/*******************************************************************************
 * 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
 ******************************************************************************/

#ifndef NDARRAY_CPP
#define NDARRAY_CPP

#include <array/NDArray.h>
#include <array/NDArrayFactory.h>
#include <legacy/NativeOpExecutioner.h>
#include <memory/Workspace.h>
#include <memory/MemoryRegistrator.h>
#include <ops/ops.h>
#include <ops/gemm.h>
#include <system/pointercast.h>
#include <stdexcept>
#include <memory>
#include <helpers/logger.h>
#include <loops/pairwise_transform.h>
#include <loops/transform_same.h>
#include <loops/random.h>
#include <loops/broadcasting.h>
#include <indexing/NDIndex.h>
#include <indexing/IndicesList.h>
#include <helpers/ShapeUtils.h>
#include <sstream>
#include <helpers/ArrayUtils.h>
#include <helpers/MmulHelper.h>
#include <helpers/threshold.h>
#include <exceptions/datatype_exception.h>
#include <exceptions/cuda_exception.h>
#include <ops/specials_cuda.h>
#include <loops/special_kernels.h>
#include <helpers/PointersManager.h>
#include <array/NDArray.hXX>
#include <helpers/ConstantShapeHelper.h>

namespace sd {

void* NDArray::platformBuffer()             { return specialBuffer();    }
void const* NDArray::platformBuffer() const    { return specialBuffer(); }

Nd4jLong const* NDArray::platformShapeInfo() const { return specialShapeInfo(); }
//Nd4jLong const* NDArray::platformShapeInfo()          { return specialShapeInfo(); }

void NDArray::syncToDevice() const          {
    auto currentDeviceId = AffinityManager::currentDeviceId();
    if (currentDeviceId != _deviceId) {
        // first of all we update shapeInfo
        const_cast<NDArray*>(this)->setShapeInfo(this->shapeInfo());

        // now we actually migrate data buffer
        _buffer->migrate();
    }

    _buffer->syncToSpecial();
}

void NDArray::syncToHost() const            { _buffer->syncToPrimary(getContext()); }
void NDArray::tickWriteHost() const         { _buffer->writePrimary();   }
void NDArray::tickWriteDevice() const       { _buffer->writeSpecial();   }
void NDArray::tickReadHost() const          { _buffer->readPrimary();    }
void NDArray::tickReadDevice() const        { _buffer->readSpecial();    }
void NDArray::tickBothActual() const        { _buffer->writePrimary(); _buffer->readSpecial(); }
bool NDArray::isActualOnHostSide() const    { return _buffer->isPrimaryActual(); }
bool NDArray::isActualOnDeviceSide() const  { return _buffer->isSpecialActual(); }
void NDArray::makeBothBuffersActual() const { if(!isActualOnHostSide()) syncToHost(); if(!isActualOnDeviceSide()) syncToDevice(); }

///////////////////////////////////////////////////////////////////
template<typename T>
__global__ static void fillAsTriangularCuda(const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo, const T val, const int lower, const int upper) {

    const auto x = reinterpret_cast<const T*>(vx);
          auto z = reinterpret_cast<T*>(vz);

    __shared__ int zRank, xRank, areSameOffsets, *sharedMem;    // xRank == zRank always, except when xRank = 1, in this case zRank = 2
    __shared__ Nd4jLong zLen, totalThreads;                     // xLen == zLen, except when xRank = 1, in this case zLen = 2*xLen

    if (threadIdx.x == 0) {
        extern __shared__ unsigned char shmem[];
        sharedMem = reinterpret_cast<int*>(shmem);
        areSameOffsets = shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo);
        xRank = shape::rank(xShapeInfo);
        zRank = shape::rank(zShapeInfo);
        zLen  = shape::length(zShapeInfo);
        totalThreads = gridDim.x * blockDim.x;
    }
    __syncthreads();

    auto coords = sharedMem + threadIdx.x * zRank;

    const auto tid = blockIdx.x * blockDim.x + threadIdx.x;

    for (Nd4jLong i = tid; i < zLen; i += totalThreads) {

        shape::index2coords(i, zShapeInfo, coords);
        const auto zOffset = shape::getOffset(zShapeInfo, coords);

