[DataLoader] Add `generate_state` for NumPy seeding (#56797)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/56797
After adding default seeding strategy for NumPy random module within each worker of DataLoader #56488, two concerns are raised:
- We dropped the support for NumPy < 1.17 due to `SeedSequence`
- In order to support seeding for NumPy < 1.17, how can we provide seed for `numpy.random`?
- First option is set the same seed as `random`. But, the problem is a same algorithm is shared between `numpy.random` and `random`. With the same seed, they will have exact same state sequence. Thanks to rkern, we noticed this so-called [bad things](https://github.com/PyTorchLightning/pytorch-lightning/pull/6960#issuecomment-818393659).
- Considering most of users do not aware this problem, we can provide a better seed by default for `numpy.random` using same `SeedSequence` algorithm as numpy. This is just a workaround with hard-coded function to generate an array of four int32 as the seed.
To better coping with this problem since there are amount of 3rd party libraries not just `NumPy` having random module. We may at the end need to implement a `SeedSequence` within `torch.random` module, then users can `spawn` a new `SeedSequence` for each library.
Test Plan: Imported from OSS
Reviewed By: H-Huang
Differential Revision: D28000619
Pulled By: ejguan
fbshipit-source-id: 5701c8124a38ea5ded69eb8eee70f9680877ffa6
