cavis/libnd4j/include/ops/declarable/generic/parity_ops/bias_add.cpp

/*******************************************************************************
 * Copyright (c) 2015-2018 Skymind, Inc.
 *
 * 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.
 *
 * 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
// @author Yurii Shyrma (iuriish@yahoo.com)
//

#include <op_boilerplate.h>
#if NOT_EXCLUDED(OP_biasadd)

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

namespace nd4j {
namespace ops {

////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(biasadd, 2, 1, true, 0, 0) {

    auto input = INPUT_VARIABLE(0);
    auto bias = INPUT_VARIABLE(1);

    auto output = OUTPUT_VARIABLE(0);

    const bool isNCHW = !block.getBArguments()->empty() ? B_ARG(0) : false;
    const int channelDim = isNCHW ? 1 : input->rankOf() - 1;      // second or last

    REQUIRE_TRUE(bias->rankOf() == 1, 0, "BIASADD CUSTOM_OP: bias array should have rank = 1, but got %i instead !", bias->rankOf());

    REQUIRE_TRUE(bias->sizeAt(0) == input->sizeAt(channelDim), 0, "BIASADD CUSTOM_OP: shapes of bias %s and input %s arrays are not suitable for broadcast operation along channel dimension %i !", ShapeUtils::shapeAsString(bias).c_str(), ShapeUtils::shapeAsString(input).c_str(), channelDim);

    REQUIRE_TRUE(output->isSameShape(input), 0, "BIASADD CUSTOM_OP: wrong shape of output array, expected is %s but got %s instead !", ShapeUtils::shapeAsString(input).c_str(), ShapeUtils::shapeAsString(output).c_str());

    helpers::addBias(block, *input, *bias, *output, isNCHW);
    // input->applyBroadcast(nd4j::broadcast::Add, {channelDim}, bias, output);

    return Status::OK();
}
DECLARE_SYN(bias_add, biasadd);

////////////////////////////////////////////////////////////////////
DECLARE_SHAPE_FN(biasadd) {
    auto xShape = inputShape->at(0);
    auto yShape = inputShape->at(1);

    auto dtype = ArrayOptions::dataType(yShape);
    return SHAPELIST(ConstantShapeHelper::getInstance()->createShapeInfo(ShapeDescriptor(xShape, dtype)));
}

DECLARE_TYPES(biasadd) {
    getOpDescriptor()
            ->setAllowedInputTypes(nd4j::DataType::ANY)
            ->setAllowedOutputTypes({ALL_FLOATS});
}

////////////////////////////////////////////////////////////////////
CUSTOM_OP_IMPL(biasadd_bp, 3, 2, false, 0, 0) {

    auto input = INPUT_VARIABLE(0);
    auto bias  = INPUT_VARIABLE(1);
    auto gradO = INPUT_VARIABLE(2);

    auto gradI = OUTPUT_VARIABLE(0);
    auto gradB = OUTPUT_VARIABLE(1);

    const bool isNCHW = !block.getBArguments()->empty() ? B_ARG(0) : false;
    const int channelDim = isNCHW ? 1 : input->rankOf() - 1;      // second or last

    gradI->assign(gradO);

    gradO->reduceAlongDimension(nd4j::reduce::Sum, *gradB, ShapeUtils::evalDimsToExclude(gradO->rankOf(), {channelDim}));

    return ND4J_STATUS_OK;
}
DECLARE_SYN(BiasAddGrad, biasadd_bp);

////////////////////////////////////////////////////////////////////
DECLARE_SHAPE_FN(biasadd_bp) {
    auto input = inputShape->at(0);
    auto bias = inputShape->at(1);

    Nd4jLong* epsShape;
    Nd4jLong* gradShape;

    COPY_SHAPE(input, epsShape);
    COPY_SHAPE(bias, gradShape);

    return SHAPELIST(CONSTANT(epsShape), CONSTANT(gradShape));
}

DECLARE_TYPES(biasadd_bp) {
    getOpDescriptor()
            ->setAllowedInputTypes(nd4j::DataType::ANY)
            ->setAllowedOutputTypes({ALL_FLOATS});
}


}
}

#endif