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

/*******************************************************************************
 * 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 {
    nd4j::LaunchContext* defaultContext = nd4j::LaunchContext::defaultContext();

//    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) {
        T *inverted = reinterpret_cast<T *>(invertedBuf);
        T *input = reinterpret_cast<T *>(inputBuf);

        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};
            Nd4jLong posX[] = {i, i};
            Nd4jLong posY[] = {i - 1, i - 1};
            auto xIndex = shape::getOffset(0, shape::shapeOf(inputShape), shape::stride(inputShape), pos, 2);
            auto dxIndex = shape::getOffset(0, shape::shapeOf(inputShape), shape::stride(inputShape), posX, 2);
            auto dyIndex = shape::getOffset(0, shape::shapeOf(inputShape), shape::stride(inputShape), posY, 2);
            auto zIndex = shape::getOffset(0, shape::shapeOf(invertedShape), shape::stride(invertedShape), pos, 2);
            inverted[zIndex] = -input[xIndex] / (input[dxIndex] * input[dyIndex]);
//            math::atomics::nd4j_atomicAdd(&inverted[zIndex], - input[xIndex] * inverted[iIndex] / input[dIndex]);
        }
    }

    template<typename T>
    static __global__ void
    upvertKernel(void *invertedBuf, Nd4jLong *invertedShape, void *inputBuf, Nd4jLong *inputShape, Nd4jLong n) {
        T *inverted = reinterpret_cast<T *>(invertedBuf);
        T *input = reinterpret_cast<T *>(inputBuf);

        auto start = threadIdx.x + blockIdx.x * blockDim.x;
        auto step = blockDim.x * gridDim.x;

        for (int i = start; 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);
//            math::atomics::nd4j_atomicDiv(&inverted[zIndex], input[xIndex]);
            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;
        __shared__ Nd4jLong* inputStride;
        __shared__ Nd4jLong* invertedStride;
        __shared__ Nd4jLong* invertedShapeOf;
        __shared__ Nd4jLong* inputShapeOf;
        if (threadIdx.x == 0) {
            inverted = reinterpret_cast<T *>(invertedBuf);
            input = reinterpret_cast<T *>(inputBuf);
            inputStride = shape::stride(inputShape);
            invertedStride = shape::stride(invertedShape);
            invertedShapeOf = shape::shapeOf(invertedShape);
            inputShapeOf = shape::shapeOf(inputShape);

        }
        __syncthreads();

        auto start = threadIdx.x + blockIdx.x * blockDim.x;
        auto step = blockDim.x * gridDim.x;

        for (int i = start; 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, inputShapeOf, shape::stride(inputShape), pos, 2);
//            auto yIndex = shape::getOffset(0, shape::shapeOf(inputShape), shape::stride(inputShape), posY, 2);
//            auto yIndex = shape::getOffset(0, shape::shapeOf(inputShape), shape::stride(inputShape), pos, 2);
            auto iIndex = shape::getOffset(0, invertedShapeOf, invertedStride, posX, 2);
            auto zIndex = shape::getOffset(0, invertedShapeOf, invertedStride, pos, 2);
            math::atomics::nd4j_atomicAdd(&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) {
        T *inverted = reinterpret_cast<T *>(invertedBuf);
        T *input = reinterpret_cast<T *>(inputBuf);

        for (int i = blockIdx.x + 2; i < n; i += gridDim.x) {
            for (int j = i - 2; j >= 0; --j)
                for (int k = threadIdx.x; k < i; 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);
                    math::atomics::nd4j_atomicAdd(&inverted[zIndex], -inverted[yIndex] * input[xIndex] / input[dIndex]);
                }
        }
    }

