cavis/libnd4j/include/ops/declarable/helpers/cuda/svd.cu

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/*******************************************************************************
* Copyright (c) 2015-2018 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
// @author Yurii Shyrma (iuriish@yahoo.com)
//
#include <svd.h>
#include <cuda_runtime.h>
#include <cublas_v2.h>
#include <cusolverDn.h>
#include <exceptions/cuda_exception.h>
#include <PointersManager.h>
#include <ShapeUtils.h>
namespace nd4j {
namespace ops {
namespace helpers {
// FIXME -> we should optimize these helpers for the case when input matrices have c order (perform transpositions appropriately)
template <typename T>
__global__ static void inverseColumnSignCuda(void* vu, const Nd4jLong* uShapeInfo, void* vv, const Nd4jLong* vShapeInfo) {
T* u = reinterpret_cast<T*>(vu);
T* v = reinterpret_cast<T*>(vv);
__shared__ int rank, uLastButOneColumn, vLastButOneColumn; // uRank = vRank
__shared__ Nd4jLong uLen, vLen;
__shared__ Nd4jLong *sharedMem;
if (threadIdx.x == 0) {
extern __shared__ unsigned char shmem[];
sharedMem = reinterpret_cast<Nd4jLong*>(shmem);
rank = shape::rank(uShapeInfo);
uLen = shape::length(uShapeInfo);
vLen = shape::length(vShapeInfo);
uLastButOneColumn = uShapeInfo[rank] - 2;
vLastButOneColumn = vShapeInfo[rank - 1] - 2;
}
__syncthreads();
const auto ind = threadIdx.x + blockIdx.x * blockDim.x;
auto coords = sharedMem + threadIdx.x * rank;
// u
for (Nd4jLong i = ind; i < uLen; i += gridDim.x * blockDim.x) {
shape::index2coords(rank, uShapeInfo + 1, i, uLen, coords);
if(coords[rank - 1] == 0 || coords[rank - 1] == uLastButOneColumn) // do not change sign in first and last but one columns
continue;
const auto uOffset = shape::getOffset(0, uShapeInfo + 1, uShapeInfo + rank + 1, coords, rank);
u[uOffset] = -u[uOffset];
}
// v
for (Nd4jLong i = ind; i < vLen; i += gridDim.x * blockDim.x) {
shape::index2coords(rank, vShapeInfo + 1, i, vLen, coords);
if(coords[rank - 2] == 0 || coords[rank - 2] == vLastButOneColumn) // do not change sign in first and last but one columns
continue;
const auto vOffset = shape::getOffset(0, vShapeInfo + 1, vShapeInfo + rank + 1, coords, rank);
v[vOffset] = -v[vOffset];
}
}
//////////////////////////////////////////////////////////////////////////
template <typename T>
static void inverseColumnSignCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream,
void* vu, const Nd4jLong* uShapeInfo,
void* vv, const Nd4jLong* vShapeInfo) {
inverseColumnSignCuda<T><<<blocksPerGrid, threadsPerBlock, sharedMem, *stream>>>(vu, uShapeInfo, vv, vShapeInfo);
}
BUILD_SINGLE_TEMPLATE(template void inverseColumnSignCudaLauncher, (const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t* stream, void* vu, const Nd4jLong* uShapeInfo, void* vv, const Nd4jLong* vShapeInfo), FLOAT_TYPES);
//////////////////////////////////////////////////////////////////////////
static void svdQR(nd4j::LaunchContext* context, const NDArray* A, NDArray* S, NDArray* U, NDArray* VT, const bool fullUV, const bool calcUV) {
