Fix Trainer tests in a multiGPU env (#7458)

tests/test_trainer.py

@@ -109,12 +109,15 @@ if is_torch_available():
             loss = torch.nn.functional.mse_loss(y, labels)
             return (loss, y, y) if self.double_output else (loss, y)
 
-    def get_regression_trainer(a=0, b=0, double_output=False, train_len=64, eval_len=64, **kwargs):
+    def get_regression_trainer(a=0, b=0, double_output=False, train_len=64, eval_len=64, pretrained=True, **kwargs):
         label_names = kwargs.get("label_names", None)
         train_dataset = RegressionDataset(length=train_len, label_names=label_names)
         eval_dataset = RegressionDataset(length=eval_len, label_names=label_names)
-        config = RegressionModelConfig(a=a, b=b, double_output=double_output)
-        model = RegressionPreTrainedModel(config)
+        if pretrained:
+            config = RegressionModelConfig(a=a, b=b, double_output=double_output)
+            model = RegressionPreTrainedModel(config)
+        else:
+            model = RegressionModel(a=a, b=b, double_output=double_output)
         compute_metrics = kwargs.pop("compute_metrics", None)
         data_collator = kwargs.pop("data_collator", None)
         optimizers = kwargs.pop("optimizers", (None, None))
@@ -178,6 +181,7 @@ class TrainerIntegrationTest(unittest.TestCase):
             best_model = RegressionModel()
             state_dict = torch.load(os.path.join(checkpoint, WEIGHTS_NAME))
             best_model.load_state_dict(state_dict)
+            best_model.to(trainer.args.device)
         self.assertTrue(torch.allclose(best_model.a, trainer.model.a))
         self.assertTrue(torch.allclose(best_model.b, trainer.model.b))
 
@@ -360,8 +364,7 @@ class TrainerIntegrationTest(unittest.TestCase):
 
         # With a regular model that is not a PreTrainedModel
         with tempfile.TemporaryDirectory() as tmpdir:
-            trainer = get_regression_trainer(output_dir=tmpdir, save_steps=5)
-            trainer.model = RegressionModel()
+            trainer = get_regression_trainer(output_dir=tmpdir, save_steps=5, pretrained=False)
             trainer.train()
             self.check_saved_checkpoints(tmpdir, 5, int(self.n_epochs * 64 / self.batch_size), False)
 
@@ -426,8 +429,8 @@ class TrainerIntegrationTest(unittest.TestCase):
                 eval_steps=5,
                 evaluation_strategy="steps",
                 load_best_model_at_end=True,
+                pretrained=False,
             )
-            trainer.model = RegressionModel(a=1.5, b=2.5)
             self.assertFalse(trainer.args.greater_is_better)
             trainer.train()
             self.check_saved_checkpoints(tmpdir, 5, total, is_pretrained=False)