Avoid raising the sampler warning if num_replicas=1 (#14097)

Co-authored-by: Carlos Mocholí <[email protected]>
Co-authored-by: otaj <[email protected]>

src/pytorch_lightning/CHANGELOG.md

@@ -89,6 +89,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed epoch-end logging results not being reset after the end of the epoch ([#14061](https://github.com/Lightning-AI/lightning/pull/14061))
 
 
+- Avoid raising the sampler warning if num_replicas=1 ([#14097](https://github.com/Lightning-AI/lightning/pull/14097))
+
+
 - Fixed saving hyperparameters in a composition where the parent class is not a `LightningModule` or `LightningDataModule` ([#14151](https://github.com/Lightning-AI/lightning/pull/14151))
 
 

src/pytorch_lightning/trainer/connectors/data_connector.py

@@ -298,10 +298,14 @@ def _resolve_sampler(self, dataloader: DataLoader, shuffle: bool, mode: Optional
 
             # update docs too once this is resolved
             trainer_fn = self.trainer.state.fn
-            if isinstance(sampler, DistributedSampler) and trainer_fn in (TrainerFn.VALIDATING, TrainerFn.TESTING):
+            if (
+                isinstance(sampler, DistributedSampler)
+                and sampler.num_replicas > 1
+                and trainer_fn in (TrainerFn.VALIDATING, TrainerFn.TESTING)
+            ):
                 rank_zero_warn(
-                    f"Using `DistributedSampler` with the dataloaders. During `trainer.{trainer_fn.value}()`,"
-                    " it is recommended to use `Trainer(devices=1)` to ensure each sample/batch gets evaluated"
+                    f"Using `DistributedSampler` with the dataloaders. During `trainer.{trainer_fn.value}()`, it is"
+                    " recommended to use `Trainer(devices=1, num_nodes=1)` to ensure each sample/batch gets evaluated"
                     " exactly once. Otherwise, multi-device settings use `DistributedSampler` that replicates"
                     " some samples to make sure all devices have same batch size in case of uneven inputs.",
                     category=PossibleUserWarning,

tests/tests_pytorch/trainer/connectors/test_data_connector.py

@@ -526,19 +526,20 @@ def test_invalid_hook_passed_in_datahook_selector():
         dh_selector.get_instance("setup")
 
 
-def test_eval_distributed_sampler_warning(tmpdir):
+@pytest.mark.parametrize("devices, warn_context", [(1, no_warning_call), (2, pytest.warns)])
+def test_eval_distributed_sampler_warning(devices, warn_context):
     """Test that a warning is raised when `DistributedSampler` is used with evaluation."""
 
     model = BoringModel()
-    trainer = Trainer(strategy="ddp", devices=2, accelerator="cpu", fast_dev_run=True)
+    trainer = Trainer(strategy="ddp", devices=devices, accelerator="cpu")
     trainer._data_connector.attach_data(model)
 
     trainer.state.fn = TrainerFn.VALIDATING
-    with pytest.warns(PossibleUserWarning, match="multi-device settings use `DistributedSampler`"):
+    with warn_context(PossibleUserWarning, match="multi-device settings use `DistributedSampler`"):
         trainer.reset_val_dataloader(model)
 
     trainer.state.fn = TrainerFn.TESTING
-    with pytest.warns(PossibleUserWarning, match="multi-device settings use `DistributedSampler`"):
+    with warn_context(PossibleUserWarning, match="multi-device settings use `DistributedSampler`"):
         trainer.reset_test_dataloader(model)