text: # Copyright (c) Facebook, Inc. and its affiliates.
  #
  # This source code is licensed under the MIT license found in the
  # LICENSE file in the root directory of this source tree.
  
  import math
  
  import torch
  import torch.nn.functional as F
  
  from fairseq import metrics, utils
  from fairseq.criterions import FairseqCriterion, register_criterion
  
  
  @register_criterion('masked_frame_lm')
  class MaskedFrameLmLoss(FairseqCriterion):
      """
      Implementation for the loss used in masked language model (MLM) training.
      """
  
      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
          """
          # compute MLM loss
          masked_tokens = sample['target'].ne(self.padding_idx)
          masked_tokens = torch.where(
              # (rare case) when all tokens are masked, project all tokens
              masked_tokens.any(),
              masked_tokens,
              masked_tokens.new([True]),
          )
  
          logits = model(**sample['net_input'], masked_tokens=masked_tokens)[0]
          targets = model.get_targets(sample, [logits])
          targets = targets[masked_tokens]
  
          loss = F.nll_loss(
              F.log_softmax(
                  logits.view(-1, logits.size(-1)),
                  dim=-1,
                  dtype=torch.float32,
              ),
              targets.view(-1),
              reduction='sum',
              ignore_index=self.padding_idx,
          )
  
          sample_size = masked_tokens.int().sum()
          logging_output = {
              'loss': loss.data,
              'ntokens': sample['ntokens'],
              'nsentences': sample['nsentences'],
              'sample_size': sample_size,
          }
          return loss, sample_size, logging_output
  
      @staticmethod
      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))
  
      @staticmethod
      def logging_outputs_can_be_summed() -> bool:
          """
          Whether the logging outputs returned by `forward` can be summed
          across workers prior to calling `reduce_metrics`. Setting this
          to True will improves distributed training speed.
          """
          return True
  