Add CAGRA gbench

cpp/bench/prims/CMakeLists.txt

@@ -141,6 +141,7 @@ if(BUILD_PRIMS_BENCH)
     PATH
     bench/prims/neighbors/knn/brute_force_float_int64_t.cu
     bench/prims/neighbors/knn/brute_force_float_uint32_t.cu
+    bench/prims/neighbors/knn/cagra_float_uint32_t.cu
     bench/prims/neighbors/knn/ivf_flat_float_int64_t.cu
     bench/prims/neighbors/knn/ivf_flat_int8_t_int64_t.cu
     bench/prims/neighbors/knn/ivf_flat_uint8_t_int64_t.cu

cpp/bench/prims/neighbors/cagra_bench.cuh

@@ -0,0 +1,165 @@
+/*
+ * Copyright (c) 2023, 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.
+ */
+
+#pragma once
+
+#include <common/benchmark.hpp>
+#include <raft/neighbors/cagra.cuh>
+#include <raft/random/rng.cuh>
+#include <raft/util/itertools.hpp>
+
+#include <optional>
+
+namespace raft::bench::neighbors {
+
+struct params {
+  /** Size of the dataset. */
+  size_t n_samples;
+  /** Number of dimensions in the dataset. */
+  int n_dims;
+  /** The batch size -- number of KNN searches. */
+  int n_queries;
+  /** Number of nearest neighbours to find for every probe. */
+  int k;
+  /** kNN graph degree*/
+  int degree;
+  int itopk_size;
+  int block_size;
+  int num_parents;
+  int max_iterations;
+};
+
+template <typename T, typename IdxT>
+struct CagraBench : public fixture {
+  explicit CagraBench(const params& ps)
+    : fixture(true),
+      params_(ps),
+      queries_(make_device_matrix<T, IdxT>(handle, ps.n_queries, ps.n_dims))
+  {
+    // Generate random dataset and queriees
+    auto dataset = make_device_matrix<T, IdxT>(handle, ps.n_samples, ps.n_dims);
+    raft::random::RngState state{42};
+    constexpr T kRangeMax = std::is_integral_v<T> ? std::numeric_limits<T>::max() : T(1);
+    constexpr T kRangeMin = std::is_integral_v<T> ? std::numeric_limits<T>::min() : T(-1);
+    if constexpr (std::is_integral_v<T>) {
+      raft::random::uniformInt(
+        state, dataset.data_handle(), dataset.size(), kRangeMin, kRangeMax, stream);
+      raft::random::uniformInt(
+        state, queries_.data_handle(), queries_.size(), kRangeMin, kRangeMax, stream);
+    } else {
+      raft::random::uniform(
+        state, dataset.data_handle(), dataset.size(), kRangeMin, kRangeMax, stream);
+      raft::random::uniform(
+        state, queries_.data_handle(), queries_.size(), kRangeMin, kRangeMax, stream);
+    }
+
+    // Generate random knn graph
+    auto knn_graph = make_device_matrix<IdxT, IdxT>(handle, ps.n_samples, ps.degree);
+    raft::random::uniformInt<IdxT>(
+      state, knn_graph.data_handle(), knn_graph.size(), 0, ps.n_samples - 1, stream);
+
+    auto metric = raft::distance::DistanceType::L2Expanded;
+
+    index_.emplace(raft::neighbors::experimental::cagra::index<T, IdxT>(
+      handle, metric, make_const_mdspan(dataset.view()), knn_graph.view()));
+  }
+
+  void run_benchmark(::benchmark::State& state) override
+  {
+    raft::neighbors::experimental::cagra::search_params search_params;
+    search_params.max_queries       = 1024;
+    search_params.itopk_size        = params_.itopk_size;
+    search_params.team_size         = 0;
+    search_params.thread_block_size = params_.block_size;
+    search_params.num_parents       = params_.num_parents;
+
+    auto indices   = make_device_matrix<IdxT, IdxT>(handle, params_.n_queries, params_.k);
+    auto distances = make_device_matrix<float, IdxT>(handle, params_.n_queries, params_.k);
+    auto ind_v     = make_device_matrix_view<IdxT, IdxT, row_major>(
+      indices.data_handle(), params_.n_queries, params_.k);
+    auto dist_v = make_device_matrix_view<float, IdxT, row_major>(
+      distances.data_handle(), params_.n_queries, params_.k);
+
+    auto queries_v = make_const_mdspan(queries_.view());
+    loop_on_state(state, [&]() {
