Simplify fetching's loader types

src/pytorch_lightning/utilities/fetching.py

@@ -144,23 +144,18 @@ def _store_dataloader_iter_state(
                 dataloader_iter.state.update(iter_name, state)
 
     @property
-    def loaders(self) -> List[DataLoader]:
+    def loaders(self) -> Any:
         if isinstance(self.dataloader, CombinedLoader):
-            loaders = self.dataloader.loaders
-        else:
-            loaders = [self.dataloader]
-        return loaders
+            return self.dataloader.loaders
+        return self.dataloader
 
     @property
-    def loader_iters(self) -> List[Iterator]:
+    def loader_iters(self) -> Any:
         if self.dataloader_iter is None:
             raise MisconfigurationException("The `dataloader_iter` isn't available outside the __iter__ context.")
-
         if isinstance(self.dataloader, CombinedLoader):
-            loader_iters = self.dataloader_iter.loader_iters
-        else:
-            loader_iters = [self.dataloader_iter]
-        return loader_iters
+            return self.dataloader_iter.loader_iters
+        return self.dataloader_iter
 
     @property
     def state(self) -> List[MergedIteratorState]:

tests/tests_pytorch/utilities/test_auto_restart.py

@@ -700,16 +700,8 @@ def __len__(self):
         return self.len
 
 
-# TODO: test with `RandomGeneratorGetItemDataset`
 @mock.patch.dict(os.environ, {"PL_FAULT_TOLERANT_TRAINING": "1"})
-@pytest.mark.parametrize(
-    "dataset_class",
-    [
-        SequentialGetItemDataset,
-        RandomGetItemDataset,
-        # RandomGeneratorGetItemDataset,
-    ],
-)
+@pytest.mark.parametrize("dataset_class", [SequentialGetItemDataset, RandomGetItemDataset])
 @pytest.mark.parametrize("num_workers", [0, pytest.param(2, marks=RunIf(slow=True))])
 @pytest.mark.parametrize("batch_size", [1, 2, 3])
 def test_dataset_rng_states_restart(dataset_class, num_workers, batch_size):
@@ -732,12 +724,11 @@ def fetch(fetcher, prefetch_iter, num_batches_fetched):
         _ = next(prefetch_iter)
 
         state: List[MergedIteratorState] = fetcher.state
-        assert len(state) == 1
-        assert isinstance(state[0], MergedIteratorState)
+        assert isinstance(state, MergedIteratorState)
 
         assert len(fetcher.dataloader_iter.cache_states) == 1
         if num_workers == 0:
-            assert state[0].state[0].num_batches_fetched == num_batches_fetched
+            assert state.state[0].num_batches_fetched == num_batches_fetched
         return state
 
     dataset, random_sampler = create_dataset_sampler()
@@ -754,7 +745,7 @@ def fetch(fetcher, prefetch_iter, num_batches_fetched):
 
     # (A) capture the state after fetching 4 batches
     state = fetch(fetcher, prefetch_iter, 4)
-    state = deepcopy(state[0])
+    state = deepcopy(state)
 
     # (B) simulate 2 additional batches
     batch05 = next(prefetch_iter)

tests/tests_pytorch/utilities/test_fetching.py

@@ -101,16 +101,14 @@ def test_empty_prefetch_iterator(dataset_cls, prefetch_batches):
 
 
 def test_misconfiguration_error():
-
     fetcher = DataFetcher()
+    loader = DataLoader(range(10))
+    fetcher.setup(loader)
+    assert fetcher.loaders == loader
     with pytest.raises(
         MisconfigurationException, match="The `dataloader_iter` isn't available outside the __iter__ context."
     ):
-        loader = DataLoader(range(10))
-        fetcher.setup(loader)
-        assert fetcher.loaders[0] == loader
         fetcher.loader_iters
-
     iter(fetcher)
     assert fetcher.loader_iters