    template<typename T>
    static __global__ void
    invertUpKernel(void *invertedBuf, Nd4jLong *invertedShape, void *inputBuf, Nd4jLong *inputShape, Nd4jLong n) {
        __shared__ T* inverted;
        __shared__ T* input;
        __shared__ Nd4jLong* inputShapeOf;
        __shared__ Nd4jLong* invertedShapeOf;
        __shared__ Nd4jLong* invertedStrideOf;
        __shared__ Nd4jLong* inputStrideOf;

        if (threadIdx.x == 0) {
            inverted = reinterpret_cast<T *>(invertedBuf);;
            input = reinterpret_cast<T *>(inputBuf);
            inputShapeOf = shape::shapeOf(inputShape);
            invertedShapeOf = shape::shapeOf(invertedShape);
            inputStrideOf = shape::stride(inputShape);
            invertedStrideOf = shape::stride(invertedShape);
        }
        __syncthreads();

        for (int i = (int)n - blockIdx.x - 2; i >= 0; i -= gridDim.x) {
            for (int j = i + 2; j < (int)n; j++)
                for (int k = i + threadIdx.x; k < (int)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, inputShapeOf, inputStrideOf, posX, 2);
                    auto yIndex = shape::getOffset(0, invertedShapeOf, invertedStrideOf, posY, 2);
                    //                  auto dIndex = shape::getOffset(0, shape::shapeOf(inputShape), shape::stride(inputShape), posD, 2);
                    auto zIndex = shape::getOffset(0, invertedShapeOf, invertedStrideOf, posZ, 2);
                    math::atomics::nd4j_atomicAdd(&inverted[zIndex], -inverted[yIndex] * input[xIndex]);// / input[dIndex]);
//                    printf("(%d, %d) inverted[%lld] = %lf (-inverted[%lld] * input[%lld]\n", blockIdx.x, threadIdx.x, zIndex, inverted[zIndex], yIndex, xIndex);
                }
        }
    }

    template<typename T>
    static void invertLowerMatrix_(NDArray *inputMatrix, NDArray *invertedMatrix) {
        int n = inputMatrix->rows();
        invertedMatrix->setIdentity();

        if (inputMatrix->isIdentityMatrix()) return;

        auto stream = defaultContext->getCudaStream();

        // invert main diagonal
        upvertKernel<T> << < 1, n, 512, *stream >> >
                                        (invertedMatrix->specialBuffer(), invertedMatrix->specialShapeInfo(), inputMatrix->specialBuffer(), inputMatrix->specialShapeInfo(), n);
        // invert the second diagonal
        invertKernelLow<T> << < 1, n, 512, *stream >> >
                                           (invertedMatrix->specialBuffer(), invertedMatrix->specialShapeInfo(), inputMatrix->specialBuffer(), inputMatrix->specialShapeInfo(), n);
//        invertKernelLow<T><<<1, n, 128, *stream>>>(invertedMatrix->specialBuffer(), invertedMatrix->specialShapeInfo(), inputMatrix->specialBuffer(), inputMatrix->specialShapeInfo(), n);
        invertLowKernel<T><<< n, n, 512, *stream >> >
                                           (invertedMatrix->specialBuffer(), invertedMatrix->specialShapeInfo(), inputMatrix->specialBuffer(), inputMatrix->specialShapeInfo(), n);
    }

    void invertLowerMatrix(NDArray *inputMatrix, NDArray *invertedMatrix) {
        NDArray::prepareSpecialUse({invertedMatrix}, {inputMatrix});
        BUILD_SINGLE_SELECTOR(inputMatrix->dataType(), invertLowerMatrix_, (inputMatrix, invertedMatrix), FLOAT_NATIVE);
        NDArray::registerSpecialUse({invertedMatrix}, {inputMatrix});
    }

