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Update lapack syevd/heevd implementations #1891
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3a59dd1
Use dispatch_table for syevd
vlad-perevezentsev 6174375
Implement common logic for lapack syevd/heevd
vlad-perevezentsev 33215ac
Merge master into common_evd_logic
vlad-perevezentsev c8f8eaf
Remove junk code
vlad-perevezentsev ba1a722
Add source files for syevd and heevd
vlad-perevezentsev 9ae6ea6
Merge master into common_evd_logic
vlad-perevezentsev f4cbee2
Merge branch 'master' into common_evd_logic
antonwolfy b18ae1c
Merge branch 'master' into common_evd_logic
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,178 @@ | ||
| //***************************************************************************** | ||
| // Copyright (c) 2024, Intel Corporation | ||
| // All rights reserved. | ||
| // | ||
| // Redistribution and use in source and binary forms, with or without | ||
| // modification, are permitted provided that the following conditions are met: | ||
| // - Redistributions of source code must retain the above copyright notice, | ||
| // this list of conditions and the following disclaimer. | ||
| // - 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. | ||
| // | ||
| // 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. | ||
| //***************************************************************************** | ||
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| #pragma once | ||
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| #include <oneapi/mkl.hpp> | ||
| #include <pybind11/pybind11.h> | ||
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| // dpctl tensor headers | ||
| #include "utils/memory_overlap.hpp" | ||
| #include "utils/output_validation.hpp" | ||
| #include "utils/type_dispatch.hpp" | ||
| #include "utils/type_utils.hpp" | ||
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| #include "types_matrix.hpp" | ||
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| namespace dpnp | ||
| { | ||
| namespace backend | ||
| { | ||
| namespace ext | ||
| { | ||
| namespace lapack | ||
| { | ||
| namespace evd | ||
| { | ||
| typedef sycl::event (*evd_impl_fn_ptr_t)(sycl::queue &, | ||
| const oneapi::mkl::job, | ||
| const oneapi::mkl::uplo, | ||
| const std::int64_t, | ||
| char *, | ||
| char *, | ||
| std::vector<sycl::event> &, | ||
| const std::vector<sycl::event> &); | ||
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| namespace dpctl_td_ns = dpctl::tensor::type_dispatch; | ||
| namespace py = pybind11; | ||
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| template <typename dispatchT> | ||
| std::pair<sycl::event, sycl::event> | ||
| evd_func(sycl::queue &exec_q, | ||
| const std::int8_t jobz, | ||
| const std::int8_t upper_lower, | ||
| dpctl::tensor::usm_ndarray &eig_vecs, | ||
| dpctl::tensor::usm_ndarray &eig_vals, | ||
| const std::vector<sycl::event> &depends, | ||
| const dispatchT &evd_dispatch_table) | ||
| { | ||
| const int eig_vecs_nd = eig_vecs.get_ndim(); | ||
| const int eig_vals_nd = eig_vals.get_ndim(); | ||
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| if (eig_vecs_nd != 2) { | ||
| throw py::value_error("Unexpected ndim=" + std::to_string(eig_vecs_nd) + | ||
| " of an output array with eigenvectors"); | ||
| } | ||
| else if (eig_vals_nd != 1) { | ||
| throw py::value_error("Unexpected ndim=" + std::to_string(eig_vals_nd) + | ||
| " of an output array with eigenvalues"); | ||
| } | ||
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| const py::ssize_t *eig_vecs_shape = eig_vecs.get_shape_raw(); | ||
| const py::ssize_t *eig_vals_shape = eig_vals.get_shape_raw(); | ||
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| if (eig_vecs_shape[0] != eig_vecs_shape[1]) { | ||
| throw py::value_error("Output array with eigenvectors with be square"); | ||
| } | ||
| else if (eig_vecs_shape[0] != eig_vals_shape[0]) { | ||
| throw py::value_error( | ||
