mirror of
https://github.com/OPM/opm-simulators.git
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372 lines
11 KiB
C++
372 lines
11 KiB
C++
/*
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Copyright 2013 SINTEF ICT, Applied Mathematics.
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Copyright 2016 IRIS AS
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This file is part of the Open Porous Media project (OPM).
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OPM is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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OPM is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with OPM. If not, see <http://www.gnu.org/licenses/>.
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*/
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#include <config.h>
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#if HAVE_DYNAMIC_BOOST_TEST
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#define BOOST_TEST_DYN_LINK
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#endif
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#define BOOST_TEST_MODULE AutoDiffBlockTest
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#include <opm/autodiff/AutoDiffBlock.hpp>
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#include <boost/test/unit_test.hpp>
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#include <Eigen/Eigen>
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#include <Eigen/Sparse>
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using namespace Opm;
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namespace {
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template <typename Scalar>
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bool
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operator ==(const Eigen::SparseMatrix<Scalar>& A,
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const Eigen::SparseMatrix<Scalar>& B)
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{
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// Two SparseMatrices are equal if
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// 0) They have the same ordering (enforced by equal types)
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// 1) They have the same outer and inner dimensions
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// 2) They have the same number of non-zero elements
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// 3) They have the same sparsity structure
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// 4) The non-zero elements are equal
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// 1) Outer and inner dimensions
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bool eq = (A.outerSize() == B.outerSize());
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eq = eq && (A.innerSize() == B.innerSize());
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// 2) Equal number of non-zero elements
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eq = eq && (A.nonZeros() == B.nonZeros());
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for (typename Eigen::SparseMatrix<Scalar>::Index
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k0 = 0, kend = A.outerSize(); eq && (k0 < kend); ++k0) {
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for (typename Eigen::SparseMatrix<Scalar>::InnerIterator
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iA(A, k0), iB(B, k0); eq && (iA && iB); ++iA, ++iB) {
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// 3) Sparsity structure
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eq = (iA.row() == iB.row()) && (iA.col() == iB.col());
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// 4) Equal non-zero elements
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eq = eq && (iA.value() == iB.value());
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}
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}
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return eq;
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// Note: Investigate implementing this operator as
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// return A.cwiseNotEqual(B).count() == 0;
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}
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bool operator==(const AutoDiffMatrix& lhs,
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const AutoDiffMatrix& rhs)
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{
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Eigen::SparseMatrix<double> lhs_s, rhs_s;
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lhs.toSparse(lhs_s);
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rhs.toSparse(rhs_s);
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return lhs_s == rhs_s;
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}
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void checkClose(const AutoDiffBlock<double>& lhs, const AutoDiffBlock<double>& rhs, double tolerance) {
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BOOST_CHECK(lhs.value().isApprox(rhs.value(), tolerance));
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auto lhs_d = lhs.derivative();
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auto rhs_d = rhs.derivative();
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Eigen::SparseMatrix<double> lhs_s, rhs_s;
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//If lhs has no derivatives, make sure all rhs derivatives are zero
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if (lhs_d.size() == 0) {
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for (size_t i=0; i<rhs_d.size(); ++i) {
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rhs.derivative()[i].toSparse(rhs_s);
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BOOST_CHECK_EQUAL(rhs_s.nonZeros(), 0);
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}
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}
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//If rhs has no derivatives, make sure all lhs derivatives are zero
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else if (rhs_d.size() == 0) {
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for (size_t i=0; i<lhs_d.size(); ++i) {
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lhs.derivative()[i].toSparse(lhs_s);
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BOOST_CHECK_EQUAL(lhs_s.nonZeros(), 0);
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}
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}
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//Else, check that derivatives are close to each other
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else {
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BOOST_CHECK_EQUAL(lhs_d.size(), rhs_d.size());
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for (size_t i=0; i<lhs_d.size(); ++i) {
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lhs.derivative()[i].toSparse(lhs_s);
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rhs.derivative()[i].toSparse(rhs_s);
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BOOST_CHECK(lhs_s.isApprox(rhs_s, tolerance));
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}
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}
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}
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}
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BOOST_AUTO_TEST_CASE(ConstantInitialisation)
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{
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typedef AutoDiffBlock<double> ADB;
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ADB::V v(3);
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v << 0.2, 1.2, 13.4;
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ADB a = ADB::constant(v);
