cavis/libnd4j/include/ops/declarable/helpers/cuda/sequence_mask.cu

/*******************************************************************************
 * 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 GS <sgazeos@gmail.com>
//

#include <ops/declarable/helpers/sequence_mask.h>

namespace nd4j {
namespace ops {
namespace helpers {

    template <typename I, typename B>
    static __global__ void sequenceMaskKernel(void* inputBuf, Nd4jLong* inputShape, void* outputBuf, Nd4jLong* outputShape, int maxIndex) {

        __shared__ I* input;
        __shared__ B* output;
        __shared__ Nd4jLong inputLen, outputLen;
        if (threadIdx.x == 0) {
            input = reinterpret_cast<I*>(inputBuf);
            output = reinterpret_cast<B*>(outputBuf);
            inputLen = shape::length(inputShape);
            outputLen = shape::length(outputShape);
        }

        for (auto i = blockIdx.x; i < maxIndex; i += gridDim.x)
            for(auto k = threadIdx.x; k < inputLen; k += blockDim.x)
                if (i < input[shape::getIndexOffset(k, inputShape, inputLen)])
                    output[shape::getIndexOffset(k * maxIndex + i, outputShape, outputLen)] = B(true);

    }

    template <typename I, typename B>
    static void sequenceMask_(LaunchContext* context, NDArray* input, NDArray* output, int maxIndex) {
        dim3 launchDims(maxIndex, input->lengthOf(), 128);
        NDArray::prepareSpecialUse({output}, {input});
        auto stream = context->getCudaStream();
        sequenceMaskKernel<I, B><<<launchDims.x, launchDims.y, launchDims.z, *stream>>>(input->specialBuffer(), input->specialShapeInfo(), output->specialBuffer(), output->specialShapeInfo(), maxIndex);
        NDArray::registerSpecialUse({output}, {input});
    }

    void sequenceMask(nd4j::LaunchContext * context, NDArray* input, NDArray* output, int maxIndex) {
        BUILD_DOUBLE_SELECTOR(input->dataType(), output->dataType(), sequenceMask_, (context, input, output, maxIndex), INTEGER_TYPES, BOOL_TYPES);
    }

    BUILD_DOUBLE_TEMPLATE(template void sequenceMask_, (nd4j::LaunchContext* context, NDArray* input, NDArray* output, int maxIndex), INTEGER_TYPES, BOOL_TYPES);
}
}
}