code: # 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
  from dataclasses import dataclass
  
  import torch.nn.functional as F
  from fairseq_mod import metrics, utils
  from fairseq_mod.criterions import FairseqCriterion, register_criterion
  from fairseq_mod.dataclass import FairseqDataclass
  from omegaconf import II
  
  
  @dataclass
  class CrossEntropyCriterionConfig(FairseqDataclass):
      sentence_avg: bool = II("params.optimization.sentence_avg")
  
  
  @register_criterion("cross_entropy", dataclass=CrossEntropyCriterionConfig)
  class CrossEntropyCriterion(FairseqCriterion):
      def __init__(self, task, sentence_avg):
          super().__init__(task)
          self.sentence_avg = sentence_avg
  
      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.sentence_avg else sample["ntokens"]
          )
          logging_output = {
              "loss": loss.data,
              "ntokens": sample["ntokens"],
              "nsentences": sample["target"].size(0),
              "sample_size": sample_size,
          }
          return loss, sample_size, logging_output
  
      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
  
      @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)
          ntokens = sum(log.get("ntokens", 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
          )
          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)
              )
  
      @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
  
apis: torch.nn.functional.nll_loss