Add reduce kwarg to criterions (6f6cb4ab) · Commits · Simon Will / fairseq

fairseq/criterions/cross_entropy.py

+4 −3

Changes for fairseq/criterions/cross_entropy.py: 4 added lines, 3 removed lines.
Original line number	Diff line number	Diff line
		@@ -17,7 +17,7 @@ class CrossEntropyCriterion(FairseqCriterion):
		def __init__(self, args, dst_dict):
		super().__init__(args, dst_dict)

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

		Returns a tuple with three elements:
		@@ -28,10 +28,11 @@ class CrossEntropyCriterion(FairseqCriterion):
		net_output = model(**sample['net_input'])
		input = net_output.view(-1, net_output.size(-1))
		target = sample['target'].view(-1)
		loss = F.cross_entropy(input, target, size_average=False, ignore_index=self.padding_idx)
		loss = F.cross_entropy(input, target, size_average=False, ignore_index=self.padding_idx,
		reduce=reduce)
		sample_size = sample['target'].size(0) if self.args.sentence_avg else sample['ntokens']
		logging_output = {
		'loss': loss.data[0],
		'loss': loss.data[0] if reduce else loss.data,
		'sample_size': sample_size,
		}
		return loss, sample_size, logging_output

fairseq/criterions/fairseq_criterion.py

+1 −1

Changes for fairseq/criterions/fairseq_criterion.py: 1 added line, 1 removed line.
Original line number	Diff line number	Diff line
		@@ -16,7 +16,7 @@ class FairseqCriterion(_Loss):
		self.args = args
		self.padding_idx = dst_dict.pad()

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

		Returns a tuple with three elements:

fairseq/criterions/label_smoothed_cross_entropy.py

+8 −5

Changes for fairseq/criterions/label_smoothed_cross_entropy.py: 8 added lines, 5 removed lines.
Original line number	Diff line number	Diff line
		@@ -17,7 +17,7 @@ from .fairseq_criterion import FairseqCriterion
		class LabelSmoothedNLLLoss(torch.autograd.Function):

		@staticmethod
		def forward(ctx, input, target, eps, padding_idx, weights):
		def forward(ctx, input, target, eps, padding_idx, weights, reduce=True):
		grad_input = input.new(input.size()).zero_()
		target = target.view(target.size(0), 1)
		grad_input = grad_input.scatter_(grad_input.dim() - 1, target, eps - 1)
		@@ -34,11 +34,14 @@ class LabelSmoothedNLLLoss(torch.autograd.Function):
		grad_input = grad_input.add(-eps / norm)

		ctx.grad_input = grad_input
		if reduce:
		return input.new([grad_input.view(-1).dot(input.view(-1))])
		else:
		return grad_input * input

		@staticmethod
		def backward(ctx, grad):
		return Variable(ctx.grad_input, volatile=True) * grad, None, None, None, None
		return Variable(ctx.grad_input, volatile=True) * grad, None, None, None, None, None


		class LabelSmoothedCrossEntropyCriterion(FairseqCriterion):
		@@ -48,7 +51,7 @@ class LabelSmoothedCrossEntropyCriterion(FairseqCriterion):
		self.eps = args.label_smoothing
		self.weights = weights

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

		Returns a tuple with three elements:
		@@ -59,10 +62,10 @@ class LabelSmoothedCrossEntropyCriterion(FairseqCriterion):
		net_output = model(**sample['net_input'])
		input = F.log_softmax(net_output.view(-1, net_output.size(-1)), dim=1)
		target = sample['target'].view(-1)
		loss = LabelSmoothedNLLLoss.apply(input, target, self.eps, self.padding_idx, self.weights)
		loss = LabelSmoothedNLLLoss.apply(input, target, self.eps, self.padding_idx, self.weights, reduce)
		sample_size = sample['target'].size(0) if self.args.sentence_avg else sample['ntokens']
		logging_output = {
		'loss': loss.data[0],
		'loss': loss.data[0] if reduce else loss.data,
		'sample_size': sample_size,
		}
		return loss, sample_size, logging_output