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

850 lines
39 KiB
Plaintext

/*******************************************************************************
* 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 raver119@gmail.com
//
#include <ops/declarable/helpers/top_k.h>
#include <MmulHelper.h>
#include <NDArrayFactory.h>
#include <Status.h>
#include <ConstantTadHelper.h>
#include <ShapeUtils.h>
#include <cusolverDn.h>
#include <cuda_exception.h>
namespace nd4j {
namespace ops {
namespace helpers {
template <typename T>
static __device__ void swapRows_(T* matrix, Nd4jLong* shape, int theFirst, int theSecond, Nd4jLong N) {
if (theFirst != theSecond) {
auto start = threadIdx.x + blockIdx.x * blockDim.x;
auto step = blockDim.x * gridDim.x;
for (auto i = start; i < N; i += step) {
Nd4jLong iCoord1[] = {theFirst, i};
Nd4jLong iCoord2[] = {theSecond, i};
auto iIndex1 = shape::getOffset(0, shape::shapeOf(shape), shape::stride(shape), iCoord1, 2);
auto iIndex2 = shape::getOffset(0, shape::shapeOf(shape), shape::stride(shape), iCoord2, 2);
//atomicExch(&matrix[iIndex1], matrix[iIndex2]);
T e0 = matrix[iIndex1];
T e1 = matrix[iIndex2];
matrix[iIndex1] = e0;
matrix[iIndex2] = e1;
}
}
}
// BUILD_SINGLE_TEMPLATE(template void swapRows_, (NDArray* matrix, int theFirst, int theSecond), FLOAT_TYPES);
//
// void swapRows(NDArray* matrix, int theFirst, int theSecond) {
// BUILD_SINGLE_SELECTOR(matrix->dataType(), swapRows_, (matrix, theFirst, theSecond), FLOAT_TYPES);
// }
template <typename T>
static __global__ void invertKernelLow(void* invertedBuf, Nd4jLong* invertedShape, void* inputBuf, Nd4jLong* inputShape, Nd4jLong n) {
__shared__ T* inverted;
__shared__ T* input;
if (threadIdx.x == 0) {
inverted = reinterpret_cast<T*>(invertedBuf);
input = reinterpret_cast<T*>(inputBuf);
}
__syncthreads();
auto start = threadIdx.x + blockIdx.x * blockDim.x;
auto step = blockDim.x * gridDim.x;
for (int i = start + 1; i < n; i += step) {
Nd4jLong pos[] = {i, i - 1};
auto xIndex = shape::getOffset(0, shape::shapeOf(inputShape), shape::stride(inputShape), pos, 2);
auto zIndex = shape::getOffset(0, shape::shapeOf(invertedShape), shape::stride(invertedShape), pos, 2);
inverted[zIndex] = -input[xIndex];
}
}
template <typename T>
static __global__ void upvertKernel(void* invertedBuf, Nd4jLong* invertedShape, void* inputBuf, Nd4jLong* inputShape, Nd4jLong n) {
__shared__ T* inverted;
__shared__ T* input;
if (threadIdx.x == 0) {
inverted = reinterpret_cast<T*>(invertedBuf);
input = reinterpret_cast<T*>(inputBuf);
}
__syncthreads();
auto start = threadIdx.x + blockIdx.x * blockDim.x;
auto step = blockDim.x * gridDim.x;
for (int i = start + 1; i < n; i += step) {
Nd4jLong pos[] = {i, i};
auto xIndex = shape::getOffset(0, shape::shapeOf(inputShape), shape::stride(inputShape), pos, 2);
auto zIndex = shape::getOffset(0, shape::shapeOf(invertedShape), shape::stride(invertedShape), pos, 2);
inverted[zIndex] /= input[xIndex];
}
}
template <typename T>
static __global__ void upvertKernelUp(void* invertedBuf, Nd4jLong* invertedShape, void* inputBuf, Nd4jLong* inputShape, Nd4jLong n) {
__shared__ T* inverted;
__shared__ T* input;
if (threadIdx.x == 0) {
inverted = reinterpret_cast<T*>(invertedBuf);
input = reinterpret_cast<T*>(inputBuf);
}
__syncthreads();
auto start = threadIdx.x + blockIdx.x * blockDim.x;
auto step = blockDim.x * gridDim.x;
for (int i = start + 1; i < n - 1; i += step) {
Nd4jLong pos[] = {i, i + 1};
Nd4jLong posY[] = {i, i};
Nd4jLong posX[] = {i + 1, i + 1};
auto xIndex = shape::getOffset(0, shape::shapeOf(inputShape), shape::stride(inputShape), pos, 2);
auto yIndex = shape::getOffset(0, shape::shapeOf(inputShape), shape::stride(inputShape), pos, 2);
// auto yIndex = shape::getOffset(0, shape::shapeOf(inputShape), shape::stride(inputShape), pos, 2);
auto iIndex = shape::getOffset(0, shape::shapeOf(invertedShape), shape::stride(invertedShape), posX, 2);
