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C API for MNMG ECG Closes #3571 Closes #3572 Closes #3573 Closes #3575 Authors: - Naim (https://github.com/naimnv) Approvers: - Seunghwa Kang (https://github.com/seunghwak) - Joseph Nke (https://github.com/jnke2016) - Brad Rees (https://github.com/BradReesWork) URL: #4194
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| /* | ||
| * Copyright (c) 2022-2024, NVIDIA 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 <cugraph_c/community_algorithms.h> | ||
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| #include <cugraph/algorithms.hpp> | ||
| #include <cugraph/detail/shuffle_wrappers.hpp> | ||
| #include <cugraph/detail/utility_wrappers.hpp> | ||
| #include <cugraph/graph_functions.hpp> | ||
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| #include <c_api/abstract_functor.hpp> | ||
| #include <c_api/graph.hpp> | ||
| #include <c_api/graph_helper.hpp> | ||
| #include <c_api/hierarchical_clustering_result.hpp> | ||
| #include <c_api/random.hpp> | ||
| #include <c_api/resource_handle.hpp> | ||
| #include <c_api/utils.hpp> | ||
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| #include <optional> | ||
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| namespace { | ||
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| struct ecg_functor : public cugraph::c_api::abstract_functor { | ||
| raft::handle_t const& handle_; | ||
| cugraph::c_api::cugraph_rng_state_t* rng_state_{nullptr}; | ||
| cugraph::c_api::cugraph_graph_t* graph_{nullptr}; | ||
| double min_weight_{0.1}; | ||
| size_t ensemble_size_{10}; | ||
| size_t max_level_{0}; | ||
| double threshold_{0.001}; | ||
| double resolution_{1}; | ||
| bool do_expensive_check_{false}; | ||
| cugraph::c_api::cugraph_hierarchical_clustering_result_t* result_{}; | ||
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| ecg_functor(::cugraph_resource_handle_t const* handle, | ||
| ::cugraph_rng_state_t* rng_state, | ||
| ::cugraph_graph_t* graph, | ||
| double min_weight, | ||
| size_t ensemble_size, | ||
| size_t max_level, | ||
| double threshold, | ||
| double resolution, | ||
| bool do_expensive_check) | ||
| : abstract_functor(), | ||
| handle_(*reinterpret_cast<cugraph::c_api::cugraph_resource_handle_t const*>(handle)->handle_), | ||
| rng_state_(reinterpret_cast<cugraph::c_api::cugraph_rng_state_t*>(rng_state)), | ||
| graph_(reinterpret_cast<cugraph::c_api::cugraph_graph_t*>(graph)), | ||
| max_level_(max_level), | ||
| threshold_(threshold), | ||
| resolution_(resolution), | ||
| do_expensive_check_(do_expensive_check) | ||
| { | ||
| } | ||
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| template <typename vertex_t, | ||
| typename edge_t, | ||
| typename weight_t, | ||
| typename edge_type_type_t, | ||
| bool store_transposed, | ||
| bool multi_gpu> | ||
| void operator()() | ||
| { | ||
| if constexpr (!cugraph::is_candidate<vertex_t, edge_t, weight_t>::value) { | ||
| unsupported(); | ||
| } else { | ||
| // ecg expects store_transposed == false | ||
| if constexpr (store_transposed) { | ||
| error_code_ = cugraph::c_api:: | ||
| transpose_storage<vertex_t, edge_t, weight_t, store_transposed, multi_gpu>( | ||
| handle_, graph_, error_.get()); | ||
| if (error_code_ != CUGRAPH_SUCCESS) return; | ||
| } | ||
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| auto graph = | ||
| reinterpret_cast<cugraph::graph_t<vertex_t, edge_t, false, multi_gpu>*>(graph_->graph_); | ||
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| auto graph_view = graph->view(); | ||
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| auto edge_weights = reinterpret_cast< | ||
| cugraph::edge_property_t<cugraph::graph_view_t<vertex_t, edge_t, false, multi_gpu>, | ||
| weight_t>*>(graph_->edge_weights_); | ||
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| auto number_map = reinterpret_cast<rmm::device_uvector<vertex_t>*>(graph_->number_map_); | ||
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| rmm::device_uvector<vertex_t> clusters(0, handle_.get_stream()); | ||
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| weight_t modularity; | ||
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| std::tie(clusters, std::ignore, modularity) = | ||
| cugraph::ecg(handle_, | ||
| rng_state_->rng_state_, | ||
| graph_view, | ||
| (edge_weights != nullptr) | ||
| ? std::make_optional(edge_weights->view()) | ||
| : std::make_optional(cugraph::c_api::create_constant_edge_property( | ||
| handle_, graph_view, weight_t{1}) | ||
| .view()), | ||
| static_cast<weight_t>(min_weight_), | ||
| ensemble_size_, | ||
| max_level_, | ||
| static_cast<weight_t>(threshold_), | ||
| static_cast<weight_t>(resolution_)); | ||
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| rmm::device_uvector<vertex_t> vertices(graph_view.local_vertex_partition_range_size(), | ||
| handle_.get_stream()); | ||
| raft::copy(vertices.data(), number_map->data(), vertices.size(), handle_.get_stream()); | ||
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| result_ = new cugraph::c_api::cugraph_hierarchical_clustering_result_t{ | ||
| modularity, | ||
| new cugraph::c_api::cugraph_type_erased_device_array_t(vertices, graph_->vertex_type_), | ||
| new cugraph::c_api::cugraph_type_erased_device_array_t(clusters, graph_->vertex_type_)}; | ||
| } | ||
| } | ||
| }; | ||
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| } // namespace | ||
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| extern "C" cugraph_error_code_t cugraph_ecg(const cugraph_resource_handle_t* handle, | ||
| cugraph_rng_state_t* rng_state, | ||
| cugraph_graph_t* graph, | ||
| double min_weight, | ||
| size_t ensemble_size, | ||
| size_t max_level, | ||
| double threshold, | ||
| double resolution, | ||
| bool_t do_expensive_check, | ||
| cugraph_hierarchical_clustering_result_t** result, | ||
| cugraph_error_t** error) | ||
| { | ||
| ecg_functor functor(handle, | ||
| rng_state, | ||
| graph, | ||
| min_weight, | ||
| ensemble_size, | ||
| max_level, | ||
| threshold, | ||
| resolution, | ||
| do_expensive_check); | ||
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| return cugraph::c_api::run_algorithm(graph, functor, result, error); | ||
| } |
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