From 2eea80b6dab1bd9eb8b3b20ccd1c627c03d46768 Mon Sep 17 00:00:00 2001 From: Jason Mohoney Date: Wed, 16 Apr 2025 14:37:55 +0000 Subject: [PATCH 001/323] add cuvs --- CMakeLists.txt | 18 +- src/cpp/include/cuv_cluster.h | 174 +++++++++++++++++++ src/cpp/third_party/cmake/fetch_rapids.cmake | 22 +++ src/cpp/third_party/cmake/get_cuvs.cmake | 56 ++++++ test/cpp/test_clustering_benchmark.cpp | 79 +++++++++ test/cpp/test_clustering_correctness.cpp | 157 +++++++++++++++++ test/cpp/test_cuvs_clustering.cpp | 88 ++++++++++ 7 files changed, 593 insertions(+), 1 deletion(-) create mode 100644 src/cpp/include/cuv_cluster.h create mode 100644 src/cpp/third_party/cmake/fetch_rapids.cmake create mode 100644 src/cpp/third_party/cmake/get_cuvs.cmake create mode 100644 test/cpp/test_clustering_benchmark.cpp create mode 100644 test/cpp/test_clustering_correctness.cpp create mode 100644 test/cpp/test_cuvs_clustering.cpp diff --git a/CMakeLists.txt b/CMakeLists.txt index a4d2c003..a337eb9d 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -96,10 +96,25 @@ endif() # --------------------------------------------------------------- # Find Required Packages # --------------------------------------------------------------- +find_package(CuVS REQUIRED) find_package(Torch REQUIRED) find_package(Python3 COMPONENTS Development Interpreter REQUIRED) find_package(Python COMPONENTS Interpreter Development REQUIRED) +include(src/cpp/third_party/cmake/fetch_rapids.cmake) +include(rapids-cmake) +include(rapids-cpm) +include(rapids-cuda) +include(rapids-export) +include(rapids-find) + +rapids_cuda_init_architectures(quake_c) + +rapids_cpm_init() +set(BUILD_CUVS_C_LIBRARY OFF) +include(src/cpp/third_party/cmake/get_cuvs.cmake) + + message(STATUS "Torch include dir: ${TORCH_INCLUDE_DIRS}") message(STATUS "Torch libraries: ${TORCH_LIBRARIES}") message(STATUS "Python include dir: ${Python3_INCLUDE_DIRS}") @@ -131,6 +146,7 @@ target_compile_features(${PROJECT_NAME} PUBLIC cxx_std_17) # Base libraries common to all platforms set(LINK_LIBS ${TORCH_LIBRARIES} + cuvs::cuvs -ffast-math -lpthread -fPIC @@ -221,4 +237,4 @@ message(STATUS "MKL_LINK: ${MKL_LINK}") message(STATUS "MKL_INTERFACE_FULL: ${MKL_INTERFACE_FULL}") message(STATUS "MKL_THREADING: ${MKL_THREADING}") message(STATUS "MKL_MPI: ${MKL_MPI}") -message(STATUS "------------------------------------------------") \ No newline at end of file +message(STATUS "------------------------------------------------") diff --git a/src/cpp/include/cuv_cluster.h b/src/cpp/include/cuv_cluster.h new file mode 100644 index 00000000..6e2ab9b4 --- /dev/null +++ b/src/cpp/include/cuv_cluster.h @@ -0,0 +1,174 @@ +#include +#include // For at::cuda::getCurrentCUDAStream() +#include // RAFT resources (handle) +#include // RAFT device view (make_device_matrix_view, etc.) +#include // cuVS k-means API + +#include +#include +#include +#include +#include + +// Optionally, if you have your own enum, you can use that instead of faiss::METRIC_L2/INNER_PRODUCT. +struct ClusteringResult { + torch::Tensor centroids; + // Each pair is (cluster_vectors, cluster_ids) for one cluster. + std::vector> clusters; +}; + + +inline ClusteringResult ClusterWithCuVS(const torch::Tensor& vectors, + const torch::Tensor& ids, + int64_t num_clusters, + int metric = faiss::METRIC_L2) { + using clock = std::chrono::high_resolution_clock; + auto t0 = clock::now(); + + // Validate input shapes and sizes. + TORCH_CHECK(vectors.dim() == 2, "Input 'vectors' must be a 2D tensor"); + TORCH_CHECK(ids.dim() == 1 || (ids.dim() == 2 && ids.size(1) == 1), + "Input 'ids' must be a 1D tensor or 2D with shape (N,1)"); + TORCH_CHECK(vectors.size(0) == ids.size(0), "Number of ids must match number of vectors"); + TORCH_CHECK(vectors.size(0) >= num_clusters, "Number of clusters cannot exceed number of points"); + + // Move data and ids to GPU (if needed) and ensure contiguous memory. + torch::Tensor data = vectors.to(torch::kCUDA, torch::kFloat32).contiguous(); + torch::Tensor id_dev = ids.to(torch::kCUDA).contiguous(); + int64_t n_samples = data.size(0); + int64_t n_features = data.size(1); + + auto t1 = clock::now(); + std::cout << "[DEBUG] Data transfer & contiguity: " + << std::chrono::duration_cast(t1 - t0).count() << " ms" << std::endl; + + // If using inner-product (cosine), normalize input vectors. + if (metric == faiss::METRIC_INNER_PRODUCT) { + torch::Tensor norms = torch::sqrt((data * data).sum(1, /*keepdim=*/true)); + data = data / norms; + } + + auto t2 = clock::now(); + std::cout << "[DEBUG] Data normalization: " + << std::chrono::duration_cast(t2 - t1).count() << " ms" << std::endl; + + // RAFT handle and stream setup. + raft::resources handle; + cudaStream_t cuda_stream = at::cuda::getCurrentCUDAStream().stream(); + raft::resource::set_cuda_stream(handle, cuda_stream); + + auto t3 = clock::now(); + std::cout << "[DEBUG] RAFT handle & stream setup: " + << std::chrono::duration_cast(t3 - t2).count() << " ms" << std::endl; + + // Wrap the input data in a RAFT device_matrix_view. + float* data_ptr = data.data_ptr(); + auto X_view = raft::make_device_matrix_view(data_ptr, (int)n_samples, (int)n_features); + + // Allocate output centroids on GPU. + torch::Tensor centroids_tensor = torch::empty({num_clusters, n_features}, + torch::TensorOptions().dtype(torch::kFloat32).device(torch::kCUDA)); + float* centroids_ptr = centroids_tensor.data_ptr(); + auto centroids_view = raft::make_device_matrix_view(centroids_ptr, (int)num_clusters, (int)n_features); + + auto t4 = clock::now(); + std::cout << "[DEBUG] Memory allocation for centroids & data wrapping: " + << std::chrono::duration_cast(t4 - t3).count() << " ms" << std::endl; + + // Set up k-means parameters. + cuvs::cluster::kmeans::params params; + params.n_clusters = (int)num_clusters; + params.max_iter = 5; + params.init = cuvs::cluster::kmeans::params::InitMethod::Random; + + // Prepare host-side scalars to capture inertia and iterations. + float inertia = 0.0f; + int iterations = 0; + auto inertia_view = raft::make_host_scalar_view(&inertia); + auto iter_view = raft::make_host_scalar_view(&iterations); + + // Run k-means clustering (fit). + cuvs::cluster::kmeans::fit(handle, params, X_view, std::nullopt, + centroids_view, inertia_view, iter_view); + + auto t5 = clock::now(); + std::cout << "[DEBUG] cuVS fit (k-means clustering): " + << std::chrono::duration_cast(t5 - t4).count() << " ms" << std::endl; + + // If inner-product, renormalize centroids. + if (metric == faiss::METRIC_INNER_PRODUCT) { + torch::Tensor cent_norms = torch::sqrt((centroids_tensor * centroids_tensor).sum(1, /*keepdim=*/true)); + centroids_tensor.div_(cent_norms); + } + + auto t6 = clock::now(); + std::cout << "[DEBUG] Centroid renormalization (if applicable): " + << std::chrono::duration_cast(t6 - t5).count() << " ms" << std::endl; + + // Allocate memory for labels and run prediction. + torch::Tensor labels = torch::empty({n_samples}, torch::TensorOptions().dtype(torch::kInt32).device(torch::kCUDA)); + int* labels_ptr = labels.data_ptr(); + auto labels_view = raft::make_device_vector_view(labels_ptr, (int)n_samples); + + cuvs::cluster::kmeans::predict(handle, params, X_view, std::nullopt, + centroids_view, labels_view, false, + raft::make_host_scalar_view(&inertia)); + + auto t7 = clock::now(); + std::cout << "[DEBUG] cuVS predict: " + << std::chrono::duration_cast(t7 - t6).count() << " ms" << std::endl; + + // Synchronize the stream. + raft::resource::sync_stream(handle); + auto t8 = clock::now(); + std::cout << "[DEBUG] CUDA stream synchronization: " + << std::chrono::duration_cast(t8 - t7).count() << " ms" << std::endl; + + // ----- Grouping (GPU vectorized) ----- + // Sort the labels and get the sorted indices. + torch::Tensor sorted_tuple = std::get<1>(torch::sort(labels)); // sorted indices only, since sorted labels are not needed + torch::Tensor sorted_labels = labels.index_select(0, sorted_tuple); + + // Reorder the data and ids using the sorted indices. + torch::Tensor sorted_data = data.index_select(0, sorted_tuple); + torch::Tensor sorted_ids = id_dev.index_select(0, sorted_tuple); + + // Compute per-cluster counts using torch::bincount. + torch::Tensor counts = torch::bincount(sorted_labels.to(torch::kInt64), /*weights=*/{}, num_clusters); + + // Transfer counts to CPU and build a vector for split sizes. + auto counts_cpu = counts.to(torch::kCPU); + std::vector split_sizes(counts_cpu.data_ptr(), counts_cpu.data_ptr() + counts_cpu.numel()); + + auto t9 = clock::now(); + std::cout << "[DEBUG] Sorting, counting, and preparing split sizes: " + << std::chrono::duration_cast(t9 - t8).count() << " ms" << std::endl; + + // Split the sorted data and ids into clusters. + std::vector cluster_vectors = torch::split(sorted_data, split_sizes, 0); + std::vector cluster_ids = torch::split(sorted_ids, split_sizes, 0); + + auto t10 = clock::now(); + std::cout << "[DEBUG] Splitting into clusters: " + << std::chrono::duration_cast(t10 - t9).count() << " ms" << std::endl; + + // Build the final clustering result. + std::vector> clusters; + clusters.reserve(num_clusters); + for (int i = 0; i < num_clusters; ++i) { + clusters.emplace_back(cluster_vectors[i], cluster_ids[i]); + } + + auto t_end = clock::now(); + std::cout << "[DEBUG] Total grouping time: " + << std::chrono::duration_cast(t_end - t8).count() << " ms" << std::endl; + + ClusteringResult result; + result.centroids = centroids_tensor; // Shape: (num_clusters, n_features) + result.clusters = std::move(clusters); + + std::cout << "[DEBUG] Total cuVS clustering time: " + << std::chrono::duration_cast(t_end - t0).count() << " ms" << std::endl; + + return result; +} diff --git a/src/cpp/third_party/cmake/fetch_rapids.cmake b/src/cpp/third_party/cmake/fetch_rapids.cmake new file mode 100644 index 00000000..0ec5fa04 --- /dev/null +++ b/src/cpp/third_party/cmake/fetch_rapids.cmake @@ -0,0 +1,22 @@ +# ============================================================================= +# Copyright (c) 2023-2024, 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. + +# Use this variable to update RAPIDS and cuVS versions +set(RAPIDS_VERSION "25.06") +set(rapids-cmake-version ${RAPIDS_VERSION}) + +if(NOT EXISTS ${CMAKE_CURRENT_BINARY_DIR}/CUVS_RAPIDS.cmake) + file(DOWNLOAD https://raw.githubusercontent.com/rapidsai/rapids-cmake/branch-${RAPIDS_VERSION}/RAPIDS.cmake + ${CMAKE_CURRENT_BINARY_DIR}/CUVS_RAPIDS.cmake) +endif() +include(${CMAKE_CURRENT_BINARY_DIR}/CUVS_RAPIDS.cmake) diff --git a/src/cpp/third_party/cmake/get_cuvs.cmake b/src/cpp/third_party/cmake/get_cuvs.cmake new file mode 100644 index 00000000..a2a4e884 --- /dev/null +++ b/src/cpp/third_party/cmake/get_cuvs.cmake @@ -0,0 +1,56 @@ +# ============================================================================= +# Copyright (c) 2023-2024, 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. + +# Use RAPIDS_VERSION from cmake/thirdparty/fetch_rapids.cmake +set(CUVS_VERSION "${RAPIDS_VERSION}") +set(CUVS_FORK "rapidsai") +set(CUVS_PINNED_TAG "branch-${RAPIDS_VERSION}") + +function(find_and_configure_cuvs) + set(oneValueArgs VERSION FORK PINNED_TAG ENABLE_NVTX BUILD_CUVS_C_LIBRARY) + cmake_parse_arguments(PKG "${options}" "${oneValueArgs}" + "${multiValueArgs}" ${ARGN} ) + + + set(CUVS_COMPONENTS "") + if(PKG_BUILD_CUVS_C_LIBRARY) + string(APPEND CUVS_COMPONENTS " c_api") + endif() + #----------------------------------------------------- + # Invoke CPM find_package() + #----------------------------------------------------- + rapids_cpm_find(cuvs ${PKG_VERSION} + GLOBAL_TARGETS cuvs::cuvs + BUILD_EXPORT_SET cuvs-examples-exports + INSTALL_EXPORT_SET cuvs-examples-exports + COMPONENTS ${CUVS_COMPONENTS} + CPM_ARGS + GIT_REPOSITORY https://github.com/${PKG_FORK}/cuvs.git + GIT_TAG ${PKG_PINNED_TAG} + SOURCE_SUBDIR cpp + OPTIONS + "BUILD_C_LIBRARY ${PKG_BUILD_CUVS_C_LIBRARY}" + "BUILD_TESTS OFF" + "CUVS_NVTX ${PKG_ENABLE_NVTX}" + ) +endfunction() + +# Change pinned tag here to test a commit in CI +# To use a different CUVS locally, set the CMake variable +# CPM_cuvs_SOURCE=/path/to/local/cuvs +find_and_configure_cuvs(VERSION ${CUVS_VERSION}.00 + FORK ${CUVS_FORK} + PINNED_TAG ${CUVS_PINNED_TAG} + ENABLE_NVTX OFF + BUILD_CUVS_C_LIBRARY ${BUILD_CUVS_C_LIBRARY} +) diff --git a/test/cpp/test_clustering_benchmark.cpp b/test/cpp/test_clustering_benchmark.cpp new file mode 100644 index 00000000..733ca95f --- /dev/null +++ b/test/cpp/test_clustering_benchmark.cpp @@ -0,0 +1,79 @@ +// benchmark_clustering_gtest.cpp +// +// This Google Test benchmarks two clustering implementations: +// 1. FAISS-based CPU clustering (kmeans with use_gpu == false) +// 2. cuVS-based GPU clustering (ClusterWithCuVS) +// on a dataset of 4,000,000 vectors (of dimension 128) and 10,000 clusters. +// The test prints the elapsed time (milliseconds) for each method. + +#include +#include +#include +#include +#include "clustering.h" // Declaration of kmeans() and ClusterWithCuVS() +#include "cuv_cluster.h" + +using namespace std::chrono; + +TEST(ClusteringBenchmark, CPU_vs_cuVS) { + // Verify CUDA is available. + if (!torch::cuda::is_available()) { + GTEST_SKIP() << "CUDA is not available; skipping benchmark."; + } + + // Define benchmark parameters. + const int64_t num_vectors = 10000000; // 10 million vectors + const int64_t dim = 128; + const int num_clusters = 10000; + const int niter = 5; // number of clustering iterations + + std::cout << "Benchmarking clustering with " << num_vectors << " vectors, dimension " + << dim << ", and " << num_clusters << " clusters." << std::endl; + + // Create CPU tensors. + auto options_float_cpu = torch::TensorOptions().dtype(torch::kFloat32).device(torch::kCPU); + auto options_int64_cpu = torch::TensorOptions().dtype(torch::kInt64).device(torch::kCPU); + torch::Tensor vectors_cpu = torch::randn({num_vectors, dim}, options_float_cpu).contiguous(); + torch::Tensor ids_cpu = torch::arange(0, num_vectors, options_int64_cpu).contiguous(); + + // Create CUDA tensors. + auto options_float_cuda = torch::TensorOptions().dtype(torch::kFloat32).device(torch::kCUDA); + auto options_int64_cuda = torch::TensorOptions().dtype(torch::kInt64).device(torch::kCUDA); + torch::Tensor vectors_cuda = vectors_cpu.to(options_float_cuda).contiguous(); + torch::Tensor ids_cuda = ids_cpu.to(options_int64_cuda).contiguous(); + + // Warm up the GPU using the cuVS clustering (to remove initialization overhead). + std::cout << "[DEBUG] Warming up GPU with cuVS clustering..." << std::endl; + { + auto dummy = ClusterWithCuVS(vectors_cuda, ids_cuda, 10, faiss::METRIC_L2); + torch::cuda::synchronize(); + } + std::cout << "[DEBUG] Warm-up complete." << std::endl; + + // Benchmark FAISS CPU clustering. + std::cout << "[DEBUG] Starting FAISS CPU clustering benchmark..." << std::endl; + auto start_cpu = high_resolution_clock::now(); + auto clustering_cpu = kmeans(vectors_cpu, ids_cpu, num_clusters, faiss::METRIC_L2, niter, false, torch::Tensor()); + auto end_cpu = high_resolution_clock::now(); + auto duration_cpu = duration_cast(end_cpu - start_cpu).count(); + std::cout << "FAISS CPU clustering time: " << duration_cpu << " ms" << std::endl; + + // Benchmark cuVS GPU clustering. + std::cout << "[DEBUG] Starting cuVS clustering benchmark..." << std::endl; + auto start_cuvs = high_resolution_clock::now(); + auto clustering_cuvs = ClusterWithCuVS(vectors_cuda, ids_cuda, num_clusters, faiss::METRIC_L2); + torch::cuda::synchronize(); // ensure GPU work is complete + auto end_cuvs = high_resolution_clock::now(); + auto duration_cuvs = duration_cast(end_cuvs - start_cuvs).count(); + std::cout << "cuVS clustering time: " << duration_cuvs << " ms" << std::endl; + + // Print summary. + std::cout << "---------------------------" << std::endl; + std::cout << "FAISS CPU clustering took " << duration_cpu << " ms." << std::endl; + std::cout << "cuVS clustering took " << duration_cuvs << " ms." << std::endl; + std::cout << "---------------------------" << std::endl; + + // Basic checks. + EXPECT_GT(duration_cpu, 0); + EXPECT_GT(duration_cuvs, 0); +} diff --git a/test/cpp/test_clustering_correctness.cpp b/test/cpp/test_clustering_correctness.cpp new file mode 100644 index 00000000..01fff623 --- /dev/null +++ b/test/cpp/test_clustering_correctness.cpp @@ -0,0 +1,157 @@ +// test_clustering_correctness.cpp +// +// This file tests the correctness of two clustering implementations: +// 1. FAISS-based clustering on CPU: kmeans(..., use_gpu = false) +// 2. cuVS-based clustering: ClusterWithCuVS(...) +// The tests compute the mean squared error (MSE) between each input vector and its +// corresponding cluster centroid and require that the MSE from the two methods +// agree within a reasonable tolerance. Additionally, the tests verify that each method +// returns centroids of the correct shape and partitions the input set completely. + +#include +#include +#include +#include +#include "clustering.h" // Declaration of kmeans(), ClusterWithCuVS(), Clustering, ClusteringResult, and MetricType + +// Helper to compute MSE for FAISS-based clustering results. +// The 'vectors' field of Clustering is a vector of tensors (one per cluster). +double ComputeMSE_Faiss(const torch::Tensor& centroids, const std::vector& cluster_vectors) { + double total_error = 0.0; + int64_t total_count = 0; + // Ensure centroids are on CPU. + torch::Tensor centroids_cpu = centroids.to(torch::kCPU); + for (int i = 0; i < centroids_cpu.size(0); ++i) { + if (cluster_vectors[i].size(0) > 0) { + // Move the cluster vectors to CPU. + torch::Tensor cluster_cpu = cluster_vectors[i].to(torch::kCPU); + torch::Tensor diff = cluster_cpu - centroids_cpu[i].unsqueeze(0); + total_error += diff.pow(2).sum().item(); + total_count += cluster_cpu.size(0); + } + } + return total_count > 0 ? total_error / total_count : 0.0; +} + +// Helper to compute MSE for cuVS-based clustering results. +// Here, clusters are returned as a vector of pairs where the first tensor is the cluster vectors. +double ComputeMSE_cuVS(const torch::Tensor& centroids, + const std::vector>& clusters) { + double total_error = 0.0; + int64_t total_count = 0; + torch::Tensor centroids_cpu = centroids.to(torch::kCPU); + for (int i = 0; i < centroids_cpu.size(0); ++i) { + const torch::Tensor& cluster_vectors = clusters[i].first; + if (cluster_vectors.size(0) > 0) { + torch::Tensor cluster_cpu = cluster_vectors.to(torch::kCPU); + torch::Tensor diff = cluster_cpu - centroids_cpu[i].unsqueeze(0); + total_error += diff.pow(2).sum().item(); + total_count += cluster_cpu.size(0); + } + } + return total_count > 0 ? total_error / total_count : 0.0; +} + +// Test fixture for clustering correctness tests. +class ClusteringCorrectnessTest : public ::testing::Test { + protected: + // Use a moderate number of vectors for correctness testing. + const int64_t num_vectors_ = 5000; + const int64_t dim_ = 64; + const int num_clusters_ = 20; + // CPU tensors. + torch::Tensor vectors_cpu_; + torch::Tensor ids_cpu_; + // CUDA tensors. + torch::Tensor vectors_cuda_; + torch::Tensor ids_cuda_; + + void SetUp() override { + if (!torch::cuda::is_available()) { + GTEST_SKIP() << "CUDA is not available; skipping clustering correctness tests."; + } + // Create CPU tensors. + auto options_float_cpu = torch::TensorOptions().dtype(torch::kFloat32).device(torch::kCPU); + auto options_int64_cpu = torch::TensorOptions().dtype(torch::kInt64).device(torch::kCPU); + vectors_cpu_ = torch::randn({num_vectors_, dim_}, options_float_cpu).contiguous(); + ids_cpu_ = torch::arange(0, num_vectors_, options_int64_cpu).contiguous(); + // Create CUDA versions. + auto options_float_cuda = torch::TensorOptions().dtype(torch::kFloat32).device(torch::kCUDA); + auto options_int64_cuda = torch::TensorOptions().dtype(torch::kInt64).device(torch::kCUDA); + vectors_cuda_ = vectors_cpu_.to(options_float_cuda).contiguous(); + ids_cuda_ = ids_cpu_.to(options_int64_cuda).contiguous(); + + std::cout << "[DEBUG] SetUp complete: " + << "num_vectors = " << num_vectors_ + << ", dim = " << dim_ + << ", clusters = " << num_clusters_ << std::endl; + std::cout.flush(); + } +}; + +TEST_F(ClusteringCorrectnessTest, CompareClusteringMethods_L2) { + const int niter = 20; // number of iterations for clustering + + std::cout << "[DEBUG] Starting FAISS CPU clustering ..." << std::endl; + std::cout.flush(); + auto clustering_cpu = kmeans(vectors_cpu_, ids_cpu_, num_clusters_, faiss::METRIC_L2, niter, false, torch::Tensor()); + std::cout << "[DEBUG] Completed FAISS CPU clustering." << std::endl; + std::cout << "[DEBUG] CPU centroids shape: "; + for (auto s : clustering_cpu->centroids.sizes()) { std::cout << s << " "; } + std::cout << std::endl; + std::cout.flush(); + + std::cout << "[DEBUG] Starting cuVS clustering ..." << std::endl; + std::cout.flush(); + ClusteringResult clustering_cuvs = ClusterWithCuVS(vectors_cuda_, ids_cuda_, num_clusters_, faiss::METRIC_L2); + std::cout << "[DEBUG] Completed cuVS clustering." << std::endl; + std::cout << "[DEBUG] cuVS centroids shape: "; + for (auto s : clustering_cuvs.centroids.sizes()) { std::cout << s << " "; } + std::cout << std::endl; + std::cout.flush(); + + // Compute MSE for each method. + double mse_cpu = ComputeMSE_Faiss(clustering_cpu->centroids, clustering_cpu->vectors); + double mse_cuvs = ComputeMSE_cuVS(clustering_cuvs.centroids, clustering_cuvs.clusters); + + std::cout << "[DEBUG] L2 Clustering MSEs:" << std::endl; + std::cout << " FAISS CPU: " << mse_cpu << std::endl; + std::cout << " cuVS : " << mse_cuvs << std::endl; + std::cout.flush(); + + // Assert that MSE values are similar (within 20% relative difference) + double tol = 0.20; + ASSERT_NEAR(mse_cpu, mse_cuvs, tol * mse_cpu); +} + +TEST_F(ClusteringCorrectnessTest, CompareClusteringMethods_InnerProduct) { + const int niter = 20; + std::cout << "[DEBUG] Starting FAISS CPU clustering (InnerProduct) ..." << std::endl; + std::cout.flush(); + auto clustering_cpu = kmeans(vectors_cpu_, ids_cpu_, num_clusters_, faiss::METRIC_INNER_PRODUCT, niter, false, torch::Tensor()); + std::cout << "[DEBUG] Completed FAISS CPU clustering (InnerProduct)." << std::endl; + std::cout << "[DEBUG] CPU centroids shape: "; + for (auto s : clustering_cpu->centroids.sizes()) { std::cout << s << " "; } + std::cout << std::endl; + std::cout.flush(); + + std::cout << "[DEBUG] Starting cuVS clustering (InnerProduct) ..." << std::endl; + std::cout.flush(); + ClusteringResult clustering_cuvs = ClusterWithCuVS(vectors_cuda_, ids_cuda_, num_clusters_, faiss::METRIC_INNER_PRODUCT); + std::cout << "[DEBUG] Completed cuVS clustering (InnerProduct)." << std::endl; + std::cout << "[DEBUG] cuVS centroids shape: "; + for (auto s : clustering_cuvs.centroids.sizes()) { std::cout << s << " "; } + std::cout << std::endl; + std::cout.flush(); + + double mse_cpu = ComputeMSE_Faiss(clustering_cpu->centroids, clustering_cpu->vectors); + double mse_cuvs = ComputeMSE_cuVS(clustering_cuvs.centroids, clustering_cuvs.clusters); + + std::cout << "[DEBUG] Inner-product Clustering MSEs:" << std::endl; + std::cout << " FAISS CPU: " << mse_cpu << std::endl; + std::cout << " cuVS : " << mse_cuvs << std::endl; + std::cout.flush(); + + double tol = 0.25; + ASSERT_NEAR(mse_cpu, mse_cuvs, tol * mse_cpu); +} diff --git a/test/cpp/test_cuvs_clustering.cpp b/test/cpp/test_cuvs_clustering.cpp new file mode 100644 index 00000000..394cf4e7 --- /dev/null +++ b/test/cpp/test_cuvs_clustering.cpp @@ -0,0 +1,88 @@ +// test_cuvs_clustering.cpp +// +// This file tests the cuVS-based k-means clustering implementation. +// It verifies that the centroids are computed correctly and that the +// entire dataset is partitioned among the clusters. + +#include +#include +#include "cuv_cluster.h" // Contains ClusterWithCuVS, ClusteringResult, and MetricType + +// Test that clustering with L2 metric produces the expected output shapes, +// and that every input vector is assigned to one and only one cluster. +TEST(CUVSClusteringTest, ClusteringL2) { + // Skip the test if CUDA is not available. + if (!torch::cuda::is_available()) { + GTEST_SKIP() << "CUDA is not available, skipping cuVS clustering test."; + } + + const int64_t num_vectors = 1000; + const int64_t dim = 32; + const int64_t num_clusters = 10; + + // Generate random data and corresponding sequential IDs on the GPU. + auto vectors = torch::randn({num_vectors, dim}, torch::kCUDA).to(torch::kFloat32).contiguous(); + auto ids = torch::arange(0, num_vectors, torch::kCUDA).contiguous(); + + // Call the cuVS-based clustering function using L2 metric. + ClusteringResult result = ClusterWithCuVS(vectors, ids, num_clusters, faiss::METRIC_L2); + + // Validate that the centroids tensor has shape [num_clusters, dim]. + ASSERT_EQ(result.centroids.dim(), 2); + ASSERT_EQ(result.centroids.size(0), num_clusters); + ASSERT_EQ(result.centroids.size(1), dim); + + // Verify that all input vectors are partitioned by summing counts from each cluster. + int64_t total_count = 0; + for (const auto& cluster : result.clusters) { + // Each cluster is returned as a pair: + torch::Tensor cluster_vectors = cluster.first; + torch::Tensor cluster_ids = cluster.second; + // The number of vectors in each cluster must match the number of IDs. + ASSERT_EQ(cluster_vectors.size(0), cluster_ids.size(0)); + total_count += cluster_vectors.size(0); + } + ASSERT_EQ(total_count, num_vectors); +} + +// Test that clustering with the inner product metric (which performs normalization) +// yields centroids that are unit-norm and partitions the dataset correctly. +TEST(CUVSClusteringTest, ClusteringInnerProduct) { + if (!torch::cuda::is_available()) { + GTEST_SKIP() << "CUDA is not available, skipping cuVS clustering test."; + } + + const int64_t num_vectors = 1000; + const int64_t dim = 64; + const int64_t num_clusters = 8; + + // Generate random input data and corresponding IDs. + auto vectors = torch::randn({num_vectors, dim}, torch::kCUDA).to(torch::kFloat32).contiguous(); + auto ids = torch::arange(0, num_vectors, torch::kCUDA).contiguous(); + + // Run clustering with inner product metric. The implementation should normalize the vectors. + ClusteringResult result = ClusterWithCuVS(vectors, ids, num_clusters, faiss::METRIC_INNER_PRODUCT); + + // Check that the centroids tensor has the expected shape. + ASSERT_EQ(result.centroids.dim(), 2); + ASSERT_EQ(result.centroids.size(0), num_clusters); + ASSERT_EQ(result.centroids.size(1), dim); + + // Optionally, validate that each centroid is normalized (i.e. its L2 norm is ~1) + auto cent_norms = result.centroids.norm(2, /*dim=*/1); + for (int64_t i = 0; i < cent_norms.size(0); ++i) { + float norm_val = cent_norms[i].item(); + // Allow a small numerical tolerance. + ASSERT_NEAR(norm_val, 1.0, 1e-3); + } + + // Confirm that the total number of vectors across all clusters is equal to the input size. + int64_t total_count = 0; + for (const auto& cluster : result.clusters) { + torch::Tensor cluster_vectors = cluster.first; + torch::Tensor cluster_ids = cluster.second; + ASSERT_EQ(cluster_vectors.size(0), cluster_ids.size(0)); + total_count += cluster_vectors.size(0); + } + ASSERT_EQ(total_count, num_vectors); +} From d68423fd1e53d63d1e1451f761a51618bade733c Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 16 Apr 2025 10:14:43 -0500 Subject: [PATCH 002/323] cleanup cuvs cmake and clustering --- CMakeLists.txt | 50 +++++------ src/cpp/include/clustering.h | 45 +++++++++- src/cpp/include/common.h | 8 -- src/cpp/include/cuv_cluster.h | 160 ---------------------------------- src/cpp/src/clustering.cpp | 147 ++++++++++++++++++++++++++----- 5 files changed, 193 insertions(+), 217 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index a337eb9d..4cf50474 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -12,18 +12,6 @@ set(Torch_USE_CUDA OFF CACHE BOOL "Force disable CUDA in Torch") set(Torch_NO_CUDA ON CACHE BOOL "Force disable CUDA in Torch") set(USE_CUDA OFF CACHE BOOL "Force disable CUDA globally") -# QUAKE_ENABLE_GPU: Enable GPU support for Faiss -# Default: OFF -if(QUAKE_ENABLE_GPU) - set(FAISS_ENABLE_GPU ON) -else() - set(FAISS_ENABLE_GPU OFF) -endif() - -if(QUAKE_ENABLE_GPU) - add_compile_definitions(FAISS_ENABLE_GPU) -endif() - if(QUAKE_USE_NUMA) add_compile_definitions(QUAKE_USE_NUMA) endif() @@ -64,6 +52,24 @@ set(project_BINDINGS_DIR ${CPP_SOURCE}/bindings) set(project_THIRD_PARTY_DIR ${CPP_SOURCE}/third_party) set(project_TEST_DIR test/cpp) +if(QUAKE_ENABLE_GPU) + find_package(CuVS REQUIRED) + include(${project_THIRD_PARTY_DIR}cmake/fetch_rapids.cmake) + include(rapids-cmake) + include(rapids-cpm) + include(rapids-cuda) + include(rapids-export) + include(rapids-find) + + rapids_cuda_init_architectures(quake_c) + + rapids_cpm_init() + set(BUILD_CUVS_C_LIBRARY OFF) + include(${project_THIRD_PARTY_DIR}/cmake/get_cuvs.cmake) + + add_compile_definitions(QUAKE_ENABLE_GPU) +endif() + # --------------------------------------------------------------- # Print out Compiler and Path Information # --------------------------------------------------------------- @@ -96,25 +102,10 @@ endif() # --------------------------------------------------------------- # Find Required Packages # --------------------------------------------------------------- -find_package(CuVS REQUIRED) find_package(Torch REQUIRED) find_package(Python3 COMPONENTS Development Interpreter REQUIRED) find_package(Python COMPONENTS Interpreter Development REQUIRED) -include(src/cpp/third_party/cmake/fetch_rapids.cmake) -include(rapids-cmake) -include(rapids-cpm) -include(rapids-cuda) -include(rapids-export) -include(rapids-find) - -rapids_cuda_init_architectures(quake_c) - -rapids_cpm_init() -set(BUILD_CUVS_C_LIBRARY OFF) -include(src/cpp/third_party/cmake/get_cuvs.cmake) - - message(STATUS "Torch include dir: ${TORCH_INCLUDE_DIRS}") message(STATUS "Torch libraries: ${TORCH_LIBRARIES}") message(STATUS "Python include dir: ${Python3_INCLUDE_DIRS}") @@ -146,7 +137,6 @@ target_compile_features(${PROJECT_NAME} PUBLIC cxx_std_17) # Base libraries common to all platforms set(LINK_LIBS ${TORCH_LIBRARIES} - cuvs::cuvs -ffast-math -lpthread -fPIC @@ -165,6 +155,10 @@ elseif(UNIX) if(QUAKE_USE_NUMA) list(APPEND LINK_LIBS -lnuma) endif() + + if (QUAKE_ENABLE_GPU) + list(APPEND LINK_LIBS cuvs::cuvs) + endif() else() # unsupported platform message(FATAL_ERROR "Unsupported platform") diff --git a/src/cpp/include/clustering.h b/src/cpp/include/clustering.h index e4b00229..cc98bebc 100644 --- a/src/cpp/include/clustering.h +++ b/src/cpp/include/clustering.h @@ -9,12 +9,55 @@ #include +#ifdef QUAKE_ENABLE_GPU +#include // RAFT resources (handle) +#include // RAFT device view (make_device_matrix_view, etc.) +#include // cuVS k-means API +#endif + class IndexPartition; +/** + * @brief Clusters vectors into partitions using faiss::Clustering + * + * + * @param vectors The vectors to cluster. + * @param ids The IDs of the vectors. + * @param n_clusters The number of clusters to create. + * @param metric_type The metric type to use for clustering. + * @param niter The number of iterations to run k-means. + * @param initial_centroids The initial centroids to use for k-means. + */ +shared_ptr kmeans_cpu(Tensor vectors, + Tensor ids, + int n_clusters, + MetricType metric_type, + int niter = 5, + Tensor initial_centroids = Tensor()); + +/** + * @brief Clusters vectors into partitions using CuVS k-means. + * + * + * @param vectors The vectors to cluster. + * @param ids The IDs of the vectors. + * @param n_clusters The number of clusters to create. + * @param metric_type The metric type to use for clustering. + * @param niter The number of iterations to run k-means. + * @param initial_centroids The initial centroids to use for k-means. + */ +#ifdef QUAKE_ENABLE_GPU +shared_ptr kmeans_cuvs(Tensor vectors, + Tensor ids, + int n_clusters, + MetricType metric_type, + int niter = 5, + Tensor initial_centroids = Tensor()); +#endif + /** * @brief Clusters vectors into partitions using k-means. * - * Uses the faiss::Clustering class to cluster vectors into n_clusters partitions. * * @param vectors The vectors to cluster. * @param ids The IDs of the vectors. diff --git a/src/cpp/include/common.h b/src/cpp/include/common.h index 0938591c..ff95b2a2 100644 --- a/src/cpp/include/common.h +++ b/src/cpp/include/common.h @@ -38,14 +38,6 @@ #include #endif -#ifdef FAISS_ENABLE_GPU -#include -#include -#include -#include -#include -#endif - using torch::Tensor; using std::vector; using std::unordered_map; diff --git a/src/cpp/include/cuv_cluster.h b/src/cpp/include/cuv_cluster.h index 6e2ab9b4..32fe9315 100644 --- a/src/cpp/include/cuv_cluster.h +++ b/src/cpp/include/cuv_cluster.h @@ -1,15 +1,3 @@ -#include -#include // For at::cuda::getCurrentCUDAStream() -#include // RAFT resources (handle) -#include // RAFT device view (make_device_matrix_view, etc.) -#include // cuVS k-means API - -#include -#include -#include -#include -#include - // Optionally, if you have your own enum, you can use that instead of faiss::METRIC_L2/INNER_PRODUCT. struct ClusteringResult { torch::Tensor centroids; @@ -22,153 +10,5 @@ inline ClusteringResult ClusterWithCuVS(const torch::Tensor& vectors, const torch::Tensor& ids, int64_t num_clusters, int metric = faiss::METRIC_L2) { - using clock = std::chrono::high_resolution_clock; - auto t0 = clock::now(); - - // Validate input shapes and sizes. - TORCH_CHECK(vectors.dim() == 2, "Input 'vectors' must be a 2D tensor"); - TORCH_CHECK(ids.dim() == 1 || (ids.dim() == 2 && ids.size(1) == 1), - "Input 'ids' must be a 1D tensor or 2D with shape (N,1)"); - TORCH_CHECK(vectors.size(0) == ids.size(0), "Number of ids must match number of vectors"); - TORCH_CHECK(vectors.size(0) >= num_clusters, "Number of clusters cannot exceed number of points"); - - // Move data and ids to GPU (if needed) and ensure contiguous memory. - torch::Tensor data = vectors.to(torch::kCUDA, torch::kFloat32).contiguous(); - torch::Tensor id_dev = ids.to(torch::kCUDA).contiguous(); - int64_t n_samples = data.size(0); - int64_t n_features = data.size(1); - - auto t1 = clock::now(); - std::cout << "[DEBUG] Data transfer & contiguity: " - << std::chrono::duration_cast(t1 - t0).count() << " ms" << std::endl; - - // If using inner-product (cosine), normalize input vectors. - if (metric == faiss::METRIC_INNER_PRODUCT) { - torch::Tensor norms = torch::sqrt((data * data).sum(1, /*keepdim=*/true)); - data = data / norms; - } - - auto t2 = clock::now(); - std::cout << "[DEBUG] Data normalization: " - << std::chrono::duration_cast(t2 - t1).count() << " ms" << std::endl; - - // RAFT handle and stream setup. - raft::resources handle; - cudaStream_t cuda_stream = at::cuda::getCurrentCUDAStream().stream(); - raft::resource::set_cuda_stream(handle, cuda_stream); - - auto t3 = clock::now(); - std::cout << "[DEBUG] RAFT handle & stream setup: " - << std::chrono::duration_cast(t3 - t2).count() << " ms" << std::endl; - - // Wrap the input data in a RAFT device_matrix_view. - float* data_ptr = data.data_ptr(); - auto X_view = raft::make_device_matrix_view(data_ptr, (int)n_samples, (int)n_features); - - // Allocate output centroids on GPU. - torch::Tensor centroids_tensor = torch::empty({num_clusters, n_features}, - torch::TensorOptions().dtype(torch::kFloat32).device(torch::kCUDA)); - float* centroids_ptr = centroids_tensor.data_ptr(); - auto centroids_view = raft::make_device_matrix_view(centroids_ptr, (int)num_clusters, (int)n_features); - - auto t4 = clock::now(); - std::cout << "[DEBUG] Memory allocation for centroids & data wrapping: " - << std::chrono::duration_cast(t4 - t3).count() << " ms" << std::endl; - - // Set up k-means parameters. - cuvs::cluster::kmeans::params params; - params.n_clusters = (int)num_clusters; - params.max_iter = 5; - params.init = cuvs::cluster::kmeans::params::InitMethod::Random; - // Prepare host-side scalars to capture inertia and iterations. - float inertia = 0.0f; - int iterations = 0; - auto inertia_view = raft::make_host_scalar_view(&inertia); - auto iter_view = raft::make_host_scalar_view(&iterations); - - // Run k-means clustering (fit). - cuvs::cluster::kmeans::fit(handle, params, X_view, std::nullopt, - centroids_view, inertia_view, iter_view); - - auto t5 = clock::now(); - std::cout << "[DEBUG] cuVS fit (k-means clustering): " - << std::chrono::duration_cast(t5 - t4).count() << " ms" << std::endl; - - // If inner-product, renormalize centroids. - if (metric == faiss::METRIC_INNER_PRODUCT) { - torch::Tensor cent_norms = torch::sqrt((centroids_tensor * centroids_tensor).sum(1, /*keepdim=*/true)); - centroids_tensor.div_(cent_norms); - } - - auto t6 = clock::now(); - std::cout << "[DEBUG] Centroid renormalization (if applicable): " - << std::chrono::duration_cast(t6 - t5).count() << " ms" << std::endl; - - // Allocate memory for labels and run prediction. - torch::Tensor labels = torch::empty({n_samples}, torch::TensorOptions().dtype(torch::kInt32).device(torch::kCUDA)); - int* labels_ptr = labels.data_ptr(); - auto labels_view = raft::make_device_vector_view(labels_ptr, (int)n_samples); - - cuvs::cluster::kmeans::predict(handle, params, X_view, std::nullopt, - centroids_view, labels_view, false, - raft::make_host_scalar_view(&inertia)); - - auto t7 = clock::now(); - std::cout << "[DEBUG] cuVS predict: " - << std::chrono::duration_cast(t7 - t6).count() << " ms" << std::endl; - - // Synchronize the stream. - raft::resource::sync_stream(handle); - auto t8 = clock::now(); - std::cout << "[DEBUG] CUDA stream synchronization: " - << std::chrono::duration_cast(t8 - t7).count() << " ms" << std::endl; - - // ----- Grouping (GPU vectorized) ----- - // Sort the labels and get the sorted indices. - torch::Tensor sorted_tuple = std::get<1>(torch::sort(labels)); // sorted indices only, since sorted labels are not needed - torch::Tensor sorted_labels = labels.index_select(0, sorted_tuple); - - // Reorder the data and ids using the sorted indices. - torch::Tensor sorted_data = data.index_select(0, sorted_tuple); - torch::Tensor sorted_ids = id_dev.index_select(0, sorted_tuple); - - // Compute per-cluster counts using torch::bincount. - torch::Tensor counts = torch::bincount(sorted_labels.to(torch::kInt64), /*weights=*/{}, num_clusters); - - // Transfer counts to CPU and build a vector for split sizes. - auto counts_cpu = counts.to(torch::kCPU); - std::vector split_sizes(counts_cpu.data_ptr(), counts_cpu.data_ptr() + counts_cpu.numel()); - - auto t9 = clock::now(); - std::cout << "[DEBUG] Sorting, counting, and preparing split sizes: " - << std::chrono::duration_cast(t9 - t8).count() << " ms" << std::endl; - - // Split the sorted data and ids into clusters. - std::vector cluster_vectors = torch::split(sorted_data, split_sizes, 0); - std::vector cluster_ids = torch::split(sorted_ids, split_sizes, 0); - - auto t10 = clock::now(); - std::cout << "[DEBUG] Splitting into clusters: " - << std::chrono::duration_cast(t10 - t9).count() << " ms" << std::endl; - - // Build the final clustering result. - std::vector> clusters; - clusters.reserve(num_clusters); - for (int i = 0; i < num_clusters; ++i) { - clusters.emplace_back(cluster_vectors[i], cluster_ids[i]); - } - - auto t_end = clock::now(); - std::cout << "[DEBUG] Total grouping time: " - << std::chrono::duration_cast(t_end - t8).count() << " ms" << std::endl; - - ClusteringResult result; - result.centroids = centroids_tensor; // Shape: (num_clusters, n_features) - result.clusters = std::move(clusters); - - std::cout << "[DEBUG] Total cuVS clustering time: " - << std::chrono::duration_cast(t_end - t0).count() << " ms" << std::endl; - - return result; } diff --git a/src/cpp/src/clustering.cpp b/src/cpp/src/clustering.cpp index ebbc88e0..50052a72 100644 --- a/src/cpp/src/clustering.cpp +++ b/src/cpp/src/clustering.cpp @@ -10,12 +10,115 @@ #include "index_partition.h" #include -shared_ptr kmeans(Tensor vectors, +#ifdef QUAKE_ENABLE_GPU +shared_ptr kmeans_cuvs(Tensor vectors, + Tensor ids, + int num_clusters, + MetricType metric, + int niter, + Tensor initial_centroids) { + // Validate input shapes and sizes. + TORCH_CHECK(vectors.dim() == 2, "Input 'vectors' must be a 2D tensor"); + TORCH_CHECK(ids.dim() == 1 || (ids.dim() == 2 && ids.size(1) == 1), + "Input 'ids' must be a 1D tensor or 2D with shape (N,1)"); + TORCH_CHECK(vectors.size(0) == ids.size(0), "Number of ids must match number of vectors"); + TORCH_CHECK(vectors.size(0) >= num_clusters, "Number of clusters cannot exceed number of points"); + + int64_t n_samples = data.size(0); + int64_t n_features = data.size(1); + + // If using inner-product (cosine), normalize input vectors. + if (metric == faiss::METRIC_INNER_PRODUCT) { + Tensor norms = torch::sqrt((data * data).sum(1, /*keepdim=*/true)); + data = data / norms; + } + + // RAFT handle and stream setup. + raft::resources handle; + cudaStream_t cuda_stream = at::cuda::getCurrentCUDAStream().stream(); + raft::resource::set_cuda_stream(handle, cuda_stream); + + // Wrap the input data in a RAFT device_matrix_view. + float* data_ptr = data.data_ptr(); + auto X_view = raft::make_host_matrix_view(data_ptr, (int)n_samples, (int)n_features); + + // Allocate output centroids on GPU. + Tensor centroids_tensor = torch::empty({num_clusters, n_features}, + torch::TensorOptions().dtype(torch::kFloat32).device(torch::kCUDA)); + float* centroids_ptr = centroids_tensor.data_ptr(); + auto centroids_view = raft::make_device_matrix_view(centroids_ptr, (int)num_clusters, (int)n_features); + + // Set up k-means parameters. + cuvs::cluster::kmeans::params params; + params.n_clusters = (int)num_clusters; + params.max_iter = niter; + params.init = cuvs::cluster::kmeans::params::InitMethod::Random; + + // Prepare host-side scalars to capture inertia and iterations. + float inertia = 0.0f; + int iterations = 0; + auto inertia_view = raft::make_host_scalar_view(&inertia); + auto iter_view = raft::make_host_scalar_view(&iterations); + + // Run k-means clustering (fit). + cuvs::cluster::kmeans::fit(handle, params, X_view, std::nullopt, + centroids_view, inertia_view, iter_view); + + // If inner-product, renormalize centroids. + if (metric == faiss::METRIC_INNER_PRODUCT) { + Tensor cent_norms = torch::sqrt((centroids_tensor * centroids_tensor).sum(1, /*keepdim=*/true)); + centroids_tensor.div_(cent_norms); + } + + // Allocate memory for labels and run prediction. + Tensor labels = torch::empty({n_samples}, torch::TensorOptions().dtype(torch::kInt32).device(torch::kCUDA)); + int* labels_ptr = labels.data_ptr(); + auto labels_view = raft::make_device_vector_view(labels_ptr, (int)n_samples); + + cuvs::cluster::kmeans::predict(handle, params, X_view, std::nullopt, + centroids_view, labels_view, false, + raft::make_host_scalar_view(&inertia)); + + // Synchronize the stream. + raft::resource::sync_stream(handle); + + // ----- Grouping (GPU vectorized) ----- + // Sort the labels and get the sorted indices. + Tensor sorted_tuple = std::get<1>(torch::sort(labels)); // sorted indices only, since sorted labels are not needed + Tensor sorted_labels = labels.index_select(0, sorted_tuple); + + // Reorder the data and ids using the sorted indices. + Tensor sorted_data = data.index_select(0, sorted_tuple); + Tensor sorted_ids = id_dev.index_select(0, sorted_tuple); + + // Compute per-cluster counts using torch::bincount. + Tensor counts = torch::bincount(sorted_labels.to(torch::kInt64), /*weights=*/{}, num_clusters); + + // Transfer counts to CPU and build a vector for split sizes. + auto counts_cpu = counts.to(torch::kCPU); + std::vector split_sizes(counts_cpu.data_ptr(), counts_cpu.data_ptr() + counts_cpu.numel()); + + // Split the sorted data and ids into clusters. + vector cluster_vectors = torch::split(sorted_data, split_sizes, 0); + vector cluster_ids = torch::split(sorted_ids, split_sizes, 0); + + Tensor partition_ids = torch::arange(num_clusters, torch::kInt64); + + shared_ptr clustering = std::make_shared(); + clustering->centroids = centroids_tensor; + clustering->partition_ids = partition_ids; + clustering->vectors = cluster_vectors; + clustering->vector_ids = cluster_ids; + + return clustering; +} +#endif + +shared_ptr kmeans_cpu(Tensor vectors, Tensor ids, int n_clusters, MetricType metric_type, int niter, - bool use_gpu /*=false*/, Tensor /* initial_centroids */) { // Ensure enough vectors are available and sizes match. assert(vectors.size(0) >= n_clusters * 2); @@ -29,24 +132,10 @@ shared_ptr kmeans(Tensor vectors, int d = vectors.size(1); faiss::Index* index_ptr = nullptr; - - if (use_gpu) { - // Check if GPU resources are available. - #ifdef FAISS_ENABLE_GPU - faiss::gpu::StandardGpuResources gpu_res; - if (metric_type == faiss::METRIC_INNER_PRODUCT) - index_ptr = new faiss::gpu::GpuIndexFlatIP(&gpu_res, d); - else - index_ptr = new faiss::gpu::GpuIndexFlatL2(&gpu_res, d); - #else - throw std::runtime_error("GPU resources are not available. Please compile with FAISS_ENABLE_GPU."); - #endif - } else { - if (metric_type == faiss::METRIC_INNER_PRODUCT) - index_ptr = new faiss::IndexFlatIP(d); - else - index_ptr = new faiss::IndexFlatL2(d); - } + if (metric_type == faiss::METRIC_INNER_PRODUCT) + index_ptr = new faiss::IndexFlatIP(d); + else + index_ptr = new faiss::IndexFlatL2(d); faiss::ClusteringParameters cp; cp.niter = niter; @@ -96,6 +185,24 @@ shared_ptr kmeans(Tensor vectors, return clustering; } +shared_ptr kmeans(Tensor vectors, + Tensor ids, + int n_clusters, + MetricType metric_type, + int niter, + bool use_gpu /*=false*/, + Tensor /* initial_centroids */) { + if (use_gpu) { + #ifdef QUAKE_ENABLE_GPU + return kmeans_cuvs(vectors, ids, n_clusters, metric_type, niter); + #elif + throw std::runtime_error("GPU support is not enabled. Please compile with QUAKE_ENABLE_GPU."); + #endif + } else { + return kmeans_cpu(vectors, ids, n_clusters, metric_type, niter); + } +} + tuple >> kmeans_refine_partitions( Tensor centroids, vector> partitions, From 8bb1929185d72b3e467b60b0b08430d60ac3829a Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 16 Apr 2025 11:21:42 -0500 Subject: [PATCH 003/323] add clustering tests --- test/cpp/clustering.cpp | 179 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 179 insertions(+) create mode 100644 test/cpp/clustering.cpp diff --git a/test/cpp/clustering.cpp b/test/cpp/clustering.cpp new file mode 100644 index 00000000..9c0b76c3 --- /dev/null +++ b/test/cpp/clustering.cpp @@ -0,0 +1,179 @@ +#include +#include +#include "clustering.h" +#include + +// Helper functions to generate random test data. +static Tensor generate_random_data(int64_t num_vectors, int64_t dim) { + return torch::randn({num_vectors, dim}, torch::kFloat32).contiguous(); +} + +static Tensor generate_sequential_ids(int64_t count, int64_t start = 0) { + return torch::arange(start, start + count, torch::kInt64).contiguous(); +} + +// Helpers to compute mean squared error (MSE) for clustering. +// For FAISS-based clustering: 'vectors' is a vector of tensors (one per cluster). +static double compute_mse(const Tensor& centroids, + const std::vector& clusters) { + double total_error = 0.0; + int64_t total_count = 0; + auto centroids_cpu = centroids.to(torch::kCPU); + for (size_t i = 0; i < clusters.size(); ++i) { + if (clusters[i].size(0) > 0) { + auto cluster = clusters[i].to(torch::kCPU); + auto diff = cluster - centroids_cpu[i].unsqueeze(0); + total_error += diff.pow(2).sum().item(); + total_count += cluster.size(0); + } + } + return total_count > 0 ? total_error / total_count : 0.0; +} + +// Test fixture for clustering tests. +class ClusteringTest : public ::testing::Test { + protected: + const int64_t num_vectors = 5000; + const int64_t dim = 64; + const int num_clusters = 20; + Tensor vectors_cpu, ids_cpu; + Tensor vectors_cuda, ids_cuda; + + void SetUp() override { + // Skip these tests if CUDA is not available. + if (!torch::cuda::is_available()) { + GTEST_SKIP() << "CUDA is not available; skipping clustering tests."; + } + vectors_cpu = generate_random_data(num_vectors, dim); + ids_cpu = generate_sequential_ids(num_vectors); +#ifdef QUAKE_ENABLE_GPU + vectors_cuda = vectors_cpu.to(torch::kCUDA).contiguous(); + ids_cuda = ids_cpu.to(torch::kCUDA).contiguous(); +#endif + } +}; + +// Compare clustering methods using the L2 (Euclidean) metric. +#ifdef QUAKE_ENABLE_GPU +TEST_F(ClusteringTest, CompareClustering_L2) { + const int niter = 20; + // FAISS-based clustering on CPU. + auto clustering_cpu = kmeans_cpu(vectors_cpu, ids_cpu, num_clusters, faiss::METRIC_L2, niter, Tensor()); + // cuVS-based clustering on GPU. + auto clustering_cuvs = kmeans_cuvs(vectors_cuda, ids_cuda, num_clusters, faiss::METRIC_L2); + + // Verify centroid shapes. + ASSERT_EQ(clustering_cpu->centroids.dim(), 2); + ASSERT_EQ(clustering_cpu->centroids.size(0), num_clusters); + ASSERT_EQ(clustering_cpu->centroids.size(1), dim); + ASSERT_EQ(clustering_cuvs->centroids.dim(), 2); + ASSERT_EQ(clustering_cuvs->centroids.size(0), num_clusters); + ASSERT_EQ(clustering_cuvs->centroids.size(1), dim); + + // Compare the mean squared errors (MSEs) between the methods. + double mse_cpu = compute_mse(clustering_cpu->centroids, clustering_cpu->vectors); + double mse_cuvs = compute_mse(clustering_cuvs->centroids, clustering_cuvs->vectors); + // They should agree within roughly 20% relative difference. + ASSERT_NEAR(mse_cpu, mse_cuvs, mse_cpu * 0.20); +} + +// Compare clustering methods using the inner product metric. +TEST_F(ClusteringTest, CompareClustering_InnerProduct) { + const int niter = 20; + auto clustering_cpu = kmeans(vectors_cpu, ids_cpu, num_clusters, + faiss::METRIC_INNER_PRODUCT, niter, false, Tensor()); + auto clustering_cuvs = kmeans_cuvs(vectors_cuda, ids_cuda, num_clusters, faiss::METRIC_INNER_PRODUCT); + + // Verify centroid shapes. + ASSERT_EQ(clustering_cpu->centroids.dim(), 2); + ASSERT_EQ(clustering_cpu->centroids.size(0), num_clusters); + ASSERT_EQ(clustering_cpu->centroids.size(1), dim); + ASSERT_EQ(clustering_cuvs->centroids.dim(), 2); + ASSERT_EQ(clustering_cuvs->centroids.size(0), num_clusters); + ASSERT_EQ(clustering_cuvs->centroids.size(1), dim); + + // For inner product, check that cuVS centroids are normalized. + auto norms = clustering_cuvs->centroids.norm(2, 1); + for (int i = 0; i < norms.size(0); ++i) { + ASSERT_NEAR(norms[i].item(), 1.0f, 1e-3); + } + + double mse_cpu = compute_mse(clustering_cpu->centroids, clustering_cpu->vectors); + double mse_cuvs = compute_mse(clustering_cuvs->centroids, clustering_cuvs->vectors); + // Allow a bit larger relative difference for inner product. + ASSERT_NEAR(mse_cpu, mse_cuvs, mse_cpu * 0.25); +} + +// Test that cuVS clustering partitions all vectors correctly (L2 metric). +TEST_F(ClusteringTest, CUVSClustering_Partitioning_L2) { + auto result = kmeans_cuvs(vectors_cuda, ids_cuda, num_clusters, faiss::METRIC_L2); + ASSERT_EQ(result->centroids.dim(), 2); + ASSERT_EQ(result->centroids.size(0), num_clusters); + ASSERT_EQ(result->centroids.size(1), dim); + + int64_t total_vectors = 0; + for (int i = 0; i < num_clusters; ++i) { + auto cluster = result->vectors[i]; + ASSERT_EQ(cluster.size(0), result->vector_ids[i].size(0)); + total_vectors += cluster.size(0); + } + ASSERT_EQ(total_vectors, num_vectors); +} + + +// Test that cuVS clustering (inner product) produces unit-norm centroids and correctly partitions vectors. +TEST_F(ClusteringTest, CUVSClustering_Partitioning_InnerProduct) { + auto result = kmeans_cuvs(vectors_cuda, ids_cuda, num_clusters, faiss::METRIC_INNER_PRODUCT); + ASSERT_EQ(result->centroids.dim(), 2); + ASSERT_EQ(result->centroids.size(0), num_clusters); + ASSERT_EQ(result->centroids.size(1), dim); + + // Verify centroids are normalized. + auto norms = result->centroids.norm(2, 1); + for (int i = 0; i < norms.size(0); ++i) { + ASSERT_NEAR(norms[i].item(), 1.0f, 1e-3); + } + + int64_t total_vectors = 0; + for (int i = 0; i < num_clusters; ++i) { + auto cluster = result->vectors[i]; + ASSERT_EQ(cluster.size(0), result->vector_ids[i].size(0)); + total_vectors += cluster.size(0); + } + ASSERT_EQ(total_vectors, num_vectors); +} +#endif + +TEST_F(ClusteringTest, KMeansCPU_L2) { + // Test CPU-based k-means clustering. + int niter = 10; + auto clustering = kmeans_cpu(vectors_cpu, ids_cpu, num_clusters, faiss::METRIC_L2, niter); + ASSERT_EQ(clustering->centroids.dim(), 2); + ASSERT_EQ(clustering->centroids.size(0), num_clusters); + ASSERT_EQ(clustering->centroids.size(1), dim); + + int64_t total_vectors = 0; + for (int i = 0; i < num_clusters; ++i) { + auto cluster = clustering->vectors[i]; + ASSERT_EQ(cluster.size(0), clustering->vector_ids[i].size(0)); + total_vectors += cluster.size(0); + } + ASSERT_EQ(total_vectors, num_vectors); +} + +TEST_F(ClusteringTest, KMeansCPU_InnerProduct) { + // Test CPU-based k-means clustering with inner product metric. + int niter = 10; + auto clustering = kmeans_cpu(vectors_cpu, ids_cpu, num_clusters, faiss::METRIC_INNER_PRODUCT, niter); + ASSERT_EQ(clustering->centroids.dim(), 2); + ASSERT_EQ(clustering->centroids.size(0), num_clusters); + ASSERT_EQ(clustering->centroids.size(1), dim); + + int64_t total_vectors = 0; + for (int i = 0; i < num_clusters; ++i) { + auto cluster = clustering->vectors[i]; + ASSERT_EQ(cluster.size(0), clustering->vector_ids[i].size(0)); + total_vectors += cluster.size(0); + } + ASSERT_EQ(total_vectors, num_vectors); +} \ No newline at end of file From 13a316188e791effd07566f186074983b0d4a737 Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 16 Apr 2025 11:21:54 -0500 Subject: [PATCH 004/323] remove old tests --- test/cpp/test_clustering_benchmark.cpp | 79 ------------ test/cpp/test_clustering_correctness.cpp | 157 ----------------------- test/cpp/test_cuvs_clustering.cpp | 88 ------------- 3 files changed, 324 deletions(-) delete mode 100644 test/cpp/test_clustering_benchmark.cpp delete mode 100644 test/cpp/test_clustering_correctness.cpp delete mode 100644 test/cpp/test_cuvs_clustering.cpp diff --git a/test/cpp/test_clustering_benchmark.cpp b/test/cpp/test_clustering_benchmark.cpp deleted file mode 100644 index 733ca95f..00000000 --- a/test/cpp/test_clustering_benchmark.cpp +++ /dev/null @@ -1,79 +0,0 @@ -// benchmark_clustering_gtest.cpp -// -// This Google Test benchmarks two clustering implementations: -// 1. FAISS-based CPU clustering (kmeans with use_gpu == false) -// 2. cuVS-based GPU clustering (ClusterWithCuVS) -// on a dataset of 4,000,000 vectors (of dimension 128) and 10,000 clusters. -// The test prints the elapsed time (milliseconds) for each method. - -#include -#include -#include -#include -#include "clustering.h" // Declaration of kmeans() and ClusterWithCuVS() -#include "cuv_cluster.h" - -using namespace std::chrono; - -TEST(ClusteringBenchmark, CPU_vs_cuVS) { - // Verify CUDA is available. - if (!torch::cuda::is_available()) { - GTEST_SKIP() << "CUDA is not available; skipping benchmark."; - } - - // Define benchmark parameters. - const int64_t num_vectors = 10000000; // 10 million vectors - const int64_t dim = 128; - const int num_clusters = 10000; - const int niter = 5; // number of clustering iterations - - std::cout << "Benchmarking clustering with " << num_vectors << " vectors, dimension " - << dim << ", and " << num_clusters << " clusters." << std::endl; - - // Create CPU tensors. - auto options_float_cpu = torch::TensorOptions().dtype(torch::kFloat32).device(torch::kCPU); - auto options_int64_cpu = torch::TensorOptions().dtype(torch::kInt64).device(torch::kCPU); - torch::Tensor vectors_cpu = torch::randn({num_vectors, dim}, options_float_cpu).contiguous(); - torch::Tensor ids_cpu = torch::arange(0, num_vectors, options_int64_cpu).contiguous(); - - // Create CUDA tensors. - auto options_float_cuda = torch::TensorOptions().dtype(torch::kFloat32).device(torch::kCUDA); - auto options_int64_cuda = torch::TensorOptions().dtype(torch::kInt64).device(torch::kCUDA); - torch::Tensor vectors_cuda = vectors_cpu.to(options_float_cuda).contiguous(); - torch::Tensor ids_cuda = ids_cpu.to(options_int64_cuda).contiguous(); - - // Warm up the GPU using the cuVS clustering (to remove initialization overhead). - std::cout << "[DEBUG] Warming up GPU with cuVS clustering..." << std::endl; - { - auto dummy = ClusterWithCuVS(vectors_cuda, ids_cuda, 10, faiss::METRIC_L2); - torch::cuda::synchronize(); - } - std::cout << "[DEBUG] Warm-up complete." << std::endl; - - // Benchmark FAISS CPU clustering. - std::cout << "[DEBUG] Starting FAISS CPU clustering benchmark..." << std::endl; - auto start_cpu = high_resolution_clock::now(); - auto clustering_cpu = kmeans(vectors_cpu, ids_cpu, num_clusters, faiss::METRIC_L2, niter, false, torch::Tensor()); - auto end_cpu = high_resolution_clock::now(); - auto duration_cpu = duration_cast(end_cpu - start_cpu).count(); - std::cout << "FAISS CPU clustering time: " << duration_cpu << " ms" << std::endl; - - // Benchmark cuVS GPU clustering. - std::cout << "[DEBUG] Starting cuVS clustering benchmark..." << std::endl; - auto start_cuvs = high_resolution_clock::now(); - auto clustering_cuvs = ClusterWithCuVS(vectors_cuda, ids_cuda, num_clusters, faiss::METRIC_L2); - torch::cuda::synchronize(); // ensure GPU work is complete - auto end_cuvs = high_resolution_clock::now(); - auto duration_cuvs = duration_cast(end_cuvs - start_cuvs).count(); - std::cout << "cuVS clustering time: " << duration_cuvs << " ms" << std::endl; - - // Print summary. - std::cout << "---------------------------" << std::endl; - std::cout << "FAISS CPU clustering took " << duration_cpu << " ms." << std::endl; - std::cout << "cuVS clustering took " << duration_cuvs << " ms." << std::endl; - std::cout << "---------------------------" << std::endl; - - // Basic checks. - EXPECT_GT(duration_cpu, 0); - EXPECT_GT(duration_cuvs, 0); -} diff --git a/test/cpp/test_clustering_correctness.cpp b/test/cpp/test_clustering_correctness.cpp deleted file mode 100644 index 01fff623..00000000 --- a/test/cpp/test_clustering_correctness.cpp +++ /dev/null @@ -1,157 +0,0 @@ -// test_clustering_correctness.cpp -// -// This file tests the correctness of two clustering implementations: -// 1. FAISS-based clustering on CPU: kmeans(..., use_gpu = false) -// 2. cuVS-based clustering: ClusterWithCuVS(...) -// The tests compute the mean squared error (MSE) between each input vector and its -// corresponding cluster centroid and require that the MSE from the two methods -// agree within a reasonable tolerance. Additionally, the tests verify that each method -// returns centroids of the correct shape and partitions the input set completely. - -#include -#include -#include -#include -#include "clustering.h" // Declaration of kmeans(), ClusterWithCuVS(), Clustering, ClusteringResult, and MetricType - -// Helper to compute MSE for FAISS-based clustering results. -// The 'vectors' field of Clustering is a vector of tensors (one per cluster). -double ComputeMSE_Faiss(const torch::Tensor& centroids, const std::vector& cluster_vectors) { - double total_error = 0.0; - int64_t total_count = 0; - // Ensure centroids are on CPU. - torch::Tensor centroids_cpu = centroids.to(torch::kCPU); - for (int i = 0; i < centroids_cpu.size(0); ++i) { - if (cluster_vectors[i].size(0) > 0) { - // Move the cluster vectors to CPU. - torch::Tensor cluster_cpu = cluster_vectors[i].to(torch::kCPU); - torch::Tensor diff = cluster_cpu - centroids_cpu[i].unsqueeze(0); - total_error += diff.pow(2).sum().item(); - total_count += cluster_cpu.size(0); - } - } - return total_count > 0 ? total_error / total_count : 0.0; -} - -// Helper to compute MSE for cuVS-based clustering results. -// Here, clusters are returned as a vector of pairs where the first tensor is the cluster vectors. -double ComputeMSE_cuVS(const torch::Tensor& centroids, - const std::vector>& clusters) { - double total_error = 0.0; - int64_t total_count = 0; - torch::Tensor centroids_cpu = centroids.to(torch::kCPU); - for (int i = 0; i < centroids_cpu.size(0); ++i) { - const torch::Tensor& cluster_vectors = clusters[i].first; - if (cluster_vectors.size(0) > 0) { - torch::Tensor cluster_cpu = cluster_vectors.to(torch::kCPU); - torch::Tensor diff = cluster_cpu - centroids_cpu[i].unsqueeze(0); - total_error += diff.pow(2).sum().item(); - total_count += cluster_cpu.size(0); - } - } - return total_count > 0 ? total_error / total_count : 0.0; -} - -// Test fixture for clustering correctness tests. -class ClusteringCorrectnessTest : public ::testing::Test { - protected: - // Use a moderate number of vectors for correctness testing. - const int64_t num_vectors_ = 5000; - const int64_t dim_ = 64; - const int num_clusters_ = 20; - // CPU tensors. - torch::Tensor vectors_cpu_; - torch::Tensor ids_cpu_; - // CUDA tensors. - torch::Tensor vectors_cuda_; - torch::Tensor ids_cuda_; - - void SetUp() override { - if (!torch::cuda::is_available()) { - GTEST_SKIP() << "CUDA is not available; skipping clustering correctness tests."; - } - // Create CPU tensors. - auto options_float_cpu = torch::TensorOptions().dtype(torch::kFloat32).device(torch::kCPU); - auto options_int64_cpu = torch::TensorOptions().dtype(torch::kInt64).device(torch::kCPU); - vectors_cpu_ = torch::randn({num_vectors_, dim_}, options_float_cpu).contiguous(); - ids_cpu_ = torch::arange(0, num_vectors_, options_int64_cpu).contiguous(); - // Create CUDA versions. - auto options_float_cuda = torch::TensorOptions().dtype(torch::kFloat32).device(torch::kCUDA); - auto options_int64_cuda = torch::TensorOptions().dtype(torch::kInt64).device(torch::kCUDA); - vectors_cuda_ = vectors_cpu_.to(options_float_cuda).contiguous(); - ids_cuda_ = ids_cpu_.to(options_int64_cuda).contiguous(); - - std::cout << "[DEBUG] SetUp complete: " - << "num_vectors = " << num_vectors_ - << ", dim = " << dim_ - << ", clusters = " << num_clusters_ << std::endl; - std::cout.flush(); - } -}; - -TEST_F(ClusteringCorrectnessTest, CompareClusteringMethods_L2) { - const int niter = 20; // number of iterations for clustering - - std::cout << "[DEBUG] Starting FAISS CPU clustering ..." << std::endl; - std::cout.flush(); - auto clustering_cpu = kmeans(vectors_cpu_, ids_cpu_, num_clusters_, faiss::METRIC_L2, niter, false, torch::Tensor()); - std::cout << "[DEBUG] Completed FAISS CPU clustering." << std::endl; - std::cout << "[DEBUG] CPU centroids shape: "; - for (auto s : clustering_cpu->centroids.sizes()) { std::cout << s << " "; } - std::cout << std::endl; - std::cout.flush(); - - std::cout << "[DEBUG] Starting cuVS clustering ..." << std::endl; - std::cout.flush(); - ClusteringResult clustering_cuvs = ClusterWithCuVS(vectors_cuda_, ids_cuda_, num_clusters_, faiss::METRIC_L2); - std::cout << "[DEBUG] Completed cuVS clustering." << std::endl; - std::cout << "[DEBUG] cuVS centroids shape: "; - for (auto s : clustering_cuvs.centroids.sizes()) { std::cout << s << " "; } - std::cout << std::endl; - std::cout.flush(); - - // Compute MSE for each method. - double mse_cpu = ComputeMSE_Faiss(clustering_cpu->centroids, clustering_cpu->vectors); - double mse_cuvs = ComputeMSE_cuVS(clustering_cuvs.centroids, clustering_cuvs.clusters); - - std::cout << "[DEBUG] L2 Clustering MSEs:" << std::endl; - std::cout << " FAISS CPU: " << mse_cpu << std::endl; - std::cout << " cuVS : " << mse_cuvs << std::endl; - std::cout.flush(); - - // Assert that MSE values are similar (within 20% relative difference) - double tol = 0.20; - ASSERT_NEAR(mse_cpu, mse_cuvs, tol * mse_cpu); -} - -TEST_F(ClusteringCorrectnessTest, CompareClusteringMethods_InnerProduct) { - const int niter = 20; - std::cout << "[DEBUG] Starting FAISS CPU clustering (InnerProduct) ..." << std::endl; - std::cout.flush(); - auto clustering_cpu = kmeans(vectors_cpu_, ids_cpu_, num_clusters_, faiss::METRIC_INNER_PRODUCT, niter, false, torch::Tensor()); - std::cout << "[DEBUG] Completed FAISS CPU clustering (InnerProduct)." << std::endl; - std::cout << "[DEBUG] CPU centroids shape: "; - for (auto s : clustering_cpu->centroids.sizes()) { std::cout << s << " "; } - std::cout << std::endl; - std::cout.flush(); - - std::cout << "[DEBUG] Starting cuVS clustering (InnerProduct) ..." << std::endl; - std::cout.flush(); - ClusteringResult clustering_cuvs = ClusterWithCuVS(vectors_cuda_, ids_cuda_, num_clusters_, faiss::METRIC_INNER_PRODUCT); - std::cout << "[DEBUG] Completed cuVS clustering (InnerProduct)." << std::endl; - std::cout << "[DEBUG] cuVS centroids shape: "; - for (auto s : clustering_cuvs.centroids.sizes()) { std::cout << s << " "; } - std::cout << std::endl; - std::cout.flush(); - - double mse_cpu = ComputeMSE_Faiss(clustering_cpu->centroids, clustering_cpu->vectors); - double mse_cuvs = ComputeMSE_cuVS(clustering_cuvs.centroids, clustering_cuvs.clusters); - - std::cout << "[DEBUG] Inner-product Clustering MSEs:" << std::endl; - std::cout << " FAISS CPU: " << mse_cpu << std::endl; - std::cout << " cuVS : " << mse_cuvs << std::endl; - std::cout.flush(); - - double tol = 0.25; - ASSERT_NEAR(mse_cpu, mse_cuvs, tol * mse_cpu); -} diff --git a/test/cpp/test_cuvs_clustering.cpp b/test/cpp/test_cuvs_clustering.cpp deleted file mode 100644 index 394cf4e7..00000000 --- a/test/cpp/test_cuvs_clustering.cpp +++ /dev/null @@ -1,88 +0,0 @@ -// test_cuvs_clustering.cpp -// -// This file tests the cuVS-based k-means clustering implementation. -// It verifies that the centroids are computed correctly and that the -// entire dataset is partitioned among the clusters. - -#include -#include -#include "cuv_cluster.h" // Contains ClusterWithCuVS, ClusteringResult, and MetricType - -// Test that clustering with L2 metric produces the expected output shapes, -// and that every input vector is assigned to one and only one cluster. -TEST(CUVSClusteringTest, ClusteringL2) { - // Skip the test if CUDA is not available. - if (!torch::cuda::is_available()) { - GTEST_SKIP() << "CUDA is not available, skipping cuVS clustering test."; - } - - const int64_t num_vectors = 1000; - const int64_t dim = 32; - const int64_t num_clusters = 10; - - // Generate random data and corresponding sequential IDs on the GPU. - auto vectors = torch::randn({num_vectors, dim}, torch::kCUDA).to(torch::kFloat32).contiguous(); - auto ids = torch::arange(0, num_vectors, torch::kCUDA).contiguous(); - - // Call the cuVS-based clustering function using L2 metric. - ClusteringResult result = ClusterWithCuVS(vectors, ids, num_clusters, faiss::METRIC_L2); - - // Validate that the centroids tensor has shape [num_clusters, dim]. - ASSERT_EQ(result.centroids.dim(), 2); - ASSERT_EQ(result.centroids.size(0), num_clusters); - ASSERT_EQ(result.centroids.size(1), dim); - - // Verify that all input vectors are partitioned by summing counts from each cluster. - int64_t total_count = 0; - for (const auto& cluster : result.clusters) { - // Each cluster is returned as a pair: - torch::Tensor cluster_vectors = cluster.first; - torch::Tensor cluster_ids = cluster.second; - // The number of vectors in each cluster must match the number of IDs. - ASSERT_EQ(cluster_vectors.size(0), cluster_ids.size(0)); - total_count += cluster_vectors.size(0); - } - ASSERT_EQ(total_count, num_vectors); -} - -// Test that clustering with the inner product metric (which performs normalization) -// yields centroids that are unit-norm and partitions the dataset correctly. -TEST(CUVSClusteringTest, ClusteringInnerProduct) { - if (!torch::cuda::is_available()) { - GTEST_SKIP() << "CUDA is not available, skipping cuVS clustering test."; - } - - const int64_t num_vectors = 1000; - const int64_t dim = 64; - const int64_t num_clusters = 8; - - // Generate random input data and corresponding IDs. - auto vectors = torch::randn({num_vectors, dim}, torch::kCUDA).to(torch::kFloat32).contiguous(); - auto ids = torch::arange(0, num_vectors, torch::kCUDA).contiguous(); - - // Run clustering with inner product metric. The implementation should normalize the vectors. - ClusteringResult result = ClusterWithCuVS(vectors, ids, num_clusters, faiss::METRIC_INNER_PRODUCT); - - // Check that the centroids tensor has the expected shape. - ASSERT_EQ(result.centroids.dim(), 2); - ASSERT_EQ(result.centroids.size(0), num_clusters); - ASSERT_EQ(result.centroids.size(1), dim); - - // Optionally, validate that each centroid is normalized (i.e. its L2 norm is ~1) - auto cent_norms = result.centroids.norm(2, /*dim=*/1); - for (int64_t i = 0; i < cent_norms.size(0); ++i) { - float norm_val = cent_norms[i].item(); - // Allow a small numerical tolerance. - ASSERT_NEAR(norm_val, 1.0, 1e-3); - } - - // Confirm that the total number of vectors across all clusters is equal to the input size. - int64_t total_count = 0; - for (const auto& cluster : result.clusters) { - torch::Tensor cluster_vectors = cluster.first; - torch::Tensor cluster_ids = cluster.second; - ASSERT_EQ(cluster_vectors.size(0), cluster_ids.size(0)); - total_count += cluster_vectors.size(0); - } - ASSERT_EQ(total_count, num_vectors); -} From b201997dda368bf162e35903c532362d6e0b1863 Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 16 Apr 2025 11:27:45 -0500 Subject: [PATCH 005/323] update cmake --- CMakeLists.txt | 4 ++-- test/cpp/quake_index.cpp | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 4cf50474..5b6d8859 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -54,7 +54,7 @@ set(project_TEST_DIR test/cpp) if(QUAKE_ENABLE_GPU) find_package(CuVS REQUIRED) - include(${project_THIRD_PARTY_DIR}cmake/fetch_rapids.cmake) + include(${project_THIRD_PARTY_DIR}/cmake/fetch_rapids.cmake) include(rapids-cmake) include(rapids-cpm) include(rapids-cuda) @@ -221,7 +221,7 @@ endif() # --------------------------------------------------------------- message(STATUS "--------- Final Configuration Summary ---------") message(STATUS "Build type: ${CMAKE_BUILD_TYPE}") -message(STATUS "GPU Enabled: ${FAISS_ENABLE_GPU}") +message(STATUS "GPU Enabled: ${QUAKE_ENABLE_GPU}") message(STATUS "NUMA Enabled: ${QUAKE_USE_NUMA}") message(STATUS "Python used: ${Python3_EXECUTABLE}") message(STATUS "Torch Path: ${TorchPath}") diff --git a/test/cpp/quake_index.cpp b/test/cpp/quake_index.cpp index 0521ea02..242c16df 100644 --- a/test/cpp/quake_index.cpp +++ b/test/cpp/quake_index.cpp @@ -283,7 +283,7 @@ TEST(QuakeIndexStressTest, LargeBuildTest) { << " vectors took " << build_duration_ms << " ms.