auto zIndex = shape::getOffset(0, shape::shapeOf(invertedShape), shape::stride(invertedShape), pos, 2);
inverted[zIndex] -= input[xIndex] * inverted[iIndex] / input[yIndex];
//inputMatrix->t<T>(i, i + 1) * invertedMatrix->t<T>(i + 1, i + 1) / inputMatrix->t<T>(i, i)
}
}
template <typename T>
static __global__ void invertLowKernel(void* invertedBuf, Nd4jLong* invertedShape, void* inputBuf, Nd4jLong* inputShape, Nd4jLong n) {
__shared__ T* inverted;
__shared__ T* input;
if (threadIdx.x == 0) {
inverted = reinterpret_cast<T*>(invertedBuf);
input = reinterpret_cast<T*>(inputBuf);
}
__syncthreads();
// auto start = threadIdx.x + blockIdx.x * blockDim.x;
// auto step = blockDim.x * gridDim.x;
for (int i = blockIdx.x + 2; i < n; i += gridDim.x) {
for (int j = i - 2; j > -1; --j)
for (int k = threadIdx.x; k < i; k+= blockDim.x) {
Nd4jLong posZ[] = {i, j};
Nd4jLong posX[] = {k, j};
Nd4jLong posY[] = {i, k};
auto xIndex = shape::getOffset(0, shape::shapeOf(inputShape), shape::stride(inputShape), posX, 2);
auto yIndex = shape::getOffset(0, shape::shapeOf(invertedShape), shape::stride(invertedShape), posY, 2);
auto zIndex = shape::getOffset(0, shape::shapeOf(invertedShape), shape::stride(invertedShape), posZ, 2);
inverted[zIndex] -= inverted[yIndex] * input[xIndex];
}
}
}
template <typename T>
static __global__ void invertUpKernel(void* invertedBuf, Nd4jLong* invertedShape, void* inputBuf, Nd4jLong* inputShape, Nd4jLong n) {
__shared__ T* inverted;
__shared__ T* input;
if (threadIdx.x == 0) {
inverted = reinterpret_cast<T*>(invertedBuf);
input = reinterpret_cast<T*>(inputBuf);
}
__syncthreads();
// auto start = threadIdx.x + blockIdx.x * blockDim.x;
// auto step = blockDim.x * gridDim.x;
for (int i = n - blockIdx.x - 2; i >= 0; i -= gridDim.x) {
for (int j = i + 2; j < n; j++)
for (int k = i + threadIdx.x; k < n; k+= blockDim.x) {
Nd4jLong posZ[] = {i, j};
Nd4jLong posY[] = {k, j};
Nd4jLong posX[] = {i, k};
Nd4jLong posD[] = {i, i};
auto xIndex = shape::getOffset(0, shape::shapeOf(inputShape), shape::stride(inputShape), posX, 2);
auto yIndex = shape::getOffset(0, shape::shapeOf(invertedShape), shape::stride(invertedShape), posY, 2);
auto dIndex = shape::getOffset(0, shape::shapeOf(inputShape), shape::stride(inputShape), posD, 2);
auto zIndex = shape::getOffset(0, shape::shapeOf(invertedShape), shape::stride(invertedShape), posZ, 2);
inverted[zIndex] -= inverted[yIndex] * input[xIndex] / input[dIndex];
}
}
}
template <typename T>
static void invertLowerMatrix_(NDArray* inputMatrix, NDArray* invertedMatrix) {
int n = inputMatrix->rows();
invertedMatrix->setIdentity();
if (inputMatrix->isIdentityMatrix()) return;
LaunchContext* context = inputMatrix->getContext();
auto stream = context->getCudaStream();
invertKernelLow<T><<<1, n, 128, *stream>>>(invertedMatrix->specialBuffer(), invertedMatrix->specialShapeInfo(), inputMatrix->specialBuffer(), inputMatrix->specialShapeInfo(), n);
invertLowKernel<T><<<n, n, 128, *stream>>>(invertedMatrix->specialBuffer(), invertedMatrix->specialShapeInfo(), inputMatrix->specialBuffer(), inputMatrix->specialShapeInfo(), n);
}
BUILD_SINGLE_TEMPLATE(template void invertLowerMatrix_, (NDArray* inputMatrix, NDArray* invertedMatrix);, FLOAT_TYPES);
void invertLowerMatrix(NDArray* inputMatrix, NDArray* invertedMatrix) {
BUILD_SINGLE_SELECTOR(inputMatrix->dataType(), invertLowerMatrix_, (inputMatrix, invertedMatrix), FLOAT_TYPES);
}
template <typename T>
static void invertUpperMatrix_(NDArray* inputMatrix, NDArray* invertedMatrix) {
int n = inputMatrix->rows();
invertedMatrix->setIdentity();
auto stream = inputMatrix->getContext()->getCudaStream();
if (inputMatrix->isIdentityMatrix()) { // the inverse for I is I
return;
}
upvertKernel<T><<<1, n, 128, *stream>>>(invertedMatrix->specialBuffer(), invertedMatrix->specialShapeInfo(), inputMatrix->specialBuffer(), inputMatrix->specialShapeInfo(), n);
