fairseq/criterions/sentence_ranking.py

Survived

Mutant 3306

--- fairseq/criterions/sentence_ranking.py
+++ fairseq/criterions/sentence_ranking.py
@@ -12,7 +12,7 @@
 from fairseq.criterions import FairseqCriterion, register_criterion
 
 
-@register_criterion('sentence_ranking')
+@register_criterion('XXsentence_rankingXX')
 class SentenceRankingCriterion(FairseqCriterion):
 
     def __init__(self, task, ranking_head_name, save_predictions, num_classes):

Mutant 3307

--- fairseq/criterions/sentence_ranking.py
+++ fairseq/criterions/sentence_ranking.py
@@ -11,8 +11,6 @@
 from fairseq import metrics, utils
 from fairseq.criterions import FairseqCriterion, register_criterion
 
-
-@register_criterion('sentence_ranking')
 class SentenceRankingCriterion(FairseqCriterion):
 
     def __init__(self, task, ranking_head_name, save_predictions, num_classes):

Mutant 3308

--- fairseq/criterions/sentence_ranking.py
+++ fairseq/criterions/sentence_ranking.py
@@ -28,7 +28,6 @@
         if self.prediction_h is not None:
             self.prediction_h.close()
 
-    @staticmethod
     def add_args(parser):
         # fmt: off
         parser.add_argument('--save-predictions', metavar='FILE',

Mutant 3309

--- fairseq/criterions/sentence_ranking.py
+++ fairseq/criterions/sentence_ranking.py
@@ -38,7 +38,7 @@
                             help='name of the ranking head to use')
         # fmt: on
 
-    def forward(self, model, sample, reduce=True):
+    def forward(self, model, sample, reduce=False):
         """Compute ranking loss for the given sample.
 
         Returns a tuple with three elements:

Mutant 3310

--- fairseq/criterions/sentence_ranking.py
+++ fairseq/criterions/sentence_ranking.py
@@ -90,7 +90,6 @@
 
         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)

Mutant 3311

--- fairseq/criterions/sentence_ranking.py
+++ fairseq/criterions/sentence_ranking.py
@@ -106,7 +106,6 @@
             ncorrect = sum(log.get('ncorrect', 0) for log in logging_outputs)
             metrics.log_scalar('accuracy', 100.0 * ncorrect / nsentences, nsentences, round=1)
 
-    @staticmethod
     def logging_outputs_can_be_summed() -> bool:
         """
         Whether the logging outputs returned by `forward` can be summed