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 George A. Shulinok <sgazeos@gmail.com>
//

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

#include <ops/declarable/headers/random.h>
#include <ops/declarable/helpers/random.h>

namespace sd {
namespace ops {
CUSTOM_OP_IMPL(random_gamma, 2, 1, false, 0, 0) {
  // gamma distribution
  auto rng = block.randomGenerator();
  auto shape = INPUT_VARIABLE(0);
  auto alpha = INPUT_VARIABLE(1);
  NDArray* beta = nullptr;

  if (block.width() > 2) {
    beta = INPUT_VARIABLE(2);
    REQUIRE_TRUE(ShapeUtils::areShapesBroadcastable(*alpha, *beta), 0,
                 "random_gamma: alpha and beta shapes should be broadcastable.");
  }

  auto output = OUTPUT_VARIABLE(0);
  auto seed = 0;

  if (block.getIArguments()->size()) {
    seed = INT_ARG(0);
  }

  rng.setSeed(seed);

  helpers::fillRandomGamma(block.launchContext(), rng, alpha, beta, output);

  return sd::Status::OK;
}

DECLARE_SHAPE_FN(random_gamma) {
  auto in = INPUT_VARIABLE(0);
  auto shape = in->template asVectorT<sd::LongType>();
  auto alphaShape = inputShape->at(1);
  auto additionalShape = alphaShape;
  if (inputShape->size() > 2) {
    auto rest = inputShape->at(2);
    additionalShape = nullptr;
    REQUIRE_TRUE(ShapeUtils::areShapesBroadcastable(alphaShape, rest), 0,
                 "random_gamma: alpha and beta shapes should be broadcastable.");
    const sd::LongType* additionalShapeBroadcasted = nullptr;
    ShapeUtils::evalBroadcastShapeInfo(alphaShape, rest, true, additionalShapeBroadcasted, block.workspace());
    additionalShape = additionalShapeBroadcasted;
  }
  auto lastDim = shape::sizeAt(alphaShape, static_cast<sd::LongType>(0));
  auto dtype = block.numD() > 0 ? D_ARG(0) : ArrayOptions::dataType(alphaShape);
  for (sd::LongType i = 0; i < shape::rank(additionalShape); i++) shape.push_back(shape::sizeAt(additionalShape, i));
  auto newShape = ConstantShapeHelper::getInstance().createShapeInfo(dtype, 'c', shape);
  return SHAPELIST(newShape);
}

DECLARE_TYPES(random_gamma) {
  getOpDescriptor()
      ->setAllowedInputTypes(0, {ALL_INTS})
      ->setAllowedInputTypes(1, {ALL_FLOATS})
      ->setAllowedInputTypes(2, {ALL_FLOATS})
      ->setAllowedOutputTypes({ALL_FLOATS});
}
}  // namespace ops
}  // namespace sd

#endif