upvertKernelUp<T><<<1, n, 128, *stream>>>(invertedMatrix->specialBuffer(), invertedMatrix->specialShapeInfo(), inputMatrix->specialBuffer(), inputMatrix->specialShapeInfo(), n);
invertUpKernel<T><<<n, n, 256, *stream>>>(invertedMatrix->specialBuffer(), invertedMatrix->specialShapeInfo(), inputMatrix->specialBuffer(), inputMatrix->specialShapeInfo(), n);
}
BUILD_SINGLE_TEMPLATE(template void invertUpperMatrix_, (NDArray* inputMatrix, NDArray* invertedMatrix);, FLOAT_TYPES);
void invertUpperMatrix(NDArray* inputMatrix, NDArray* invertedMatrix) {
BUILD_SINGLE_SELECTOR(inputMatrix->dataType(), invertUpperMatrix_, (inputMatrix, invertedMatrix), FLOAT_TYPES);
}
template <typename T>
static __global__ void lupKernel(T* compound, Nd4jLong* compoundShape, T* permutation, Nd4jLong* permutationShape, Nd4jLong rowNum) {
int swapCount = 0;
for(int i = blockIdx.x; i < rowNum; i += gridDim.x ) {
auto pivotValue = T(0.0);
auto pivot = -1;
for(int rowCounter = i; rowCounter < rowNum; rowCounter++ ) {
Nd4jLong rowCoord[] = {rowCounter, i};
auto rowPos = shape::getOffset(0, shape::shapeOf(compoundShape), shape::stride(compoundShape), rowCoord, 2);
if(nd4j::math::nd4j_abs(compound[rowPos]) > pivotValue ) {
pivotValue = nd4j::math::nd4j_abs(compound[rowPos]);
pivot = rowCounter;
}
}
if( pivotValue != T(0.0) ) {
swapRows_<T>(compound, compoundShape, pivot, i, rowNum);
swapRows_<T>(permutation, permutationShape, pivot, i, rowNum);
if (pivot != i)
swapCount++;
for( int j = i + 1; j < rowNum; j++ ) {
Nd4jLong posJIbuf[] = {j, i};
Nd4jLong posIIbuf[] = {i, i};
auto posJI = shape::getOffset(0, shape::shapeOf(compoundShape), shape::stride(compoundShape), posJIbuf, 2);
auto posII = shape::getOffset(0, shape::shapeOf(compoundShape), shape::stride(compoundShape), posIIbuf, 2);
compound[posJI] /= compound[posII];
for( int k = i + 1; k < rowNum; k++ ) {
Nd4jLong posJKbuf[] = {j, k};
Nd4jLong posIKbuf[] = {i, k};
auto posJK = shape::getOffset(0, shape::shapeOf(compoundShape), shape::stride(compoundShape), posJKbuf, 2);
auto posIK = shape::getOffset(0, shape::shapeOf(compoundShape), shape::stride(compoundShape), posIKbuf, 2);
T arg = compound[posJI] * compound[posIK];
compound[posJK] -= arg;
}
}
}
}
}
template <typename T, typename F>
static __global__ void determinantKernel(T* compound, T* result, Nd4jLong len) {
__shared__ F tempRes;
if (blockIdx.x == 0) {
tempRes = (F)result[0];
}
__syncthreads();
auto start = blockIdx.x * blockDim.x + threadIdx.x;
auto step = blockDim.x * gridDim.x;
for (auto i = start; i < len; i += step) {
auto pos = i * len + i; //shape::getOffset(0, shape::shapeOf(shape), shape::stride(shape), di, 2);
math::atomics::nd4j_atomicMul<F>(&tempRes, (F)compound[pos]);
}
__syncthreads();
if (blockIdx.x == 0) {
result[0] = (T)tempRes;
}
}
template <typename T, typename F>
static __global__ void determinantLogKernel(T* compound, T* result, Nd4jLong len) {
__shared__ F tempRes;
if (blockIdx.x == 0) {
tempRes = (F)result[0];
}
__syncthreads();
auto start = blockIdx.x * blockDim.x + threadIdx.x;
auto step = blockDim.x * gridDim.x;
for (auto i = start; i < len; i += step) {
auto pos = i * len + i; //shape::getOffset(0, shape::shapeOf(shape), shape::stride(shape), di, 2);
math::atomics::nd4j_atomicMul<F>(&tempRes, (F)compound[pos]);
}
__syncthreads();
if (blockIdx.x == 0) {
result[0] = (T)math::nd4j_log<F,F>(math::nd4j_abs(tempRes));
}
}
template <typename T, typename F>
static __global__ void fillMatrix(void* output, Nd4jLong* outShape, void* input, Nd4jLong* inputShape, Nd4jLong pos, Nd4jLong rowLen) {
__shared__ F* matrix;
__shared__ T* inputBuf;
__shared__ Nd4jLong inputLen;
__shared__ Nd4jLong n2;
if (threadIdx.x == 0) {
matrix = reinterpret_cast<F*>(output);
inputBuf = reinterpret_cast<T*>(input);
inputLen = shape::length(inputShape);
