CSPRNG is a PyTorch C++/CUDA extension that provides cryptographically secure pseudorandom number generators for PyTorch.
CSPRNG exposes four methods to create crypto-secure and non-crypto-secure PRNGs:
| PRNG | Is crypto-secure? | Has seed? |
|---|---|---|
| create_random_device_generator | yes | no |
| create_random_device_generator_with_token | yes | no |
| create_mt19937_generator | no | yes |
| create_mt19937_generator_with_seed | no | yes |
The following list of methods supports all forementioned PRNGs:
| Kernel | CUDA | CPU |
|---|---|---|
| random_() | yes | yes(slow, parallelization TBD) |
| random_(to) | yes | yes(slow, parallelization TBD) |
| random_(from, to) | yes | yes(slow, parallelization TBD) |
| uniform_(from, to) | yes | yes(slow, parallelization TBD) |
| normal_(mean, std) | yes | yes(slow, parallelization TBD) |
| cauchy_(median, sigma) | yes | yes(slow, parallelization TBD) |
| log_normal_(mean, std) | yes | yes(slow, parallelization TBD) |
| geometric_(p) | yes | yes(slow, parallelization TBD) |
| exponential_(lambda) | yes | yes(slow, parallelization TBD) |
Since CSPRNG is C++/CUDA extension it uses setuptools, just run python setup.py install to build and install it.
import torch
import torch_csprng as csprng
# Create crypto-secure PRNG from /dev/urandom:
urandom_gen = csprng.create_random_device_generator_with_token('/dev/urandom')
# Create empty boolean tensor on CUDA and initialize it with random values from urandom_gen:
print(torch.empty(10, dtype=torch.bool, device='cuda').random_(generator=urandom_gen))
tensor([ True, False, False, True, False, False, False, True, False, False],
device='cuda:0')
# Create empty int16 tensor on CUDA and initialize it with random values in range [0, 100) from urandom_gen:
print(torch.empty(10, dtype=torch.int16, device='cuda').random_(100, generator=urandom_gen))
tensor([59, 20, 68, 51, 18, 37, 7, 54, 74, 85], device='cuda:0',
dtype=torch.int16)
# Create non-crypto-secure MT19937 PRNG:
mt19937_gen = csprng.create_mt19937_generator()
print(torch.empty(10, dtype=torch.int64, device='cuda').random_(torch.iinfo(torch.int64).min, to=None, generator=mt19937_gen))
tensor([-7584783661268263470, 2477984957619728163, -3472586837228887516,
-5174704429717287072, 4125764479102447192, -4763846282056057972,
-182922600982469112, -498242863868415842, 728545841957750221,
7740902737283645074], device='cuda:0')
# Create crypto-secure PRNG from default random device:
default_device_gen = csprng.create_random_device_generator()
print(torch.randn(10, device='cuda', generator=default_device_gen))
tensor([ 1.2885, 0.3240, -1.1813, 0.8629, 0.5714, 2.3720, -0.5627, -0.5551,
-0.6304, 0.1090], device='cuda:0')
# Create non-crypto-secure MT19937 PRNG with seed
mt19937_gen = csprng.create_mt19937_generator_with_seed(42)
print(torch.empty(10, device='cuda').geometric_(p=0.2, generator=mt19937_gen))
tensor([ 7., 1., 8., 1., 11., 3., 1., 1., 5., 10.], device='cuda:0')
print(torch.empty(10, device='cuda').geometric_(p=0.2, generator=mt19937_gen))
tensor([ 1., 1., 1., 6., 1., 13., 5., 1., 3., 4.], device='cuda:0')
print(torch.empty(10, device='cuda').geometric_(p=0.2, generator=mt19937_gen))
tensor([14., 5., 4., 5., 1., 1., 8., 1., 7., 10.], device='cuda:0')
# Recreate MT19937 PRNG with the same seed
mt19937_gen = csprng.create_mt19937_generator_with_seed(42)
print(torch.empty(10, device='cuda').geometric_(p=0.2, generator=mt19937_gen))
tensor([ 7., 1., 8., 1., 11., 3., 1., 1., 5., 10.], device='cuda:0')
print(torch.empty(10, device='cuda').geometric_(p=0.2, generator=mt19937_gen))
tensor([ 1., 1., 1., 6., 1., 13., 5., 1., 3., 4.], device='cuda:0')
print(torch.empty(10, device='cuda').geometric_(p=0.2, generator=mt19937_gen))
tensor([14., 5., 4., 5., 1., 1., 8., 1., 7., 10.], device='cuda:0')