cavis/libnd4j/include/ops/declarable/helpers/cuda/scatter_update.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 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 scatterUpdateCuda(const int opCode, const int numOfInd,
                                                     void* vx, const Nd4jLong *xShapeInfo, const Nd4jLong *xOffsets,
                                                     void* vy, const Nd4jLong *yShapeInfo, const Nd4jLong *yOffsets,
                                                     const int* indexes) {

                __shared__ T *x, *y;
                __shared__ Nd4jLong arrLenX, arrLenY;

                for (int e = 0; e < numOfInd; e++ ) {

                    const auto xIndex = indexes[e];
                    const bool isOwner = xIndex < gridDim.x ? blockIdx.x == xIndex : blockIdx.x == xIndex % gridDim.x;

                    if (!isOwner)
                        continue;

                    if (threadIdx.x == 0) {
                        x = reinterpret_cast<T*>(vx) + xOffsets[xIndex];
                        y = reinterpret_cast<T*>(vy) + yOffsets[e];
                        arrLenX = shape::length(xShapeInfo);
                        arrLenY = shape::length(yShapeInfo);
                    }
                    __syncthreads();

                    if (arrLenX != arrLenY)
                        return;

                    for (Nd4jLong i = threadIdx.x; i < arrLenX; i += blockDim.x) {

                        const auto xOffset = shape::getIndexOffset(i, xShapeInfo);
                        const auto yOffset = shape::getIndexOffset(i, yShapeInfo);

                        switch (opCode) {
                            case 0:
                                x[xOffset] += y[yOffset];
                                break;
                            case 1:
                                x[xOffset] -= y[yOffset];
                                break;
                            case 2:
                                x[xOffset] *= y[yOffset];
                                break;
                            case 3:
                                x[xOffset] /= y[yOffset];
                                break;
                            case 4:
                                x[xOffset] = y[yOffset] - x[xOffset];
                                break;
                            case 5:
                                x[xOffset] = y[yOffset] / x[xOffset];
                                break;
                            case 6:
                                x[xOffset] = y[yOffset];
                                break;
                            default:
                                continue;
                        }
                    }
                    __syncthreads();
                }
            }

            template<typename T>
            __host__ static void scatterUpdateCudaLauncher(const cudaStream_t* stream, const int opCode, const int numOfInd, void* vx, const Nd4jLong *xShapeInfo, const Nd4jLong *xOffsets, void* vy, const Nd4jLong *yShapeInfo, const Nd4jLong *yOffsets, const int* indexes) {

                scatterUpdateCuda<T><<<512, 256, MAX_NUM_THREADS, *stream>>>(opCode, numOfInd, vx, xShapeInfo, xOffsets, vy, yShapeInfo, yOffsets, indexes);
            }


//////////////////////////////////////////////////////////////////////////
            void scatterUpdate(sd::LaunchContext* context, NDArray& input, NDArray& updates, const std::vector<int>* intArgs) {

                const int opCode    = (*intArgs)[0];
                const int numOfDims = (*intArgs)[1];
                const int numOfInd  = (*intArgs)[2 + numOfDims];

                std::vector<int> tadDimensions(numOfDims);
                for (int e = 2; e < 2 + numOfDims; e++)
                    tadDimensions[e-2] = (*intArgs)[e];

                auto packX = ConstantTadHelper::getInstance()->tadForDimensions(input.shapeInfo(), tadDimensions);
                auto packY = ConstantTadHelper::getInstance()->tadForDimensions(updates.shapeInfo(), tadDimensions);

                NDArray indices(const_cast<int*>(intArgs->data()) + numOfDims + 3, 'c', {numOfInd}, sd::DataType::INT32, context);

                PointersManager manager(context, "scatterUpdate");

                NDArray::prepareSpecialUse({&input}, {&input, &updates, &indices});
                BUILD_SINGLE_SELECTOR(input.dataType(), scatterUpdateCudaLauncher, (context->getCudaStream(), opCode, numOfInd, input.specialBuffer(), packX.platformShapeInfo(), packX.platformOffsets(), updates.specialBuffer(), packY.platformShapeInfo(), packY.platformOffsets(), reinterpret_cast<int*>(indices.specialBuffer())), LIBND4J_TYPES);
                NDArray::registerSpecialUse({&input}, {&input, &updates, &indices});

                manager.synchronize();
            }
        }
    }
}