n2 = rowLen * rowLen;
}
__syncthreads();
auto start = blockIdx.x * blockDim.x + threadIdx.x;
auto step = blockDim.x * gridDim.x;
for (int k = pos + start, j = start; j < n2; k += step, j += step) {
auto xIndex = shape::getIndexOffset(k, inputShape, inputLen);
matrix[j] = (F)inputBuf[xIndex];
}
}
template <typename F>
static __global__ void fillUpPermutation(void* output, Nd4jLong* shape, int* source, int rowNum) {
__shared__ F* permutation;
if (threadIdx.x == 0) {
permutation = reinterpret_cast<F*>(output);
}
auto start = blockIdx.x * blockDim.x + threadIdx.x;
auto step = blockDim.x * gridDim.x;
for (auto i = start; i < rowNum; i += step) {
int val = source[i] - 1;
Nd4jLong posF[] = {i, val};
auto pos = shape::getOffset(0, shape::shapeOf(shape), shape::stride(shape), posF, 2);
permutation[pos] = F(1.f);
}
}
template <typename T>
static void lup_(LaunchContext* context, NDArray* input, NDArray* compound, NDArray* permutation) {
auto stream = context->getCudaStream();
auto n = input->rows();
cusolverDnHandle_t cusolverH = nullptr;
cusolverStatus_t status = cusolverDnCreate(&cusolverH);
if (CUSOLVER_STATUS_SUCCESS != status) {
throw cuda_exception::build("Cannot create cuSolver handle", status);
}
status = cusolverDnSetStream(cusolverH, *stream);
if (CUSOLVER_STATUS_SUCCESS != status) {
throw cuda_exception::build("Cannot set up stream for cuda solver", status);
}
int lwork = 0;
int *d_info = nullptr;
auto err = cudaMalloc((void **) &d_info, sizeof(int));
if (err) {
throw cuda_exception::build("helpers::lup_: Cannot allocate memory for solver info buffer", err);
}
DataType dtype = input->dataType();
switch(dtype) {
case DataType::DOUBLE: {
double *d_work = nullptr;
err = cudaMalloc((void **) &d_work, sizeof(float) * lwork);
if (err) {
throw cuda_exception::build("helpers::lup_: Cannot allocate memory for solver data buffer", err);
}
double *matrix = reinterpret_cast<double*>(input->specialBuffer());
status = cusolverDnDgetrf_bufferSize(
cusolverH,
n,
n,
matrix,
n,
&lwork);
if (CUSOLVER_STATUS_SUCCESS != status) {
throw cuda_exception::build("helpers::lup_: Cannot create cuSolver handle", status);
}
if (permutation == nullptr)
status = cusolverDnDgetrf(
cusolverH,
n,
n,
matrix,
n,
d_work,
nullptr,
d_info);
else {
NDArray permutVector('c', {n}, nd4j::DataType::INT32, context);
int *permutationBuf = reinterpret_cast<int *>(permutVector.specialBuffer());
status = cusolverDnDgetrf(
cusolverH,
n,
n,
matrix,
n,
d_work,
permutationBuf,
d_info);
fillUpPermutation<double><<<n, n, 128, *stream>>>(permutation->specialBuffer(), permutation->specialShapeInfo(), permutationBuf, n);
permutation->tickWriteDevice();
}
err = cudaFree(d_work);
if (err) {
throw cuda_exception::build("helpers::lup_: Cannot deallocate memory for solver data buffer", err);
}
}
break;
case DataType::FLOAT32: {
float *matrix = reinterpret_cast<float*>(input->specialBuffer());
float *d_work = nullptr;
err = cudaMalloc((void **) &d_work, sizeof(float) * lwork);
if (err) {
throw cuda_exception::build("helpers::lup_: Cannot allocate memory for solver data buffer", err);
}
status = cusolverDnSgetrf_bufferSize(
cusolverH,
n,
n,
matrix,
n,
&lwork);
if (CUSOLVER_STATUS_SUCCESS != status) {
throw cuda_exception::build("helpers::lup_: Cannot create cuSolver handle", status);
}
if (permutation == nullptr)
status = cusolverDnSgetrf(
cusolverH,
n,
n,
matrix,
n,
d_work,
nullptr,
d_info);
else {
NDArray permutVector('c', {n}, nd4j::DataType::INT32, context);
int *permutationBuf = reinterpret_cast<int *>(permutVector.specialBuffer());
status = cusolverDnSgetrf(
cusolverH,
n,
n,
matrix,
n,
d_work,
permutationBuf,
d_info);
fillUpPermutation<float><<<n, n, 128, *stream>>>(permutation->specialBuffer(), permutation->specialShapeInfo(), permutationBuf, n);
permutation->tickWriteDevice();
}
err = cudaFree(d_work);
if (err) {
