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

/*******************************************************************************
 * Copyright (c) 2020 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 GS <sgazeos@gmail.com>
//
#include <system/op_boilerplate.h>
#include <array/NDArray.h>
#include <helpers/MmulHelper.h>
#include <helpers/ShapeUtils.h>
#include <helpers/ConstantTadHelper.h>

#include <ops/declarable/helpers/triangular_solve.h>
#include <ops/declarable/helpers/lup.h>
#include <ops/declarable/helpers/qr.h>
#include <ops/declarable/helpers/lstsq.h>

namespace sd {
namespace ops {
namespace helpers {

    template <typename T>
    static __global__ void fillRegularizerKernel(T* ioMatrixData, Nd4jLong* ioMatrixShape, Nd4jLong* ioMatrixTads, Nd4jLong* ioMatrixOffsets, Nd4jLong batchSize, Nd4jLong rows, T const value) {

        for (auto x = blockIdx.x; x < batchSize; x += gridDim.x) {
            auto z = ioMatrixData + ioMatrixOffsets[x];
            for (auto r = threadIdx.x; r < rows; r += blockDim.x) {
                Nd4jLong pos[] = {r,r};
                auto zIndex = shape::getOffset(ioMatrixTads, pos);
                z[zIndex] = value;
            }
        }

    }

    template <typename T>
    static void fillRegularizer(sd::LaunchContext* context, NDArray& ioMatrix, double const value) {
        auto lastDimsTads = ConstantTadHelper::getInstance()->tadForDimensions(ioMatrix.shapeInfo(), {-2, -1});
        auto stream = context->getCudaStream();
        auto rows = ioMatrix.sizeAt(-2);
        //auto cols = ioMatrix.sizeAt(-1);
        fillRegularizerKernel<T><<<256, 256, 128, *stream>>>(ioMatrix.dataBuffer()->specialAsT<T>(), ioMatrix.specialShapeInfo(), lastDimsTads.specialShapeInfo(), lastDimsTads.specialOffsets(), lastDimsTads.numberOfTads(), rows, (T)value);

    }

    template <typename T>
    int leastSquaresSolveFunctor_(sd::LaunchContext* context, NDArray const* leftInput, NDArray const* rightInput, double const l2Regularizer, bool const fast, NDArray* output) {
        if (fast) { // Cholesky decomposition approach
            // Equation for solve A^T * Ax = A^T * b, so
            // 1. Computing A2:
            auto tAtShape = ShapeUtils::evalShapeForMatmul(leftInput->getShapeInfo(), leftInput->getShapeInfo(), true, false);
            //tAtShape[tAtShape.size() - 2] = output->sizeAt(-2);
            NDArray leftOutput(leftInput->ordering(), tAtShape, output->dataType(), context);
            MmulHelper::matmul(leftInput, leftInput, &leftOutput, true, false); // Computing A2 = A^T * A
            // 2. Computing B' = A^T * b
            auto rightOutput = output->ulike();

            MmulHelper::matmul(leftInput, rightInput, &rightOutput, true, false); // Computing B' = A^T * b
            // 3. Regularization ( indeed A' = A2 - l2Regularizer * I)
            if (l2Regularizer != 0.0) {
                auto regularizer = leftOutput.ulike(); regularizer.nullify();
                fillRegularizer<T>(context, regularizer, (T)l2Regularizer);
                leftOutput += regularizer;
            }

            // 4. Cholesky decomposition -- output matrix is square and lower triangular
            helpers::cholesky(context, &leftOutput, &leftOutput, true); // inplace decomposition
            // 5. Solve two triangular systems:
            auto rightB = rightOutput.ulike(); rightB.nullify();

            helpers::triangularSolveFunctor(context, &leftOutput, &rightOutput, true, false, &rightB);

            helpers::adjointMatrix(context, &leftOutput, true, &leftOutput);
            helpers::triangularSolveFunctor(context, &leftOutput, &rightB, false, false, output);
            // All done
        }
        else { // QR decomposition approach
            // Equation for solve Rx = Q^T * b, where A = Q * R, where Q - orthogonal matrix, and R - upper triangular
            // 1. QR decomposition
            auto qShape = leftInput->getShapeAsVector();
            auto rShape = leftInput->getShapeAsVector();
            qShape[leftInput->rankOf() - 1] = leftInput->sizeAt(-2);

            NDArray Q(leftInput->ordering(), qShape, leftInput->dataType(), context);// = leftInput->ulike();
            NDArray R(leftInput->ordering(), rShape, leftInput->dataType(), context); // = rightInput->ulike();
            helpers::qr(context, leftInput, &Q, &R, true);
            // 2. b` = Q^t * b:
            auto rightOutput = rightInput->ulike();
            MmulHelper::matmul(&Q, rightInput, &rightOutput, true, false);
            // 3. Solve triangular system
            helpers::triangularSolveFunctor(context, &R, &rightOutput, false, false, output);
        }
        return Status::OK();
    }

    int leastSquaresSolveFunctor(sd::LaunchContext* context, NDArray const* leftInput, NDArray const* rightInput, double const l2Regularizer, bool const fast, NDArray* output) {
        BUILD_SINGLE_SELECTOR(leftInput->dataType(), return leastSquaresSolveFunctor_, (context, leftInput, rightInput, l2Regularizer, fast, output), FLOAT_TYPES);
    }

}
}
}