cu-rand.cc
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// cudamatrix/cu-rand.cc
// Copyright 2016-2017 Brno University of Technology (author Karel Vesely)
// See ../../COPYING for clarification regarding multiple authors
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
// WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
// MERCHANTABLITY OR NON-INFRINGEMENT.
// See the Apache 2 License for the specific language governing permissions and
// limitations under the License.
#include "cudamatrix/cu-rand.h"
namespace kaldi {
#if HAVE_CUDA == 1
/// Wrappers of curand functions to interface both float and double as 1 function,
/// Wrapper of curandGenerateUniform(), curandGenerateUniformDouble(),
template<typename Real>
curandStatus_t curandGenerateUniformWrap(curandGenerator_t gen, Real *ptr, size_t num);
//
template<>
curandStatus_t curandGenerateUniformWrap(curandGenerator_t gen, float *ptr, size_t num) {
return curandGenerateUniform(gen, ptr, num);
}
template<>
curandStatus_t curandGenerateUniformWrap(curandGenerator_t gen, double *ptr, size_t num) {
return curandGenerateUniformDouble(gen, ptr, num);
}
/// Wrapper of curandGenerateNormal(), curandGenerateNormalDouble(),
template<typename Real>
curandStatus_t curandGenerateNormalWrap(
curandGenerator_t gen, Real *ptr, size_t num);
//
template<>
curandStatus_t curandGenerateNormalWrap<float>(
curandGenerator_t gen, float *ptr, size_t num) {
return curandGenerateNormal(gen, ptr, num, 0.0 /*mean*/, 1.0 /*stddev*/);
}
template<>
curandStatus_t curandGenerateNormalWrap<double>(
curandGenerator_t gen, double *ptr, size_t num) {
return curandGenerateNormalDouble(gen, ptr, num, 0.0 /*mean*/, 1.0 /*stddev*/);
}
/// End of wrappers.
#endif
template<typename Real>
void CuRand<Real>::RandUniform(CuMatrixBase<Real> *tgt) {
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
CuTimer tim;
// Better use 'tmp' matrix, 'tgt' can be a window into a larger matrix,
// so we should not use it to generate random numbers over whole stride.
// Use the option kStrideEqualNumCols to ensure consistency
// (because when memory is nearly exhausted, the stride of CudaMallocPitch
// may vary).
CuMatrix<Real> tmp(tgt->NumRows(), tgt->NumCols(), kUndefined,
kStrideEqualNumCols);
size_t s = static_cast<size_t>(tmp.NumRows()) * static_cast<size_t>(tmp.Stride());
CURAND_SAFE_CALL(curandGenerateUniformWrap(
GetCurandHandle(), tmp.Data(), s));
tgt->CopyFromMat(tmp);
CuDevice::Instantiate().AccuProfile(__func__, tim);
} else
#endif
{
tgt->Mat().SetRandUniform();
}
}
template<typename Real>
void CuRand<Real>::RandUniform(CuMatrix<Real> *tgt) {
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
CuTimer tim;
// Here we don't need to use 'tmp' matrix,
size_t s = static_cast<size_t>(tgt->NumRows()) * static_cast<size_t>(tgt->Stride());
CURAND_SAFE_CALL(curandGenerateUniformWrap(
GetCurandHandle(), tgt->Data(), s));
CuDevice::Instantiate().AccuProfile(__func__, tim);
} else
#endif
{
tgt->Mat().SetRandUniform();
}
}
template<typename Real>
void CuRand<Real>::RandUniform(CuVectorBase<Real> *tgt) {
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
CuTimer tim;
CURAND_SAFE_CALL(curandGenerateUniformWrap(
GetCurandHandle(), tgt->Data(), tgt->Dim()));
CuDevice::Instantiate().AccuProfile(__func__, tim);
} else
#endif
{
tgt->Vec().SetRandUniform();
}
}
template<typename Real>
void CuRand<Real>::RandGaussian(CuMatrixBase<Real> *tgt) {
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
CuTimer tim;
// Better use 'tmp' matrix, 'tgt' can be a window into a larger matrix,
// so we should not use it to generate random numbers over whole stride.
// Also, we ensure to have 'even' number of elements for calling 'curand'
// by possibly adding one column. Even number of elements is required by
// curandGenerateUniform(), curandGenerateUniformDouble().
