Add possibility to evaluate every epoch (#7302)

* Add possibility to evaluate every epoch

* Remove multitype arg

* Remove needless import

* Use a proper enum

* Apply suggestions from @LysandreJik

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

* One else and formatting

Co-authored-by: Lysandre Debut <lysandre@huggingface.co>

src/transformers/trainer.py

@@ -39,6 +39,7 @@ from .trainer_utils import (
     PREFIX_CHECKPOINT_DIR,
     BestRun,
     EvalPrediction,
+    EvaluationStrategy,
     HPSearchBackend,
     PredictionOutput,
     TrainOutput,
@@ -782,7 +783,10 @@ class Trainer:
 
                         self.log(logs)
 
-                    if self.args.evaluate_during_training and self.global_step % self.args.eval_steps == 0:
+                    if (
+                        self.args.evaluation_strategy == EvaluationStrategy.STEPS
+                        and self.global_step % self.args.eval_steps == 0
+                    ):
                         metrics = self.evaluate()
                         self._report_to_hp_search(trial, epoch, metrics)
 
@@ -820,6 +824,9 @@ class Trainer:
                             torch.save(self.lr_scheduler.state_dict(), os.path.join(output_dir, "scheduler.pt"))
 
                 epoch_pbar.update(1)
+                if self.args.evaluation_strategy == EvaluationStrategy.EPOCH:
+                    metrics = self.evaluate()
+                    self._report_to_hp_search(trial, epoch, metrics)
                 if self.args.max_steps > 0 and self.global_step >= self.args.max_steps:
                     break
             epoch_pbar.close()