    template<typename T>
    static void invertUpperMatrix_(NDArray* inputMatrix, NDArray* invertedMatrix) {
        int n = inputMatrix->rows();
        invertedMatrix->setIdentity();
        auto stream = defaultContext->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, 512, *stream >>>(invertedMatrix->specialBuffer(), invertedMatrix->specialShapeInfo(),
                inputMatrix->specialBuffer(), inputMatrix->specialShapeInfo(), n);
        invertedMatrix->tickWriteDevice();
        invertedMatrix->printIndexedBuffer("Step1 UP inversion");
        invertUpKernel<T><<<n, n, 512, *stream >>>(invertedMatrix->specialBuffer(), invertedMatrix->specialShapeInfo(),
                inputMatrix->specialBuffer(), inputMatrix->specialShapeInfo(), n);
    }

    void invertUpperMatrix(NDArray *inputMatrix, NDArray *invertedMatrix) {
        NDArray::prepareSpecialUse({invertedMatrix}, {inputMatrix});
        BUILD_SINGLE_SELECTOR(invertedMatrix->dataType(), invertUpperMatrix_, (inputMatrix, invertedMatrix), FLOAT_NATIVE);
        NDArray::prepareSpecialUse({invertedMatrix}, {inputMatrix});
    }

//    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>
        template<typename T>
        static __global__ void determinantKernel(T *compound, T *result, Nd4jLong len) {
            //F tempRes = result[0];

            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(&result[0], compound[pos]);
            }
        }

        template<typename T>
        static __global__ void determinantLogKernel(T *compound, T *result, Nd4jLong len) {
//            F tempRes =  (F)result[0];

            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_atomicAdd(result, math::nd4j_log<T,T>(math::nd4j_abs(compound[pos])));
            }
//            __syncthreads();
//
//            if (threadIdx.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 T>
        static __global__ void
        returnMatrix(void *output, Nd4jLong *outputShape, void *input, Nd4jLong *inputShape, Nd4jLong pos,
                     Nd4jLong rowLen) {
            __shared__            T *matrix;
            __shared__            T *outputBuf;
            __shared__            Nd4jLong outputLen;
            __shared__            Nd4jLong n2;

            if (threadIdx.x == 0) {
                matrix = reinterpret_cast<T *>(input);
                outputBuf = reinterpret_cast<T *>(output);
                outputLen = 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 zIndex = shape::getIndexOffset(k, outputShape, outputLen);
                outputBuf[zIndex] = (T) matrix[j];
            }
        }

        template<typename F>
        static __global__ void fillUpPermutation(void *output, Nd4jLong *shape, int *source, int rowNum) {
            F *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);
            defaultContext = context;
            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, 1024, *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<T> <<< 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_NATIVE);

        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;
            defaultContext = context;
            auto matrix = NDArrayFactory::create(input->ordering(), {n, n}, DataTypeUtils::fromT<T>(),
                                                 defaultContext); //, 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> << < 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();
        }

        int determinant(nd4j::LaunchContext *context, NDArray *input, NDArray *output) {
            defaultContext = context;
            NDArray::prepareSpecialUse({output}, {input});
            BUILD_SINGLE_SELECTOR(input->dataType(), return determinant_, (context, input, output), FLOAT_NATIVE);
            NDArray::registerSpecialUse({output}, {input});
        }

        template<typename T>
        int logAbsDeterminant_(LaunchContext *context, NDArray *input, NDArray *output) {
            defaultContext = context;
            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,
                                                 defaultContext); //, block.getWorkspace());
            auto det = NDArrayFactory::create<T>(1);
            auto stream = context->getCudaStream();
            NDArray::prepareSpecialUse({output}, {input});
            dim3 launchDims(256, 256, 1024);
            output->assign(0.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> << < 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;
        }

        int logAbsDeterminant(nd4j::LaunchContext *context, NDArray *input, NDArray *output) {
            defaultContext = context;
            NDArray::prepareSpecialUse({output}, {input});
            BUILD_SINGLE_SELECTOR(input->dataType(), return logAbsDeterminant_, (context, input, output), FLOAT_NATIVE);
            NDArray::registerSpecialUse({output}, {input});
        }

