From: MY_ <lartpang@163.com>
Date: Mon, 16 Aug 2021 21:07:06 +0000 (-0700)
Subject: A re-open PR: Avoid re-creating the random number generator in RandomSampler (#63026)
X-Git-Tag: accepted/tizen/8.0/unified/20231005.095509~990
X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=dc5ce22a1a8bd075141f7310fa6cb503db4d6e08;p=platform%2Fupstream%2Fpytorch.git

A re-open PR: Avoid re-creating the random number generator in RandomSampler (#63026)

Summary:
More details can be found in the old pr: https://github.com/pytorch/pytorch/pull/53085

ejguan  Thanks for your guidance. I tried to reopen this PR following your instructions.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/63026

Reviewed By: anjali411

Differential Revision: D30224920

Pulled By: ejguan

fbshipit-source-id: 2fa83bd4a2661485e553447fe3e57ce723f2716d
---

diff --git a/torch/utils/data/sampler.py b/torch/utils/data/sampler.py
index 894f775..7903347 100644
--- a/torch/utils/data/sampler.py
+++ b/torch/utils/data/sampler.py
@@ -112,16 +112,15 @@ class RandomSampler(Sampler[int]):
     def __iter__(self) -> Iterator[int]:
         n = len(self.data_source)
         if self.generator is None:
-            generator = torch.Generator()
-            generator.manual_seed(int(torch.empty((), dtype=torch.int64).random_().item()))
-        else:
-            generator = self.generator
+            self.generator = torch.Generator()
+            self.generator.manual_seed(int(torch.empty((), dtype=torch.int64).random_().item()))
+
         if self.replacement:
             for _ in range(self.num_samples // 32):
-                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()
+                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()
         else:
-            yield from torch.randperm(n, generator=generator).tolist()
+            yield from torch.randperm(n, generator=self.generator).tolist()
 
     def __len__(self) -> int:
         return self.num_samples