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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, field
import torch
import torch.nn.functional as F
from fairseq import metrics, utils
from fairseq.criterions import FairseqCriterion, register_criterion
from fairseq.dataclass import FairseqDataclass
@dataclass
class SentencePredictionConfig(FairseqDataclass):
classification_head_name: str = field(
default="sentence_classification_head",
metadata={"help": "name of the classification head to use"},
)
regression_target: bool = field(
default=False,
)
@register_criterion("sentence_prediction", dataclass=SentencePredictionConfig)
class SentencePredictionCriterion(FairseqCriterion):
def __init__(self, cfg: SentencePredictionConfig, task):
super().__init__(task)
self.classification_head_name = cfg.classification_head_name
self.regression_target = cfg.regression_target
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, "classification_heads")
and self.classification_head_name in model.classification_heads
), "model must provide sentence classification head for --criterion=sentence_prediction"
logits, _ = model(
**sample["net_input"],
features_only=True,
classification_head_name=self.classification_head_name,
)
targets = model.get_targets(sample, [logits]).view(-1)
sample_size = targets.numel()
if not self.regression_target:
lprobs = F.log_softmax(logits, dim=-1, dtype=torch.float32)
loss = F.nll_loss(lprobs, targets, reduction="sum")
else:
logits = logits.view(-1).float()
targets = targets.float()
loss = F.mse_loss(logits, targets, reduction="sum")
logging_output = {
"loss": loss.data,
"ntokens": sample["ntokens"],
"nsentences": sample_size,
"sample_size": sample_size,
}
if not self.regression_target:
preds = logits.argmax(dim=1)
logging_output["ncorrect"] = (preds == targets).sum()
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)
nsentences = sum(log.get("nsentences", 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
)
if len(logging_outputs) > 0 and "ncorrect" in logging_outputs[0]:
ncorrect = sum(log.get("ncorrect", 0) for log in logging_outputs)
metrics.log_scalar(
"accuracy", 100.0 * ncorrect / nsentences, nsentences, round=1
)
@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
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