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

80 lines
3.1 KiB
C++

/*******************************************************************************
* 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
******************************************************************************/
//
// Created by george@skymind.io on 11/13/2018.
//
#include <ops/declarable/CustomOperations.h>
#include <ops/declarable/helpers/axis.h>
namespace nd4j {
namespace ops {
#if NOT_EXCLUDED(OP_reduce_logsumexp)
CUSTOM_OP_IMPL(reduce_logsumexp, 1, 1, false, 0, 0) {
auto input = INPUT_VARIABLE(0);
auto output = OUTPUT_VARIABLE(0);
std::vector<int> axes;// = *block.getIArguments();
if (block.width() > 1) {
auto axisVector = INPUT_VARIABLE(1);
helpers::adjustAxis(input->rankOf(), axisVector, axes );
}
else if (block.getIArguments()->size() > 0) {
axes = *block.getIArguments();
}
for(const auto& item : axes)
REQUIRE_TRUE(item >= -input->shapeInfo()[0] && item <input->shapeInfo()[0], 0, "REDUCE_LOGSUMEXP: the input dimension to reduce along must be in range [-%i, %i), but got %i instead !" , input->rankOf(), input->rankOf(), item);
const bool keepDims = block.getTArguments()->size() > 0 ? (bool)T_ARG(0) : false;
Nd4jLong maxI = input->argMax();
auto maxVals = input->e(maxI);
//void* whereMax = (void*)();
auto internal = (*input);
internal -= maxVals;
internal.applyTransform(transform::Exp, internal);
internal.reduceAlongDimension(reduce::Sum, *output, axes, keepDims, false); //, (void*)&maxVals);
output->applyTransform(transform::Log, *output);
(*output) += maxVals;
return ND4J_STATUS_OK;
}
DECLARE_TYPES(reduce_logsumexp) {
getOpDescriptor()
-> setAllowedInputTypes({ALL_INTS, ALL_FLOATS})
-> setAllowedOutputTypes({ALL_FLOATS});
}
DECLARE_SHAPE_FN(reduce_logsumexp) {
const bool keepDims = block.getTArguments()->size() > 0 ? (bool)T_ARG(0) : false;
auto input = INPUT_VARIABLE(0);
std::vector<int> axes; // = *block.getIArguments();
if (block.width() > 1) {
auto axisVector = INPUT_VARIABLE(1);
helpers::adjustAxis(input->rankOf(), axisVector, axes );
}
else if (block.getIArguments()->size() > 0) {
axes = *block.getIArguments();
}
Nd4jLong* outShapeInfo = ShapeUtils::evalReduceShapeInfo(shape::order(inputShape->at(0)), axes, inputShape->at(0), keepDims, false, block.getWorkspace());
return SHAPELIST(outShapeInfo);
}
#endif
}
}