[DataLoader2] Saving and restoring initial seed generator

test/dataloader2/test_mprs.py

@@ -4,7 +4,6 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
-
 import multiprocessing as mp
 import unittest
 from unittest import TestCase
@@ -14,7 +13,7 @@
 from torch.utils.data.datapipes.iter.sharding import SHARDING_PRIORITIES
 
 from torchdata.dataloader2 import DataLoader2, DataLoader2Iterator, MultiProcessingReadingService
-from torchdata.datapipes.iter import IterableWrapper
+from torchdata.datapipes.iter import IterableWrapper, IterDataPipe
 
 
 def _add_one(x: int) -> int:
@@ -46,6 +45,17 @@ def _dispatching_dp(n_elements=1000):
  return dp
 
 
+class NonShardableDataPipe(IterDataPipe):
+ def __init__(self, dp: IterDataPipe):
+ self.dp = dp
+
+ def is_replicable(self):
+ return False
+
+ def __iter__(self):
+ yield from self.dp
+
+
 class TestMultiProcessingReadingService(TestCase):
  r"""
  This tests specific functionalities of MultiProcessingReadingService, notably
@@ -64,7 +74,7 @@ def test_early_exit(self, ctx, dp_fn, main_prefetch, worker_prefetch) -> None:
  worker_prefetch_cnt=worker_prefetch,
  multiprocessing_context=ctx,
  )
- dl = DataLoader2(dp, reading_service=rs)
+ dl: DataLoader2 = DataLoader2(dp, reading_service=rs)
  it = iter(dl)
  for _ in range(10):
  _ = next(it)
@@ -82,7 +92,7 @@ def test_exit(self, ctx, dp_fn, main_prefetch, worker_prefetch) -> None:
  worker_prefetch_cnt=worker_prefetch,
  multiprocessing_context=ctx,
  )
- dl = DataLoader2(dp, reading_service=rs)
+ dl: DataLoader2 = DataLoader2(dp, reading_service=rs)
  _ = list(dl)
  dl.shutdown()
 
@@ -248,6 +258,54 @@ def test_reading_service_limit(self, dp, n_workers, worker_prefetch_cnt, main_pr
  res.append(x)
  self.assertEqual(9, len(res))
 
+ def test_initial_epoch_checkpointing(self):
+ dp = IterableWrapper(range(20)).shuffle().sharding_filter()
+ # Note that the second `shuffle` occurs in the main process, which uses a different RNG from
+ # the `shuffle` done in the worker processes
+ dp = NonShardableDataPipe(dp).shuffle() # type: ignore[assignment, arg-type]
+ rs = MultiProcessingReadingService(num_workers=2)
+
+ # Functional Test: Saving state before iterator is created
+ dl: DataLoader2 = DataLoader2(datapipe=dp, reading_service=rs)
+ dl.seed(1)
+ initial_state = dl.state_dict()
+ it1 = iter(dl)
+
+ restored_dl: DataLoader2 = DataLoader2.from_state(initial_state, rs) # type: ignore[arg-type]
+ restored_dl._restore_checkpoint_beginning_of_epoch()
+ self.assertEqual(list(it1), list(restored_dl))
+
+ dl.shutdown()
+ restored_dl.shutdown()
+
+ # Functional Test: Saving state after iterator is created
+ dl = DataLoader2(datapipe=dp, reading_service=rs)
+ dl.seed(1)
+ it1 = iter(dl)
+ initial_state = dl.state_dict()
+
+ restored_dl = DataLoader2.from_state(initial_state, rs) # type: ignore[arg-type]
+ restored_dl._restore_checkpoint_beginning_of_epoch()
+ self.assertEqual(list(it1), list(restored_dl))
+
+ dl.shutdown()
+ restored_dl.shutdown()
+
+ # Functional Test: Saving state after iterator is created and began iterating
+ dl = DataLoader2(datapipe=dp, reading_service=rs)
+ dl.seed(1)
+ it1 = iter(dl)
+ temp = next(it1) # Starts iterating
+ initial_state = dl.state_dict()
+
+ restored_dl = DataLoader2.from_state(initial_state, rs) # type: ignore[arg-type]
+ restored_dl._restore_checkpoint_beginning_of_epoch()
+
+ self.assertEqual([temp] + list(it1), list(restored_dl)) # Note skipping over 1st element from actual result
+
+ dl.shutdown()
+ restored_dl.shutdown()
+
  # TODO: Test cases when there is official support of `pause` and `resume` with round-robin sharding
  # Currently, using sharding_round_robin raises a warning
  # def test_round_robin_dispatching_pause_limit(self):

torchdata/dataloader2/dataloader2.py

@@ -4,7 +4,7 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
-
+import pickle
 import warnings
 
