Python API Reference

Python API Reference#

This chapter describes the rocRAND Python module API.

class PRNG#

class rocrand.PRNG(rngtype=400, seed=None, offset=None, stream=None)#
DEFAULT = 400#

Default pseudo-random generator type, XORWOW

LFSR113 = 406#

LFSR113 pseudo-random generator type

MRG31K3P = 405#

MRG31k3p pseudo-random generator type

MRG32K3A = 402#

MRG32k3a pseudo-random generator type

MT19937 = 407#

Mersenne Twister pseudo-random generator type

MTGP32 = 403#

Mersenne Twister MTGP32 pseudo-random generator type

PHILOX4_32_10 = 404#

PHILOX_4x32 (10 rounds) pseudo-random generator type

THREEFRY2_32_20 = 408#

THREEFRY2_32_20 pseudo-random generator type

THREEFRY2_64_20 = 409#

THREEFRY2_64_20 pseudo-random generator type

THREEFRY4_32_20 = 410#

THREEFRY4_32_20 pseudo-random generator type

THREEFRY4_64_20 = 411#

THREEFRY4_64_20 pseudo-random generator type

XORWOW = 401#

XORWOW pseudo-random generator type

generate(ary, size=None)#

Generates uniformly distributed integers.

Generates size (if present) or ary.size uniformly distributed integers and saves them to ary.

Supported dtype of ary: numpy.uint32, numpy.int32.

Parameters:

  • ary – NumPy array (numpy.ndarray) or HIP device-side array (DeviceNDArray)

  • size – Number of samples to generate, default to ary.size

lognormal(ary, mean, stddev, size=None)#

Generates log-normally distributed floats.

Generates size (if present) or ary.size log-normally distributed floats and saves them to ary.

Supported dtype of ary: numpy.float32, numpy.float64.

Parameters:

  • ary – NumPy array (numpy.ndarray) or HIP device-side array (DeviceNDArray)

  • mean – Mean value of log normal distribution

  • stddev – Standard deviation value of log normal distribution

  • size – Number of samples to generate, default to ary.size

normal(ary, mean, stddev, size=None)#

Generates normally distributed floats.

Generates size (if present) or ary.size normally distributed floats and saves them to ary.

Supported dtype of ary: numpy.float32, numpy.float64.

Parameters:

  • ary – NumPy array (numpy.ndarray) or HIP device-side array (DeviceNDArray)

  • mean – Mean value of normal distribution

  • stddev – Standard deviation value of normal distribution

  • size – Number of samples to generate, default to ary.size

property offset#

Mutable attribute of the offset of random numbers sequence.

Setting this attribute resets the sequence.

poisson(ary, lmbd, size=None)#

Generates Poisson-distributed integers.

Generates size (if present) or ary.size Poisson-distributed integers and saves them to ary.

Supported dtype of ary: numpy.uint32, numpy.int32.

Parameters:

  • ary – NumPy array (numpy.ndarray) or HIP device-side array (DeviceNDArray)

  • lmbd – lambda for the Poisson distribution

  • size – Number of samples to generate, default to ary.size

property seed#

Mutable attribute of the seed of random numbers sequence.

Setting this attribute resets the sequence.

uniform(ary, size=None)#

Generates uniformly distributed floats.

Generates size (if present) or ary.size uniformly distributed floats and saves them to ary.

Supported dtype of ary: numpy.float32, numpy.float64.

Generated numbers are between 0.0 and 1.0, excluding 0.0 and including 1.0.

Parameters:

  • ary – NumPy array (numpy.ndarray) or HIP device-side array (DeviceNDArray)

  • size – Number of samples to generate, default to ary.size

class QRNG#

class rocrand.QRNG(rngtype=500, ndim=None, offset=None, stream=None)#
DEFAULT = 500#

Default quasi-random generator type, SOBOL32

SCRAMBLED_SOBOL32 = 502#

Scrambled Sobol32 quasi-random generator type

SCRAMBLED_SOBOL64 = 505#

Scrambled Sobol64 quasi-random generator type

SOBOL32 = 501#

Sobol32 quasi-random generator type

SOBOL64 = 504#

Sobol64 quasi-random generator type

generate(ary, size=None)#

Generates uniformly distributed integers.