        // if( (row + upper < col) || (row + lower > col) )
        if((coords[zRank - 2] + upper < coords[zRank - 1]) || (coords[zRank - 2] + lower > coords[zRank - 1]))
            z[zOffset] = val;
        else if(vx != vz) {      // when x and z are different arrays
            if(xRank != zRank)
                coords[0] = coords[1];
            const auto xOffset = areSameOffsets ? zOffset : shape::getOffset(xShapeInfo, coords);
            z[zOffset] = x[xOffset];
        }
    }
}

///////////////////////////////////////////////////////////////////
template<typename T>
void NDArray::fillAsTriangular(const float val, int lower, int upper, NDArray& target, const char direction) {

    if (isS())
        throw std::runtime_error("NDArray::fillAsTriangular: you can't use this method on String array!");

    if(!isSameShape(target) && !(rankOf() == 1 && target.rankOf() == 2 && sizeAt(0) == target.sizeAt(0) && sizeAt(0) == target.sizeAt(1)))
        throw std::string("NDArray::fillAsTriangular method: wrong shape of target array !");

     if (direction == 'u')
        lower = -target.sizeAt(-2);
    else if (direction == 'l')
        upper = target.sizeAt(-1);

    const int threadsPerBlock = MAX_NUM_THREADS / 4;
    const int blocksPerGrid = (target.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
    const int sharedMem = threadsPerBlock * sizeof(int) * target.rankOf() + 128;

    PointersManager manager(getContext(), "NDArray::fillAsTriangular");

    NDArray::prepareSpecialUse({&target}, {this});
    fillAsTriangularCuda<T><<<blocksPerGrid, threadsPerBlock, sharedMem, *getContext()->getCudaStream()>>>(platformBuffer(), platformShapeInfo(), target.platformBuffer(), target.platformShapeInfo(), static_cast<T>(val), lower, upper);
    NDArray::registerSpecialUse({&target}, {this});

    manager.synchronize();
}
BUILD_SINGLE_TEMPLATE(template ND4J_EXPORT void NDArray::fillAsTriangular, (const float val, int lower, int upper, NDArray& target, const char direction), LIBND4J_TYPES);

////////////////////////////////////////////////////////////////////////
template<typename T>
__global__ static void identityMatrixCuda(void* vx, const Nd4jLong* xShapeInfo, const T val) {

    auto x = reinterpret_cast<T*>(vx);

    __shared__ int rank, *sharedMem;
    __shared__ Nd4jLong len, totalThreads;  // xLen == zLen, except when xRank = 1, in this case zLen = 2*xLen

    if (threadIdx.x == 0) {
        extern __shared__ unsigned char shmem[];
        sharedMem = reinterpret_cast<int*>(shmem);
        rank = shape::rank(xShapeInfo);
        len  = shape::length(xShapeInfo);
        totalThreads = gridDim.x * blockDim.x;
    }
    __syncthreads();

    auto coords = sharedMem + threadIdx.x * rank;

    const auto tid = blockIdx.x * blockDim.x + threadIdx.x;

    for (Nd4jLong i = tid; i < len; i += totalThreads) {

        shape::index2coords(i, xShapeInfo, coords);
        const auto offset = shape::getOffset(xShapeInfo, coords);

        if(coords[rank - 2] == coords[rank - 1]) // row == col -> on diagonal
            x[offset] = val;
        else
            x[offset] = static_cast<T>(0);
    }
}

///////////////////////////////////////////////////////////////////
template<typename T>
static void identityMatrixCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream, void* vx, const Nd4jLong *xShapeInfo, const float val) {

    identityMatrixCuda<T><<<blocksPerGrid, threadsPerBlock, sharedMem, *stream>>>(vx, xShapeInfo, static_cast<T>(val));
}
BUILD_SINGLE_TEMPLATE(template void identityMatrixCudaLauncher, (const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream, void* vx, const Nd4jLong *xShapeInfo, const float val), LIBND4J_TYPES);

////////////////////////////////////////////////////////////////////////
void NDArray::setIdentity() {
    if (isS())
        throw std::runtime_error("NDArray::setIdentity: you can't use this method on String array!");

