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

/*******************************************************************************
 * 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 sgazeos@gmail.com
//
#ifndef __MIN_I_MAX_H_HELPERS__
#define __MIN_I_MAX_H_HELPERS__
#include <op_boilerplate.h>
#include <NDArray.h>
#include <helpers/ShapeUtils.h>

namespace nd4j {
namespace ops {
namespace helpers {

    template <typename T>
    static void minimumBPFunctor_(NDArray* x, NDArray* y, NDArray* epsNext, NDArray* gradX, NDArray* gradY) {

            auto lambdaX = LAMBDA_TTT(_e, _x, _y) {
                return _x <= _y ? _e : (T) 0.;
            };

            auto lambdaY = LAMBDA_TTT(_e, _x, _y) {
                return _x >= _y ? _e : (T) 0.;
            };


            if (x->isSameShape(y)) {
                // PWT case case

                // X gradient
                epsNext->applyTriplewiseLambda<T>(*x, *y, lambdaX, *gradX);

                // Y gradient
                epsNext->applyTriplewiseLambda<T>(*x, *y, lambdaY, *gradY);

            } else if (y->isScalar()) {
                T s = y->e<T>(0);
                auto lambdaS = LAMBDA_TT(_e, _x, s) {
                    return _x <= s ? _e : (T) 0.;
                };

                // scalar case
                auto tmp = epsNext->reduceNumber(reduce::Sum);
                if (x <= y)
                    gradY->assign(tmp);
                else
                    gradY->assign(0.0f);

                epsNext->applyPairwiseLambda<T>(*x, lambdaS, *gradX);
            } else {
                // broadcast case

                // in this case we want to boost our X and Y shapes to the size of FF pass output (or epsNext, which has the same shape)
                auto preX = x->dup();
                auto preY = y->dup();

                auto targetShape = epsNext->getShapeAsVector();

                preX.tileToShape(targetShape, preX);
                preY.tileToShape(targetShape, preY);

                epsNext->applyTriplewiseLambda<T>(preX, preY, lambdaX, preX);
                epsNext->applyTriplewiseLambda<T>(preX, preY, lambdaY, preY);

                auto axisX = ShapeUtils::evalBroadcastBackwardAxis(x->shapeInfo(), epsNext->shapeInfo());
                auto axisY = ShapeUtils::evalBroadcastBackwardAxis(y->shapeInfo(), epsNext->shapeInfo());

                if (axisX.size() > 0) {
                    auto sum = preX.reduceAlongDimension(reduce::Sum, axisX);
                    gradX->assign(sum);
                } else
                    gradX->assign(preX);

                if (axisY.size() > 0) {
                    auto sum = preY.reduceAlongDimension(reduce::Sum, axisY);
                    gradY->assign(sum);
                } else
                    gradY->assign(preY);
            }

    }
    template <typename T>
    void maximumBPFunctor_(NDArray* x, NDArray* y, NDArray* epsNext, NDArray* gradX, NDArray* gradY) {

            auto lambdaX = LAMBDA_TTT(_e, _x, _y) {
                return _x >= _y ? _e : (T) 0.;
            };

            auto lambdaY = LAMBDA_TTT(_e, _x, _y) {
                return _x <= _y ? _e : (T) 0.;
            };


            if (x->isSameShape(y)) {
                // PWT case case

                // X gradient
                epsNext->applyTriplewiseLambda<T>(*x, *y, lambdaX, *gradX);

                // Y gradient
                epsNext->applyTriplewiseLambda<T>(*x, *y, lambdaY, *gradY);

            } else if (y->isScalar()) {
                T s = y->e<T>(0);
                auto lambdaS = LAMBDA_TT(_e, _x, s) {
                    return _x >= s ? _e : (T) 0.;
                };

                // scalar case
                auto tmp = epsNext->reduceNumber(reduce::Sum);
                if (x <= y)
                    gradY->assign(tmp);
                else
                    gradY->assign(0.0f);

                epsNext->applyPairwiseLambda<T>(*x, lambdaS, *gradX);
            } else {
                // broadcast case

                // in this case we want to boost our X and Y shapes to the size of FF pass output (or epsNext, which has the same shape)
                auto preX = x->dup();
                auto preY = y->dup();

                auto targetShape = epsNext->getShapeAsVector();

                preX.tileToShape(targetShape, preX);
                preY.tileToShape(targetShape, preY);

                epsNext->applyTriplewiseLambda<T>(preX, preY, lambdaX, preX);
                epsNext->applyTriplewiseLambda<T>(preX, preY, lambdaY, preY);

                auto axisX = ShapeUtils::evalBroadcastBackwardAxis(x->shapeInfo(), epsNext->shapeInfo());
                auto axisY = ShapeUtils::evalBroadcastBackwardAxis(y->shapeInfo(), epsNext->shapeInfo());

                if (axisX.size() > 0) {
                    auto sum = preX.reduceAlongDimension(reduce::Sum, axisX);
                    gradX->assign(sum);
                } else
                    gradX->assign(preX);

                if (axisY.size() > 0) {
                    auto sum = preY.reduceAlongDimension(reduce::Sum, axisY);
                    gradY->assign(sum);
                } else
                    gradY->assign(preY);
            }
    }

    void minimumBPFunctor(nd4j::LaunchContext * context, NDArray* x, NDArray* y, NDArray* epsNext, NDArray* gradX, NDArray* gradY) {
        BUILD_SINGLE_SELECTOR(x->dataType(), minimumBPFunctor_, (x, y, epsNext, gradX, gradY), NUMERIC_TYPES);
    }

    void maximumBPFunctor(nd4j::LaunchContext * context, NDArray* x, NDArray* y, NDArray* epsNext, NDArray* gradX, NDArray* gradY) {
        BUILD_SINGLE_SELECTOR(x->dataType(), maximumBPFunctor_, (x, y, epsNext, gradX, gradY), NUMERIC_TYPES);
    }
    BUILD_SINGLE_TEMPLATE(template void minimumBPFunctor_, (NDArray* x, NDArray* y, NDArray* epsNext, NDArray* gradX, NDArray* gradY), NUMERIC_TYPES);
    BUILD_SINGLE_TEMPLATE(template void maximumBPFunctor_, (NDArray* x, NDArray* y, NDArray* epsNext, NDArray* gradX, NDArray* gradY), NUMERIC_TYPES);

}
}
}
#endif