// Use the option kStrideEqualNumCols to ensure consistency
// (because when memory is nearly exhausted, the stride of CudaMallocPitch
// may vary).
MatrixIndexT num_cols_even = tgt->NumCols() + (tgt->NumCols() % 2); // + 0 or 1,
CuMatrix<Real> tmp(tgt->NumRows(), num_cols_even, kUndefined,
kStrideEqualNumCols);
CURAND_SAFE_CALL(curandGenerateNormalWrap(
GetCurandHandle(), tmp.Data(), tmp.NumRows()*tmp.Stride()));
tgt->CopyFromMat(tmp.ColRange(0,tgt->NumCols()));
CuDevice::Instantiate().AccuProfile(__func__, tim);
} else
#endif
{
tgt->Mat().SetRandn();
}
}
template<typename Real>
void CuRand<Real>::RandGaussian(CuMatrix<Real> *tgt) {
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
CuTimer tim;
// Here we don't need to use 'tmp' matrix, if the number of elements is even,
MatrixIndexT num_elements = tgt->NumRows() * tgt->Stride();
if (0 == (num_elements % 2)) {
CURAND_SAFE_CALL(curandGenerateNormalWrap(
GetCurandHandle(), tgt->Data(), num_elements));
} else {
// We use 'tmp' matrix with one column added, this guarantees an even
// number of elements. Use the option kStrideEqualNumCols to ensure
// consistency (because when memory is nearly exhausted, the stride of
// CudaMallocPitch may vary).
MatrixIndexT num_cols_even = tgt->NumCols() + (tgt->NumCols() % 2); // + 0 or 1,
CuMatrix<Real> tmp(tgt->NumRows(), num_cols_even, kUndefined,
kStrideEqualNumCols);
CURAND_SAFE_CALL(curandGenerateNormalWrap(
GetCurandHandle(), tmp.Data(), tmp.NumRows() * tmp.Stride()));
tgt->CopyFromMat(tmp.ColRange(0,tgt->NumCols()));
}
CuDevice::Instantiate().AccuProfile(__func__, tim);
} else
#endif
{
tgt->Mat().SetRandn();
}
}
template<typename Real>
void CuRand<Real>::RandGaussian(CuVectorBase<Real> *tgt) {
#if HAVE_CUDA == 1
if (CuDevice::Instantiate().Enabled()) {
CuTimer tim;
// To ensure 'even' number of elements, we use 'tmp' vector of even length.
// Even number of elements is required by 'curand' functions:
// curandGenerateUniform(), curandGenerateUniformDouble().
MatrixIndexT num_elements = tgt->Dim();
if (0 == (num_elements % 2)) {
CURAND_SAFE_CALL(curandGenerateNormalWrap(
GetCurandHandle(), tgt->Data(), tgt->Dim()));
} else {
MatrixIndexT dim_even = tgt->Dim() + (tgt->Dim() % 2); // + 0 or 1,
CuVector<Real> tmp(dim_even, kUndefined);
CURAND_SAFE_CALL(curandGenerateNormalWrap(
GetCurandHandle(), tmp.Data(), tmp.Dim()));
tgt->CopyFromVec(tmp.Range(0,tgt->Dim()));
}
CuDevice::Instantiate().AccuProfile(__func__, tim);
} else
#endif
{
tgt->Vec().SetRandn();
}
}
/// convert probabilities binary values,
template<typename Real>
void CuRand<Real>::BinarizeProbs(const CuMatrix<Real> &probs, CuMatrix<Real> *states) {
CuMatrix<Real> tmp(probs.NumRows(), probs.NumCols());
this->RandUniform(&tmp); // [0..1]
tmp.Scale(-1.0); // [-1..0]
tmp.AddMat(1.0, probs); // [-1..+1]
states->Heaviside(tmp); // negative
}
/// add gaussian noise to each element
template<typename Real>
void CuRand<Real>::AddGaussNoise(CuMatrix<Real> *tgt, Real gscale) {
// Use the option kStrideEqualNumCols to ensure consistency (because when
// memory is nearly exhausted, the stride of CudaMallocPitch may vary).
CuMatrix<Real> tmp(tgt->NumRows(), tgt->NumCols(),
kUndefined, kStrideEqualNumCols);
this->RandGaussian(&tmp);
tgt->AddMat(gscale, tmp);
}
// explicit instantiation,
template class CuRand<float>;
template class CuRand<double>;
} // namespace,