cavis/libnd4j/include/ops/declarable/helpers/cpu/clip.cpp

/* ******************************************************************************
 *
 *
 * 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.
 *
 *  See the NOTICE file distributed with this work for additional
 *  information regarding copyright ownership.
 * 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)
// @author sgazeos@gmail.com
// @author raver119@gmail.com
//


#include <ops/declarable/helpers/transforms.h>
#include <execution/Threads.h>

namespace sd 	  {
namespace ops 	  {
namespace helpers {

//////////////////////////////////////////////////////////////////////////
void clipByNorm(sd::LaunchContext* context, NDArray& input, NDArray& output, const std::vector<int>& dimensions, const NDArray& clipNorm, const bool isInplace, const bool useAverage) {

    NDArray* z = nullptr;

    if(isInplace) {
        z = &input;
    }
    else {
        output.assign(input);
        z = &output;
    }

    if(dimensions.empty()) {

        const NDArray actualNorm = useAverage ? z->reduceAlongDimension(reduce::Norm2, {}) / z->lengthOf() : z->reduceAlongDimension(reduce::Norm2, {});

        if(actualNorm.e<float>(0) > clipNorm.e<float>(0))
            *z *= clipNorm / actualNorm;
    }
    else {

        auto listOfSubArrs = z->allTensorsAlongDimension(dimensions);

        auto func = PRAGMA_THREADS_FOR {
            for (auto i = start; i < stop; i++) {
                const NDArray actualNorm = useAverage ? listOfSubArrs.at(i)->reduceAlongDimension(reduce::Norm2, {}) / listOfSubArrs.at(i)->lengthOf() : listOfSubArrs.at(i)->reduceAlongDimension(reduce::Norm2, {});
                if(actualNorm.e<float>(0) > clipNorm.e<float>(0))
                    *listOfSubArrs.at(i) *= clipNorm / actualNorm;
            }
        };
        samediff::Threads::parallel_tad(func, 0, listOfSubArrs.size());
    }
}


//////////////////////////////////////////////////////////////////////////
template<typename T>
static void clipByNormBp_(const NDArray& input, const NDArray& gradO, NDArray& gradI, const std::vector<int>& dimensions, const NDArray& clipNorm, const bool useAverage) {

    const int rank = input.rankOf();

    auto norm2 = input.reduceAlongDimension(reduce::Norm2, dimensions);
    auto sums  = input.reduceAlongDimension(reduce::Sum, dimensions);

    if(norm2.lengthOf() == 1) {

        const T norm = useAverage ? norm2.e<T>(0) / input.lengthOf() : norm2.e<T>(0);

        auto clipVal = clipNorm.e<T>(0);

        if(norm > clipVal) {

            const T sum = sums.e<T>(0);    // reduce to scalar
            const T factor1 =  clipVal / norm;
            const T factor2 = static_cast<T>(1.f) / (norm * norm);                                            // 1 / (norm*norm*norm)

            auto lambda = LAMBDA_TT(x, y, sum, factor1, factor2) {
                return factor1 * y * (static_cast<T>(1.f) - factor2 * x * sum);
            };

            const_cast<NDArray&>(input).applyPairwiseLambda<T>(const_cast<NDArray&>(gradO), lambda, gradI);
        }
        else
            gradI.assign(gradO);
    }
    else {

        auto gradISubArrs = gradI.allTensorsAlongDimension({dimensions});
        auto gradOSubArrs = gradO.allTensorsAlongDimension({dimensions});
        auto inputSubArrs = input.allTensorsAlongDimension({dimensions});

        auto clipVal = clipNorm.e<T>(0);

        auto func = PRAGMA_THREADS_FOR {

            for (auto i = start; i < stop; i++) {

                auto gradOSubArr = gradOSubArrs.at(i);
                auto gradISubArr = gradISubArrs.at(i);

                const T norm = useAverage ? norm2.e<T>(i) / gradISubArr->lengthOf() : norm2.e<T>(i);

                if (norm > clipVal) {

                    auto inputSubArr = inputSubArrs.at(i);

                    const T sum = sums.e<T>(i);    // reduce to scalar
                    const T factor1 =  clipVal / norm;
                    const T factor2 = static_cast<T>(1.f) / (norm * norm);                                            // 1 / (norm*norm*norm)

