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| /* | ||
| * Copyright (c) 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. | ||
| */ | ||
| #include <common/benchmark.hpp> | ||
| #include <cusparse_v2.h> | ||
| #include <raft/core/device_resources.hpp> | ||
| #include <raft/core/resource/cublas_handle.hpp> | ||
| #include <raft/distance/distance.cuh> | ||
| #include <raft/distance/distance_types.hpp> | ||
| #include <raft/random/rng.cuh> | ||
| #include <raft/sparse/linalg/sddmm.hpp> | ||
| #include <raft/util/itertools.hpp> | ||
|
|
||
| #include <raft/core/resource/cuda_stream.hpp> | ||
| #include <raft/core/resources.hpp> | ||
|
|
||
| #include <random> | ||
| #include <sstream> | ||
| #include <vector> | ||
|
|
||
| namespace raft::bench::linalg { | ||
|
|
||
| template <typename ValueType> | ||
| struct SDDMMBenchParams { | ||
| size_t m; | ||
| size_t k; | ||
| size_t n; | ||
| float sparsity; | ||
| bool transpose_a; | ||
| bool transpose_b; | ||
| ValueType alpha = 1.0; | ||
| ValueType beta = 0.0; | ||
| }; | ||
|
|
||
| enum Alg { SDDMM, Inner }; | ||
|
|
||
| template <typename ValueType> | ||
| inline auto operator<<(std::ostream& os, const SDDMMBenchParams<ValueType>& params) -> std::ostream& | ||
| { | ||
| os << " m*k*n=" << params.m << "*" << params.k << "*" << params.n | ||
| << "\tsparsity=" << params.sparsity << "\ttrans_a=" << (params.transpose_a ? "T" : "F") | ||
| << " trans_b=" << (params.transpose_b ? "T" : "F"); | ||
| return os; | ||
| } | ||
|
|
||
| template <typename ValueType, | ||
| typename LayoutPolicyA = row_major, | ||
| typename LayoutPolicyB = col_major, | ||
| const int SDDMMorInner = Alg::SDDMM, | ||
| typename IndexType = int64_t> | ||
| struct SDDMMBench : public fixture { | ||
| SDDMMBench(const SDDMMBenchParams<ValueType>& p) | ||
| : fixture(true), | ||
| params(p), | ||
| handle(stream), | ||
| a_data_d(0, stream), | ||
| b_data_d(0, stream), | ||
| c_indptr_d(0, stream), | ||
| c_indices_d(0, stream), | ||
| c_data_d(0, stream), | ||
| c_dense_data_d(0, stream) | ||
| { | ||
| a_data_d.resize(params.m * params.k, stream); | ||
| b_data_d.resize(params.k * params.n, stream); | ||
|
|
||
| raft::random::RngState rng(2024ULL); | ||
| raft::random::uniform( | ||
| handle, rng, a_data_d.data(), params.m * params.k, ValueType(-1.0), ValueType(1.0)); | ||
| raft::random::uniform( | ||
| handle, rng, b_data_d.data(), params.k * params.n, ValueType(-1.0), ValueType(1.0)); | ||
|
|
||
| std::vector<bool> c_dense_data_h(params.m * params.n); | ||
|
|
||
| c_true_nnz = create_sparse_matrix(c_dense_data_h); | ||
| std::vector<ValueType> values(c_true_nnz); | ||
| std::vector<IndexType> indices(c_true_nnz); | ||
| std::vector<IndexType> indptr(params.m + 1); | ||
|
|
||
| c_data_d.resize(c_true_nnz, stream); | ||
| c_indptr_d.resize(params.m + 1, stream); | ||
| c_indices_d.resize(c_true_nnz, stream); | ||
|
|
||
| if (SDDMMorInner == Alg::Inner) { c_dense_data_d.resize(params.m * params.n, stream); } | ||
|
|
||
| convert_to_csr(c_dense_data_h, params.m, params.n, values, indices, indptr); | ||
