cavis/libnd4j/include/ops/declarable/helpers/cuda/roll.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 raver119@gmail.com
//

#include <ops/declarable/helpers/roll.h>
#include <helpers/ConstantTadHelper.h>
#include <helpers/PointersManager.h>

namespace sd {
namespace ops {
namespace helpers {

    template <typename T>
    static void _CUDA_D rollKernelLinearStage1Dev(const void *vx, const Nd4jLong *xShapeInfo, void *vz, const Nd4jLong *zShapeInfo, Nd4jLong fullLength, int actualShift) {
        auto x = reinterpret_cast<const T*>(vx);
        auto z = reinterpret_cast<T*>(vz);

        auto xEws = shape::elementWiseStride(xShapeInfo);
        auto zEws = shape::elementWiseStride(zShapeInfo);

        auto xOrder = shape::order(xShapeInfo);
        auto zOrder = shape::order(zShapeInfo);

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

        if (xEws > 0 && zEws > 0 && xOrder == zOrder) {
            for (int i = tid; i < actualShift; i += blockDim.x * gridDim.x) {
                int sourceIndex = fullLength - actualShift + i;

                auto eA = x[sourceIndex * xEws];
                auto eB = x[i * xEws];

                z[i * zEws] = eA;
                z[sourceIndex * zEws] = eB;
            }
        } else {
            for (int i = tid; i < actualShift; i += blockDim.x * gridDim.x) {
                int sourceIndex = fullLength - actualShift + i;

                auto xOffsetA = shape::getIndexOffset(i, xShapeInfo);
                auto xOffsetB = shape::getIndexOffset(sourceIndex, xShapeInfo);

                auto zOffsetA = shape::getIndexOffset(i, zShapeInfo);
                auto zOffsetB = shape::getIndexOffset(sourceIndex, zShapeInfo);

                auto eA = x[xOffsetA];
                auto eB = x[xOffsetB];

                z[zOffsetA] = eB;
                z[zOffsetB] = eA;
            }
        }
    }

    template <typename T>
    static void _CUDA_G rollKernelLinearStage1(const void *vx, const Nd4jLong *xShapeInfo, void *vz, const Nd4jLong *zShapeInfo, Nd4jLong fullLength, int actualShift) {
        rollKernelLinearStage1Dev<T>(vx, xShapeInfo, vz, zShapeInfo, fullLength, actualShift);
    }

    template <typename T>
    static void _CUDA_G rollKernelLinearStage2(const void *vx, const Nd4jLong *xShapeInfo, void *vz, const Nd4jLong *zShapeInfo, Nd4jLong fullLength, int actualShift, int shiftCount) {
        auto x = reinterpret_cast<const T*>(vx);
        auto z = reinterpret_cast<T*>(vz);

        auto xEws = shape::elementWiseStride(xShapeInfo);
        auto zEws = shape::elementWiseStride(zShapeInfo);

        auto xOrder = shape::order(xShapeInfo);
        auto zOrder = shape::order(zShapeInfo);

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

        if (xEws > 0 && zEws > 0 && xOrder == zOrder) {
            for (int count = 1; count < shiftCount; ++count) {
                for (int i = tid; i < actualShift; i += blockDim.x * gridDim.x) {
                    int destinationIndex = fullLength - (count + 1) * actualShift + i;
                    int sourceIndex = fullLength - count * actualShift + i;

                    auto eA = x[sourceIndex * xEws];
                    auto eB = x[destinationIndex * xEws];

                    z[destinationIndex * zEws] = eA;
                    z[sourceIndex * zEws] = eB;
                }

                __syncthreads();
            }
        } else {
            for (int count = 1; count < shiftCount; ++count) {
                for (int i = tid; i < actualShift; i += blockDim.x * gridDim.x) {
                    int destinationIndex = fullLength - (count + 1) * actualShift + i;
                    int sourceIndex = fullLength - count * actualShift + i;

                    auto xOffsetA = shape::getIndexOffset(destinationIndex, xShapeInfo);
                    auto xOffsetB = shape::getIndexOffset(sourceIndex, xShapeInfo);

                    auto zOffsetA = shape::getIndexOffset(destinationIndex, zShapeInfo);
                    auto zOffsetB = shape::getIndexOffset(sourceIndex, zShapeInfo);

                    auto eA = x[xOffsetA];
                    auto eB = x[xOffsetB];

                    z[zOffsetA] = eB;
                    z[zOffsetB] = eA;
                }

                __syncthreads();
            }
        }
    }

    template <typename T>
    static void _CUDA_G rollKernelLinearStage3(const void *vx, const Nd4jLong *xShapeInfo, void *vz, const Nd4jLong *zShapeInfo, Nd4jLong fullLength, int actualShift, int remainShift) {
        auto x = reinterpret_cast<const T*>(vx);
        auto z = reinterpret_cast<T*>(vz);

        auto xEws = shape::elementWiseStride(xShapeInfo);
        auto zEws = shape::elementWiseStride(zShapeInfo);

        auto xOrder = shape::order(xShapeInfo);
        auto zOrder = shape::order(zShapeInfo);

