deeplearning4j--deeplearning4j/libnd4j/include/ops/declarable/generic/reduce/reduce_sqnorm.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 6/4/2018.
//

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


#if NOT_EXCLUDED(OP_reduce_sqnorm)

namespace sd {
namespace ops {
//////////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(reduce_sqnorm, -1, 1, false, 0, 0) {
  auto input = INPUT_VARIABLE(0);
  auto gradI = OUTPUT_VARIABLE(0);

  bool keepDims = false;

  auto dimensions = *block.getIArguments();

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

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

  REQUIRE_TRUE(
      dimensions.size() <= static_cast<size_t>(input->rankOf()), 0,
      "REDUCE_SQNORM 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->rankOf() && item < input->rankOf(), 0,
        "REDUCE_SQNORM OP: the input dimension to reduce along must be in range [-%i, %i), but got %i instead !",
        input->rankOf(), input->rankOf(), item);

  input->reduceAlongDimension(reduce::SquaredNorm, gradI, &dimensions, keepDims);

  return sd::Status::OK;
}

DECLARE_SHAPE_FN(reduce_sqnorm) {
  auto dimensions = *block.getIArguments();
  bool keepDims = false;

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

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

  REQUIRE_TRUE(
      dimensions.size() <= static_cast<size_t>(inputShape->at(0)[0]), 0,
      "REDUCE_SQNORM 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_SQNORM 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_sqnorm) {
  getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS});
}

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

  if (gradO->lengthOf() == 1) {
    auto* temp1 = (*input) * 2.0;
    auto* assign = (*temp1) * gradO->e(0);
    delete temp1;
    gradI->assign(assign);
    delete assign;
  } else {
    bool keepDims = false;
    auto dimensions = *block.getIArguments();

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

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

    REQUIRE_TRUE(
        dimensions.size() <= static_cast<size_t>(input->rankOf()), 0,
        "REDUCE_SQNORM_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->rankOf() && item < input->rankOf(), 0,
          "REDUCE_SQNORM_BP OP: the input dimension to reduce along must be in range [-%i, %i), but got %i instead !",
          input->rankOf(), input->rankOf(), item);

    // *** calculations *** //

    if (!keepDims) {
      auto gradOShapeKeepDims =
          ShapeUtils::evalReduceShapeInfo(gradO->ordering(), &dimensions, *input, true, false, block.getWorkspace());
      std::vector<sd::LongType> shape =  ShapeUtils::pullShapeFromShapeInfo(
          gradOShapeKeepDims);
      auto* reshaped = gradO->reshape(gradO->ordering(), shape);
      
      // Break down: 2. * (*input) * *reshaped
      auto* temp1 = (*input) * 2.0;
      auto* gradITemp1 = (*temp1) * (*reshaped);
      delete temp1;
      delete reshaped;
      
      gradI->assign(gradITemp1);
      delete gradITemp1;
    } else {
      // Break down: 2. * (*input) * *gradO
      auto* temp2 = (*input) * 2.0;
      auto* gradITemp2 = (*temp2) * (*gradO);
      delete temp2;
      
      gradI->assign(gradITemp2);
      delete gradITemp2;
    }
  }
  return sd::Status::OK;
}

DECLARE_SHAPE_FN(reduce_sqnorm_bp) {
  if (shape::length(inputShape->at(1)) > 1) {
    auto 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_SQNORM_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_SQNORM_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_sqnorm_bp) {
  getOpDescriptor()->setAllowedInputTypes(sd::DataType::ANY)->setAllowedOutputTypes({ALL_FLOATS});
}


}  // namespace ops
}  // namespace sd

#endif