fairseq/fairseq/criterions/label_smoothed_cross_entropy_with_alignment.py

# 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 fairseq import metrics, utils
from fairseq.criterions import register_criterion

from .label_smoothed_cross_entropy import (
    LabelSmoothedCrossEntropyCriterion,
    LabelSmoothedCrossEntropyCriterionConfig,
)

from dataclasses import dataclass, field


@dataclass
class LabelSmoothedCrossEntropyCriterionWithAlignmentConfig(
    LabelSmoothedCrossEntropyCriterionConfig
):
    alignment_lambda: float = field(
        default=0.05, metadata={"help": "weight for the alignment loss"}
    )


@register_criterion(
    "label_smoothed_cross_entropy_with_alignment",
    dataclass=LabelSmoothedCrossEntropyCriterionWithAlignmentConfig,
)
class LabelSmoothedCrossEntropyCriterionWithAlignment(
    LabelSmoothedCrossEntropyCriterion
):
    def __init__(self, task, sentence_avg, label_smoothing, alignment_lambda):
        super().__init__(task, sentence_avg, label_smoothing)
        self.alignment_lambda = alignment_lambda

    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, nll_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": utils.item(loss.data) if reduce else loss.data,
            "nll_loss": utils.item(nll_loss.data) if reduce else nll_loss.data,
            "ntokens": sample["ntokens"],
            "nsentences": sample["target"].size(0),
            "sample_size": sample_size,
        }

        alignment_loss = None

        # Compute alignment loss only for training set and non dummy batches.
        if "alignments" in sample and sample["alignments"] is not None:
            alignment_loss = self.compute_alignment_loss(sample, net_output)

        if alignment_loss is not None:
            logging_output["alignment_loss"] = utils.item(alignment_loss.data)
            loss += self.alignment_lambda * alignment_loss

        return loss, sample_size, logging_output

    def compute_alignment_loss(self, sample, net_output):
        attn_prob = net_output[1]["attn"][0]
        bsz, tgt_sz, src_sz = attn_prob.shape
        attn = attn_prob.view(bsz * tgt_sz, src_sz)

        align = sample["alignments"]
        align_weights = sample["align_weights"].float()

        if len(align) > 0:
            # Alignment loss computation. align (shape [:, 2]) contains the src-tgt index pairs corresponding to
            # the alignments. align_weights (shape [:]) contains the 1 / frequency of a tgt index for normalizing.
            loss = -(
                (attn[align[:, 1][:, None], align[:, 0][:, None]]).log()
                * align_weights[:, None]
            ).sum()
        else:
            return None

        return loss

    @staticmethod
    def reduce_metrics(logging_outputs) -> None:
        """Aggregate logging outputs from data parallel training."""
        loss_sum = utils.item(sum(log.get("loss", 0) for log in logging_outputs))
        nll_loss_sum = utils.item(
            sum(log.get("nll_loss", 0) for log in logging_outputs)
        )
        alignment_loss_sum = utils.item(
            sum(log.get("alignment_loss", 0) for log in logging_outputs)
        )
        ntokens = utils.item(sum(log.get("ntokens", 0) for log in logging_outputs))
        sample_size = utils.item(
            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_scalar(
            "nll_loss", nll_loss_sum / ntokens / math.log(2), ntokens, round=3
        )
        metrics.log_scalar(
            "alignment_loss",
            alignment_loss_sum / sample_size / math.log(2),
            sample_size,
            round=3,
        )
        metrics.log_derived(
            "ppl", lambda meters: utils.get_perplexity(meters["nll_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