throw cuda_exception::build("helpers::lup_: Cannot deallocate memory for solver data buffer", err);
}
}
}
if (CUSOLVER_STATUS_SUCCESS != status) {
throw cuda_exception::build("helpers::lup_: Cannot make LU decomposition", status);
}
err = cudaFree(d_info);
if (err) {
throw cuda_exception::build("helpers::lup_: Cannot deallocate memory for solver info buffer", err);
}
cusolverDnDestroy(cusolverH);
// NDArray::registerSpecialUse({input}, {input});
input->tickWriteDevice();
}
BUILD_SINGLE_TEMPLATE(template void lup_, (LaunchContext* context, NDArray* input, NDArray* output, NDArray* permutation), FLOAT_TYPES);
template <typename T>
static int determinant_(nd4j::LaunchContext* context, NDArray* input, NDArray* output) {
Nd4jLong n = input->sizeAt(-1);
Nd4jLong n2 = n * n;
std::vector<int> dims();
auto packX = ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), {input->rankOf() - 2, input->rankOf() - 1});
//auto packZ = ConstantTadHelper::getInstance()->tadForDimensions(output->shapeInfo(), {output->rankOf() - 1});
DataType dtype = input->dataType();
if (dtype != DataType::DOUBLE)
dtype = DataType::FLOAT32;
auto matrix = NDArrayFactory::create(input->ordering(), {n, n}, dtype, input->getContext()); //, block.getWorkspace());
auto det = NDArrayFactory::create<T>(1);
auto stream = context->getCudaStream();
NDArray::prepareSpecialUse({output}, {input});
dim3 launchDims(256, 256, 1024);
output->assign(1.f);
for (int e = 0; e < output->lengthOf(); e++) {
Nd4jLong pos = e * n2;
if (matrix.dataType() == input->dataType())
fillMatrix<T, T><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(matrix.specialBuffer(), matrix.specialShapeInfo(), input->specialBuffer(), input->specialShapeInfo(), pos, n);
else
fillMatrix<T, float><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(matrix.specialBuffer(), matrix.specialShapeInfo(), input->specialBuffer(), input->specialShapeInfo(), pos, n);
if (matrix.dataType() == input->dataType())
lup_<T>(context, &matrix, nullptr, nullptr);
else
lup_<float>(context, &matrix, nullptr, nullptr);
auto offset = shape::getIndexOffset(e, output->shapeInfo(), output->lengthOf());
auto inputBuf = reinterpret_cast<T*>(matrix.specialBuffer());
auto outputBuf = reinterpret_cast<T*>(output->specialBuffer()) + offset;
if (matrix.dataType() == input->dataType())
determinantKernel<T, T><<<launchDims.x, launchDims.y, launchDims.z, *stream >>> (inputBuf, outputBuf, n);
else
determinantKernel<T, float><<<launchDims.x, launchDims.y, launchDims.z, *stream >>> (inputBuf, outputBuf, n);
}
NDArray::registerSpecialUse({output}, {input});
return Status::OK();
}
BUILD_SINGLE_TEMPLATE(template int determinant_, (nd4j::LaunchContext* context, NDArray* input, NDArray* output), FLOAT_TYPES);
int determinant(nd4j::LaunchContext * context, NDArray* input, NDArray* output) {
BUILD_SINGLE_SELECTOR(input->dataType(), return determinant_, (context, input, output), FLOAT_TYPES);
}
template <typename T>
int logAbsDeterminant_(LaunchContext* context, NDArray* input, NDArray* output) {
Nd4jLong n = input->sizeAt(-1);
Nd4jLong n2 = n * n;
std::vector<int> dims();
auto packX = ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), {input->rankOf() - 2, input->rankOf() - 1});
//auto packZ = ConstantTadHelper::getInstance()->tadForDimensions(output->shapeInfo(), {output->rankOf() - 1});
DataType dtype = input->dataType();
if (dtype != DataType::DOUBLE)
dtype = DataType::FLOAT32;
auto matrix = NDArrayFactory::create(input->ordering(), {n, n}, dtype, input->getContext()); //, block.getWorkspace());
auto det = NDArrayFactory::create<T>(1);
auto stream = context->getCudaStream();
NDArray::prepareSpecialUse({output}, {input});
dim3 launchDims(256, 256, 1024);
output->assign(1.f);
for (int e = 0; e < output->lengthOf(); e++) {
Nd4jLong pos = e * n2;
if (matrix.dataType() == input->dataType())
