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

/*******************************************************************************
 * Copyright (c) 2015-2018 Skymind, Inc.
 * Copyright (c) 2019 Konduit K.K.
 *
 * This program and the accompanying materials are made available under the
 * terms of the Apache License, Version 2.0 which is available at
 * https://www.apache.org/licenses/LICENSE-2.0.
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
 * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
 * License for the specific language governing permissions and limitations
 * under the License.
 *
 * SPDX-License-Identifier: Apache-2.0
 ******************************************************************************/

//
// @author Yurii Shyrma (iuriish@yahoo.com), created on 20.04.2018
//


#include<ops/declarable/helpers/transforms.h>
#include <array/ResultSet.h>
#include <helpers/ShapeUtils.h>
#include <numeric>
#include <array/NDArrayFactory.h>
#include <helpers/TAD.h>
#include <exceptions/cuda_exception.h>
#include <helpers/PointersManager.h>
#include <helpers/ConstantTadHelper.h>

namespace sd 	  {
namespace ops 	  {
namespace helpers {

///////////////////////////////////////////////////////////////////
template<typename T>
__global__ static void invertPermutationCuda(const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo) {

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

    __shared__ Nd4jLong len, totalThreads;

    if (threadIdx.x == 0) {

        len  = shape::length(xShapeInfo);
        totalThreads = gridDim.x * blockDim.x;
    }

    __syncthreads();

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

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

        const auto xOffset = shape::getIndexOffset(i, xShapeInfo);
        const Nd4jLong index = x[xOffset];
        const auto zOffset = shape::getIndexOffset(index, zShapeInfo);
        z[zOffset] = i;
    }
}

///////////////////////////////////////////////////////////////////
template<typename T>
__host__ static void invertPermutationCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const cudaStream_t *stream,
                                                   const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo) {

    invertPermutationCuda<T><<<blocksPerGrid, threadsPerBlock, 1024, *stream>>>(vx, xShapeInfo, vz, zShapeInfo);
}

////////////////////////////////////////////////////////////////////////
void invertPermutation(sd::LaunchContext* context, const NDArray& input, NDArray& output) {

    const int threadsPerBlock = MAX_NUM_THREADS;
    const int blocksPerGrid = (input.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;

    PointersManager manager(context, "invertPermutation");

    NDArray::prepareSpecialUse({&output}, {&input});
    BUILD_SINGLE_SELECTOR(input.dataType(), invertPermutationCudaLauncher, (blocksPerGrid, threadsPerBlock, context->getCudaStream(), input.specialBuffer(), input.specialShapeInfo(), output.specialBuffer(), output.specialShapeInfo()), LIBND4J_TYPES);
    NDArray::registerSpecialUse({&output}, {&input});

    manager.synchronize();
}

//////////////////////////////////////////////////////////////////////////
template<typename T>
__global__ static void traceCuda(const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo, const uint diagLen) {

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

    __shared__ T sharedMem[CUDA_BLOCK_SIZE];
    __shared__ int xRank, zRank;        // xRank = zRank + 2
    __shared__ Nd4jLong xLen, zLen;

    if (threadIdx.x == 0) {
        xRank = shape::rank(xShapeInfo);
        zRank = shape::rank(zShapeInfo);
        xLen = shape::length(xShapeInfo);
        zLen = shape::length(zShapeInfo);   // corresponds to number of matrices

    }
    __syncthreads();

    Nd4jLong coords[MAX_RANK];

    for (uint m = blockIdx.x; m < zLen; m += gridDim.x) {   // one block per each element of z, that is per each matrix

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

        sharedMem[threadIdx.x] = 0;

          for (uint i = threadIdx.x; i < diagLen; i += blockDim.x) {

            coords[zRank] = coords[zRank + 1] = i;
            const auto xOffset = shape::getOffset(xShapeInfo, coords);
            sharedMem[threadIdx.x] += x[xOffset];
          }

          __syncthreads();

        // aggregate sum
        for (Nd4jLong activeThreads = blockDim.x / 2; activeThreads > 0; activeThreads /= 2) {
            if (threadIdx.x < activeThreads)
                sharedMem[threadIdx.x] += sharedMem[threadIdx.x + activeThreads];
            __syncthreads();
        }

        if (threadIdx.x == 0)
            z[zOffset] = *sharedMem;
        __syncthreads();
    }

}

///////////////////////////////////////////////////////////////////
template<typename T>
static void traceCudaLauncher(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 uint diagLen) {

    traceCuda<T><<<blocksPerGrid, threadsPerBlock, sharedMem, *stream>>>(vx, xShapeInfo, vz, zShapeInfo, diagLen);
}


///////////////////////////////////////////////////////////////////
void trace(sd::LaunchContext* context, const NDArray& input, NDArray& output) {

    PointersManager manager(context, "trace");

    const uint diagLen = input.sizeAt(-1) < input.sizeAt(-2) ? input.sizeAt(-1) : input.sizeAt(-2);
    const int threadsPerBlock = CUDA_BLOCK_SIZE;
    const int blocksPerGrid = (output.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
    const int sharedMem = 1024;

    NDArray::prepareSpecialUse({&output}, {&input});
    BUILD_SINGLE_SELECTOR(input.dataType(), traceCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, context->getCudaStream(), input.specialBuffer(), input.specialShapeInfo(), output.specialBuffer(), output.specialShapeInfo(), diagLen), LIBND4J_TYPES);
    NDArray::registerSpecialUse({&output}, {&input});

    manager.synchronize();
}

///////////////////////////////////////////////////////////////////
template<typename T>
__global__ static void triuBPCuda(const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo, const int diag) {

