None metrics

parlai/core/metrics.py

@@ -292,7 +292,8 @@ def from_mask(
         cls, metric_per_token: torch.Tensor, mask: torch.Tensor
     ) -> List[Metric]:
         """
-        From token-level metrics, returns an aggregate MyMetric per example in the batch.
+        From token-level metrics, returns an aggregate MyMetric per example in the
+        batch.
 
         :param metric_per_token:
             a (batchsize x num_tokens) Tensor
@@ -1097,6 +1098,8 @@ def _consume_user_metrics(self, observation):
         # User-reported metrics
         if 'metrics' in observation:
             for uk, v in observation['metrics'].items():
+                if v is None:
+                    continue
                 if uk in ALL_METRICS:
                     # don't let the user override our metrics
                     uk = f'USER_{uk}'

projects/cringe/cringe_loss.py

@@ -88,9 +88,7 @@ def __call__(self, x, y, classifier_labels=None, **kwargs):
 
         # concatenate the logits of the preds with the actual label's logits
         x_target = x[torch.arange(x.shape[0]), y]
-        x_ct = torch.concat(
-            [x_target.unsqueeze(1), sample_preds_values.unsqueeze(1)], -1
-        )
+        x_ct = torch.cat([x_target.unsqueeze(1), sample_preds_values.unsqueeze(1)], -1)
         # get the y's for the x_ct (the correct label is index 0 if
         # the target is positive and index 1 if the target is negative)
         y_ct = torch.abs(torch.abs(classifier_labels) - 1).type(y.dtype).to(x_ct.device)