// since cusa api cusolverDnDgesvd/cusolverDnSgesvd have following constrain on input matrix A: A_rows >= A_columns && A_order = 'f'
// we make this function to have deal with 2 valid cases only:
// 1) A_rows >= A_columns and A_corder = 'f'
// 2) A_rows <= A_columns and A_corder = 'c' - int this case perform transposition to get f order
// if 1) or 2) are not met then throw exception
// A [m, n]
// S [n]
// U [m, m] or [m, n] if fullUV = false and m > n
// VT [n, n] or [m, n] if fullUV = false and m < n
if(A->rankOf() != 2)
throw std::runtime_error("svdQR: rank of A array is not equal 2 !");
auto m = A->sizeAt(0);
auto n = A->sizeAt(1);
const int minDim = m < n ? m : n;
const char orderA = A->ordering();
if(m < n)
throw std::runtime_error("svdQR: due to cuda api input constrains given shape of A array are not valid !");
if(ShapeUtils::shapeAsString({minDim}) != ShapeUtils::shapeAsString(S))
throw std::runtime_error("svdQR: wrong shape of S array !");
if(calcUV) {
if(fullUV && ShapeUtils::shapeAsString({m,m}) != ShapeUtils::shapeAsString(U))
throw std::runtime_error("svdQR: wrong shape of U array !");
else if(!fullUV && ShapeUtils::shapeAsString({m,minDim}) != ShapeUtils::shapeAsString(U))
throw std::runtime_error("svdQR: wrong shape of U array !");
if(fullUV && ShapeUtils::shapeAsString({n,n}) != ShapeUtils::shapeAsString(VT))
throw std::runtime_error("svdQR: wrong shape of VT array !");
else if(!fullUV && ShapeUtils::shapeAsString({minDim,n}) != ShapeUtils::shapeAsString(VT))
throw std::runtime_error("svdQR: wrong shape of VT array !");
}
NDArray* pA = const_cast<NDArray*>(A);
NDArray* pS = S;
NDArray* pU = U;
NDArray* pVT = VT;
std::vector<NDArray*> toDelete;
if(pA->ews() != 1 || pA->ordering() == 'c') {
pA = A->dup('f');
toDelete.push_back(pA);
}
if(S->ews() != 1) {
pS = S->dup('f');
toDelete.push_back(pS);
}
if(calcUV) {
if(pU->ews() != 1 || pU->ordering() == 'c') {
pU = U->dup('f');
toDelete.push_back(pU);
}
if(pVT->ews() != 1 || pVT->ordering() == 'c') {
pVT = VT->dup('f');
toDelete.push_back(pVT);
}
}
// create cusolverDn handle
cusolverDnHandle_t handle = nullptr;
cusolverStatus_t status = cusolverDnCreate(&handle);
if(status != CUSOLVER_STATUS_SUCCESS)
throw cuda_exception::build("svdQR: cuda failed !", status);
// stream
status = cusolverDnSetStream(handle, *context->getCudaStream());
if(status != CUSOLVER_STATUS_SUCCESS)
throw cuda_exception::build("svdQR: cuda failed !", status);
// query working space of SVD
int lwork = 0;
if(A->dataType() == DataType::DOUBLE)
status = cusolverDnDgesvd_bufferSize(handle, m, n, &lwork);
else if(A->dataType() == DataType::FLOAT32)
status = cusolverDnSgesvd_bufferSize(handle, m, n, &lwork);
else
throw std::invalid_argument("svdQR: given data type is unsupported !");
if(status != CUSOLVER_STATUS_SUCCESS)
throw cuda_exception::build("svdQR: cuda failed !", status);
// allocate memory for dWork
void* dWork = nullptr;
