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feature: Linear Regression online spmd support (#2846)
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cpp/oneapi/dal/algo/linear_regression/backend/gpu/finalize_train_kernel_norm_eq_impl.hpp
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| /******************************************************************************* | ||
| * Copyright contributors to the oneDAL project | ||
| * | ||
| * 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/linear_regression/backend/gpu/finalize_train_kernel.hpp" | ||
| #include "oneapi/dal/backend/primitives/utils.hpp" | ||
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| #ifdef ONEDAL_DATA_PARALLEL | ||
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| namespace oneapi::dal::linear_regression::backend { | ||
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| namespace bk = dal::backend; | ||
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| template <typename Float, typename Task> | ||
| class finalize_train_kernel_norm_eq_impl { | ||
| using comm_t = bk::communicator<spmd::device_memory_access::usm>; | ||
| using input_t = partial_train_result<Task>; | ||
| using result_t = train_result<Task>; | ||
| using descriptor_t = detail::descriptor_base<Task>; | ||
| using train_parameters_t = detail::train_parameters<Task>; | ||
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| public: | ||
| finalize_train_kernel_norm_eq_impl(const bk::context_gpu& ctx) | ||
| : q(ctx.get_queue()), | ||
| comm_(ctx.get_communicator()) {} | ||
| result_t operator()(const descriptor_t& desc, | ||
| const train_parameters_t& params, | ||
| const input_t& input); | ||
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| private: | ||
| sycl::queue q; | ||
| comm_t comm_; | ||
| }; | ||
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| } // namespace oneapi::dal::linear_regression::backend | ||
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| #endif // ONEDAL_DATA_PARALLEL |
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cpp/oneapi/dal/algo/linear_regression/backend/gpu/finalize_train_kernel_norm_eq_impl_dpc.cpp
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,127 @@ | ||
| /******************************************************************************* | ||
| * Copyright contributors to the oneDAL project | ||
| * | ||
| * 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 "oneapi/dal/algo/linear_regression/backend/gpu/finalize_train_kernel_norm_eq_impl.hpp" | ||
| #include "oneapi/dal/algo/linear_regression/backend/gpu/misc.hpp" | ||
| #include "oneapi/dal/algo/linear_regression/backend/model_impl.hpp" | ||
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| #include "oneapi/dal/backend/primitives/lapack.hpp" | ||
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| namespace oneapi::dal::linear_regression::backend { | ||
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| namespace be = dal::backend; | ||
| namespace pr = be::primitives; | ||
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| using be::context_gpu; | ||
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| template <typename Float, typename Task> | ||
| train_result<Task> finalize_train_kernel_norm_eq_impl<Float, Task>::operator()( | ||
| const detail::descriptor_base<Task>& desc, | ||
| const detail::train_parameters<Task>& params, | ||
| const partial_train_result<Task>& input) { | ||
| using dal::detail::check_mul_overflow; | ||
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| using model_t = model<Task>; | ||
| using model_impl_t = detail::model_impl<Task>; | ||
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| const bool compute_intercept = desc.get_compute_intercept(); | ||
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| constexpr auto uplo = pr::mkl::uplo::upper; | ||
| constexpr auto alloc = sycl::usm::alloc::device; | ||
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| const auto response_count = input.get_partial_xty().get_row_count(); | ||
| const auto ext_feature_count = input.get_partial_xty().get_column_count(); | ||
| const auto feature_count = ext_feature_count - compute_intercept; | ||
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| const pr::ndshape<2> xtx_shape{ ext_feature_count, ext_feature_count }; | ||
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| const auto xtx_nd = | ||
| pr::table2ndarray<Float>(q, input.get_partial_xtx(), sycl::usm::alloc::device); | ||
| const auto xty_nd = pr::table2ndarray<Float, pr::ndorder::f>(q, | ||
| input.get_partial_xty(), | ||
| sycl::usm::alloc::device); | ||
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| const pr::ndshape<2> betas_shape{ response_count, feature_count + 1 }; | ||
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| const auto betas_size = check_mul_overflow(response_count, feature_count + 1); | ||
| auto betas_arr = array<Float>::zeros(q, betas_size, alloc); | ||
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| if (comm_.get_rank_count() > 1) { | ||
| { | ||
| ONEDAL_PROFILER_TASK(xtx_allreduce); | ||
| auto xtx_arr = | ||
| dal::array<Float>::wrap(q, xtx_nd.get_mutable_data(), xtx_nd.get_count()); | ||
| comm_.allreduce(xtx_arr).wait(); | ||
| } | ||
| { | ||
| ONEDAL_PROFILER_TASK(xty_allreduce); | ||
| auto xty_arr = | ||
| dal::array<Float>::wrap(q, xty_nd.get_mutable_data(), xty_nd.get_count()); | ||
| comm_.allreduce(xty_arr).wait(); | ||
| } | ||
| } | ||
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| double alpha = desc.get_alpha(); | ||
| sycl::event ridge_event; | ||
| if (alpha != 0.0) { | ||
| ridge_event = add_ridge_penalty<Float>(q, xtx_nd, compute_intercept, alpha); | ||
| } | ||
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| auto nxtx = pr::ndarray<Float, 2>::empty(q, xtx_shape, alloc); | ||
| auto nxty = pr::ndview<Float, 2>::wrap_mutable(betas_arr, betas_shape); | ||
| auto solve_event = | ||
| pr::solve_system<uplo>(q, compute_intercept, xtx_nd, xty_nd, nxtx, nxty, { ridge_event }); | ||
| sycl::event::wait_and_throw({ solve_event }); | ||
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| auto betas = homogen_table::wrap(betas_arr, response_count, feature_count + 1); | ||
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| const auto model_impl = std::make_shared<model_impl_t>(betas); | ||
| const auto model = dal::detail::make_private<model_t>(model_impl); | ||
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| const auto options = desc.get_result_options(); | ||
| auto result = train_result<Task>().set_model(model).set_result_options(options); | ||
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| if (options.test(result_options::intercept)) { | ||
| auto arr = array<Float>::zeros(q, response_count, alloc); | ||
| auto dst = pr::ndview<Float, 2>::wrap_mutable(arr, { 1l, response_count }); | ||
| const auto src = nxty.get_col_slice(0l, 1l).t(); | ||
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| pr::copy(q, dst, src).wait_and_throw(); | ||
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| auto intercept = homogen_table::wrap(arr, 1l, response_count); | ||
| result.set_intercept(intercept); | ||
| } | ||
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| if (options.test(result_options::coefficients)) { | ||
| const auto size = check_mul_overflow(response_count, feature_count); | ||
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| auto arr = array<Float>::zeros(q, size, alloc); | ||
| const auto src = nxty.get_col_slice(1l, feature_count + 1); | ||
| auto dst = pr::ndview<Float, 2>::wrap_mutable(arr, { response_count, feature_count }); | ||
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| pr::copy(q, dst, src).wait_and_throw(); | ||
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| auto coefficients = homogen_table::wrap(arr, response_count, feature_count); | ||
| result.set_coefficients(coefficients); | ||
| } | ||
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| return result; | ||
| } | ||
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| template class finalize_train_kernel_norm_eq_impl<float, task::regression>; | ||
| template class finalize_train_kernel_norm_eq_impl<double, task::regression>; | ||
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| } // namespace oneapi::dal::linear_regression::backend |
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