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| # 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 register_criterion | |
| from fairseq.criterions.cross_entropy import CrossEntropyCriterion | |
| from fairseq.dataclass import FairseqDataclass | |
| from omegaconf import II | |
| class AdaptiveSpanCriterionConfig(FairseqDataclass): | |
| sentence_avg: bool = II("optimization.sentence_avg") | |
| class AdaptiveSpanCriterion(CrossEntropyCriterion): | |
| def __init__(self, task, sentence_avg): | |
| super().__init__(task, 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 here is summed, different from the adaptive span code | |
| 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, aux_loss, avg_span, max_span = self.compute_loss( | |
| model, net_output, sample, reduce=reduce | |
| ) | |
| sample_size = ( | |
| sample["target"].size(0) if self.sentence_avg else sample["ntokens"] | |
| ) | |
| loss /= sample_size | |
| total_loss = loss + aux_loss | |
| sample_size = 1 | |
| logging_output = { | |
| "loss": loss.data, | |
| "ntokens": sample["ntokens"], | |
| "nsentences": sample["target"].size(0), | |
| "sample_size": sample_size, | |
| "total_loss": total_loss.data, | |
| "avg_span": avg_span * sample_size, | |
| "max_span": max_span * sample_size, | |
| } | |
| return total_loss, sample_size, logging_output | |
| def compute_loss(self, model, net_output, sample, reduce=True): | |
| loss, _ = super().compute_loss(model, net_output, sample, reduce) | |
| aux_loss = model.get_aux_loss() | |
| avg_span = model.get_current_avg_span() | |
| max_span = model.get_current_max_span() | |
| return loss, aux_loss, avg_span, max_span | |
| 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) | |
| total_loss_sum = sum(log.get("total_loss", 0) for log in logging_outputs) | |
| avg_span_sum = sum(log.get("avg_span", 0) for log in logging_outputs) | |
| max_span_sum = sum(log.get("max_span", 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 | |
| ) | |
| metrics.log_scalar("avg_span", avg_span_sum / sample_size, sample_size, round=3) | |
| metrics.log_scalar("max_span", max_span_sum / sample_size, sample_size, round=3) | |
| # total loss contains the L1 norm on adaptive-span | |
| metrics.log_scalar( | |
| "total_loss", | |
| total_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) | |
| ) | |
| 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 | |