    // x and z have same shapes
    const auto x = reinterpret_cast<const T*>(vx);  // gradO
          auto z = reinterpret_cast<T*>(vz);        // gradI

    __shared__ int rank, areSameOffsets;
    __shared__ Nd4jLong len, totalThreads;  // xLen = zLen

    if (threadIdx.x == 0) {
        areSameOffsets = shape::haveSameShapeAndStrides(xShapeInfo, zShapeInfo);
        rank = shape::rank(xShapeInfo);
        len  = shape::length(zShapeInfo);
        totalThreads = gridDim.x * blockDim.x;
    }

    __syncthreads();

    Nd4jLong coords[MAX_RANK];

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

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

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

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

        if((coords[rank - 2] + diag > coords[rank - 1]))    // row + diag > col
            z[zOffset] = 0;
        else
            z[zOffset] = x[areSameOffsets ? zOffset : shape::getOffset(xShapeInfo, coords)];
    }
}

///////////////////////////////////////////////////////////////////
template<typename T>
static void triuBPCudaLauncher(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 diag) {

    triuBPCuda<T><<<blocksPerGrid, threadsPerBlock, sharedMem, *stream>>>(vx, xShapeInfo, vz, zShapeInfo, diag);
}

///////////////////////////////////////////////////////////////////
void triuBP(sd::LaunchContext* context, const NDArray& input, const NDArray& gradO, NDArray& gradI, const int diagonal) {

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

    PointersManager manager(context, "triuBP");

    NDArray::prepareSpecialUse({&gradI}, {&gradO});
    BUILD_SINGLE_SELECTOR(gradI.dataType(), triuBPCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, context->getCudaStream(), gradO.specialBuffer(), gradO.specialShapeInfo(), gradI.specialBuffer(), gradI.specialShapeInfo(), diagonal), LIBND4J_TYPES);
    NDArray::registerSpecialUse({&gradI}, {&gradO});

    manager.synchronize();
}

///////////////////////////////////////////////////////////////////
template<typename T>
__global__ static void tileBPCuda(const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo, Nd4jLong* globMem) {

    // x and z have same shapes
    const auto x = reinterpret_cast<const T*>(vx);  // gradO
          auto z = reinterpret_cast<T*>(vz);        // gradI

    __shared__ int xRank, zRank;                // xRank >= zRank
    __shared__ Nd4jLong numOfXOffsets, zLen, totalThreads;  // xLen >= zLen

    if (threadIdx.x == 0) {
        xRank = shape::rank(zShapeInfo);
        zLen  = shape::length(zShapeInfo);
        numOfXOffsets = shape::length(xShapeInfo) / zLen;

        totalThreads = gridDim.x * blockDim.x;
    }

    __syncthreads();

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

    int memBuff[MAX_RANK * 2];
    auto xOffsets = globMem + tid * numOfXOffsets;

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

        const auto zOffset = shape::getIndexOffset(i, zShapeInfo);

        shape::outerArrayOffsets(xOffsets, i, xShapeInfo, zShapeInfo, memBuff);

        z[zOffset] = x[xOffsets[0]];                    // first offset
        for (Nd4jLong j = 1; j < numOfXOffsets; ++j)    // rest offsets
            z[zOffset] += x[xOffsets[j]];
    }
}

///////////////////////////////////////////////////////////////////
template<typename T>
static void tileBPCudaLauncher(const int blocksPerGrid, const int threadsPerBlock, const int sharedMem, const cudaStream_t *stream,  const void* vx, const Nd4jLong* xShapeInfo, void* vz, const Nd4jLong* zShapeInfo, Nd4jLong* globMem) {

    tileBPCuda<T><<<blocksPerGrid, threadsPerBlock, sharedMem, *stream>>>(vx, xShapeInfo, vz, zShapeInfo, globMem);
}


//////////////////////////////////////////////////////////////////////////
void tileBP(sd::LaunchContext * context, const NDArray& gradO /*input*/, NDArray& gradI /*output*/, const std::vector<Nd4jLong> reps) {

    NDArray memBuff('c', gradO.getShapeAsVector(), sd::DataType::INT64, context);        // empty auxiliary array for storing device memory which will be used in kernel calculations

    const int threadsPerBlock = MAX_NUM_THREADS / 4;
    const int blocksPerGrid = (gradI.lengthOf() + threadsPerBlock - 1) / threadsPerBlock;
    const int sharedMem = threadsPerBlock * sizeof(int) * 2 * gradO.rankOf() + 128;

    PointersManager manager(context, "tileBP");

    NDArray::prepareSpecialUse({&gradI}, {&gradO, &memBuff});
    BUILD_SINGLE_SELECTOR(gradI.dataType(), tileBPCudaLauncher, (blocksPerGrid, threadsPerBlock, sharedMem, context->getCudaStream(), gradO.specialBuffer(), gradO.specialShapeInfo(), gradI.specialBuffer(), gradI.specialShapeInfo(), reinterpret_cast<Nd4jLong*>(memBuff.specialBuffer())), FLOAT_TYPES);
    NDArray::registerSpecialUse({&gradI}, {&gradO, &memBuff});

    manager.synchronize();
}

    //////////////////////////////////////////////////////////////////////////
    void eye(sd::LaunchContext * context, NDArray& output) {

        output.setIdentity();
    }

}
}
}