cavis/libnd4j/include/loops/cuda/legacy/scalar_temp.legacy

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


#include <system/pointercast.h>




/**
* Cuda implementation of transform
* @param dx
* @param xShapeInfo
* @param result
* @param resultShapeInfo
* @param extraParams
* @param n
*/
template<typename OpType>
static inline __device__ void transform(Nd4jLong n, T scalar,T *dy,T *params, T *result, int *indexes, int *allocationBuffer, UnifiedSharedMemory *manager) {
    int totalThreads = gridDim.x * blockDim.x;
    int tid = threadIdx.x;
    Nd4jLong i = blockIdx.x * blockDim.x + tid;

    /* equal, positive, non-unit increments. */
    for (; i < n; i+= totalThreads) {
        result[indexes[i]] = OpType::op(dy[indexes[i]],scalar, params);
    }
}


/**
 * Cuda implementation of transform
 * @param dx
 * @param xShapeInfo
 * @param result
 * @param resultShapeInfo
 * @param extraParams
 * @param n
 */
template<typename OpType>
static inline __device__ void transformCuda(T scalar, T *dy, int *shapeInfo, T *params, T *result, int *resultShapeInfo, int *allocationBuffer, UnifiedSharedMemory *manager) {

    int *xShape = shape::shapeOf(shapeInfo);
    int *xStride = shape::stride(shapeInfo);
    char xOrder = shape::order(shapeInfo);
    int xRank = shape::rank(shapeInfo);
    int xOffset = shape::offset(shapeInfo);
    int xElementWiseStride = shape::elementWiseStride(shapeInfo);
    int resultElementWiseStride = shape::elementWiseStride(resultShapeInfo);
    int *zShape = shape::shapeOf(resultShapeInfo);
    int *zStride = shape::stride(resultShapeInfo);
    int zRank = shape::rank(resultShapeInfo);

    int totalThreads = gridDim.x * blockDim.x;
    int tid = blockIdx.x * blockDim.x + threadIdx.x;

    __shared__ int length;
    if(threadIdx.x == 0)
        length = shape::length(shapeInfo);
    __syncthreads();


    if(xElementWiseStride >= 1 && resultElementWiseStride >= 1 && xOrder == shape::order(resultShapeInfo)) {
        transformCuda<OpType>(
                length,
                        scalar,
                        dy,
                        xElementWiseStride,
                        params,
                        result,resultElementWiseStride, allocationBuffer, manager);
    }
    else {
        int xIdx[MAX_RANK];

        for (Nd4jLong i = tid; i < length; i+= totalThreads) {
            shape::ind2sub(xRank, xShape, i, length, xIdx);
            int xOffset2 = shape::getOffset(shapeInfo, xIdx);
            int resultOffset = shape::getOffset(0resultShapeInfo, xIdx);
            result[resultOffset] = OpType::op(dy[xOffset2],scalar, params);
        }
    }
}

/**
  * ScalarOp along dimension
**/
template<typename OpType>
static inline void __device__ transformCuda(T *x, int *xShapeInfo, T *extraParams, T *z, int *zShapeInfo, T *scalars, int *dimension, int dimensionLength, int *tadShapeInfo, Nd4jLong *tadOffsets, int *tadShapeInfoZ, Nd4jLong *tadOffsetsZ) {

    if (tadShapeInfoZ == nullptr) {
        tadShapeInfoZ = tadShapeInfo;
        tadOffsetsZ = tadOffsets;
    }

    // tad preparation
    int tadEWS = shape::elementWiseStride(tadShapeInfo);
    int zEWS = shape::elementWiseStride(tadShapeInfo);
    int tadRank = shape::rank(tadShapeInfo);
    int tadLength = shape::length(tadShapeInfo);//shape::tadLength(xShapeInfo, dimension, dimensionLength);
    int numTads =shape::length(xShapeInfo) / tadLength;

    // main loop, rolling over tads
    for (int r = blockIdx.x; r < numTads; r+=gridDim.x) {
        Nd4jLong offset = tadOffsets[r];
        Nd4jLong offsetZ = tadOffsetsZ[r];
        T scalar = scalars[r];

        if (tadEWS >= 1 && zEWS >= 1) {
            T *oZ = z + offsetZ;
            T *oX = x + offset;

            for (int f = threadIdx.x; f < tadLength; f+= blockDim.x) {
                oZ[f] = OpType::op(oX[f], scalar, extraParams);
            }
        } else {
            // ind2sub loop
            printf("Super-bad loop visited. Shouldn't ever happen\n");
        }
    }
}
/**
 *
 * @param n
 * @param idx
 * @param dx
 * @param dy
 * @param incy
 * @param params
 * @param result
 * @param blockSize
 */
template<typename OpType>
static inline __device__ void transformCuda(
        Nd4jLong n,
        T dx,
        T *dy,
        int incy,
        T *params,
        T *result,
        int resultStride,
        int *allocationBuffer,
        UnifiedSharedMemory *manager) {

    int totalThreads = gridDim.x * blockDim.x;
    int tid = blockIdx.x * blockDim.x + threadIdx.x;

    Nd4jLong i = tid;
    if(incy == 1 && resultStride == 1) {
        for (; i < n; i += totalThreads) {
            result[i] = OpType::op(dy[i],dx, params);
        }
    }
    else {
        for (; i < n; i += totalThreads) {
            result[i * resultStride] = OpType::op(dy[i * incy],dx, params);
        }
    }
}