Distributed sampling parity between Lite and PyTorch (#16101)

src/lightning_fabric/CHANGELOG.md

@@ -43,6 +43,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Merged the implementation of `DDPSpawnStrategy` into `DDPStrategy` and removed `DDPSpawnStrategy` ([#14952](https://github.com/Lightning-AI/lightning/issues/14952))
 
 
+- The dataloader wrapper returned from `.setup_dataloaders()` now calls `.set_epoch()` on the distributed sampler if one is used ([#16101](https://github.com/Lightning-AI/lightning/issues/16101))
+
+
 ### Deprecated
 
 -

src/lightning_fabric/fabric.py

@@ -25,7 +25,7 @@
 from lightning_utilities.core.rank_zero import rank_zero_warn
 from torch import Tensor
 from torch.optim import Optimizer
-from torch.utils.data import BatchSampler, DataLoader, DistributedSampler, RandomSampler
+from torch.utils.data import BatchSampler, DataLoader, DistributedSampler, RandomSampler, SequentialSampler
 
 from lightning_fabric.plugins import Precision  # avoid circular imports: # isort: split
 from lightning_fabric.accelerators.accelerator import Accelerator
@@ -639,6 +639,8 @@ def _requires_distributed_sampler(self, dataloader: DataLoader) -> bool:
     def _get_distributed_sampler(dataloader: DataLoader, **kwargs: Any) -> DistributedSampler:
         kwargs.setdefault("shuffle", isinstance(dataloader.sampler, RandomSampler))
         kwargs.setdefault("seed", int(os.getenv("PL_GLOBAL_SEED", 0)))
+        if isinstance(dataloader.sampler, (RandomSampler, SequentialSampler)):
+            return DistributedSampler(dataloader.dataset, **kwargs)
         return DistributedSamplerWrapper(dataloader.sampler, **kwargs)
 
     def _prepare_run_method(self) -> None:

src/lightning_fabric/utilities/distributed.py

@@ -294,6 +294,11 @@ class DistributedSamplerWrapper(DistributedSampler):
 
     Allows you to use any sampler in distributed mode. It will be automatically used by Lightning in distributed mode if
     sampler replacement is enabled.
+
+    Note:
+        The purpose of this wrapper is to take care of sharding the sampler indices. It is up to the underlying
+        sampler to handle randomness and shuffling. The ``shuffle`` and ``seed`` arguments on this wrapper won't
+        have any effect.
     """
 
     def __init__(self, sampler: Union[Sampler, Iterable], *args: Any, **kwargs: Any) -> None:

src/lightning_fabric/wrappers.py

@@ -151,6 +151,7 @@ def __init__(self, dataloader: DataLoader, device: Optional[torch.device] = None
         self.__dict__.update(dataloader.__dict__)
         self._dataloader = dataloader
         self._device = device
+        self._num_iter_calls = 0
 
     @property
     def device(self) -> Optional[torch.device]:
@@ -160,6 +161,13 @@ def __len__(self) -> int:
         return len(self._dataloader)
 
     def __iter__(self) -> Union[Iterator[Any], Generator[Any, None, None]]:
+        if hasattr(self._dataloader.sampler, "set_epoch"):
+            # Without setting the epoch, the distributed sampler would return the same indices every time, even when
+            # shuffling is enabled. In PyTorch, the user would normally have to call `.set_epoch()` on the sampler.
+            # In Lite, we take care of this boilerplate code.
+            self._dataloader.sampler.set_epoch(self._num_iter_calls)
+        self._num_iter_calls += 1
+
         iterator = iter(self._dataloader)
         if self._device is None:
             yield from iterator

