Avoid warning when resuming mid-epoch checkpoint and using stateful d…

…ataloader (#19475)

src/lightning/pytorch/loops/training_epoch_loop.py

@@ -18,6 +18,8 @@
 from typing_extensions import override
 
 import lightning.pytorch as pl
+from lightning.fabric.utilities.types import _Stateful
+from lightning.fabric.utilities.warnings import PossibleUserWarning
 from lightning.pytorch import loops  # import as loops to avoid circular imports
 from lightning.pytorch.loops.fetchers import _DataFetcher, _DataLoaderIterDataFetcher
 from lightning.pytorch.loops.optimization import _AutomaticOptimization, _ManualOptimization
@@ -152,10 +154,16 @@ def reset(self) -> None:
             trainer = self.trainer
             if trainer.num_training_batches != float("inf"):
                 expected_steps = math.ceil(trainer.num_training_batches / trainer.accumulate_grad_batches)
-                if self.global_step % expected_steps != 0:
+                loader = trainer.fit_loop._combined_loader
+                assert loader is not None
+                is_resumable_loader = all(isinstance(loader, _Stateful) for loader in loader.flattened)
+                if self.global_step % expected_steps != 0 and not is_resumable_loader:
                     rank_zero_warn(
-                        "You're resuming from a checkpoint that ended before the epoch ended. This can cause unreliable"
-                        " results if further training is done. Consider using an end-of-epoch checkpoint"
+                        "You're resuming from a checkpoint that ended before the epoch ended and your dataloader is"
+                        " not resumable. This can cause unreliable results if further training is done."
+                        " Consider using an end-of-epoch checkpoint or make your dataloader resumable by implementing"
+                        " the `state_dict` / `load_state_dict` interface.",
+                        category=PossibleUserWarning,
                     )
         else:
             self.batch_progress.reset_on_run()

tests/tests_pytorch/loops/test_training_epoch_loop.py

@@ -15,11 +15,15 @@
 from unittest.mock import Mock, patch
 
 import pytest
+import torch
+from lightning.fabric.utilities.warnings import PossibleUserWarning
+from lightning.pytorch.callbacks import ModelCheckpoint
 from lightning.pytorch.demos.boring_classes import BoringModel
 from lightning.pytorch.trainer.trainer import Trainer
+from lightning_utilities.test.warning import no_warning_call
 
 
-def test_no_val_on_train_epoch_loop_restart(tmpdir):
+def test_no_val_on_train_epoch_loop_restart(tmp_path):
     """Test that training validation loop doesn't get triggered at the beginning of a restart."""
     trainer_kwargs = {
         "max_epochs": 1,
@@ -31,7 +35,7 @@ def test_no_val_on_train_epoch_loop_restart(tmpdir):
     trainer = Trainer(**trainer_kwargs)
     model = BoringModel()
     trainer.fit(model)
-    ckpt_path = str(tmpdir / "last.ckpt")
+    ckpt_path = str(tmp_path / "last.ckpt")
     trainer.save_checkpoint(ckpt_path)
 
     trainer_kwargs["max_epochs"] = 2
@@ -157,3 +161,59 @@ def optimizer_step(self, epoch, batch_idx, *args, **kwargs):
     model = MyModel()
     trainer.fit(model)
     assert model.last_batch_idx == 3
+
+
+def test_resume_mid_epoch_warning(tmp_path):
+    """Test that resuming from a mid-epoch checkpoint raises a warning unless the dataloader is stateful."""
+
+    class NotStatefulIterable:
+        def __init__(self):
+            self.index = 0
+
+        def __iter__(self):
+            for i in range(self.index, len(self)):
+                yield torch.ones(2, 32) * i
+
+        def __len__(self):
+            return 3
+
+    class StatefulIterable(NotStatefulIterable):
+        def state_dict(self):
+            return {"index": self.index}
+
+        def load_state_dict(self, state_dict):
+            self.index = state_dict["index"]
+
+    trainer_kwargs = {
+        "default_root_dir": tmp_path,
+        "accelerator": "cpu",
+        "max_epochs": 1,
+        "enable_model_summary": False,
+        "enable_progress_bar": False,
+        "logger": False,
+    }
+
+    def train_and_resume(dataloader, resume_step, expected_warning):
+        # Initial training
+        checkpoint_dir = tmp_path / "checkpoints"
+        trainer = Trainer(
+            **trainer_kwargs,
+            callbacks=ModelCheckpoint(dirpath=checkpoint_dir, every_n_train_steps=1, save_top_k=-1),
+        )
+        trainer.fit(BoringModel(), dataloader)
+
+        # Resume
+        trainer = Trainer(**trainer_kwargs, enable_checkpointing=False)
+        resume_from = checkpoint_dir / f"epoch=0-step={resume_step}.ckpt"
+        warn_assert = pytest.warns if expected_warning else no_warning_call
+        with warn_assert(PossibleUserWarning, match="resuming from a checkpoint that ended before"):
+            trainer.fit(BoringModel(), dataloader, ckpt_path=resume_from)
+
+    # Resume mid-epoch, no stateful dataloader -> warning
+    train_and_resume(dataloader=NotStatefulIterable(), resume_step=1, expected_warning=True)
+
+    # Resume end-of-epoch, no stateful dataloader -> no warning
+    train_and_resume(dataloader=NotStatefulIterable(), resume_step=3, expected_warning=False)
+
+    # Resume mid-epoch, stateful dataloader -> no warning
+    train_and_resume(dataloader=StatefulIterable(), resume_step=1, expected_warning=False)