103 lines
4.8 KiB
C++
103 lines
4.8 KiB
C++
/*******************************************************************************
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* Copyright (c) 2020 Konduit, K.K.
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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 GS <sgazeos@gmail.com>
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//
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#include <op_boilerplate.h>
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#include <NDArray.h>
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#include <NDArrayFactory.h>
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#include <execution/Threads.h>
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#include <helpers/MmulHelper.h>
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#include "../triangular_solve.h"
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#include "../lup.h"
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#include "../solve.h"
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namespace nd4j {
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namespace ops {
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namespace helpers {
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// --------------------------------------------------------------------------------------------------------------------------------------- //
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template <typename T>
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static void adjointMatrix_(nd4j::LaunchContext* context, NDArray const* input, NDArray* output) {
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auto inputPart = input->allTensorsAlongDimension({-2, -1});
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auto outputPart = output->allTensorsAlongDimension({-2, -1});
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auto rows = input->sizeAt(-2);
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output->assign(input);
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auto batchLoop = PRAGMA_THREADS_FOR {
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for (auto batch = start; batch < stop; batch += increment) {
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for (auto r = 0; r < rows; r++) {
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for (auto c = 0; c < r; c++) {
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math::nd4j_swap(outputPart[batch]->t<T>(r, c) , outputPart[batch]->t<T>(c, r));
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}
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}
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}
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};
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samediff::Threads::parallel_tad(batchLoop, 0, inputPart.size(), 1);
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}
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// --------------------------------------------------------------------------------------------------------------------------------------- //
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template <typename T>
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static int solveFunctor_(nd4j::LaunchContext * context, NDArray* leftInput, NDArray* rightInput, bool const adjoint, NDArray* output) {
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// stage 1: LU decomposition batched
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auto leftOutput = leftInput->ulike();
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auto permuShape = rightInput->getShapeAsVector(); permuShape.pop_back();
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auto permutations = NDArrayFactory::create<int>('c', permuShape, context);
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helpers::lu(context, leftInput, &leftOutput, &permutations);
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auto P = leftInput->ulike(); //permutations batched matrix
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P.nullify(); // to fill up matricies with zeros
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auto PPart = P.allTensorsAlongDimension({-2,-1});
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auto permutationsPart = permutations.allTensorsAlongDimension({-1});
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for (auto batch = 0; batch < permutationsPart.size(); ++batch) {
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for (auto row = 0; row < PPart[batch]->rows(); ++row) {
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PPart[batch]->t<T>(row, permutationsPart[batch]->t<int>(row)) = T(1.f);
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}
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}
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auto leftLower = leftOutput.dup();
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auto rightOutput = rightInput->ulike();
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auto rightPermuted = rightOutput.ulike();
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MmulHelper::matmul(&P, rightInput, &rightPermuted, 0, 0);
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ResultSet leftLowerPart = leftLower.allTensorsAlongDimension({-2, -1});
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for (auto i = 0; i < leftLowerPart.size(); i++) {
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for (auto r = 0; r < leftLowerPart[i]->rows(); r++)
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leftLowerPart[i]->t<T>(r,r) = (T)1.f;
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}
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// stage 2: triangularSolveFunctor for Lower with given b
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helpers::triangularSolveFunctor(context, &leftLower, &rightPermuted, true, false, &rightOutput);
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// stage 3: triangularSolveFunctor for Upper with output of previous stage
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helpers::triangularSolveFunctor(context, &leftOutput, &rightOutput, false, false, output);
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return Status::OK();
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}
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// --------------------------------------------------------------------------------------------------------------------------------------- //
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int solveFunctor(nd4j::LaunchContext * context, NDArray* leftInput, NDArray* rightInput, bool const adjoint, NDArray* output) {
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BUILD_SINGLE_SELECTOR(leftInput->dataType(), return solveFunctor_, (context, leftInput, rightInput, adjoint, output), FLOAT_TYPES);
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}
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// --------------------------------------------------------------------------------------------------------------------------------------- //
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void adjointMatrix(nd4j::LaunchContext* context, NDArray const* input, NDArray* output) {
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BUILD_SINGLE_SELECTOR(input->dataType(), adjointMatrix_, (context, input, output), FLOAT_TYPES);
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}
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// --------------------------------------------------------------------------------------------------------------------------------------- //
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}
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}
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}
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