tests/tests_fabric/test_fabric.py

@@ -405,7 +405,46 @@ def test_setup_dataloaders_distributed_sampler_shuffle():
     for dataloader in shuffle_dataloaders:
         seed_everything(1)
         dataloader = lite.setup_dataloaders(dataloader)
-        assert list(t[0].item() for t in iter(dataloader)) == [5, 0, 2, 1]
+        assert list(t[0].item() for t in iter(dataloader)) == [5, 2, 7, 1]
+
+
+@pytest.mark.parametrize("shuffle", [True, False])
+@pytest.mark.parametrize("batch_size", [1, 2, 3])
+def test_setup_dataloaders_distributed_sampler_parity(shuffle, batch_size):
+    """Test that the distributed sampler setup in Lite leads to the same sequence of data as in raw PyTorch."""
+    torch.manual_seed(1)
+    lite = Fabric(accelerator="cpu", strategy="ddp", devices=2)
+    # no lite.launch(): pretend we are on rank 0 now
+
+    dataset = torch.arange(10)
+    torch_dataloader = DataLoader(
+        dataset,
+        sampler=DistributedSampler(dataset, num_replicas=2, rank=0, shuffle=shuffle),
+        batch_size=batch_size,
+    )
+    lite_dataloader = DataLoader(dataset, shuffle=shuffle, batch_size=batch_size)
+    lite_dataloader = lite.setup_dataloaders(lite_dataloader)
+
+    def fetch_epoch(loader):
+        iterator = iter(loader)
+        # we fetch 2 batches per epoch
+        return torch.cat((next(iterator), next(iterator)))
+
+    # 1st epoch
+    # PyTorch users needs to set the epoch, while in Lite it gets handled automatically
+    torch_dataloader.sampler.set_epoch(0)
+    torch_data = fetch_epoch(torch_dataloader)
+    lite_data = fetch_epoch(lite_dataloader)
+    assert torch.equal(torch_data, lite_data)
+
+    # 2nd epoch
+    # PyTorch users needs to set the epoch, while in Lite it gets handled automatically
+    torch_dataloader.sampler.set_epoch(1)
+    torch_data = fetch_epoch(torch_dataloader)
+    lite_data = fetch_epoch(lite_dataloader)
+    assert torch.equal(torch_data, lite_data)
+    assert torch_dataloader.sampler.epoch == 1
+    assert lite_dataloader._dataloader.sampler.epoch == 1
 
 
 @mock.patch.dict(os.environ, {}, clear=True)

tests/tests_fabric/test_wrappers.py

@@ -11,11 +11,12 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from unittest.mock import Mock
+from unittest.mock import call, Mock
 
 import pytest
 import torch
 from tests_fabric.helpers.runif import RunIf
+from torch.utils.data import DistributedSampler
 from torch.utils.data.dataloader import DataLoader
 
 from lightning_fabric.fabric import Fabric
@@ -230,6 +231,26 @@ def test_lite_dataloader_device_placement(src_device_str, dest_device_str):
     assert torch.equal(batch1["data"], torch.tensor([2, 3], device=dest_device))
 
 
+def test_lite_dataloader_distributed_sampler_set_epoch():
+    """Test that the LiteDataLoader calls `set_epoch()` on the wrapped sampler if applicable."""
+    sampler = DistributedSampler(range(3), num_replicas=2, rank=0)
+    sampler.set_epoch = Mock()
+    dataloader = DataLoader(range(3), sampler=sampler)
+    lite_dataloader = _FabricDataLoader(dataloader)
+    iterator_epoch_0 = iter(lite_dataloader)
+    dataloader.sampler.set_epoch.assert_not_called()
+    next(iterator_epoch_0)
+    # .set_epoch() gets called before the first sample gets fetched from the wrapped dataloader
+    assert dataloader.sampler.set_epoch.call_args_list == [call(0)]
+    next(iterator_epoch_0)
+    assert dataloader.sampler.set_epoch.call_args_list == [call(0)]
+    iterator_epoch_1 = iter(lite_dataloader)
+    assert dataloader.sampler.set_epoch.call_args_list == [call(0)]
+    next(iterator_epoch_1)
+    # with every new iterator call, the epoch increases
+    assert dataloader.sampler.set_epoch.call_args_list == [call(0), call(1)]
+
+
 def test_lite_optimizer_wraps():
     """Test that the FabricOptimizer fully wraps the optimizer."""
     optimizer_cls = torch.optim.SGD