Fix overlapping samples in DDP when no global seed is set (#17713)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

(cherry picked from commit 53815e6)

src/lightning/pytorch/CHANGELOG.md

@@ -38,6 +38,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed inconsistent settings for FSDP Precision ([#17670](https://github.com/Lightning-AI/lightning/issues/17670))
 
 
+- Fixed an edge case causing overlapping samples in DDP when no global seed is set ([#17713](https://github.com/Lightning-AI/lightning/pull/17713))
+
+
 ## [2.0.2] - 2023-04-24
 
 ### Fixed

src/lightning/pytorch/trainer/connectors/data_connector.py

@@ -16,7 +16,7 @@
 from dataclasses import dataclass, field
 from typing import Any, Iterable, Optional, Tuple, Union
 
-from torch.utils.data import BatchSampler, DataLoader, Sampler, SequentialSampler
+from torch.utils.data import BatchSampler, DataLoader, RandomSampler, Sampler, SequentialSampler
 from torch.utils.data.distributed import DistributedSampler
 
 import lightning.pytorch as pl
@@ -245,8 +245,11 @@ def _get_distributed_sampler(
     """This function is used to created the distributed sampler injected within the user DataLoader."""
     kwargs["shuffle"] = shuffle and not overfit_batches
     kwargs.setdefault("seed", int(os.getenv("PL_GLOBAL_SEED", 0)))
-    cls = UnrepeatedDistributedSamplerWrapper if mode == RunningStage.PREDICTING else DistributedSamplerWrapper
-    return cls(dataloader.sampler, **kwargs)
+    if mode == RunningStage.PREDICTING:
+        return UnrepeatedDistributedSamplerWrapper(dataloader.sampler, **kwargs)
+    if isinstance(dataloader.sampler, (RandomSampler, SequentialSampler)):
+        return DistributedSampler(dataloader.dataset, **kwargs)
+    return DistributedSamplerWrapper(dataloader.sampler, **kwargs)
 
 
 def _resolve_overfit_batches(combined_loader: CombinedLoader, mode: RunningStage) -> None:

tests/tests_pytorch/trainer/test_dataloaders.py

@@ -807,6 +807,40 @@ def test_dataloader_distributed_sampler(tmpdir):
     trainer.test(model)
 
 
+class TestModelUniqueDDPSampling(BoringModel):
+    def __init__(self):
+        super().__init__()
+        self.seen_samples = []
+
+    def training_step(self, batch):
+        self.seen_samples.extend(batch.tolist())
+
+    def on_train_end(self):
+        seen_samples = self.all_gather(self.seen_samples)
+        # The samples should be unique across all processes
+        assert set(torch.cat(seen_samples).view(-1).tolist()) == set(range(32))
+
+
+@RunIf(standalone=True)
+def test_distributed_sampler_without_global_seed(tmpdir):
+    """Test that the samples are non-overlapping in DDP when shuffling is enabled and no global seed is set."""
+    # This test must run without a global seed set (e.g. through `seed_everything`), to ensure that each process
+    # starts with a different initial state.
+    assert "PL_GLOBAL_SEED" not in os.environ
+    train_dataloader = DataLoader(range(32), shuffle=True, batch_size=4)
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        num_sanity_val_steps=False,
+        logger=False,
+        enable_progress_bar=False,
+        accelerator="cpu",
+        devices=2,
+        strategy="ddp",
+        max_epochs=1,
+    )
+    trainer.fit(TestModelUniqueDDPSampling(), train_dataloader)
+
+
 class ModelWithDataLoaderDistributedSampler(BoringModel):
     def train_dataloader(self):
         dataloader = super().train_dataloader()