cavis/libnd4j/include/ops/declarable/generic/loss/hingeLoss.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 Yurii Shyrma (iuriish@yahoo.com), created on 23.11.2017
//

#include <system/op_boilerplate.h>
#if NOT_EXCLUDED(OP_hinge_loss)

#include <ops/declarable/CustomOperations.h>

namespace sd {
    namespace ops {

//////////////////////////////////////////////////////////////////////////
        CUSTOM_OP_IMPL(hinge_loss, 3, 1, false, 0, 1) {
            auto logits  = INPUT_VARIABLE(0);
            auto weights = INPUT_VARIABLE(1);
            auto labels  = INPUT_VARIABLE(2);

            auto output  = OUTPUT_VARIABLE(0);

            int reductionMode = INT_ARG(0);			// 0 - "none"; 1 - "weighted_sum";  2 - "weighted_mean";  3 - "weighted_sum_by_nonzero_weights"

            // input validation
            REQUIRE_TRUE(labels->isSameShape(logits), 0, "HINGE_LOSS OP: labels and logits arrays must have the same shapes, but got %s and %s correspondingly !", ShapeUtils::shapeAsString(labels).c_str(), ShapeUtils::shapeAsString(logits).c_str());
            // weights array can be single scalar or has the same rank as labels, and must be broadcastable to labels
            REQUIRE_TRUE(weights->isScalar() || weights->rankOf() == labels->rankOf(), 0, "HINGE_LOSS OP: weights array should be scalar or have the same rank as labels array, but got %i and %i correspondingly!", weights->rankOf(), labels->rankOf());
            // check whether broadcast operation is possible for weights array
            REQUIRE_TRUE(weights->isScalar() || ShapeUtils::areShapesBroadcastable(*weights, *labels), 0, "HINGE_LOSS OP: shapes of weights and labels arrays should be broadcastable, but got weights = %s and labels = %s instead!", ShapeUtils::shapeAsString(weights).c_str(), ShapeUtils::shapeAsString(labels).c_str());
            // only 4 possible reduction modes exist
            REQUIRE_TRUE(reductionMode==0 || reductionMode==1 || reductionMode==2 || reductionMode==3, 0, "HINGE_LOSS OP: reduction mode value is not acceptable, possible values are 0, 1, 2, 3, but got %i instead!", reductionMode);

            // perform weights broadcasting/tile to logits if needed
            auto weightsBroad = weights;
            if(!weights->isScalar() && !weights->isSameShape(logits))
                weightsBroad = new NDArray(weights->tileToShape(logits->shapeInfo()));

            // We first need to convert binary labels to -1/1 labels (as floats)
            NDArray E = 1.f - (*labels * 2.f - 1.f) * (*logits);
            E.applyScalar(scalar::RELU, 0.0f, E);

            // multiply E on weights
            E *= *weightsBroad;

            switch (reductionMode) {
                case 0: {											// 0 - "none", un-reduced weighted losses with the same shape as labels.
                    output->assign(E);
                    break;
                }
                case 1: {											// 1 - "weighted_sum", output is scalar and equal to sum of all elements of E array
                    E.reduceNumber(reduce::Sum, *output);
                    break;
                }
                case 2: {											// 2 - "weighted_mean", output is scalar and equal to sum of all elements of E array divided by sum of all elements of weightsBroad array
                    NDArray sum;
                    sum.setContext(block.launchContext());
                    if (weights->isScalar())
                        sum = *weights * E.lengthOf();
                    else
                        sum = weightsBroad->reduceNumber(reduce::Sum);

                    if (sum.e<double>(0) == 0.)
                        *output = 0.;
                    else
                        output->assign(E.reduceNumber(reduce::Sum) / sum);
                    break;
                }
                case 3: {											// 3 - "weighted_sum_by_nonzero_weights", output is scalar and equal to scalar sum of all elements of E array divided by number of non-zero weights
                    Nd4jLong numOfNonZeroWeights = 0;
                    if(weights->isScalar()) {
                        if(weights->e<double>(0) != 0.)
                            numOfNonZeroWeights = E.lengthOf();
                    }
                    else {
                        numOfNonZeroWeights = weightsBroad->reduceNumber(reduce::CountNonZero).e<Nd4jLong>(0);
                    }

