96 lines
3.7 KiB
Plaintext
96 lines
3.7 KiB
Plaintext
/*******************************************************************************
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* Copyright (c) 2015-2018 Skymind, Inc.
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*
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* This program and the accompanying materials are made available under the
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* terms of the Apache License, Version 2.0 which is available at
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* https://www.apache.org/licenses/LICENSE-2.0.
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
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* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
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* License for the specific language governing permissions and limitations
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* under the License.
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*
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* SPDX-License-Identifier: Apache-2.0
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******************************************************************************/
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//
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// @author Yurii Shyrma, created on 21.09.2018
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// @author raver119@gmail.com
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//
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#include <helpers/TAD.h>
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#include<ops/declarable/helpers/ismax.h>
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#include<loops/special_kernels.h>
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#include <helpers/DebugHelper.h>
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#include <cuda_exception.h>
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#include <PointersManager.h>
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#include <helpers/ConstantTadHelper.h>
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namespace nd4j {
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namespace ops {
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namespace helpers {
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template <typename T>
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static void ismax_(nd4j::LaunchContext * context, const NDArray* input, NDArray* output, const std::vector<int>& dimensions) {
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auto stream = context->getCudaStream();
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auto xRank = input->rankOf();
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auto zRank = output->rankOf();
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auto xType = input->dataType();
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auto zType = output->dataType();
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input->syncToDevice();
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Nd4jLong* special = nullptr;
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PointersManager manager(context, "IsMaxHelper");
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if (dimensions.size() == 0) {
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/**
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* In case of vector-input for IsMax, it just turns into IndexReduce call + subsequent filler call
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*/
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auto indexMax = input->applyIndexReduce(indexreduce::IndexMax, dimensions);
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auto targetIdx = indexMax->e<Nd4jLong>(0);
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dim3 launchDims(128, 512, 1024);
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BUILD_SINGLE_SELECTOR(zType, fillIsMaxGeneric, (launchDims, stream, output->specialBuffer(), output->specialShapeInfo(), output->lengthOf(), targetIdx), LIBND4J_TYPES);
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manager.synchronize();
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delete indexMax;
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} else {
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Nd4jLong* hostYShapeInfo = nullptr;
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Nd4jLong* hostTShapeInfo = nullptr;
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int* dimension = nullptr;
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int dimensionLength = dimensions.size();
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std::vector<int> copy(dimensions);
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auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), copy.data(), copy.size());
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// we launch legacy IndexMax op, to get indices of max values along dimension
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auto indexMaxArr = input->applyIndexReduce(indexreduce::IndexMax, dimensions);
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dim3 launchDims(256, 256, 16384);
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dimension = (int *) manager.replicatePointer(dimensions.data(), dimensions.size() * sizeof(int));
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// at this point, all IMax indexes are gathered, and we execute filler
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BUILD_SINGLE_SELECTOR(zType, fillDimensionalIsMaxGeneric, (launchDims, stream, indexMaxArr->specialBuffer(), output->specialBuffer(), output->specialShapeInfo(), packZ.specialShapeInfo(), dimension, dimensionLength, packZ.specialOffsets()), LIBND4J_TYPES);
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manager.synchronize();
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delete indexMaxArr;
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}
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}
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void ismax(nd4j::LaunchContext * context, const NDArray *input, NDArray *output, const std::vector<int>& dimensions) {
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NDArray::prepareSpecialUse({output}, {input});
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BUILD_SINGLE_SELECTOR(input->dataType(), ismax_, (context, input, output, dimensions), LIBND4J_TYPES);
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NDArray::registerSpecialUse({output}, {input});
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}
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BUILD_SINGLE_TEMPLATE(template void ismax_, (nd4j::LaunchContext * context, const NDArray *input, NDArray *output, const std::vector<int>& dimensions), LIBND4J_TYPES);
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}
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}
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}
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