def run_train_single_device(run_configuration):
"""Runs the training workflow without pmap or jit."""
config = run_configuration.config
run_dir = run_configuration.run_dir
adapter = run_configuration.adapter
checkpoint_path = run_configuration.original_checkpoint_path
dataset = run_configuration.dataset_info.dataset
random_seed = 0
rng = jax.random.PRNGKey(random_seed)
rng = jax.random.fold_in(rng, jax.host_id())
dropout_rng, init_rng = jax.random.split(rng)
# Set up optimizer.
optimizer = adapter.create_optimizer(run_configuration, rng=init_rng)
# Set up train step.
train_step = adapter.make_train_step(single_device=True)
# Set up checkpointing.
# TODO(dbieber): Set up phoenix.
checkpoint_dir = checkpoint_utils.build_checkpoint_dir(run_dir)
if checkpoint_path is None:
checkpoint_path = checkpoint_utils.latest_checkpoint(checkpoint_dir)
optimizer = checkpoint_utils.handle_restart_behavior(
checkpoint_path, optimizer, config)
start_step = int(optimizer.state.step)
num_train_steps = config.train.total_steps
# Begin training loop.
dataset_iter_raw = iter(dataset)
dataset_iter = adapter.preprocess(dataset_iter_raw, single_device=True)
for step, example in zip(range(start_step, num_train_steps), dataset_iter):
print(f'Step #{step}')
train_inputs = adapter.get_train_inputs(example)
optimizer, metrics, dropout_rng, logits, state = train_step(
optimizer, train_inputs, dropout_rng)
del metrics, logits, state # Unused.
# Save a Checkpoint.
if ((step % config.logging.save_freq == 0 and step > 0)
or step == num_train_steps - 1):
if jax.host_id() == 0 and config.logging.save_freq:
# Save unreplicated optimizer + model state.
checkpoint_utils.save_checkpoint(checkpoint_dir, optimizer,
step)
def run_train(run_configuration):
"""Runs the training workflow."""
config = run_configuration.config
run_dir = run_configuration.run_dir
adapter = run_configuration.adapter
log_dir = os.path.join(run_dir, 'train')
checkpoint_path = run_configuration.original_checkpoint_path
dataset = run_configuration.dataset_info.dataset
info = run_configuration.dataset_info.info
random_seed = 0
rng = jax.random.PRNGKey(random_seed)
rng = jax.random.fold_in(rng, jax.host_id())
rng, init_rng = jax.random.split(rng)
dropout_rngs = jax.random.split(rng, jax.local_device_count())
# Set up optimizer.
optimizer = adapter.create_optimizer(run_configuration, rng=init_rng)
# Set up train step.
train_step = adapter.make_train_step()
if jax.host_id() == 0:
summary_writer = tensorboard.SummaryWriter(log_dir)
# Set up checkpointing.
# TODO(dbieber): Set up phoenix.
checkpoint_dir = checkpoint_utils.build_checkpoint_dir(run_dir)
if checkpoint_path is None:
checkpoint_path = checkpoint_utils.latest_checkpoint(checkpoint_dir)
optimizer = checkpoint_utils.handle_restart_behavior(
checkpoint_path, optimizer, config)
start_step = int(optimizer.state.step)
num_train_steps = config.train.total_steps
# Replicate optimizer.
optimizer = flax.jax_utils.replicate(optimizer)
# Begin training loop.
dataset_iter_raw = iter(dataset)
dataset_iter = adapter.preprocess(dataset_iter_raw)
summary_freq = config.logging.summary_freq
metrics_all = []
tick = time.time()
for step, example in zip(range(start_step, num_train_steps), dataset_iter):
train_inputs = adapter.get_train_inputs(example)
optimizer, metrics, dropout_rngs, logits, state = train_step(
optimizer, train_inputs, dropout_rngs)
metrics_all.append(metrics)
# Save a Checkpoint
if ((step % config.logging.save_freq == 0 and step > 0)
or step == num_train_steps - 1):
if jax.host_id() == 0 and config.logging.save_freq:
# Save unreplicated optimizer + model state.
checkpoint_utils.save_checkpoint(
checkpoint_dir, jax_utils.unreplicate(optimizer), step)
# Periodic metric handling.
if summary_freq and step % summary_freq == 0 and step > 0:
metrics_all = common_utils.get_metrics(metrics_all)
lr = metrics_all.pop('learning_rate').mean()
metrics_sums = jax.tree_map(jnp.sum, metrics_all)
denominator = metrics_sums.pop('denominator')
summary = jax.tree_map(lambda x: x / denominator, metrics_sums) # pylint: disable=cell-var-from-loop
summary['learning_rate'] = lr
# Calculate (clipped) perplexity after averaging log-perplexities:
summary['perplexity'] = jnp.clip(jnp.exp(summary['loss']),
a_max=1.0e4)
logging.info('train step: %d, loss: %.4f', step, summary['loss'])
if jax.host_id() == 0:
tock = time.time()
steps_per_sec = summary_freq / (tock - tick)
examples_per_sec = denominator / (tock - tick)
tick = tock
summary_writer.scalar('per-second/steps', steps_per_sec, step)
summary_writer.scalar('per-second/examples', examples_per_sec,
step)
for key, val in summary.items():
summary_writer.scalar(key, val, step)
adapter.write_summaries(example, logits, summary_writer, info,
step, state)
summary_writer.flush()
# Reset metric accumulation for next evaluation cycle.
metrics_all = []