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BOOST_REQUIRE(a.value().matrix() == v.matrix());
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const std::vector<ADB::M>& J = a.derivative();
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for (std::vector<ADB::M>::const_iterator
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b = J.begin(), e = J.end(); b != e; ++b) {
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BOOST_REQUIRE(b->nonZeros() == 0);
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}
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}
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BOOST_AUTO_TEST_CASE(VariableInitialisation)
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{
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typedef AutoDiffBlock<double> ADB;
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std::vector<int> blocksizes = { 3, 1, 2 };
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ADB::V v(3);
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v << 1.0, 2.2, 3.4;
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enum { FirstVar = 0, SecondVar = 1, ThirdVar = 2 };
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ADB x = ADB::variable(FirstVar, v, blocksizes);
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BOOST_REQUIRE(x.value().matrix() == v.matrix());
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const std::vector<ADB::M>& J = x.derivative();
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BOOST_REQUIRE(J[0].nonZeros() == v.size());
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const ADB::M& d = J[0];
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for (int i=0; i<d.cols(); ++i) {
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for (int j=0; j<d.rows(); ++j) {
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if (i==j) {
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BOOST_REQUIRE_EQUAL(d.coeff(j, i), 1.0);
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}
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else {
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BOOST_REQUIRE_EQUAL(d.coeff(j, i), 0.0);
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}
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}
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}
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for (std::vector<ADB::M>::const_iterator
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b = J.begin() + 1, e = J.end(); b != e; ++b) {
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BOOST_REQUIRE(b->nonZeros() == 0);
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}
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}
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BOOST_AUTO_TEST_CASE(FunctionInitialisation)
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{
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typedef AutoDiffBlock<double> ADB;
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std::vector<int> blocksizes = { 3, 1, 2 };
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std::vector<int>::size_type num_blocks = blocksizes.size();
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enum { FirstVar = 0, SecondVar = 1, ThirdVar = 2 };
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ADB::V v(3);
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v << 1.0, 2.2, 3.4;
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std::vector<ADB::M> jacs(num_blocks);
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for (std::vector<int>::size_type j = 0; j < num_blocks; ++j) {
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Eigen::SparseMatrix<double> sm(blocksizes[FirstVar], blocksizes[j]);
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sm.insert(0,0) = -1.0;
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jacs[j] = ADB::M(sm);
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}
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ADB::V v_copy(v);
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std::vector<ADB::M> jacs_copy(jacs);
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ADB f = ADB::function(std::move(v_copy), std::move(jacs_copy));
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BOOST_REQUIRE(f.value().matrix() == v.matrix());
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const std::vector<ADB::M>& J = f.derivative();
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for (std::vector<ADB::M>::const_iterator
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bf = J.begin(), ef = J.end(), bj = jacs.begin();
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bf != ef; ++bf, ++bj) {
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BOOST_CHECK(*bf == *bj);
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}
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}
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BOOST_AUTO_TEST_CASE(Addition)
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{
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typedef AutoDiffBlock<double> ADB;
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std::vector<int> blocksizes = { 3, 1, 2 };
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ADB::V va(3);
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va << 0.2, 1.2, 13.4;
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ADB::V vx(3);
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vx << 1.0, 2.2, 3.4;
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enum { FirstVar = 0, SecondVar = 1, ThirdVar = 2 };
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ADB a = ADB::constant(va, blocksizes);
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ADB x = ADB::variable(FirstVar, vx, blocksizes);
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ADB xpx = x + x;
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BOOST_CHECK_EQUAL(xpx.value().cwiseNotEqual(2 * x.value()).count(), 0);
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const std::vector<ADB::M>& J1x = x .derivative();
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const std::vector<ADB::M>& J2x = xpx.derivative();
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BOOST_CHECK_EQUAL(J1x.size(), J2x.size());
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for (std::vector<ADB::M>::const_iterator
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j1b = J1x.begin(), j1e = J1x.end(), j2b = J2x.begin();
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j1b != j1e; ++j1b, ++j2b) {
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BOOST_CHECK(*j2b == ADB::M((*j1b) * 2));
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}
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ADB::V r = 2*x.value() + a.value();
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ADB xpxpa = x + x + a;
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BOOST_CHECK_EQUAL(xpxpa.value().cwiseNotEqual(r).count(), 0);
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const std::vector<ADB::M>& J3 = xpxpa.derivative();
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for (std::vector<ADB::M>::const_iterator
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j1b = J1x.begin(), j1e = J1x.end(), j3b = J3.begin();
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j1b != j1e; ++j1b, ++j3b) {
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BOOST_CHECK(*j3b == ADB::M((*j1b) * 2));
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}
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}
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BOOST_AUTO_TEST_CASE(AssignAddSubtractOperators)
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{
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typedef AutoDiffBlock<double> ADB;
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// Basic testing of += and -=.
