Make predict() close progress bars after finishing (#17952) (#18078)

* Make Trainer.predict call on_evaluate (#17952)

* Add on_predict

* Small fix

* Small and different fix

* Add tests

src/transformers/trainer.py

@@ -2713,6 +2713,7 @@ class Trainer:
             )
         )
 
+        self.control = self.callback_handler.on_predict(self.args, self.state, self.control, output.metrics)
         self._memory_tracker.stop_and_update_metrics(output.metrics)
 
         return PredictionOutput(predictions=output.predictions, label_ids=output.label_ids, metrics=output.metrics)

src/transformers/trainer_callback.py

@@ -262,6 +262,12 @@ class TrainerCallback:
         """
         pass
 
+    def on_predict(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, metrics, **kwargs):
+        """
+        Event called after a successful prediction.
+        """
+        pass
+
     def on_save(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs):
         """
         Event called after a checkpoint save.
@@ -372,6 +378,9 @@ class CallbackHandler(TrainerCallback):
         control.should_evaluate = False
         return self.call_event("on_evaluate", args, state, control, metrics=metrics)
 
+    def on_predict(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, metrics):
+        return self.call_event("on_predict", args, state, control, metrics=metrics)
+
     def on_save(self, args: TrainingArguments, state: TrainerState, control: TrainerControl):
         control.should_save = False
         return self.call_event("on_save", args, state, control)
@@ -484,6 +493,12 @@ class ProgressCallback(TrainerCallback):
                 self.prediction_bar.close()
             self.prediction_bar = None
 
+    def on_predict(self, args, state, control, **kwargs):
+        if state.is_local_process_zero:
+            if self.prediction_bar is not None:
+                self.prediction_bar.close()
+            self.prediction_bar = None
+
     def on_log(self, args, state, control, logs=None, **kwargs):
         if state.is_local_process_zero and self.training_bar is not None:
             _ = logs.pop("total_flos", None)

src/transformers/utils/notebook.py

@@ -307,6 +307,11 @@ class NotebookProgressCallback(TrainerCallback):
         else:
             self.prediction_bar.update(self.prediction_bar.value + 1)
 
+    def on_predict(self, args, state, control, **kwargs):
+        if self.prediction_bar is not None:
+            self.prediction_bar.close()
+        self.prediction_bar = None
+
     def on_log(self, args, state, control, logs=None, **kwargs):
         # Only for when there is no evaluation
         if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:

tests/trainer/test_trainer_callback.py

@@ -66,6 +66,9 @@ class MyTestTrainerCallback(TrainerCallback):
     def on_evaluate(self, args, state, control, **kwargs):
         self.events.append("on_evaluate")
 
+    def on_predict(self, args, state, control, **kwargs):
+        self.events.append("on_predict")
+
     def on_save(self, args, state, control, **kwargs):
         self.events.append("on_save")