cudaError_t status2 = cudaMalloc((void**)&dWork , A->sizeOfT() * lwork);
if(status2 != cudaSuccess)
throw cuda_exception::build("svdQR: cuda failed !", status2);
signed char jobu, jobvt;
if(calcUV) {
if(fullUV)
jobu = jobvt = 'A';
else
jobu = jobvt = 'S';
}
else {
jobu = jobvt = 'N';
}
int *devInfo = nullptr;
void* rWork = nullptr;
int lda(m), ldu, ldvt;
if(calcUV) {
ldu = pU->sizeAt(0);
ldvt = pVT->sizeAt(0);
}
PointersManager manager(context, "svdQR");
NDArray::prepareSpecialUse({pS, pU, pVT}, {pA});
// choose appropriate cuda gemm api depending on data types
if(A->dataType() == DataType::DOUBLE) {
status = cusolverDnDgesvd(handle, jobu, jobvt, m, n, reinterpret_cast<double*>(pA->getSpecialBuffer()), lda, reinterpret_cast<double*>(pS->getSpecialBuffer()), calcUV ? reinterpret_cast<double*>(pU->getSpecialBuffer()) : nullptr, ldu, calcUV ? reinterpret_cast<double*>(pVT->getSpecialBuffer()) : nullptr, ldvt, reinterpret_cast<double*>(dWork), lwork, reinterpret_cast<double*>(rWork), devInfo);
}
else if(A->dataType() == DataType::FLOAT32) {
status = cusolverDnSgesvd(handle, jobu, jobvt, m, n, reinterpret_cast<float*>(pA->getSpecialBuffer()), lda, reinterpret_cast<float*>(pS->getSpecialBuffer()), calcUV ? reinterpret_cast<float*>(pU->getSpecialBuffer()) : nullptr, ldu, calcUV ? reinterpret_cast<float*>(pVT->getSpecialBuffer()) : nullptr, ldvt, reinterpret_cast<float*>(dWork), lwork, reinterpret_cast<float*>(rWork), devInfo);
}
else
throw std::invalid_argument("svdQR: given data type is unsupported !");
if(status != CUSOLVER_STATUS_SUCCESS)
throw cuda_exception::build("svdQR: cuda failed !", status);
manager.synchronize();
NDArray::registerSpecialUse({pS, pU, pVT}, {pA});
S->assign(pS);
if(calcUV) {
U->assign(pU);
VT->assign(pVT);
}
for (int i = toDelete.size() - 1; i >= 0; --i)
delete toDelete[i];
if (devInfo)
cudaFree(devInfo);
if (dWork )
cudaFree(dWork);
if (rWork)
cudaFree(rWork);
if(handle)
cusolverDnDestroy(handle);
// cudaDeviceReset();
}
//////////////////////////////////////////////////////////////////////////
static void svdJcb(nd4j::LaunchContext* context, const NDArray* A, NDArray* S, NDArray* U, NDArray* V, const bool fullUV, const bool calcUV) {
// A [m, n]
// S [n]
// U [m, m] or [m, n] if fullUV = false and m > n
// V [n, n] or [n, m] if fullUV = false and m < n
if(A->rankOf() != 2)
throw std::runtime_error("svdJcb: rank of A array is not equal 2 !");
auto m = A->sizeAt(0);
auto n = A->sizeAt(1);
const int minDim = m < n ? m : n;
if(ShapeUtils::shapeAsString({minDim}) != ShapeUtils::shapeAsString(S))
throw std::runtime_error("svdJcb: wrong shape of S array !");
if(calcUV) {
if(fullUV && ShapeUtils::shapeAsString({m,m}) != ShapeUtils::shapeAsString(U))
throw std::runtime_error("svdJcb: wrong shape of U array !");
else if(!fullUV && ShapeUtils::shapeAsString({m,minDim}) != ShapeUtils::shapeAsString(U))
throw std::runtime_error("svdJcb: wrong shape of U array !");
if(fullUV && ShapeUtils::shapeAsString({n,n}) != ShapeUtils::shapeAsString(V))
