cavis/libnd4j/include/ops/declarable/generic/thrid_party/firas_sparse.cpp

111 lines
3.2 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
******************************************************************************/
//
// This file contains operations added by 3rd parties
//
// @author raver119@gmail.com
//
#include <op_boilerplate.h>
#if NOT_EXCLUDED(OP_firas_sparse)
#ifndef LIBND4J_THIRD_PARTY_H
#define LIBND4J_THIRD_PARTY_H
#include <op_boilerplate.h>
#include <memory>
#include <shape.h>
#include <loops/random.h>
#include <NDArray.h>
#include <ops/declarable/DeclarableOp.h>
#include <ops/declarable/OpRegistrator.h>
#include <ops/declarable/CustomOperations.h>
namespace nd4j {
namespace ops {
/**
* This op is special one, and suited only for ProjectionLayer by @firasdib
*
*
*
* @tparam T
*/
CUSTOM_OP_IMPL(firas_sparse, 1, 1, false, 0, -1) {
auto x = INPUT_VARIABLE(0);
auto z = OUTPUT_VARIABLE(0);
int batchSize = x->sizeAt(0);
int numColumns = x->sizeAt(1);
std::vector<int> indices(*block.getIArguments());
std::map<int, int> sparse2dense;
int cnt = 0;
for (auto v: indices) {
std::pair<int, int> pair(v, cnt++);
sparse2dense.insert(pair);
}
std::unique_ptr<ResultSet> rows(x->allTensorsAlongDimension({1}));
//PRAGMA_OMP_PARALLEL_FOR
for (int r = 0; r < batchSize; r++) {
auto row = rows->at(r);
for (int e = 0; e < numColumns; e += 2) {
int idx = row->e<int>(e);
if (idx < 0)
break;
int denseIdx = sparse2dense.at(idx);
float value = row->e<float>(e);
float current = z->e<float>(r, denseIdx);
z->p(r, denseIdx, value + current);
}
}
//STORE_RESULT(*z);
return Status::OK();
}
DECLARE_SHAPE_FN(firas_sparse) {
auto inP = inputShape->at(0);
std::vector<Nd4jLong> shape({shape::shapeOf(inP)[0], (Nd4jLong) block.getIArguments()->size()});
auto newShape = ConstantShapeHelper::getInstance()->createShapeInfo(ArrayOptions::dataType(inP), 'c', shape);
return SHAPELIST(newShape);
}
DECLARE_TYPES(firas_sparse) {
getOpDescriptor()
->setAllowedInputTypes(nd4j::DataType::ANY)
->setAllowedOutputTypes({ALL_FLOATS});
}
}
}
#endif
#endif //LIBND4J_THIRD_PARTY_H