Source code for fairseq.criterions.cross_entropy

# 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 import metrics, utils
from fairseq.criterions import FairseqCriterion, register_criterion
from fairseq.dataclass import FairseqDataclass
from omegaconf import II

class CrossEntropyCriterionConfig(FairseqDataclass):
    sentence_avg: bool = II("optimization.sentence_avg")

[docs]@register_criterion("cross_entropy", dataclass=CrossEntropyCriterionConfig) class CrossEntropyCriterion(FairseqCriterion): def __init__(self, task, sentence_avg): super().__init__(task) self.sentence_avg = sentence_avg
[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.sentence_avg else sample["ntokens"] ) logging_output = { "loss":, "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 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) # we divide by log(2) to convert the loss from base e to base 2 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) )
[docs] @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