Fix end of epoch StatefulDataLoader restart

test/stateful_dataloader/test_state_dict.py

@@ -1314,7 +1314,7 @@ def test(self):
                 dataset=dataset,
                 num_workers=num_workers,
                 collate_fn=identity,
-                multiprocessing_context="forkserver" if IS_MACOS and num_workers else None,
+                multiprocessing_context=("forkserver" if IS_MACOS and num_workers else None),
             )
             it = iter(dl)
             # Fetch at least one batch from each worker
@@ -1325,7 +1325,10 @@ def test(self):
             if num_workers > 0:
                 for i in range(num_workers):
                     # Ensure worker state is stored only once if the dataset is also the iterator
-                    self.assertEqual(state_dict["_snapshot"]["_worker_snapshots"][f"worker_{i}"]["dataset_state"], None)
+                    self.assertEqual(
+                        state_dict["_snapshot"]["_worker_snapshots"][f"worker_{i}"]["dataset_state"],
+                        None,
+                    )
                     self.assertTrue(
                         state_dict["_snapshot"]["_worker_snapshots"][f"worker_{i}"]["fetcher_state"][
                             "dataset_iter_state"
@@ -1441,6 +1444,79 @@ def test_fast_state_dict_request_skip_steps(self) -> None:
         self._run_test(17, 19)
 
 
+class TestMultiEpochSDL_shard0(TestCase):
+    def get_map_dl(self, data_size=100, num_workers=0, batch_size=1, shuffle=False):
+        dataset = DummyMapDataset(data_size, shuffle=False)
+        return StatefulDataLoader(
+            dataset=dataset,
+            num_workers=num_workers,
+            batch_size=batch_size,
+            shuffle=shuffle,
+            multiprocessing_context=("forkserver" if IS_MACOS and num_workers else None),
+        )
+
+    def _run(self, data_size, num_workers, batch_size, shuffle=False):
+        dl1 = self.get_map_dl(
+            data_size=data_size,
+            num_workers=num_workers,
+            batch_size=batch_size,
+            shuffle=shuffle,
+        )
+        # Run through the dataloader for 2 epochs and count the number of items yielded
+        num_items_yielded = 0
+        dl1_items = []
+        for _ in range(2):
+            for batch in dl1:
+                dl1_items.append(batch)
+                num_items_yielded += 1
+        # Save the state dict
+        state_dict = dl1.state_dict()
+        # Create a new StatefulDataLoader instance and load the state dict
+        new_dl1 = self.get_map_dl(
+            data_size=data_size,
+            num_workers=num_workers,
+            batch_size=batch_size,
+            shuffle=shuffle,
+        )
+        new_dl1.load_state_dict(state_dict)
+        # Run through the new dataloader for another 2 epochs and count the number of items yielded
+        additional_num_items_yielded = 0
+        for i in range(2):
+            epoch_num_items_yielded = 0
+            for batch in new_dl1:
+                dl1_items.append(batch)
+                epoch_num_items_yielded += 1
+            additional_num_items_yielded += epoch_num_items_yielded
+        # Check that the total number of items yielded is correct
+        self.assertEqual(num_items_yielded + additional_num_items_yielded, data_size * 4)
+
+        # now run a second dataloder for 4 epochs and check if the order is same.
+        dl2 = self.get_map_dl(
+            data_size=data_size,
+            num_workers=num_workers,
+            batch_size=batch_size,
+            shuffle=shuffle,
+        )
+        dl2_items = []
+        for _ in range(4):
+            for batch in dl2:
+                dl2_items.append(batch)
+
+        self.assertEqual(dl1_items, dl2_items)
+
+    def test_main_process(self):
+        self._run(100, 0, 1, False)
+
+    def test_multiprocess(self):
+        self._run(100, 2, 1, False)
+
+    def test_main_process_shuffle(self):
+        self._run(100, 0, 1, True)
+
+    def test_multiprocess_shuffle(self):
+        self._run(100, 2, 1, True)
+
+
 class TestMultiEpochState_shard0(TestCase):
     def get_iterable_dl(self, pw, num_workers):
         data_size = [25, 50, 100, 75]

torchdata/stateful_dataloader/sampler.py

@@ -48,7 +48,11 @@ def state_dict(self) -> Dict[str, Any]:
 
 class RandomSampler(torch.utils.data.sampler.RandomSampler):
     def __init__(
-        self, data_source: Sized, replacement: bool = False, num_samples: Optional[int] = None, generator=None
+        self,
+        data_source: Sized,
+        replacement: bool = False,
+        num_samples: Optional[int] = None,
+        generator=None,
     ):
         if generator is None:
             # Ensure that underlying sampler has something repeatable
@@ -60,16 +64,30 @@ def __iter__(self):
         return _StatefulRandomSamplerIterator(self, super().__iter__())
 