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

        if (xEws > 0 && zEws > 0 && xOrder == zOrder) {
            for (int i = tid ; i < actualShift; i += blockDim.x * gridDim.x) {
                int remainIdx = i + actualShift;
                int sourceIndex = remainIdx + remainShift;

                auto eA = x[sourceIndex * xEws];
                auto eB = x[remainIdx * xEws];

                z[remainIdx * zEws] = eA;
                z[sourceIndex * zEws] = eB;
            }
        } else {
            for (int i = tid; i < actualShift; i += blockDim.x * gridDim.x) {
                int remainIdx = i + actualShift;
                int sourceIndex = remainIdx + remainShift;

                auto xOffsetA = shape::getIndexOffset(remainIdx, xShapeInfo);
                auto xOffsetB = shape::getIndexOffset(sourceIndex, xShapeInfo);

                auto zOffsetA = shape::getIndexOffset(remainIdx, zShapeInfo);
                auto zOffsetB = shape::getIndexOffset(sourceIndex, zShapeInfo);

                auto eA = x[xOffsetA];
                auto eB = x[xOffsetB];

                z[zOffsetA] = eB;
                z[zOffsetB] = eA;
            }
        }
    }

    template <typename T>
    static void _CUDA_D swapTadsKernel(void *vx, void *vz, const Nd4jLong *zShapeInfo, Nd4jLong tadLength) {
        auto x = reinterpret_cast<T*>(vx);
        auto z = reinterpret_cast<T*>(vz);

        auto zEws = shape::elementWiseStride(zShapeInfo);

        auto zOrder = shape::order(zShapeInfo);

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

        if (zEws > 0) {
            for (int e = threadIdx.x; e < tadLength; e += blockDim.x) {
                auto eA = x[e * zEws];
                auto eB = z[e * zEws];

                x[e * zEws] = eB;
                z[e * zEws] = eA;
            }
        } else {
            for (int e = threadIdx.x; e < tadLength; e += blockDim.x) {
                auto zOffset = shape::getIndexOffset(e, zShapeInfo);

                auto eA = x[zOffset];
                auto eB = z[zOffset];

                x[zOffset] = eB;
                z[zOffset] = eA;
            }
        }
    }

    template <typename T>
    static void _CUDA_G rollKernelFullAnyDimensionStage1(const void *vx, const Nd4jLong *xTadShapeInfo, const Nd4jLong *xTadOffsets, void *vz, const Nd4jLong *zTadShapeInfo, const Nd4jLong *zTadOffsets, int numTads, Nd4jLong tadLength, int dim, Nd4jLong sizeAt, int theShift) {
        auto x = reinterpret_cast<const T *>(vx);
        auto z = reinterpret_cast<T *>(vz);

        for (int e = blockIdx.x + theShift; e < sizeAt - theShift; e += gridDim.x) {
            int sourceIndex = dim * sizeAt + e - theShift;
            int targetIndex = dim * sizeAt + e;

            swapTadsKernel<T>(z + xTadOffsets[sourceIndex], z + xTadOffsets[targetIndex], zTadShapeInfo, tadLength);
        }
    }

    template <typename T>
    static void _CUDA_G rollKernelFullAnyDimensionStage2(void *vx, const Nd4jLong *xTadShapeInfo, const Nd4jLong *xTadOffsets, void *vz, const Nd4jLong *zTadShapeInfo, const Nd4jLong *zTadOffsets, int numTads, Nd4jLong tadLength, int dim, Nd4jLong sizeAt, int theShift) {
        auto x = reinterpret_cast<const T *>(vx);
        auto z = reinterpret_cast<T *>(vz);

        for (int e = blockIdx.x; e < theShift; e += gridDim.x) {
            int sourceIndex = dim * sizeAt + sizeAt - theShift + e;
            int targetIndex = dim * sizeAt + e;

            swapTadsKernel<T>(z + zTadOffsets[sourceIndex], z + zTadOffsets[targetIndex], zTadShapeInfo, tadLength);
        }
    }

    template <typename T>
    static void rollFunctorFull_(NDArray* input, NDArray* output, std::vector<int> const& shifts, std::vector<int> const& axes, bool inplace){
        if (!inplace)
            output->assign(input);