        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);
                xStrideOf = shape::stride(lowerShape);

                yShapeOf = shape::shapeOf(upperShape);
                yStrideOf = shape::stride(upperShape);

                zShapeOf = shape::shapeOf(matrixShape);
                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[] = {k, j};
                    Nd4jLong posD[] = {j, j};
                    auto xPos = shape::getOffset(0, xShapeOf, xStrideOf, posX, 2);
                    auto yPos = shape::getOffset(0, yShapeOf, yStrideOf, posX, 2);
                    auto iPos = shape::getOffset(0, zShapeOf, zStrideOf, posX, 2);
                    auto dPos = shape::getOffset(0, zShapeOf, zStrideOf, posD, 2);
                    if (k >= j)
                        lowerMatrix[xPos] = matrix[iPos];//(k, j);
                    else
                        upperMatrix[yPos] = matrix[iPos]; //k, j);
                }
            }
        }

        template<typename T>
        static int inverse_(nd4j::LaunchContext *context, NDArray *input, NDArray *output) {
            defaultContext = context;
            auto n = input->sizeAt(-1);
            auto n2 = n * n;
            auto dtype = DataTypeUtils::fromT<T>(); //input->dataType();
//            if (dtype != DataType::DOUBLE)
//                dtype = DataType::FLOAT32;
            NDArray matrix = NDArrayFactory::create('c', {n, n}, dtype, defaultContext);
            NDArray upper = NDArrayFactory::create('c', {n, n}, dtype, defaultContext);
            NDArray lower = NDArrayFactory::create('c', {n, n}, dtype, defaultContext);
            NDArray compound = NDArrayFactory::create('c', {n, n}, dtype, defaultContext);
            NDArray permutation = NDArrayFactory::create('c', {n, n}, dtype, defaultContext);
            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, T> << < 1, n2, 1024, *stream >> >
                                                   (matrix.specialBuffer(), matrix.specialShapeInfo(), input->specialBuffer(), input->specialShapeInfo(),
                                                           i * n2, n);
                matrix.tickWriteDevice();
                compound.assign(matrix);
                lup_<T>(context, &compound, nullptr, nullptr);
                fillLowerUpperKernel<T> << < n, n, 1024, *stream >> >
                                                         (lower.specialBuffer(), lower.specialShapeInfo(), upper.specialBuffer(), upper.specialShapeInfo(), compound.specialBuffer(), compound.specialShapeInfo(), n);
                matrix.assign(0);
                invertUpperMatrix(&upper, &matrix); // U^{-1}
                matrix.tickWriteDevice();
//                matrix.printIndexedBuffer("Upper Inverted");
                compound.assign(0);
                invertLowerMatrix(&lower, &compound); // L{-1}
                compound.tickWriteDevice();
//                compound.printIndexedBuffer("Lower Inverted");
//                matrix.tickWriteDevice();
//                compound.tickWriteDevice();
                nd4j::MmulHelper::mmul(&matrix, &compound, &upper, 1.0, 0.0);
                upper.tickWriteDevice();
//                upper.printIndexedBuffer("Full inverted");
                returnMatrix<T> << < 1, n2, 1024, *stream >> >
                                                     (output->specialBuffer(), output->specialShapeInfo(), upper.specialBuffer(), upper.specialShapeInfo(),
                                                             i * n2, n);
            }
            return Status::OK();
        }

        int inverse(nd4j::LaunchContext *context, NDArray *input, NDArray *output) {
            defaultContext = context;
            NDArray::prepareSpecialUse({output}, {input});
            BUILD_SINGLE_SELECTOR(input->dataType(), return inverse_, (context, input, output), FLOAT_NATIVE);
            NDArray::registerSpecialUse({output}, {input});
        }

        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;
            Nd4jLong *shapeOf = shape::shapeOf(shape);
            Nd4jLong *strideOf = shape::stride(shape);