 from typing import Any, Dict, Generic, Iterable, Iterator, Optional, TypeVar, Union
@@ -19,6 +19,7 @@
 T_co = TypeVar("T_co", covariant=True)
 SERIALIZED_DATAPIPE_KEY_NAME = "serialized_datapipe"
 READING_SERVICE_STATE_KEY_NAME = "reading_service_state"
+RANDOMNESS_STATE_KEY_NAME = "randomness_state"
 
 
 class DataLoader2Iterator(Iterator[T_co]):
@@ -176,6 +177,8 @@ def __init__(
  self._seed_generator: SeedGenerator = SeedGenerator()
  self._seed: Optional[int] = None
  self._reset_seed: bool = True
+ # Seed generator as of beginning of each epoch
+ self._initial_seed_generator: SeedGenerator = clone(self._seed_generator)
 
  def __iter__(self) -> DataLoader2Iterator[T_co]:
  r"""
@@ -198,6 +201,9 @@ def __iter__(self) -> DataLoader2Iterator[T_co]:
  else:
  self._seed_generator.seed()
 
+ # Saving initial seed generator state
+ self._initial_seed_generator = clone(self._seed_generator)
+
  if not self._adapted and self.reading_service is not None:
  if self.reading_service_state is None:
  self.datapipe = self.reading_service.initialize(self.datapipe)
@@ -269,10 +275,17 @@ def state_dict(self) -> Dict[str, Any]:
 
  # Serialize datapipe after applying adapters and before reading service adaption
  serialized_datapipe = serialize_datapipe(self._datapipe_before_reading_service_adapt)
+ serialized_randomness_state = (
+ self._seed,
+ self._reset_seed,
+ pickle.dumps(self._seed_generator),
+ pickle.dumps(self._initial_seed_generator),
+ )
 
  return {
  SERIALIZED_DATAPIPE_KEY_NAME: serialized_datapipe,
  READING_SERVICE_STATE_KEY_NAME: reading_service_state,
+ RANDOMNESS_STATE_KEY_NAME: serialized_randomness_state,
  }
 
  @classmethod
@@ -294,6 +307,12 @@ def from_state(
  reading_service=reading_service,
  )
  data_loader.reading_service_state = reading_service_state
+
+ randomness_state = state[RANDOMNESS_STATE_KEY_NAME]
+ data_loader._seed, data_loader._reset_seed = randomness_state[0], randomness_state[1]
+ data_loader._seed_generator = pickle.loads(randomness_state[2])
+ data_loader._initial_seed_generator = pickle.loads(randomness_state[3])
+
  return data_loader
 
  def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
@@ -320,12 +339,28 @@ def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
  self.datapipe = deserialized_datapipe
  self.reading_service_state = reading_service_state
 
+ randomness_state = state_dict[RANDOMNESS_STATE_KEY_NAME]
+ self._seed, self._reset_seed = randomness_state[0], randomness_state[1]
+ self._seed_generator = pickle.loads(randomness_state[2])
+ self._initial_seed_generator = pickle.loads(randomness_state[3])
+
  # re-initialize datapipe_adapter_fn and _datapipe_before_reading_service_adapt
  if self.datapipe_adapter_fns is not None:
  for adapter_fn in self.datapipe_adapter_fns:
  self.datapipe = adapter_fn(self.datapipe)
  self._datapipe_before_reading_service_adapt = clone(self.datapipe)
 
+ def _restore_checkpoint_beginning_of_epoch(self) -> None:
+ r"""
+ At the beginning of each iteration (epoch), the initial state of randomness is automatically saved.
+ That state is also saved as part of ``state_dict``. This method restores the current DataLoader2 RNG state
+ to that initial state.
+
+ The common use case is to invoke this method after ``DataLoader2``'s state is restored (through
+ ``.from_state(...)`` or ``load_state_dict(...)``) in order to resume from the beginning of the last-ran epoch.
+ """
+ self._seed_generator = self._initial_seed_generator
+
  def _pause(self):
  if hasattr(self.reading_service, "_pause"):
  self._is_paused = True

torchdata/dataloader2/random/seed_generator.py

@@ -83,3 +83,13 @@ def spawn(self, worker_id: int, inplace: bool = False) -> "SeedGenerator":
  self._worker_rng = self._worker_rng.spawn(worker_id)
  return self
  return SeedGenerator(seed=None, _rngs=(self._shared_rng.clone(), self._worker_rng.spawn(worker_id)))
+
+ def __getstate__(self):
+ state = (
+ self._shared_rng,
+ self._worker_rng,
+ )
+ return state
+
+ def __setstate__(self, state):
+ self._shared_rng, self._worker_rng = state

torchdata/dataloader2/reading_service.py

@@ -312,6 +312,8 @@ def initialize_iteration(
  ) -> Optional[Callable[[DataPipe], DataPipe]]:
  assert self._end_datapipe is not None
 
+ # Set random seeds for DataPipe that are in the main process (NOT those in worker processes)
+ # Worker seeds are set in `process_reset_fn`
  set_graph_random_seed(self._end_datapipe, seed_generator)
 
  if self._mp: