Avoid accessing .dataset of a DataLoader in Trainer (#16451)

* Avoid accessing .dataset of a dataloader

* style

* fix

* cleaning up, reverting some misunderstandings

* black

* add train_dataset argument to get_train_dataloader, and fix other instances of length checks

* flake8

* address comments

* fix bug

* cleanup

* add test

* Update tests/trainer/test_trainer.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* under torch

* merge

* stylistic suggestion

Co-authored-by: Sander Land <sander@chatdesk.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

tests/trainer/test_trainer.py

@@ -189,6 +189,26 @@ if is_torch_available():
                 yield self.dataset[self.current_sample]
                 self.current_sample += 1
 
+    class MultiLoader:
+        def __init__(self, loaders):
+            self.loaders = loaders
+
+        def __len__(self):
+            return sum(len(loader) for loader in self.loaders)
+
+        def __iter__(self):
+            for loader in self.loaders:
+                yield from loader
+
+    class CustomDataloaderTrainer(Trainer):
+        def get_train_dataloader(self):
+            dataloaders = [super().get_train_dataloader(), super().get_train_dataloader()]
+            return MultiLoader(dataloaders)
+
+        def get_eval_dataloader(self, eval_dataset):
+            dataloaders = [super().get_eval_dataloader(eval_dataset), super().get_eval_dataloader(eval_dataset)]
+            return MultiLoader(dataloaders)
+
     class RegressionModel(nn.Module):
         def __init__(self, a=0, b=0, double_output=False):
             super().__init__()
@@ -647,6 +667,15 @@ class TrainerIntegrationTest(TestCasePlus, TrainerIntegrationCommon):
         new_eval_dataset = RegressionDataset(length=128)
         self.assertEqual(len(trainer.get_eval_dataloader(new_eval_dataset)), 128 // (32 * n_gpu))
 
+    # tests that we do not require dataloader to have a .dataset attribute
+    def test_dataloader_without_dataset(self):
+        train_dataset = RegressionDataset(length=128)
+        trainer = CustomDataloaderTrainer(
+            model=RegressionModel(), train_dataset=train_dataset, eval_dataset=train_dataset
+        )
+        trainer.train()
+        trainer.evaluate()
+
     def test_sampler_seed(self):
         # nb: we don't want to inherit from IterableDataset to hit the right code path
         class DummyDataset(torch.utils.data.Dataset):