+      raft::neighbors::experimental::cagra::search(
+        this->handle, search_params, *this->index_, queries_v, ind_v, dist_v);
+    });
+
+    double data_size  = params_.n_samples * params_.n_dims * sizeof(T);
+    double graph_size = params_.n_samples * params_.degree * sizeof(IdxT);
+
+    int iterations = params_.max_iterations;
+    if (iterations == 0) {
+      // see search_plan_impl::adjust_search_params()
+      double r   = params_.itopk_size / static_cast<float>(params_.num_parents);
+      iterations = 1 + std::min(r * 1.1, r + 10);
+    }
+    state.counters["dataset (GiB)"] = data_size / (1 << 30);
+    state.counters["graph (GiB)"]   = graph_size / (1 << 30);
+    state.counters["n_rows"]        = params_.n_samples;
+    state.counters["n_cols"]        = params_.n_dims;
+    state.counters["degree"]        = params_.degree;
+    state.counters["n_queries"]     = params_.n_queries;
+    state.counters["k"]             = params_.k;
+    state.counters["itopk_size"]    = params_.itopk_size;
+    state.counters["block_size"]    = params_.block_size;
+    state.counters["num_parents"]   = params_.num_parents;
+    state.counters["iterations"]    = iterations;
+  }
+
+ private:
+  const params params_;
+  std::optional<const raft::neighbors::experimental::cagra::index<T, IdxT>> index_;
+  raft::device_matrix<T, IdxT, row_major> queries_;
+};
+
+inline const std::vector<params> generate_inputs()
+{
+  std::vector<params> inputs =
+    raft::util::itertools::product<params>({2000000ull},           // n_samples
+                                           {128, 256, 512, 1024},  // dataset dim
+                                           {1000},                 // n_queries
+                                           {32},                   // k
+                                           {64},                   // knn graph degree
+                                           {64},                   // itopk_size
+                                           {0},                    // block_size
+                                           {1},                    // num_parents
+                                           {0}                     // max_iterations
+    );
+  auto inputs2 = raft::util::itertools::product<params>({2000000ull, 10000000ull},  // n_samples
+                                                        {128},                      // dataset dim
+                                                        {1000},                     // n_queries
+                                                        {32},                       // k
+                                                        {64},  // knn graph degree
+                                                        {64},  // itopk_size
+                                                        {64, 128, 256, 512, 1024},  // block_size
+                                                        {1},                        // num_parents
+                                                        {0}  // max_iterations
+  );
+  inputs.insert(inputs.end(), inputs2.begin(), inputs2.end());
+  return inputs;
+}
+
+const std::vector<params> kCagraInputs = generate_inputs();
+
+#define CAGRA_REGISTER(ValT, IdxT, inputs)                \
+  namespace BENCHMARK_PRIVATE_NAME(knn) {                 \
+  using AnnCagra = CagraBench<ValT, IdxT>;                \
+  RAFT_BENCH_REGISTER(AnnCagra, #ValT "/" #IdxT, inputs); \
+  }
+
+}  // namespace raft::bench::neighbors

cpp/bench/prims/neighbors/knn/cagra_float_uint32_t.cu

@@ -0,0 +1,23 @@
+/*
+ * Copyright (c) 2023, 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 "../cagra_bench.cuh"
+
+namespace raft::bench::neighbors {
+
+CAGRA_REGISTER(float, uint32_t, kCagraInputs);
+
+}  // namespace raft::bench::neighbors