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feature: online pca algorithm (#2550)
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31 changes: 31 additions & 0 deletions
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cpp/oneapi/dal/algo/pca/backend/cpu/finalize_train_kernel.hpp
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| /******************************************************************************* | ||
| * Copyright 2023 Intel Corporation | ||
| * | ||
| * Licensed under the Apache License, Version 2.0 (the "License"); | ||
| * you may not use this file except in compliance with the License. | ||
| * You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * Unless required by applicable law or agreed to in writing, software | ||
| * distributed under the License is distributed on an "AS IS" BASIS, | ||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| * See the License for the specific language governing permissions and | ||
| * limitations under the License. | ||
| *******************************************************************************/ | ||
|
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| #pragma once | ||
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| #include "oneapi/dal/algo/pca/train_types.hpp" | ||
| #include "oneapi/dal/backend/dispatcher.hpp" | ||
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| namespace oneapi::dal::pca::backend { | ||
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| template <typename Float, typename Method, typename Task> | ||
| struct finalize_train_kernel_cpu { | ||
| train_result<Task> operator()(const dal::backend::context_cpu& ctx, | ||
| const detail::descriptor_base<Task>& params, | ||
| const partial_train_result<Task>& input) const; | ||
| }; | ||
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| } // namespace oneapi::dal::pca::backend |
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cpp/oneapi/dal/algo/pca/backend/cpu/finalize_train_kernel_cov.cpp
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| Original file line number | Diff line number | Diff line change |
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| /******************************************************************************* | ||
| * Copyright 2023 Intel Corporation | ||
| * | ||
| * Licensed under the Apache License, Version 2.0 (the "License"); | ||
| * you may not use this file except in compliance with the License. | ||
| * You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * Unless required by applicable law or agreed to in writing, software | ||
| * distributed under the License is distributed on an "AS IS" BASIS, | ||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| * See the License for the specific language governing permissions and | ||
| * limitations under the License. | ||
| *******************************************************************************/ | ||
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| #include <daal/src/algorithms/pca/pca_dense_correlation_online_kernel.h> | ||
| #include <daal/src/algorithms/covariance/covariance_hyperparameter_impl.h> | ||
| #include "daal/src/algorithms/covariance/covariance_kernel.h" | ||
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| #include "oneapi/dal/algo/pca/backend/common.hpp" | ||
| #include "oneapi/dal/algo/pca/backend/cpu/finalize_train_kernel.hpp" | ||
| #include "oneapi/dal/backend/interop/common.hpp" | ||
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| #include "oneapi/dal/backend/interop/error_converter.hpp" | ||
| #include "oneapi/dal/backend/interop/table_conversion.hpp" | ||
| #include "oneapi/dal/table/row_accessor.hpp" | ||
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| namespace oneapi::dal::pca::backend { | ||
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| using dal::backend::context_cpu; | ||
| using descriptor_t = detail::descriptor_base<task::dim_reduction>; | ||
| using model_t = model<task::dim_reduction>; | ||
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| namespace interop = dal::backend::interop; | ||
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| namespace daal_pca = daal::algorithms::pca; | ||
| namespace daal_cov = daal::algorithms::covariance; | ||
| namespace interop = dal::backend::interop; | ||
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| template <typename Float, daal::CpuType Cpu> | ||
| using daal_pca_cor_kernel_t = daal_pca::internal::PCACorrelationKernel<daal::online, Float, Cpu>; | ||
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| template <typename Float, daal::CpuType Cpu> | ||
| using daal_cov_kernel_t = | ||
| daal_cov::internal::CovarianceDenseOnlineKernel<Float, daal_cov::Method::defaultDense, Cpu>; | ||
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| template <typename Float, typename Task> | ||
| static train_result<Task> call_daal_kernel_finalize_train(const context_cpu& ctx, | ||
| const descriptor_t& desc, | ||
| const partial_train_result<Task>& input) { | ||
| const std::int64_t component_count = | ||
