deeplearning4j/deeplearning4j

/* ******************************************************************************
 *
 *
 * 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 raver119@gmail.com
// @author Oleh Semeniv (oleg.semeniv@gmail.com)
//

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

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

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

  BROADCAST_CHECK_EMPTY(x, y, z);

  // REQUIRE_TRUE(!y->isB(), 0, "Pairwise OP: you can't divide by bool array!");

  auto tZ = BroadcastHelper::broadcastApply({scalar::Pow, pairwise::Pow, broadcast::Pow}, x, y, z);
  if (tZ == nullptr)
    return sd::Status::KERNEL_FAILURE;
  else if (tZ != z) {
    OVERWRITE_RESULT(tZ);
  }

  return sd::Status::OK;
}

DECLARE_TYPES(Pow) {
  getOpDescriptor()
      ->setAllowedInputTypes(0, {ALL_FLOATS, ALL_INTS})
      ->setAllowedInputTypes(1, {ALL_FLOATS, ALL_INTS})
      ->setAllowedOutputTypes(0, {ALL_FLOATS, ALL_INTS});
}

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

  auto dLdx = OUTPUT_VARIABLE(0);
  auto dLdy = OUTPUT_VARIABLE(1);

  const sd::LongType* dLdzShapeInfo = nullptr;
  const bool areShapesBroadcastable =
      ShapeUtils::evalBroadcastShapeInfo(x->shapeInfo(), y->shapeInfo(), true, dLdzShapeInfo, block.getWorkspace());
  REQUIRE_TRUE(areShapesBroadcastable, 0,
               "POW_BP OP: the shapes of x %s"
               " and y %s are not suitable for broadcast !",
               ShapeUtils::shapeAsString(x).c_str(), ShapeUtils::shapeAsString(y).c_str());
  REQUIRE_TRUE(shape::equalsSoft(dLdz->shapeInfo(), dLdzShapeInfo), 0,
               "POW_BP OP: wrong shape of next epsilon array (dLdOut),"
               " expected is %s, but got %s instead !",
               ShapeUtils::shapeAsString(dLdzShapeInfo).c_str(), ShapeUtils::shapeAsString(dLdz).c_str());

  // dL/dy = x^y * log(x) * dL/dz
  auto temp = x->applyTrueBroadcast(BroadcastOpsTuple::Pow(), *y);  // a = x^y
  x->applyTransform(transform::Log, *dLdx);                         // b = log(x)
  dLdx->applyScalar(sd::scalar::ReplaceNans, 0, *dLdx);
  temp *= *dLdx;  // c = b*a
  temp *= *dLdz;  // dL/dy = c * dL/dz
  if (dLdy->isSameShape(*dLdz)) {
    dLdy->assign(temp);
  } else {
    std::vector<sd::LongType> axesForY = ShapeUtils::evalBroadcastBackwardAxis(y->shapeInfo(), dLdz->shapeInfo());
    dLdy->assign(temp.reduceAlongDimension(reduce::Sum, &axesForY));  // dL/dy = sum(c * dL/dz)
  }

  // dL/dx = y*x^(y-1) * dL/dz
  x->applyTrueBroadcast(BroadcastOpsTuple::PowDerivative(), *y, temp);  // a = y*x^(y-1)
  temp *= *dLdz;                                                        // dLdx = a*dL/dz

  if (dLdx->isSameShape(*dLdz)) {
    dLdx->assign(temp);  // dLdx = a*dL/dz
  } else {
    std::vector<sd::LongType> axesForX = ShapeUtils::evalBroadcastBackwardAxis(x->shapeInfo(), dLdz->shapeInfo());
    dLdx->assign(temp.reduceAlongDimension(reduce::Sum, &axesForX));  // dLdx = a*dL/dz
  }

  return sd::Status::OK;
}

DECLARE_SHAPE_FN(Pow_bp) {
  auto xShapeInfo = inputShape->at(0);
  auto yShapeInfo = inputShape->at(1);

  sd::LongType* dLdxShapeInfo = nullptr;
  sd::LongType* dLdyShapeInfo = nullptr;

  COPY_SHAPE(xShapeInfo, dLdxShapeInfo);
  COPY_SHAPE(yShapeInfo, dLdyShapeInfo);

  return SHAPELIST(CONSTANT(dLdxShapeInfo), CONSTANT(dLdyShapeInfo));
}

DECLARE_TYPES(Pow_bp) {
  getOpDescriptor()->setAllowedInputTypes({ALL_FLOATS, ALL_INTS})->setAllowedOutputTypes({ALL_FLOATS});
}

}  // namespace ops
}  // namespace sd

#endif