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Merge pull request apache#47 from tqchen/master
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change random to cxx11.
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@tqchen
tqchen committed Sep 15, 2015
2 parents 208a198 + 2f32325 commit 811c396
Showing 1 changed file with 96 additions and 62 deletions.
158 changes: 96 additions & 62 deletions mshadow/random.h
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Original file line number Diff line number Diff line change
Expand Up @@ -7,10 +7,16 @@
*/
#ifndef MSHADOW_RANDOM_H_
#define MSHADOW_RANDOM_H_

#include <cstdlib>
#include "./base.h"
#include "./tensor.h"
#include "./tensor_container.h"

#if MSHADOW_IN_CXX11
#include <random> // use cxx11 random by default
#endif

#if _MSC_VER
#define rand_r(x) rand()
#endif
Expand Down Expand Up @@ -38,19 +44,14 @@ class Random<cpu, DType> {
buffer_.Resize(Shape1(kRandBufferSize));
}
~Random(void) {
#if MSHADOW_USE_MKL
vslDeleteStream(&vStream_);
#endif
}
/*!
* \brief seed random number generator using this seed
* \param seed seed of prng
*/
inline void Seed(int seed) {
#if MSHADOW_USE_MKL
int status = vslNewStream(&vStream_, VSL_BRNG_MT19937, seed);
utils::Check(status == VSL_STATUS_OK,
"MKL VSL Random engine failed to be initialized.\n");
#if MSHADOW_IN_CXX11
rnd_engine_.seed(seed);
#else
this->rseed_ = static_cast<unsigned>(seed);
#endif
Expand All @@ -71,9 +72,13 @@ class Random<cpu, DType> {
template<int dim>
inline void SampleUniform(Tensor<cpu, dim, DType> *dst,
DType a = 0.0f, DType b = 1.0f) {
Tensor<cpu, 2, DType> mat = dst->FlatTo2D();
for (index_t i = 0; i < mat.size(0); ++i) {
this->GenUniform(mat[i].dptr_, mat.size(1), a, b);
if (dst->CheckContiguous()) {
this->GenUniform(dst->dptr_, dst->shape_.Size(), a, b);
} else {
Tensor<cpu, 2, DType> mat = dst->FlatTo2D();
for (index_t i = 0; i < mat.size(0); ++i) {
this->GenUniform(mat[i].dptr_, mat.size(1), a, b);
}
}
}
/*!
Expand All @@ -89,9 +94,13 @@ class Random<cpu, DType> {
if (sigma <= 0.0f) {
*dst = mu; return;
}
Tensor<cpu, 2, DType> mat = dst->FlatTo2D();
for (index_t i = 0; i < mat.size(0); ++i) {
this->GenGaussian(mat[i].dptr_, mat.size(1), mu, sigma);
if (dst->CheckContiguous()) {
this->GenGaussian(dst->dptr_, dst->shape_.Size(), mu, sigma);
} else {
Tensor<cpu, 2, DType> mat = dst->FlatTo2D();
for (index_t i = 0; i < mat.size(0); ++i) {
this->GenGaussian(mat[i].dptr_, mat.size(1), mu, sigma);
}
}
}
/*!
Expand Down Expand Up @@ -132,32 +141,25 @@ class Random<cpu, DType> {
}

private:
#if MSHADOW_USE_MKL
/*! \brief stream used by MKL VSL */
VSLStreamStatePtr vStream_;
// generate uniform distribution
inline void GenUniform(float *dptr, index_t size, float a, float b) {
int status = vsRngUniform(0, vStream_, size, dptr, a, b);
utils::Check(status == VSL_STATUS_OK,
"Failed to generate random number by MKL.");
}
inline void GenUniform(double *dptr, index_t size, double a, double b) {
int status = vdRngUniform(0, vStream_, size, dptr, a, b);
utils::Check(status == VSL_STATUS_OK,
"Failed to generate random number by MKL.");
}
inline void GenGaussian(float *dptr, index_t size, float mu, float sigma) {
int status = vsRngGaussian(0, vStream_, size, dptr, mu, sigma);
utils::Check(status == VSL_STATUS_OK,
"Failed to generate random number by MKL.");
#if MSHADOW_IN_CXX11
/*! \brief use c++11 random engine. */
std::mt19937 rnd_engine_;
// implementing generators.
inline void GenUniform(DType *dptr, index_t size, DType a, DType b) {
std::uniform_real_distribution<DType> dist_uniform(a, b);
for (size_t i = 0; i < size; ++i) {
dptr[i] = dist_uniform(rnd_engine_);
}
}
inline void GenGaussian(double *dptr, index_t size, double mu, double sigma) {
int status = vdRngGaussian(0, vStream_, size, dptr, mu, sigma);
utils::Check(status == VSL_STATUS_OK,
"Failed to generate random number by MKL.");
inline void GenGaussian(DType *dptr, index_t size, DType mu, DType sigma) {
std::normal_distribution<DType> dist_normal(mu, sigma);
for (size_t i = 0; i < size; ++i) {
dptr[i] = dist_normal(rnd_engine_);
}
}

