Deprecate _aggregate_logging_outputs (8a8df81d) · Commits · Simon Will / fairseq

fairseq/criterions/init.py

+1 −1

Original line number	Diff line number	Diff line
		@@ -16,7 +16,7 @@ CRITERION_CLASS_NAMES = set()


		def build_criterion(args, task):
		return CRITERION_REGISTRY[args.criterion](args, task)
		return CRITERION_REGISTRY[args.criterion].build_criterion(args, task)


		def register_criterion(name):

fairseq/criterions/composite_loss.py

+27 −10

Original line number	Diff line number	Diff line
		@@ -24,19 +24,23 @@ class CompositeLoss(FairseqCriterion):
		help='underlying criterion to use for the composite loss')
		# fmt: on

		def __init__(self, args, task):
		super().__init__(args, task)
		@staticmethod
		def build_underlying_criterion(args, task):
		saved_criterion = args.criterion
		args.criterion = args.underlying_criterion

		assert saved_criterion != args.underlying_criterion

		self.underlying_criterion = task.build_criterion(args)
		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(CompositeLoss.FakeModel, self).__init__()
		super().__init__()
		self.model = model
		self.net_out = net_out
		self.target = target
		@@ -44,6 +48,9 @@ class CompositeLoss(FairseqCriterion):
		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

		@@ -51,17 +58,24 @@ class CompositeLoss(FairseqCriterion):
		def decoder(self):
		return self.model.decoder

		class _CompositeLoss(FairseqCriterion):

		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).zero_()
		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 = CompositeLoss.FakeModel(model, (o, net_outputs[1]), t)
		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
		@@ -72,5 +86,8 @@ class CompositeLoss(FairseqCriterion):
		logging_output['loss'] = utils.item(loss.data) if reduce else loss.data
		return loss, sample_size, logging_output

		def _aggregate_logging_outputs(self, logging_outputs):
		return self.underlying_criterion._aggregate_logging_outputs(logging_outputs)
		@staticmethod
		def aggregate_logging_outputs(logging_outputs):
		return underlying_criterion.__class__.aggregate_logging_outputs(logging_outputs)

		return _CompositeLoss(args, task, underlying_criterion)

fairseq/criterions/fairseq_criterion.py

+4 −9

Original line number	Diff line number	Diff line
		@@ -20,6 +20,10 @@ class FairseqCriterion(_Loss):
		"""Add criterion-specific arguments to the parser."""
		pass

		@classmethod
		def build_criterion(cls, args, task):
		return cls(args, task)

		def forward(self, model, sample, reduce=True):
		"""Compute the loss for the given sample.

		@@ -35,15 +39,6 @@ class FairseqCriterion(_Loss):
		"""Aggregate logging outputs from data parallel training."""
		raise NotImplementedError

		def _aggregate_logging_outputs(self, logging_outputs):
		"""An instance method version of :func:`aggregate_logging_outputs`.

		This can be overridden if needed, but please be careful not to rely
		on shared state when aggregating logging outputs otherwise you may
		get incorrect results.
		"""
		return self.__class__.aggregate_logging_outputs(logging_outputs)

		@staticmethod
		def grad_denom(sample_sizes):
		"""Compute the gradient denominator for a set of sample sizes."""

fairseq/tasks/fairseq_task.py

+1 −1

Original line number	Diff line number	Diff line
		@@ -245,7 +245,7 @@ class FairseqTask(object):
		return criterion.__class__.grad_denom(sample_sizes)

		def aggregate_logging_outputs(self, logging_outputs, criterion):
		return criterion._aggregate_logging_outputs(logging_outputs)
		return criterion.__class__.aggregate_logging_outputs(logging_outputs)

		def max_positions(self):
		"""Return the max input length allowed by the task."""

fairseq/tasks/translation_moe.py

+1 −1

Original line number	Diff line number	Diff line
		@@ -222,7 +222,7 @@ class TranslationMoETask(TranslationTask):
		)

		def aggregate_logging_outputs(self, logging_outputs, criterion):
		agg_logging_outputs = criterion._aggregate_logging_outputs(logging_outputs)
		agg_logging_outputs = criterion.__class__.aggregate_logging_outputs(logging_outputs)
		agg_logging_outputs['posterior'] = sum(
		log['posterior'] for log in logging_outputs if 'posterior' in log
		)

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