fix overfit_batch sampler replacement logic

CHANGELOG.md

@@ -151,6 +151,12 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Squeeze the early stopping monitor to remove empty tensor dimensions ([#10461](https://github.com/PyTorchLightning/pytorch-lightning/issues/10461))
 
 
+- Fixed sampler replacement logic with `overfit_batches` to only replace the sample when `SequentialSampler` is not used ([#10486](https://github.com/PyTorchLightning/pytorch-lightning/issues/10486))
+
+
+-
+
+
 -
 
 

pytorch_lightning/trainer/data_loading.py

@@ -438,8 +438,7 @@ def _reset_eval_dataloader(
         for loader_i in range(len(dataloaders)):
             loader = dataloaders[loader_i]
 
-            if hasattr(loader, "sampler") and isinstance(loader.sampler, RandomSampler):
-
+            if hasattr(loader, "sampler") and not isinstance(loader.sampler, SequentialSampler):
                 # when overfitting, the dataloader should not have sampler
                 if self.overfit_batches > 0 and mode.evaluating:
                     rank_zero_warn(
@@ -591,16 +590,17 @@ def _add_sampler_metadata_collate(dataloader: DataLoader) -> None:
 
     @staticmethod
     def _resolve_overfit_batches(dataloader: Collection[DataLoader]) -> Collection[DataLoader]:
-        has_random_sampler = False
+        all_have_sequential_sampler = True
 
-        def resolve_had_random_sampler(dataloader: DataLoader):
-            nonlocal has_random_sampler
-            if not has_random_sampler:
-                has_random_sampler = isinstance(dataloader.sampler, RandomSampler)
+        def resolve_has_no_sequential_sampler(dataloader: DataLoader):
+            nonlocal all_have_sequential_sampler
+            all_have_sequential_sampler = all_have_sequential_sampler & isinstance(
+                dataloader.sampler, SequentialSampler
+            )
 
-        apply_to_collection(dataloader, DataLoader, resolve_had_random_sampler)
+        apply_to_collection(dataloader, DataLoader, resolve_has_no_sequential_sampler)
 
-        if has_random_sampler:
+        if not all_have_sequential_sampler:
             rank_zero_warn(
                 "You requested to overfit but enabled training dataloader shuffling."
                 " We are turning off the training dataloader shuffling for you."

tests/trainer/flags/test_overfit_batches.py

@@ -13,13 +13,16 @@
 # limitations under the License.
 import pytest
 import torch
+from torch.utils.data.sampler import Sampler, SequentialSampler
 
 from pytorch_lightning import Trainer
 from tests.helpers.boring_model import BoringModel, RandomDataset
 
 
 def test_overfit_multiple_val_loaders(tmpdir):
-    """Tests that only training_step can be used."""
+    """Tests that overfit batches works with multiple val dataloaders."""
+    val_dl_count = 2
+    overfit_batches = 3
 
     class TestModel(BoringModel):
         def validation_step(self, batch, batch_idx, dataloader_idx):
@@ -31,25 +34,65 @@ def validation_epoch_end(self, outputs) -> None:
             pass
 
         def val_dataloader(self):
-            dl1 = torch.utils.data.DataLoader(RandomDataset(32, 64))
-            dl2 = torch.utils.data.DataLoader(RandomDataset(32, 64))
-            return [dl1, dl2]
+            dls = [torch.utils.data.DataLoader(RandomDataset(32, 64)) for _ in range(val_dl_count)]
+            return dls
 
     model = TestModel()
 
     trainer = Trainer(
-        default_root_dir=tmpdir, max_epochs=2, overfit_batches=1, log_every_n_steps=1, enable_model_summary=False
+        default_root_dir=tmpdir,
+        max_epochs=2,
+        overfit_batches=overfit_batches,
+        log_every_n_steps=1,
+        enable_model_summary=False,
     )
 
     trainer.fit(model)
+    assert trainer.num_training_batches == overfit_batches
+    assert len(trainer.num_val_batches) == val_dl_count
+    assert all(nbatches == overfit_batches for nbatches in trainer.num_val_batches)
 
 
-@pytest.mark.parametrize("overfit", [1, 2, 0.1, 0.25, 1.0])
-def test_overfit_basic(tmpdir, overfit):
-    """Tests that only training_step can be used."""
+@pytest.mark.parametrize("overfit_batches", [1, 2, 0.1, 0.25, 1.0])
+def test_overfit_basic(tmpdir, overfit_batches):
+    """Tests that only training_step can be used when overfitting."""
 
     model = BoringModel()
+    model.validation_step = None
+    total_train_samples = len(BoringModel().train_dataloader())
 
-    trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, overfit_batches=overfit, enable_model_summary=False)
-
+    trainer = Trainer(
+        default_root_dir=tmpdir, max_epochs=1, overfit_batches=overfit_batches, enable_model_summary=False
+    )
     trainer.fit(model)
+
+    assert trainer.num_val_batches == []
+    assert trainer.num_training_batches == int(
+        overfit_batches * (1 if isinstance(overfit_batches, int) else total_train_samples)
+    )
+
+
+def test_overfit_batches_raises_warning_in_case_of_sequential_sampler(tmpdir):
+    class NonSequentialSampler(Sampler):
+        def __init__(self, data_source):
+            self.data_source = data_source
+
+        def __iter__(self):
+            return iter(range(len(self.data_source)))
+
+        def __len__(self):
+            return len(self.data_source)
+
+    class TestModel(BoringModel):
+        def train_dataloader(self):
+            dataset = RandomDataset(32, 64)
+            sampler = NonSequentialSampler(dataset)
+            return torch.utils.data.DataLoader(dataset, sampler=sampler)
+
+    model = TestModel()
+    trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, overfit_batches=2)
+
+    with pytest.warns(UserWarning, match="requested to overfit but enabled training dataloader shuffling"):
+        trainer.fit(model)
+
+    assert isinstance(trainer.train_dataloader.loaders.sampler, SequentialSampler)