Source code for fairseq.criterions.adaptive_loss

# 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 fairseq.dataclass.constants import DDP_BACKEND_CHOICES
from omegaconf import II

class AdaptiveLossConfig(FairseqDataclass):
    sentence_avg: bool = II("optimization.sentence_avg")
    ddp_backend: DDP_BACKEND_CHOICES = II("distributed_training.ddp_backend")

[docs]@register_criterion("adaptive_loss", dataclass=AdaptiveLossConfig) class AdaptiveLoss(FairseqCriterion): """This is an implementation of the loss function accompanying the adaptive softmax approximation for graphical processing units (GPU), described in the paper "Efficient softmax approximation for GPUs" (""" def __init__(self, task, sentence_avg): super().__init__(task) self.sentence_avg = sentence_avg
[docs] @classmethod def build_criterion(cls, cfg: AdaptiveLossConfig, task): if cfg.ddp_backend in {"c10d", "pytorch_ddp"}: raise Exception( "AdaptiveLoss is not compatible with the PyTorch " "version of DistributedDataParallel. Please use " "`--ddp-backend=legacy_ddp` instead." ) return cls(task, cfg.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 """ assert ( hasattr(model.decoder, "adaptive_softmax") and model.decoder.adaptive_softmax is not None ) adaptive_softmax = model.decoder.adaptive_softmax net_output = model(**sample["net_input"]) orig_target = model.get_targets(sample, net_output) nsentences = orig_target.size(0) orig_target = orig_target.view(-1) bsz = orig_target.size(0) logits, target = adaptive_softmax(net_output[0], orig_target) assert len(target) == len(logits) loss = net_output[0].new(1 if reduce else bsz).zero_() for i in range(len(target)): if target[i] is not None: assert target[i].min() >= 0 and target[i].max() <= logits[i].size(1) loss += F.cross_entropy( logits[i], target[i], ignore_index=self.padding_idx, reduction="sum" if reduce else "none", ) orig = utils.strip_pad(orig_target, self.padding_idx) ntokens = orig.numel() sample_size = sample["target"].size(0) if self.sentence_avg else ntokens logging_output = { "loss":, "ntokens": ntokens, "nsentences": nsentences, "sample_size": sample_size, } return loss, sample_size, logging_output
[docs] @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)) 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 ) 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