                    if (numOfNonZeroWeights == 0)
                        (*output) = 0.;
                    else
                        output->assign(E.reduceNumber(reduce::Sum) / numOfNonZeroWeights);
                    break;
                }
            }

            if(weightsBroad != weights)
                delete weightsBroad;

            return Status::OK();
        }

//////////////////////////////////////////////////////////////////////////
        DECLARE_TYPES(hinge_loss) {

            getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS});
        }

//////////////////////////////////////////////////////////////////////////
        DECLARE_SHAPE_FN(hinge_loss) {

            auto logitsShapeInfo  = inputShape->at(0);
            auto weightsShapeInfo = inputShape->at(1);
            auto labelsShapeInfo  = inputShape->at(2);

            // labels and predictions must have the same shapes
            REQUIRE_TRUE(shape::shapeEquals(labelsShapeInfo, logitsShapeInfo), 0, "HINGE_LOSS OP: labels and predictions arrays must have the same shapes, but got %s and %s correspondingly !", ShapeUtils::shapeAsString(labelsShapeInfo).c_str(), ShapeUtils::shapeAsString(logitsShapeInfo).c_str());
            // weights array can be single scalar or has the same rank as labels, and must be broadcastable to labels
            REQUIRE_TRUE(shape::isScalar(weightsShapeInfo) || shape::rank(weightsShapeInfo) == shape::rank(labelsShapeInfo), 0, "HINGE_LOSS OP: weights array should be scalar or have the same rank as labels array, but got %i and %i correspondingly!", shape::rank(weightsShapeInfo), shape::rank(labelsShapeInfo));
            // check whether broadcast operation is possible for weights array
            REQUIRE_TRUE(shape::isScalar(weightsShapeInfo) || ShapeUtils::areShapesBroadcastable(weightsShapeInfo, labelsShapeInfo), 0, "HINGE_LOSS OP: shapes of weights and labels arrays should be broadcastable, but got weights = %s and labels = %s instead!", ShapeUtils::shapeAsString(weightsShapeInfo).c_str(), ShapeUtils::shapeAsString(labelsShapeInfo).c_str());

            DataType outType = DataTypeUtils::pickFloatingType(ArrayOptions::dataType(logitsShapeInfo));
            Nd4jLong const* outShapeInfo = nullptr;

            if(INT_ARG(0) != 0) 			// in this case output is scalar
                outShapeInfo = ConstantShapeHelper::getInstance()->scalarShapeInfo(outType);
            else 							// in this case output has the same shape as labels and predictions
                outShapeInfo = ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(outType, shape::order(labelsShapeInfo), shape::shapeOf(labelsShapeInfo), shape::rank(labelsShapeInfo)));

            return SHAPELIST(outShapeInfo);

        }




//////////////////////////////////////////////////////////////////////////
        CUSTOM_OP_IMPL(hinge_loss_grad, 3, 3, false, 0, 1) {

            auto logits  = INPUT_VARIABLE(0);
            auto weights = INPUT_VARIABLE(1);
            auto labels  = INPUT_VARIABLE(2);

            auto dLdp = OUTPUT_VARIABLE(0);		// dL/dpredictions
            auto dLdw = OUTPUT_VARIABLE(1);		// dL/dweights
            auto dLdl = OUTPUT_VARIABLE(2);		// dL/dlabels

            int reductionMode = INT_ARG(0);			// 0 - "none"; 1 - "weighted_sum";  2 - "weighted_mean";  3 - "weighted_sum_by_nonzero_weights"
            // take into account Alex's proposition to treat "none" the same as "weighted_sum" mode when calculating gradients
            if(reductionMode == 0)
                reductionMode = 1;

