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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 | |
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, | |
) | |
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 | |
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 | |
) | |
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 | |