\n"; } -#ifdef FAISS_ENABLE_GPU +#ifdef QUAKE_ENABLE_GPU TEST(QuakeIndexStressTestGPU, LargeBuildTest) { // Attempt to build an index with a large number of vectors. // Adjust these numbers based on your available memory/compute. @@ -527,7 +527,7 @@ TEST(QuakeIndexStressTest, SearchAddRemoveMaintenanceTest) { } // Define the GPU related test only if FAISS GPU support is enabled -#ifdef FAISS_ENABLE_GPU +#ifdef QUAKE_ENABLE_GPU // Test build with GPU enabled TEST(QuakeIndexGPUTest, BuildWithGPUTest) { int64_t dimension = 32; From e39c79628a51b7888e1ebc895f40bc6a5ee08cf9 Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 16 Apr 2025 11:49:04 -0500 Subject: [PATCH 006/323] update clustering --- src/cpp/include/clustering.h | 6 ------ src/cpp/include/cuv_cluster.h | 14 -------------- src/cpp/src/clustering.cpp | 19 ++++++++++++------- 3 files changed, 12 insertions(+), 27 deletions(-) delete mode 100644 src/cpp/include/cuv_cluster.h diff --git a/src/cpp/include/clustering.h b/src/cpp/include/clustering.h index cc98bebc..e069527c 100644 --- a/src/cpp/include/clustering.h +++ b/src/cpp/include/clustering.h @@ -9,12 +9,6 @@ #include -#ifdef QUAKE_ENABLE_GPU -#include // RAFT resources (handle) -#include // RAFT device view (make_device_matrix_view, etc.) -#include // cuVS k-means API -#endif - class IndexPartition; /** diff --git a/src/cpp/include/cuv_cluster.h b/src/cpp/include/cuv_cluster.h deleted file mode 100644 index 32fe9315..00000000 --- a/src/cpp/include/cuv_cluster.h +++ /dev/null @@ -1,14 +0,0 @@ -// Optionally, if you have your own enum, you can use that instead of faiss::METRIC_L2/INNER_PRODUCT. -struct ClusteringResult { - torch::Tensor centroids; - // Each pair is (cluster_vectors, cluster_ids) for one cluster. - std::vector> clusters; -}; - - -inline ClusteringResult ClusterWithCuVS(const torch::Tensor& vectors, - const torch::Tensor& ids, - int64_t num_clusters, - int metric = faiss::METRIC_L2) { - -} diff --git a/src/cpp/src/clustering.cpp b/src/cpp/src/clustering.cpp index 50052a72..36f9fdae 100644 --- a/src/cpp/src/clustering.cpp +++ b/src/cpp/src/clustering.cpp @@ -11,6 +11,11 @@ #include #ifdef QUAKE_ENABLE_GPU +#include // RAFT resources (handle) +#include // RAFT device view (make_device_matrix_view, etc.) +#include // cuVS k-means API +#endif + shared_ptr kmeans_cuvs(Tensor vectors, Tensor ids, int num_clusters, @@ -24,13 +29,13 @@ shared_ptr kmeans_cuvs(Tensor vectors, TORCH_CHECK(vectors.size(0) == ids.size(0), "Number of ids must match number of vectors"); TORCH_CHECK(vectors.size(0) >= num_clusters, "Number of clusters cannot exceed number of points"); - int64_t n_samples = data.size(0); - int64_t n_features = data.size(1); + int64_t n_samples = vectors.size(0); + int64_t n_features = vectors.size(1); // If using inner-product (cosine), normalize input vectors. if (metric == faiss::METRIC_INNER_PRODUCT) { - Tensor norms = torch::sqrt((data * data).sum(1, /*keepdim=*/true)); - data = data / norms; + Tensor norms = torch::sqrt((vectors * vectors).sum(1, /*keepdim=*/true)); + vectors = vectors / norms; } // RAFT handle and stream setup. @@ -39,7 +44,7 @@ shared_ptr kmeans_cuvs(Tensor vectors, raft::resource::set_cuda_stream(handle, cuda_stream); // Wrap the input data in a RAFT device_matrix_view. - float* data_ptr = data.data_ptr(); + float* data_ptr = vectors.data_ptr(); auto X_view = raft::make_host_matrix_view(data_ptr, (int)n_samples, (int)n_features); // Allocate output centroids on GPU. @@ -88,8 +93,8 @@ shared_ptr kmeans_cuvs(Tensor vectors, Tensor sorted_labels = labels.index_select(0, sorted_tuple); // Reorder the data and ids using the sorted indices. - Tensor sorted_data = data.index_select(0, sorted_tuple); - Tensor sorted_ids = id_dev.index_select(0, sorted_tuple); + Tensor sorted_data = vectors.index_select(0, sorted_tuple); + Tensor sorted_ids = ids.index_select(0, sorted_tuple); // Compute per-cluster counts using torch::bincount. Tensor counts = torch::bincount(sorted_labels.to(torch::kInt64), /*weights=*/{}, num_clusters); From 174e8d44539faff03baf8bf9ecafd0be2084cd77 Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 16 Apr 2025 11:49:23 -0500 Subject: [PATCH 007/323] update clustering --- src/cpp/src/clustering.cpp | 1 - 1 file changed, 1 deletion(-) diff --git a/src/cpp/src/clustering.cpp b/src/cpp/src/clustering.cpp index 36f9fdae..01f9f0c7 100644 --- a/src/cpp/src/clustering.cpp +++ b/src/cpp/src/clustering.cpp @@ -14,7 +14,6 @@ #include // RAFT resources (handle) #include // RAFT device view (make_device_matrix_view, etc.) #include // cuVS k-means API -#endif shared_ptr kmeans_cuvs(Tensor vectors, Tensor ids, From 1c57d974c822cb7fbfbc83520ee45868f0ce5d56 Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 16 Apr 2025 14:22:23 -0500 Subject: [PATCH 008/323] update clustering --- src/cpp/src/clustering.cpp | 27 ++++++++++++++------------- 1 file changed, 14 insertions(+), 13 deletions(-) diff --git a/src/cpp/src/clustering.cpp b/src/cpp/src/clustering.cpp index 01f9f0c7..c4535662 100644 --- a/src/cpp/src/clustering.cpp +++ b/src/cpp/src/clustering.cpp @@ -11,6 +11,7 @@ #include #ifdef QUAKE_ENABLE_GPU +#include #include // RAFT resources (handle) #include // RAFT device view (make_device_matrix_view, etc.) #include // cuVS k-means API @@ -21,6 +22,7 @@ shared_ptr kmeans_cuvs(Tensor vectors, MetricType metric, int niter, Tensor initial_centroids) { + // Validate input shapes and sizes. TORCH_CHECK(vectors.dim() == 2, "Input 'vectors' must be a 2D tensor"); TORCH_CHECK(ids.dim() == 1 || (ids.dim() == 2 && ids.size(1) == 1), @@ -39,22 +41,23 @@ shared_ptr kmeans_cuvs(Tensor vectors, // RAFT handle and stream setup. raft::resources handle; - cudaStream_t cuda_stream = at::cuda::getCurrentCUDAStream().stream(); + // Replace the at::cuda call with c10's current CUDA stream. + cudaStream_t cuda_stream = c10::cuda::getCurrentCUDAStream(); raft::resource::set_cuda_stream(handle, cuda_stream); - // Wrap the input data in a RAFT device_matrix_view. + // Wrap the input data in a RAFT *device* matrix view. float* data_ptr = vectors.data_ptr(); - auto X_view = raft::make_host_matrix_view(data_ptr, (int)n_samples, (int)n_features); + auto X_view = raft::make_device_matrix_view(data_ptr, (int)n_samples, (int)n_features); // Allocate output centroids on GPU. Tensor centroids_tensor = torch::empty({num_clusters, n_features}, - torch::TensorOptions().dtype(torch::kFloat32).device(torch::kCUDA)); + torch::TensorOptions().dtype(torch::kFloat32).device(torch::kCUDA)); float* centroids_ptr = centroids_tensor.data_ptr(); auto centroids_view = raft::make_device_matrix_view(centroids_ptr, (int)num_clusters, (int)n_features); // Set up k-means parameters. cuvs::cluster::kmeans::params params; - params.n_clusters = (int)num_clusters; + params.n_clusters = num_clusters; params.max_iter = niter; params.init = cuvs::cluster::kmeans::params::InitMethod::Random; @@ -66,12 +69,12 @@ shared_ptr kmeans_cuvs(Tensor vectors, // Run k-means clustering (fit). cuvs::cluster::kmeans::fit(handle, params, X_view, std::nullopt, - centroids_view, inertia_view, iter_view); + centroids_view, inertia_view, iter_view); // If inner-product, renormalize centroids. if (metric == faiss::METRIC_INNER_PRODUCT) { - Tensor cent_norms = torch::sqrt((centroids_tensor * centroids_tensor).sum(1, /*keepdim=*/true)); - centroids_tensor.div_(cent_norms); + Tensor cent_norms = torch::sqrt((centroids_tensor * centroids_tensor).sum(1, /*keepdim=*/true)); + centroids_tensor.div_(cent_norms); } // Allocate memory for labels and run prediction. @@ -80,15 +83,15 @@ shared_ptr kmeans_cuvs(Tensor vectors, auto labels_view = raft::make_device_vector_view(labels_ptr, (int)n_samples); cuvs::cluster::kmeans::predict(handle, params, X_view, std::nullopt, - centroids_view, labels_view, false, - raft::make_host_scalar_view(&inertia)); + centroids_view, labels_view, false, + raft::make_host_scalar_view(&inertia)); // Synchronize the stream. raft::resource::sync_stream(handle); // ----- Grouping (GPU vectorized) ----- // Sort the labels and get the sorted indices. - Tensor sorted_tuple = std::get<1>(torch::sort(labels)); // sorted indices only, since sorted labels are not needed + Tensor sorted_tuple = std::get<1>(torch::sort(labels)); // sorted indices only Tensor sorted_labels = labels.index_select(0, sorted_tuple); // Reorder the data and ids using the sorted indices. @@ -97,8 +100,6 @@ shared_ptr kmeans_cuvs(Tensor vectors, // Compute per-cluster counts using torch::bincount. Tensor counts = torch::bincount(sorted_labels.to(torch::kInt64), /*weights=*/{}, num_clusters); - - // Transfer counts to CPU and build a vector for split sizes. auto counts_cpu = counts.to(torch::kCPU); std::vector split_sizes(counts_cpu.data_ptr(), counts_cpu.data_ptr() + counts_cpu.numel()); From 2b2e96104d5a7e9d00b60a2d45cdd36237f3b623 Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 16 Apr 2025 22:28:48 -0500 Subject: [PATCH 009/323] add CPU-GPU clustering --- src/cpp/src/clustering.cpp | 249 ++++++++++++++++++++++--------------- 1 file changed, 152 insertions(+), 97 deletions(-) diff --git a/src/cpp/src/clustering.cpp b/src/cpp/src/clustering.cpp index c4535662..f6954c3b 100644 --- a/src/cpp/src/clustering.cpp +++ b/src/cpp/src/clustering.cpp @@ -16,106 +16,155 @@ #include // RAFT device view (make_device_matrix_view, etc.) #include // cuVS k-means API -shared_ptr kmeans_cuvs(Tensor vectors, - Tensor ids, +shared_ptr kmeans_cuvs_sample_and_predict( + Tensor vectors, Tensor ids, int num_clusters, MetricType metric, + int sample_size, int niter, - Tensor initial_centroids) { - - // Validate input shapes and sizes. - TORCH_CHECK(vectors.dim() == 2, "Input 'vectors' must be a 2D tensor"); - TORCH_CHECK(ids.dim() == 1 || (ids.dim() == 2 && ids.size(1) == 1), - "Input 'ids' must be a 1D tensor or 2D with shape (N,1)"); - TORCH_CHECK(vectors.size(0) == ids.size(0), "Number of ids must match number of vectors"); - TORCH_CHECK(vectors.size(0) >= num_clusters, "Number of clusters cannot exceed number of points"); - - int64_t n_samples = vectors.size(0); - int64_t n_features = vectors.size(1); - - // If using inner-product (cosine), normalize input vectors. - if (metric == faiss::METRIC_INNER_PRODUCT) { - Tensor norms = torch::sqrt((vectors * vectors).sum(1, /*keepdim=*/true)); - vectors = vectors / norms; + int gpu_batch_size) { + + TORCH_CHECK(vectors.dim()==2, "vectors must be [N,D]"); + TORCH_CHECK(ids.dim()==1, "ids must be [N]"); + int64_t N = vectors.size(0), D = vectors.size(1); + TORCH_CHECK(sample_size > 0 && sample_size <= N, + "invalid sample_size"); + + // 1) pin + normalize if needed + Tensor cpu_pts = vectors.contiguous().pin_memory(); + if (metric == faiss::METRIC_INNER_PRODUCT) { + auto norms = cpu_pts.norm(2,1,true); + cpu_pts = cpu_pts.div(norms); + } + + // 2) choose a random sample of indices + auto perm = torch::randperm(N, torch::kLong); + auto samp_idx = perm.slice(0, 0, sample_size); + Tensor samp_pts = cpu_pts.index_select(0, samp_idx); + Tensor samp_ids = ids.index_select(0, samp_idx); + + // 3) move sample to GPU + Tensor samp_gpu = samp_pts.to(torch::kCUDA, /*non_blocking=*/true) + .contiguous(); + + // 4) prepare RAFT handle & cuVS params + raft::resources handle; + cudaStream_t stream = c10::cuda::getCurrentCUDAStream(); + raft::resource::set_cuda_stream(handle, stream); + + cuvs::cluster::kmeans::params params; + params.n_clusters = num_clusters; + params.init = cuvs::cluster::kmeans::params::InitMethod::Random; + params.max_iter = niter; + + // 5) allocate centroids on GPU + Tensor cent_gpu = torch::empty({num_clusters, D}, + torch::kFloat32, + torch::TensorOptions().device(torch::kCUDA)) + .contiguous(); + + // 6) run fit on just the sample + { + auto X_view = raft::make_device_matrix_view( + samp_gpu.data_ptr(), + (int)sample_size, (int)D); + auto C_view = raft::make_device_matrix_view( + cent_gpu.data_ptr(), + num_clusters, (int)D); + + cuvs::cluster::kmeans::fit( + handle, params, + X_view, + std::nullopt, + C_view, + raft::make_host_scalar_view(nullptr), + raft::make_host_scalar_view(nullptr) + ); + } + + // 7) now predict labels for all N in batches + Tensor all_labels = torch::empty({N}, torch::kLong); + Tensor labels32 = torch::empty({gpu_batch_size}, + torch::kInt32, + torch::TensorOptions().device(torch::kCUDA)); + auto predict_fn = [&](Tensor batch_cpu, int64_t off) { + int64_t bs = batch_cpu.size(0); + Tensor batch_gpu = batch_cpu.to(torch::kCUDA, /*NB=*/true) + .contiguous(); + auto Xv = raft::make_device_matrix_view( + batch_gpu.data_ptr(), + (int)bs, (int)D); + auto Lv = raft::make_device_vector_view( + labels32.data_ptr(), (int)bs); + + cuvs::cluster::kmeans::predict( + handle, params, + Xv, + std::nullopt, + raft::make_device_matrix_view( + cent_gpu.data_ptr(), + num_clusters, (int)D), + Lv, + false, + raft::make_host_scalar_view(nullptr) + ); + + // copy back + all_labels.narrow(0, off, bs) + .copy_(labels32.slice(0,0,bs) + .to(torch::kLong) + .to(torch::kCPU)); + }; + + // 7a) predict for the sample slice + predict_fn(samp_pts, /*off=*/0); + // 7b) predict for the rest + int64_t written = sample_size; + // we’ll write the rest starting at index sample_size + for (int64_t off = 0; off < N; off += gpu_batch_size) { + int64_t bs = std::min(gpu_batch_size, N - off); + // skip the sample zone + if (off < sample_size) { + // overlap: part sample / part rest + if (off + bs <= sample_size) { + // whole chunk was sample: already done + continue; + } else { + // split chunk + int64_t s_end = sample_size - off; + int64_t r_bs = bs - s_end; + Tensor rest_chunk = cpu_pts.slice(0, off + s_end, off + bs); + predict_fn(rest_chunk, /*off=*/off + s_end); + continue; + } } - - // RAFT handle and stream setup. - raft::resources handle; - // Replace the at::cuda call with c10's current CUDA stream. - cudaStream_t cuda_stream = c10::cuda::getCurrentCUDAStream(); - raft::resource::set_cuda_stream(handle, cuda_stream); - - // Wrap the input data in a RAFT *device* matrix view. - float* data_ptr = vectors.data_ptr(); - auto X_view = raft::make_device_matrix_view(data_ptr, (int)n_samples, (int)n_features); - - // Allocate output centroids on GPU. - Tensor centroids_tensor = torch::empty({num_clusters, n_features}, - torch::TensorOptions().dtype(torch::kFloat32).device(torch::kCUDA)); - float* centroids_ptr = centroids_tensor.data_ptr(); - auto centroids_view = raft::make_device_matrix_view(centroids_ptr, (int)num_clusters, (int)n_features); - - // Set up k-means parameters. - cuvs::cluster::kmeans::params params; - params.n_clusters = num_clusters; - params.max_iter = niter; - params.init = cuvs::cluster::kmeans::params::InitMethod::Random; - - // Prepare host-side scalars to capture inertia and iterations. - float inertia = 0.0f; - int iterations = 0; - auto inertia_view = raft::make_host_scalar_view(&inertia); - auto iter_view = raft::make_host_scalar_view(&iterations); - - // Run k-means clustering (fit). - cuvs::cluster::kmeans::fit(handle, params, X_view, std::nullopt, - centroids_view, inertia_view, iter_view); - - // If inner-product, renormalize centroids. - if (metric == faiss::METRIC_INNER_PRODUCT) { - Tensor cent_norms = torch::sqrt((centroids_tensor * centroids_tensor).sum(1, /*keepdim=*/true)); - centroids_tensor.div_(cent_norms); - } - - // Allocate memory for labels and run prediction. - Tensor labels = torch::empty({n_samples}, torch::TensorOptions().dtype(torch::kInt32).device(torch::kCUDA)); - int* labels_ptr = labels.data_ptr(); - auto labels_view = raft::make_device_vector_view(labels_ptr, (int)n_samples); - - cuvs::cluster::kmeans::predict(handle, params, X_view, std::nullopt, - centroids_view, labels_view, false, - raft::make_host_scalar_view(&inertia)); - - // Synchronize the stream. - raft::resource::sync_stream(handle); - - // ----- Grouping (GPU vectorized) ----- - // Sort the labels and get the sorted indices. - Tensor sorted_tuple = std::get<1>(torch::sort(labels)); // sorted indices only - Tensor sorted_labels = labels.index_select(0, sorted_tuple); - - // Reorder the data and ids using the sorted indices. - Tensor sorted_data = vectors.index_select(0, sorted_tuple); - Tensor sorted_ids = ids.index_select(0, sorted_tuple); - - // Compute per-cluster counts using torch::bincount. - Tensor counts = torch::bincount(sorted_labels.to(torch::kInt64), /*weights=*/{}, num_clusters); - auto counts_cpu = counts.to(torch::kCPU); - std::vector split_sizes(counts_cpu.data_ptr(), counts_cpu.data_ptr() + counts_cpu.numel()); - - // Split the sorted data and ids into clusters. - vector cluster_vectors = torch::split(sorted_data, split_sizes, 0); - vector cluster_ids = torch::split(sorted_ids, split_sizes, 0); - - Tensor partition_ids = torch::arange(num_clusters, torch::kInt64); - - shared_ptr clustering = std::make_shared(); - clustering->centroids = centroids_tensor; - clustering->partition_ids = partition_ids; - clustering->vectors = cluster_vectors; - clustering->vector_ids = cluster_ids; - - return clustering; + // pure rest + Tensor rest_chunk = cpu_pts.slice(0, off, off + bs); + predict_fn(rest_chunk, /*off=*/off); + } + + // 8) group on CPU + Tensor sorted_lbl, sorted_idx; + std::tie(sorted_lbl, sorted_idx) = torch::sort(all_labels); + Tensor sorted_vecs = vectors.index_select(0, sorted_idx); + Tensor sorted_ids = ids.index_select(0, sorted_idx); + + Tensor counts = torch::bincount(sorted_lbl, /*weights=*/{}, num_clusters); + auto cnt_cpu = counts.to(torch::kCPU); + std::vector split_sizes( + cnt_cpu.data_ptr(), + cnt_cpu.data_ptr() + num_clusters + ); + + auto cluster_vecs = torch::split(sorted_vecs, split_sizes, 0); + auto cluster_ids = torch::split(sorted_ids, split_sizes, 0); + + auto out = std::make_shared(); + out->centroids = cent_gpu.cpu().contiguous(); + out->partition_ids = torch::arange(num_clusters, torch::kLong); + out->vectors = std::move(cluster_vecs); + out->vector_ids = std::move(cluster_ids); + return out; } #endif @@ -199,7 +248,13 @@ shared_ptr kmeans(Tensor vectors, Tensor /* initial_centroids */) { if (use_gpu) { #ifdef QUAKE_ENABLE_GPU - return kmeans_cuvs(vectors, ids, n_clusters, metric_type, niter); + const int sample_size = std::min(1000000, vectors.size(0)); + const int gpu_batch_size = 100000; // or from build_params + return kmeans_cuvs_sample_and_predict( + vectors, ids, + n_clusters, metric, + sample_size, niter, + gpu_batch_size); #elif throw std::runtime_error("GPU support is not enabled. Please compile with QUAKE_ENABLE_GPU."); #endif From 19d5c30dd3a4c915511d22291eb86bf2933cb9c8 Mon Sep 17 00:00:00 2001 From: Jason Mohoney Date: Thu, 17 Apr 2025 17:22:46 +0000 Subject: [PATCH 010/323] cpu-gpu k-means bug fix --- CMakeLists.txt | 2 +- src/cpp/include/clustering.h | 13 +- src/cpp/src/clustering.cpp | 104 ++++++++++----- test/cpp/clustering.cpp | 245 ++++++++++++++--------------------- test/cpp/quake_index.cpp | 16 ++- 5 files changed, 184 insertions(+), 196 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 5b6d8859..fa71a403 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -70,6 +70,7 @@ if(QUAKE_ENABLE_GPU) add_compile_definitions(QUAKE_ENABLE_GPU) endif() +set(FAISS_ENABLE_GPU OFF) # --------------------------------------------------------------- # Print out Compiler and Path Information # --------------------------------------------------------------- @@ -87,7 +88,6 @@ message(STATUS "QUAKE_USE_NUMA: ${QUAKE_USE_NUMA}") # Apple-specific adjustments if(APPLE) include_directories("/opt/homebrew/opt/openblas/include") - set(FAISS_ENABLE_GPU OFF) endif() # Compiler options and definitions diff --git a/src/cpp/include/clustering.h b/src/cpp/include/clustering.h index e069527c..130b5a1d 100644 --- a/src/cpp/include/clustering.h +++ b/src/cpp/include/clustering.h @@ -41,12 +41,13 @@ shared_ptr kmeans_cpu(Tensor vectors, * @param initial_centroids The initial centroids to use for k-means. */ #ifdef QUAKE_ENABLE_GPU -shared_ptr kmeans_cuvs(Tensor vectors, - Tensor ids, - int n_clusters, - MetricType metric_type, - int niter = 5, - Tensor initial_centroids = Tensor()); +shared_ptr kmeans_cuvs_sample_and_predict( + Tensor vectors, Tensor ids, + int num_clusters, + MetricType metric, + int sample_size, + int niter, + int gpu_batch_size); #endif /** diff --git a/src/cpp/src/clustering.cpp b/src/cpp/src/clustering.cpp index f6954c3b..d5520320 100644 --- a/src/cpp/src/clustering.cpp +++ b/src/cpp/src/clustering.cpp @@ -24,8 +24,15 @@ shared_ptr kmeans_cuvs_sample_and_predict( int niter, int gpu_batch_size) { - TORCH_CHECK(vectors.dim()==2, "vectors must be [N,D]"); - TORCH_CHECK(ids.dim()==1, "ids must be [N]"); + std::cout << "[kmeans] Starting sample_and_predict: N=" + << vectors.size(0) << ", D=" << vectors.size(1) + << ", clusters=" << num_clusters + << ", sample_size=" << sample_size + << ", niter=" << niter + << ", batch_size=" << gpu_batch_size << std::endl; + + TORCH_CHECK(vectors.dim() == 2, "vectors must be [N,D]"); + TORCH_CHECK(ids.dim() == 1, "ids must be [N]"); int64_t N = vectors.size(0), D = vectors.size(1); TORCH_CHECK(sample_size > 0 && sample_size <= N, "invalid sample_size"); @@ -33,19 +40,20 @@ shared_ptr kmeans_cuvs_sample_and_predict( // 1) pin + normalize if needed Tensor cpu_pts = vectors.contiguous().pin_memory(); if (metric == faiss::METRIC_INNER_PRODUCT) { - auto norms = cpu_pts.norm(2,1,true); + auto norms = cpu_pts.norm(2, 1, true); cpu_pts = cpu_pts.div(norms); + std::cout << "[kmeans] Normalized for inner product metric" << std::endl; } // 2) choose a random sample of indices - auto perm = torch::randperm(N, torch::kLong); + std::cout << "[kmeans] Sampling " << sample_size << " points" << std::endl; + auto perm = torch::randperm(N, torch::kLong); auto samp_idx = perm.slice(0, 0, sample_size); Tensor samp_pts = cpu_pts.index_select(0, samp_idx); - Tensor samp_ids = ids.index_select(0, samp_idx); // 3) move sample to GPU - Tensor samp_gpu = samp_pts.to(torch::kCUDA, /*non_blocking=*/true) - .contiguous(); + std::cout << "[kmeans] Uploading sample to GPU" << std::endl; + Tensor samp_gpu = samp_pts.to(torch::kCUDA, /*non_blocking=*/true).contiguous(); // 4) prepare RAFT handle & cuVS params raft::resources handle; @@ -59,12 +67,20 @@ shared_ptr kmeans_cuvs_sample_and_predict( // 5) allocate centroids on GPU Tensor cent_gpu = torch::empty({num_clusters, D}, - torch::kFloat32, - torch::TensorOptions().device(torch::kCUDA)) + torch::TensorOptions() + .dtype(torch::kFloat32) + .device(torch::kCUDA)) .contiguous(); // 6) run fit on just the sample { + std::cout << "[kmeans] Running cuVS fit on sample" << std::endl; + // host scalars + float inertia = 0.0f; + int actual_iter= 0; + auto host_inertia = raft::make_host_scalar_view(&inertia); + auto host_iter = raft::make_host_scalar_view(&actual_iter); + auto X_view = raft::make_device_matrix_view( samp_gpu.data_ptr(), (int)sample_size, (int)D); @@ -77,26 +93,39 @@ shared_ptr kmeans_cuvs_sample_and_predict( X_view, std::nullopt, C_view, - raft::make_host_scalar_view(nullptr), - raft::make_host_scalar_view(nullptr) + host_inertia, + host_iter ); + + std::cout << "[kmeans] Fit complete: inertia=" << inertia + << ", iterations=" << actual_iter << std::endl; } - // 7) now predict labels for all N in batches + std::cout << "[kmeans] Beginning predict over all " << N << " points" << std::endl; Tensor all_labels = torch::empty({N}, torch::kLong); - Tensor labels32 = torch::empty({gpu_batch_size}, - torch::kInt32, - torch::TensorOptions().device(torch::kCUDA)); + auto predict_fn = [&](Tensor batch_cpu, int64_t off) { int64_t bs = batch_cpu.size(0); + + // allocate exactly bs labels on the GPU + Tensor labels32 = torch::empty( + {bs}, + torch::TensorOptions() + .dtype(torch::kInt32) + .device(torch::kCUDA)); + Tensor batch_gpu = batch_cpu.to(torch::kCUDA, /*NB=*/true) - .contiguous(); + .contiguous(); + auto Xv = raft::make_device_matrix_view( batch_gpu.data_ptr(), (int)bs, (int)D); auto Lv = raft::make_device_vector_view( labels32.data_ptr(), (int)bs); + float pred_inertia = 0.0f; + auto host_pred = raft::make_host_scalar_view(&pred_inertia); + cuvs::cluster::kmeans::predict( handle, params, Xv, @@ -106,44 +135,44 @@ shared_ptr kmeans_cuvs_sample_and_predict( num_clusters, (int)D), Lv, false, - raft::make_host_scalar_view(nullptr) + host_pred ); - // copy back + std::cout << "[kmeans] Predict batch off=" << off + << ", bs=" << bs + << ", inertia=" << pred_inertia << std::endl; + + // now safe to copy back exactly bs elements all_labels.narrow(0, off, bs) - .copy_(labels32.slice(0,0,bs) - .to(torch::kLong) - .to(torch::kCPU)); + .copy_( + labels32.to(torch::kLong) + .to(torch::kCPU) + ); }; - // 7a) predict for the sample slice + // predict the sample slice + std::cout << "[kmeans] Predicting sample slice [0," << sample_size << ")" << std::endl; predict_fn(samp_pts, /*off=*/0); - // 7b) predict for the rest - int64_t written = sample_size; - // we’ll write the rest starting at index sample_size + + // predict the rest + std::cout << "[kmeans] Predicting remaining chunks" << std::endl; for (int64_t off = 0; off < N; off += gpu_batch_size) { int64_t bs = std::min(gpu_batch_size, N - off); - // skip the sample zone if (off < sample_size) { - // overlap: part sample / part rest if (off + bs <= sample_size) { - // whole chunk was sample: already done continue; } else { - // split chunk - int64_t s_end = sample_size - off; - int64_t r_bs = bs - s_end; - Tensor rest_chunk = cpu_pts.slice(0, off + s_end, off + bs); - predict_fn(rest_chunk, /*off=*/off + s_end); + int64_t overlap = sample_size - off; + Tensor rest_chunk = cpu_pts.slice(0, off + overlap, off + bs); + predict_fn(rest_chunk, /*off=*/off + overlap); continue; } } - // pure rest Tensor rest_chunk = cpu_pts.slice(0, off, off + bs); predict_fn(rest_chunk, /*off=*/off); } - // 8) group on CPU + std::cout << "[kmeans] Grouping on CPU" << std::endl; Tensor sorted_lbl, sorted_idx; std::tie(sorted_lbl, sorted_idx) = torch::sort(all_labels); Tensor sorted_vecs = vectors.index_select(0, sorted_idx); @@ -164,8 +193,11 @@ shared_ptr kmeans_cuvs_sample_and_predict( out->partition_ids = torch::arange(num_clusters, torch::kLong); out->vectors = std::move(cluster_vecs); out->vector_ids = std::move(cluster_ids); + + std::cout << "[kmeans] Completed sample_and_predict\n"; return out; } + #endif shared_ptr kmeans_cpu(Tensor vectors, @@ -252,7 +284,7 @@ shared_ptr kmeans(Tensor vectors, const int gpu_batch_size = 100000; // or from build_params return kmeans_cuvs_sample_and_predict( vectors, ids, - n_clusters, metric, + n_clusters, metric_type, sample_size, niter, gpu_batch_size); #elif diff --git a/test/cpp/clustering.cpp b/test/cpp/clustering.cpp index 9c0b76c3..c94cb44a 100644 --- a/test/cpp/clustering.cpp +++ b/test/cpp/clustering.cpp @@ -1,179 +1,132 @@ #include #include #include "clustering.h" -#include -// Helper functions to generate random test data. -static Tensor generate_random_data(int64_t num_vectors, int64_t dim) { - return torch::randn({num_vectors, dim}, torch::kFloat32).contiguous(); +// Helpers to generate random data and sequential ids +static torch::Tensor generate_random_data(int64_t N, int64_t D) { + return torch::randn({N, D}, torch::kFloat32).contiguous(); } - -static Tensor generate_sequential_ids(int64_t count, int64_t start = 0) { - return torch::arange(start, start + count, torch::kInt64).contiguous(); +static torch::Tensor generate_sequential_ids(int64_t N, int64_t start = 0) { + return torch::arange(start, start + N, torch::kInt64).contiguous(); } -// Helpers to compute mean squared error (MSE) for clustering. -// For FAISS-based clustering: 'vectors' is a vector of tensors (one per cluster). -static double compute_mse(const Tensor& centroids, - const std::vector& clusters) { - double total_error = 0.0; - int64_t total_count = 0; - auto centroids_cpu = centroids.to(torch::kCPU); +// Compute mean squared error for clustering (for CPU sanity) +static double compute_mse(const torch::Tensor& centroids, + const std::vector& clusters) { + double total_err = 0.0; + int64_t count = 0; + auto C = centroids.to(torch::kCPU); for (size_t i = 0; i < clusters.size(); ++i) { - if (clusters[i].size(0) > 0) { - auto cluster = clusters[i].to(torch::kCPU); - auto diff = cluster - centroids_cpu[i].unsqueeze(0); - total_error += diff.pow(2).sum().item(); - total_count += cluster.size(0); - } + auto cl = clusters[i].to(torch::kCPU); + if (cl.size(0) == 0) continue; + auto diff = cl - C[i].unsqueeze(0); + total_err += diff.pow(2).sum().item(); + count += cl.size(0); } - return total_count > 0 ? total_error / total_count : 0.0; + return count>0 ? total_err / count : 0.0; } -// Test fixture for clustering tests. +// Fixture class ClusteringTest : public ::testing::Test { protected: const int64_t num_vectors = 5000; - const int64_t dim = 64; - const int num_clusters = 20; - Tensor vectors_cpu, ids_cpu; - Tensor vectors_cuda, ids_cuda; + const int64_t dim = 64; + const int num_clusters= 20; + + torch::Tensor vectors_cpu, ids_cpu; +#ifdef QUAKE_ENABLE_GPU + torch::Tensor vectors_cuda, ids_cuda; +#endif void SetUp() override { - // Skip these tests if CUDA is not available. - if (!torch::cuda::is_available()) { - GTEST_SKIP() << "CUDA is not available; skipping clustering tests."; - } vectors_cpu = generate_random_data(num_vectors, dim); - ids_cpu = generate_sequential_ids(num_vectors); + ids_cpu = generate_sequential_ids(num_vectors); + #ifdef QUAKE_ENABLE_GPU + if (!torch::cuda::is_available()) { + GTEST_SKIP() << "CUDA not available"; + } vectors_cuda = vectors_cpu.to(torch::kCUDA).contiguous(); - ids_cuda = ids_cpu.to(torch::kCUDA).contiguous(); + ids_cuda = ids_cpu.to(torch::kCUDA).contiguous(); #endif } }; -// Compare clustering methods using the L2 (Euclidean) metric. -#ifdef QUAKE_ENABLE_GPU -TEST_F(ClusteringTest, CompareClustering_L2) { - const int niter = 20; - // FAISS-based clustering on CPU. - auto clustering_cpu = kmeans_cpu(vectors_cpu, ids_cpu, num_clusters, faiss::METRIC_L2, niter, Tensor()); - // cuVS-based clustering on GPU. - auto clustering_cuvs = kmeans_cuvs(vectors_cuda, ids_cuda, num_clusters, faiss::METRIC_L2); - - // Verify centroid shapes. - ASSERT_EQ(clustering_cpu->centroids.dim(), 2); - ASSERT_EQ(clustering_cpu->centroids.size(0), num_clusters); - ASSERT_EQ(clustering_cpu->centroids.size(1), dim); - ASSERT_EQ(clustering_cuvs->centroids.dim(), 2); - ASSERT_EQ(clustering_cuvs->centroids.size(0), num_clusters); - ASSERT_EQ(clustering_cuvs->centroids.size(1), dim); - - // Compare the mean squared errors (MSEs) between the methods. - double mse_cpu = compute_mse(clustering_cpu->centroids, clustering_cpu->vectors); - double mse_cuvs = compute_mse(clustering_cuvs->centroids, clustering_cuvs->vectors); - // They should agree within roughly 20% relative difference. - ASSERT_NEAR(mse_cpu, mse_cuvs, mse_cpu * 0.20); -} - -// Compare clustering methods using the inner product metric. -TEST_F(ClusteringTest, CompareClustering_InnerProduct) { - const int niter = 20; - auto clustering_cpu = kmeans(vectors_cpu, ids_cpu, num_clusters, - faiss::METRIC_INNER_PRODUCT, niter, false, Tensor()); - auto clustering_cuvs = kmeans_cuvs(vectors_cuda, ids_cuda, num_clusters, faiss::METRIC_INNER_PRODUCT); - - // Verify centroid shapes. - ASSERT_EQ(clustering_cpu->centroids.dim(), 2); - ASSERT_EQ(clustering_cpu->centroids.size(0), num_clusters); - ASSERT_EQ(clustering_cpu->centroids.size(1), dim); - ASSERT_EQ(clustering_cuvs->centroids.dim(), 2); - ASSERT_EQ(clustering_cuvs->centroids.size(0), num_clusters); - ASSERT_EQ(clustering_cuvs->centroids.size(1), dim); - - // For inner product, check that cuVS centroids are normalized. - auto norms = clustering_cuvs->centroids.norm(2, 1); - for (int i = 0; i < norms.size(0); ++i) { - ASSERT_NEAR(norms[i].item(), 1.0f, 1e-3); +// Test existing CPU kmeans +TEST_F(ClusteringTest, KMeansCPU_L2) { + auto cl = kmeans_cpu(vectors_cpu, ids_cpu, num_clusters, + faiss::METRIC_L2, /*niter=*/10, torch::Tensor()); + ASSERT_EQ(cl->centroids.sizes(), (std::vector{num_clusters, dim})); + int64_t tot=0; + for (int i=0;ivectors[i].size(0), cl->vector_ids[i].size(0)); + tot += cl->vectors[i].size(0); } - - double mse_cpu = compute_mse(clustering_cpu->centroids, clustering_cpu->vectors); - double mse_cuvs = compute_mse(clustering_cuvs->centroids, clustering_cuvs->vectors); - // Allow a bit larger relative difference for inner product. - ASSERT_NEAR(mse_cpu, mse_cuvs, mse_cpu * 0.25); + ASSERT_EQ(tot, num_vectors); } -// Test that cuVS clustering partitions all vectors correctly (L2 metric). -TEST_F(ClusteringTest, CUVSClustering_Partitioning_L2) { - auto result = kmeans_cuvs(vectors_cuda, ids_cuda, num_clusters, faiss::METRIC_L2); - ASSERT_EQ(result->centroids.dim(), 2); - ASSERT_EQ(result->centroids.size(0), num_clusters); - ASSERT_EQ(result->centroids.size(1), dim); - - int64_t total_vectors = 0; - for (int i = 0; i < num_clusters; ++i) { - auto cluster = result->vectors[i]; - ASSERT_EQ(cluster.size(0), result->vector_ids[i].size(0)); - total_vectors += cluster.size(0); +// Compare CPU vs CPU wrapper +TEST_F(ClusteringTest, KMeansWrapper_CPU) { + auto cl = kmeans(vectors_cpu, ids_cpu, num_clusters, + faiss::METRIC_L2, /*niter=*/10, /*use_gpu=*/false, torch::Tensor()); + ASSERT_EQ(cl->centroids.sizes(), (std::vector{num_clusters, dim})); + int64_t tot=0; + for (int i=0;ivectors[i].size(0); } - ASSERT_EQ(total_vectors, num_vectors); + ASSERT_EQ(tot, num_vectors); } - -// Test that cuVS clustering (inner product) produces unit-norm centroids and correctly partitions vectors. -TEST_F(ClusteringTest, CUVSClustering_Partitioning_InnerProduct) { - auto result = kmeans_cuvs(vectors_cuda, ids_cuda, num_clusters, faiss::METRIC_INNER_PRODUCT); - ASSERT_EQ(result->centroids.dim(), 2); - ASSERT_EQ(result->centroids.size(0), num_clusters); - ASSERT_EQ(result->centroids.size(1), dim); - - // Verify centroids are normalized. - auto norms = result->centroids.norm(2, 1); - for (int i = 0; i < norms.size(0); ++i) { - ASSERT_NEAR(norms[i].item(), 1.0f, 1e-3); - } - - int64_t total_vectors = 0; - for (int i = 0; i < num_clusters; ++i) { - auto cluster = result->vectors[i]; - ASSERT_EQ(cluster.size(0), result->vector_ids[i].size(0)); - total_vectors += cluster.size(0); +#ifdef QUAKE_ENABLE_GPU +TEST_F(ClusteringTest, SampleAndPredict_GPU_L2) { + const int sample_size = 1000; + const int niter = 5; + const int gpu_batch_size = 512; + + auto cl = kmeans_cuvs_sample_and_predict( + vectors_cpu, ids_cpu, + num_clusters, + faiss::METRIC_L2, + sample_size, + niter, + gpu_batch_size); + + // centroids must live on CPU and have correct shape + ASSERT_EQ(cl->centroids.device().type(), torch::kCPU); + ASSERT_EQ(cl->centroids.sizes(), (std::vector{num_clusters, dim})); + + // all vectors accounted for + int64_t tot=0; + for (int i=0;ivectors[i]; + ASSERT_EQ(part.device().type(), torch::kCPU); + ASSERT_EQ(part.size(0), cl->vector_ids[i].size(0)); + tot += part.size(0); } - ASSERT_EQ(total_vectors, num_vectors); + ASSERT_EQ(tot, num_vectors); + + // Optional quality check: rough MSE vs CPU run + auto cl_cpu = kmeans_cpu(vectors_cpu, ids_cpu, num_clusters, + faiss::METRIC_L2, niter, torch::Tensor()); + double mse_cpu = compute_mse(cl_cpu->centroids, cl_cpu->vectors); + double mse_gpu = compute_mse(cl->centroids, cl->vectors); + ASSERT_NEAR(mse_cpu, mse_gpu, mse_cpu * 0.30); } -#endif - -TEST_F(ClusteringTest, KMeansCPU_L2) { - // Test CPU-based k-means clustering. - int niter = 10; - auto clustering = kmeans_cpu(vectors_cpu, ids_cpu, num_clusters, faiss::METRIC_L2, niter); - ASSERT_EQ(clustering->centroids.dim(), 2); - ASSERT_EQ(clustering->centroids.size(0), num_clusters); - ASSERT_EQ(clustering->centroids.size(1), dim); - int64_t total_vectors = 0; - for (int i = 0; i < num_clusters; ++i) { - auto cluster = clustering->vectors[i]; - ASSERT_EQ(cluster.size(0), clustering->vector_ids[i].size(0)); - total_vectors += cluster.size(0); - } - ASSERT_EQ(total_vectors, num_vectors); +// Full wrapper test for GPU +TEST_F(ClusteringTest, KMeansWrapper_GPU) { + const int gpu_sample = 2000; + const int gpu_iter = 3; + const int gpu_batch = 1024; + + auto cl = kmeans(vectors_cpu, ids_cpu, num_clusters, + faiss::METRIC_L2, gpu_iter, + /*use_gpu=*/true, + torch::Tensor()); + ASSERT_EQ(cl->centroids.device().type(), torch::kCPU); + ASSERT_EQ(cl->vectors.size(), size_t(num_clusters)); + int64_t tot=0; + for (auto &p : cl->vectors) tot += p.size(0); + ASSERT_EQ(tot, num_vectors); } - -TEST_F(ClusteringTest, KMeansCPU_InnerProduct) { - // Test CPU-based k-means clustering with inner product metric. - int niter = 10; - auto clustering = kmeans_cpu(vectors_cpu, ids_cpu, num_clusters, faiss::METRIC_INNER_PRODUCT, niter); - ASSERT_EQ(clustering->centroids.dim(), 2); - ASSERT_EQ(clustering->centroids.size(0), num_clusters); - ASSERT_EQ(clustering->centroids.size(1), dim); - - int64_t total_vectors = 0; - for (int i = 0; i < num_clusters; ++i) { - auto cluster = clustering->vectors[i]; - ASSERT_EQ(cluster.size(0), clustering->vector_ids[i].size(0)); - total_vectors += cluster.size(0); - } - ASSERT_EQ(total_vectors, num_vectors); -} \ No newline at end of file +#endif // QUAKE_ENABLE_GPU diff --git a/test/cpp/quake_index.cpp b/test/cpp/quake_index.cpp index 242c16df..12dd3bf0 100644 --- a/test/cpp/quake_index.cpp +++ b/test/cpp/quake_index.cpp @@ -257,14 +257,14 @@ TEST(QuakeIndexStressTest, LargeBuildTest) { // Attempt to build an index with a large number of vectors. // Adjust these numbers based on your available memory/compute. int64_t dimension = 128; // Medium-high dimension - int64_t num_vectors = 1e6; // 1 million vectors + int64_t num_vectors = 10e6; // 1 million vectors auto data_vectors = generate_random_data(num_vectors, dimension); auto data_ids = generate_sequential_ids(num_vectors, 0); QuakeIndex index; auto build_params = std::make_shared(); - build_params->nlist = 512; + build_params->nlist = 5120; build_params->metric = "l2"; // Keep the iteration count modest to avoid overly long tests build_params->niter = 5; @@ -288,14 +288,16 @@ TEST(QuakeIndexStressTestGPU, LargeBuildTest) { // Attempt to build an index with a large number of vectors. // Adjust these numbers based on your available memory/compute. int64_t dimension = 128; // Medium-high dimension - int64_t num_vectors = 1e6; // 1 million vectors - auto data_vectors = generate_random_data(num_vectors, dimension); - auto data_ids = generate_sequential_ids(num_vectors, 0); + int64_t num_vectors = 10e6; // 1 million vectors + auto data_vectors = generate_random_data(num_vectors, dimension).contiguous(); + auto data_ids = generate_sequential_ids(num_vectors, 0).contiguous(); QuakeIndex index; + std::cout << "generated\n"; + auto build_params = std::make_shared(); - build_params->nlist = 512; + build_params->nlist = 5120; build_params->metric = "l2"; build_params->use_gpu = true; // Keep the iteration count modest to avoid overly long tests @@ -554,4 +556,4 @@ TEST(QuakeIndexGPUTest, BuildWithGPUTest) { EXPECT_EQ(timing_info->n_vectors, data_vectors.size(0)); EXPECT_EQ(timing_info->d, data_vectors.size(1)); } -#endif \ No newline at end of file +#endif From 62650c942d15f97ab8766b7e1485d4e9fc0f874f Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 17 Apr 2025 12:24:37 -0500 Subject: [PATCH 011/323] remove logs --- src/cpp/src/clustering.cpp | 28 ++-------------------------- 1 file changed, 2 insertions(+), 26 deletions(-) diff --git a/src/cpp/src/clustering.cpp b/src/cpp/src/clustering.cpp index d5520320..b2a3f15a 100644 --- a/src/cpp/src/clustering.cpp +++ b/src/cpp/src/clustering.cpp @@ -10,7 +10,7 @@ #include "index_partition.h" #include -#ifdef QUAKE_ENABLE_GPU +// #ifdef QUAKE_ENABLE_GPU #include #include // RAFT resources (handle) #include // RAFT device view (make_device_matrix_view, etc.) @@ -24,13 +24,6 @@ shared_ptr kmeans_cuvs_sample_and_predict( int niter, int gpu_batch_size) { - std::cout << "[kmeans] Starting sample_and_predict: N=" - << vectors.size(0) << ", D=" << vectors.size(1) - << ", clusters=" << num_clusters - << ", sample_size=" << sample_size - << ", niter=" << niter - << ", batch_size=" << gpu_batch_size << std::endl; - TORCH_CHECK(vectors.dim() == 2, "vectors must be [N,D]"); TORCH_CHECK(ids.dim() == 1, "ids must be [N]"); int64_t N = vectors.size(0), D = vectors.size(1); @@ -42,17 +35,14 @@ shared_ptr kmeans_cuvs_sample_and_predict( if (metric == faiss::METRIC_INNER_PRODUCT) { auto norms = cpu_pts.norm(2, 1, true); cpu_pts = cpu_pts.div(norms); - std::cout << "[kmeans] Normalized for inner product metric" << std::endl; } // 2) choose a random sample of indices - std::cout << "[kmeans] Sampling " << sample_size << " points" << std::endl; auto perm = torch::randperm(N, torch::kLong); auto samp_idx = perm.slice(0, 0, sample_size); Tensor samp_pts = cpu_pts.index_select(0, samp_idx); // 3) move sample to GPU - std::cout << "[kmeans] Uploading sample to GPU" << std::endl; Tensor samp_gpu = samp_pts.to(torch::kCUDA, /*non_blocking=*/true).contiguous(); // 4) prepare RAFT handle & cuVS params @@ -74,7 +64,6 @@ shared_ptr kmeans_cuvs_sample_and_predict( // 6) run fit on just the sample { - std::cout << "[kmeans] Running cuVS fit on sample" << std::endl; // host scalars float inertia = 0.0f; int actual_iter= 0; @@ -96,12 +85,8 @@ shared_ptr kmeans_cuvs_sample_and_predict( host_inertia, host_iter ); - - std::cout << "[kmeans] Fit complete: inertia=" << inertia - << ", iterations=" << actual_iter << std::endl; } - std::cout << "[kmeans] Beginning predict over all " << N << " points" << std::endl; Tensor all_labels = torch::empty({N}, torch::kLong); auto predict_fn = [&](Tensor batch_cpu, int64_t off) { @@ -138,10 +123,6 @@ shared_ptr kmeans_cuvs_sample_and_predict( host_pred ); - std::cout << "[kmeans] Predict batch off=" << off - << ", bs=" << bs - << ", inertia=" << pred_inertia << std::endl; - // now safe to copy back exactly bs elements all_labels.narrow(0, off, bs) .copy_( @@ -151,11 +132,9 @@ shared_ptr kmeans_cuvs_sample_and_predict( }; // predict the sample slice - std::cout << "[kmeans] Predicting sample slice [0," << sample_size << ")" << std::endl; predict_fn(samp_pts, /*off=*/0); // predict the rest - std::cout << "[kmeans] Predicting remaining chunks" << std::endl; for (int64_t off = 0; off < N; off += gpu_batch_size) { int64_t bs = std::min(gpu_batch_size, N - off); if (off < sample_size) { @@ -172,7 +151,6 @@ shared_ptr kmeans_cuvs_sample_and_predict( predict_fn(rest_chunk, /*off=*/off); } // 8) group on CPU - std::cout << "[kmeans] Grouping on CPU" << std::endl; Tensor sorted_lbl, sorted_idx; std::tie(sorted_lbl, sorted_idx) = torch::sort(all_labels); Tensor sorted_vecs = vectors.index_select(0, sorted_idx); @@ -194,11 +172,9 @@ shared_ptr kmeans_cuvs_sample_and_predict( out->vectors = std::move(cluster_vecs); out->vector_ids = std::move(cluster_ids); - std::cout << "[kmeans] Completed sample_and_predict\n"; return out; } - -#endif +// #endif shared_ptr kmeans_cpu(Tensor vectors, Tensor ids, From c71326616ded0de9efed1f5bea14e028bce24c61 Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 17 Apr 2025 16:01:09 -0500 Subject: [PATCH 012/323] pass build params directly to kmeans --- src/cpp/include/clustering.h | 16 +-- src/cpp/include/common.h | 8 +- src/cpp/src/clustering.cpp | 174 +++++++++++++++--------------- src/cpp/src/partition_manager.cpp | 6 +- src/cpp/src/quake_index.cpp | 5 +- test/cpp/clustering.cpp | 51 +++++---- 6 files changed, 132 insertions(+), 128 deletions(-) diff --git a/src/cpp/include/clustering.h b/src/cpp/include/clustering.h index 130b5a1d..d83d563b 100644 --- a/src/cpp/include/clustering.h +++ b/src/cpp/include/clustering.h @@ -24,9 +24,7 @@ class IndexPartition; */ shared_ptr kmeans_cpu(Tensor vectors, Tensor ids, - int n_clusters, - MetricType metric_type, - int niter = 5, + shared_ptr build_params, Tensor initial_centroids = Tensor()); /** @@ -42,12 +40,7 @@ shared_ptr kmeans_cpu(Tensor vectors, */ #ifdef QUAKE_ENABLE_GPU shared_ptr kmeans_cuvs_sample_and_predict( - Tensor vectors, Tensor ids, - int num_clusters, - MetricType metric, - int sample_size, - int niter, - int gpu_batch_size); + Tensor vectors, Tensor ids, shared_ptr build_params); #endif /** @@ -63,10 +56,7 @@ shared_ptr kmeans_cuvs_sample_and_predict( */ shared_ptr kmeans(Tensor vectors, Tensor ids, - int n_clusters, - MetricType metric_type, - int niter = 5, - bool use_gpu = false, + shared_ptr build_params, Tensor initial_centroids = Tensor()); diff --git a/src/cpp/include/common.h b/src/cpp/include/common.h index ff95b2a2..b9ac90ed 100644 --- a/src/cpp/include/common.h +++ b/src/cpp/include/common.h @@ -63,6 +63,8 @@ constexpr int DEFAULT_NLIST = 0; ///< Default number of cluste constexpr int DEFAULT_NITER = 5; ///< Default number of k-means iterations used during clustering. constexpr const char* DEFAULT_METRIC = "l2"; ///< Default distance metric (either "l2" for Euclidean or "ip" for inner product). constexpr int DEFAULT_NUM_WORKERS = 0; ///< Default number of workers (0 means single-threaded). +constexpr int DEFAULT_GPU_BATCH_SIZE = 100000; ///< Default batch size for GPU index building. +constexpr int DEFAULT_GPU_SAMPLE_SIZE = 1000000; ///< Default sample size for GPU index building. // Default constants for search parameters constexpr int DEFAULT_K = 1; ///< Default number of neighbors to return. @@ -124,11 +126,15 @@ struct IndexBuildParams { bool use_adaptive_nprobe = false; bool use_numa = false; - bool use_gpu = false; bool verify_numa = false; bool same_core = true; bool verbose = false; + // gpu index build params + bool use_gpu = false; + int gpu_batch_size = DEFAULT_GPU_BATCH_SIZE; + int gpu_sample_size = DEFAULT_GPU_SAMPLE_SIZE; + shared_ptr parent_params = nullptr; IndexBuildParams() = default; diff --git a/src/cpp/src/clustering.cpp b/src/cpp/src/clustering.cpp index b2a3f15a..b5f82682 100644 --- a/src/cpp/src/clustering.cpp +++ b/src/cpp/src/clustering.cpp @@ -10,7 +10,7 @@ #include "index_partition.h" #include -// #ifdef QUAKE_ENABLE_GPU +#ifdef QUAKE_ENABLE_GPU #include #include // RAFT resources (handle) #include // RAFT device view (make_device_matrix_view, etc.) @@ -18,52 +18,54 @@ shared_ptr kmeans_cuvs_sample_and_predict( Tensor vectors, Tensor ids, - int num_clusters, - MetricType metric, - int sample_size, - int niter, - int gpu_batch_size) { - - TORCH_CHECK(vectors.dim() == 2, "vectors must be [N,D]"); - TORCH_CHECK(ids.dim() == 1, "ids must be [N]"); - int64_t N = vectors.size(0), D = vectors.size(1); - TORCH_CHECK(sample_size > 0 && sample_size <= N, + shared_ptr build_params) { + + int num_clusters = build_params->nlist; + int niter = build_params->niter; + int gpu_batch_size = build_params->gpu_batch_size; + int gpu_sample_size = build_params->gpu_sample_size; + MetricType metric = str_to_metric_type(build_params->metric); + + TORCH_CHECK(vectors.dim() == 2, "vectors must be [N,D]"); + TORCH_CHECK(ids.dim() == 1, "ids must be [N]"); + int64_t N = vectors.size(0), D = vectors.size(1); + TORCH_CHECK(gpu_sample_size > 0 && gpu_sample_size <= N, "invalid sample_size"); - // 1) pin + normalize if needed - Tensor cpu_pts = vectors.contiguous().pin_memory(); - if (metric == faiss::METRIC_INNER_PRODUCT) { + // 1) pin + normalize if needed + Tensor cpu_pts = vectors.contiguous().pin_memory(); + if (metric == faiss::METRIC_INNER_PRODUCT) { auto norms = cpu_pts.norm(2, 1, true); cpu_pts = cpu_pts.div(norms); - } + } - // 2) choose a random sample of indices - auto perm = torch::randperm(N, torch::kLong); - auto samp_idx = perm.slice(0, 0, sample_size); - Tensor samp_pts = cpu_pts.index_select(0, samp_idx); + // 2) choose a random sample of indices + auto perm = torch::randperm(N, torch::kLong); + auto samp_idx = perm.slice(0, 0, gpu_sample_size); + Tensor samp_pts = cpu_pts.index_select(0, samp_idx); - // 3) move sample to GPU - Tensor samp_gpu = samp_pts.to(torch::kCUDA, /*non_blocking=*/true).contiguous(); + // 3) move sample to GPU + Tensor samp_gpu = samp_pts.to(torch::kCUDA, /*non_blocking=*/true).contiguous(); - // 4) prepare RAFT handle & cuVS params - raft::resources handle; - cudaStream_t stream = c10::cuda::getCurrentCUDAStream(); - raft::resource::set_cuda_stream(handle, stream); + // 4) prepare RAFT handle & cuVS params + raft::resources handle; + cudaStream_t stream = c10::cuda::getCurrentCUDAStream(); + raft::resource::set_cuda_stream(handle, stream); - cuvs::cluster::kmeans::params params; - params.n_clusters = num_clusters; - params.init = cuvs::cluster::kmeans::params::InitMethod::Random; - params.max_iter = niter; + cuvs::cluster::kmeans::params params; + params.n_clusters = num_clusters; + params.init = cuvs::cluster::kmeans::params::InitMethod::Random; + params.max_iter = niter; - // 5) allocate centroids on GPU - Tensor cent_gpu = torch::empty({num_clusters, D}, + // 5) allocate centroids on GPU + Tensor cent_gpu = torch::empty({num_clusters, D}, torch::TensorOptions() .dtype(torch::kFloat32) .device(torch::kCUDA)) .contiguous(); - // 6) run fit on just the sample - { + // 6) run fit on just the sample + { // host scalars float inertia = 0.0f; int actual_iter= 0; @@ -72,7 +74,7 @@ shared_ptr kmeans_cuvs_sample_and_predict( auto X_view = raft::make_device_matrix_view( samp_gpu.data_ptr(), - (int)sample_size, (int)D); + (int) gpu_sample_size, (int)D); auto C_view = raft::make_device_matrix_view( cent_gpu.data_ptr(), num_clusters, (int)D); @@ -85,11 +87,11 @@ shared_ptr kmeans_cuvs_sample_and_predict( host_inertia, host_iter ); - } + } - Tensor all_labels = torch::empty({N}, torch::kLong); + Tensor all_labels = torch::empty({N}, torch::kLong); - auto predict_fn = [&](Tensor batch_cpu, int64_t off) { + auto predict_fn = [&](Tensor batch_cpu, int64_t off) { int64_t bs = batch_cpu.size(0); // allocate exactly bs labels on the GPU @@ -129,19 +131,19 @@ shared_ptr kmeans_cuvs_sample_and_predict( labels32.to(torch::kLong) .to(torch::kCPU) ); - }; + }; - // predict the sample slice - predict_fn(samp_pts, /*off=*/0); + // predict the sample slice + predict_fn(samp_pts, /*off=*/0); - // predict the rest - for (int64_t off = 0; off < N; off += gpu_batch_size) { + // predict the rest + for (int64_t off = 0; off < N; off += gpu_batch_size) { int64_t bs = std::min(gpu_batch_size, N - off); - if (off < sample_size) { - if (off + bs <= sample_size) { + if (off < gpu_sample_size) { + if (off + bs <= gpu_sample_size) { continue; } else { - int64_t overlap = sample_size - off; + int64_t overlap = gpu_sample_size - off; Tensor rest_chunk = cpu_pts.slice(0, off + overlap, off + bs); predict_fn(rest_chunk, /*off=*/off + overlap); continue; @@ -149,43 +151,43 @@ shared_ptr kmeans_cuvs_sample_and_predict( } Tensor rest_chunk = cpu_pts.slice(0, off, off + bs); predict_fn(rest_chunk, /*off=*/off); - } - // 8) group on CPU - Tensor sorted_lbl, sorted_idx; - std::tie(sorted_lbl, sorted_idx) = torch::sort(all_labels); - Tensor sorted_vecs = vectors.index_select(0, sorted_idx); - Tensor sorted_ids = ids.index_select(0, sorted_idx); - - Tensor counts = torch::bincount(sorted_lbl, /*weights=*/{}, num_clusters); - auto cnt_cpu = counts.to(torch::kCPU); - std::vector split_sizes( + } + // 8) group on CPU + Tensor sorted_lbl, sorted_idx; + std::tie(sorted_lbl, sorted_idx) = torch::sort(all_labels); + Tensor sorted_vecs = vectors.index_select(0, sorted_idx); + Tensor sorted_ids = ids.index_select(0, sorted_idx); + + Tensor counts = torch::bincount(sorted_lbl, /*weights=*/{}, num_clusters); + auto cnt_cpu = counts.to(torch::kCPU); + std::vector split_sizes( cnt_cpu.data_ptr(), cnt_cpu.data_ptr() + num_clusters - ); + ); - auto cluster_vecs = torch::split(sorted_vecs, split_sizes, 0); - auto cluster_ids = torch::split(sorted_ids, split_sizes, 0); + auto cluster_vecs = torch::split(sorted_vecs, split_sizes, 0); + auto cluster_ids = torch::split(sorted_ids, split_sizes, 0); - auto out = std::make_shared(); - out->centroids = cent_gpu.cpu().contiguous(); - out->partition_ids = torch::arange(num_clusters, torch::kLong); - out->vectors = std::move(cluster_vecs); - out->vector_ids = std::move(cluster_ids); + auto out = std::make_shared(); + out->centroids = cent_gpu.cpu().contiguous(); + out->partition_ids = torch::arange(num_clusters, torch::kLong); + out->vectors = std::move(cluster_vecs); + out->vector_ids = std::move(cluster_ids); - return out; + return out; } -// #endif +#endif shared_ptr kmeans_cpu(Tensor vectors, Tensor ids, - int n_clusters, - MetricType metric_type, - int niter, + shared_ptr build_params, Tensor /* initial_centroids */) { // Ensure enough vectors are available and sizes match. - assert(vectors.size(0) >= n_clusters * 2); + assert(vectors.size(0) >= build_params->nlist * 2); assert(vectors.size(0) == ids.size(0)); + MetricType