dvclive callback: warn instead of fail when logging non-scalars (#27608)

* dvclive callback: warn instead of fail when logging non-scalars

* tests: log lr as scalar

src/transformers/integrations/integration_utils.py

@@ -1680,10 +1680,19 @@ class DVCLiveCallback(TrainerCallback):
         if not self._initialized:
             self.setup(args, state, model)
         if state.is_world_process_zero:
+            from dvclive.plots import Metric
             from dvclive.utils import standardize_metric_name
 
             for key, value in logs.items():
+                if Metric.could_log(value):
                     self.live.log_metric(standardize_metric_name(key, "dvclive.huggingface"), value)
+                else:
+                    logger.warning(
+                        "Trainer is attempting to log a value of "
+                        f'"{value}" of type {type(value)} for key "{key}" as a scalar. '
+                        "This invocation of DVCLive's Live.log_metric() "
+                        "is incorrect so we dropped this attribute."
+                    )
             self.live.next_step()
 
     def on_save(self, args, state, control, **kwargs):

tests/trainer/test_trainer.py

@@ -672,7 +672,7 @@ class TrainerIntegrationPrerunTest(TestCasePlus, TrainerIntegrationCommon):
             def log(self, logs):
                 # the LR is computed after metrics and does not exist for the first epoch
                 if hasattr(self.lr_scheduler, "_last_lr"):
-                    logs["learning_rate"] = self.lr_scheduler._last_lr
+                    logs["learning_rate"] = self.lr_scheduler._last_lr[0]
                 super().log(logs)
 
         train_dataset = RegressionDataset(length=64)
@@ -702,14 +702,14 @@ class TrainerIntegrationPrerunTest(TestCasePlus, TrainerIntegrationCommon):
             if loss > best_loss:
                 bad_epochs += 1
                 if bad_epochs > patience:
-                    self.assertLess(logs[i + 1]["learning_rate"][0], log["learning_rate"][0])
+                    self.assertLess(logs[i + 1]["learning_rate"], log["learning_rate"])
                     just_decreased = True
                     bad_epochs = 0
             else:
                 best_loss = loss
                 bad_epochs = 0
             if not just_decreased:
-                self.assertEqual(logs[i + 1]["learning_rate"][0], log["learning_rate"][0])
+                self.assertEqual(logs[i + 1]["learning_rate"], log["learning_rate"])
 
     def test_adafactor_lr_none(self):
         # test the special case where lr=None, since Trainer can't not have lr_scheduler