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))
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)
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."""
sample_size = utils.item(
sum(log.get("sample_size", 0) for log in logging_outputs))
loss = utils.item(sum(log.get("loss", 0) for log in logging_outputs))
nll_loss = utils.item(
sum(log.get("nll_loss", 0) for log in logging_outputs))
metrics.log_scalar('loss',
loss / sample_size / math.log(2),
sample_size,
round=3)
metrics.log_scalar('nll_loss',
nll_loss / sample_size / math.log(2),
sample_size,
round=3)
metrics.log_derived(
'ppl', lambda meters: utils.get_perplexity(meters['loss'].avg))
for key in logging_outputs[0]:
if key[-5:] == "-loss":
val = sum(log.get(key, 0) for log in logging_outputs)
metrics.log_scalar(
key[:-5],
val / sample_size /
math.log(2) if sample_size > 0 else 0.0,
sample_size,
round=3,
)
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_derived(
'ppl', lambda meters: utils.get_perplexity(meters['loss'].avg))