def test_train_one_epoch(self):
"""Tests training loop over one epoch."""
trainer = training.Trainer(self._optimizer, self._model, self._state,
self._dataset)
with self.subTest(name='trainer_instantiation'):
self.assertIsInstance(trainer, training.Trainer)
best_model, best_metrics = trainer.train(self._num_epochs)
with self.subTest(name='best_model_type'):
self.assertIsInstance(best_model, flax.deprecated.nn.Model)
with self.subTest(name='train_accuracy'):
self.assertBetween(best_metrics['train_accuracy'], 0., 1.)
with self.subTest(name='train_avg_loss'):
self.assertBetween(best_metrics['train_avg_loss'], self._min_loss,
self._max_loss)
with self.subTest(name='step'):
self.assertGreater(best_metrics['step'], 0)
with self.subTest(name='cosine_distance'):
self.assertBetween(best_metrics['cosine_distance'], 0., 1.)
with self.subTest(name='cumulative_gradient_norm'):
self.assertGreater(best_metrics['cumulative_gradient_norm'], 0)
with self.subTest(name='test_accuracy'):
self.assertBetween(best_metrics['test_accuracy'], 0., 1.)
with self.subTest(name='test_avg_loss'):
self.assertBetween(best_metrics['test_avg_loss'], self._min_loss,
self._max_loss)
def test_train_one_epoch_pruning_local_schedule(self):
"""Tests training loop over one epoch with local pruning rate schedule."""
trainer = training.Trainer(self._optimizer, self._model, self._state,
self._dataset)
with self.subTest(name='trainer_instantiation'):
self.assertIsInstance(trainer, training.Trainer)
best_model, best_metrics = trainer.train(
self._num_epochs,
pruning_rate_fn={'MaskedModule_0': lambda _: 0.5})
with self.subTest(name='best_model_type'):
self.assertIsInstance(best_model, flax.nn.Model)
with self.subTest(name='train_accuracy'):
self.assertBetween(best_metrics['train_accuracy'], 0., 1.)
with self.subTest(name='train_avg_loss'):
self.assertBetween(best_metrics['train_avg_loss'], self._min_loss,
self._max_loss)
with self.subTest(name='step'):
self.assertGreater(best_metrics['step'], 0)
with self.subTest(name='cosine_distance'):
self.assertBetween(best_metrics['cosine_distance'], 0., 1.)
with self.subTest(name='cumulative_gradient_norm'):
self.assertGreater(best_metrics['cumulative_gradient_norm'], 0.)
with self.subTest(name='test_accuracy'):
self.assertBetween(best_metrics['test_accuracy'], 0., 1.)
with self.subTest(name='test_avg_loss'):
self.assertBetween(best_metrics['test_avg_loss'], self._min_loss,
self._max_loss)
if isinstance(pruning_schedule, collections.Mapping):
pruning_rate_fn = {
f'MaskedModule_{layer_num}': pruning_fn_p(schedule)
for layer_num, schedule in pruning_schedule.items()
}
else:
pruning_rate_fn = pruning_fn_p(pruning_schedule)
else:
pruning_rate_fn = lr_schedule.create_constant_learning_rate_schedule(
FLAGS.pruning_rate, steps_per_epoch)
if jax.host_id() == 0:
trainer = training.Trainer(
optimizer,
initial_model,
initial_state,
dataset,
rng,
summary_writer=summary_writer,
)
else:
trainer = training.Trainer(
optimizer, initial_model, initial_state, dataset, rng)
_, best_metrics = trainer.train(
FLAGS.epochs,
lr_fn=lr_fn,
pruning_rate_fn=pruning_rate_fn,
update_iter=FLAGS.update_iterations,
update_epoch=FLAGS.update_epoch,
)
def run_training():
"""Trains a model."""
print('Logging to {}'.format(FLAGS.log_dir))
work_unit_id = uuid.uuid4()
experiment_dir = path.join(FLAGS.experiment_dir, str(work_unit_id))
logging.info('Saving experimental results to %s', experiment_dir)
host_count = jax.host_count()
local_device_count = jax.local_device_count()
logging.info('Device count: %d, host count: %d, local device count: %d',
jax.device_count(), host_count, local_device_count)
if jax.host_id() == 0:
summary_writer = tensorboard.SummaryWriter(experiment_dir)
dataset = dataset_factory.create_dataset(
FLAGS.dataset,
FLAGS.batch_size,
FLAGS.batch_size_test,
shuffle_buffer_size=FLAGS.shuffle_buffer_size)
logging.info('Training %s on the %s dataset...', FLAGS.model,
FLAGS.dataset)
rng = jax.random.PRNGKey(FLAGS.random_seed)
input_shape = (1, ) + dataset.shape
model, initial_state = model_factory.create_model(
FLAGS.model,
rng, ((input_shape, np.float32), ),
num_classes=dataset.num_classes)
if FLAGS.optimizer == 'Adam':
optimizer = flax.optim.Adam(learning_rate=FLAGS.lr,
weight_decay=FLAGS.weight_decay)
elif FLAGS.optimizer == 'Momentum':
optimizer = flax.optim.Momentum(learning_rate=FLAGS.lr,
beta=FLAGS.momentum,
weight_decay=FLAGS.weight_decay,
nesterov=False)
steps_per_epoch = dataset.get_train_len() // FLAGS.batch_size
if FLAGS.lr_schedule == 'constant':
lr_fn = lr_schedule.create_constant_learning_rate_schedule(
FLAGS.lr, steps_per_epoch)
elif FLAGS.lr_schedule == 'stepped':
lr_schedule_steps = ast.literal_eval(FLAGS.lr_schedule_steps)
lr_fn = lr_schedule.create_stepped_learning_rate_schedule(
FLAGS.lr, steps_per_epoch, lr_schedule_steps)
elif FLAGS.lr_schedule == 'cosine':
lr_fn = lr_schedule.create_cosine_learning_rate_schedule(
FLAGS.lr, steps_per_epoch, FLAGS.epochs)
else:
raise ValueError('Unknown LR schedule type {}'.format(
FLAGS.lr_schedule))
if jax.host_id() == 0:
trainer = training.Trainer(
optimizer,
model,
initial_state,
dataset,
rng,
summary_writer=summary_writer,
)
else:
trainer = training.Trainer(optimizer, model, initial_state, dataset,
rng)
_, best_metrics = trainer.train(FLAGS.epochs,
lr_fn=lr_fn,
update_iter=FLAGS.update_iterations,
update_epoch=FLAGS.update_epoch)
logging.info('Best metrics: %s', str(best_metrics))
if jax.host_id() == 0:
for label, value in best_metrics.items():
summary_writer.scalar('best/{}'.format(label), value,
FLAGS.epochs * steps_per_epoch)
summary_writer.close()
