def aggregate_logging_outputs(logging_outputs):
"""Aggregate logging outputs from data parallel training."""
agg_output = LabelSmoothedCrossEntropyCriterion.aggregate_logging_outputs(logging_outputs)
word_error = sum(log.get('word_error', 0) for log in logging_outputs)
word_count = sum(log.get('word_count', 0) for log in logging_outputs)
char_error = sum(log.get('char_error', 0) for log in logging_outputs)
char_count = sum(log.get('char_count', 0) for log in logging_outputs)
if word_count > 0: # model.training == False
agg_output['word_error'] = word_error
agg_output['word_count'] = word_count
if char_count > 0: # model.training == False
agg_output['char_error'] = char_error
agg_output['char_count'] = char_count
return agg_output
def aggregate_logging_outputs(logging_outputs):
"""Aggregate logging outputs from data parallel training."""
xent_outputs_dict = LabelSmoothedCrossEntropyCriterion.aggregate_logging_outputs(logging_outputs)
ntokens = sum(log.get('ntokens', 0) for log in logging_outputs)
nsentences = sum(log.get('nsentences', 0) for log in logging_outputs)
sample_size = sum(log.get('sample_size', 0) for log in logging_outputs)
if 'editability_loss' not in logging_outputs[0]:
return xent_outputs_dict
xent_outputs_dict['editability_loss'] = sum(log['editability_loss'] for log in logging_outputs) / len(
logging_outputs)
xent_outputs_dict['main_loss'] = sum(
log.get('main_loss', 0) for log in logging_outputs) / sample_size / math.log(2) if sample_size > 0 else 0.
xent_outputs_dict['stability_loss'] = sum(log['stability_loss'] for log in logging_outputs) / len(
logging_outputs)
xent_outputs_dict['edit_complexity'] = sum(log['edit_complexity'] for log in logging_outputs) / len(
logging_outputs)
return xent_outputs_dict
def aggregate_logging_outputs(cls, logging_outputs):
"""Aggregate logging outputs from data parallel training."""
return LabelSmoothedCrossEntropyCriterion.aggregate_logging_outputs(
logging_outputs)