metric_type = str_to_metric_type(build_params->metric); + // Normalize vectors for inner product if (metric_type == faiss::METRIC_INNER_PRODUCT) vectors = vectors / vectors.norm(2, 1).unsqueeze(1); @@ -200,13 +202,13 @@ shared_ptr kmeans_cpu(Tensor vectors, index_ptr = new faiss::IndexFlatL2(d); faiss::ClusteringParameters cp; - cp.niter = niter; + cp.niter = build_params->niter; - faiss::Clustering clus(d, n_clusters, cp); + faiss::Clustering clus(d, build_params->nlist, cp); clus.train(n, vectors.data_ptr(), *index_ptr); // Retrieve centroids as a torch Tensor. - Tensor centroids = torch::from_blob(clus.centroids.data(), {n_clusters, d}, torch::kFloat32).clone(); + Tensor centroids = torch::from_blob(clus.centroids.data(), {build_params->nlist, d}, torch::kFloat32).clone(); if (metric_type == faiss::METRIC_INNER_PRODUCT) centroids = centroids / centroids.norm(2, 1).unsqueeze(1); @@ -223,7 +225,7 @@ shared_ptr kmeans_cpu(Tensor vectors, Tensor sorted_ids = ids.index_select(0, sorted_indices); // Compute counts per cluster using bincount. - Tensor counts_tensor = torch::bincount(sorted_assignments, /*weights=*/{}, n_clusters); + Tensor counts_tensor = torch::bincount(sorted_assignments, /*weights=*/{}, build_params->nlist); // Ensure counts are on CPU to extract split sizes. counts_tensor = counts_tensor.to(torch::kCPU); // Convert counts tensor to std::vector @@ -234,7 +236,7 @@ shared_ptr kmeans_cpu(Tensor vectors, vector cluster_vectors = torch::split(sorted_vectors, counts_vector, 0); vector cluster_ids = torch::split(sorted_ids, counts_vector, 0); - Tensor partition_ids = torch::arange(n_clusters, torch::kInt64); + Tensor partition_ids = torch::arange(build_params->nlist, torch::kInt64); shared_ptr clustering = std::make_shared(); clustering->centroids = centroids; @@ -249,25 +251,23 @@ shared_ptr kmeans_cpu(Tensor vectors, shared_ptr kmeans(Tensor vectors, Tensor ids, - int n_clusters, - MetricType metric_type, - int niter, - bool use_gpu /*=false*/, + shared_ptr build_params, Tensor /* initial_centroids */) { - if (use_gpu) { + if (build_params->use_gpu) { #ifdef QUAKE_ENABLE_GPU - const int sample_size = std::min(1000000, vectors.size(0)); - const int gpu_batch_size = 100000; // or from build_params return kmeans_cuvs_sample_and_predict( - vectors, ids, - n_clusters, metric_type, - sample_size, niter, - gpu_batch_size); - #elif + vectors, + ids, + n_clusters, + metric_type, + build_params->gpu-sample_size, + niter, + build_params->gpu_batch_size); + #else throw std::runtime_error("GPU support is not enabled. Please compile with QUAKE_ENABLE_GPU."); #endif } else { - return kmeans_cpu(vectors, ids, n_clusters, metric_type, niter); + return kmeans_cpu(vectors, ids, build_params); } } diff --git a/src/cpp/src/partition_manager.cpp b/src/cpp/src/partition_manager.cpp index 3b738856..9e7695fd 100644 --- a/src/cpp/src/partition_manager.cpp +++ b/src/cpp/src/partition_manager.cpp @@ -412,11 +412,13 @@ shared_ptr PartitionManager::split_partitions(const Tensor &partitio for (int64_t i = 0; i < partition_ids.size(0); ++i) { // Ensure enough vectors to split assert(clustering->cluster_size(i) >= 4 && "Partition must have at least 8 vectors to split."); + shared_ptr build_params = make_shared(); + build_params->nlist = num_splits; + build_params->metric = parent_->metric_; shared_ptr curr_split_clustering = kmeans( clustering->vectors[i], clustering->vector_ids[i], - num_splits, - parent_->metric_ + build_params ); for (size_t j = 0; j < curr_split_clustering->nlist(); ++j) { diff --git a/src/cpp/src/quake_index.cpp b/src/cpp/src/quake_index.cpp index 1f7cc853..9fb6f5ff 100644 --- a/src/cpp/src/quake_index.cpp +++ b/src/cpp/src/quake_index.cpp @@ -44,10 +44,7 @@ shared_ptr QuakeIndex::build(Tensor x, Tensor ids, shared_ptr clustering = kmeans( x, ids, - build_params_->nlist, - metric_, - build_params_->niter, - build_params_->use_gpu + build_params_ ); auto e1 = std::chrono::high_resolution_clock::now(); timing_info->train_time_us = std::chrono::duration_cast(e1 - s1).count(); diff --git a/test/cpp/clustering.cpp b/test/cpp/clustering.cpp index c94cb44a..bd373ba0 100644 --- a/test/cpp/clustering.cpp +++ b/test/cpp/clustering.cpp @@ -54,8 +54,11 @@ class ClusteringTest : public ::testing::Test { // Test existing CPU kmeans TEST_F(ClusteringTest, KMeansCPU_L2) { - auto cl = kmeans_cpu(vectors_cpu, ids_cpu, num_clusters, - faiss::METRIC_L2, /*niter=*/10, torch::Tensor()); + shared_ptr build_params = std::make_shared(); + build_params->nlist = num_clusters; + build_params->metric = "l2"; + build_params->niter = 10; + auto cl = kmeans_cpu(vectors_cpu, ids_cpu, build_params, torch::Tensor()); ASSERT_EQ(cl->centroids.sizes(), (std::vector{num_clusters, dim})); int64_t tot=0; for (int i=0;i build_params = std::make_shared(); + build_params->nlist = num_clusters; + build_params->metric = "l2"; + build_params->niter = 10; + build_params->use_gpu = false; + auto cl = kmeans(vectors_cpu, ids_cpu, build_params, torch::Tensor()); ASSERT_EQ(cl->centroids.sizes(), (std::vector{num_clusters, dim})); int64_t tot=0; for (int i=0;i build_params = std::make_shared(); + build_params->nlist = num_clusters; + build_params->metric = "l2"; + build_params->niter = 10; + build_params->use_gpu = true; + build_params->gpu_sample_size = 2000; + build_params->gpu_batch_size = 100; auto cl = kmeans_cuvs_sample_and_predict( - vectors_cpu, ids_cpu, - num_clusters, - faiss::METRIC_L2, - sample_size, - niter, - gpu_batch_size); + vectors_cpu, ids_cpu, build_params); // centroids must live on CPU and have correct shape ASSERT_EQ(cl->centroids.device().type(), torch::kCPU); @@ -105,9 +111,10 @@ TEST_F(ClusteringTest, SampleAndPredict_GPU_L2) { } ASSERT_EQ(tot, num_vectors); + build_params->use_gpu = false; + // Optional quality check: rough MSE vs CPU run - auto cl_cpu = kmeans_cpu(vectors_cpu, ids_cpu, num_clusters, - faiss::METRIC_L2, niter, torch::Tensor()); + auto cl_cpu = kmeans_cpu(vectors_cpu, ids_cpu, build_params, torch::Tensor()); double mse_cpu = compute_mse(cl_cpu->centroids, cl_cpu->vectors); double mse_gpu = compute_mse(cl->centroids, cl->vectors); ASSERT_NEAR(mse_cpu, mse_gpu, mse_cpu * 0.30); @@ -115,13 +122,15 @@ TEST_F(ClusteringTest, SampleAndPredict_GPU_L2) { // Full wrapper test for GPU TEST_F(ClusteringTest, KMeansWrapper_GPU) { - const int gpu_sample = 2000; - const int gpu_iter = 3; - const int gpu_batch = 1024; + shared_ptr build_params = std::make_shared(); + build_params->nlist = num_clusters; + build_params->metric = "l2"; + build_params->niter = 10; + build_params->use_gpu = true; + build_params->gpu_sample_size = 2000; + build_params->gpu_batch_size = 100; - auto cl = kmeans(vectors_cpu, ids_cpu, num_clusters, - faiss::METRIC_L2, gpu_iter, - /*use_gpu=*/true, + auto cl = kmeans(vectors_cpu, ids_cpu, build_params, torch::Tensor()); ASSERT_EQ(cl->centroids.device().type(), torch::kCPU); ASSERT_EQ(cl->vectors.size(), size_t(num_clusters)); From 65eb128c60f8f82595e11100950b7c014b16a4ce Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 18 Apr 2025 13:21:54 -0500 Subject: [PATCH 013/323] update conda --- environments/ubuntu-cuda/conda.yaml | 4 ++++ src/cpp/src/partition_manager.cpp | 6 +++--- 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/environments/ubuntu-cuda/conda.yaml b/environments/ubuntu-cuda/conda.yaml index 715744f2..99265b48 100644 --- a/environments/ubuntu-cuda/conda.yaml +++ b/environments/ubuntu-cuda/conda.yaml @@ -3,6 +3,8 @@ channels: - pytorch - defaults - conda-forge + - nvidia + - libcuvs dependencies: - python=3.11 - numpy @@ -10,6 +12,8 @@ dependencies: - faiss-gpu - matplotlib - pytest + - libcuvs + - cuda-version=12.8 - pip - pip: - sphinx diff --git a/src/cpp/src/partition_manager.cpp b/src/cpp/src/partition_manager.cpp index 9e7695fd..c838541b 100644 --- a/src/cpp/src/partition_manager.cpp +++ b/src/cpp/src/partition_manager.cpp @@ -409,12 +409,12 @@ shared_ptr PartitionManager::split_partitions(const Tensor &partitio shared_ptr clustering = select_partitions(partition_ids); + shared_ptr build_params = make_shared(); + build_params->nlist = num_splits; + build_params->metric = metric_type_to_str(parent_->metric_); for (int64_t i = 0; i < partition_ids.size(0); ++i) { // Ensure enough vectors to split assert(clustering->cluster_size(i) >= 4 && "Partition must have at least 8 vectors to split."); - shared_ptr build_params = make_shared(); - build_params->nlist = num_splits; - build_params->metric = parent_->metric_; shared_ptr curr_split_clustering = kmeans( clustering->vectors[i], clustering->vector_ids[i], From d10cd64918db57164438f54c5e27ff5a0d9e3890 Mon Sep 17 00:00:00 2001 From: Jason Mohoney Date: Fri, 18 Apr 2025 20:12:47 +0000 Subject: [PATCH 014/323] fix gpu clustering --- src/cpp/src/clustering.cpp | 11 ++++++----- test/cpp/quake_index.cpp | 12 ++++++------ 2 files changed, 12 insertions(+), 11 deletions(-) diff --git a/src/cpp/src/clustering.cpp b/src/cpp/src/clustering.cpp index b5f82682..903996ea 100644 --- a/src/cpp/src/clustering.cpp +++ b/src/cpp/src/clustering.cpp @@ -26,9 +26,14 @@ shared_ptr kmeans_cuvs_sample_and_predict( int gpu_sample_size = build_params->gpu_sample_size; MetricType metric = str_to_metric_type(build_params->metric); + TORCH_CHECK(vectors.dim() == 2, "vectors must be [N,D]"); TORCH_CHECK(ids.dim() == 1, "ids must be [N]"); int64_t N = vectors.size(0), D = vectors.size(1); + + gpu_sample_size = std::min(gpu_sample_size, (int) N); + gpu_batch_size = std::min(gpu_batch_size, (int) N); + TORCH_CHECK(gpu_sample_size > 0 && gpu_sample_size <= N, "invalid sample_size"); @@ -258,11 +263,7 @@ shared_ptr kmeans(Tensor vectors, return kmeans_cuvs_sample_and_predict( vectors, ids, - n_clusters, - metric_type, - build_params->gpu-sample_size, - niter, - build_params->gpu_batch_size); + build_params); #else throw std::runtime_error("GPU support is not enabled. Please compile with QUAKE_ENABLE_GPU."); #endif diff --git a/test/cpp/quake_index.cpp b/test/cpp/quake_index.cpp index 12dd3bf0..b8f0199c 100644 --- a/test/cpp/quake_index.cpp +++ b/test/cpp/quake_index.cpp @@ -257,14 +257,14 @@ TEST(QuakeIndexStressTest, LargeBuildTest) { // Attempt to build an index with a large number of vectors. // Adjust these numbers based on your available memory/compute. int64_t dimension = 128; // Medium-high dimension - int64_t num_vectors = 10e6; // 1 million vectors + int64_t num_vectors = 1e6; // 1 million vectors auto data_vectors = generate_random_data(num_vectors, dimension); auto data_ids = generate_sequential_ids(num_vectors, 0); QuakeIndex index; auto build_params = std::make_shared(); - build_params->nlist = 5120; + build_params->nlist = 512; build_params->metric = "l2"; // Keep the iteration count modest to avoid overly long tests build_params->niter = 5; @@ -288,7 +288,7 @@ TEST(QuakeIndexStressTestGPU, LargeBuildTest) { // Attempt to build an index with a large number of vectors. // Adjust these numbers based on your available memory/compute. int64_t dimension = 128; // Medium-high dimension - int64_t num_vectors = 10e6; // 1 million vectors + int64_t num_vectors = 1e6; // 1 million vectors auto data_vectors = generate_random_data(num_vectors, dimension).contiguous(); auto data_ids = generate_sequential_ids(num_vectors, 0).contiguous(); @@ -297,7 +297,7 @@ TEST(QuakeIndexStressTestGPU, LargeBuildTest) { std::cout << "generated\n"; auto build_params = std::make_shared(); - build_params->nlist = 5120; + build_params->nlist = 512; build_params->metric = "l2"; build_params->use_gpu = true; // Keep the iteration count modest to avoid overly long tests @@ -533,8 +533,8 @@ TEST(QuakeIndexStressTest, SearchAddRemoveMaintenanceTest) { // Test build with GPU enabled TEST(QuakeIndexGPUTest, BuildWithGPUTest) { int64_t dimension = 32; - int64_t num_vectors = 200; - int64_t nlist = 5; + int64_t num_vectors = 10000; + int64_t nlist = 10; torch::Tensor data_vectors = generate_random_data(num_vectors, dimension); torch::Tensor data_ids = generate_sequential_ids(num_vectors, 0); From b8b714e2cf17f30a4f245b1c0808c22f38705646 Mon Sep 17 00:00:00 2001 From: Kefan Zheng Date: Sun, 20 Apr 2025 18:22:33 -0500 Subject: [PATCH 015/323] adapt cuvs to cuda 12.4 (#61) * adapt cuvs to cuda 12.4 * separate compilation scripts for cuda118 and cuda124 --- .../Dockerfile | 0 .../conda.yaml | 0 environments/ubuntu-cuda124/Dockerfile | 64 +++++++++++++++++++ environments/ubuntu-cuda124/conda.yaml | 24 +++++++ 4 files changed, 88 insertions(+) rename environments/{ubuntu-cuda => ubuntu-cuda118}/Dockerfile (100%) rename environments/{ubuntu-cuda => ubuntu-cuda118}/conda.yaml (100%) create mode 100644 environments/ubuntu-cuda124/Dockerfile create mode 100644 environments/ubuntu-cuda124/conda.yaml diff --git a/environments/ubuntu-cuda/Dockerfile b/environments/ubuntu-cuda118/Dockerfile similarity index 100% rename from environments/ubuntu-cuda/Dockerfile rename to environments/ubuntu-cuda118/Dockerfile diff --git a/environments/ubuntu-cuda/conda.yaml b/environments/ubuntu-cuda118/conda.yaml similarity index 100% rename from environments/ubuntu-cuda/conda.yaml rename to environments/ubuntu-cuda118/conda.yaml diff --git a/environments/ubuntu-cuda124/Dockerfile b/environments/ubuntu-cuda124/Dockerfile new file mode 100644 index 00000000..0462c5c9 --- /dev/null +++ b/environments/ubuntu-cuda124/Dockerfile @@ -0,0 +1,64 @@ +# Use a CUDA-enabled Ubuntu base image +FROM nvidia/cuda:12.4.1-cudnn-devel-ubuntu22.04 + +# ----------------------------- +# Set up environment variables +# ----------------------------- +ENV CONDA_DIR=/opt/miniconda +ENV PATH="${CONDA_DIR}/bin:${PATH}" +ENV DEBIAN_FRONTEND=noninteractive + +# ----------------------------- +# Install system dependencies +# ----------------------------- +RUN apt-get update && \ + apt-get install -y --no-install-recommends \ + wget \ + curl \ + build-essential \ + ca-certificates \ + swig \ + git \ + libomp5 \ + libomp-dev \ + graphviz \ + libnuma-dev \ + && rm -rf /var/lib/apt/lists/* + + +# Install CMake 3.24.2 +RUN wget -qO /tmp/cmake.sh https://github.com/Kitware/CMake/releases/download/v3.30.4/cmake-3.30.4-linux-x86_64.sh && \ + chmod +x /tmp/cmake.sh && \ + /tmp/cmake.sh --skip-license --prefix=/usr/local && \ + rm /tmp/cmake.sh + + +# ----------------------------- +# Install Miniconda +# ----------------------------- +RUN wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O /tmp/miniconda.sh && \ + bash /tmp/miniconda.sh -b -p $CONDA_DIR && \ + rm /tmp/miniconda.sh + +# ----------------------------- +# Copy in your conda environment YAML +# ----------------------------- +COPY environments/ubuntu-cuda/conda.yaml /tmp/conda.yaml + +# Create quake-env +RUN conda env create -f /tmp/conda.yaml && conda clean -afy + +# ----------------------------- +# Install GPU-enabled PyTorch +# ----------------------------- +RUN conda run -n quake-env pip install --no-cache-dir torch --index-url https://download.pytorch.org/whl/cu124 + +# ----------------------------- +# Debug: show conda information +# ----------------------------- +RUN echo "===== DEBUG: which conda =====" && which conda +RUN echo "===== DEBUG: conda info =====" && conda info +RUN echo "===== DEBUG: conda env list =====" && conda env list +RUN echo "===== DEBUG: quake-env check =====" && conda run -n quake-env python -c "import sys; print('OK in quake-env; python:', sys.executable)" + +CMD ["/bin/bash"] \ No newline at end of file diff --git a/environments/ubuntu-cuda124/conda.yaml b/environments/ubuntu-cuda124/conda.yaml new file mode 100644 index 00000000..f895341b --- /dev/null +++ b/environments/ubuntu-cuda124/conda.yaml @@ -0,0 +1,24 @@ +name: quake-env +channels: + - rapidsai + - pytorch + - defaults + - conda-forge + - nvidia +dependencies: + - python=3.11 + - numpy + - pandas + - faiss-gpu + - matplotlib + - pytest + - libcuvs + - cuda-nvrtc-dev=12.4 + - cuda-version=12.4 + - pip + - pip: + - sphinx + - sphinx_rtd_theme + - sphinxcontrib-mermaid + - graphviz + - pyyaml \ No newline at end of file From 741bd0362f6e97dc291741d77b7854a04ca8f4d0 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 21 Apr 2025 13:05:47 -0500 Subject: [PATCH 016/323] prepare camera ready experiments --- test/experiments/osdi2025/ablation/run.py | 2 ++ test/experiments/osdi2025/aps_recall_targets/run.py | 2 ++ test/experiments/osdi2025/early_termination/run.py | 2 ++ test/experiments/osdi2025/experiment_runner.py | 5 +++++ test/experiments/osdi2025/measure_wiki_skew_and_perf/run.py | 2 ++ test/experiments/osdi2025/multi_level/run.py | 2 ++ test/experiments/osdi2025/numa_single_query/run.py | 2 ++ test/experiments/osdi2025/workload_comparison/run.py | 2 ++ 8 files changed, 19 insertions(+) create mode 100644 test/experiments/osdi2025/ablation/run.py create mode 100644 test/experiments/osdi2025/aps_recall_targets/run.py create mode 100644 test/experiments/osdi2025/early_termination/run.py create mode 100644 test/experiments/osdi2025/experiment_runner.py create mode 100644 test/experiments/osdi2025/measure_wiki_skew_and_perf/run.py create mode 100644 test/experiments/osdi2025/multi_level/run.py create mode 100644 test/experiments/osdi2025/numa_single_query/run.py create mode 100644 test/experiments/osdi2025/workload_comparison/run.py diff --git a/test/experiments/osdi2025/ablation/run.py b/test/experiments/osdi2025/ablation/run.py new file mode 100644 index 00000000..139597f9 --- /dev/null +++ b/test/experiments/osdi2025/ablation/run.py @@ -0,0 +1,2 @@ + + diff --git a/test/experiments/osdi2025/aps_recall_targets/run.py b/test/experiments/osdi2025/aps_recall_targets/run.py new file mode 100644 index 00000000..139597f9 --- /dev/null +++ b/test/experiments/osdi2025/aps_recall_targets/run.py @@ -0,0 +1,2 @@ + + diff --git a/test/experiments/osdi2025/early_termination/run.py b/test/experiments/osdi2025/early_termination/run.py new file mode 100644 index 00000000..139597f9 --- /dev/null +++ b/test/experiments/osdi2025/early_termination/run.py @@ -0,0 +1,2 @@ + + diff --git a/test/experiments/osdi2025/experiment_runner.py b/test/experiments/osdi2025/experiment_runner.py new file mode 100644 index 00000000..97eaea67 --- /dev/null +++ b/test/experiments/osdi2025/experiment_runner.py @@ -0,0 +1,5 @@ + + +EXPERIMENTS_LIST = { + "numa_single_query" +} \ No newline at end of file diff --git a/test/experiments/osdi2025/measure_wiki_skew_and_perf/run.py b/test/experiments/osdi2025/measure_wiki_skew_and_perf/run.py new file mode 100644 index 00000000..139597f9 --- /dev/null +++ b/test/experiments/osdi2025/measure_wiki_skew_and_perf/run.py @@ -0,0 +1,2 @@ + + diff --git a/test/experiments/osdi2025/multi_level/run.py b/test/experiments/osdi2025/multi_level/run.py new file mode 100644 index 00000000..139597f9 --- /dev/null +++ b/test/experiments/osdi2025/multi_level/run.py @@ -0,0 +1,2 @@ + + diff --git a/test/experiments/osdi2025/numa_single_query/run.py b/test/experiments/osdi2025/numa_single_query/run.py new file mode 100644 index 00000000..139597f9 --- /dev/null +++ b/test/experiments/osdi2025/numa_single_query/run.py @@ -0,0 +1,2 @@ + + diff --git a/test/experiments/osdi2025/workload_comparison/run.py b/test/experiments/osdi2025/workload_comparison/run.py new file mode 100644 index 00000000..139597f9 --- /dev/null +++ b/test/experiments/osdi2025/workload_comparison/run.py @@ -0,0 +1,2 @@ + + From 73b3901b73886c4360a32cb402760a0410a6e37d Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 22 Apr 2025 14:01:36 -0500 Subject: [PATCH 017/323] add numa multi and single query experiment --- test/__init__.py | 0 test/experiments/__init__.py | 0 test/experiments/osdi2025/__init__.py | 0 .../experiments/osdi2025/experiment_runner.py | 63 +++++- .../osdi2025/numa_multi_query/__init__.py | 0 .../numa_multi_query/configs/sift1m.yaml | 18 ++ .../osdi2025/numa_multi_query/run.py | 186 ++++++++++++++++++ .../osdi2025/numa_single_query/__init__.py | 0 .../numa_single_query/configs/sift1m.yaml | 15 ++ .../osdi2025/numa_single_query/run.py | 115 +++++++++++ 10 files changed, 394 insertions(+), 3 deletions(-) create mode 100644 test/__init__.py create mode 100644 test/experiments/__init__.py create mode 100644 test/experiments/osdi2025/__init__.py create mode 100644 test/experiments/osdi2025/numa_multi_query/__init__.py create mode 100644 test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml create mode 100644 test/experiments/osdi2025/numa_multi_query/run.py create mode 100644 test/experiments/osdi2025/numa_single_query/__init__.py create mode 100644 test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml diff --git a/test/__init__.py b/test/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/test/experiments/__init__.py b/test/experiments/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/test/experiments/osdi2025/__init__.py b/test/experiments/osdi2025/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/test/experiments/osdi2025/experiment_runner.py b/test/experiments/osdi2025/experiment_runner.py index 97eaea67..fd3a02cf 100644 --- a/test/experiments/osdi2025/experiment_runner.py +++ b/test/experiments/osdi2025/experiment_runner.py @@ -1,5 +1,62 @@ +#!/usr/bin/env python +""" +Master runner for OSDI2025 vector‐search experiments. +Usage: + python -m test.experiments.osdi2025.experiment_runner \ + --experiment \ + --config \ + --output-dir +""" -EXPERIMENTS_LIST = { - "numa_single_query" -} \ No newline at end of file +import argparse +from pathlib import Path + +# Make sure `test/`, `test/experiments/`, and subfolders all have an __init__.py +from test.experiments.osdi2025.numa_single_query.run import run_experiment as run_numa_single +from test.experiments.osdi2025.numa_multi_query.run import run_experiment as run_numa_multi + +EXPERIMENTS = { + "numa_single_query": run_numa_single, + "numa_multi_query": run_numa_multi, +} + +def main(): + parser = argparse.ArgumentParser( + description="Run one OSDI2025 experiment" + ) + parser.add_argument( + "-x", "--experiment", + choices=EXPERIMENTS.keys(), + required=True, + help="Which experiment to run" + ) + parser.add_argument( + "-c", "--config", + required=True, + help="Config name (without .yaml) under configs/" + ) + parser.add_argument( + "-o", "--output-dir", + default=None, + help="Where to write results (csv + plots)" + ) + args = parser.parse_args() + + exp = args.experiment + cfg_name = args.config + base = Path(__file__).resolve().parent + cfg_path = base / exp / "configs" / f"{cfg_name}.yaml" + if not cfg_path.is_file(): + raise FileNotFoundError(f"Missing config: {cfg_path}") + + # compute output directory + out_dir = Path(args.output_dir) if args.output_dir else base / exp / "results" / cfg_name + out_dir.mkdir(parents=True, exist_ok=True) + + # call the appropriate experiment + runner = EXPERIMENTS[exp] + runner(str(cfg_path), str(out_dir)) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/test/experiments/osdi2025/numa_multi_query/__init__.py b/test/experiments/osdi2025/numa_multi_query/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml new file mode 100644 index 00000000..b6b5b768 --- /dev/null +++ b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml @@ -0,0 +1,18 @@ +# existing keys... +dataset: + name: sift1m + num_queries: 10000 + k: 10 + nprobe: 10 + +#n_workers: [0,1,2,4,8,16] +#batch_sizes: [1, 10, 100, 100] +n_workers: [1, 2, 4, 8, 16] +batch_sizes: [1, 10, 100, 1000, 10000] + +# new keys: +trials: 5 +warmup: 10 + +output: + results_csv: numa_multi_query_results.csv \ No newline at end of file diff --git a/test/experiments/osdi2025/numa_multi_query/run.py b/test/experiments/osdi2025/numa_multi_query/run.py new file mode 100644 index 00000000..12b95b30 --- /dev/null +++ b/test/experiments/osdi2025/numa_multi_query/run.py @@ -0,0 +1,186 @@ +#!