        for (size_t i = 0; i < axes.size(); i++) {
            int axe = axes[i];
            if (axe == input->rankOf() - 1) { // last dimension
                ResultSet listOfTensors = output->allTensorsAlongDimension({axe});
                ResultSet listOfOutTensors = output->allTensorsAlongDimension({axe});
                int fullLen = listOfTensors.size();
                int theShift = shifts[i];
//                if (theShift > 0) {
//                    theShift %= fullLen;
//                }
//                else {
//                    theShift -= fullLen * (theShift / fullLen - 1);
//                }
                for (int k = 0; k < fullLen; k++) {
                    rollFunctorLinear(output->getContext(), listOfTensors.at(k), listOfOutTensors.at(k), theShift, true);
                }
            } else {
                std::vector<int> dims(input->rankOf() - axe - 1);
                for (int i = 0; i < dims.size(); ++i)
                    dims[i] = axe + 1 + i;

                auto packZ = ConstantTadHelper::getInstance()->tadForDimensions(output->shapeInfo(), dims);

                int numTads = packZ.numberOfTads();
                int sizeAt = input->sizeAt(axe);
                auto tadLength = shape::length(packZ.primaryShapeInfo());

                int theShift = shifts[i];

//                if (theShift > 0)
//                    theShift %= sizeAt;
//                else
//                    theShift -= sizeAt * (theShift / sizeAt - 1);

                if (theShift) {
                    for (int dim = 0; dim < numTads / sizeAt; ++dim) {

                        rollKernelFullAnyDimensionStage1<T><<<1, 256, 1024, *(output->getContext()->getCudaStream())>>>(output->specialBuffer(), packZ.platformShapeInfo(), packZ.platformOffsets(), output->specialBuffer(), packZ.platformShapeInfo(), packZ.platformOffsets(), numTads, tadLength, dim, sizeAt, theShift);

                        rollKernelFullAnyDimensionStage2<T><<<1, 256, 1024, *(output->getContext()->getCudaStream())>>>(output->specialBuffer(), packZ.platformShapeInfo(), packZ.platformOffsets(), output->specialBuffer(), packZ.platformShapeInfo(), packZ.platformOffsets(), numTads, tadLength, dim, sizeAt, theShift);
                    }
                }
            }
        }
    }

    template <typename T>
    static void rollFunctorLinear_(NDArray* input, NDArray* output, int shift, bool inplace){
        if (!inplace)
            output->assign(input);

        auto fullLen = input->lengthOf();
        int actualShift = shift; // % fullLen; // shift already non-negative then
        if (actualShift < 0) {
            actualShift -= fullLen * (actualShift / fullLen - 1);
        }
        else
            actualShift %= fullLen;

        if (actualShift) {
            int shiftCount = fullLen / actualShift - 1;
            int remainShift = fullLen % actualShift;

            // stage 1) swap last actualShift elements with first ones.
            rollKernelLinearStage1<T><<<1, 1, 1024, *(output->getContext()->getCudaStream())>>>(output->specialBuffer(), output->specialShapeInfo(), output->specialBuffer(), output->specialShapeInfo(), fullLen, actualShift);

            // stage 2) swap swapped actualShift elements with rest remainShiftCount times.
            rollKernelLinearStage2<T><<<1, 1, 1024, *(output->getContext()->getCudaStream())>>>(output->specialBuffer(), output->specialShapeInfo(), output->specialBuffer(), output->specialShapeInfo(), fullLen, actualShift, shiftCount);

            // FIXME: no parallelism here :(
            // stage 3) swap remainer of items.
            if (remainShift && shiftCount)
                rollKernelLinearStage3<T><<<1, 1, 1024, *(output->getContext()->getCudaStream())>>>(output->specialBuffer(), output->specialShapeInfo(), output->specialBuffer(), output->specialShapeInfo(), fullLen, actualShift, remainShift);
        }
    }

    void rollFunctorFull(sd::LaunchContext * context, NDArray* input, NDArray* output, std::vector<int> const& shifts, std::vector<int> const& axes, bool inplace){
        input->syncToDevice();

        BUILD_SINGLE_SELECTOR(input->dataType(), rollFunctorFull_, (input, output, shifts, axes, inplace), LIBND4J_TYPES);

        output->tickWriteDevice();
    }

    void rollFunctorLinear(sd::LaunchContext * context, NDArray* input, NDArray* output, int shift, bool inplace){
        input->syncToDevice();

        BUILD_SINGLE_SELECTOR(input->dataType(), rollFunctorLinear_, (input, output, shift, inplace), LIBND4J_TYPES);

        output->tickWriteDevice();
    }

    BUILD_SINGLE_TEMPLATE(template void rollFunctorLinear_, (NDArray* input, NDArray* output, int shift, bool inplace), LIBND4J_TYPES);
    BUILD_SINGLE_TEMPLATE(template void rollFunctorFull_, (NDArray* input, NDArray* output, std::vector<int> const& shifts, std::vector<int> const& axes, bool inplace), LIBND4J_TYPES);
}
}
}