fillMatrix<T, T><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(matrix.specialBuffer(), matrix.specialShapeInfo(), input->specialBuffer(), input->specialShapeInfo(), pos, n);
else
fillMatrix<T, float><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(matrix.specialBuffer(), matrix.specialShapeInfo(), input->specialBuffer(), input->specialShapeInfo(), pos, n);
if (matrix.dataType() == input->dataType())
lup_<T>(context, &matrix, nullptr, nullptr);
else
lup_<float>(context, &matrix, nullptr, nullptr);
auto offset = shape::getIndexOffset(e, output->shapeInfo(), output->lengthOf());
auto inputBuf = reinterpret_cast<T*>(matrix.specialBuffer());
auto outputBuf = reinterpret_cast<T*>(output->specialBuffer()) + offset;
if (matrix.dataType() == input->dataType())
determinantLogKernel<T, T><<<launchDims.x, launchDims.y, launchDims.z, *stream >>> (inputBuf, outputBuf, n);
else
determinantLogKernel<T, float><<<launchDims.x, launchDims.y, launchDims.z, *stream >>> (inputBuf, outputBuf, n);
}
NDArray::registerSpecialUse({output}, {input});
return Status::OK();
return ND4J_STATUS_OK;
}
BUILD_SINGLE_TEMPLATE(template int logAbsDeterminant_, (LaunchContext* context, NDArray* input, NDArray* output), FLOAT_TYPES);
int logAbsDeterminant(nd4j::LaunchContext * context, NDArray* input, NDArray* output) {
BUILD_SINGLE_SELECTOR(input->dataType(), return logAbsDeterminant_, (context, input, output), FLOAT_TYPES);
}
template <typename T>
static __global__ void fillLowerUpperKernel(void* lowerBuf, Nd4jLong* lowerShape, void* upperBuf, Nd4jLong* upperShape, void* matrixBuf, Nd4jLong* matrixShape, Nd4jLong n) {
__shared__ Nd4jLong* xShapeOf;
__shared__ Nd4jLong* yShapeOf;
__shared__ Nd4jLong* zShapeOf;
__shared__ Nd4jLong* xStrideOf;
__shared__ Nd4jLong* yStrideOf;
__shared__ Nd4jLong* zStrideOf;
__shared__ T* lowerMatrix;
__shared__ T* upperMatrix;
__shared__ T* matrix;
if (threadIdx.x == 0) {
xShapeOf = shape::shapeOf(lowerShape);
yShapeOf = shape::shapeOf(upperShape);
zShapeOf = shape::shapeOf(matrixShape);
xStrideOf = shape::stride(lowerShape);
yStrideOf = shape::stride(upperShape);
zStrideOf = shape::stride(matrixShape);
lowerMatrix = reinterpret_cast<T*>(lowerBuf);
upperMatrix = reinterpret_cast<T*>(upperBuf);
matrix = reinterpret_cast<T*>(matrixBuf);
}
__syncthreads();
for (int k = blockIdx.x; k < n; k += gridDim.x) { // and then put all values under main diagonal on to it
for (int j = threadIdx.x; j < n; j += blockDim.x) {
Nd4jLong posX[] = {j, k};
auto xPos = shape::getOffset(0, xShapeOf, xStrideOf, posX, 2);
auto yPos = shape::getOffset(0, yShapeOf, yStrideOf, posX, 2);
auto pos = shape::getOffset(0, zShapeOf, zStrideOf, posX, 2);
if (k <= j)
lowerMatrix[xPos] = matrix[pos];//(k, j);
else
upperMatrix[yPos] = matrix[pos]; //k, j);
}
}
}
template <typename T>
static int inverse_(nd4j::LaunchContext* context, NDArray* input, NDArray* output) {
auto n = input->sizeAt(-1);
auto n2 = n * n;
auto dtype = input->dataType();
if (dtype != DataType::DOUBLE)
dtype = DataType::FLOAT32;
NDArray matrix = NDArrayFactory::create('c', {n, n}, dtype, input->getContext());
NDArray upper = NDArrayFactory::create('c', {n, n}, dtype, input->getContext());
NDArray lower = NDArrayFactory::create('c', {n, n}, dtype, input->getContext());
NDArray compound = NDArrayFactory::create('c', {n, n}, dtype, input->getContext());
NDArray permutation = NDArrayFactory::create('c', {n, n}, dtype, input->getContext());
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), {input->rankOf() - 2, input->rankOf() - 1});
auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), {output->rankOf() - 2, output->rankOf() - 1});
auto stream = context->getCudaStream();
for (auto i = 0LL; i < packX.numberOfTads(); i++) {
fillMatrix<T, float><<<1, n2, 128, *stream>>>(matrix.specialBuffer(), matrix.specialShapeInfo(), input->specialBuffer(), input->specialShapeInfo(), i * n2, n);
permutation.assign(0.f);