            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);
            defaultContext = context;
            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());
            else
                input->assign(tempOutput.get());

            NDArray::registerSpecialUse({output}, {input});
            return Status::OK();
        }

//    template <typename T>
        int cholesky_(LaunchContext *context, NDArray *input, NDArray *output, bool inplace) {
            defaultContext = context;
            NDArray::prepareSpecialUse({output}, {input});
            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,
                                                defaultContext));
                tempOutput->assign(input);
                cholesky__<float>(context, tempOutput.get(), tempOutput.get(), true);
                output->assign(tempOutput.get());
            }
            NDArray::registerSpecialUse({output}, {input});
            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);
            defaultContext = context;
            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_NATIVE);

        template<typename T>
        __global__ void
        logDetKernel(T *inputBuf, Nd4jLong *inputShape, Nd4jLong batchNum, Nd4jLong *tadShape, Nd4jLong *tadOffsets,
                     T *outputBuf, Nd4jLong *outputShape) {

            __shared__ int n;
            if (threadIdx.x == 0) {
                n = shape::sizeAt(inputShape, -1); // * shape::sizeAt(inputShape, -1);
            }
            __syncthreads();

            T *output = outputBuf;
            T *input = inputBuf;

            Nd4jLong *shapeOf = shape::shapeOf(tadShape);
            Nd4jLong *strideOf = shape::stride(tadShape);

            for (auto i = blockIdx.x; i < batchNum; i += gridDim.x) {
                T *current = input + tadOffsets[i];

                auto zIndex = shape::getIndexOffset(i, outputShape, batchNum);
                for (auto e = threadIdx.x; e < n; 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<T, T>(current[xIndex] * current[xIndex]));
                }
            }
        }

        template<typename T>
        int logdetFunctor_(nd4j::LaunchContext *context, NDArray *input, NDArray *output) {
            defaultContext = context;
            NDArray::prepareSpecialUse({output}, {input});
            auto n2 = input->sizeAt(-1) * input->sizeAt(-2);
            auto stream = context->getCudaStream();
            std::unique_ptr<NDArray> tempOutput(input->dup());
//        auto inputs = tempOutput->allTensorsAlongDimension({input->rankOf() - 2, input->rankOf() - 1});
//        for (Nd4jLong e = 0; e < packX.numberOfTads(); e++) {
//            auto subArray = inputs->at(e);
//            cholesky(context, subArray, subArray, true);
//        }
//        delete inputs;
            cholesky(context, input, tempOutput.get(), false);
            tempOutput->syncToHost();
            tempOutput->printIndexedBuffer("Cholesky res!!!");
            auto outputBuf = reinterpret_cast<T*>(output->specialBuffer()); // + e * n2; // + e * n2;
            auto inputBuf = reinterpret_cast<T*>(tempOutput->specialBuffer());
            output->assign(0);
            output->syncToDevice();
            auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(tempOutput->getShapeInfo(),
                                                                                  {input->rankOf() - 2,
                                                                                   input->rankOf() - 1});
            logDetKernel<T> << < packX.numberOfTads(), n2, 128, *stream >> >
                                                                (inputBuf, tempOutput->specialShapeInfo(), packX.numberOfTads(), packX.specialShapeInfo(), packX.specialOffsets(), outputBuf, output->specialShapeInfo());
//        }
            NDArray::registerSpecialUse({output}, {input});
            //delete tempOutput;
            return Status::OK();
        }

        int logdetFunctor(nd4j::LaunchContext *context, NDArray *input, NDArray *output) {
            defaultContext = context;
            BUILD_SINGLE_SELECTOR(output->dataType(), logdetFunctor_, (context, input, output), FLOAT_NATIVE);
        }

        BUILD_SINGLE_TEMPLATE(template int logdetFunctor_,
                              (nd4j::LaunchContext * context, NDArray * input, NDArray * output), FLOAT_NATIVE);
    }
}
}