Update on "Convert generator in Sampler back to lazy construction"

Fixes #63609


- Revert #63026 
  - Sampler is expected to be re-seeded if user specify seed before each epoch
  - Can not attach generator to self with `__iter__` because multiple iterators will ruin the use case
- Add tests to prevent the same case for different Samplers


Differential Revision: [D30451774](https://our.internmc.facebook.com/intern/diff/D30451774)

[ghstack-poisoned]

test/test_dataloader.py

@@ -1547,26 +1547,6 @@ def test_sampler_reproducibility(self):
                     ls[i].append(next(its[i]))
             self.assertEqual(ls[0], ls[1])
 
-    def test_serialize_sampler(self):
-        from torch.utils.data import RandomSampler
-        sampler = RandomSampler(self.dataset, num_samples=5, replacement=True)
-        it1 = iter(sampler)
-        torch.manual_seed(0)
-        _ = next(it1)
-
-        torch.manual_seed(0)
-        seed = torch.empty((), dtype=torch.int64).random_()
-        self.assertEqual(list(sampler._iterators.values())[0].initial_seed(), seed)
-
-        it2 = iter(sampler)
-        torch.manual_seed(1)
-        _ = next(it2)
-
-        torch.manual_seed(1)
-        seed = torch.empty((), dtype=torch.int64).random_()
-        _ = list(it1)
-        self.assertEqual(list(sampler._iterators.values())[0].initial_seed(), seed)
-
     def _test_sampler(self, **kwargs):
         indices = range(2, 12)  # using a regular iterable
         dl = self._get_data_loader(self.dataset, sampler=indices, batch_size=2, **kwargs)

torch/utils/data/sampler.py

@@ -89,8 +89,6 @@ def __init__(self, data_source: Sized, replacement: bool = False,
         self.replacement = replacement
         self._num_samples = num_samples
         self.generator = generator
-        # Used to save state of RNG per iterator
-        self._iterators = {}
 
         if not isinstance(self.replacement, bool):
             raise TypeError("replacement should be a boolean value, but got "
@@ -112,27 +110,20 @@ def num_samples(self) -> int:
         return self._num_samples
 
     def __iter__(self) -> Iterator[int]:
+        n = len(self.data_source)
         if self.generator is None:
             seed = int(torch.empty((), dtype=torch.int64).random_().item())
             generator = torch.Generator()
             generator.manual_seed(seed)
         else:
             generator = self.generator
 
-        it = self.iter_fn(generator)
-        self._iterators[it] = generator
-        yield from it
-        del self._iterators[it]
-
-    def iter_fn(self, rng) -> Iterator[int]:
-        n = len(self.data_source)
-
         if self.replacement:
             for _ in range(self.num_samples // 32):
-                yield from torch.randint(high=n, size=(32,), dtype=torch.int64, generator=rng).tolist()
-            yield from torch.randint(high=n, size=(self.num_samples % 32,), dtype=torch.int64, generator=rng).tolist()
+                yield from torch.randint(high=n, size=(32,), dtype=torch.int64, generator=generator).tolist()
+            yield from torch.randint(high=n, size=(self.num_samples % 32,), dtype=torch.int64, generator=generator).tolist()
         else:
-            yield from torch.randperm(n, generator=rng).tolist()
+            yield from torch.randperm(n, generator=generator).tolist()
 
     def __len__(self) -> int:
         return self.num_samples