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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.
from fairseq import utils
from fairseq.criterions import LegacyFairseqCriterion, register_criterion
from torch import nn
@register_criterion("composite_loss")
class CompositeLoss(LegacyFairseqCriterion):
"""This is a composite loss that, given a list of model outputs and a list of targets,
computes an average of losses for each output-target pair"""
def __init__(self, args, task):
super().__init__(args, task)
self.underlying_criterion = args.underlying_criterion
@staticmethod
def add_args(parser):
"""Add criterion-specific arguments to the parser."""
# fmt: off
parser.add_argument('--underlying-criterion', type=str, metavar='VAL', required=True,
help='underlying criterion to use for the composite loss')
# fmt: on
@staticmethod
def build_underlying_criterion(args, task):
saved_criterion = args.criterion
args.criterion = args.underlying_criterion
assert saved_criterion != args.underlying_criterion
underlying_criterion = task.build_criterion(args)
args.criterion = saved_criterion
return underlying_criterion
@classmethod
def build_criterion(cls, args, task):
underlying_criterion = CompositeLoss.build_underlying_criterion(args, task)
class FakeModel(nn.Module):
def __init__(self, model, net_out, target):
super().__init__()
self.model = model
self.net_out = net_out
self.target = target
def forward(self, **unused):
return self.net_out
def get_normalized_probs(self, net_output, log_probs, sample=None):
return self.model.get_normalized_probs(
net_output, log_probs, sample=sample
)
def get_targets(self, *unused):
return self.target
@property
def decoder(self):
return self.model.decoder
class _CompositeLoss(LegacyFairseqCriterion):
def __init__(self, args, task, underlying_criterion):
super().__init__(args, task)
self.underlying_criterion = underlying_criterion
def forward(self, model, sample, reduce=True):
net_outputs = model(**sample["net_input"])
targets = sample["target"]
bsz = targets[0].size(0)
loss = net_outputs[0][0].new(1 if reduce else bsz).float().zero_()
sample_size = 0
logging_output = {}
for o, t in zip(net_outputs[0], targets):
m = FakeModel(model, (o, net_outputs[1]), t)
sample["target"] = t
l, ss, logging_output = self.underlying_criterion(m, sample, reduce)
loss += l
sample_size += ss
loss.div_(len(targets))
sample_size /= len(targets)
logging_output["loss"] = utils.item(loss.data) if reduce else loss.data
return loss, sample_size, logging_output
@staticmethod
def aggregate_logging_outputs(logging_outputs):
return underlying_criterion.__class__.aggregate_logging_outputs(
logging_outputs
)
@staticmethod
def reduce_metrics(logging_outputs) -> None:
underlying_criterion.__class__.reduce_metrics(logging_outputs)
return _CompositeLoss(args, task, underlying_criterion)
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