| import numpy as np |
| import torch |
|
|
| TORCH_RNG_MAX = 0xFFFF_FFFF_FFFF_FFFF |
| TORCH_RNG_MIN = -0x8000_0000_0000_0000 |
|
|
| NP_RNG_MAX = np.iinfo(np.uint32).max |
| NP_RNG_MIN = 0 |
|
|
|
|
| def torch_rng(seed: int): |
| torch.manual_seed(seed) |
| random_float = torch.empty(1).uniform_().item() |
| torch_rn = int(random_float * (TORCH_RNG_MAX - TORCH_RNG_MIN) + TORCH_RNG_MIN) |
| np_rn = int(random_float * (NP_RNG_MAX - NP_RNG_MIN) + NP_RNG_MIN) |
| return torch_rn, np_rn |
|
|
|
|
| def convert_np_to_torch(np_rn: int): |
| random_float = (np_rn - NP_RNG_MIN) / (NP_RNG_MAX - NP_RNG_MIN) |
| torch_rn = int(random_float * (TORCH_RNG_MAX - TORCH_RNG_MIN) + TORCH_RNG_MIN) |
| return torch_rn |
|
|
|
|
| def np_rng(): |
| return int(np.random.randint(NP_RNG_MIN, NP_RNG_MAX, dtype=np.uint32)) |
|
|
|
|
| if __name__ == "__main__": |
| import random |
|
|
| print(TORCH_RNG_MIN, TORCH_RNG_MAX) |
| s1 = np_rng() |
| s2 = torch_rng(s1) |
| print(f"s1 {s1} => s2: {s2}") |
|
|
