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
//

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

#include <ops/declarable/headers/parity_ops.h>
#include <ops/declarable/helpers/transforms.h>

#include <array>

namespace sd {
namespace ops {
CUSTOM_OP_IMPL(split, 1, -1, false, 0, 1) {
  NDArray *input = nullptr;
  int num_splits = INT_ARG(0);

  // axis is 0 by default
  sd::LongType axis = 0;

  if (block.width() == 1) {
    input = INPUT_VARIABLE(0);
  } else {
    auto a = INPUT_VARIABLE(0);
    auto b = INPUT_VARIABLE(1);

    if (a->isScalar()) {
      // axis goes first
      axis = a->e<sd::LongType>(0);
      input = b;
    } else if (b->isScalar()) {
      axis = b->e<sd::LongType>(0);
      input = a;
    }
  }

  // Edge case: splitting empty array (mainly for TF import compatibility) -> return N empty arrays
  if (input->isEmpty()) {
    for (int i = 0; i < num_splits; i++) {
      REQUIRE_TRUE(OUTPUT_VARIABLE(i)->isEmpty(), 0,
                   "Split: When input array is empty, all output arrays must be empty");
    }
    // No op
    return sd::Status::OK;
  }

  if (block.numI() == 2) axis = INT_ARG(1);

  if (axis < 0) axis += input->rankOf();

  REQUIRE_TRUE(input->sizeAt(axis) % num_splits == 0, 0,
               "Split: num_splits has wrong value, remainder of division should be 0, but it's %i",
               input->sizeAt(axis) % num_splits);

  std::vector<NDArray *> outArrs(num_splits);
  for (int e = 0; e < num_splits; e++) {
    outArrs[e] = OUTPUT_VARIABLE(e);
  }

  helpers::split(block.launchContext(), *input, outArrs, axis);

  return sd::Status::OK;
}

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

DECLARE_SHAPE_FN(split) {
  int num_splits = INT_ARG(0);
  auto input = inputShape->at(0);
  sd::DataType dataType = ArrayOptions::dataType(input);

  // axis is 0 by default
  int axis = 0;

  int inputVar = 0;
  if (inputShape->size() != 1) {
    auto shape0 = inputShape->at(0);
    auto shape1 = inputShape->at(1);

    if (shape::isScalar(shape0)) {
      input = shape1;
      auto _a = INPUT_VARIABLE(0);
      axis = _a->e<sd::LongType>(0);
      dataType = ArrayOptions::dataType(shape1);
      inputVar = 1;
    } else if (shape::isScalar(shape1)) {
      input = shape0;
      auto _a = INPUT_VARIABLE(1);
      axis = _a->e<sd::LongType>(0);
      dataType = ArrayOptions::dataType(shape0);
      inputVar = 0;
    }
  }

  auto shapes = SHAPELIST();

  // Edge case: splitting empty array (mainly for TF import compatibility) -> return N empty arrays
  // if(INPUT_VARIABLE(inputVar)->isEmpty()){
  //     for (int e = 0; e < num_splits; e++) {
  //               auto empty = ConstantShapeHelper::getInstance().emptyShapeInfo(dataType);
  //         shapes->push_back(empty);
  //     }
  //     return shapes;
  // }

  if (block.numI() == 2) axis = INT_ARG(1);

  if (axis < 0) axis += shape::rank(input);

  std::vector<sd::LongType> shape(shape::rank(input));

  for (sd::LongType e = 0; e < shape::rank(input); e++)
    if (e == axis)
      shape[e] = shape::sizeAt(input, e) / num_splits;
    else
      shape[e] = shape::sizeAt(input, e);

  for (int e = 0; e < num_splits; e++) {
    auto newShape = ConstantShapeHelper::getInstance().createShapeInfo(dataType, shape::order(input), shape);
    shapes->push_back(newShape);
  }

  return shapes;
}
}  // namespace ops
}  // namespace sd

#endif