lup_<float>(context, &matrix, &compound, &permutation);
matrix.tickWriteDevice();
permutation.tickWriteDevice();
permutation.printIndexedBuffer("PERMUTE");
lower.setIdentity(); // set up U to identity matrix
upper.setIdentity();
fillLowerUpperKernel<float><<<1, n2, 128>>>(lower.specialBuffer(), lower.specialShapeInfo(), upper.specialBuffer(), upper.specialShapeInfo(), matrix.specialBuffer(), matrix.specialShapeInfo(), n);
lower.tickWriteDevice();
upper.tickWriteDevice();
invertUpperMatrix(&upper, &matrix);
invertLowerMatrix(&lower, &upper);
lower.tickWriteDevice();
upper.tickWriteDevice();
lower.printIndexedBuffer("LOWER");
upper.printIndexedBuffer("UPPER");
nd4j::MmulHelper::mmul(&matrix, &upper, &compound, 1.0, 0.0);
nd4j::MmulHelper::mmul(&compound, &permutation, &matrix, 1.0, 0.0);
// for (int k = e * n2, row = 0; k < (e + 1) * n2; k++) {
// output->t<T>(k) = matrix.template t<T>(row++);
// }
}
return Status::OK();
}
int inverse(nd4j::LaunchContext * context, NDArray* input, NDArray* output) {
BUILD_SINGLE_SELECTOR(input->dataType(), return inverse_, (context, input, output), FLOAT_TYPES);
}
bool checkCholeskyInput(nd4j::LaunchContext * context, NDArray const* input) {
return true;
}
template <typename F>
__global__ void fillBatchKernel(F** dArrayBatch, F* buf, Nd4jLong* offsets, Nd4jLong batchSize) {
auto start = blockIdx.x * blockDim.x + threadIdx.x;
auto step = blockDim.x * gridDim.x;
for (auto i = start; i < batchSize; i += step) {
dArrayBatch[i] = buf + offsets[i];
}
}
template <typename F>
__global__ void adjustResultsKernel(F* dArray, Nd4jLong* shape, Nd4jLong* offsets, Nd4jLong batchSize, Nd4jLong n) {
//auto i = blockIdx.x * blockDim.x + threadIdx.x;
__shared__ Nd4jLong* shapeOf;
__shared__ Nd4jLong* strideOf;
if (blockIdx.x == 0 && threadIdx.x == 0) {
shapeOf = shape::shapeOf(shape);
strideOf = shape::stride(shape);
}
__syncthreads();
for (auto i = blockIdx.x; i < batchSize; i+= gridDim.x) {
auto current = dArray + offsets[i];
for (auto r = threadIdx.x; r < n; r += blockDim.x) {
for (auto c = r + 1; c < n; c++) {
Nd4jLong posRC[] = {r, c};
auto pos = r * n + c; //shape::getOffset(0, shapeOf, strideOf, posRC, 2);
current[pos] = 0.;
}
}
}
}
template <typename F>
int cholesky__(LaunchContext* context, NDArray* input, NDArray* output, bool inplace) {
if (!inplace)
output->assign(input);
std::unique_ptr<NDArray> tempOutput(output->dup());
cusolverDnHandle_t handle = nullptr;
auto n = input->sizeAt(-1);
auto n2 = n * n;
NDArray::prepareSpecialUse({output}, {input});
auto status = cusolverDnCreate(&handle);
if (CUSOLVER_STATUS_SUCCESS != status) {
throw cuda_exception::build("helpers::cholesky_: Cannot create solver handle", status);
}
F** dArrayBatch = nullptr;
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(tempOutput->getShapeInfo(), {tempOutput->rankOf() - 2, tempOutput->rankOf() - 1});
const Nd4jLong batchSize = packX.numberOfTads();
int* dInfoArray = nullptr;
auto err = cudaMalloc((void**)&dArrayBatch, sizeof(F*) * batchSize);
if (err) {
throw cuda_exception::build("helpers::cholesky_: Cannot allocate memory for solver batch data buffer", err);
}
err = cudaMalloc ((void**)&dInfoArray, sizeof(int) * batchSize);
if (err) {
throw cuda_exception::build("helpers::cholesky_: Cannot allocate memory for solver errors buffer", err);
}
auto stream = context->getCudaStream();
fillBatchKernel<F><<<1, batchSize, 128, *stream>>>(dArrayBatch, reinterpret_cast<F*>(tempOutput->specialBuffer()), packX.specialOffsets(), batchSize);
status = cusolverDnSetStream(handle, *stream);
if (CUSOLVER_STATUS_SUCCESS != status) {
throw cuda_exception::build("helpers::cholesky_: Cannot set stream to solver handle", status);
}
const cublasFillMode_t uplo = CUBLAS_FILL_MODE_UPPER;
if (input->dataType() == DataType::DOUBLE)
status = cusolverDnDpotrfBatched(
handle,
uplo,
n,
(double**)dArrayBatch,
n,
