fairseq/model_parallel/criterions/vocab_parallel_cross_entropy.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 FairseqCriterion, register_criterion


try:
    from fairseq.model_parallel.megatron.mpu.cross_entropy import (
        vocab_parallel_cross_entropy,
    )

    has_megatron_submodule = True
except (ImportError, ModuleNotFoundError):
    has_megatron_submodule = False


@register_criterion("vocab_parallel_cross_entropy")
class VocabParallelCrossEntropyCriterion(FairseqCriterion):
    def __init__(self, task, sentence_avg):
        super().__init__(task)
        self.sentence_avg = sentence_avg
        if not has_megatron_submodule:
            raise ImportError(
                "\n\nPlease install the megatron submodule:"
                "\n\n  git submodule update --init "
                "fairseq/model_parallel/megatron"
            )

    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"])
        target = sample["target"]

        loss = vocab_parallel_cross_entropy(net_output[0].float(), target)
        loss = (loss * (target != self.padding_idx)).sum()
        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,
            "ntokens": sample["ntokens"],
            "nsentences": sample["target"].size(0),
            "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)
        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