    // if (rankOf() != 2)
    //     throw std::runtime_error("NDArray::setIdentity: method should work only for 2D tensors. But " + toStringValue(rankOf()) + " was given.");

    const int threadsPerBlock = MAX_NUM_THREADS / 4;
    const int blocksPerGrid = (lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
    const int sharedMem = threadsPerBlock * sizeof(int) * rankOf() + 128;

    PointersManager manager(getContext(), "NDArray::setIdentity");

    syncToDevice();
    BUILD_SINGLE_SELECTOR(dataType(), identityMatrixCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, getContext()->getCudaStream(), platformBuffer(), platformShapeInfo(), 1.f), LIBND4J_TYPES);
    tickWriteDevice();

    manager.synchronize();
}

////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
void NDArray::swapUnsafe(NDArray& other) {
    auto xType = this->dataType();

    if (xType != other.dataType())
        throw std::runtime_error("NDArray::swapUnsage method: both arrays must have the same data type");

    if(specialBuffer() == nullptr || other.specialBuffer() == nullptr)
        throw std::runtime_error("NDArray::swapUnsafe method: input array should not be empty!");

    if(lengthOf() != other.lengthOf())
        throw std::runtime_error("NDArray::swapUnsafe method: input arrays should have the same length!");

    BUILD_SINGLE_SELECTOR(xType, templatedSwapUnsafe, (specialBuffer(), specialShapeInfo(), other.specialBuffer(), other.specialShapeInfo(), getContext()->getCudaStream()), LIBND4J_TYPES);
}

////////////////////////////////////////////////////////////////////////
void NDArray::synchronize(const char* msg) const {
    auto res = cudaStreamSynchronize(*(getContext()->getCudaStream()));
    if (res != 0)
        throw std::runtime_error(msg + std::string(": synchronization failed !"));
}

////////////////////////////////////////////////////////////////////////
void NDArray::prepareSpecialUse(const std::vector<const NDArray*>& writeList, const std::vector<const NDArray*>& readList, bool synchronizeWritables) {

    for (const auto& a : readList)
        if(a != nullptr)
            a->syncToDevice();

    for (const auto& a : writeList) {
        if (a != nullptr) {
            a->getDataBuffer()->allocateSpecial();
            if (synchronizeWritables)
                a->syncToDevice();
        }
    }
}

////////////////////////////////////////////////////////////////////////
void NDArray::registerSpecialUse(const std::vector<const NDArray*>& writeList, const std::vector<const NDArray*>& readList) {

    for (const auto& p : readList)
        if(p != nullptr)
            p->tickReadDevice();

    for (const auto& p : writeList)
        if (p != nullptr)
            p->tickWriteDevice();
}

////////////////////////////////////////////////////////////////////////
void NDArray::preparePrimaryUse(const std::vector<const NDArray*>& writeList, const std::vector<const NDArray*>& readList, bool synchronizeWritables) {

    for (const auto& a : readList)
        if(a != nullptr)
            a->syncToHost();

    for (const auto& a : writeList) {
        if (a != nullptr) {
            a->getDataBuffer()->allocatePrimary();
            if (synchronizeWritables)
                a->syncToHost();
        }
    }
}

////////////////////////////////////////////////////////////////////////
void NDArray::registerPrimaryUse(const std::vector<const NDArray*>& writeList, const std::vector<const NDArray*>& readList) {

    for (const auto& p : readList)
        if(p != nullptr)
            p->tickReadHost();

    for (const auto& p : writeList)
        if (p != nullptr)
            p->tickWriteHost();
}

//////////////////////////////////////////////////////////////////////////
void NDArray::syncShape() const {
    cudaMemcpy(const_cast<Nd4jLong*>(specialShapeInfo()), shapeInfo(), shape::shapeInfoByteLength(shapeInfo()), cudaMemcpyHostToDevice);
}

//////////////////////////////////////////////////////////////////////////
void const* NDArray::specialBufferWithOffset(Nd4jLong offset) const {
    return specialBuffer() != nullptr ? static_cast<int8_t const*>(specialBuffer()) + (offset * sizeOfT()) : nullptr;
}

void* NDArray::specialBufferWithOffset(Nd4jLong offset){
    return specialBuffer() != nullptr ? static_cast<int8_t*>(specialBuffer()) + (offset * sizeOfT()) : nullptr;
}