| "Eigenvectors and eigenvalues have different shapes"); | ||
| } | ||
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| size_t src_nelems(1); | ||
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| for (int i = 0; i < eig_vecs_nd; ++i) { | ||
| src_nelems *= static_cast<size_t>(eig_vecs_shape[i]); | ||
| } | ||
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| if (src_nelems == 0) { | ||
| // nothing to do | ||
| return std::make_pair(sycl::event(), sycl::event()); | ||
| } | ||
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| dpctl::tensor::validation::CheckWritable::throw_if_not_writable(eig_vecs); | ||
| dpctl::tensor::validation::CheckWritable::throw_if_not_writable(eig_vals); | ||
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| // check compatibility of execution queue and allocation queue | ||
| if (!dpctl::utils::queues_are_compatible(exec_q, {eig_vecs, eig_vals})) { | ||
| throw py::value_error( | ||
| "Execution queue is not compatible with allocation queues"); | ||
| } | ||
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| auto const &overlap = dpctl::tensor::overlap::MemoryOverlap(); | ||
| if (overlap(eig_vecs, eig_vals)) { | ||
| throw py::value_error("Arrays with eigenvectors and eigenvalues are " | ||
| "overlapping segments of memory"); | ||
| } | ||
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| bool is_eig_vecs_f_contig = eig_vecs.is_f_contiguous(); | ||
| bool is_eig_vals_c_contig = eig_vals.is_c_contiguous(); | ||
| if (!is_eig_vecs_f_contig) { | ||
| throw py::value_error( | ||
| "An array with input matrix / output eigenvectors " | ||
| "must be F-contiguous"); | ||
| } | ||
| else if (!is_eig_vals_c_contig) { | ||
| throw py::value_error( | ||
| "An array with output eigenvalues must be C-contiguous"); | ||
| } | ||
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| auto array_types = dpctl_td_ns::usm_ndarray_types(); | ||
| int eig_vecs_type_id = | ||
| array_types.typenum_to_lookup_id(eig_vecs.get_typenum()); | ||
| int eig_vals_type_id = | ||
| array_types.typenum_to_lookup_id(eig_vals.get_typenum()); | ||
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| evd_impl_fn_ptr_t evd_fn = | ||
| evd_dispatch_table[eig_vecs_type_id][eig_vals_type_id]; | ||
| if (evd_fn == nullptr) { | ||
| throw py::value_error( | ||
| "Types of input vectors and result array are mismatched."); | ||
| } | ||
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| char *eig_vecs_data = eig_vecs.get_data(); | ||
| char *eig_vals_data = eig_vals.get_data(); | ||
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| const std::int64_t n = eig_vecs_shape[0]; | ||
| const oneapi::mkl::job jobz_val = static_cast<oneapi::mkl::job>(jobz); | ||
| const oneapi::mkl::uplo uplo_val = | ||
| static_cast<oneapi::mkl::uplo>(upper_lower); | ||
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| std::vector<sycl::event> host_task_events; | ||
| sycl::event evd_ev = evd_fn(exec_q, jobz_val, uplo_val, n, eig_vecs_data, | ||
| eig_vals_data, host_task_events, depends); | ||
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| sycl::event args_ev = dpctl::utils::keep_args_alive( | ||
| exec_q, {eig_vecs, eig_vals}, host_task_events); | ||
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| return std::make_pair(args_ev, evd_ev); | ||
| } | ||
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| template <typename dispatchT, | ||
| template <typename fnT, typename T, typename RealT> | ||
| typename factoryT> | ||
| void init_evd_dispatch_table( | ||
| dispatchT evd_dispatch_table[][dpctl_td_ns::num_types]) | ||
| { | ||
| dpctl_td_ns::DispatchTableBuilder<dispatchT, factoryT, | ||
| dpctl_td_ns::num_types> | ||
| contig; | ||
| contig.populate_dispatch_table(evd_dispatch_table); | ||
| } | ||
| } // namespace evd | ||
| } // namespace lapack | ||
| } // namespace ext | ||
| } // namespace backend | ||
| } // namespace dpnp | ||
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