            // inputs validation
            REQUIRE_TRUE(labels->isSameShape(logits), 0, "HINGE_LOSS_GRAD OP: labels and logits arrays must have the same shapes, but got %s and %s correspondingly !", ShapeUtils::shapeAsString(labels).c_str(), ShapeUtils::shapeAsString(logits).c_str());
            // weights array can be single scalar or has the same rank as labels, and must be broadcastable to labels
            REQUIRE_TRUE(weights->isScalar() || weights->rankOf() == labels->rankOf(), 0, "HINGE_LOSS_GRAD OP: weights array should be scalar or have the same rank as labels array, but got %i and %i correspondingly!", weights->rankOf(), labels->rankOf());
            // check whether broadcast operation is possible for weights array
            REQUIRE_TRUE(weights->isScalar() || ShapeUtils::areShapesBroadcastable(*weights, *labels), 0, "HINGE_LOSS_GRAD OP: shapes of weights and labels arrays should be broadcastable, but got weights = %s and labels = %s instead!", ShapeUtils::shapeAsString(weights).c_str(), ShapeUtils::shapeAsString(labels).c_str());
            // only 4 possible reduction modes exist
            REQUIRE_TRUE(reductionMode==0 || reductionMode==1 || reductionMode==2 || reductionMode==3, 0, "HINGE_LOSS_GRAD OP: reduction mode value is not acceptable, possible values are 0, 1, 2, 3, but got %i instead!", reductionMode);

            // perform weights broadcasting/tile to labels if needed
            auto weightsBroad = weights;
            if(!weights->isScalar() && !weights->isSameShape(logits))
                weightsBroad = new NDArray(weights->tileToShape(logits->shapeInfo()));

            // We first need to convert binary labels to -1/1 labels (as floats)
            NDArray z = (*labels * 2.f - 1.f);

            NDArray E = 1.f - z * (*logits);
            E.applyScalar(scalar::RELU, 0.0f, E);
            // turn E into gradient mask

            NDArray gradientMask(E.shapeInfo(), block.getWorkspace());
            E.applyTransform(sd::transform::Sign, gradientMask);

            dLdp->assign(-z * gradientMask);
            dLdl->assign(-2.f * (*logits) * gradientMask);

            switch (reductionMode) {

                case 1: {											// 1 - "none" and "weighted_sum", output is scalar and equal to sum of all elements of E array

                    *dLdp *= *weightsBroad;
                    *dLdl *= *weightsBroad;

                    if(weights->isScalar())
                        dLdw->assign(E.reduceNumber(reduce::Sum));
                    else if(weights != weightsBroad) {
                        std::vector<int> axesToReduceAlong = ShapeUtils::evalBroadcastBackwardAxis(weights->shapeInfo(), weightsBroad->shapeInfo());
                        E.reduceAlongDimension(reduce::Sum, *dLdw, axesToReduceAlong, true, false, false);
                    }
                    else
                        dLdw->assign(E);
                    break;
                }
                case 2: {											// 2 - "weighted_mean", output is scalar and equal to sum of all elements of E array divided by sum of all elements of weightsBroad array

                    NDArray sum;
                    sum.setContext(block.launchContext());
                    if (weights->isScalar())
                        sum = (*weights) * E.lengthOf();
                    else
                        sum = weightsBroad->reduceNumber(reduce::Sum);

                    if (sum.e<double>(0) == 0.) {
                        *dLdp = 0.;
                        *dLdl = 0.;
                        *dLdw = 0.;
                    }
                    else {

                        *dLdp *= *weightsBroad / sum;
                        *dLdl *= *weightsBroad / sum;

                        if(weights->isScalar())
                            *dLdw = 0.;
                        else if(weights != weightsBroad) {
                            std::vector<int> axesToReduceAlong = ShapeUtils::evalBroadcastBackwardAxis(weights->shapeInfo(), weightsBroad->shapeInfo());
                            ((E * sum - (E * *weightsBroad).reduceNumber(reduce::Sum)) / (sum*sum)).reduceAlongDimension(reduce::Sum, *dLdw, axesToReduceAlong, true, false, false);
                        }
                        else
                            dLdw->assign((E * sum - (E * *weightsBroad).reduceNumber(reduce::Sum)) / (sum*sum));
                    }
                    break;
                }
                case 3: {											// 3 - "weighted_sum_by_nonzero_weights", output is scalar and equal to scalar sum of all elements of E array divided by number of non-zero weights