throw std::runtime_error("svdJcb: wrong shape of V array !");
else if(!fullUV && ShapeUtils::shapeAsString({n,minDim}) != ShapeUtils::shapeAsString(V))
throw std::runtime_error("svdJcb: wrong shape of V array !");
}
NDArray* pA = const_cast<NDArray*>(A);
NDArray* pS = S;
NDArray* pU = U;
NDArray* pV = V;
std::vector<NDArray*> toDelete;
if(pA->ews() != 1 || pA->ordering() == 'c') {
pA = A->dup('f');
toDelete.push_back(pA);
}
if(S->ews() != 1) {
pS = S->dup('f');
toDelete.push_back(pS);
}
if(calcUV) {
if(pU->ews() != 1 || pU->ordering() == 'c') {
pU = U->dup('f');
toDelete.push_back(pU);
}
if(pV->ews() != 1 || pV->ordering() == 'c') {
pV = V->dup('f');
toDelete.push_back(pV);
}
}
// create cusolverDn handle
cusolverDnHandle_t handle = nullptr;
cusolverStatus_t status = cusolverDnCreate(&handle);
if(status != CUSOLVER_STATUS_SUCCESS)
throw cuda_exception::build("svdJcb: cuda failed !", status);
// stream
status = cusolverDnSetStream(handle, *context->getCudaStream());
if(status != CUSOLVER_STATUS_SUCCESS)
throw cuda_exception::build("svdJcb: cuda failed !", status);
// set parameters
gesvdjInfo_t gesvdjParams = nullptr;
status = cusolverDnCreateGesvdjInfo(&gesvdjParams);
if(status != CUSOLVER_STATUS_SUCCESS)
throw cuda_exception::build("svdJcb: cuda failed !", status);
status = cusolverDnXgesvdjSetTolerance(gesvdjParams, 1.e-7); // tolerance
if(status != CUSOLVER_STATUS_SUCCESS)
throw cuda_exception::build("svdJcb: cuda failed !", status);
status = cusolverDnXgesvdjSetMaxSweeps(gesvdjParams, 15); // max_sweeps
if(status != CUSOLVER_STATUS_SUCCESS)
throw cuda_exception::build("svdJcb: cuda failed !", status);
int *devInfo = nullptr;
const cusolverEigMode_t jobz = calcUV ? CUSOLVER_EIG_MODE_VECTOR : CUSOLVER_EIG_MODE_NOVECTOR;
const int econ = !fullUV;
int lda(m), ldu(m), ldv(m);
if(calcUV) {
ldu = pU->sizeAt(0);
ldv = pV->sizeAt(0);
}
// query working space of SVD
int lwork = 0;
if(A->dataType() == DataType::DOUBLE)
status = cusolverDnDgesvdj_bufferSize(handle, jobz, econ, m, n, reinterpret_cast<double*>(pA->getSpecialBuffer()), lda, reinterpret_cast<double*>(pS->getSpecialBuffer()), calcUV ? reinterpret_cast<double*>(pU->getSpecialBuffer()) : nullptr, ldu, calcUV ? reinterpret_cast<double*>(pV->getSpecialBuffer()) : nullptr, ldv, &lwork, gesvdjParams);
else if(A->dataType() == DataType::FLOAT32)
status = cusolverDnSgesvdj_bufferSize(handle, jobz, econ, m, n, reinterpret_cast<float*>(pA->getSpecialBuffer()), lda, reinterpret_cast<float*>(pS->getSpecialBuffer()), calcUV ? reinterpret_cast<float*>(pU->getSpecialBuffer()) : nullptr, ldu, calcUV ? reinterpret_cast<float*>(pV->getSpecialBuffer()) : nullptr, ldv, &lwork, gesvdjParams);
else
throw std::invalid_argument("svdJcb: given data type is unsupported !");
if(status != CUSOLVER_STATUS_SUCCESS)
throw cuda_exception::build("svdJcb: cuda failed !", status);
// allocate memory dWork
void* dWork = nullptr;
auto status2 = cudaMalloc((void**)&dWork , A->sizeOfT() * lwork);
if(status2 != cudaSuccess)
