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Merge: Scalar Jacobi fixes and update to unified kernels.
This PR aims to fix some of the incomplete functionalities that were left during the specialization for Scalar Jacobi. Fixes #838 Related PR: #854
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| /*******************************<GINKGO LICENSE>****************************** | ||
| Copyright (c) 2017-2021, the Ginkgo authors | ||
| All rights reserved. | ||
| Redistribution and use in source and binary forms, with or without | ||
| modification, are permitted provided that the following conditions | ||
| are met: | ||
| 1. Redistributions of source code must retain the above copyright | ||
| notice, this list of conditions and the following disclaimer. | ||
| 2. Redistributions in binary form must reproduce the above copyright | ||
| notice, this list of conditions and the following disclaimer in the | ||
| documentation and/or other materials provided with the distribution. | ||
| 3. Neither the name of the copyright holder nor the names of its | ||
| contributors may be used to endorse or promote products derived from | ||
| this software without specific prior written permission. | ||
| THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS | ||
| IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED | ||
| TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A | ||
| PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT | ||
| HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, | ||
| SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT | ||
| LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, | ||
| DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY | ||
| THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT | ||
| (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE | ||
| OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. | ||
| ******************************<GINKGO LICENSE>*******************************/ | ||
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| #include "core/preconditioner/jacobi_kernels.hpp" | ||
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| #include <ginkgo/core/base/math.hpp> | ||
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| #include "common/base/kernel_launch.hpp" | ||
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| namespace gko { | ||
| namespace kernels { | ||
| namespace GKO_DEVICE_NAMESPACE { | ||
| /** | ||
| * @brief The Jacobi preconditioner namespace. | ||
| * | ||
| * @ingroup jacobi | ||
| */ | ||
| namespace jacobi { | ||
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| template <typename ValueType> | ||
| void scalar_conj(std::shared_ptr<const DefaultExecutor> exec, | ||
| const Array<ValueType> &diag, Array<ValueType> &conj_diag) | ||
| { | ||
| run_kernel( | ||
| exec, | ||
| [] GKO_KERNEL(auto elem, auto diag, auto conj_diag) { | ||
| conj_diag[elem] = conj(diag[elem]); | ||
| }, | ||
| diag.get_num_elems(), diag, conj_diag); | ||
| } | ||
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| GKO_INSTANTIATE_FOR_EACH_VALUE_TYPE(GKO_DECLARE_JACOBI_SCALAR_CONJ_KERNEL); | ||
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| template <typename ValueType> | ||
| void invert_diagonal(std::shared_ptr<const DefaultExecutor> exec, | ||
| const Array<ValueType> &diag, Array<ValueType> &inv_diag) | ||
| { | ||
| run_kernel( | ||
| exec, | ||
| [] GKO_KERNEL(auto elem, auto diag, auto inv_diag) { | ||
| inv_diag[elem] = safe_divide(one(diag[elem]), diag[elem]); | ||
| }, | ||
| diag.get_num_elems(), diag, inv_diag); | ||
| } | ||
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| GKO_INSTANTIATE_FOR_EACH_VALUE_TYPE(GKO_DECLARE_JACOBI_INVERT_DIAGONAL_KERNEL); | ||
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| template <typename ValueType> | ||
| void scalar_apply(std::shared_ptr<const DefaultExecutor> exec, | ||
| const Array<ValueType> &diag, | ||
| const matrix::Dense<ValueType> *alpha, | ||
| const matrix::Dense<ValueType> *b, | ||
| const matrix::Dense<ValueType> *beta, | ||
| matrix::Dense<ValueType> *x) | ||
| { | ||
| if (alpha->get_size()[1] > 1) { | ||
| run_kernel( | ||
| exec, | ||
| [] GKO_KERNEL(auto row, auto col, auto diag, auto alpha, auto b, | ||
| auto beta, auto x) { | ||
| x(row, col) = beta[col] * x(row, col) + | ||
| alpha[col] * b(row, col) * diag[row]; | ||
| }, | ||
| x->get_size(), diag, alpha->get_const_values(), b, | ||
| beta->get_const_values(), x); | ||
| } else { | ||
| run_kernel( | ||
| exec, | ||
| [] GKO_KERNEL(auto row, auto col, auto diag, auto alpha, auto b, | ||
| auto beta, auto x) { | ||
| x(row, col) = | ||
| beta[0] * x(row, col) + alpha[0] * b(row, col) * diag[row]; | ||
| }, | ||
| x->get_size(), diag, alpha->get_const_values(), b, | ||
| beta->get_const_values(), x); | ||
| } | ||
| } | ||
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| GKO_INSTANTIATE_FOR_EACH_VALUE_TYPE(GKO_DECLARE_JACOBI_SCALAR_APPLY_KERNEL); | ||
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| template <typename ValueType> | ||
| void simple_scalar_apply(std::shared_ptr<const DefaultExecutor> exec, | ||
| const Array<ValueType> &diag, | ||
| const matrix::Dense<ValueType> *b, | ||
| matrix::Dense<ValueType> *x) | ||
| { | ||
| run_kernel( | ||
| exec, | ||
| [] GKO_KERNEL(auto row, auto col, auto diag, auto b, auto x) { | ||
| x(row, col) = b(row, col) * diag[row]; | ||
| }, | ||
| x->get_size(), diag, b, x); | ||
| } | ||
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| GKO_INSTANTIATE_FOR_EACH_VALUE_TYPE( | ||
| GKO_DECLARE_JACOBI_SIMPLE_SCALAR_APPLY_KERNEL); | ||
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| template <typename ValueType> | ||
| void scalar_convert_to_dense(std::shared_ptr<const DefaultExecutor> exec, | ||
| const Array<ValueType> &blocks, | ||
| matrix::Dense<ValueType> *result) | ||
| { | ||
| run_kernel( | ||
| exec, | ||
| [] GKO_KERNEL(auto row, auto col, auto diag, auto result) { | ||
| result(row, col) = zero(diag[row]); | ||
| if (row == col) { | ||
| result(row, col) = diag[row]; | ||
| } | ||
| }, | ||
| result->get_size(), blocks, result); | ||
| } | ||
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| GKO_INSTANTIATE_FOR_EACH_VALUE_TYPE( | ||
| GKO_DECLARE_JACOBI_SCALAR_CONVERT_TO_DENSE_KERNEL); | ||
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| } // namespace jacobi | ||
| } // namespace GKO_DEVICE_NAMESPACE | ||
| } // namespace kernels | ||
| } // namespace gko |
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