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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
import torch
import torch.nn.functional as F
from fairseq import metrics, modules, utils
from fairseq.criterions import FairseqCriterion, register_criterion
@register_criterion("masked_lm")
class MaskedLmLoss(FairseqCriterion):
"""
Implementation for the loss used in masked language model (MLM) training.
"""
def __init__(self, task, tpu=False):
super().__init__(task)
self.tpu = tpu
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
"""
masked_tokens = sample["target"].ne(self.padding_idx)
sample_size = masked_tokens.int().sum()
# Rare: when all tokens are masked, project all tokens.
# We use torch.where to avoid device-to-host transfers,
# except on CPU where torch.where is not well supported
# (see github.com/pytorch/pytorch/issues/26247).
if self.tpu:
masked_tokens = None # always project all tokens on TPU
elif masked_tokens.device == torch.device("cpu"):
if not masked_tokens.any():
masked_tokens = None
else:
masked_tokens = torch.where(
masked_tokens.any(),
masked_tokens,
masked_tokens.new([True]),
)
logits = model(**sample["net_input"], masked_tokens=masked_tokens)[0]
targets = model.get_targets(sample, [logits])
if masked_tokens is not None:
targets = targets[masked_tokens]
loss = modules.cross_entropy(
logits.view(-1, logits.size(-1)),
targets.view(-1),
reduction="sum",
ignore_index=self.padding_idx,
)
logging_output = {
"loss": loss if self.tpu else loss.data,
"ntokens": sample["ntokens"],
"nsentences": sample["nsentences"],
"sample_size": sample_size,
}
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)
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
)
metrics.log_derived(
"ppl", lambda meters: utils.get_perplexity(meters["loss"].avg)
)
@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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