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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 FairseqCriterion, register_criterion | |
from fairseq.dataclass import FairseqDataclass | |
from omegaconf import II | |
class CrossEntropyCriterionConfig(FairseqDataclass): | |
sentence_avg: bool = II("optimization.sentence_avg") | |
class CrossEntropyCriterion(FairseqCriterion): | |
def __init__(self, task, sentence_avg): | |
super().__init__(task) | |
self.sentence_avg = 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 | |
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, _ = self.compute_loss(model, net_output, sample, reduce=reduce) | |
sample_size = ( | |
sample["target"].size(0) if self.sentence_avg else sample["ntokens"] | |
) | |
logging_output = { | |
"loss": loss.data, | |
"ntokens": sample["ntokens"], | |
"nsentences": sample["target"].size(0), | |
"sample_size": sample_size, | |
} | |
return loss, sample_size, logging_output | |
def compute_loss(self, model, net_output, sample, reduce=True): | |
lprobs = model.get_normalized_probs(net_output, log_probs=True) | |
lprobs = lprobs.view(-1, lprobs.size(-1)) | |
target = model.get_targets(sample, net_output).view(-1) | |
loss = F.nll_loss( | |
lprobs, | |
target, | |
ignore_index=self.padding_idx, | |
reduction="sum" if reduce else "none", | |
) | |
return loss, loss | |
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) | |
# 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 | |
) | |
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 | |