fairseq/criterions/cross_entropy.py
Killed 0 out of 6 mutantsSurvived
Survived mutation testing. These mutants show holes in your test suite.Mutant 3159
--- fairseq/criterions/cross_entropy.py
+++ fairseq/criterions/cross_entropy.py
@@ -11,7 +11,7 @@
from fairseq.criterions import FairseqCriterion, register_criterion
-@register_criterion('cross_entropy')
+@register_criterion('XXcross_entropyXX')
class CrossEntropyCriterion(FairseqCriterion):
def __init__(self, task, sentence_avg):
Mutant 3160
--- fairseq/criterions/cross_entropy.py
+++ fairseq/criterions/cross_entropy.py
@@ -10,8 +10,6 @@
from fairseq import metrics, utils
from fairseq.criterions import FairseqCriterion, register_criterion
-
-@register_criterion('cross_entropy')
class CrossEntropyCriterion(FairseqCriterion):
def __init__(self, task, sentence_avg):
Mutant 3161
--- fairseq/criterions/cross_entropy.py
+++ fairseq/criterions/cross_entropy.py
@@ -18,7 +18,7 @@
super().__init__(task)
self.sentence_avg = sentence_avg
- def forward(self, model, sample, reduce=True):
+ def forward(self, model, sample, reduce=False):
"""Compute the loss for the given sample.
Returns a tuple with three elements:
Mutant 3162
--- fairseq/criterions/cross_entropy.py
+++ fairseq/criterions/cross_entropy.py
@@ -37,7 +37,7 @@
}
return loss, sample_size, logging_output
- def compute_loss(self, model, net_output, sample, reduce=True):
+ def compute_loss(self, model, net_output, sample, reduce=False):
lprobs = model.get_normalized_probs(net_output, log_probs=True)
lprobs = lprobs.view(-1, lprobs.size(-1))
target = model.get_targets(sample, net_output).view(-1)
Mutant 3163
--- fairseq/criterions/cross_entropy.py
+++ fairseq/criterions/cross_entropy.py
@@ -49,7 +49,6 @@
)
return loss, loss
- @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 3164
--- fairseq/criterions/cross_entropy.py
+++ fairseq/criterions/cross_entropy.py
@@ -63,7 +63,6 @@
else:
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