def reduce_metrics(logging_outputs) -> None:
"""Aggregate logging outputs from data parallel training."""
loss_sum = sum(log.get('loss', 0) for log in logging_outputs)
ntokens = sum(log.get('ntokens', 0) for log in logging_outputs)
sample_size = sum(log.get('sample_size', 0) for log in logging_outputs)
match = sum(log.get('match', 0) for log in logging_outputs)
total = sum(log.get('total', 0) for log in logging_outputs)
metrics.log_scalar('match', value=match, round=0)
metrics.log_scalar('total', value=total, round=0)
metrics.log_scalar('loss',
loss_sum / sample_size / math.log(2),
sample_size,
round=3)
if sample_size != ntokens:
metrics.log_scalar('nll_loss',
loss_sum / ntokens / math.log(2),
ntokens,
round=3)
metrics.log_derived(
'ppl',
lambda meters: utils.get_perplexity(meters['nll_loss'].avg))
else:
metrics.log_derived(
'ppl', lambda meters: utils.get_perplexity(meters['loss'].avg))
def reduce_metrics(logging_outputs) -> None:
"""Aggregate logging outputs from data parallel training."""
loss_sum = utils.item(
sum(log.get('loss', 0) for log in logging_outputs))
nll_loss_sum = utils.item(
sum(log.get('nll_loss', 0) for log in logging_outputs))
alignment_loss_sum = utils.item(
sum(log.get('alignment_loss', 0) for log in logging_outputs))
ntokens = utils.item(
sum(log.get('ntokens', 0) for log in logging_outputs))
sample_size = utils.item(
sum(log.get('sample_size', 0) for log in logging_outputs))
metrics.log_scalar('loss',
loss_sum / sample_size / math.log(2),
sample_size,
round=3)
metrics.log_scalar('nll_loss',
nll_loss_sum / ntokens / math.log(2),
ntokens,
round=3)
metrics.log_scalar('alignment_loss',
alignment_loss_sum / sample_size / math.log(2),
sample_size,
round=3)
metrics.log_derived(
'ppl', lambda meters: utils.get_perplexity(meters['nll_loss'].avg))
def reduce_metrics(logging_outputs) -> None:
"""Aggregate logging outputs from data parallel training."""
loss_sum = sum(log.get('loss', 0) for log in logging_outputs)
sample_size = sum(log.get('sample_size', 0) for log in logging_outputs)
# metrics.log_scalar('loss', loss_sum / sample_size / math.log(2), sample_size, round=3)
metrics.log_scalar('loss',
loss_sum / sample_size,
sample_size,
round=3)
metrics.log_derived(
'ppl', lambda meters: utils.get_perplexity(meters['loss'].avg))
def get_valid_stats(args, trainer, stats):
if 'nll_loss' in stats and 'ppl' not in stats:
stats['ppl'] = utils.get_perplexity(stats['nll_loss'])
stats['num_updates'] = trainer.get_num_updates()
if hasattr(checkpoint_utils.save_checkpoint, 'best'):
key = 'best_{0}'.format(args['checkpoint']['best_checkpoint_metric'])
best_function = max if args['checkpoint']['maximize_best_checkpoint_metric'] else min
stats[key] = best_function(
checkpoint_utils.save_checkpoint.best,
stats[args['checkpoint']['best_checkpoint_metric']],
)
return stats
def get_training_stats(stats):
if 'nll_loss' in stats and 'ppl' not in stats:
stats['ppl'] = utils.get_perplexity(stats['nll_loss'])
stats['wall'] = round(metrics.get_meter('default', 'wall').elapsed_time, 0)
return stats
def get_valid_stats(args, trainer, stats):
if 'nll_loss' in stats and 'ppl' not in stats:
stats['ppl'] = utils.get_perplexity(stats['nll_loss'])
stats['num_updates'] = trainer.get_num_updates()
return stats
