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

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/*******************************************************************************
* 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 Yurii Shyrma, created on 21.09.2018
// @author raver119@gmail.com
//
#include <helpers/TAD.h>
#include<ops/declarable/helpers/ismax.h>
#include<loops/special_kernels.h>
#include <helpers/DebugHelper.h>
#include <cuda_exception.h>
#include <PointersManager.h>
#include <helpers/ConstantTadHelper.h>
namespace nd4j {
namespace ops {
namespace helpers {
template <typename T>
static void ismax_(nd4j::LaunchContext * context, const NDArray* input, NDArray* output, const std::vector<int>& dimensions) {
auto stream = context->getCudaStream();
auto xRank = input->rankOf();
auto zRank = output->rankOf();
auto xType = input->dataType();
auto zType = output->dataType();
input->syncToDevice();
Nd4jLong* special = nullptr;
PointersManager manager(context, "IsMaxHelper");
if (dimensions.size() == 0) {
/**
* In case of vector-input for IsMax, it just turns into IndexReduce call + subsequent filler call
*/
auto indexMax = input->applyIndexReduce(indexreduce::IndexMax, dimensions);
auto targetIdx = indexMax.e<Nd4jLong>(0);
dim3 launchDims(128, 512, 1024);
BUILD_SINGLE_SELECTOR(zType, fillIsMaxGeneric, (launchDims, stream, output->specialBuffer(), output->specialShapeInfo(), output->lengthOf(), targetIdx), LIBND4J_TYPES);
manager.synchronize();
} else {
Nd4jLong* hostYShapeInfo = nullptr;
Nd4jLong* hostTShapeInfo = nullptr;
int* dimension = nullptr;
int dimensionLength = dimensions.size();
std::vector<int> copy(dimensions);
auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), copy.data(), copy.size());
// we launch legacy IndexMax op, to get indices of max values along dimension
auto indexMaxArr = input->applyIndexReduce(indexreduce::IndexMax, dimensions);
dim3 launchDims(256, 256, 16384);
dimension = (int *) manager.replicatePointer(dimensions.data(), dimensions.size() * sizeof(int));
// at this point, all IMax indexes are gathered, and we execute filler
BUILD_SINGLE_SELECTOR(zType, fillDimensionalIsMaxGeneric, (launchDims, stream, indexMaxArr.specialBuffer(), output->specialBuffer(), output->specialShapeInfo(), packZ.specialShapeInfo(), dimension, dimensionLength, packZ.specialOffsets()), LIBND4J_TYPES);
manager.synchronize();
}
}
void ismax(nd4j::LaunchContext * context, const NDArray *input, NDArray *output, const std::vector<int>& dimensions) {
NDArray::prepareSpecialUse({output}, {input});
BUILD_SINGLE_SELECTOR(input->dataType(), ismax_, (context, input, output, dimensions), LIBND4J_TYPES);
NDArray::registerSpecialUse({output}, {input});
}
BUILD_SINGLE_TEMPLATE(template void ismax_, (nd4j::LaunchContext * context, const NDArray *input, NDArray *output, const std::vector<int>& dimensions), LIBND4J_TYPES);
}
}
}