deeplearning4j--deeplearning4j/libnd4j/include/ops/declarable/generic/broadcastable/squared_subtract.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
 ******************************************************************************/

//
// Created by raver119 on 23.11.17.
//

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

#include <ops/declarable/CustomOperations.h>
#include <ops/declarable/generic/helpers/BroadcastHelper.h>

namespace sd {
namespace ops {
BROADCASTABLE_OP_IMPL(squaredsubtract, 0, 0) {
  auto x = INPUT_VARIABLE(0);
  auto y = INPUT_VARIABLE(1);
  auto z = OUTPUT_VARIABLE(0);

  BROADCAST_CHECK_EMPTY(x, y, z);

  auto tZ = BroadcastHelper::broadcastApply(BROADCAST(SquaredSubtract), x, y, z);
  if (tZ == nullptr)
    return Status::KERNEL_FAILURE;
  else if (tZ != z) {
    OVERWRITE_RESULT(tZ);
  }

  return Status::OK;
}
DECLARE_SYN(squareddifference, squaredsubtract);

DECLARE_TYPES(squaredsubtract) {
  getOpDescriptor()
      ->setAllowedInputTypes(0, ANY)
      ->setAllowedInputTypes(1, ANY)
      ->setAllowedOutputTypes(0, INHERIT);
}

CUSTOM_OP_IMPL(squaredsubtract_bp, 3, 2, false, 0, 0) {
  auto x = INPUT_VARIABLE(0);
  auto y = INPUT_VARIABLE(1);
  auto epsNext = INPUT_VARIABLE(2);

  auto gradX = OUTPUT_VARIABLE(0);
  auto gradY = OUTPUT_VARIABLE(1);


  auto* ts = NDArrayFactory::create(x->dataType(), 2, block.launchContext());

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

    // X gradient
    auto* diff1 = (*x) - (*y);
    auto* temp1 = (*ts) * (*diff1);
    delete diff1;
    auto* gradXTemp = (*epsNext) * (*temp1);
    delete temp1;
    gradX->assign(gradXTemp);
    delete gradXTemp;

    // Y gradient
    auto* diff2 = (*y) - (*x);
    auto* temp2 = (*ts) * (*diff2);
    delete diff2;
    auto* gradYTemp = (*epsNext) * (*temp2);
    delete temp2;
    gradY->assign(gradYTemp);
    delete gradYTemp;

  } else if (y->isScalar()) {
    // scalar case
    auto* tmpX = x->reduceNumber(reduce::Sum);
    gradY->assign(tmpX);
    delete tmpX;
    
    // X gradient
    auto* diff3 = (*x) - (*y);
    auto* temp3 = (*ts) * (*diff3);
    delete diff3;
    auto* gradXTemp = (*epsNext) * (*temp3);
    delete temp3;
    gradX->assign(gradXTemp);
    delete gradXTemp;
  } else {
    // broadcast case

    auto* preX = x->dup(x->ordering());
    auto* preY = y->dup(y->ordering());

    auto* targetShape = epsNext->getShapeAsVector();

    preX->tileToShape(*targetShape, *preX);
    preY->tileToShape(*targetShape, *preY);
    delete targetShape;
    
    auto* diff4 = (*x) - (*y);
    auto* temp4 = (*ts) * (*diff4);
    delete diff4;
    auto* resX = (*epsNext) * (*temp4);
    delete temp4;
    preX->assign(resX);
    delete resX;
    
    auto* diff5 = (*y) - (*x);
    auto* temp5 = (*ts) * (*diff5);
    delete diff5;
    auto* resY = (*epsNext) * (*temp5);
    delete temp5;
    preY->assign(resY);
    delete resY;

    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);
      delete sum;
    } else
      gradX->assign(preX);

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

    delete preX;
    delete preY;
  }

  delete ts;

  return Status::OK;
}

DECLARE_SHAPE_FN(squaredsubtract_bp) {
  auto x = inputShape->at(0);
  auto y = inputShape->at(1);
  auto e = inputShape->at(2);

  // eps always has shape of x
  // grad always has shape of y

  return SHAPELIST(CONSTANT(x), CONSTANT(y));
}

DECLARE_TYPES(squaredsubtract_bp) {
  getOpDescriptor()->setAllowedInputTypes(ANY)->setAllowedOutputTypes({ALL_FLOATS});
}

}  // namespace ops
}  // namespace sd

#endif