| get_component_count(desc, input.get_partial_crossproduct()); | ||
| const std::int64_t column_count = input.get_partial_crossproduct().get_column_count(); | ||
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| auto result = train_result<task::dim_reduction>{}.set_result_options(desc.get_result_options()); | ||
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| auto arr_eigvec = array<Float>::empty(column_count * component_count); | ||
| auto arr_eigval = array<Float>::empty(1 * component_count); | ||
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| const auto daal_eigenvectors = | ||
| interop::convert_to_daal_homogen_table(arr_eigvec, component_count, column_count); | ||
| const auto daal_eigenvalues = | ||
| interop::convert_to_daal_homogen_table(arr_eigval, 1, component_count); | ||
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| auto rows_count_global = | ||
| row_accessor<const Float>(input.get_partial_n_rows()).pull({ 0, -1 })[0]; | ||
| auto arr_means = array<Float>::empty(column_count); | ||
| const auto daal_means = interop::convert_to_daal_homogen_table(arr_means, 1, column_count); | ||
| daal_cov::internal::Hyperparameter daal_hyperparameter; | ||
| /// the logic of block size calculation is copied from DAAL, | ||
| /// to be changed to passing the values from the performance model | ||
| std::int64_t blockSize = 140; | ||
| if (ctx.get_enabled_cpu_extensions() == dal::detail::cpu_extension::avx512) { | ||
| const std::int64_t row_count = rows_count_global; | ||
| if (5000 < row_count && row_count <= 50000) { | ||
| blockSize = 1024; | ||
| } | ||
| } | ||
| interop::status_to_exception( | ||
| daal_hyperparameter.set(daal_cov::internal::denseUpdateStepBlockSize, blockSize)); | ||
| auto daal_crossproduct = | ||
| interop::convert_to_daal_table<Float>(input.get_partial_crossproduct()); | ||
| auto daal_sums = interop::convert_to_daal_table<Float>(input.get_partial_sum()); | ||
| const auto daal_nobs = interop::convert_to_daal_table<Float>(input.get_partial_n_rows()); | ||
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| auto arr_cor_matrix = array<Float>::empty(column_count * column_count); | ||
| const auto daal_cor_matrix = | ||
| interop::convert_to_daal_homogen_table(arr_cor_matrix, column_count, column_count); | ||
| daal_cov::Parameter daal_parameter; | ||
| daal_parameter.outputMatrixType = daal_cov::correlationMatrix; | ||
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| interop::status_to_exception( | ||
| interop::call_daal_kernel_finalize_compute<Float, daal_cov_kernel_t>( | ||
| ctx, | ||
| daal_nobs.get(), | ||
| daal_crossproduct.get(), | ||
| daal_sums.get(), | ||
| daal_cor_matrix.get(), | ||
| daal_means.get(), | ||
| &daal_parameter, | ||
| &daal_hyperparameter)); | ||
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| const auto data_to_compute = daal_cor_matrix; | ||
| { | ||
| const auto status = dal::backend::dispatch_by_cpu(ctx, [&](auto cpu) { | ||
| constexpr auto cpu_type = interop::to_daal_cpu_type<decltype(cpu)>::value; | ||
| return daal_pca_cor_kernel_t<Float, cpu_type>().computeCorrelationEigenvalues( | ||
| *data_to_compute, | ||
| *daal_eigenvectors, | ||
| *daal_eigenvalues); | ||
| }); | ||
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| interop::status_to_exception(status); | ||
| } | ||
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| if (desc.get_result_options().test(result_options::eigenvectors)) { | ||
| const auto mdl = model_t{}.set_eigenvectors( | ||
| homogen_table::wrap(arr_eigvec, component_count, column_count)); | ||
| result.set_model(mdl); | ||
| } | ||
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| if (desc.get_result_options().test(result_options::eigenvalues)) { | ||
| result.set_eigenvalues(homogen_table::wrap(arr_eigval, 1, component_count)); | ||
| } | ||
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| return result; | ||
| } | ||
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| template <typename Float, typename Task> | ||
| static train_result<Task> finalize_train(const context_cpu& ctx, | ||
| const descriptor_t& desc, | ||
| const partial_train_result<Task>& input) { | ||
| return call_daal_kernel_finalize_train<Float>(ctx, desc, input); | ||
| } | ||
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| template <typename Float> | ||
| struct finalize_train_kernel_cpu<Float, method::cov, task::dim_reduction> { | ||
| train_result<task::dim_reduction> operator()( | ||
| const context_cpu& ctx, | ||
| const descriptor_t& desc, | ||
| const partial_train_result<task::dim_reduction>& input) const { | ||
| return finalize_train<Float, task::dim_reduction>(ctx, desc, input); | ||
| } | ||
| }; | ||
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| template struct finalize_train_kernel_cpu<float, method::cov, task::dim_reduction>; | ||
| template struct finalize_train_kernel_cpu<double, method::cov, task::dim_reduction>; | ||
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| } // namespace oneapi::dal::pca::backend |
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