Disable attaching samplers when using IterableDataset (#11507)

CHANGELOG.md

@@ -17,6 +17,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Changed `LSFEnvironment` to use `LSB_DJOB_RANKFILE` environment variable instead of `LSB_HOSTS` for determining node rank and main address ([#10825](https://github.com/PyTorchLightning/pytorch-lightning/pull/10825))
 
 
+- Disbled sampler replacement when using `IterableDataset` ([#11507](https://github.com/PyTorchLightning/pytorch-lightning/pull/11507))
+
+
 ## [1.5.8] - 2022-01-05
 
 ### Fixed

pytorch_lightning/trainer/data_loading.py

@@ -272,9 +272,13 @@ def _get_dataloader_init_kwargs(
 
         # kwargs to re-construct the dataloader
         dl_kwargs = {k: v for k, v in attrs.items() if k in non_defaults}
-        dl_kwargs.update(
-            TrainerDataLoadingMixin._dataloader_init_kwargs_resolve_sampler(dataloader, sampler, mode=mode)
-        )
+        if isinstance(dl_kwargs["dataset"], IterableDataset):
+            dl_kwargs["batch_sampler"] = None
+            dl_kwargs["sampler"] = None
+        else:
+            dl_kwargs.update(
+                TrainerDataLoadingMixin._dataloader_init_kwargs_resolve_sampler(dataloader, sampler, mode=mode)
+            )
 
         required_args = {
             p.name

tests/trainer/test_data_loading.py

@@ -20,11 +20,12 @@
 from torch.utils.data.sampler import BatchSampler, Sampler, SequentialSampler
 
 from pytorch_lightning import Trainer
+from pytorch_lightning.trainer.data_loading import TrainerDataLoadingMixin
 from pytorch_lightning.trainer.states import RunningStage
 from pytorch_lightning.trainer.supporters import CombinedLoader
 from pytorch_lightning.utilities.enums import DistributedType
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
-from tests.helpers import BoringModel, RandomDataset
+from tests.helpers.boring_model import BoringModel, RandomDataset, RandomIterableDataset
 from tests.helpers.runif import RunIf
 
 
@@ -389,3 +390,16 @@ def test_non_sequential_sampler_warning_is_raised_for_eval_dataloader(val_dl):
     trainer._data_connector.attach_data(model, val_dataloaders=val_dl)
     with pytest.warns(UserWarning, match="recommended .* turn this off for val/test/predict"):
         trainer._reset_eval_dataloader(RunningStage.VALIDATING, model)
+
+
+@pytest.mark.parametrize("mode", [RunningStage.TRAINING, RunningStage.PREDICTING, RunningStage.TESTING])
+def test_dataloader_kwargs_replacement_with_iterable_dataset(mode):
+    """Test that DataLoader kwargs are not replaced when using Iterable Dataset."""
+    dataset = RandomIterableDataset(7, 100)
+    dataloader = DataLoader(dataset, batch_size=32)
+    dl_kwargs = TrainerDataLoadingMixin._get_dataloader_init_kwargs(dataloader, dataloader.sampler, mode=mode)
+    assert dl_kwargs["sampler"] is None
+    assert dl_kwargs["batch_sampler"] is None
+    assert dl_kwargs["batch_size"] is dataloader.batch_size
+    assert dl_kwargs["dataset"] is dataloader.dataset
+    assert dl_kwargs["collate_fn"] is dataloader.collate_fn