cavis/libnd4j/include/ops/declarable/helpers/cuda/reverse.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, created on 16.04.2018
//

#include <ops/declarable/helpers/reverse.h>
#include <helpers/ShapeUtils.h>
#include <array/ResultSet.h>
#include <helpers/TAD.h>
#include <helpers/PointersManager.h>
#include <helpers/ConstantTadHelper.h>


namespace sd    {
namespace ops     {
namespace helpers {

    template <typename T>
    static __global__ void reverseTadKernel(const void* vinput, const Nd4jLong *inputShape, void* voutput, const Nd4jLong *outputShape, const Nd4jLong *inputTadShape, const Nd4jLong *inputTadOffsets, const Nd4jLong *outputTadShape, const Nd4jLong *outputTadOffsets, uint64_t limit, uint64_t numOfElemsToReverse, uint64_t numTads) {
        auto input = reinterpret_cast<const T*>(vinput);
        auto output = reinterpret_cast<T*>(voutput);
        const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
        const auto step = gridDim.x * blockDim.x;

        // this means that we'll have additional cycle, to move middle element
        auto div = numOfElemsToReverse / 2;
        auto odd = numOfElemsToReverse % 2 != 0;
        auto rlimit = odd ? limit / 2 + 1 : limit / 2;

        // all threads operate in the same input/output space
        for (uint64_t e = tid; e < rlimit; e += step) {
            // finding out the TAD we're going to process
            auto tadId = e / div;

            if (tadId >= numTads)
                continue;

            // now finding out element within tad
            auto idx = e % div;

            //printf("TID: %i; numTads: %lld; tadLength: %lld; tadId: %i, idx: %lld\n", tid, numTads, numOfElemsToReverse, tadId, idx);

            auto tadInput = input + inputTadOffsets[tadId];
            auto tadOutput = output + outputTadOffsets[tadId];

            // we're calculating offsets within input TAD
            auto fOffset = shape::getIndexOffset(idx, inputTadShape);
            auto lOffset = shape::getIndexOffset(numOfElemsToReverse - idx - 1, inputTadShape);

            // now we're storing input values
            auto v1 = tadInput[fOffset];
            auto v2 = tadInput[lOffset];

            // now we're calculating offsets within output TAD
            auto zfOffset = shape::getIndexOffset(idx, outputTadShape);
            auto zlOffset = shape::getIndexOffset(numOfElemsToReverse - idx - 1, outputTadShape);

            // and saving values to output arrays
            tadOutput[zfOffset] = v2;
            tadOutput[zlOffset] = v1;
        }

        // moving odd element in blocks
        if (odd && threadIdx.x == 0) {
            for (uint64_t e = blockIdx.x; e < numTads; e += gridDim.x) {
                auto tadInput = input + inputTadOffsets[e];
                auto tadOutput = output + outputTadOffsets[e];

                auto xOffset = shape::getIndexOffset(numOfElemsToReverse / 2, inputTadShape);
                auto zOffset = shape::getIndexOffset(numOfElemsToReverse / 2, outputTadShape);

                tadOutput[zOffset] = tadInput[xOffset];
            }
        }

    }


    template <typename T>
    static __global__ void reverseArrayKernel(const void* input, const Nd4jLong *inputShape, void* output, const Nd4jLong *outputShape, Nd4jLong numOfElemsToReverse) {
        const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
        const auto step = gridDim.x * blockDim.x;
        __shared__ int linearStatus;
        __shared__ const T* inputArr;
        __shared__ T* outputArr;
        __shared__ char inputOrder, outputOrder;

        if (threadIdx.x == 0) {
            linearStatus = (shape::elementWiseStride(inputShape) == shape::elementWiseStride(outputShape)) && (inputOrder == outputOrder)? shape::elementWiseStride(inputShape):0;

            char inputOrder = shape::order(inputShape);
            char outputOrder = shape::order(outputShape);
            inputArr = reinterpret_cast<const T*>(input);
            outputArr = reinterpret_cast<T*>(output);
        }
        __syncthreads();

        auto odd = numOfElemsToReverse % 2 != 0;
        auto limit = numOfElemsToReverse / 2;

        for (uint64_t e = tid; e < limit; e += step) {
            // we're calculating offsets within input array
            auto fOffset = shape::getIndexOffset(e, inputShape);
            auto lOffset = shape::getIndexOffset(numOfElemsToReverse - e - 1, inputShape);

            // now we're storing input values
            auto v1 = inputArr[fOffset];
            auto v2 = inputArr[lOffset];

            // now we're calculating offsets within output array
            auto zfOffset = shape::getIndexOffset(e, outputShape);
            auto zlOffset = shape::getIndexOffset(numOfElemsToReverse - e - 1, outputShape);

            // and saving values to output arrays
            outputArr[zfOffset] = v2;
            outputArr[zlOffset] = v1;
        }

