cavis/libnd4j/include/ops/declarable/generic/transforms/cumsum.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
//

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

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

namespace nd4j {
namespace ops  {

CONFIGURABLE_OP_IMPL(cumsum, 1, 1, true, 0, 2) {
    auto input = INPUT_VARIABLE(0);
    auto output = OUTPUT_VARIABLE(0);

    const bool exclusive = INT_ARG(0) == 1;
    const bool reverse = INT_ARG(1) == 1;

    REQUIRE_TRUE(input->dataType() == output->dataType(), 0, "CumSum: input and output data types must be equal");

    if(input->isEmpty()){
        //No-op
        return Status::OK();
    }

    if (block.getIArguments()->size() == 2 && block.width() == 1) {
        // all at once case
        nd4j::ops::helpers::_prefix(block.launchContext(), scalar::Add, input, output, exclusive, reverse);
    } 
    else {
        std::vector<int> dims(block.numI() - 2);

        if (block.width() == 1) {

            for (int e = 0; e < block.numI() - 2; e++)
                dims[e] = INT_ARG(e + 2);
        } 
        else {
            auto ax = INPUT_VARIABLE(1);
            dims = ax->template asVectorT<int>();
        }

        for (int e = 0; e < dims.size(); e++)
            if (dims[e] < 0)
                dims[e] += input->rankOf();
        
        nd4j::ops::helpers::_prefix(block.launchContext(), scalar::Add, input, output, dims, exclusive, reverse);
    }
    
    return Status::OK();
}
    DECLARE_TYPES(cumsum) {

        getOpDescriptor()
            ->setAllowedInputTypes(0, {ALL_FLOATS, ALL_INTS})
            ->setAllowedInputTypes(1, {ALL_INTS})
            ->setAllowedOutputTypes({ALL_FLOATS})
            ->setSameMode(false);
    }

CUSTOM_OP_IMPL(cumsum_bp, 2, -1, true, 0, 2) {
    auto input = INPUT_VARIABLE(0);
    auto axis = block.width() == 3 ? INPUT_VARIABLE(1) : nullptr;
    auto gradOut = block.width() == 3 ? INPUT_VARIABLE(2) : INPUT_VARIABLE(1);
    auto output = OUTPUT_VARIABLE(0);
//    output->assign(gradOut);
    const bool exclusive = INT_ARG(0) == 1;
    const bool reverse = INT_ARG(1) == 1;

    std::vector<int> dims;

    if (block.width() > 2) {
        dims = axis->template asVectorT<int>();
        OUTPUT_VARIABLE(1)->assign(1.0f);
    } else if (int newSize = (block.numI() - 2)) {
        dims.resize(newSize);

        for (int e = 0; e < newSize; e++)
            dims[e] = INT_ARG(e + 2);
    }
    if (!exclusive && !reverse) {
        if (dims.size())
            nd4j::ops::helpers::_prefix(block.launchContext(), scalar::Add, gradOut, output, dims, false, true);
        else
            nd4j::ops::helpers::_prefix(block.launchContext(), scalar::Add, gradOut, output, false, true);

    }
    else if (!exclusive && reverse){
        if (dims.size())
            nd4j::ops::helpers::_prefix(block.launchContext(), scalar::Add, gradOut, output, dims, false, false);
        else
            nd4j::ops::helpers::_prefix(block.launchContext(), scalar::Add, gradOut, output, false, false);
    }
    else if (exclusive && !reverse) {
        if (dims.size())
            nd4j::ops::helpers::_prefix(block.launchContext(), scalar::Add, gradOut, output, dims, true, true);
        else
            nd4j::ops::helpers::_prefix(block.launchContext(), scalar::Add, gradOut, output, true, true);
    }
    else {
        if (dims.size())
            nd4j::ops::helpers::_prefix(block.launchContext(), scalar::Add, gradOut, output, dims, true, false);
        else
            nd4j::ops::helpers::_prefix(block.launchContext(), scalar::Add, gradOut, output, true, false);
    }
        
    return Status::OK();
}
    DECLARE_TYPES(cumsum_bp) {
        getOpDescriptor()->setAllowedInputTypes(0, {ALL_FLOATS, ALL_INTS});
        getOpDescriptor()->setAllowedInputTypes(1, {ALL_FLOATS, ALL_INTS}); // axes can be set as the second param
        getOpDescriptor()->setAllowedInputTypes(2, {ALL_FLOATS});
        getOpDescriptor()->setAllowedOutputTypes(0, {ALL_FLOATS});
    }

    DECLARE_SHAPE_FN(cumsum_bp) {
        auto inp = inputShape->at(0);
        Nd4jLong *newShapeX = nullptr;
        COPY_SHAPE(inp, newShapeX);

        if (block.width() == 2) {
            return SHAPELIST(CONSTANT(newShapeX));
        } else {
            Nd4jLong *newShapeA = nullptr;
            COPY_SHAPE(inputShape->at(1), newShapeA);

            return SHAPELIST(CONSTANT(newShapeX), CONSTANT(newShapeA));
        }
    }
}
}

#endif