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 Yurii Shyrma (iuriish@yahoo.com), created on 01.02.2018
//

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

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

namespace sd {
namespace ops {

DECLARE_TYPES(log_softmax) { getOpDescriptor()->setAllowedInputTypes({ALL_FLOATS})->setSameMode(true); }

CONFIGURABLE_OP_IMPL(log_softmax, 1, 1, true, 0, 0) {
  auto input = INPUT_VARIABLE(0);
  auto output = OUTPUT_VARIABLE(0);

  const int rank = input->rankOf();
  const int dim = block.getIArguments()->size() > 0 ? INT_ARG(0) : rank - 1;

  REQUIRE_TRUE(dim < rank, 0,
               "LOG_SOFTMAX OP: the value of input integer parameter (dimension) must be less than input array rank "
               "%i, but got dimension = %i instead !",
               rank, dim);

  helpers::logSoftmax(block.launchContext(), *input, *output, dim);

  return sd::Status::OK;
}

DECLARE_TYPES(log_softmax_bp) {
  getOpDescriptor()
      ->setAllowedInputTypes(0, DataType::ANY)
      ->setAllowedInputTypes(1, {ALL_FLOATS})
      ->setAllowedOutputTypes({ALL_FLOATS});
}

CONFIGURABLE_OP_IMPL(log_softmax_bp, 3, 1, true, 0, 0) {
  auto input = INPUT_VARIABLE(0);
  auto gradO = INPUT_VARIABLE(1);
  auto softmaxOut = INPUT_VARIABLE(2);
  auto gradI = OUTPUT_VARIABLE(0);
  gradI->assign(softmaxOut);
  const int rank = input->rankOf();
  const int dim = block.getIArguments()->size() > 0 ? INT_ARG(0) : rank - 1;

  REQUIRE_TRUE(dim < rank, 0,
               "LOG_SOFTMAX_BP OP: the value of input integer parameter (dimension) must be less than input array rank "
               "%i, but got dimension = %i instead !",
               rank, dim);

  helpers::softmax(block.launchContext(), *input, *gradI, dim);

  std::vector<sd::LongType> dimVec;
  dimVec.push_back(dim);
  auto sumGradOj = gradO->reduceAlongDimension(reduce::Sum,&dimVec, true);
  //we stored softmax inside gradI
  gradI->assign(*gradO - *gradI * sumGradOj);

  return sd::Status::OK;
}

}  // namespace ops
}  // namespace sd

#endif