# Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import math
import torch.nn.functional as F
from fairseq import utils
from . import FairseqCriterion, register_criterion
[docs]@register_criterion('cross_entropy')
class CrossEntropyCriterion(FairseqCriterion):
def __init__(self, args, task):
super().__init__(args, task)
[docs] def forward(self, model, sample, reduce=True):
"""Compute the loss for the given sample.
Returns a tuple with three elements:
1) the loss
2) the sample size, which is used as the denominator for the gradient
3) logging outputs to display while training
"""
net_output = model(**sample['net_input'])
loss, _ = self.compute_loss(model, net_output, sample, reduce=reduce)
sample_size = sample['target'].size(0) if self.args.sentence_avg else sample['ntokens']
logging_output = {
'loss': utils.item(loss.data) if reduce else loss.data,
'ntokens': sample['ntokens'],
'nsentences': sample['target'].size(0),
'sample_size': sample_size,
}
return loss, sample_size, logging_output
[docs] def compute_loss(self, model, net_output, sample, reduce=True):
lprobs = model.get_normalized_probs(net_output, log_probs=True)
lprobs = lprobs.view(-1, lprobs.size(-1))
target = model.get_targets(sample, net_output).view(-1)
loss = F.nll_loss(
lprobs,
target,
ignore_index=self.padding_idx,
reduction='sum' if reduce else 'none',
)
return loss, loss
[docs] @staticmethod
def aggregate_logging_outputs(logging_outputs):
"""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)
nsentences = sum(log.get('nsentences', 0) for log in logging_outputs)
sample_size = sum(log.get('sample_size', 0) for log in logging_outputs)
agg_output = {
'loss': loss_sum / sample_size / math.log(2),
'ntokens': ntokens,
'nsentences': nsentences,
'sample_size': sample_size,
}
if sample_size != ntokens:
agg_output['nll_loss'] = loss_sum / ntokens / math.log(2)
return agg_output