                    Nd4jLong numOfNonZeroWeights = 0;
                    if(weights->isScalar()) {
                        if(weights->e<double>(0) != 0.)
                            numOfNonZeroWeights = E.lengthOf();
                    }
                    else
                        numOfNonZeroWeights = weightsBroad->reduceNumber(reduce::CountNonZero).e<Nd4jLong>(0);

                    if (numOfNonZeroWeights == 0) {
                        *dLdp = 0.;
                        *dLdl = 0.;
                        *dLdw = 0.;
                    }
                    else {
                        auto numOfNonZeroWeightsScalar = NDArrayFactory::create(dLdw->dataType(), numOfNonZeroWeights, block.launchContext());

                        if(weights->isScalar())
                            dLdw->assign(E.reduceNumber(reduce::Sum) / double(numOfNonZeroWeights));
                        else if(weights != weightsBroad) {
                            std::vector<int> axesToReduceAlong = ShapeUtils::evalBroadcastBackwardAxis(weights->shapeInfo(), weightsBroad->shapeInfo());
                            E.reduceAlongDimension(reduce::Sum, *dLdw, axesToReduceAlong, true, false, false);
                            *dLdw /= numOfNonZeroWeightsScalar;
                        }
                        else
                            dLdw->assign(E / numOfNonZeroWeightsScalar);

                        NDArray temp = *weightsBroad / numOfNonZeroWeightsScalar;
                        *dLdp *= temp;
                        *dLdl *= temp;
                    }
                    break;
                }
            }

            if(weightsBroad != weights)
                delete weightsBroad;

            return Status::OK();
        }

        DECLARE_TYPES(hinge_loss_grad) {

            getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS});
        }

        DECLARE_SHAPE_FN(hinge_loss_grad) {

            auto predictionsShapeInfo = inputShape->at(0);
            auto weightsShapeInfo 	  = inputShape->at(1);
            auto labelsShapeInfo  	  = inputShape->at(2);

            // labels and predictions must have the same shapes
            REQUIRE_TRUE(shape::shapeEquals(labelsShapeInfo, predictionsShapeInfo), 0, "HINGE_LOSS_GRAD OP: labels and predictions arrays must have the same shapes, but got %s and %s correspondingly !", ShapeUtils::shapeAsString(labelsShapeInfo).c_str(), ShapeUtils::shapeAsString(predictionsShapeInfo).c_str());
            // weights array can be single scalar or has the same rank as labels, and must be broadcastable to labels
            REQUIRE_TRUE(shape::isScalar(weightsShapeInfo) || shape::rank(weightsShapeInfo) == shape::rank(labelsShapeInfo), 0, "HINGE_LOSS_GRAD OP: weights array should be scalar or have the same rank as labels array, but got %i and %i correspondingly!", shape::rank(weightsShapeInfo), shape::rank(labelsShapeInfo));
            // check whether broadcast operation is possible for weights array
            REQUIRE_TRUE(shape::isScalar(weightsShapeInfo) || ShapeUtils::areShapesBroadcastable(weightsShapeInfo, labelsShapeInfo), 0, "HINGE_LOSS_GRAD OP: shapes of weights and labels arrays should be broadcastable, but got weights = %s and labels = %s instead!", ShapeUtils::shapeAsString(weightsShapeInfo).c_str(), ShapeUtils::shapeAsString(labelsShapeInfo).c_str());

            DataType outType = DataTypeUtils::pickFloatingType(ArrayOptions::dataType(predictionsShapeInfo));

            Nd4jLong *dLdpShapeInfo = ShapeBuilders::copyShapeInfoAndType(predictionsShapeInfo, outType, false, block.getWorkspace());
            Nd4jLong *dLdwShapeInfo = ShapeBuilders::copyShapeInfoAndType(weightsShapeInfo, outType, false, block.getWorkspace());
            Nd4jLong *dLdlShapeInfo = ShapeBuilders::copyShapeInfoAndType(labelsShapeInfo, outType, false, block.getWorkspace());

            return SHAPELIST(dLdpShapeInfo, dLdwShapeInfo, dLdlShapeInfo);
        }

    }
}

#endif