Enable saving and loading stateful DataLoaders in Trainer (#19361)

src/lightning/pytorch/CHANGELOG.md

@@ -30,6 +30,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - The TQDM progress bar now respects the env variable `TQDM_MINITERS` for setting the refresh rate ([#19381](https://github.com/Lightning-AI/lightning/pull/19381))
 
 
+- Added support for saving and loading stateful training DataLoaders ([#19361](https://github.com/Lightning-AI/lightning/pull/19361))
+
+
 ### Changed
 
 - `seed_everything()` without passing in a seed no longer randomly selects a seed, and now defaults to `0` ([#18846](https://github.com/Lightning-AI/lightning/pull/18846))

src/lightning/pytorch/loops/fit_loop.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import logging
-from typing import Optional, Union
+from typing import Any, Dict, List, Optional, Union
 
 import torch
 from typing_extensions import override
@@ -94,6 +94,7 @@ class _FitLoop(_Loop):
 
         self._data_source = _DataLoaderSource(None, "train_dataloader")
         self._combined_loader: Optional[CombinedLoader] = None
+        self._combined_loader_states_to_load: List[Dict[str, Any]] = []
         self._data_fetcher: Optional[_DataFetcher] = None
         self._last_train_dl_reload_epoch = float("-inf")
 
@@ -255,6 +256,8 @@ class _FitLoop(_Loop):
 
         combined_loader.limits = limits
 
+        self._load_combined_loader_states()
+
         self._data_fetcher = _select_data_fetcher(trainer, RunningStage.TRAINING)
         self._data_fetcher.setup(combined_loader)
         iter(self._data_fetcher)  # creates the iterator inside the fetcher
@@ -409,9 +412,27 @@ class _FitLoop(_Loop):
             self._data_fetcher = None
         self.epoch_loop.teardown()
 
+    @override
+    def on_save_checkpoint(self) -> Dict:
+        state_dict = super().on_save_checkpoint()
+        if self._combined_loader is not None and (loader_states := self._combined_loader._state_dicts()):
+            state_dict["combined_loader"] = loader_states
+        return state_dict
+
+    @override
+    def on_load_checkpoint(self, state_dict: Dict) -> None:
+        self._combined_loader_states_to_load = state_dict.get("combined_loader", [])
+        super().on_load_checkpoint(state_dict)
+
     def _should_accumulate(self) -> bool:
         """Whether the gradients should be accumulated."""
         return self.epoch_loop._should_accumulate()
 
     def _iteration_based_training(self) -> bool:
         return self.trainer.max_steps != -1
+
+    def _load_combined_loader_states(self) -> None:
+        if not self.restarting or not self._combined_loader_states_to_load or self._combined_loader is None:
+            return
+        self._combined_loader._load_state_dicts(self._combined_loader_states_to_load)
+        self._combined_loader_states_to_load = []  # release memory

src/lightning/pytorch/utilities/combined_loader.py

@@ -19,6 +19,7 @@ from torch.utils.data.dataloader import _BaseDataLoaderIter, _MultiProcessingDat
 from typing_extensions import Self, TypedDict, override
 
 from lightning.fabric.utilities.data import sized_len
+from lightning.fabric.utilities.types import _Stateful
 from lightning.pytorch.utilities._pytree import _map_and_unflatten, _tree_flatten, tree_unflatten
 
 _ITERATOR_RETURN = Tuple[Any, int, int]  # batch, batch_idx, dataloader_idx
@@ -374,6 +375,24 @@ class CombinedLoader(Iterable):
         fn = _SUPPORTED_MODES[self._mode]["fn"]
         return fn(lengths)
 
