CSPRNG is a PyTorch C++/CUDA extension that provides cryptographically secure pseudorandom number generators for PyTorch.

CSPRNG generates a random 128-bits key on CPU using one of its generators and runs AES128 in CTR mode mode either on CPU or on GPU using CUDA to generate random 128 bits state and apply transformation function to map it to target tensor values. This approach is based on Parallel Random Numbers: As Easy as 1, 2, 3(John K. Salmon, Mark A. Moraes, Ron O. Dror, and David E. Shaw, D. E. Shaw Research). It makes CSPRNG both crypto-secure and parallel on CUDA and CPU.

Advantages:

• The user can choose either seed-based(for testing) or random device based(fully crypto-secure) generators
• One generator instance for both CPU and CUDA tensors(because the encryption key is always generated on CPU)
• CPU random number generation is also parallel(unlike default PyTorch CPU generator)

CSPRNG exposes two methods to create crypto-secure and non-crypto-secure PRNGs:

The following list of methods supports all forementioned PRNGs:

CSPRNG works with Python 3.6/3.7/3.8 on the following operating systems and can be used with PyTorch tensors on the following devices:

Anaconda:

conda install torchcsprng -c pytorch

pip:

pip install torchcsprng

Nightly builds:

Anaconda:

pip:

CSPRNG is a Python C++/CUDA extension that depends on PyTorch. In order to build CSPRNG from source it is required to have Python(>=3.6) with PyTorch(>=1.6.0) installed and C++ compiler(gcc/clang for Linux, XCode for macOS, Visual Studio for MS Windows). To build CSPRNG run the following:

python setup.py install

By default, GPU support is built if CUDA is found and torch.cuda.is_available() is true. It's possible to force building GPU support by setting FORCE_CUDA=1 environment variable, which is useful when building a docker image.

CSPRNG API is available in torchcsprng module:

import torch
import torchcsprng as csprng

Create crypto-secure PRNG from /dev/urandom:

urandom_gen = csprng.create_random_device_generator('/dev/urandom')

Create empty boolean tensor on CUDA and initialize it with random values from urandom_gen:

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:

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()
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()
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(42)
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')

Recreate MT19937 PRNG with the same seed:

mt19937_gen = csprng.create_mt19937_generator(42)
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')

CSPRNG is BSD-licensed.