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ADB::V vx(3);
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vx << 0.2, 1.2, 13.4;
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ADB::V vy(3);
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vy << 1.0, 2.2, 3.4;
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std::vector<ADB::V> vals{ vx, vy };
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std::vector<ADB> vars = ADB::variables(vals);
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const ADB x = vars[0];
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const ADB y = vars[1];
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ADB z = x;
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z += y;
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ADB sum = x + y;
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const double tolerance = 1e-14;
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checkClose(z, sum, tolerance);
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z -= y;
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checkClose(z, x, tolerance);
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// Testing the case when the left hand side has empty() jacobian.
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ADB yconst = ADB::constant(vy);
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z = yconst;
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z -= x;
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ADB diff = yconst - x;
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checkClose(z, diff, tolerance);
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z += x;
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checkClose(z, yconst, tolerance);
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}
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BOOST_AUTO_TEST_CASE(Pow)
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{
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typedef AutoDiffBlock<double> ADB;
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// Basic testing of derivatives
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ADB::V vx(3);
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vx << 0.2, 1.2, 13.4;
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ADB::V vy(3);
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vy << 2.0, 3.0, 0.5;
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std::vector<ADB::V> vals{ vx, vy };
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std::vector<ADB> vars = ADB::variables(vals);
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const ADB x = vars[0];
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const ADB y = vars[1];
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const double tolerance = 1e-14;
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// test exp = double
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const ADB xx = x * x;
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ADB xxpow2 = Opm::pow(x,2.0);
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checkClose(xxpow2, xx, tolerance);
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const ADB xy = x * y;
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const ADB xxyy = xy * xy;
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ADB xypow2 = Opm::pow(xy,2.0);
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checkClose(xypow2, xxyy, tolerance);
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const ADB xxx = x * x * x;
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ADB xpow3 = Opm::pow(x,3.0);
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checkClose(xpow3, xxx, tolerance);
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ADB xpowhalf = Opm::pow(x,0.5);
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ADB::V x_sqrt(3);
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x_sqrt << 0.447214 , 1.095445 , 3.6606;
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for (int i = 0 ; i < 3; ++i){
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BOOST_CHECK_CLOSE(xpowhalf.value()[i], x_sqrt[i], 1e-4);
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}
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// test exp = ADB::V
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ADB xpowyval = Opm::pow(x,y.value());
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// each of the component of y is tested in the test above
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// we compare with the results from the above tests.
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ADB::V pick1(3);
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pick1 << 1,0,0;
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ADB::V pick2(3);
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pick2 << 0,1,0;
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ADB::V pick3(3);
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pick3 << 0,0,1;
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ADB compare = pick1 * xx + pick2 * xxx + pick3 * xpowhalf;
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checkClose(xpowyval, compare, tolerance);
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// test exponent = ADB::V and base = ADB
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ADB xvalpowy = Opm::pow(x.value(),y);
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// the value should be equal to xpowyval
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// the first jacobian should be trivial
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// the second jacobian is hand calculated
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// log(0.2)*0.2^2.0, log(1.2) * 1.2^3.0, log(13.4) * 13.4^0.5
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ADB::V jac2(3);
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jac2 << -0.0643775165 , 0.315051650 , 9.50019208855;
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for (int i = 0 ; i < 3; ++i){
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BOOST_CHECK_CLOSE(xvalpowy.value()[i], xpowyval.value()[i], tolerance);
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BOOST_CHECK_CLOSE(xvalpowy.derivative()[0].coeff(i,i), 0.0, tolerance);
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BOOST_CHECK_CLOSE(xvalpowy.derivative()[1].coeff(i,i), jac2[i], 1e-4);
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}
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// test exp = ADB
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ADB xpowy = Opm::pow(x,y);
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// the first jacobian should be equal to the xpowyval
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// the second jacobian should be equal to the xvalpowy
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for (int i = 0 ; i < 3; ++i){
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BOOST_CHECK_CLOSE(xpowy.value()[i], xpowyval.value()[i], tolerance);
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BOOST_CHECK_CLOSE(xpowy.derivative()[0].coeff(i,i), xpowyval.derivative()[0].coeff(i,i), tolerance);
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BOOST_CHECK_CLOSE(xpowy.derivative()[1].coeff(i,i), xvalpowy.derivative()[1].coeff(i,i), tolerance);
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}
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}
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