+    def _state_dicts(self) -> List[Dict[str, Any]]:
+        """Returns the list of state dicts for iterables in `self.flattened` that are stateful."""
+        return [loader.state_dict() for loader in self.flattened if isinstance(loader, _Stateful)]
+
+    def _load_state_dicts(self, states: List[Dict[str, Any]]) -> None:
+        """Loads the state dicts for iterables in `self.flattened` that are stateful."""
+        if not states:
+            return
+        stateful_loaders = [loader for loader in self.flattened if isinstance(loader, _Stateful)]
+        if len(stateful_loaders) != len(states):
+            raise RuntimeError(
+                f"The CombinedLoader has {len(stateful_loaders)} stateful loaders, but found {len(states)} states"
+                " in the checkpoint. Please make sure you define the same dataloaders that were used when saving"
+                " the checkpoint."
+            )
+        for loader, state_dict in zip(stateful_loaders, states):
+            loader.load_state_dict(state_dict)
+
 
 def _shutdown_workers_and_reset_iterator(dataloader: object) -> None:
     if hasattr(dataloader, "_iterator"):

tests/tests_pytorch/loops/test_loops.py

@@ -24,6 +24,7 @@ from lightning.pytorch.callbacks import Callback, ModelCheckpoint, OnExceptionCh
 from lightning.pytorch.demos.boring_classes import BoringModel, RandomDataset
 from lightning.pytorch.loops import _Loop
 from lightning.pytorch.loops.progress import _BaseProgress
+from lightning.pytorch.utilities import CombinedLoader
 from torch.utils.data.dataloader import DataLoader, _MultiProcessingDataLoaderIter
 
 from tests_pytorch.helpers.runif import RunIf
@@ -882,3 +883,94 @@ def test_validation_during_gradient_accumulation_window(tmp_path):
     )
     trainer.fit(model)
     assert model.ran_assert
+
+
+class NotStatefulIterable:
+    def __init__(self, start=0):
+        self.index = start
+
+    def __iter__(self):
+        for i in range(self.index, len(self)):
+            self.index = i
+            yield self.index
+
+    def __len__(self):
+        return 10
+
+
+class StatefulIterable(NotStatefulIterable):
+    def state_dict(self):
+        return {"index": self.index}
+
+    def load_state_dict(self, state_dict):
+        self.index = state_dict["index"] + 1
+
+
+@pytest.mark.parametrize(
+    ("train_dataloader_factory", "has_state", "batches_before", "batches_after"),
+    [
+        # No dataloader
+        (lambda: [], False, [], []),
+        # Single stateful DataLoader
+        (lambda: StatefulIterable(), True, [0, 1], [2, 3]),
+        # Single, not stateful DataLoader
+        (lambda: CombinedLoader(NotStatefulIterable()), False, [0, 1], [0, 1]),
+        # Single stateful DataLoader
+        (lambda: CombinedLoader(StatefulIterable()), True, [0, 1], [2, 3]),
+        # Multiple stateful DataLoaders
+        (lambda: CombinedLoader([StatefulIterable(3), StatefulIterable(1)]), True, [[3, 1], [4, 2]], [[5, 3], [6, 4]]),
+        # Mix of stateful and not stateful DataLoaders
+        (
+            lambda: CombinedLoader([NotStatefulIterable(3), StatefulIterable(1), NotStatefulIterable(2)]),
+            True,
+            [[3, 1, 2], [4, 2, 3]],
+            [[3, 3, 2], [4, 4, 3]],
+        ),
+    ],
+)
+def test_fit_loop_save_and_restore_dataloaders(
+    train_dataloader_factory, has_state, batches_before, batches_after, tmp_path
+):
+    """Test that the CheckpointConnector saves the state of stateful dataloaders."""
+
+    class DummyModel(BoringModel):
+        def __init__(self):
+            super().__init__()
+            self.seen_data = []
+
+        def training_step(self, batch, batch_idx):
+            self.seen_data.append(batch)
+            print(batch)
+
+        def train_dataloader(self):
+            return train_dataloader_factory()
+
+    trainer_kwargs = {
+        "default_root_dir": tmp_path,
+        "accelerator": "cpu",
+        "enable_checkpointing": False,
+        "enable_model_summary": False,
+        "enable_progress_bar": False,
+        "logger": False,
+        "num_sanity_val_steps": 0,
+    }
+
+    # Train for 2 steps
+    model = DummyModel()
+    trainer = Trainer(**trainer_kwargs, max_steps=2)
+    trainer.fit(model)
+    assert model.seen_data == batches_before
+
+    # Save a checkpoint
+    trainer.save_checkpoint(tmp_path / "checkpoint.ckpt")
+    checkpoint = torch.load(tmp_path / "checkpoint.ckpt")
+    if has_state:
+        assert checkpoint["loops"]["fit_loop"]["state_dict"]["combined_loader"]
+    else:
+        assert "combined_loader" not in checkpoint["loops"]["fit_loop"]["state_dict"]
+
+    # Restore training from step 2 and continue 2 more steps
+    model = DummyModel()
+    trainer = Trainer(**trainer_kwargs, max_steps=4)
+    trainer.fit(model, ckpt_path=(tmp_path / "checkpoint.ckpt"))
+    assert model.seen_data == batches_after

