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, modules, utils
  from fairseq.criterions import FairseqCriterion, register_criterion
  
  
  @register_criterion('masked_lm')
  class MaskedLmLoss(FairseqCriterion):
      """
      Implementation for the loss used in masked language model (MLM) training.
      """
  
      def __init__(self, task, tpu):
          super().__init__(task)
          self.tpu = tpu
  
      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
          """
          masked_tokens = sample['target'].ne(self.padding_idx)
          sample_size = masked_tokens.int().sum()
  
          # Rare: when all tokens are masked, project all tokens.
          # We use torch.where to avoid device-to-host transfers,
          # except on CPU where torch.where is not well supported
          # (see github.com/pytorch/pytorch/issues/26247).
          if self.tpu:
              masked_tokens = None  # always project all tokens on TPU
          elif masked_tokens.device == torch.device('cpu'):
              if not masked_tokens.any():
                  masked_tokens = None
          else:
              masked_tokens = torch.where(
                  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])
          if masked_tokens is not None:
              targets = targets[masked_tokens]
  
          loss = modules.cross_entropy(
              logits.view(-1, logits.size(-1)),
              targets.view(-1),
              reduction='sum',
              ignore_index=self.padding_idx,
          )
  
          logging_output = {
              'loss': loss if self.tpu else 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
  