wehub-resource-sync
100 lines
3.7 KiB
Python
100 lines
3.7 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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import torch
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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 FairseqCriterion, register_criterion
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@register_criterion("sentence_prediction")
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class SentencePredictionCriterion(FairseqCriterion):
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def __init__(self, task, classification_head_name, regression_target):
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super().__init__(task)
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self.classification_head_name = classification_head_name
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self.regression_target = regression_target
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@staticmethod
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def add_args(parser):
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# fmt: off
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parser.add_argument('--classification-head-name',
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default='sentence_classification_head',
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help='name of the classification head to use')
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# fmt: on
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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
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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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assert (
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hasattr(model, "classification_heads")
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and self.classification_head_name in model.classification_heads
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), "model must provide sentence classification head for --criterion=sentence_prediction"
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logits, _ = model(
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**sample["net_input"],
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features_only=True,
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classification_head_name=self.classification_head_name,
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)
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targets = model.get_targets(sample, [logits]).view(-1)
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sample_size = targets.numel()
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if not self.regression_target:
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lprobs = F.log_softmax(logits, dim=-1, dtype=torch.float32)
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loss = F.nll_loss(lprobs, targets, reduction="sum")
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else:
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logits = logits.view(-1).float()
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targets = targets.float()
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loss = F.mse_loss(logits, targets, reduction="sum")
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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_size,
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"sample_size": sample_size,
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}
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if not self.regression_target:
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preds = logits.argmax(dim=1)
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logging_output["ncorrect"] = (preds == targets).sum()
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return loss, sample_size, logging_output
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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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nsentences = sum(log.get("nsentences", 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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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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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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if len(logging_outputs) > 0 and "ncorrect" in logging_outputs[0]:
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ncorrect = sum(log.get("ncorrect", 0) for log in logging_outputs)
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metrics.log_scalar(
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"accuracy", 100.0 * ncorrect / nsentences, nsentences, round=1
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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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