[PrototypeRS] Adding support for naive snapshotting

ghstack-source-id: 713a9b208cf7596f0f81c305396e4c27ef51e950
Pull Request resolved: #915

test/dataloader2/test_proto_multi_rs.py

@@ -180,12 +180,41 @@ def test_reading_service_limit(self) -> None:
                     f"worker_prefetch_cnt = {rs.worker_prefetch_cnt}, main_prefetch_cnt = {rs.main_prefetch_cnt}",
                 )
 
-    # TODO: Implemented in an upcoming PR
-    # def test_reading_service_snapshot(self) -> None:
-    #     pass
-    #
-    # def test_dataloader2_snapshot(self) -> None:
-    #     pass
+    def test_dataloader2_snapshot(self) -> None:
+
+        rs1 = PrototypeMultiProcessingReadingService(num_workers=1, worker_prefetch_cnt=0, main_prefetch_cnt=0)
+        # rs2 = PrototypeMultiProcessingReadingService(num_workers=1, worker_prefetch_cnt=0, main_prefetch_cnt=2)
+        # rs3 = PrototypeMultiProcessingReadingService(num_workers=2, worker_prefetch_cnt=0, main_prefetch_cnt=0)
+        # rs4 = PrototypeMultiProcessingReadingService(num_workers=2, worker_prefetch_cnt=2, main_prefetch_cnt=0)
+        # rs5 = PrototypeMultiProcessingReadingService(num_workers=2, worker_prefetch_cnt=0, main_prefetch_cnt=2)
+        # rs6 = PrototypeMultiProcessingReadingService(num_workers=2, worker_prefetch_cnt=2, main_prefetch_cnt=2)
+
+        n_samples_before_snapshot = 3
+
+        n_samples_yielded = 0
+        initial_seed_rng = None
+
+        test_rss = [rs1]
+        for rs in test_rss:
+            dl: DataLoader2 = DataLoader2(self.dp1, reading_service=rs)
+            res = []
+            for i, x in enumerate(dl):
+                res.append(x)
+                if i in {n_samples_before_snapshot - 1}:
+                    n_samples_yielded, initial_seed_rng = dl._get_naive_datapipe_snapshot()
+                    break
+            dl.shutdown()
+            self.assertEqual(n_samples_before_snapshot, len(res))
+            self.assertEqual(n_samples_before_snapshot, n_samples_yielded)
+
+            dl_restored: DataLoader2 = DataLoader2(self.dp1, reading_service=rs)
+            dl_restored._restore_naive_datapipe_snapshot(n_samples_yielded, initial_seed_rng)
+            restored_res = list(dl_restored)
+            self.assertEqual(res, restored_res[0 : n_samples_before_snapshot - 1])  # Check if elements are the same
+            self.assertEqual(list(range(self.n_elements)), sorted(restored_res))
+            dl_restored.shutdown()
+
+            # TODO: Need to figure out the reset situation within `_simple_graph_snapshot_restoration` and ProtoRS
 
 
 if __name__ == "__main__":

torchdata/dataloader2/dataloader2.py

@@ -19,7 +19,11 @@
     serialize_datapipe,
     wrap_datapipe_for_serialization,
 )
-from torchdata.dataloader2.reading_service import CheckpointableReadingServiceInterface, ReadingServiceInterface
+from torchdata.dataloader2.reading_service import (
+    CheckpointableReadingServiceInterface,
+    PrototypeMultiProcessingReadingService,
+    ReadingServiceInterface,
+)
 
 T_co = TypeVar("T_co", covariant=True)
 SERIALIZED_DATAPIPE_KEY_NAME = "serialized_datapipe"
@@ -203,6 +207,7 @@ def __iter__(self) -> DataLoader2Iterator[T_co]:
         if self._reset_iter:
             if not self._adapted and self.reading_service is not None:
                 if self.reading_service_state is None:
+                    # Only called once when `self._adapted = False`
                     self.datapipe = self.reading_service.initialize(self.datapipe)
                 else:
                     if not isinstance(self.reading_service, CheckpointableReadingServiceInterface):
@@ -328,3 +333,30 @@ def resume(self):
         else:
             warnings.warn("ReadingService doesn't support resume.")
         self._is_paused = False
+
+    def _get_naive_datapipe_snapshot(self):
+        """
+        Return a snapshot of the DataPipe
+        """
+        if not isinstance(self.reading_service, PrototypeMultiProcessingReadingService):
+            raise RuntimeError(
+                "Only `PrototypeMultiProcessingReadingService` " "currently supports naive DataPipe snapshotting."
+            )
+        self.pause()
+        n_samples_yielded, _initial_seed = self.reading_service._get_naive_datapipe_snapshot()
+        self.resume()
+        return n_samples_yielded, _initial_seed
+
+    def _restore_naive_datapipe_snapshot(self, n_samples_yielded, initial_seed) -> None:
+        if not isinstance(self.reading_service, PrototypeMultiProcessingReadingService):
+            raise RuntimeError(
+                "Only `PrototypeMultiProcessingReadingService` " "currently supports naive DataPipe snapshotting."
+            )
+        if not self._adapted:
+            self.datapipe = self.reading_service.initialize(self.datapipe)
+            self._adapted = True
+        self.reading_service._restore_naive_datapipe_snapshot(n_samples_yielded, initial_seed)
+        # TODO: I might want to skip `initialize_iteration` after this????
+
+        # TODO: Integrate this with the existing API? Is anyone using these at the moment?
+>>>>>>> 99bf9cf5 ([PrototypeRS] Adding support for naive snapshotting)

torchdata/dataloader2/reading_service.py

@@ -19,6 +19,7 @@
 
 from torch.utils.data import DataLoader
 from torch.utils.data.datapipes.iter.grouping import SHARDING_PRIORITIES
+from torch.utils.data.datapipes.utils.snapshot import _simple_graph_snapshot_restoration
 
 from torchdata._constants import default_dl2_worker_join_timeout_in_s, default_timeout_in_s
 from torchdata.dataloader2 import communication
@@ -189,6 +190,7 @@ def __init__(
         self._pg = None
         self._world_size = 1
         self._rank = 0
+        self._initial_seed = None
 
     def initialize(self, datapipe: DataPipe) -> DataPipe:
         r"""
@@ -277,6 +279,7 @@ def initialize_iteration(self) -> None:
         shared_seed_int: int = shared_seed.item()  # type: ignore[assignment]
         _seed_generator = torch.Generator()
         _seed_generator.manual_seed(shared_seed_int)
+        self._initial_seed = shared_seed_int
         torch.utils.data.graph_settings.apply_random_seed(
             self.end_datapipe,  # type: ignore[arg-type]
             _seed_generator,
@@ -394,6 +397,15 @@ def _resume(self):
         if self.main_prefetch_cnt > 0 and self.num_workers > 0:
             self.end_datapipe.resume()  # type: ignore[union-attr]
 
+    def _get_naive_datapipe_snapshot(self):
+        return self.end_datapipe._number_of_samples_yielded, self._initial_seed
+
+    def _restore_naive_datapipe_snapshot(self, n_samples_yielded, initial_seed):
+        initial_seed_generator = torch.Generator()
+        initial_seed_generator.manual_seed(initial_seed)
+        _simple_graph_snapshot_restoration(self.end_datapipe, n_samples_yielded, initial_seed_generator)
+        # TODO: I might want to skip `initialize_iteration` after this????
+
 
 class MultiProcessingReadingService(ReadingServiceInterface):
     r"""