//////////////////////////////////////////////////////////////////////////
// change an array by repeating it the number of times given by reps.
NDArray NDArray::tile(const std::vector<Nd4jLong>& reps) const {
    int dim = reps.size();
    Nd4jLong product = 1;
    for(const auto& item : reps)
        product *= item;

    if(product < 1)
        throw std::runtime_error("NDArray::tile method: one of the elements in reps array is zero !");

    int rankOld = rankOf();
    int diff = rankOld - dim;
    if(product==1) {        // in this case 2 possibilities are present: just reshape or nothing to do
        NDArray result(*this);
        if(diff < 0) {      // reshape to higher dimension
            std::vector<Nd4jLong> shapeNew = reps;               // need to have unities at first "diff" positions of new shape
            memcpy(&shapeNew[-diff], result.shapeInfo()+1, rankOld * sizeof(Nd4jLong));   // put old shape numbers at rest of positions
            result.reshapei(ordering(), shapeNew);
        }
        return result;             // nothing to do, if diff >= 0 -> identity tile
    }

    // evaluate shapeInfo for resulting array
    auto newShapeInfo = ShapeUtils::evalTileShapeInfo(*this, reps, getContext()->getWorkspace());
    // create new buffer, in any case the memory amount new buffer points to is bigger then those for old _buffer
    std::shared_ptr<DataBuffer> newBuff = std::make_shared<DataBuffer>(shape::length(newShapeInfo) * sizeOfT(), dataType(), getContext()->getWorkspace(), true);
    // assign new shape and new buffer to resulting array
    NDArray result(newBuff, ShapeDescriptor(newShapeInfo), getContext());

    // fill newBuff, loop through all elements of newBuff
    // looping through buffer() goes automatically by means of getSubArrayIndex applying
    const auto resultLen = result.lengthOf();
    auto xType = this->dataType();
    auto stream = getContext()->getCudaStream();

    prepareSpecialUse({&result}, {this});
    BUILD_SINGLE_SELECTOR(xType, tileKernelH, (this->specialBuffer(), this->specialShapeInfo(), result.specialBuffer(), result.specialShapeInfo(), resultLen, stream), LIBND4J_TYPES);
    registerSpecialUse({&result}, {this});

    return result;
}

//////////////////////////////////////////////////////////////////////////
// change an array by repeating it the number of times given by reps.
void NDArray::tile(const std::vector<Nd4jLong>& reps, NDArray& target) const {

    auto repProd = shape::prodLong(reps.data(), reps.size());
    if (repProd < 1)
        throw std::runtime_error("NDArray::tile: reps can't contain 0s");

    // evaluate true tile shapeInfo for comparison with target shapeInfo
    auto newShapeInfo = ShapeUtils::evalTileShapeInfo(*this, reps, getContext()->getWorkspace());
    if(!shape::equalsSoft(newShapeInfo, target.shapeInfo()))  {
        throw std::runtime_error("NDArray::tile method - shapeInfo of target array is not suitable for tile operation !");
    }

    // fill newBuff, loop through all elements of newBuff
    // looping through buffer() goes automatically by means of getSubArrayIndex applying
    const int ews = target.ews();
    const int targetLen = target.lengthOf();
    auto stream = getContext()->getCudaStream();

    prepareSpecialUse({&target}, {this});
    BUILD_SINGLE_SELECTOR_TWICE(target.dataType(), tileKernelHH, (specialBuffer(), specialShapeInfo(), target.specialBuffer(), target.specialShapeInfo(), targetLen, ews, stream), LIBND4J_TYPES);
    registerSpecialUse({&target}, {this});
}

//////////////////////////////////////////////////////////////////////////
void NDArray::tile(NDArray& target) const {
    if(rankOf() > target.rankOf())
        throw std::runtime_error("NDArray::tile method - rank of target array must be bigger or equal to the rank of this array !");

    if(!ShapeUtils::areShapesBroadcastable(*this, target))
        throw std::runtime_error("NDArray::tile method - shapeInfo of target array is not suitable for tile operation !");

