Flos fix (#7384)

src/transformers/trainer.py

@@ -695,7 +695,7 @@ class Trainer:
             # set global_step to global_step of last saved checkpoint from model path
             try:
                 self.global_step = int(model_path.split("-")[-1].split(os.path.sep)[0])
-                self.total_flos = getattr(model.config, "total_flos", 0)
+                self.total_flos = getattr(self._actual_model(model).config, "total_flos", 0)
 
                 epochs_trained = self.global_step // num_update_steps_per_epoch
                 steps_trained_in_current_epoch = self.global_step % (num_update_steps_per_epoch)
@@ -1448,15 +1448,29 @@ class Trainer:
             :obj:`int`: The number of floating-point operations.
         """
 
-        if isinstance(self.model, torch.nn.DataParallel) or isinstance(
+        model = self._actual_model(self.model)
-            self.model, torch.nn.parallel.DistributedDataParallel
-        ):
-            model = self.model.module
-        else:
-            model = self.model
 
         if hasattr(model, "floating_point_ops"):
             return model.floating_point_ops(inputs)
 
         else:
             return 0
+
+    @staticmethod
+    def _actual_model(
+        model: Union[torch.nn.DataParallel, torch.nn.parallel.DistributedDataParallel, torch.nn.modules.Module]
+    ) -> torch.nn.modules.Module:
+        """
+
+        Args:
+            model: (:obj:`Union[torch.nn.DataParallel, torch.nn.parallel.DistributedDataParallel, torch.nn.modules.Module]`):
+                Model object used during training
+
+        Returns:
+            :obj:`torch.nn.modules.Module`: unwrapped module
+        """
+        if isinstance(model, torch.nn.DataParallel) or isinstance(model, torch.nn.parallel.DistributedDataParallel):
+            model = model.module
+        else:
+            model = model
+        return model

tests/test_trainer.py

@@ -336,3 +336,16 @@ class TrainerIntegrationTest(unittest.TestCase):
         trainer = get_regression_trainer(train_len=64, per_device_train_batch_size=16, gradient_accumulation_steps=5)
         train_output = trainer.train()
         self.assertEqual(train_output.global_step, int(self.n_epochs))
+
+    def test_flos_extraction(self):
+        trainer = get_regression_trainer(learning_rate=0.1)
+
+        def assert_flos_extraction(trainer, wrapped_model_to_check):
+            self.assertEqual(trainer.model, trainer._actual_model(wrapped_model_to_check))
+            self.assertGreaterEqual(getattr(trainer._actual_model(wrapped_model_to_check).config, "total_flos", 0), 0)
+
+        # with plain model
+        assert_flos_extraction(trainer, trainer.model)
+
+        # with enforced DataParallel
+        assert_flos_extraction(trainer, torch.nn.DataParallel(trainer.model))