deeplearning4j--deeplearning4j/libnd4j/include/ops/declarable/generic/reduce/reduce_max.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 george@skymind.io on 6/1/2018.
// @author Yurii Shyrma (iuriish@yahoo.com)
//

#include <ops/declarable/CustomOperations.h>
#include <ops/declarable/helpers/axis.h>
#include <ops/declarable/helpers/transforms.h>

#if NOT_EXCLUDED(OP_reduce_max)

namespace sd {
namespace ops {


//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(reduce_max, -1, 1, false, 0, 0) {
  auto input = INPUT_VARIABLE(0);
  auto output = OUTPUT_VARIABLE(0);
  //numpy compat: default is 1 for 0 length arrays https://stackoverflow.com/questions/66746566/numpy-explanation-of-numpy-prod
  if(input->lengthOf() == 0) {
    int one = 1;
    output->assign(one);
    return sd::Status::OK;
  }
  std::vector<sd::LongType> dimensions = *block.getIArguments();

  if (block.width() > 1) {
    auto axesVector = INPUT_VARIABLE(1);
    helpers::adjustAxis(input->rankOf(), axesVector, dimensions);
  }

  REQUIRE_TRUE(
      dimensions.size() <= static_cast<size_t>(input->rankOf()), 0,
      "REDUCE_MAX OP: the number of dimensions to reduce along must be <= input array rank, but got %i instead",
      dimensions.size());

  for (const auto& item : dimensions)
    REQUIRE_TRUE(item >= -input->shapeInfo()[0] && item < input->shapeInfo()[0], 0,
                 "REDUCE_MAX OP: the input dimension to reduce along must be in range [-%i, %i), but got %i instead !",
                 input->rankOf(), input->rankOf(), item);

  bool keepDims = false;  //: false;
  if (block.getBArguments()->size() > 0)
    keepDims = B_ARG(0);
  else if (block.getTArguments()->size() > 0)
    keepDims = (bool)T_ARG(0);

  input->reduceAlongDimension(reduce::Max, output, &dimensions, keepDims);

  return sd::Status::OK;
}

DECLARE_SHAPE_FN(reduce_max) {
  bool keepDims = false;  //: false;

  if (block.getBArguments()->size() > 0)
    keepDims = B_ARG(0);
  else if (block.getTArguments()->size() > 0)
    keepDims = (bool)T_ARG(0);

  auto dimensions = *block.getIArguments();
  if (block.width() > 1) {
    auto axesVector = INPUT_VARIABLE(1);
    helpers::adjustAxis(INPUT_VARIABLE(0)->rankOf(), axesVector, dimensions);
  }

  REQUIRE_TRUE(
      dimensions.size() <= static_cast<size_t>(inputShape->at(0)[0]), 0,
      "REDUCE_MAX OP: the number of dimensions to reduce along must be <= input array rank, but got %i instead",
      dimensions.size());

  for (const auto& item : dimensions)
    REQUIRE_TRUE(item >= -inputShape->at(0)[0] && item < inputShape->at(0)[0], 0,
                 "REDUCE_MAX OP: the input dimension to reduce along must be in range [-%i, %i), but got %i instead !",
                 inputShape->at(0)[0], inputShape->at(0)[0], item);

  auto outShapeInfo = ShapeUtils::evalReduceShapeInfo(shape::order(inputShape->at(0)), &dimensions, inputShape->at(0),
                                                      keepDims, false, block.getWorkspace());

  return SHAPELIST(outShapeInfo);
}

DECLARE_TYPES(reduce_max) { getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setSameMode(true); }


//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(reduce_max_bp, -1, 1, false, 0, 0) {
  auto input = INPUT_VARIABLE(0);
  auto gradO = INPUT_VARIABLE(1);
  auto gradI = OUTPUT_VARIABLE(0);

  std::vector<sd::LongType> dimensions = *block.getIArguments();

  if (block.width() > 2) {
    auto axesVector = INPUT_VARIABLE(2);
    helpers::adjustAxis(input->rankOf(), axesVector, dimensions);
  }

  REQUIRE_TRUE(
      dimensions.size() <= static_cast<size_t>(input->rankOf()), 0,
      "REDUCE_MAX_BP OP: the number of dimensions to reduce along must be <= input array rank, but got %i instead",
      dimensions.size());

  for (const auto& item : dimensions)
    REQUIRE_TRUE(
        item >= -input->shapeInfo()[0] && item < input->shapeInfo()[0], 0,
        "REDUCE_MAX_BP OP: the input dimension to reduce along must be in range [-%i, %i), but got %i instead !",
        input->rankOf(), input->rankOf(), item);

  // *** calculations *** //

  *gradI = 0;

  if (gradO->lengthOf() == 1) {
    auto indOfMaxElem = input->indexReduceNumber(sd::indexreduce::IndexMax);
    NDArray right2 = gradO->e(0);
    gradI->p(indOfMaxElem->t<sd::LongType>(0),&right2);
    delete indOfMaxElem;

  } else {
    auto indicesArr = input->applyIndexReduce(sd::indexreduce::IndexMax, &dimensions);
  auto vec = ShapeUtils::evalDimsToExclude(gradI->rankOf(), dimensions.size(),dimensions.data());
    helpers::scatterSimple(
        block.launchContext(), 6, *gradI, *gradO, *indicesArr,
        *vec);  // 6 corresponds to copy operation
    delete vec;
    delete indicesArr;
  }

  return sd::Status::OK;
}

DECLARE_SHAPE_FN(reduce_max_bp) {
  std::vector<sd::LongType> dimensions = *block.getIArguments();

  if (block.width() > 2) {
    auto axesVector = INPUT_VARIABLE(2);
    helpers::adjustAxis(INPUT_VARIABLE(0)->rankOf(), axesVector, dimensions);
  }

  REQUIRE_TRUE(
      dimensions.size() <= static_cast<size_t>(inputShape->at(0)[0]), 0,
      "REDUCE_MAX_BP OP: the number of dimensions to reduce along must be <= input array rank, but got %i instead",
      dimensions.size());

  for (const auto& item : dimensions)
    REQUIRE_TRUE(
        item >= -inputShape->at(0)[0] && item < inputShape->at(0)[0], 0,
        "REDUCE_MAX_BP OP: the input dimension to reduce along must be in range [-%i, %i), but got %i instead !",
        inputShape->at(0)[0], inputShape->at(0)[0], item);
  return SHAPELIST(CONSTANT(inputShape->at(0)));
}

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


}  // namespace ops
}  // namespace sd

#endif