None,
summary_interval=params.trainer.summary_interval if
(save_summary) else None)
logging.info('Starts to execute mode: %s', mode)
with distribution_strategy.scope():
if mode == 'train':
controller.train(steps=params.trainer.train_steps)
elif mode == 'train_and_eval':
controller.train_and_evaluate(
train_steps=params.trainer.train_steps,
eval_steps=params.trainer.validation_steps,
eval_interval=params.trainer.validation_interval)
elif mode == 'eval':
controller.evaluate(steps=params.trainer.validation_steps)
elif mode == 'continuous_eval':
def timeout_fn():
if trainer.global_step.numpy() >= params.trainer.train_steps:
return True
return False
controller.evaluate_continuously(
steps=params.trainer.validation_steps,
timeout=params.trainer.continuous_eval_timeout,
timeout_fn=timeout_fn)
else:
raise NotImplementedError('The mode is not implemented: %s' % mode)
if run_post_eval:
with distribution_strategy.scope():
return trainer.model, trainer.evaluate(
tf.convert_to_tensor(params.trainer.validation_steps))
else:
return trainer.model, {}