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