tests/tests_pytorch/utilities/test_combined_loader.py

@@ -14,9 +14,11 @@
 import math
 import pickle
 from typing import Any, NamedTuple, Sequence, get_args
+from unittest.mock import Mock
 
 import pytest
 import torch
+from lightning.fabric.utilities.types import _Stateful
 from lightning.pytorch import Trainer
 from lightning.pytorch.demos.boring_classes import BoringModel, RandomDataset
 from lightning.pytorch.utilities.combined_loader import (
@@ -602,3 +604,57 @@ def test_combined_loader_can_be_pickled():
 
     # no error
     pickle.dumps(cl)
+
+
+def test_state_dicts():
+    state1, state2, state3 = Mock(), Mock(), Mock()
+    stateful1 = Mock(spec=_Stateful, state_dict=Mock(return_value=state1))
+    stateful2 = Mock(spec=_Stateful, state_dict=Mock(return_value=state2))
+    stateful3 = Mock(spec=_Stateful, state_dict=Mock(return_value=state3))
+
+    cl = CombinedLoader([])
+    assert cl._state_dicts() == []
+    cl = CombinedLoader([range(2)])
+    assert cl._state_dicts() == []
+    cl = CombinedLoader([stateful1])
+    assert cl._state_dicts() == [state1]
+    cl = CombinedLoader([range(2), stateful1])
+    assert cl._state_dicts() == [state1]
+    cl = CombinedLoader([range(2), stateful1, range(3), stateful2])
+    assert cl._state_dicts() == [state1, state2]
+    cl = CombinedLoader({"a": [range(2), stateful1], "b": [stateful2], "c": stateful3})
+    assert cl._state_dicts() == [state1, state2, state3]
+
+
+def test_load_state_dicts():
+    stateful1 = Mock(spec=_Stateful)
+    stateful2 = Mock(spec=_Stateful)
+    state1 = Mock()
+    state2 = Mock()
+
+    # 0 stateful loaders, 1 state to load
+    cl = CombinedLoader([range(2), range(3)])
+    with pytest.raises(RuntimeError, match="has 0 stateful loaders, but found 1 states"):
+        cl._load_state_dicts([{"state": 0}])
+
+    # 1 stateful loader, 0 states to load
+    cl = CombinedLoader([stateful1, range(3)])
+    cl._load_state_dicts([])
+    stateful1.load_state_dict.assert_not_called()
+
+    # 1 stateful loader, 1 state to load
+    cl = CombinedLoader([range(2), stateful1, range(3)])
+    cl._load_state_dicts([state1])
+    stateful1.load_state_dict.assert_called_with(state1)
+    stateful1.reset_mock()
+
+    # 1 stateful loader, 2 states to load
+    cl = CombinedLoader([range(2), stateful1, range(3)])
+    with pytest.raises(RuntimeError, match="has 1 stateful loaders, but found 2 states"):
+        cl._load_state_dicts([state1, state2])
+
+    # 2 stateful loaders, 2 states to load
+    cl = CombinedLoader([range(2), stateful1, range(3), stateful2])
+    cl._load_state_dicts([state1, state2])
+    stateful1.load_state_dict.assert_called_with(state1)
+    stateful2.load_state_dict.assert_called_with(state2)