def predict_once(run_configuration, optimizer=None):
"""Predict the result once for each element in the dataset."""
adapter = run_configuration.adapter
checkpoint_path = run_configuration.original_checkpoint_path
optimizer = optimizer or adapter.create_optimizer(run_configuration)
dataset = run_configuration.dataset_info.dataset
# Restore checkpoint
optimizer = checkpoint_utils.restore_checkpoint(checkpoint_path, optimizer)
# Replicate optimizer.
optimizer = flax.jax_utils.replicate(optimizer)
predict_step = adapter.make_predict_step()
predict_step_parallel = jax.pmap(predict_step, axis_name='batch')
# Perform inference
dataset_iter_raw = iter(dataset)
dataset_iter = adapter.preprocess(dataset_iter_raw)
metrics_all = []
for example in itertools.islice(dataset_iter, 200):
train_inputs = adapter.get_train_inputs(example)
metrics, logits, state = predict_step_parallel(optimizer.target,
train_inputs)
adapter.handle_predict(metrics, logits, state)
metrics_all.append(metrics)
metrics_all = common_utils.get_metrics(metrics_all)
metrics = jax.tree_map(jnp.sum, metrics_all)
return metrics
def eval_once(run_configuration, checkpoint_path, optimizer=None):
"""Evaluates a single checkpoint on a single epoch of data."""
config = run_configuration.config
run_dir = run_configuration.run_dir
adapter = run_configuration.adapter
optimizer = optimizer or adapter.create_optimizer(run_configuration)
dataset = run_configuration.dataset_info.dataset
info = run_configuration.dataset_info.info
eval_name = config.eval_name or 'eval'
log_dir = os.path.join(run_dir, eval_name)
if jax.host_id() == 0:
summary_writer = tensorboard.SummaryWriter(log_dir)
# Restore checkpoint
optimizer = checkpoint_utils.restore_checkpoint(checkpoint_path, optimizer)
step = int(optimizer.state.step)
# Replicate optimizer.
optimizer = flax.jax_utils.replicate(optimizer)
eval_step = adapter.make_eval_step()
eval_step_parallel = jax.pmap(eval_step, axis_name='batch')
# Perform evaluation
tick = time.time()
metrics_all = []
example = None
dataset_iter_raw = iter(dataset)
dataset_iter = adapter.preprocess(dataset_iter_raw)
for unused_eval_step, example in zip(range(config.eval_steps),
dataset_iter):
train_inputs = adapter.get_train_inputs(example)
metrics, logits, state = eval_step_parallel(optimizer.target,
train_inputs)
metrics_all.append(metrics)
# Write results.
metrics_all = common_utils.get_metrics(metrics_all)
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['perplexity'] = jnp.clip(jnp.exp(summary['loss']), a_max=1.0e4)
logging.info('eval @ train step: %d, loss: %.4f', step, summary['loss'])
if jax.host_id() == 0:
tock = time.time()
steps_per_sec = len(metrics_all) / (tock - tick)
examples_per_sec = denominator / (tock - tick)
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()