throw cuda_exception::build("svdJcb: cuda failed !", status2);
PointersManager manager(context, "svdJcb");
NDArray::prepareSpecialUse({pS, pU, pV}, {pA});
// choose appropriate cuda gemm api depending on data types
if(A->dataType() == DataType::DOUBLE) {
status = cusolverDnDgesvdj(handle, jobz, econ, m, n, reinterpret_cast<double*>(pA->getSpecialBuffer()), lda, reinterpret_cast<double*>(pS->getSpecialBuffer()), calcUV ? reinterpret_cast<double*>(pU->getSpecialBuffer()) : nullptr, ldu, calcUV ? reinterpret_cast<double*>(pV->getSpecialBuffer()) : nullptr, ldv, reinterpret_cast<double*>(dWork), lwork, devInfo, gesvdjParams);
}
else if(A->dataType() == DataType::FLOAT32) {
status = cusolverDnSgesvdj(handle, jobz, econ, m, n, reinterpret_cast<float*>(pA->getSpecialBuffer()), lda, reinterpret_cast<float*>(pS->getSpecialBuffer()), calcUV ? reinterpret_cast<float*>(pU->getSpecialBuffer()) : nullptr, ldu, calcUV ? reinterpret_cast<float*>(pV->getSpecialBuffer()) : nullptr, ldv, reinterpret_cast<float*>(dWork), lwork, devInfo, gesvdjParams);
}
else
throw std::invalid_argument("svdJcb: given data type is unsupported !");
if(status != CUSOLVER_STATUS_SUCCESS)
throw cuda_exception::build("svdJcb: cuda failed !", status);
manager.synchronize();
NDArray::registerSpecialUse({pS, pU, pV}, {pA});
S->assign(pS);
if(calcUV) {
U->assign(pU);
V->assign(pV);
}
for (int i = toDelete.size() - 1; i >= 0; --i)
delete toDelete[i];
if (devInfo)
cudaFree(devInfo);
if (dWork )
cudaFree(dWork);
if(handle)
cusolverDnDestroy(handle);
if(gesvdjParams)
cusolverDnDestroyGesvdjInfo(gesvdjParams);
// cudaDeviceReset();
}
//////////////////////////////////////////////////////////////////////////
static void svdBatched(nd4j::LaunchContext* context, const NDArray* A, NDArray* S, NDArray* U, NDArray* V, const bool fullUV, const bool calcUV) {
// A [..., m, n]
// S [..., n]
// U [..., m, m] or [..., m, n] if fullUV = false and m > n
// V [..., n, n] or [..., n, m] if fullUV = false and m < n
auto m = A->sizeAt(-2);
auto n = A->sizeAt(-1);
const int minDim = m < n ? m : n;
const Nd4jLong bS = A->lengthOf() / (m * n);
if(m > 32 || n > 32)
throw std::runtime_error("svdBatched: numbers of rows and columns should be <= 32 !");
if(minDim != S->sizeAt(-1))
throw std::runtime_error("svdBatched: wrong shape of S array !");
if(calcUV) {
if(U->sizeAt(-2) != m)
throw std::runtime_error("svdBatched: wrong shape of U array !");
if(U->sizeAt(-1) != (fullUV ? m : minDim))
throw std::runtime_error("svdBatched: wrong shape of U array !");
if(U->lengthOf() / (U->sizeAt(-2) * U->sizeAt(-1)) != bS)
throw std::runtime_error("svdBatched: wrong shape of U array !");
if(V->sizeAt(-2) != n)
throw std::runtime_error("svdBatched: wrong shape of V array !");
if(V->sizeAt(-1) != (fullUV ? n : minDim))
throw std::runtime_error("svdBatched: wrong shape of V array !");
if(V->lengthOf() / (V->sizeAt(-2) * V->sizeAt(-1)) != bS)
throw std::runtime_error("svdBatched: wrong shape of V array !");
}