| RAFT_EXPECTS(c_true_nnz == c_indices_d.size(), | ||
| "Something wrong. The c_true_nnz != c_indices_d.size()!"); | ||
|
|
||
| update_device(c_data_d.data(), values.data(), c_true_nnz, stream); | ||
| update_device(c_indices_d.data(), indices.data(), c_true_nnz, stream); | ||
| update_device(c_indptr_d.data(), indptr.data(), params.m + 1, stream); | ||
| } | ||
|
|
||
| void convert_to_csr(std::vector<bool>& matrix, | ||
| IndexType rows, | ||
| IndexType cols, | ||
| std::vector<ValueType>& values, | ||
| std::vector<IndexType>& indices, | ||
| std::vector<IndexType>& indptr) | ||
| { | ||
| IndexType offset_indptr = 0; | ||
| IndexType offset_values = 0; | ||
| indptr[offset_indptr++] = 0; | ||
|
|
||
| for (IndexType i = 0; i < rows; ++i) { | ||
| for (IndexType j = 0; j < cols; ++j) { | ||
| if (matrix[i * cols + j]) { | ||
| values[offset_values] = static_cast<ValueType>(1.0); | ||
| indices[offset_values] = static_cast<IndexType>(j); | ||
| offset_values++; | ||
| } | ||
| } | ||
| indptr[offset_indptr++] = static_cast<IndexType>(offset_values); | ||
| } | ||
| } | ||
|
|
||
| size_t create_sparse_matrix(std::vector<bool>& matrix) | ||
| { | ||
| size_t total_elements = static_cast<size_t>(params.m * params.n); | ||
| size_t num_ones = static_cast<size_t>((total_elements * 1.0f) * params.sparsity); | ||
| size_t res = num_ones; | ||
|
|
||
| for (size_t i = 0; i < total_elements; ++i) { | ||
| matrix[i] = false; | ||
| } | ||
|
|
||
| std::random_device rd; | ||
| std::mt19937 gen(rd()); | ||
| std::uniform_int_distribution<> dis(0, total_elements - 1); | ||
|
|
||
| while (num_ones > 0) { | ||
| size_t index = dis(gen); | ||
|
|
||
| if (matrix[index] == false) { | ||
| matrix[index] = true; | ||
| num_ones--; | ||
| } | ||
| } | ||
| return res; | ||
| } | ||
|
|
||
| ~SDDMMBench() {} | ||
|
|
||
| void run_benchmark(::benchmark::State& state) override | ||
| { | ||
| std::ostringstream label_stream; | ||
| label_stream << params; | ||
| state.SetLabel(label_stream.str()); | ||
|
|
||
| auto a = raft::make_device_matrix_view<const ValueType, IndexType, LayoutPolicyA>( | ||
| a_data_d.data(), | ||
| (!params.transpose_a ? params.m : params.k), | ||
| (!params.transpose_a ? params.k : params.m)); | ||
|
|
||
| auto b = raft::make_device_matrix_view<const ValueType, IndexType, LayoutPolicyB>( | ||
| b_data_d.data(), | ||
| (!params.transpose_b ? params.k : params.n), | ||
| (!params.transpose_b ? params.n : params.k)); | ||
|
|
||
| auto c_structure = raft::make_device_compressed_structure_view<int64_t, int64_t, int64_t>( | ||
| c_indptr_d.data(), | ||
| c_indices_d.data(), | ||
| params.m, | ||
| params.n, | ||
| static_cast<IndexType>(c_indices_d.size())); | ||
|
|
||
| auto c = raft::make_device_csr_matrix_view<ValueType>(c_data_d.data(), c_structure); | ||
| raft::resource::get_cusparse_handle(handle); | ||
|
|
||
| resource::sync_stream(handle); | ||
|
|
||
| auto op_a = params.transpose_a ? raft::linalg::Operation::TRANSPOSE | ||
| : raft::linalg::Operation::NON_TRANSPOSE; | ||
| auto op_b = params.transpose_b ? raft::linalg::Operation::TRANSPOSE | ||
