cavis/libnd4j/include/ops/declarable/generic/loss/cosineDistance.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 22.11.2017
//

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

#include <ops/declarable/CustomOperations.h>
#include <helpers/ShapeUtils.h>

namespace sd {
namespace ops  {


//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(cosine_distance_loss, 3, 1, false, 0, 2) {

  	auto predictions = 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"
    int dim = INT_ARG(1);					// axis along which sum will be made
    if(dim < 0)
    	dim += labels->rankOf();

    // labels and predictions must have the same shapes
    REQUIRE_TRUE(labels->isSameShape(predictions), 0, "COSINE_DISTANCE_LOSS OP: labels and predictions arrays must have the same shapes, but got %s and %s correspondingly !", ShapeUtils::shapeAsString(labels).c_str(), ShapeUtils::shapeAsString(predictions).c_str());
    // regard 4 possible reduction modes below
    REQUIRE_TRUE(reductionMode==0 || reductionMode==1 || reductionMode==2 || reductionMode==3, 0, "COSINE_DISTANCE_LOSS OP: reduction mode value is not acceptable, possible values are 0, 1, 2, 3, but got %i instead!", reductionMode);
	// input dimension can't be larger than labels/predictions/weights rank
    REQUIRE_TRUE(dim < labels->rankOf(), 0, "COSINE_DISTANCE_LOSS OP: input reduction dimension (got %i) must be < labels rank %i!", dim, labels->rankOf());

    if(!output->isScalar()) {
    	// weights array can be single scalar or has the same shape as output, and must be broadcastable to output shape
    	REQUIRE_TRUE(weights->isScalar() || weights->rankOf() == output->rankOf(), 0, "SOFTMAX_CROSS_ENTROPY_LOSS OP: weights array should be scalar or have the same rank as output array, but got %i and %i correspondingly!", weights->rankOf(), output->rankOf());
    	// check whether broadcast operation is possible for weights array
	    REQUIRE_TRUE(weights->isScalar() || ShapeUtils::areShapesBroadcastable(*weights, *output), 0, "COSINE_DISTANCE_LOSS OP: shapes of weights and output arrays should be broadcastable, but got weights = %s and output = %s instead!", ShapeUtils::shapeAsString(weights).c_str(), ShapeUtils::shapeAsString(labels).c_str());
    }

    NDArray E = 1. - (*predictions * *labels).reduceAlongDimension(reduce::Sum, {dim}, true);

	// perform weights broadcasting/tile to E if it is necessary
	auto weightsBroad = weights;
	if(!weights->isScalar() && !weights->isSameShape(&E))
			weightsBroad = new NDArray(weights->tileToShape(E.shapeInfo()));

 	// 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
			output->assign(E.reduceNumber(reduce::Sum));
			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;
			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 = E.reduceNumber(reduce::CountNonZero).e<Nd4jLong>(0);

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

			break;
		}
	}


    STORE_RESULT(*output);

    if(weightsBroad != weights)
    	delete weightsBroad;

    return Status::OK();
}

//////////////////////////////////////////////////////////////////////////
DECLARE_TYPES(cosine_distance_loss) {

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

//////////////////////////////////////////////////////////////////////////
DECLARE_SHAPE_FN(cosine_distance_loss) {

	// labels and predictions must have the same shapes
	auto predictionsShapeInfo = inputShape->at(0);
	auto weightsShapeInfo     = inputShape->at(1);
    auto labelsShapeInfo  	  = inputShape->at(2);

    int dim = INT_ARG(1);
    if(dim < 0)
    	dim += labelsShapeInfo[0];

    // labels and predictions must have the same shapes
    REQUIRE_TRUE(shape::shapeEquals(labelsShapeInfo, predictionsShapeInfo), 0, "COSINE_DISTANCE_LOSS 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());
    // input dimension can't be larger than labels/predictions/weights rank
    REQUIRE_TRUE(dim < labelsShapeInfo[0], 0, "COSINE_DISTANCE_LOSS OP: input reduction dimension (got %i) must be < labels rank %i!", dim, labelsShapeInfo[0]);

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

 	// evaluate output shapeInfo
    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 reduced  by dim axis

    	std::vector<int> dimensions = {dim};
    	outShapeInfo = ShapeUtils::evalReduceShapeInfo(shape::order(predictionsShapeInfo), dimensions, predictionsShapeInfo, outType, true, false, block.getWorkspace());

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

    return SHAPELIST(outShapeInfo);
}



//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(cosine_distance_loss_grad, 3, 3, false, 0, 2) {

  	auto predictions = 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;

    int dim = INT_ARG(1);					// axis along which sum will be made
    if(dim < 0)
    	dim += labels->rankOf();

    std::vector<int> dimensions = {dim};