NDArray* pA = const_cast<NDArray*>(A);
NDArray* pS = S;
NDArray* pU = U;
NDArray* pV = V;
std::vector<NDArray*> toDelete;
if(pA->ews() != 1 || pA->ordering() == 'c') {
pA = A->dup('f');
toDelete.push_back(pA);
}
if(S->ews() != 1) {
pS = S->dup('f');
toDelete.push_back(pS);
}
if(calcUV) {
if(pU->ews() != 1 || pU->ordering() == 'c') {
pU = U->dup('f');
toDelete.push_back(pU);
}
if(pV->ews() != 1 || pV->ordering() == 'c') {
pV = V->dup('f');
toDelete.push_back(pV);
}
}
// create cusolverDn handle
cusolverDnHandle_t handle = nullptr;
cusolverStatus_t status = cusolverDnCreate(&handle);
if(status != CUSOLVER_STATUS_SUCCESS)
throw cuda_exception::build("svdBatched: cuda failed !", status);
// stream
status = cusolverDnSetStream(handle, *context->getCudaStream());
if(status != CUSOLVER_STATUS_SUCCESS)
throw cuda_exception::build("svdBatched: cuda failed !", status);
// set parameters
gesvdjInfo_t gesvdjParams = nullptr;
status = cusolverDnCreateGesvdjInfo(&gesvdjParams);
if(status != CUSOLVER_STATUS_SUCCESS)
throw cuda_exception::build("svdBatched: cuda failed !", status);
status = cusolverDnXgesvdjSetTolerance(gesvdjParams, 1.e-7); // tolerance
if(status != CUSOLVER_STATUS_SUCCESS)
throw cuda_exception::build("svdBatched: cuda failed !", status);
status = cusolverDnXgesvdjSetMaxSweeps(gesvdjParams, 15); // max_sweeps
if(status != CUSOLVER_STATUS_SUCCESS)
throw cuda_exception::build("svdBatched: cuda failed !", status);
// devInfo
int *devInfo = nullptr;
auto status2 = cudaMalloc((void**)&devInfo, sizeof(int) * bS);
if(status2 != cudaSuccess)
throw cuda_exception::build("svdBatched: cuda failed !", status2);
status2 = cudaDeviceSynchronize();
if(status2 != cudaSuccess)
throw cuda_exception::build("svdJcb: cuda failed !", status2);
const cusolverEigMode_t jobz = calcUV ? CUSOLVER_EIG_MODE_VECTOR : CUSOLVER_EIG_MODE_NOVECTOR;
int lda(m), ldu, ldv;
if(calcUV) {
ldu = pU->sizeAt(-2);
ldv = pV->sizeAt(-2);
}
// Ak (i,j) = A[i + 5*j + 25*k]
// query working space of SVD
int lwork = 0;
if(A->dataType() == DataType::DOUBLE)
status = cusolverDnDgesvdjBatched_bufferSize(handle, jobz, m, n, reinterpret_cast<double*>(pA->getSpecialBuffer()), lda, reinterpret_cast<double*>(pS->getSpecialBuffer()), calcUV ? reinterpret_cast<double*>(pU->getSpecialBuffer()) : nullptr, ldu, calcUV ? reinterpret_cast<double*>(pV->getSpecialBuffer()) : nullptr, ldv, &lwork, gesvdjParams, bS);
else if(A->dataType() == DataType::FLOAT32)
status = cusolverDnSgesvdjBatched_bufferSize(handle, jobz, m, n, reinterpret_cast<float*>(pA->getSpecialBuffer()), lda, reinterpret_cast<float*>(pS->getSpecialBuffer()), calcUV ? reinterpret_cast<float*>(pU->getSpecialBuffer()) : nullptr, ldu, calcUV ? reinterpret_cast<float*>(pV->getSpecialBuffer()) : nullptr, ldv, &lwork, gesvdjParams, bS);
else
throw std::invalid_argument("svdBatched: given data type is unsupported !");
if(status != CUSOLVER_STATUS_SUCCESS)
throw cuda_exception::build("svdBatched: cuda failed !", status);
// allocate memory dWork
