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Convert generator in Sampler back to lazy construction #63646

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Convert generator in Sampler back to lazy construction #63646

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8b4ddf7
Convert Sampler back to lazily construction
ejguan Aug 20, 2021
468acb4
Update on "Convert Sampler back to lazily construction"
ejguan Aug 20, 2021
1b1f1e9
Update on "Convert generator in Sampler back to lazy construction"
ejguan Aug 20, 2021
8251153
Update on "Convert generator in Sampler back to lazy construction"
ejguan Aug 20, 2021
4c639d5
Update on "Convert generator in Sampler back to lazy construction"
ejguan Aug 20, 2021
8aee201
Update on "Convert generator in Sampler back to lazy construction"
ejguan Sep 29, 2021
725eb13
Update on "Convert generator in Sampler back to lazy construction"
ejguan Sep 29, 2021
d506308
Update on "Convert generator in Sampler back to lazy construction"
ejguan Sep 29, 2021
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22 changes: 22 additions & 0 deletions test/test_dataloader.py
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Expand Up @@ -1524,6 +1524,28 @@ def test_sampler_reproducibility(self):
):
self.assertEqual(list(fn()), list(fn()))

for sampler in (
RandomSampler(self.dataset, num_samples=5, replacement=True),
RandomSampler(self.dataset, replacement=False),
WeightedRandomSampler(weights, num_samples=5, replacement=True),
WeightedRandomSampler(weights, num_samples=5, replacement=False),
SubsetRandomSampler(range(10)),
):
torch.manual_seed(0)
l1 = list(sampler) + list(sampler)

torch.manual_seed(0)
l2 = list(sampler) + list(sampler)
self.assertEqual(l1, l2)

its = (iter(sampler), iter(sampler))
ls = ([], [])
for idx in range(len(sampler)):
for i in range(2):
if idx == 0:
torch.manual_seed(0)
ls[i].append(next(its[i]))
self.assertEqual(ls[0], ls[1])

def _test_sampler(self, **kwargs):
indices = range(2, 12) # using a regular iterable
Expand Down
18 changes: 11 additions & 7 deletions torch/utils/data/sampler.py
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Expand Up @@ -112,15 +112,18 @@ def num_samples(self) -> int:
def __iter__(self) -> Iterator[int]:
n = len(self.data_source)
if self.generator is None:
self.generator = torch.Generator()
self.generator.manual_seed(int(torch.empty((), dtype=torch.int64).random_().item()))
seed = int(torch.empty((), dtype=torch.int64).random_().item())
generator = torch.Generator()
generator.manual_seed(seed)
else:
generator = self.generator

if self.replacement:
for _ in range(self.num_samples // 32):
yield from torch.randint(high=n, size=(32,), dtype=torch.int64, generator=self.generator).tolist()
yield from torch.randint(high=n, size=(self.num_samples % 32,), dtype=torch.int64, generator=self.generator).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=self.generator).tolist()
yield from torch.randperm(n, generator=generator).tolist()

def __len__(self) -> int:
return self.num_samples
Expand All @@ -140,7 +143,8 @@ def __init__(self, indices: Sequence[int], generator=None) -> None:
self.generator = generator

def __iter__(self) -> Iterator[int]:
return (self.indices[i] for i in torch.randperm(len(self.indices), generator=self.generator))
for i in torch.randperm(len(self.indices), generator=self.generator):
yield self.indices[i]

def __len__(self) -> int:
return len(self.indices)
Expand Down Expand Up @@ -183,7 +187,7 @@ def __init__(self, weights: Sequence[float], num_samples: int,

def __iter__(self) -> Iterator[int]:
rand_tensor = torch.multinomial(self.weights, self.num_samples, self.replacement, generator=self.generator)
return iter(rand_tensor.tolist())
yield from iter(rand_tensor.tolist())

def __len__(self) -> int:
return self.num_samples
Expand Down