                    auto lambda = LAMBDA_TT(x, y, sum, factor1, factor2) {
                        return factor1 * y * (static_cast<T>(1.f) - factor2 * x * sum);
                    };

                    inputSubArr->applyPairwiseLambda<T>(*gradOSubArr, lambda, *gradISubArr);
                }
                else
                    gradISubArr->assign(gradOSubArr);
            }
        };
        samediff::Threads::parallel_tad(func, 0, gradISubArrs.size());
    }
}
BUILD_SINGLE_TEMPLATE(template void clipByNormBp_, (const NDArray& input, const NDArray& gradO, NDArray& gradI, const std::vector<int>& dimensions, const NDArray& clipNorm, const bool useAverage), FLOAT_TYPES);

//////////////////////////////////////////////////////////////////////////
void clipByNormBp(sd::LaunchContext* context, const NDArray& input, const NDArray& gradO, NDArray& gradI, const std::vector<int>& dimensions, const NDArray& clipNorm, const bool useAverage) {

    const NDArray& castedInput = gradI.dataType() == input.dataType() ? input : input.cast(gradI.dataType());

    BUILD_SINGLE_SELECTOR(gradI.dataType(), clipByNormBp_, (castedInput, gradO, gradI, dimensions, clipNorm, useAverage), FLOAT_TYPES);
}




    template <typename T>
    static void clipByGlobalNorm_(std::vector<NDArray*> const& inputs, double clipNorm, sd::memory::Workspace* workspace, std::vector<NDArray*>& outputs, bool isInplace) {
        T globalNorm = 0; //NDArrayFactory::create<T>(0, inputs[0]->getContext()); //sqrt(sum([l2norm(t)**2 for t in t_list]))
//        PRAGMA_OMP_PARALLEL_FOR_SIMD_REDUCTION(sumT : globalNorm)
        for (size_t i = 0; i < inputs.size(); i++) {
            auto input = inputs[i];
            auto l2norm = input->reduceNumber(reduce::Norm2);
            globalNorm += l2norm.t<T>(0) * l2norm.t<T>(0);
        }

        //globalNorm.applyTransform(transform::Sqrt, nullptr, nullptr);// = sd::math::nd4j_sqrt(globalNorm);
        auto normS = sd::math::nd4j_sqrt<T,T>(globalNorm);
        outputs[inputs.size()]->p(0, normS);

        const T factor = clipNorm / normS;

//        PRAGMA_OMP_PARALLEL_FOR
        for (size_t e = 0; e < inputs.size(); e++) {
            // all-reduce
            auto input = inputs[e];
            auto output = outputs[e];

            if (normS <= clipNorm) {
                output->assign(input);
            }
            else {

                auto lambda = LAMBDA_T(_x, factor) { return _x * factor; };
                input->applyLambda<T>(lambda, *output);
            }
        }
    }
    void clipByGlobalNorm(sd::LaunchContext * context, std::vector<NDArray*> const& inputs, double clipNorm, sd::memory::Workspace* workspace, std::vector<NDArray*>& outputs, bool isInplace) {
        BUILD_SINGLE_SELECTOR(outputs[0]->dataType(), clipByGlobalNorm_, (inputs, clipNorm, workspace, outputs, isInplace), FLOAT_TYPES);
    }

    BUILD_SINGLE_TEMPLATE(template void clipByGlobalNorm_, (std::vector<NDArray*> const& inputs, double clipNorm, sd::memory::Workspace* workspace, std::vector<NDArray*>& outputs, bool isInplace), FLOAT_TYPES);


    template <typename T>
    static void clipByValue_(NDArray& input, double leftBound, double rightBound, NDArray& output) {
        auto routine = LAMBDA_T(_x, leftBound, rightBound) {
            if (_x > rightBound) return rightBound;
            if (_x < leftBound)  return leftBound;
            return _x;
        };

        input.applyLambda<T>(routine, output);
    }

    void clipByValue(sd::LaunchContext * context, NDArray& input, double leftBound, double rightBound, NDArray& output) {
        BUILD_SINGLE_SELECTOR(input.dataType(), clipByValue_, (input, leftBound, rightBound, output), FLOAT_TYPES);
    }

    BUILD_SINGLE_TEMPLATE(template void clipByValue_, (NDArray& input, double leftBound, double rightBound, NDArray& output);, FLOAT_TYPES);

}
}
}