    // fill newBuff, loop through all elements of newBuff
    // looping through getBuffer() goes automatically by means of getSubArrayIndex applying
    const auto ews = target.ews();
    const auto targetLen = target.lengthOf();
    auto stream = getContext()->getCudaStream();

    prepareSpecialUse({&target}, {this});
    BUILD_SINGLE_SELECTOR_TWICE(target.dataType(),  tileKernelHH, (specialBuffer(), specialShapeInfo(), target.specialBuffer(), target.specialShapeInfo(), targetLen, ews, stream), LIBND4J_TYPES);
    registerSpecialUse({&target}, {this});
}

////////////////////////////////////////////////////////////////////////
template<typename X, typename Z>
__global__ static void repeatCuda(const void* vx, const Nd4jLong* xShapeInfo,
                                        void* vz, const Nd4jLong* zShapeInfo,
                                  const int* repeats, const int repSize,
                                  const int axis) {

    const X* x = reinterpret_cast<const X*>(vx);
          Z* z = reinterpret_cast<Z*>(vz);

    __shared__ int rank, *sharedMem;
    __shared__ Nd4jLong zLen, totalThreads;  // xLen = zLen

    if (threadIdx.x == 0) {

        extern __shared__ unsigned char shmem[];
        sharedMem = reinterpret_cast<int*>(shmem);

        rank = shape::rank(zShapeInfo);     // xRank = zRank
        zLen = shape::length(zShapeInfo);    // xLen <= zLen

        totalThreads = gridDim.x * blockDim.x;
    }

    __syncthreads();

    auto coords = sharedMem + threadIdx.x * rank;

    const auto tid = blockIdx.x * blockDim.x + threadIdx.x;

    for (Nd4jLong i = tid; i < zLen; i += totalThreads) {

        shape::index2coords(i, zShapeInfo, coords);

        const auto zOffset = shape::getOffset(zShapeInfo, coords);

        if(repSize > 1) {
            for (uint j = 0; j < repSize; ++j) {
                coords[axis] -= repeats[j];
                if (coords[axis] < 0) {
                    coords[axis] = j;
                    break;
                }
            }
        }
        else
            coords[axis] /= repeats[0];

        z[zOffset] = x[shape::getOffset(xShapeInfo, coords)];
    }
}

//////////////////////////////////////////////////////////////////////////
template<typename X, typename Z>
static void repeatCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream,
                               const void* vx, const Nd4jLong* xShapeInfo,
                                     void* vz, const Nd4jLong* zShapeInfo,
                               const int* repeats, const int repSize,
                               const int axis) {

    repeatCuda<X,Z><<<blocksPerGrid, threadsPerBlock, sharedMem, *stream>>>(vx, xShapeInfo, vz, zShapeInfo, repeats, repSize, axis);
}
BUILD_DOUBLE_TEMPLATE(template void repeatCudaLauncher, (const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream, const void *vx, const Nd4jLong *xShapeInfo, void *vz, const Nd4jLong *zShapeInfo, const int* repeats, const int repSize, const int axis), LIBND4J_TYPES, LIBND4J_TYPES);

//////////////////////////////////////////////////////////////////////////
// create new array by repeating it the number of times given by repeats
NDArray NDArray::repeat(const int axis, const std::vector<int>& repeats) const {

    NDArray output('c', ShapeUtils::evalRepeatShape(axis, repeats, *this), dataType(),  getContext());

    const int threadsPerBlock = MAX_NUM_THREADS / 2;
    const int blocksPerGrid = (output.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
    const int sharedMem = output.rankOf() * sizeof(int) * threadsPerBlock  + 128;

    PointersManager manager(getContext(), "NDArray::repeat(const int axis, const std::vector<int>& repeats)");

    const int* reps = reinterpret_cast<int*>(manager.replicatePointer(repeats.data(), repeats.size() * sizeof(int)));

    prepareSpecialUse({&output}, {this});
    BUILD_SINGLE_SELECTOR_TWICE(dataType(), repeatCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, getContext()->getCudaStream(), specialBuffer(), specialShapeInfo(), output.specialBuffer(), output.specialShapeInfo(), reps, repeats.size(), axis), LIBND4J_TYPES);
    prepareSpecialUse({&output}, {this});

    manager.synchronize();

    return output;
}

//////////////////////////////////////////////////////////////////////////
// fill array by repeating it the number of times given by repeats
void NDArray::repeat(const int axis, const std::vector<int>& repeats, NDArray& target) const {

    if(!target.isSameShape(ShapeUtils::evalRepeatShape(axis, repeats, *this)))
        throw std::invalid_argument("NDArray::repeat(const int axis, const std::vector<int>& repeats, NDArray& target) method: wrong shape of target array!");

    const int threadsPerBlock = MAX_NUM_THREADS / 2;
    const int blocksPerGrid = (target.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
    const int sharedMem = target.rankOf() * sizeof(int) * threadsPerBlock  + 128;