void* dWork = nullptr;
status2 = cudaMalloc((void**)&dWork , A->sizeOfT() * lwork);
if(status2 != cudaSuccess)
throw cuda_exception::build("svdBatched: cuda failed !", status2);
status2 = cudaDeviceSynchronize();
if(status2 != cudaSuccess)
throw cuda_exception::build("svdBatched: cuda failed !", status2);
PointersManager manager(context, "svdBatched");
NDArray::prepareSpecialUse({pS, pU, pV}, {pA});
// choose appropriate cuda gemm api depending on data types
if(A->dataType() == DataType::DOUBLE) {
status = cusolverDnDgesvdjBatched(handle, jobz, m, n, reinterpret_cast<double*>(pA->getSpecialBuffer()), lda, reinterpret_cast<double*>(pS->getSpecialBuffer()), calcUV ? reinterpret_cast<double*>(pU->getSpecialBuffer()) : nullptr, ldu, calcUV ? reinterpret_cast<double*>(pV->getSpecialBuffer()) : nullptr, ldv, reinterpret_cast<double*>(dWork), lwork, devInfo, gesvdjParams, bS);
}
else if(A->dataType() == DataType::FLOAT32) {
status = cusolverDnSgesvdjBatched(handle, jobz, m, n, reinterpret_cast<float*>(pA->getSpecialBuffer()), lda, reinterpret_cast<float*>(pS->getSpecialBuffer()), calcUV ? reinterpret_cast<float*>(pU->getSpecialBuffer()) : nullptr, ldu, calcUV ? reinterpret_cast<float*>(pV->getSpecialBuffer()) : nullptr, ldv, reinterpret_cast<float*>(dWork), lwork, devInfo, gesvdjParams, bS);
}
else
throw std::invalid_argument("svdBatched: given data type is unsupported !");
if(status != CUSOLVER_STATUS_SUCCESS)
throw cuda_exception::build("svdBatched: cuda failed !", status);
manager.synchronize();
NDArray::registerSpecialUse({pS, pU, pV}, {pA});
S->assign(pS);
if(calcUV) {
U->assign(pU);
V->assign(pV);
}
for (int i = toDelete.size() - 1; i >= 0; --i)
delete toDelete[i];
if (devInfo)
cudaFree(devInfo);
if (dWork )
cudaFree(dWork);
if(handle)
cusolverDnDestroy(handle);
if(gesvdjParams)
cusolverDnDestroyGesvdjInfo(gesvdjParams);
// cudaDeviceReset();
}
////////////////////////////////////////////////////////////////////
void svd(nd4j::LaunchContext* context, const NDArray* x, const std::vector<NDArray*>& outArrs, const bool fullUV, const bool calcUV, const int switchNum) {
NDArray* S = outArrs[0];
NDArray* U = outArrs[1];
// NDArray VT = outArrs[2]->transpose();
NDArray* V = outArrs[2];
if(x->rankOf() == 2) {
// svdQR(context, x, S, U, VT, fullUV, calcUV);
svdJcb(context, x, S, U, V, fullUV, calcUV);
}
else {
// svdBatched(context, *x, *S, *U, *V, fullUV, calcUV);
ResultSet *tadsU(nullptr), *tadsV(nullptr);
auto tadsX = x->allTensorsAlongDimension({x->rankOf() - 2, x->rankOf() - 1});
auto tadsS = S->allTensorsAlongDimension({S->rankOf() - 1});
if(calcUV) {
tadsU = U->allTensorsAlongDimension({U->rankOf() - 2, U->rankOf() - 1});
tadsV = V->allTensorsAlongDimension({V->rankOf() - 2, V->rankOf() - 1});
}
for (int i = 0; i < tadsX->size(); ++i)
svdJcb(context, tadsX->at(i), tadsS->at(i), calcUV ? tadsU->at(i) : nullptr, calcUV ? tadsV->at(i) : nullptr, fullUV, calcUV);
delete tadsX;
delete tadsS;
if(calcUV) {
delete tadsU;
delete tadsV;
}
}
}
}
}
}