dInfoArray,
batchSize);
else
status = cusolverDnSpotrfBatched(
handle,
uplo,
n,
(float**)dArrayBatch,
n,
dInfoArray,
batchSize);
if (CUSOLVER_STATUS_SUCCESS != status) {
throw cuda_exception::build("helpers::cholesky_: Cholesky factorization failed for batch", status);
}
adjustResultsKernel<F><<<batchSize, n2, 128, *stream>>>(reinterpret_cast<F*>(tempOutput->specialBuffer()), packX.specialShapeInfo(), packX.specialOffsets(), batchSize, n);
err = cudaFree(dArrayBatch);
if (err) {
throw cuda_exception::build("helpers::cholesky_: Cannot deallocate memory for solver batch data buffer", err);
}
err = cudaFree(dInfoArray);
if (err) {
throw cuda_exception::build("helpers::cholesky_: Cannot allocate memory for solver errors buffer", err);
}
if(!inplace)
output->assign(tempOutput.get());
NDArray::registerSpecialUse({output}, {input});
return Status::OK();
}
// template <typename T>
int cholesky_(LaunchContext* context, NDArray* input, NDArray* output, bool inplace) {
if (input->dataType() == DataType::DOUBLE)
cholesky__<double>(context, input, output, inplace);
else if (input->dataType() == DataType::FLOAT32)
cholesky__<float>(context, input, output, inplace);
else {
std::unique_ptr<NDArray> tempOutput(NDArrayFactory::create_('c', input->getShapeAsVector(), DataType::FLOAT32, input->getContext()));
tempOutput->assign(input);
cholesky__<float>(context, tempOutput.get(), tempOutput.get(), true);
output->assign(tempOutput.get());
}
return Status::OK();
}
int cholesky(nd4j::LaunchContext* context, NDArray* input, NDArray* output, bool inplace) {
// BUILD_SINGLE_SELECTOR(input->dataType(), return cholesky_, (context, input, output, inplace), FLOAT_TYPES);
return cholesky_(context, input, output, inplace);
}
// BUILD_SINGLE_TEMPLATE(template int cholesky_, (LaunchContext* context, NDArray* input, NDArray* output, bool inplace), FLOAT_TYPES);
BUILD_SINGLE_TEMPLATE(template int inverse_, (nd4j::LaunchContext* context, NDArray* input, NDArray* output), FLOAT_TYPES);
__global__ void logDetKernel(void* inputBuf, Nd4jLong* inputShape, Nd4jLong batchNum, Nd4jLong* tadShape, Nd4jLong* tadOffsets, void* outputBuf, Nd4jLong* outputShape) {
__shared__ double* output;
__shared__ double* input;
__shared__ int n2;
if (threadIdx.x == 0) {
output = reinterpret_cast<double*>(outputBuf);
input = reinterpret_cast<double*>(inputBuf);
n2 = shape::sizeAt(inputShape, -1) * shape::sizeAt(inputShape, -1);
}
__syncthreads();
for (Nd4jLong i = blockIdx.x; i < batchNum; i += gridDim.x) {
double* current = input + tadOffsets[i];
Nd4jLong* shapeOf = shape::shapeOf(tadShape);
Nd4jLong* strideOf = shape::stride(tadShape);
auto zIndex = shape::getIndexOffset(i, outputShape, batchNum);
for (Nd4jLong e = threadIdx.x; e < n2; e += blockDim.x) {
Nd4jLong diag[] = {e, e};
auto xIndex = shape::getOffset(0, shapeOf, strideOf, diag, 2);
math::atomics::nd4j_atomicAdd(&output[zIndex], math::nd4j_log<double,double>(current[xIndex] * current[xIndex]));
}
}
}
int logdetFunctor(nd4j::LaunchContext* context, NDArray* input, NDArray* output) {
NDArray::prepareSpecialUse({output}, {input});
auto tempOutput = input->dup('c');
auto n2 = input->sizeAt(-1) * input->sizeAt(-2);
auto stream = context->getCudaStream();
cholesky(context, tempOutput, tempOutput, true);
auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(tempOutput->getShapeInfo(), {tempOutput->rankOf() - 2, tempOutput->rankOf() - 1});
//for (Nd4jLong e = 0; e < output->lengthOf(); e++) {
auto outputBuf = reinterpret_cast<double*>(output->specialBuffer()); // + e * n2;
logDetKernel<<<packX.numberOfTads(), n2, 128, *stream>>>(tempOutput->specialBuffer(), tempOutput->specialShapeInfo(), packX.numberOfTads(), packX.specialShapeInfo(), packX.specialOffsets(), outputBuf, output->specialShapeInfo());
//}
NDArray::registerSpecialUse({output}, {input});
delete tempOutput;
return Status::OK();
}
}
}
}