| : raft::linalg::Operation::NON_TRANSPOSE; | ||
|
|
||
| raft::sparse::linalg::sddmm(handle, | ||
| a, | ||
| b, | ||
| c, | ||
| op_a, | ||
| op_b, | ||
| raft::make_host_scalar_view<ValueType>(¶ms.alpha), | ||
| raft::make_host_scalar_view<ValueType>(¶ms.beta)); | ||
| resource::sync_stream(handle); | ||
|
|
||
| loop_on_state(state, [this, &a, &b, &c, &op_a, &op_b]() { | ||
| if (SDDMMorInner == Alg::SDDMM) { | ||
| raft::sparse::linalg::sddmm(handle, | ||
| a, | ||
| b, | ||
| c, | ||
| op_a, | ||
| op_b, | ||
| raft::make_host_scalar_view<ValueType>(¶ms.alpha), | ||
| raft::make_host_scalar_view<ValueType>(¶ms.beta)); | ||
| resource::sync_stream(handle); | ||
| } else { | ||
| raft::distance::pairwise_distance(handle, | ||
| a_data_d.data(), | ||
| b_data_d.data(), | ||
| c_dense_data_d.data(), | ||
| static_cast<int>(params.m), | ||
| static_cast<int>(params.n), | ||
| static_cast<int>(params.k), | ||
| raft::distance::DistanceType::InnerProduct, | ||
| std::is_same_v<LayoutPolicyA, row_major>); | ||
| resource::sync_stream(handle); | ||
| } | ||
| }); | ||
| } | ||
|
|
||
| private: | ||
| const raft::device_resources handle; | ||
| SDDMMBenchParams<ValueType> params; | ||
|
|
||
| rmm::device_uvector<ValueType> a_data_d; | ||
| rmm::device_uvector<ValueType> b_data_d; | ||
| rmm::device_uvector<ValueType> c_dense_data_d; | ||
|
|
||
| size_t c_true_nnz = 0; | ||
| rmm::device_uvector<IndexType> c_indptr_d; | ||
| rmm::device_uvector<IndexType> c_indices_d; | ||
| rmm::device_uvector<ValueType> c_data_d; | ||
| }; | ||
|
|
||
| template <typename ValueType> | ||
| static std::vector<SDDMMBenchParams<ValueType>> getInputs() | ||
| { | ||
| std::vector<SDDMMBenchParams<ValueType>> param_vec; | ||
| struct TestParams { | ||
| bool transpose_a; | ||
| bool transpose_b; | ||
| size_t m; | ||
| size_t k; | ||
| size_t n; | ||
| float sparsity; | ||
| }; | ||
|
|
||
| const std::vector<TestParams> params_group = | ||
| raft::util::itertools::product<TestParams>({false, true}, | ||
| {false, true}, | ||
| {size_t(10), size_t(1024)}, | ||
| {size_t(128), size_t(1024)}, | ||
| {size_t(1024 * 1024)}, | ||
| {0.01f, 0.1f, 0.2f, 0.5f}); | ||
|
|
||
| param_vec.reserve(params_group.size()); | ||
| for (TestParams params : params_group) { | ||
| param_vec.push_back(SDDMMBenchParams<ValueType>( | ||
| {params.m, params.k, params.n, params.sparsity, params.transpose_a, params.transpose_b})); | ||
| } | ||
| return param_vec; | ||
| } | ||
|
|
||
| RAFT_BENCH_REGISTER((SDDMMBench<float, row_major, col_major, Alg::SDDMM>), "", getInputs<float>()); | ||
| RAFT_BENCH_REGISTER((SDDMMBench<float, col_major, row_major, Alg::SDDMM>), "", getInputs<float>()); | ||
| RAFT_BENCH_REGISTER((SDDMMBench<float, row_major, row_major, Alg::SDDMM>), "", getInputs<float>()); | ||
| RAFT_BENCH_REGISTER((SDDMMBench<float, col_major, col_major, Alg::SDDMM>), "", getInputs<float>()); | ||
|
|
||
| RAFT_BENCH_REGISTER((SDDMMBench<float, row_major, col_major, Alg::Inner>), "", getInputs<float>()); | ||
|
|
||
| } // namespace raft::bench::linalg |
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