    PointersManager manager(getContext(), "NDArray::repeat(const int axis, const std::vector<int>& repeats)");

    const int* reps = reinterpret_cast<int*>(manager.replicatePointer(repeats.data(), repeats.size() * sizeof(int)));

    prepareSpecialUse({&target}, {this});
    BUILD_DOUBLE_SELECTOR(dataType(), target.dataType(), repeatCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, getContext()->getCudaStream(), specialBuffer(), specialShapeInfo(), target.specialBuffer(), target.specialShapeInfo(), reps, repeats.size(), axis), LIBND4J_TYPES, LIBND4J_TYPES);
    prepareSpecialUse({&target}, {this});

    manager.synchronize();
}


////////////////////////////////////////////////////////////////////////
void* NDArray::specialBuffer() {

    if (_buffer->special() == nullptr) {
        syncToDevice();
        tickReadHost();
    }
    // FIXME: this should be fixed once CUDA backend added
    return static_cast<int8_t*>(_buffer->special()) + (_offset * sizeOfT());
}

////////////////////////////////////////////////////////////////////////
void const* NDArray::specialBuffer() const {
    if (_buffer->special() == nullptr) {
        syncToDevice();
        tickReadHost();
    }
    // FIXME: this should be fixed once CUDA backend added
    return static_cast<int8_t*>(_buffer->special()) + (_offset * sizeOfT());
}

//////////////////////////////////////////////////////////////////////////
template<typename T>
void NDArray::printCurrentBuffer(const bool host, const char* msg, const int precision) const {

    if(_length == 0)
            { printf("NDArray::printActualBuffer: array length is zero !\n"); return; }

    if(msg)
        printf("%s", msg);

    if(host) {
        if(buffer() == nullptr || _length == 0)
            { printf("NDArray::printActualBuffer: host buffer is nullptr !\n"); return; }

        const T* buff = bufferAsT<T>();
        for (uint i = 0; i < _length; i++)
            printf("%.*f, ", precision, (double)buff[getOffset(i)]);
        printf("\n");
    }
    else {
        if(specialBuffer() == nullptr || _length == 0)
            { printf("NDArray::printSpecialBuffer: special buffer is nullptr !\n"); return; }

        void* pHost = operator new(sizeof(T) * _length);

        if (ews() != 1) {
            for (uint i = 0; i < _length; i++)
                cudaMemcpyAsync(reinterpret_cast<T*>(pHost) + i, specialBufferWithOffset(i), sizeof(T), cudaMemcpyDeviceToHost, *(getContext()->getCudaStream()));
        }
        else
            cudaMemcpyAsync(pHost, specialBuffer(), sizeOfT() * _length, cudaMemcpyDeviceToHost, *getContext()->getCudaStream());

        cudaError_t cudaResult = cudaStreamSynchronize(*getContext()->getCudaStream());
        if(cudaResult != 0)
            throw std::runtime_error("NDArray::printSpecialBuffer: cudaStreamSynchronize failed!");

        for (uint i = 0; i < _length; i++)
            printf("%.*f, ", precision, (double)reinterpret_cast<T*>(pHost)[i]);
        printf("\n");

        operator delete(pHost);
    }
}
template void NDArray::printCurrentBuffer<int>(const bool host,const char* msg, const int precision) const;
template void NDArray::printCurrentBuffer<float>(const bool host, const char* msg, const int precision) const;
template void NDArray::printCurrentBuffer<double>(const bool host, const char* msg, const int precision) const;


#if defined(__CUDACC__) && !defined(BUILD_TESTS)

//#include <cpu/NDArrayLambda.hpp>

#endif

} // end namespace sd
#endif