        // in case of odd array we'll have to move middle value
        if (odd && tid == 0) {
            auto xOffset = shape::getIndexOffset(limit, inputShape);
            auto zOffset = shape::getIndexOffset(limit, outputShape);

            outputArr[zOffset] = inputArr[xOffset];
        }
    }

    template<typename T>
    static void reverseTad(sd::LaunchContext * context, const NDArray* input, NDArray* output, const Nd4jLong *inputTadShape, const Nd4jLong *inputTadOffsets, const Nd4jLong *outputTadShape, const Nd4jLong *outputTadOffsets, uint64_t tadLength) {
        auto stream = context->getCudaStream();
        reverseTadKernel<T><<<256, 512, 8192, *stream>>>(input->specialBuffer(), input->specialShapeInfo(), output->specialBuffer(), output->specialShapeInfo(), inputTadShape, inputTadOffsets, outputTadShape, outputTadOffsets, input->lengthOf(), tadLength, input->lengthOf() / tadLength);
    }

    template<typename T>
    static void reverseArray(sd::LaunchContext * context, const NDArray* input, NDArray* output, Nd4jLong numOfElemsToReverse) {
        auto stream = context->getCudaStream();
        Nd4jLong numOfReverse = numOfElemsToReverse;
        if (numOfElemsToReverse == 0)
            numOfReverse = input->lengthOf();

        reverseArrayKernel<T><<<256, 512, 8192, *stream>>>(input->specialBuffer(), input->specialShapeInfo(), output->specialBuffer(), output->specialShapeInfo(), numOfReverse);
    }


    ///////////////////////////////////////////////////////////////////
    template <typename T>
    static void reverseSequence_(sd::LaunchContext * context, const NDArray* input, const NDArray* seqLengths, NDArray* output, int seqDim, const int batchDim){
        int posOfNonUnityDim = -1;
        seqLengths->syncToHost();
        auto stream = context->getCudaStream();

        if(input->isVector() || shape::isLikeVector(input->shapeInfo(), posOfNonUnityDim) || seqLengths->lengthOf() == 1) {
            int numOfElemsToReverse = seqLengths->e<int>(0);
            if((seqDim == 0 && input->sizeAt(0) == 1) || (batchDim == posOfNonUnityDim))
                output->assign(input);
            else
                reverseArrayKernel<T><<<256, 512, 8192, *stream>>>(input->specialBuffer(), input->specialShapeInfo(), output->specialBuffer(), output->specialShapeInfo(), numOfElemsToReverse);//helpers::reverseArray<T>(context, const_cast<NDArray*>(input), output, numOfElemsToReverse);
        }
        else {

            if(seqDim > batchDim)
                --seqDim;

            std::vector<int> dimensions = ShapeUtils::evalDimsToExclude(input->rankOf(), {batchDim});

            auto inSubArrsSet  = input->allTensorsAlongDimension(dimensions);
            auto outSubArrsSet = output->allTensorsAlongDimension(dimensions);

            for(int i = 0; i < inSubArrsSet.size(); ++i) {

                int numOfElemsToReverse = seqLengths->e<int>(i);

                if(numOfElemsToReverse == 0 || numOfElemsToReverse == 1) {
                    outSubArrsSet.at(i)->assign(inSubArrsSet.at(i));
                }
                else {
                    auto inInnerSet  = inSubArrsSet.at(i)->allTensorsAlongDimension({seqDim});
                    auto outInnerSet = outSubArrsSet.at(i)->allTensorsAlongDimension({seqDim});
                    for(int j = 0; j < inInnerSet.size(); ++j)
                        reverseArray<T>(context, inInnerSet.at(j), outInnerSet.at(j), numOfElemsToReverse);
                }
            }
        }
    }

    void reverseSequence(sd::LaunchContext * context, const NDArray* input, const NDArray* seqLengths, NDArray* output, int seqDim, const int batchDim) {
        NDArray::prepareSpecialUse({output}, {input, seqLengths});

        // if op isn't inplace - copy original data into output array
        if (output->specialBuffer() != input->specialBuffer())
            output->assign(input);

        BUILD_SINGLE_SELECTOR(input->dataType(), reverseSequence_, (context, input, seqLengths, output, seqDim, batchDim), LIBND4J_TYPES);
        NDArray::registerSpecialUse({output}, {input, seqLengths});
    }

    //////////////////////////////////////////////////////////////////////////
    void reverse(sd::LaunchContext * context, const NDArray* input, NDArray* output, const std::vector<int>* intArgs, bool isBackProp) {
        // we need to reverse axis only if that's new op
        std::vector<int> dimensions = isBackProp ? ShapeUtils::evalDimsToExclude(input->rankOf(), *intArgs) : *intArgs;
        std::vector<int> axis = ShapeUtils::evalDimsToExclude(input->rankOf(), dimensions);
        auto packX = sd::ConstantTadHelper::getInstance()->tadForDimensions(input->shapeInfo(), dimensions);
        auto packZ = sd::ConstantTadHelper::getInstance()->tadForDimensions(output->shapeInfo(), dimensions);



        NDArray::prepareSpecialUse({output}, {input});

        if (packX.numberOfTads() == 1) {
            BUILD_SINGLE_SELECTOR(input->dataType(), reverseArray, (context, input, output, 0),  LIBND4J_TYPES);
        } else {
            BUILD_SINGLE_SELECTOR(input->dataType(), reverseTad, (context, input, output, packX.platformShapeInfo(), packX.platformOffsets(), packZ.platformShapeInfo(), packZ.platformOffsets(), (uint64_t) (input->lengthOf() / packX.numberOfTads())),  LIBND4J_TYPES);
        }

        NDArray::registerSpecialUse({output}, {input});
    }
}
}
}