Generates size (if present) or ary.size uniformly distributed integers and saves them to ary.

Supported dtype of ary: numpy.uint32, numpy.int32.

Parameters:

  • ary – NumPy array (numpy.ndarray) or HIP device-side array (DeviceNDArray)

  • size – Number of samples to generate, default to ary.size

lognormal(ary, mean, stddev, size=None)#

Generates log-normally distributed floats.

Generates size (if present) or ary.size log-normally distributed floats and saves them to ary.

Supported dtype of ary: numpy.float32, numpy.float64.

Parameters:

  • ary – NumPy array (numpy.ndarray) or HIP device-side array (DeviceNDArray)

  • mean – Mean value of log normal distribution

  • stddev – Standard deviation value of log normal distribution

  • size – Number of samples to generate, default to ary.size

property ndim#

Mutable attribute of the number of dimensions of random numbers sequence.

Supported values are 1 to 20000. Setting this attribute resets the sequence.

normal(ary, mean, stddev, size=None)#

Generates normally distributed floats.

Generates size (if present) or ary.size normally distributed floats and saves them to ary.

Supported dtype of ary: numpy.float32, numpy.float64.

Parameters:

  • ary – NumPy array (numpy.ndarray) or HIP device-side array (DeviceNDArray)

  • mean – Mean value of normal distribution

  • stddev – Standard deviation value of normal distribution

  • size – Number of samples to generate, default to ary.size

property offset#

Mutable attribute of the offset of random numbers sequence.

Setting this attribute resets the sequence.

poisson(ary, lmbd, size=None)#

Generates Poisson-distributed integers.

Generates size (if present) or ary.size Poisson-distributed integers and saves them to ary.

Supported dtype of ary: numpy.uint32, numpy.int32.

Parameters:

  • ary – NumPy array (numpy.ndarray) or HIP device-side array (DeviceNDArray)

  • lmbd – lambda for the Poisson distribution

  • size – Number of samples to generate, default to ary.size

uniform(ary, size=None)#

Generates uniformly distributed floats.

Generates size (if present) or ary.size uniformly distributed floats and saves them to ary.

Supported dtype of ary: numpy.float32, numpy.float64.

Generated numbers are between 0.0 and 1.0, excluding 0.0 and including 1.0.

Parameters:

  • ary – NumPy array (numpy.ndarray) or HIP device-side array (DeviceNDArray)

  • size – Number of samples to generate, default to ary.size

Exceptions#

exception rocrand.RocRandError(value)#

Run-time rocRAND error.

exception rocrand.HipError(value)#

Run-time HIP error.

Utilities#

class rocrand.DeviceNDArray(shape, dtype, data=None)#
copy_to_host(ary=None)#

Copy from data device memory to host memory.

If ary is passed then ary must have the same dtype and greater or equal size as self has.

If ary is not passed then a new numpy.ndarray will be created.

Parameters:

ary – NumPy array (numpy.ndarray)

Returns:

a new array or ary

rocrand.empty(shape, dtype)#

Create a new device-side array of given shape and type, without initializing entries.

This function is a limited version of numpy.empty() for device-side arrays.

Example:

import rocrand
import numpy as np

gen = rocrand.QRNG(ndim=5)
d_a = rocrand.empty((5, 10000), dtype=np.float32)
gen.uniform(d_a)
a = d_a.copy_to_host()
print(a)

See DeviceNDArray

Parameters:

  • shape (int or tuple of int) – Shape of the array (see numpy.ndarray.shape)

  • dtype – Type of the array (see numpy.ndarray.dtype)

rocrand.get_version()#

Returns the version number of the rocRAND library.

Contents