#else
/*! \brief random number seed used by PRNG*/
/*! \brief random number seed used by PRNG */
unsigned rseed_;
// functions
inline void GenUniform(float *dptr, index_t size, float a, float b) {
Expand Down Expand Up @@ -217,8 +219,9 @@ class Random<cpu, DType> {
/*! \brief temporal space used to store random numbers */
TensorContainer<cpu, 1, DType> buffer_;
}; // class Random<cpu, DType>
// only allow GPU PRNG in CUDACC
#ifdef __CUDACC__

// only allow GPU PRNG when cuda is enabled
#if MSHADOW_USE_CUDA
/*! \brief GPU random number generator */
template<typename DType>
class Random<gpu, DType> {
Expand Down Expand Up @@ -271,13 +274,8 @@ class Random<gpu, DType> {
*/
template<int dim>
inline void SampleUniform(Tensor<gpu, dim, DType> *dst,
DType a = 0.0f, DType b = 1.0f) {
if (a == 0.0f && b == 1.0f) {
*dst = this->uniform(dst->shape_);
} else {
*dst = this->uniform(dst->shape_) * (b - a) + a;
}
}
DType a = 0.0f, DType b = 1.0f);

/*!
* \brief generate data from standard gaussian
* \param dst destination
Expand All @@ -287,9 +285,7 @@ class Random<gpu, DType> {
*/
template<int dim>
inline void SampleGaussian(Tensor<gpu, dim, DType> *dst,
DType mu = 0.0f, DType sigma = 1.0f) {
*dst = this->gaussian(dst->shape_, mu, sigma);
}
DType mu = 0.0f, DType sigma = 1.0f);
/*!
* \brief return a temporal expression storing standard gaussian random variables
* the temporal tensor is only valid before next call of gaussian or uniform
Expand All @@ -305,14 +301,7 @@ class Random<gpu, DType> {
*/
template<int dim>
inline expr::ReshapeExp<Tensor<gpu, 1, DType>, DType, dim, 1>
gaussian(Shape<dim> shape, DType mu = 0.0f, DType sigma = 1.0f) {
size_t aligned_sz = ((shape.Size() + 1UL) >> 1) << 1;
// allocate alligned size
buffer_.Resize(Shape1(aligned_sz));
buffer_.Resize(Shape1(shape.Size()));
this->GenGaussian(buffer_.dptr_, aligned_sz, mu, sigma);
return expr::reshape(buffer_, shape);
}
gaussian(Shape<dim> shape, DType mu = 0.0f, DType sigma = 1.0f);
/*!
* \brief return a temporal expression storing standard uniform [0,1)
* the temporal tensor is only valid before next call of gaussian or uniform
Expand All @@ -326,11 +315,7 @@ class Random<gpu, DType> {
*/
template<int dim>
inline expr::ReshapeExp<Tensor<gpu, 1, DType>, DType, dim, 1>
uniform(Shape<dim> shape) {
buffer_.Resize(Shape1(shape.Size()));
this->GenUniform(buffer_.dptr_, buffer_.size(0));
return expr::reshape(buffer_, shape);
}
uniform(Shape<dim> shape);

private:
inline void GenGaussian(float *dptr, size_t size, float mu, float sigma) {
Expand Down Expand Up @@ -358,6 +343,55 @@ class Random<gpu, DType> {
/*! \brief templ buffer */
TensorContainer<gpu, 1, DType> buffer_;
}; // class Random<gpu, DType>
#endif
#endif // MSHADOW_USE_CUDA

#ifdef __CUDACC__
// implementations that depends on cuda kernels
template<typename DType>
template<int dim>
inline void Random<gpu, DType>::SampleUniform(
Tensor<gpu, dim, DType> *dst, DType a, DType b) {
if (a == 0.0f && b == 1.0f) {
if (dst->CheckContiguous()) {
this->GenUniform(dst->dptr_, dst->shape_.Size());
} else {
*dst = this->uniform(dst->shape_);
}
} else {
*dst = this->uniform(dst->shape_) * (b - a) + a;
}
}
template<typename DType>
template<int dim>
inline void Random<gpu, DType>::SampleGaussian(
Tensor<gpu, dim, DType> *dst, DType mu, DType sigma) {
if (dst->CheckContiguous()) {
this->GenGaussian(dst->dptr_, dst->shape_.Size(), mu, sigma);
} else {
*dst = this->gaussian(dst->shape_, mu, sigma);
}
}

template<typename DType>
template<int dim>
inline expr::ReshapeExp<Tensor<gpu, 1, DType>, DType, dim, 1>
Random<gpu, DType>::gaussian(Shape<dim> shape, DType mu, DType sigma) {
size_t aligned_sz = ((shape.Size() + 1UL) >> 1) << 1;
// allocate alligned size
buffer_.Resize(Shape1(aligned_sz));
buffer_.Resize(Shape1(shape.Size()));
this->GenGaussian(buffer_.dptr_, aligned_sz, mu, sigma);
return expr::reshape(buffer_, shape);
}

template<typename DType>
template<int dim>
inline expr::ReshapeExp<Tensor<gpu, 1, DType>, DType, dim, 1>
Random<gpu, DType>::uniform(Shape<dim> shape) {
buffer_.Resize(Shape1(shape.Size()));
this->GenUniform(buffer_.dptr_, buffer_.size(0));
return expr::reshape(buffer_, shape);
}
#endif // __CUDACC__
} // namespace mshadow
#endif // MSHADOW_RANDOM_H_

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