wehub-resource-sync
107 lines
4.1 KiB
Python
107 lines
4.1 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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import math
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from dataclasses import dataclass
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import torch.nn.functional as F
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from fairseq import metrics, utils
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from fairseq.criterions import register_criterion
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from fairseq.criterions.cross_entropy import CrossEntropyCriterion
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from fairseq.dataclass import FairseqDataclass
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from omegaconf import II
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@dataclass
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class AdaptiveSpanCriterionConfig(FairseqDataclass):
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sentence_avg: bool = II("optimization.sentence_avg")
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@register_criterion("adaptive_span_loss", dataclass=AdaptiveSpanCriterionConfig)
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class AdaptiveSpanCriterion(CrossEntropyCriterion):
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def __init__(self, task, sentence_avg):
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super().__init__(task, sentence_avg)
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def forward(self, model, sample, reduce=True):
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"""Compute the loss for the given sample.
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Returns a tuple with three elements:
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1) the loss here is summed, different from the adaptive span code
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2) the sample size, which is used as the denominator for the gradient
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3) logging outputs to display while training
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"""
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net_output = model(**sample["net_input"])
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loss, aux_loss, avg_span, max_span = self.compute_loss(
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model, net_output, sample, reduce=reduce
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)
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sample_size = (
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sample["target"].size(0) if self.sentence_avg else sample["ntokens"]
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)
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loss /= sample_size
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total_loss = loss + aux_loss
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sample_size = 1
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logging_output = {
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"loss": loss.data,
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"ntokens": sample["ntokens"],
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"nsentences": sample["target"].size(0),
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"sample_size": sample_size,
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"total_loss": total_loss.data,
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"avg_span": avg_span * sample_size,
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"max_span": max_span * sample_size,
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}
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return total_loss, sample_size, logging_output
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def compute_loss(self, model, net_output, sample, reduce=True):
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loss, _ = super().compute_loss(model, net_output, sample, reduce)
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aux_loss = model.get_aux_loss()
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avg_span = model.get_current_avg_span()
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max_span = model.get_current_max_span()
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return loss, aux_loss, avg_span, max_span
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@staticmethod
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def reduce_metrics(logging_outputs) -> None:
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"""Aggregate logging outputs from data parallel training."""
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loss_sum = sum(log.get("loss", 0) for log in logging_outputs)
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ntokens = sum(log.get("ntokens", 0) for log in logging_outputs)
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sample_size = sum(log.get("sample_size", 0) for log in logging_outputs)
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total_loss_sum = sum(log.get("total_loss", 0) for log in logging_outputs)
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avg_span_sum = sum(log.get("avg_span", 0) for log in logging_outputs)
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max_span_sum = sum(log.get("max_span", 0) for log in logging_outputs)
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# we divide by log(2) to convert the loss from base e to base 2
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metrics.log_scalar(
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"loss", loss_sum / sample_size / math.log(2), sample_size, round=3
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)
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metrics.log_scalar("avg_span", avg_span_sum / sample_size, sample_size, round=3)
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metrics.log_scalar("max_span", max_span_sum / sample_size, sample_size, round=3)
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# total loss contains the L1 norm on adaptive-span
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metrics.log_scalar(
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"total_loss",
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total_loss_sum / sample_size / math.log(2),
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sample_size,
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round=3,
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)
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if sample_size != ntokens:
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metrics.log_scalar(
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"nll_loss", loss_sum / ntokens / math.log(2), ntokens, round=3
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)
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metrics.log_derived(
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"ppl", lambda meters: utils.get_perplexity(meters["nll_loss"].avg)
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)
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else:
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metrics.log_derived(
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"ppl", lambda meters: utils.get_perplexity(meters["loss"].avg)
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)
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@staticmethod
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def logging_outputs_can_be_summed() -> bool:
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"""
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Whether the logging outputs returned by `forward` can be summed
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across workers prior to calling `reduce_metrics`. Setting this
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to True will improves distributed training speed.
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"""
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return True
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