    // input validation
    REQUIRE_TRUE(labels->isSameShape(predictions), 0, "COSINE_DISTANCE_LOSS_GRAD OP: labels and predictions arrays must have the same shapes, but got %s and %s correspondingly !", ShapeUtils::shapeAsString(labels).c_str(), ShapeUtils::shapeAsString(predictions).c_str());
	// only 4 possible reduction modes exist
    REQUIRE_TRUE(reductionMode==0 || reductionMode==1 || reductionMode==2 || reductionMode==3, 0, "COSINE_DISTANCE_LOSS_GRAD OP: reduction mode value is not acceptable, possible values are 0, 1, 2, 3, but got %i instead!", reductionMode);
   	auto lossShapeInfo = ShapeUtils::evalReduceShapeInfo(predictions->ordering(), dimensions, predictions->shapeInfo(), true, false, block.getWorkspace());
   	// weights array can be single scalar or has the same shape as loss, and must be broadcastable to loss shape
   	REQUIRE_TRUE(weights->isScalar() || weights->rankOf() == shape::rank(lossShapeInfo), 0, "COSINE_DISTANCE_LOSS_GRAD OP: weights array should be scalar or have the same rank as loss array, but got %i and %i correspondingly!", weights->rankOf(), shape::rank(lossShapeInfo));
   	// check whether broadcast operation is possible for weights array
    REQUIRE_TRUE(weights->isScalar() || ShapeUtils::areShapesBroadcastable(weights->shapeInfo(), lossShapeInfo), 0, "COSINE_DISTANCE_LOSS_GRAD OP: shapes of weights and loss arrays should be broadcastable, but got weights = %s and loss = %s instead!", ShapeUtils::shapeAsString(weights).c_str(), ShapeUtils::shapeAsString(lossShapeInfo).c_str());
    // input dimension can't be larger than labels/predictions/weights rank
    REQUIRE_TRUE(dim < labels->rankOf(), 0, "COSINE_DISTANCE_LOSS_GRAD OP: input reduction dimension (got %i) must be < labels rank %i!", dim, labels->rankOf());

    NDArray E = 1. - (*predictions * *labels).reduceAlongDimension(reduce::Sum, {dim}, true);

	// perform weights broadcasting/tile to E if it is necessary
	auto weightsBroad = weights;
	if(!weights->isScalar() && !weights->isSameShape(&E))
			weightsBroad = new NDArray(weights->tileToShape(E.shapeInfo()));

	dLdp->assign(-*labels);
	dLdl->assign(-*predictions);

	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() || weights->lengthOf() == 1) {
				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);
				}
				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;
			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 {

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

				if(weights->isScalar() || weights->lengthOf() == 1) {
					*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);
					}
					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 {

				NDArray temp = *weightsBroad / numOfNonZeroWeights;
				*dLdp *= temp;
				*dLdl *= temp;

				if(weights->isScalar() || weights->lengthOf() == 1) {
					dLdw->assign(E.reduceNumber(reduce::Sum) / numOfNonZeroWeights);
				}
				else {

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

	if(weightsBroad != weights)
    	delete weightsBroad;

    return Status::OK();
}

//////////////////////////////////////////////////////////////////////////
DECLARE_TYPES(cosine_distance_loss_grad) {

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

//////////////////////////////////////////////////////////////////////////
DECLARE_SHAPE_FN(cosine_distance_loss_grad) {

	/// labels and predictions must have the same shapes
	auto predictionsShapeInfo = inputShape->at(0);
	auto weightsShapeInfo     = inputShape->at(1);
    auto labelsShapeInfo  	  = inputShape->at(2);

    int dim = INT_ARG(1);
    if(dim < 0)
    	dim += labelsShapeInfo[0];

    std::vector<int> dimensions = {dim};

    // labels and predictions must have the same shapes
    REQUIRE_TRUE(shape::shapeEquals(labelsShapeInfo, predictionsShapeInfo), 0, "COSINE_DISTANCE_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());
    auto lossShapeInfo = ShapeUtils::evalReduceShapeInfo(shape::order(predictionsShapeInfo), dimensions, predictionsShapeInfo, true, false, block.getWorkspace());
	// weights array can be single scalar or has the same rank as loss, and must be broadcastable to loss
    REQUIRE_TRUE(shape::isScalar(weightsShapeInfo) || shape::rank(weightsShapeInfo) == shape::rank(lossShapeInfo), 0, "COSINE_DISTANCE_LOSS_GRAD OP: weights array should be scalar or have the same rank as loss array, but got %i and %i correspondingly!", shape::rank(weightsShapeInfo), shape::rank(lossShapeInfo));
    // check whether broadcast operation is possible for weights array
	REQUIRE_TRUE(shape::isScalar(weightsShapeInfo) || ShapeUtils::areShapesBroadcastable(weightsShapeInfo, lossShapeInfo), 0, "COSINE_DISTANCE_LOSS_GRAD OP: shapes of weights and loss arrays should be broadcastable, but got weights = %s and loss = %s instead!", ShapeUtils::shapeAsString(weightsShapeInfo).c_str(), ShapeUtils::shapeAsString(lossShapeInfo).c_str());
	// input dimension can't be larger than labels/predictions/weights rank
    REQUIRE_TRUE(dim < labelsShapeInfo[0], 0, "COSINE_DISTANCE_LOSS_GRAD OP: input reduction dimension (got %i) must be < labels rank %i!", dim, labelsShapeInfo[0]);

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

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

    return SHAPELIST(CONSTANT(dLdpShapeInfo), CONSTANT(dLdwShapeInfo), CONSTANT(dLdlShapeInfo));
}



}
}

#endif