 
-class BatchSampler(torch.utils.data.sampler.BatchSampler, Stateful):
+class _BatchSamplerIterator(Iterator[list[int]], Stateful):
     _SAMPLES_YIELDED = "samples_yielded"
     _SAMPLER_STATE = "sampler_state"
     _SAMPLER_ITER_STATE = "sampler_iter_state"
 
-    def __init__(self, sampler, batch_size, drop_last):
-        super().__init__(sampler, batch_size, drop_last)
+    def __init__(self, sampler, batch_size: int, drop_last: bool):
+        self.sampler = sampler
+        self.sampler_iter = iter(self.sampler)
+        self.batch_size = batch_size
+        self.drop_last = drop_last
         self.samples_yielded = 0
-        self.next_yielded = None
-        self.sampler_iter = iter(sampler)
+
+    def __next__(self) -> list[int]:
+        batch = []
+        try:
+            for _ in range(self.batch_size):
+                batch.append(next(self.sampler_iter))
+                self.samples_yielded += 1
+            return batch
+        except StopIteration:
+            if self.drop_last or len(batch) == 0:
+                raise StopIteration
+            else:
+                return batch
 
     def state_dict(self) -> Dict[str, Any]:
         sd: Dict[str, Any] = {self._SAMPLES_YIELDED: self.samples_yielded}
@@ -80,7 +98,7 @@ def state_dict(self) -> Dict[str, Any]:
         return sd
 
     def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
-        self.next_yielded = state_dict[self._SAMPLES_YIELDED]
+        self.samples_yielded = state_dict[self._SAMPLES_YIELDED]
         if self._SAMPLER_STATE in state_dict:
             assert isinstance(self.sampler, Stateful)
             self.sampler.load_state_dict(state_dict[self._SAMPLER_STATE])
@@ -89,44 +107,29 @@ def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
             assert isinstance(self.sampler_iter, Stateful)
             self.sampler_iter.load_state_dict(state_dict[self._SAMPLER_ITER_STATE])
 
+        if not (isinstance(self.sampler, Stateful) or isinstance(self.sampler_iter, Stateful)) and not isinstance(
+            self.sampler, _InfiniteConstantSampler
+        ):
+            # We skip x samples if underlying sampler is not stateful
+            for _ in range(self.samples_yielded):
+                next(self.sampler_iter)
+
+        # Skip one epoch if we were at the end of the last epoch
+        if hasattr(self.sampler, "__len__") and self.samples_yielded == len(self.sampler):
+            for _ in self.sampler_iter:
+                pass
+
+
+class BatchSampler(torch.utils.data.sampler.BatchSampler):
+    def __init__(self, sampler, batch_size, drop_last):
+        super().__init__(sampler, batch_size, drop_last)
+
     def __iter__(self):
-        if self.next_yielded is not None:
-            self.samples_yielded = self.next_yielded
-            if not (isinstance(self.sampler, Stateful) or isinstance(self.sampler_iter, Stateful)) and not isinstance(
-                self.sampler, _InfiniteConstantSampler
-            ):
-                # We skip x samples if underlying sampler is not stateful
-                for _ in range(self.next_yielded):
-                    next(self.sampler_iter)
-            self.next_yielded = None
-        elif self.samples_yielded > 0:
-            # don't re-create sampler_iter unless necessary, we may already have one from init
-            self.sampler_iter = iter(self.sampler)
-            self.samples_yielded = 0
-
-        if self.drop_last:
-            while True:
-                try:
-                    batch = []
-                    for _ in range(self.batch_size):
-                        batch.append(next(self.sampler_iter))
-                        self.samples_yielded += 1
-                    yield batch
-                except StopIteration:
-                    break
-        else:
-            batch = [0] * self.batch_size
-            idx_in_batch = 0
-            for idx in self.sampler_iter:
-                self.samples_yielded += 1
-                batch[idx_in_batch] = idx
-                idx_in_batch += 1
-                if idx_in_batch == self.batch_size:
-                    yield batch
-                    idx_in_batch = 0
-                    batch = [0] * self.batch_size
-            if idx_in_batch > 0:
-                yield batch[:idx_in_batch]
+        return _BatchSamplerIterator(
+            sampler=self.sampler,
+            batch_size=self.batch_size,
+            drop_last=self.drop_last,
+        )
 
 
 class StatefulDistributedSampler(torch.utils.data.distributed.DistributedSampler):