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Cryptographically secure pseudorandom number generators for PyTorch

Project description

PyTorch/CSPRNG

CircleCI

torchcsprng is a PyTorch C++/CUDA extension that provides:

Design

torchcsprng generates a random 128-bit key on CPU using one of its generators and runs AES128 in CTR mode either on CPU or on GPU using CUDA to generate a random 128 bit state and apply a 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 torchcsprng both crypto-secure and parallel on CUDA and CPU.

CSPRNG architecture

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 the default PyTorch CPU generator)

Features

torchcsprng 0.2.0 exposes new API for tensor encryption/decryption. Tensor encryption/decryption API is dtype agnostic, so a tensor of any dtype can be encrypted and the result can be stored to a tensor of any dtype. An encryption key also can be a tensor of any dtype. Currently torchcsprng supports AES cipher with 128-bit key in two modes: ECB and CTR.

  • torchcsprng.encrypt(input: Tensor, output: Tensor, key: Tensor, cipher: string, mode: string)
  • input tensor can be any CPU or CUDA tensor of any dtype and size in bytes(zero-padding is used to make its size in bytes divisible by block size in bytes)
  • output tensor can have any dtype and the same device as input tensor and the size in bytes rounded up to the block size in bytes(16 bytes for AES 128)
  • key tensor can have any dtype and the same device as input tensor and size in bytes equal to 16 for AES 128
  • cipher currently can be only one supported value "aes128"
  • mode currently can be either "ecb" or "ctr"
  • torchcsprng.decrypt(input: Tensor, output: Tensor, key: Tensor, cipher: string, mode: string)
  • input tensor can be any CPU or CUDA tensor of any dtype with size in bytes divisible by the block size in bytes(16 bytes for AES 128)
  • output tensor can have any dtype but the same device as input tensor and the same size in bytes as input tensor
  • key tensor can have any dtype and the same device as input tensor and size in bytes equal to 16 for AES 128
  • cipher currently can be only one supported value "aes128"
  • mode currently can be either "ecb" or "ctr"

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

Method to create PRNG Is crypto-secure? Has seed? Underlying implementation
create_random_device_generator(token: string=None) yes no See std::random_device and its constructor. The implementation in libstdc++ expects token to name the source of random bytes. Possible token values include "default", "rand_s", "rdseed", "rdrand", "rdrnd", "/dev/urandom", "/dev/random", "mt19937", and integer string specifying the seed of the mt19937 engine. (Token values other than "default" are only valid for certain targets.) If token=None then constructs a new std::random_device object with an implementation-defined token.
create_mt19937_generator(seed: int=None) no yes See std::mt19937 and its constructor. Constructs a mersenne_twister_engine object, and initializes its internal state sequence to pseudo-random values. If seed=None then seeds the engine with default_seed.

The following list of methods supports all forementioned PRNGs:

Kernel CUDA CPU
random_() yes yes
random_(to) yes yes
random_(from, to) yes yes
uniform_(from, to) yes yes
normal_(mean, std) yes yes
cauchy_(median, sigma) yes yes
log_normal_(mean, std) yes yes
geometric_(p) yes yes
exponential_(lambda) yes yes
randperm(n) yes* yes
  • the calculations are done on CPU and the result is copied to CUDA

Installation

CSPRNG works with Python 3.6-3.9 on the following operating systems and can be used with PyTorch tensors on the following devices:

Tensor Device Type Linux macOS MS Window
CPU Supported Supported Supported
CUDA Supported Not Supported Supported since 0.2.0

The following is the corresponding CSPRNG versions and supported Python versions.

PyTorch CSPRNG Python CUDA
1.8.0 0.2.0 3.7-3.9 10.1, 10.2, 11.1
1.7.1 0.1.4 3.6-3.8 9.2, 10.1, 10.2
1.7.0 0.1.3 3.6-3.8 9.2, 10.1, 10.2
1.6.0 0.1.2 3.6-3.8 9.2, 10.1, 10.2

Binary Installation

Anaconda:

OS CUDA
Linux/Windows 10.1

10.2

11.1

None
conda install torchcsprng cudatoolkit=10.1 -c pytorch -c conda-forge

conda install torchcsprng cudatoolkit=10.2 -c pytorch -c conda-forge

conda install torchcsprng cudatoolkit=11.1 -c pytorch -c conda-forge

conda install torchcsprng cpuonly -c pytorch -c conda-forge
macOS None conda install torchcsprng -c pytorch

pip:

OS CUDA
Linux/Windows 10.1

10.2

11.1

None
pip install torchcsprng==0.2.0+cu101 torch==1.8.0+cu101 -f https://download.pytorch.org/whl/cu101/torch_stable.html

pip install torchcsprng==0.2.0 torch==1.8.0 -f https://download.pytorch.org/whl/cu102/torch_stable.html

pip install torchcsprng==0.2.0+cu111 torch==1.8.0+cu111 -f https://download.pytorch.org/whl/cu111/torch_stable.html

pip install torchcsprng==0.2.0+cpu torch==1.8.0+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html
macOS None pip install torchcsprng torch

Nightly builds:

Anaconda:

OS CUDA
Linux/Windows 10.1

10.2

11.1

None
conda install torchcsprng cudatoolkit=10.1 -c pytorch-nightly -c conda-forge

conda install torchcsprng cudatoolkit=10.2 -c pytorch-nightly -c conda-forge

conda install torchcsprng cudatoolkit=11.1 -c pytorch-nightly -c conda-forge

conda install torchcsprng cpuonly -c pytorch-nightly -c conda-forge
macOS None conda install torchcsprng -c pytorch-nightly

pip:

OS CUDA
Linux/Windows 10.1

10.2

11.1

None
pip install --pre torchcsprng -f https://download.pytorch.org/whl/nightly/cu101/torch_nightly.html

pip install --pre torchcsprng -f https://download.pytorch.org/whl/nightly/cu102/torch_nightly.html

pip install --pre torchcsprng -f https://download.pytorch.org/whl/nightly/cu111/torch_nightly.html

pip install --pre torchcsprng -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
macOS None pip install --pre torchcsprng -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html

From Source

torchcsprng is a Python C++/CUDA extension that depends on PyTorch. In order to build CSPRNG from source it is required to have Python(>=3.7) with PyTorch(>=1.8.0) installed and C++ compiler(gcc/clang for Linux, XCode for macOS, Visual Studio for MS Windows). To build torchcsprng you can run the following:

python setup.py install

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

Getting Started

The torchcsprng 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')

Contributing

We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us.

License

torchcsprng is BSD 3-clause licensed. See the license file here

Terms of Use

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