test restore

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

@@ -398,7 +398,9 @@ def _restore_train_dataloaders(self) -> None:
         if not state_dicts:
             return
 
-        for train_dataloader, state_dict in zip(self.trainer.train_dataloader, state_dicts):
+        combined_loader = self.trainer.fit_loop._combined_loader
+        iterables = combined_loader.flattened if combined_loader is not None else []
+        for train_dataloader, state_dict in zip(iterables, state_dicts):
             if isinstance(train_dataloader, _Stateful):
                 train_dataloader.load_state_dict(state_dict)
 

tests/tests_pytorch/trainer/connectors/test_checkpoint_connector.py

@@ -254,7 +254,7 @@ def __init__(self, *args, **kwargs):
     (CombinedLoader([NotStatefulDataLoader(3), StatefulDataLoader(1), NotStatefulDataLoader(2)]), [{"label": 1}]),
 ])
 def test_train_dataloaders_restore(train_dataloaders, expected_states, tmp_path):
-    """Test that the CheckpointConnector saves the state of stateful dataloaders and can reloead them."""
+    """Test that the CheckpointConnector saves the state of stateful dataloaders and can reload them."""
     class DataLoaderModel(BoringModel):
         def training_step(self, batch, batch_idx):
             if isinstance(batch, list):
@@ -264,8 +264,7 @@ def training_step(self, batch, batch_idx):
         def train_dataloader(self):
             return train_dataloaders
 
-    model = DataLoaderModel()
-    trainer = Trainer(
+    trainer_kwargs = dict(
         default_root_dir=tmp_path,
         accelerator="cpu",
         max_steps=1,
@@ -275,6 +274,10 @@ def train_dataloader(self):
         logger=False,
         num_sanity_val_steps=0,
     )
+
+    model = DataLoaderModel()
+    trainer = Trainer(**trainer_kwargs)
+
     # Fit to init the state of CheckpointConnector
     trainer.fit(model)
     checkpoint = trainer._checkpoint_connector.dump_checkpoint()
@@ -283,3 +286,10 @@ def train_dataloader(self):
         assert "train_dataloaders" not in checkpoint
     else:
         assert checkpoint["train_dataloaders"] == expected_states
+
+    torch.save(checkpoint, tmp_path / "checkpoint.ckpt")
+
+    model = DataLoaderModel()
+    trainer = Trainer(**trainer_kwargs)
+    trainer.fit(model, ckpt_path=(tmp_path / "checkpoint.ckpt"))
+    # TODO: Test here