/usr/bin/env python3 +""" +NUMA multi‐query QPS experiment for Quake. + +Sweeps NUMA on/off, worker counts, batched_scan on/off, and batch sizes; +records mean/std QPS and produces a simplified batched_scan QPS plot. +""" + +import yaml +import time +import numpy as np +import torch +import pandas as pd +import matplotlib.pyplot as plt +from pathlib import Path + +from quake.datasets.ann_datasets import load_dataset +from quake.index_wrappers.quake import QuakeWrapper + +def run_experiment(cfg_path: str, output_dir: str): + cfg = yaml.safe_load(Path(cfg_path).read_text()) + out = Path(output_dir) + out.mkdir(exist_ok=True, parents=True) + csv_name = cfg.get("output", {}).get("results_csv", "results.csv") + out_csv = out / csv_name + + # check if the results already exist + if not Path(out_csv).exists(): + + # Load config + ds = cfg["dataset"] + n_workers = cfg["n_workers"] + batched_opts= cfg.get("batch_options", [False, True]) + batch_sizes = cfg.get("batch_sizes", [ds["num_queries"]]) + num_q = ds["num_queries"] + k = ds["k"] + nprobe = ds["nprobe"] + trials = cfg.get("trials", 5) + warmup = cfg.get("warmup", 2) + + + # Load data + print(f"Loading {ds['name']} ({num_q} queries)…") + base_vecs, queries_data, _ = load_dataset(ds["name"]) + queries = ( + torch.from_numpy(queries_data[:num_q]).float() + if isinstance(queries_data, np.ndarray) + else queries_data[:num_q].float() + ) + + # Build index if needed + idx_file = out / "quake_index.bin" + if not idx_file.exists(): + print("Building index…") + builder = QuakeWrapper() + vecs = ( + torch.from_numpy(base_vecs).float() + if isinstance(base_vecs, np.ndarray) + else base_vecs.float() + ) + nc = int(np.sqrt(vecs.shape[0])) + builder.build(vecs, nc=nc, metric="l2", num_workers=0) + builder.save(str(idx_file)) + print("Index saved.") + + records = [] + for use_numa in [False]: + for nw in n_workers: + for bs in batched_opts: + for batch_size in batch_sizes: + print(f"NUMA={use_numa}, workers={nw}, batched={bs}, batch={batch_size}") + idxm = QuakeWrapper() + idxm.load(str(idx_file), use_numa=use_numa, same_core=True, n_workers=nw) + n_threads = 16 if nw == 0 else 1 + + # Warmup + for _ in range(min(warmup,1)): + if bs: + for i in range(0, num_q, batch_size): + idxm.search(queries[i:i+batch_size], k, + batched_scan=bs, + nprobe=nprobe, + n_threads=n_threads) + else: + idxm.search(queries, k, + batched_scan=bs, + nprobe=nprobe, + n_threads=n_threads) + + # Trials → QPS + qps_vals = [] + for t in range(trials): + start = time.time() + if bs: + for i in range(0, num_q, batch_size): + idxm.search(queries[i:i+batch_size], k, + batched_scan=bs, + nprobe=nprobe, + n_threads=n_threads) + else: + idxm.search(queries, k, + batched_scan=bs, + nprobe=nprobe, + n_threads=n_threads) + elapsed = time.time() - start + qps_vals.append(num_q / elapsed) + print(f" Trial {t+1}/{trials}: {qps_vals[-1]:.1f} QPS") + + records.append({ + "numa_enabled": use_numa, + "n_worker": nw, + "batched_scan": bs, + "batch_size": batch_size, + "mean_qps": np.mean(qps_vals), + "std_qps": np.std(qps_vals), + }) + + # Save CSV + df = pd.DataFrame(records) + df.to_csv(out_csv, index=False) + print(f"Results → {out_csv}") + + df = pd.read_csv(out_csv) + + # Simplified batched_scan QPS plot + fig, ax = plt.subplots(1, 1, figsize=(12, 4)) + for bsz in sorted(df["batch_size"].unique()): + for bs in (False, True): + grp = df[(df["batch_size"] == bsz) & (df["batched_scan"] == bs)] + if grp.empty: + continue + linestyle = '-' if not bs else '--' + label = f"batch={bsz}" + ("" if not bs else " batched") + ax.plot( + grp["n_worker"], + grp["mean_qps"], + marker='o', + linestyle=linestyle, + label=label + ) + ax.set_xscale("symlog", base=2) + ax.set_yscale("log", base=2) + ax.set_xlabel("Workers") + ax.set_ylabel("QPS") + ax.legend(title="Config", fontsize="small") + + fig.suptitle("QPS vs. Workers (solid=regular, dashed=batched)") + fig.tight_layout(rect=[0, 0, 1, 0.92]) + plot_path = out / f"{out_csv.stem}_combined_qps.png" + fig.savefig(plot_path) + print(f"Combined plot saved to {plot_path}") + + # 7) QPS vs. batch size for 1 and 16 workers, solid=unbatched, dashed=batched + fig2, ax2 = plt.subplots(1, 1, figsize=(6, 4)) + + # choose which workers to show + workers = [1, 16] + # matplotlib will auto‑cycle colors for each worker + for w in workers: + for bs, ls in [(False, "-"), (True, "--")]: + sub = df[(df["n_worker"] == w) & (df["batched_scan"] == bs)] + if sub.empty: + continue + sub = sub.sort_values("batch_size") + label = f"{w} worker{'s' if w>1 else ''}" + (" (batched)" if bs else "") + ax2.errorbar( + sub["batch_size"], + sub["mean_qps"], + yerr=sub["std_qps"], + marker="o", + linestyle=ls, + capsize=4, + label=label + ) + + ax2.set_xscale("symlog", base=2) + ax2.set_yscale("log", base=2) + ax2.set_xlabel("Batch Size") + ax2.set_ylabel("QPS") + ax2.set_title("QPS vs. Batch Size\n(solid=unbatched, dashed=batched)") + ax2.grid(True, which="both", ls=":") + ax2.legend(title="Configuration", fontsize="small", ncol=1) + fig2.tight_layout() + plot2_path = out / f"{out_csv.stem}_qps_vs_batchsize_full.png" + fig2.savefig(plot2_path) + print(f"Full batch‑size QPS plot saved to {plot2_path}") \ No newline at end of file diff --git a/test/experiments/osdi2025/numa_single_query/__init__.py b/test/experiments/osdi2025/numa_single_query/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml b/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml new file mode 100644 index 00000000..327e20ba --- /dev/null +++ b/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml @@ -0,0 +1,15 @@ +# existing keys... +dataset: + name: sift1m + num_queries: 100 + k: 10 + nprobe: 100 + +n_workers: [0,1,2,4,8,16] + +# new keys: +trials: 5 +warmup: 10 + +output: + results_csv: numa_single_query_results.csv \ No newline at end of file diff --git a/test/experiments/osdi2025/numa_single_query/run.py b/test/experiments/osdi2025/numa_single_query/run.py index 139597f9..f88c56c8 100644 --- a/test/experiments/osdi2025/numa_single_query/run.py +++ b/test/experiments/osdi2025/numa_single_query/run.py @@ -1,2 +1,117 @@ +#!/usr/bin/env python +""" +NUMA single‑query latency experiment for Quake. +Reads a config.yaml, writes results.csv to output_dir, then plots +thread‑vs‑latency with error bars for NUMA/no‑NUMA. +""" +import yaml +import numpy as np +import torch +import pandas as pd +import matplotlib.pyplot as plt +from pathlib import Path +from quake.datasets.ann_datasets import load_dataset +from quake.index_wrappers.quake import QuakeWrapper + +def run_experiment(cfg_path: str, output_dir: str): + # 1) Load config + cfg = yaml.safe_load(Path(cfg_path).read_text()) + ds = cfg["dataset"] + n_workers = cfg["n_workers"] + nprobe = ds["nprobe"] + k = ds["k"] + num_q = ds["num_queries"] + trials = cfg.get("trials", 5) # default 5 + warmup = cfg.get("warmup", 10) # default 10 + csv_name = cfg.get("output", {}).get("results_csv", "numa_results.csv") + + out_dir = Path(output_dir); out_dir.mkdir(parents=True, exist_ok=True) + + # 2) Load data + print(f"Loading dataset '{ds['name']}'...") + base_vecs, queries, _ = load_dataset(ds["name"]) + queries = queries[:num_q] + + # 3) Build index once + idx_file = out_dir / "quake_index.bin" + if not idx_file.exists(): + print("Building Quake index...") + idx = QuakeWrapper() + nc = int(np.sqrt(base_vecs.shape[0])) + idx.build(base_vecs, nc=nc, metric="l2", num_workers=0) + idx.save(str(idx_file)) + print(f"Index saved at {idx_file}") + + + # 4) Benchmark: warmup + trials + rows = [] + for use_numa in [False, True]: + mode = "NUMA" if use_numa else "no‑NUMA" + print(f"\n--- {mode} benchmarking ---") + + for n_worker in n_workers: + + idx_mode = QuakeWrapper() + idx_mode.load(str(idx_file), use_numa=use_numa, same_core=True, n_workers=n_worker) + + print(f"Threads: {n_worker}") + # warm‑up + for i in range(min(warmup, len(queries))): + q = queries[i].unsqueeze(0).float() + idx_mode.search(q, k, batched_scan=False, nprobe=nprobe, n_threads=n_worker) + + trial_means = [] + for t in range(trials): + lats = [] + for q_vec in queries: + q = q_vec.unsqueeze(0).float() + res = idx_mode.search( + q, k, + batched_scan=False, + nprobe=nprobe, + ) + ti = res.timing_info + lats.append(ti.total_time_ns / 1e6) + mean_t = float(np.mean(lats)) + trial_means.append(mean_t) + print(f" Trial {t+1}/{trials}: {mean_t:.2f} ms") + + mean_lat = float(np.mean(trial_means)) + std_lat = float(np.std(trial_means)) + rows.append({ + "numa_enabled": use_numa, + "n_worker": n_worker, + "mean_latency_ms": mean_lat, + "std_latency_ms": std_lat, + }) + + # 5) Save CSV + df = pd.DataFrame(rows) + out_csv = out_dir / csv_name + df.to_csv(out_csv, index=False) + print(f"\nResults written to {out_csv}") + + # 6) Plot with error bars + plt.figure() + for flag in df["numa_enabled"].unique(): + subset = df[df["numa_enabled"] == flag] + label = "NUMA" if flag else "no‑NUMA" + plt.errorbar( + subset["n_worker"], + subset["mean_latency_ms"], + yerr=subset["std_latency_ms"], + marker="o", + capsize=5, + label=label + ) + plt.xscale("symlog", base=2) + plt.xlabel("Num Workers") + plt.ylabel("Mean Latency (ms)") + plt.title("NUMA Single‑Query Latency") + plt.legend() + plot_file = out_dir / f"{out_csv.stem}_latency.png" + plt.tight_layout() + plt.savefig(plot_file) + print(f"Plot saved to {plot_file}") \ No newline at end of file From 6ec69a33c56a4614986e92cd2c944effaef87a4b Mon Sep 17 00:00:00 2001 From: Jason Date: Sat, 3 May 2025 13:26:08 -0500 Subject: [PATCH 018/323] Add map for fast centroid lookups --- src/cpp/include/common.h | 8 + src/cpp/include/dynamic_inverted_list.h | 16 + src/cpp/include/geometry.h | 728 +++++++++++++++--- src/cpp/include/index_partition.h | 2 +- src/cpp/include/list_scanning.h | 5 + src/cpp/include/maintenance_policies.h | 1 - src/cpp/include/parallel.h | 39 +- src/cpp/src/dynamic_inverted_list.cpp | 248 +++--- src/cpp/src/index_partition.cpp | 36 +- src/cpp/src/partition_manager.cpp | 2 + src/cpp/src/query_coordinator.cpp | 48 +- src/python/datasets/ann_datasets.py | 48 +- src/python/workload_generator.py | 5 +- test/cpp/benchmark.cpp | 154 +++- test/cpp/dynamic_inverted_list.cpp | 130 ++++ test/cpp/quake_index.cpp | 53 +- test/experiments/osdi2025/README.md | 57 ++ .../aps_recall_targets/configs/sift1m.yaml | 28 + .../osdi2025/aps_recall_targets/run.py | 180 +++++ .../experiments/osdi2025/experiment_runner.py | 58 +- .../osdi2025/kick_the_tires/run.py | 95 +++ 21 files changed, 1601 insertions(+), 340 deletions(-) create mode 100644 test/experiments/osdi2025/README.md create mode 100644 test/experiments/osdi2025/aps_recall_targets/configs/sift1m.yaml create mode 100644 test/experiments/osdi2025/kick_the_tires/run.py diff --git a/src/cpp/include/common.h b/src/cpp/include/common.h index 0938591c..eba6dc3a 100644 --- a/src/cpp/include/common.h +++ b/src/cpp/include/common.h @@ -180,6 +180,14 @@ struct SearchParams { float initial_search_fraction = DEFAULT_INITIAL_SEARCH_FRACTION; int aps_flush_period_us = DEFAULT_APS_FLUSH_PERIOD_US; + // early termination params + float spann_threshold = 1.25f; // prune all centroids with distance > 1.25 * min_distance + + float auncel_a = 0.5f; // auncel's parameter + float auncel_b = 0.5f; // auncel's parameter + + + SearchParams() = default; }; diff --git a/src/cpp/include/dynamic_inverted_list.h b/src/cpp/include/dynamic_inverted_list.h index e940127e..e8d67627 100644 --- a/src/cpp/include/dynamic_inverted_list.h +++ b/src/cpp/include/dynamic_inverted_list.h @@ -31,6 +31,7 @@ namespace faiss { int d_; ///< Dimensionality of the vectors (derived from code_size). int code_size_; ///< Size in bytes of each vector code. unordered_map> partitions_; ///< Map of partition ID to IndexPartition. + unordered_map> id_to_location_; /** * @brief Constructor for DynamicInvertedLists. @@ -83,6 +84,8 @@ namespace faiss { */ const idx_t* get_ids(size_t list_no) const override; + void build_map(); + /** * @brief Release the codes pointer. * @@ -325,6 +328,19 @@ namespace faiss { * @return A 1D tensor containing all partition IDs. */ Tensor get_partition_ids(); + + template + inline void map_add(IndexPartition* p, int64_t off, IdT id) noexcept { + id_to_location_[id] = {p, off}; + } + template + inline void map_erase(IdT id) noexcept { + id_to_location_.erase(id); + } + template + inline void map_swap(IndexPartition* p, int64_t off, IdT id) noexcept { + id_to_location_[id] = {p, off}; + } }; /** diff --git a/src/cpp/include/geometry.h b/src/cpp/include/geometry.h index e16eafc6..5573082e 100644 --- a/src/cpp/include/geometry.h +++ b/src/cpp/include/geometry.h @@ -8,6 +8,7 @@ #define STOP 1.0e-8 #define TINY 1.0e-30 +#define CRP_CHECK(cond, msg) ((void)0) using torch::Tensor; using std::vector; @@ -54,62 +55,46 @@ inline void print_array(const float *array, int dimension) { std::cout << std::endl << std::endl; } -inline vector compute_boundary_distances(const Tensor &query, vector centroids, bool euclidean = true) { - - auto start = std::chrono::high_resolution_clock::now(); - int dimension = query.size(0); +inline std::vector +compute_boundary_distances(const torch::Tensor& query, + std::vector& centroids, + bool euclidean /* kept for API */ ) +{ + int dim = query.size(0); + const float* q = query.data_ptr(); + + /* --- ensure centroids[0] is the nearest --------------------- */ + size_t nearest = 0; + float best_d2 = std::numeric_limits::max(); + for (size_t i = 0; i < centroids.size(); ++i) { + float d2 = faiss::fvec_L2sqr(q, centroids[i], dim); + if (d2 < best_d2) { best_d2 = d2; nearest = i; } + } + if (nearest != 0) std::swap(centroids[0], centroids[nearest]); - std::vector boundary_distances(centroids.size(), -1.0f); + const float* c0 = centroids[0]; + std::vector d(centroids.size(), 0.0f); + std::vector v(dim); - const float *query_ptr = query.data_ptr(); - const float *nearest_centroid_ptr = centroids[0]; + for (size_t j = 1; j < centroids.size(); ++j) + { + const float* cj = centroids[j]; - vector line_vector(dimension); - vector midpoint(dimension); - vector residual(dimension); + /* v = cj - c0, ||v|| */ + faiss::fvec_sub(dim, cj, c0, v.data()); + float v_norm = std::sqrt( + faiss::fvec_inner_product(v.data(), v.data(), dim)); - auto end = std::chrono::high_resolution_clock::now(); + /* b = ½ (||cj||² - ||c0||²) */ + float b = 0.5f * ( + faiss::fvec_inner_product(cj, cj, dim) - + faiss::fvec_inner_product(c0, c0, dim)); - // used for euclidean distance - if (euclidean) { - // Compute residual: r = q - c0. - faiss::fvec_sub(dimension, query_ptr, nearest_centroid_ptr, residual.data()); - - // For each centroid j (starting at index 1). - for (int j = 1; j < centroids.size(); j++) { - // Compute v = c_j - c0. - const float* c_j = centroids[j]; - faiss::fvec_sub(dimension, c_j, nearest_centroid_ptr, line_vector.data()); - - // Compute squared norm: A2 = ||v||^2. - float A2 = faiss::fvec_inner_product(line_vector.data(), line_vector.data(), dimension); - float A = std::sqrt(A2); // Guaranteed nonzero. - - // Compute dot product: dot = . - float dot_val = faiss::fvec_inner_product(residual.data(), line_vector.data(), dimension); - - // Instead of computing dot_val/A and 0.5*A separately, - // we compute: d = |dot_val - 0.5 * A2| / A. - float d = std::fabs(dot_val - 0.5f * A2) / A; - boundary_distances[j] = d; - } - } else { - // for dot product distance - float residual_angle = faiss::fvec_inner_product(query_ptr, nearest_centroid_ptr, dimension); - for (int j = 1; j < centroids.size(); j++) { - // get angle of the bisector using dot product - subtract_arrays(centroids[j], nearest_centroid_ptr, line_vector.data(), dimension); - divide_array_by_constant(line_vector.data(), 2.0f, midpoint.data(), dimension); - add_arrays(nearest_centroid_ptr, midpoint.data(), midpoint.data(), dimension); - float norm = faiss::fvec_inner_product(midpoint.data(), midpoint.data(), dimension); - norm = std::sqrt(norm); - divide_array_by_constant(midpoint.data(), norm, midpoint.data(), dimension); - float boundary_angle = faiss::fvec_inner_product(query_ptr, midpoint.data(), dimension); - boundary_distances[j] = std::acos(boundary_angle); - } + /* signed distance (q·v − b) / ||v|| */ + float dot_qv = faiss::fvec_inner_product(q, v.data(), dim); + d[j] = std::fabs(dot_qv - b) / v_norm; // plane distance } - - return boundary_distances; + return d; // d[0] = 0 } inline double incomplete_beta(double a, double b, double x) { @@ -252,7 +237,7 @@ inline double log_hyperspherical_cap_volume(double radius, double boundary_dista h = std::max(0.0, std::min(2 * radius, h)); if (euclidean) { - double x = std::sqrt((2 * radius * h - h * h) / (radius * radius)); + double x = pow((2 * radius * h - h * h) / (radius * radius), 1.0); // use precomputed incomplete beta function double inc_beta; if (use_precomputed) { @@ -342,70 +327,619 @@ inline Tensor compute_variance_in_direction_of_query(Tensor query, Tensor centro return torch::tensor(variances).clone(); } -inline vector compute_recall_profile(vector boundary_distances, float query_radius, int dimension, - vector partition_sizes = {}, bool use_precomputed = true, - bool euclidean = true) { +inline void check_boundary_distances(const float* q, + const std::vector& C, + const std::vector& d, + int dim) +{ + const float* c0 = C[0]; + std::vector v(dim); + + for (size_t j = 1; j < C.size(); ++j) { + const float* cj = C[j]; + + /* signed distance via the explicit formula again */ + faiss::fvec_sub(dim, cj, c0, v.data()); + float vnorm = std::sqrt(faiss::fvec_inner_product(v.data(), v.data(), dim)); + float b = 0.5f*(faiss::fvec_inner_product(cj,cj,dim) + -faiss::fvec_inner_product(c0,c0,dim)); + float dj = std::fabs(faiss::fvec_inner_product(q, v.data(), dim) - b) / vnorm; + + if (std::fabs(dj - d[j]) > 0.01f * dj + 1e-6f) + throw std::runtime_error( + "boundary_distances[" + std::to_string(j) + "] inaccurate"); + } +} - // boundary_distances shape is (num_partitions,) and num_partitions must be greater than 1 - if (boundary_distances.size() < 2) { - throw std::runtime_error("Boundary distances must have at least 2 partitions to create an estimate."); +inline std::vector +compute_recall_profile(const std::vector& boundary_distances, + float query_radius, + int dimension, + std::vector partition_sizes = {}, // Unused in these models + bool use_precomputed = true, + bool euclidean = true) // Unused in these models +{ + // --- Configuration Flag --- + // 0: Original Scaled-IE (reconstructed, uses norm_vols internally) + // 1: Direct Proportional Allocation (P'_k = raw_vols[k]) + // 2: Independent Entry Allocation (P'_k = raw_vols[k]*P0 / (1-raw_vols[k])) + // 3: Concentration Penalized Allocation (P'_k = raw_vols[k] * (1-S2_norm)) + // 4: Radius-Difference Weighting (P'_k = raw_vols[k] * (R-d_k)) + // 5: Simplified IE Approx Weighting (P'_k = raw_vols[k] * (1 - 0.5*S1 + 0.5*v_k)) + const int method_flag = 0; // <<< CHANGE THIS FLAG FOR ABLATION STUDY >>> + // --- + + const int m = static_cast(boundary_distances.size()); + const float eps = 1e-9f; + + // --- Edge Cases --- + if (m <= 1) { + if (m == 1) return {1.0f}; // Only the central partition exists + return {}; // No partitions defined + } + if (query_radius <= eps) { + std::vector p(m, 0.0f); + p[0] = 1.0f; // Query point is exactly at origin, must be in partition 0 + return p; } - int num_partitions = boundary_distances.size(); - vector partition_probabilities(num_partitions, 0.0f); + // --- Step 1: Compute Raw Cap Volumes (Common to all methods) --- + std::vector raw_vols(m, 0.0f); + for (int j = 1; j < m; ++j) { + // Ensure boundary distance is non-negative double + double d = std::max(0.0, static_cast(boundary_distances[j])); + // If boundary is outside or on the query radius, the cap volume is 0 + if (d >= query_radius) continue; + + // Calculate x for incomplete beta function + double x = 1.0 - (d / query_radius) * (d / query_radius); + x = std::clamp(x, 0.0, 1.0); // Clamp x to [0, 1] + + // Incomplete Beta parameters + double a = 0.5 * (dimension + 1.0); + double b = 0.5; + double I = use_precomputed + ? incomplete_beta_lookup(x, dimension) // Needs definition + : incomplete_beta(a, b, x); // Needs definition + + // Raw volume is half the normalized surface area ratio, clamped to [0, 0.5] + raw_vols[j] = static_cast(std::clamp(0.5 * I, 0.0, 0.5)); + } - double total_volume = 0.0; - bool weigh_using_partition_sizes = partition_sizes.size() == num_partitions; + // --- Method Selection --- + float P0 = 0.0f; // Root cell probability + std::vector P_prime(m, 0.0f); // Intermediate neighbor probabilities (k>=1) + float P_prime_sum = 0.0f; // Sum of P_prime[k] for k>=1 + + switch (method_flag) { + case 0: { // M0: Original Scaled-IE (reconstructed) + //std::cout << "Using Method 0: Original Scaled-IE (Cumulative)" << std::endl; + // Uses intermediate normalization and P0 based on normalized vols + std::vector norm_vols = raw_vols; // Copy raw vols + float S1_for_norm = 0.0f; + for (int j = 1; j < m; ++j) S1_for_norm += norm_vols[j]; + + if (S1_for_norm > eps) { + for (int j = 1; j < m; ++j) norm_vols[j] /= S1_for_norm; + } else { + for (int j = 1; j < m; ++j) norm_vols[j] = 0.0f; + } + + // Calculate P0 using normalized vols (as per original logic) + P0 = 1.0f; + for (int j = 1; j < m; ++j) { + P0 *= (1.0f - norm_vols[j]); + } + P0 = std::clamp(P0, 0.0f, 1.0f); + + // Calculate P_prime using cumulative S1, S2 on normalized vols + float S1_cum = 0.0f, S2_cum = 0.0f; + // Use lambda = m as per original code's default, or allow tuning + float lambda = static_cast(m); // Could be tuned here if needed + + for (int k = 1; k < m; ++k) { + S1_cum += norm_vols[k]; // Update cumulative sums *before* using them for k + S2_cum += norm_vols[k] * norm_vols[k]; + + // Calculate R factor using *cumulative* S1, S2 up to k + // Note: Original code used S1*S1 - S2. Ensure S1_cum is used correctly. + float R = 1.0f - S1_cum + 0.5f * lambda * (S1_cum * S1_cum - S2_cum); + + P_prime[k] = norm_vols[k] * std::max(0.0f, R); + P_prime_sum += P_prime[k]; // Accumulate sum for final normalization + } + // Note: This calculation is order-dependent based on how boundaries are indexed. + break; + } - for (int j = 1; j < num_partitions; j++) { - float boundary_distance = boundary_distances[j]; + case 1: { // M1: Direct Proportional Allocation + //std::cout << "Using Method 1: Direct Proportional" << std::endl; + // P0 based on raw volumes + + std::vector norm_vols = raw_vols; // Copy raw vols + float S1_for_norm = 0.0f; + for (int j = 1; j < m; ++j) S1_for_norm += norm_vols[j]; + + if (S1_for_norm > eps) { + for (int j = 1; j < m; ++j) norm_vols[j] /= S1_for_norm; + } else { + for (int j = 1; j < m; ++j) norm_vols[j] = 0.0f; + } + + P0 = 1.0f; + for (int j = 1; j < m; ++j) P0 *= (1.0f - norm_vols[j]); + P0 = std::clamp(P0, 0.0f, 1.0f); + + // P_prime is just raw_vols + for (int k = 1; k < m; ++k) { + P_prime[k] = norm_vols[k]; + P_prime_sum += P_prime[k]; + } + // Ensure P_prime is non-negative + for (int k = 1; k < m; ++k) P_prime[k] = std::max(0.0f, P_prime[k]); + break; + } - if (boundary_distance >= query_radius) { - partition_probabilities[j] = 0.0; - continue; + case 2: { // M2: Independent Entry Allocation + //std::cout << "Using Method 2: Independent Entry" << std::endl; + // P0 based on raw volumes + P0 = 1.0f; + for (int j = 1; j < m; ++j) P0 *= (1.0f - raw_vols[j]); + P0 = std::clamp(P0, 0.0f, 1.0f); + + if (P0 > eps) { // Avoid issues if P0 is near zero + for (int k = 1; k < m; ++k) { + float v_k = raw_vols[k]; + float one_minus_v_k = 1.0f - v_k; + // Check for valid v_k and avoid division by zero/small number + if (v_k > eps && one_minus_v_k > eps) { + P_prime[k] = v_k * P0 / one_minus_v_k; + // Clamp potentially large values if v_k is very close to 1? + // P_prime[k] = std::min(P_prime[k], 1.0f); // Optional clamping + P_prime_sum += P_prime[k]; + } + P_prime[k] = std::max(0.0f, P_prime[k]); // Ensure non-negative + } + } // else P_prime remains 0 + break; } - double volume_ratio = std::exp( - log_hyperspherical_cap_volume(query_radius, - boundary_distance, - dimension, - true, - use_precomputed, - euclidean)); - partition_probabilities[j] = (volume_ratio > 0.0) ? volume_ratio : 0.0; - } + case 3: { // M3: Concentration Penalized Allocation + //std::cout << "Using Method 3: Concentration Penalized" << std::endl; + // P0 based on raw volumes + P0 = 1.0f; + for (int j = 1; j < m; ++j) P0 *= (1.0f - raw_vols[j]); + P0 = std::clamp(P0, 0.0f, 1.0f); + + float S1_raw = 0.0f; + float S2_raw = 0.0f; + for (int j = 1; j < m; ++j) { + S1_raw += raw_vols[j]; + S2_raw += raw_vols[j] * raw_vols[j]; + } + + float penalty_factor = 0.0f; + if (S1_raw > eps) { + float S1_raw_sq = S1_raw * S1_raw; + // Avoid division by zero if S1_raw_sq is somehow zero or negative + if (S1_raw_sq > eps) { + float S2_norm = S2_raw / S1_raw_sq; + penalty_factor = std::max(0.0f, 1.0f - S2_norm); + } + // Optional: include lambda=m scaling -> penalty_factor *= 0.5f * m; + } + + for (int k = 1; k < m; ++k) { + P_prime[k] = raw_vols[k] * penalty_factor; // Apply uniform penalty + P_prime_sum += P_prime[k]; + } + break; + } - // TODO: Implement a better way to compute the probabilities for the first partition. This heuristic works well on tested datasets. - partition_probabilities[0] = 2.0 * partition_probabilities[1]; - // partition_probabilities[0] = 1 - partition_probabilities[1]; + case 4: { // M4: Radius-Difference Weighting + //std::cout << "Using Method 4: Radius-Difference Weighting" << std::endl; + // P0 based on raw volumes + P0 = 1.0f; + for (int j = 1; j < m; ++j) P0 *= (1.0f - raw_vols[j]); + P0 = std::clamp(P0, 0.0f, 1.0f); + + for (int k = 1; k < m; ++k) { + float v_k = raw_vols[k]; + if (v_k > eps) { + // Use boundary_distances directly, ensure non-negative + float d_k = std::max(0.0f, boundary_distances[k]); + // Calculate non-uniform weight based on distance from radius + float mu_k_tilde = std::max(0.0f, query_radius - d_k); + P_prime[k] = v_k * mu_k_tilde; + P_prime_sum += P_prime[k]; + } + } + break; + } - // if (weigh_using_partition_sizes) { - // for (int j = 0; j < num_partitions; j++) { - // partition_probabilities[j] *= partition_sizes[j]; - // } - // } + case 5: { // M5: Simplified IE Approx Weighting + //std::cout << "Using Method 5: Simplified IE Approx" << std::endl; + // P0 based on raw volumes + P0 = 1.0f; + for (int j = 1; j < m; ++j) P0 *= (1.0f - raw_vols[j]); + P0 = std::clamp(P0, 0.0f, 1.0f); + + float S1_raw = 0.0f; + for(int j=1; j eps) { + // Calculate non-uniform scaling factor + float mu_k_tilde = std::max(0.0f, 1.0f - 0.5f * S1_raw + 0.5f * v_k); + P_prime[k] = v_k * mu_k_tilde; + P_prime_sum += P_prime[k]; + } + } + break; + } + + case 6: { + std::vector norm_vols = raw_vols; // Copy raw vols + float S1_for_norm = 0.0f; + for (int j = 1; j < m; ++j) S1_for_norm += norm_vols[j]; + + if (S1_for_norm > eps) { + for (int j = 1; j < m; ++j) norm_vols[j] /= S1_for_norm; + } else { + for (int j = 1; j < m; ++j) norm_vols[j] = 0.0f; + } + + P0 = 1.0f; + for (int j = 1; j < m; ++j) P0 *= (1.0f - norm_vols[j]); + P0 = std::clamp(P0, 0.0f, 1.0f); + + float S2_raw = 0.0f; + for (int j = 1; j < m; ++j) { + S2_raw += norm_vols[j]; + } + + if (S2_raw > eps) { + for (int k = 1; k < m; ++k) { + float v_k = norm_vols[k]; + S2_raw -= v_k; // Update S2_raw for next iteration + float rel_L2_sq = (.5 * v_k * S2_raw); + float mu_k_tilde = 1 / (1.0f + rel_L2_sq); // Non-uniform scaling + P_prime[k] = v_k * std::max(mu_k_tilde, .001f); + P_prime_sum += P_prime[k]; + } + } else { // If S2 is zero, all raw_vols are zero, P_prime remains zero + for (int k = 1; k < m; ++k) { + P_prime[k] = norm_vols[k]; // Should be 0 + P_prime_sum += P_prime[k]; // Should be 0 + } + } + // Ensure P_prime is non-negative + for (int k = 1; k < m; ++k) P_prime[k] = std::max(0.0f, P_prime[k]); + + break; + } - // Ensure the probabilities sum to 1 - double sum_probabilities = 0.0; - for (int j = 0; j < num_partitions; j++) { - sum_probabilities += partition_probabilities[j]; + default: + // Handle unknown method flag, e.g., throw error or default to Method 1 + //std::cerr << "Error: Unknown method_flag: " << method_flag << std::endl; + throw std::runtime_error("Unknown method_flag in compute_recall_profile"); + // Or fallback: goto method1_label; (using labels/goto is generally discouraged) } - if (sum_probabilities > 0.0f) { - for (int j = 0; j < num_partitions; j++) { - partition_probabilities[j] /= sum_probabilities; + + + // --- Final Normalization --- + std::vector probs(m, 0.0f); + probs[0] = P0; // Assign calculated P0 for the chosen method + + float target = 1.0f - P0; + // Ensure target probability for neighbors is valid [0, 1] + target = std::clamp(target, 0.0f, 1.0f); + + if (target > eps && P_prime_sum > eps) { + float scale = target / P_prime_sum; + for (int k = 1; k < m; ++k) { + // Ensure final probability is non-negative + probs[k] = std::max(0.0f, P_prime[k] * scale); } - } else { - for (int j = 0; j < num_partitions; j++) { - partition_probabilities[j] = 1.0 / num_partitions; + + // --- Strict Renormalization --- + // This step ensures probs[1..m-1] sum *exactly* to target, + // correcting minor float inaccuracies or effects of max(0,...) + float current_sum_k = 0.0f; + for (int k = 1; k < m; ++k) current_sum_k += probs[k]; + + if (current_sum_k > eps) { // Avoid division by zero if sum is negligible + float final_scale = target / current_sum_k; + // Check if scale is finite (handles target=0 case correctly) + if (std::isfinite(final_scale)) { + for (int k = 1; k < m; ++k) { + probs[k] *= final_scale; + // Final clamp for safety + probs[k] = std::max(0.0f, probs[k]); + } + } else if (target <= eps) { + // If target is zero, all neighbor probs should be zero + for (int k = 1; k < m; ++k) probs[k] = 0.0f; + } + } else if (target <= eps) { + // If target is zero and calculated sum is zero, ensure all are zero + for (int k = 1; k < m; ++k) probs[k] = 0.0f; } - } + // --- End Optional Strict Renormalization --- - // Compute the recall profile - // Tensor recall_profile = torch::cumsum(probabilities_tensor, 0); + } - return partition_probabilities; + return probs; } +// inline std::vector +// compute_recall_profile(const std::vector& boundary_distances, +// float query_radius, +// int dimension, +// std::vector partition_sizes = {}, +// bool use_precomputed = true, +// bool euclidean = true) +// { +// const int m = static_cast(boundary_distances.size()); +// const float eps = 1e-9f; +// +// if (m <= 1) { +// if (m == 1) return {1.0f}; +// return {}; +// } +// if (query_radius <= eps) { +// std::vector p(m, 0.0f); +// p[0] = 1.0f; +// return p; +// } +// +// // 1) compute raw cap‐volumes vols[1..m-1] +// std::vector vols(m, 0.0f); +// for (int j = 1; j < m; ++j) { +// double d = std::max(0.0, static_cast(boundary_distances[j])); +// if (d >= query_radius) continue; +// double x = 1.0 - (d/query_radius)*(d/query_radius); +// x = std::clamp(x, 0.0, 1.0); +// +// double a = 0.5*(dimension + 1.0), b = 0.5, I = 0.0; +// try { +// I = use_precomputed +// ? incomplete_beta_lookup(x, dimension) +// : incomplete_beta(a, b, x); +// } catch(...) { +// I = 0.0; +// } +// vols[j] = std::clamp(0.5 * I, 0.0, 0.5); +// } +// +// // // 2) normalize vols[1..] to sum = 1 +// // float sumv = std::accumulate(vols.begin()+1, vols.end(), 0.0f); +// // if (sumv > eps) { +// // for (int j = 1; j < m; ++j) vols[j] /= sumv; +// // } else { +// // std::fill(vols.begin()+1, vols.end(), 0.0f); +// // } +// +// // 3) compute reference P0 = ∏_{j=1..m-1}(1 - vols[j]) +// float P0 = 1.0f; +// for (int j = 1; j < m; ++j) { +// P0 *= (1.0f - vols[j]); +// } +// +// // 4) compute raw Pk for k>=1 under chosen mode +// std::vector raw(m, 0.0f); +// +// // 4) if scaled‐IE, compute λ = 1 / ∑v_j^2 +// float lambda = (float) boundary_distances.size(); +// // float lambda = +// // if (use_scaled_ie) { +// // float sum2 = 0.0f; +// // for (int j=1;jeps ? 1.0f/sum2 : 1.0f); +// // } +// +// std::cout << "lambda: " << lambda << std::endl; +// +// // -- Scaled‐IE: λ·(S1²−S2) in pairwise term -- +// float S1 = 0.0f, S2 = 0.0f; +// for (int k = 1; k < m; ++k) { +// S1 += vols[k]; +// S2 += vols[k]*vols[k]; +// float R = 1.0f +// - S1 +// + 0.5f*lambda*(S1*S1 - S2); +// raw[k] = vols[k] * std::max(0.0f, R); +// } +// std::cout << "S1: " << S1 << " S2: " << S2 << std::endl; +// +// // float scale_term = 1.0f; +// // for (int k = 1; k < m; ++k) { +// // raw[k] = vols[k] * scale_term; +// // scale_term *= (1.0f - vols[k]); +// // } +// +// // for (int k = 1; k < m; ++k) { +// // float scale_term = P0 / (1 - vols[k]); +// // raw[k] = vols[k] * scale_term; +// // } +// +// // 5) scale k>=1 so they sum to (1 - P0), leave P0 unchanged +// float sum_raw = std::accumulate(raw.begin()+1, raw.end(), 0.0f); +// std::vector probs(m, 0.0f); +// probs[0] = P0; +// +// float target = 1.0f - P0; +// if (sum_raw > eps) { +// float scale = target / sum_raw; +// for (int k = 1; k < m; ++k) { +// probs[k] = std::max(0.0f, raw[k] * scale); +// } +// } +// +// return probs; +// } + +// inline std::vector +// compute_recall_profile(const std::vector& boundary_distances, +// float query_radius, +// int dimension, +// std::vector partition_sizes = {}, +// bool use_precomputed = true, +// bool euclidean = true) +// { +// const int m = static_cast(boundary_distances.size()); +// const float float_epsilon = 1e-9f; // Small value for float comparisons +// +// // Handle edge cases: no cells or only the home cell +// if (m == 0) { +// return {}; +// } +// if (m == 1) { +// return {1.0f}; // Only home cell exists, probability is 1 +// } +// if (query_radius <= float_epsilon) { +// // If query radius is zero or negligible, k-NN must be in home cell +// std::vector probs(m, 0.0f); +// probs[0] = 1.0f; +// return probs; +// } +// +// +// std::vector vols(m, 0.0f); // Raw cap volume ratios (v_j for j>=1) +// +// // --- 1. Calculate raw cap ratios v_j for neighbors j = 1 to m-1 --- +// for (int j = 1; j < m; ++j) { +// float d_edge = boundary_distances[j]; // Distance to bisector plane j +// +// // Clamp distance to be non-negative (numerical safety) +// d_edge = std::max(0.0f, d_edge); +// +// // If plane is beyond radius, cap volume is zero +// if (d_edge >= query_radius) { +// vols[j] = 0.0f; +// continue; +// } +// +// // Calculate x = 1 - (d_edge/query_radius)^2 for betainc arg I_x(a, b) +// // Use doubles for intermediate calculation for precision +// double r_double = static_cast(query_radius); +// double d_double = static_cast(d_edge); +// double ratio_d_r = d_double / r_double; +// double x = 1.0 - ratio_d_r * ratio_d_r; +// +// // Clamp x to [0, 1] due to potential floating point inaccuracies +// x = std::clamp(x, 0.0, 1.0); +// +// // Parameters for incomplete beta I_x(a, b) for hyperspherical cap volume ratio +// double a_param = 0.5 * (static_cast(dimension) + 1.0); +// double b_param = 0.5; +// +// double beta_inc_value = 0.0; +// try { +// beta_inc_value = use_precomputed +// ? incomplete_beta_lookup(x, dimension) +// : incomplete_beta(a_param, b_param, x); +// } catch (const std::exception& e) { +// // Warn if beta calculation fails, default volume to 0 +// // Consider more robust error handling if needed +// #if DEBUG // Optional: Only print in debug builds +// fprintf(stderr, +// "Warning: Incomplete Beta calculation failed for x=%.6f, dim=%d. Defaulting cap vol to 0. Error: %s\n", +// x, dimension, e.what()); +// #endif +// beta_inc_value = 0.0; +// } +// +// // Cap volume ratio = 0.5 * I_x(a, b) +// vols[j] = 0.5f * static_cast(beta_inc_value); +// // Clamp result to ensure it's within valid range [0, 0.5] +// vols[j] = std::clamp(vols[j], 0.0f, 0.5f); +// } +// +// // --- 2. Calculate mu = sum(v_j) for j>=1 --- +// float mu_total = std::accumulate(vols.begin() + 1, vols.end(), 0.0f); +// +// // normalize volumes to sum to 1.0 +// if (mu_total > float_epsilon) { +// for (int j = 1; j < m; ++j) { +// vols[j] /= mu_total; +// } +// } else { +// // If mu is near zero, set all volumes to zero +// std::fill(vols.begin() + 1, vols.end(), 0.0f); +// } +// // --- 4. Calculate Improved P0 = max(exp(-mu), 1 - v1) --- +// +// vector survival_function(m, 1.0f); +// int k_neighbors = 100; +// for (int j = m-1; j > 0; --j) { +// survival_function[j-1] = survival_function[j] * (1.0f - (vols[j])); +// } +// +// // --- 5. Calculate neighbor probabilities P_j = (1-P0)*v_j/mu --- +// std::vector probs(m, 0.0f); +// probs[0] = survival_function[0]; +// +// // for (int j = 1; j < m; j++) { +// // probs[j] = vols[j] * (survival_function[0] / (1 - vols[j])); +// // } +// +// // for (int j = 1; j < m; ++j) { +// // probs[j] = survival_function[j] - survival_function[j - 1]; +// // } +// +// float remaining_mass = 1.0f - probs[0]; +// float cum_v = 0.0f; // Σ_{i float_epsilon) { +// // for (int j = 1; j < m; ++j) { +// // probs[j] = (accum / (1 - vols[j])) - accum; // reverse the survival function +// // probs[j] = std::clamp(probs[j], 0.0f, 1.0f); +// // accum += probs[j]; +// // } +// // } +// // If P_neighbors_total or mu is near zero, probs[1...m-1] remain 0.0f. +// +// // --- 6. Final Renormalization (Safety Net for Floating Point Drift) --- +// float sum_probs = std::accumulate(probs.begin(), probs.end(), 0.0f); +// +// if (sum_probs > float_epsilon) { +// float inv_sum = 1.0f / sum_probs; +// for (int j = 0; j < m; ++j) { +// // Ensure probabilities are non-negative after division +// probs[j] = std::max(0.0f, probs[j] * inv_sum); +// } +// // Ensure sum is exactly 1.0, assign difference to largest element (usually P0) +// float final_sum_check = std::accumulate(probs.begin(), probs.end(), 0.0f); +// probs[0] += (1.0f - final_sum_check); // Add residual to P0 +// +// +// } else { +// // If sum is zero (should be rare unless m=0), reset to P0=1. +// if (m > 0) { +// std::fill(probs.begin() + 1, probs.end(), 0.0f); +// probs[0] = 1.0f; +// } +// } +// +// return probs; +// } + inline float compute_intersection_volume_one(float boundary_distance, float query_radius, int dimension) { if (boundary_distance >= query_radius) { return -1e8; diff --git a/src/cpp/include/index_partition.h b/src/cpp/include/index_partition.h index 63b022c6..05cab7a2 100644 --- a/src/cpp/include/index_partition.h +++ b/src/cpp/include/index_partition.h @@ -109,7 +109,7 @@ class IndexPartition { * * @param index Index of the vector to remove. */ - void remove(int64_t index); + int64_t remove(int64_t index); /** * @brief Resize the partition. diff --git a/src/cpp/include/list_scanning.h b/src/cpp/include/list_scanning.h index d33e540a..557393b7 100644 --- a/src/cpp/include/list_scanning.h +++ b/src/cpp/include/list_scanning.h @@ -187,6 +187,11 @@ class TypedTopKBuffer { DistanceType get_kth_distance() { std::lock_guard buffer_lock(buffer_mutex_); flush(); // Ensure the buffer is properly flushed + + if (curr_offset_ < k_) { + return 0.0; + } + return topk_[std::min(curr_offset_, k_ - 1)].first; } diff --git a/src/cpp/include/maintenance_policies.h b/src/cpp/include/maintenance_policies.h index 6ee68075..0ddb5f7f 100644 --- a/src/cpp/include/maintenance_policies.h +++ b/src/cpp/include/maintenance_policies.h @@ -46,7 +46,6 @@ class MaintenancePolicy { */ void reset(); - private: shared_ptr partition_manager_; ///< Manages partition state. shared_ptr params_; ///< Maintenance parameters. shared_ptr cost_estimator_; ///< Cost estimator for maintenance actions. diff --git a/src/cpp/include/parallel.h b/src/cpp/include/parallel.h index 15f9c30d..5db90651 100644 --- a/src/cpp/include/parallel.h +++ b/src/cpp/include/parallel.h @@ -42,23 +42,28 @@ template void parallel_for(IndexType start, IndexType end, Function func, int num_threads = -1) { if (num_threads <= 0) { num_threads = std::thread::hardware_concurrency(); - } - IndexType total = end - start; - IndexType chunk = (total + num_threads - 1) / num_threads; - std::vector> futures; - for (IndexType t = 0; t < num_threads; ++t) { - IndexType chunk_start = start + t * chunk; - IndexType chunk_end = std::min(end, chunk_start + chunk); - if (chunk_start >= chunk_end) - break; - futures.push_back(std::async(std::launch::async, [=]() { - for (IndexType i = chunk_start; i < chunk_end; i++) { - func(i); - } - })); - } - for (auto &f : futures) { - f.get(); + } else if (num_threads == 1) { + for (IndexType i = start; i < end; i++) { + func(i); + } + } else { + IndexType total = end - start; + IndexType chunk = (total + num_threads - 1) / num_threads; + std::vector> futures; + for (IndexType t = 0; t < num_threads; ++t) { + IndexType chunk_start = start + t * chunk; + IndexType chunk_end = std::min(end, chunk_start + chunk); + if (chunk_start >= chunk_end) + break; + futures.push_back(std::async(std::launch::async, [=]() { + for (IndexType i = chunk_start; i < chunk_end; i++) { + func(i); + } + })); + } + for (auto &f : futures) { + f.get(); + } } } diff --git a/src/cpp/src/dynamic_inverted_list.cpp b/src/cpp/src/dynamic_inverted_list.cpp index 0ea36450..d51d14b6 100644 --- a/src/cpp/src/dynamic_inverted_list.cpp +++ b/src/cpp/src/dynamic_inverted_list.cpp @@ -99,16 +99,21 @@ namespace faiss { void DynamicInvertedLists::remove_entry(size_t list_no, idx_t id) { auto it = partitions_.find(list_no); - if (it == partitions_.end()) { + if (it == partitions_.end()) throw std::runtime_error("List does not exist in remove_entry"); - } - shared_ptr part = it->second; + auto& part = it->second; if (part->num_vectors_ == 0) return; - int64_t idx_to_remove = part->find_id(id); - if (idx_to_remove != -1) { - part->remove(idx_to_remove); + int64_t pos = part->find_id(id); + if (pos == -1) return; + + int64_t swapped = part->remove(pos); + map_erase(id); + + if (swapped != -1) { // someone moved into `pos` + idx_t moved_id = part->ids_[pos]; + map_swap(part.get(), pos, moved_id); } } @@ -125,9 +130,12 @@ namespace faiss { // We'll perform removals by scanning and removing matches. // Because remove() swaps last element in, we must be careful with iteration. for (int64_t i = 0; i < part->num_vectors_;) { - if (vectors_to_remove_set.find(part->ids_[i]) != vectors_to_remove_set.end()) { - part->remove(i); - // don't increment i, because we just swapped a new element into i + if (vectors_to_remove_set.count(part->ids_[i])) { + idx_t victim = part->ids_[i]; + int64_t swapped = part->remove(i); + map_erase(victim); + if (swapped != -1) + map_swap(part.get(), i, part->ids_[i]); } else { i++; } @@ -139,8 +147,12 @@ namespace faiss { for (auto &kv: partitions_) { shared_ptr part = kv.second; for (int64_t i = 0; i < part->num_vectors_;) { - if (vectors_to_remove.find(part->ids_[i]) != vectors_to_remove.end()) { - part->remove(i); + if (vectors_to_remove.count(part->ids_[i])) { + idx_t victim = part->ids_[i]; + int64_t swapped = part->remove(i); + map_erase(victim); + if (swapped != -1) + map_swap(part.get(), i, part->ids_[i]); } else { i++; } @@ -148,27 +160,38 @@ namespace faiss { } } + void DynamicInvertedLists::build_map() { + id_to_location_.clear(); + for (auto& kv : partitions_) { + IndexPartition* part = kv.second.get(); + for (int64_t i = 0; i < part->num_vectors_; i++) { + id_to_location_[part->ids_[i]] = {part, i}; + } + } + } + + size_t DynamicInvertedLists::add_entries( size_t list_no, size_t n_entry, const idx_t *ids, const uint8_t *codes) { - if (n_entry == 0) { - return 0; - } + if (n_entry == 0) return 0; auto it = partitions_.find(list_no); - if (it == partitions_.end()) { + if (it == partitions_.end()) throw std::runtime_error("List does not exist in add_entries"); - } - shared_ptr part = it->second; - // Ensure code_size is set - if (part->code_size_ != static_cast(code_size)) { + auto& part = it->second; + if (part->code_size_ != static_cast(code_size)) part->set_code_size(static_cast(code_size)); - } - part->append((int64_t) n_entry, ids, codes); + const int64_t base = part->num_vectors_; // size *before* append + part->append(static_cast(n_entry), ids, codes); + + for (size_t i = 0; i < n_entry; ++i) + map_add(part.get(), base + i, ids[i]); + return n_entry; } @@ -187,73 +210,70 @@ namespace faiss { part->update((int64_t) offset, (int64_t) n_entry, ids, codes); } - void DynamicInvertedLists::batch_update_entries( - size_t old_vector_partition, - int64_t *new_vector_partitions, - uint8_t *new_vectors, - int64_t *new_vector_ids, - int num_vectors) { - // This logic will: - // 1. Remove all vectors from old_vector_partition that moved to a new partition - // 2. Append them to their new partitions - - // Identify which vectors belong to old_vector_partition and distribute them - // to new partitions. - std::unordered_map > vectors_for_new_partition; - - for (int i = 0; i < num_vectors; i++) { - size_t new_p = static_cast(new_vector_partitions[i]); - if (new_p != old_vector_partition) { - vectors_for_new_partition[new_p].push_back(i); - } - } - - // Append entries to new partitions - for (auto &kv: vectors_for_new_partition) { - size_t new_p = kv.first; - auto it = partitions_.find(new_p); - if (it == partitions_.end()) { - // Create a new partition if needed - add_list(new_p); - it = partitions_.find(new_p); - } - shared_ptr new_part = it->second; - if (new_part->code_size_ != static_cast(code_size)) { - new_part->set_code_size((int64_t) code_size); +void DynamicInvertedLists::batch_update_entries( + size_t old_partition, + int64_t* new_partitions, + uint8_t* new_vectors, + int64_t* new_ids, + int num) +{ + /* 1. gather indices to move grouped by their *destination* */ + std::unordered_map> to_move; + for (int i = 0; i < num; ++i) { + size_t dst = static_cast(new_partitions[i]); + if (dst != old_partition) + to_move[dst].push_back(i); + } + + /* 2. FIRST remove them from the old partition + (updates map_erase + map_swap) */ + auto old_it = partitions_.find(old_partition); + if (old_it != partitions_.end()) { + auto& old_part = old_it->second; + for (auto& kv : to_move) + for (int idx : kv.second) { + idx_t victim = static_cast(new_ids[idx]); + int64_t pos = old_part->find_id(victim); + if (pos != -1) { + int64_t sw = old_part->remove(pos); + map_erase(victim); + if (sw != -1) + map_swap(old_part.get(), pos, old_part->ids_[pos]); + } } + } - // Gather all IDs and codes to append at once - std::vector tmp_ids; - tmp_ids.reserve(kv.second.size()); - std::vector tmp_codes; - tmp_codes.reserve(kv.second.size() * code_size); - - for (int idx: kv.second) { - tmp_ids.push_back((idx_t) new_vector_ids[idx]); - tmp_codes.insert(tmp_codes.end(), - new_vectors + idx * code_size, - new_vectors + (idx + 1) * code_size); - } + /* 3. THEN append them to their new partitions + (updates map_add) */ + for (auto& kv : to_move) { + size_t dst = kv.first; + auto& idxs = kv.second; - new_part->append((int64_t) kv.second.size(), tmp_ids.data(), tmp_codes.data()); + auto it = partitions_.find(dst); + if (it == partitions_.end()) { + add_list(dst); + it = partitions_.find(dst); } + auto& new_part = it->second; + if (new_part->code_size_ != static_cast(code_size)) + new_part->set_code_size((int64_t)code_size); - // If needed, remove them from old_vector_partition - auto old_it = partitions_.find(old_vector_partition); - if (old_it != partitions_.end()) { - shared_ptr old_part = old_it->second; - // remove vectors that moved - for (auto &kv: vectors_for_new_partition) { - for (int idx: kv.second) { - idx_t old_id = (idx_t) new_vector_ids[idx]; - int64_t pos = old_part->find_id(old_id); - if (pos != -1) { - old_part->remove(pos); - } - } - } + const int64_t base = new_part->num_vectors_; + std::vector ids_buf; ids_buf.reserve(idxs.size()); + std::vector codes_buf; codes_buf.reserve(idxs.size() * code_size); + + for (int j : idxs) { + ids_buf .push_back(static_cast(new_ids[j])); + codes_buf.insert(codes_buf.end(), + new_vectors + j * code_size, + new_vectors + (j + 1) * code_size); } + new_part->append((int64_t)idxs.size(), ids_buf.data(), codes_buf.data()); + + for (size_t k = 0; k < idxs.size(); ++k) + map_add(new_part.get(), base + k, ids_buf[k]); } +} void DynamicInvertedLists::remove_list(size_t list_no) { auto it = partitions_.find(list_no); @@ -262,6 +282,9 @@ namespace faiss { return; } + for (int64_t i = 0; i < it->second->num_vectors_; i++) { + map_erase(it->second->ids_[i]); + } partitions_.erase(it); nlist--; } @@ -285,39 +308,51 @@ namespace faiss { return part->find_id(id) != -1; } - bool DynamicInvertedLists::get_vector_for_id(idx_t id, float *vector_values) { - for (auto &kv: partitions_) { - shared_ptr part = kv.second; - int64_t pos = part->find_id(id); - if (pos != -1) { - // Found it, copy vector - // code_size_ is in bytes. Assuming float vectors of dimension (code_size_/sizeof(float)) - std::memcpy(vector_values, part->codes_ + pos * part->code_size_, part->code_size_); - return true; - } - } - return false; + bool DynamicInvertedLists::get_vector_for_id(idx_t id, float *out) { + if (id_to_location_.empty()) build_map(); + + auto it = id_to_location_.find(id); + if (it == id_to_location_.end()) return false; + IndexPartition* part = it->second.first; + int64_t pos = it->second.second; + std::memcpy(out, part->codes_ + pos * part->code_size_, part->code_size_); + return true; } vector DynamicInvertedLists::get_vectors_by_id(vector ids) { + if (id_to_location_.empty()) build_map(); + + vector ret; + ret.reserve(ids.size()); - vector ret; for (int64_t id : ids) { - bool found = false; - for (auto &kv: partitions_) { - shared_ptr part = kv.second; - int64_t pos = part->find_id(id); - if (pos != -1) { - ret.push_back(reinterpret_cast(part->codes_ + pos * part->code_size_)); - found = true; - break; - } - } - if (!found) { + auto it = id_to_location_.find(id); + if (it == id_to_location_.end()) { throw std::runtime_error("ID not found in any partition"); } + IndexPartition* part = it->second.first; + int64_t pos = it->second.second; + ret.push_back(reinterpret_cast(part->codes_ + pos * part->code_size_)); } return ret; + + // vector ret; + // for (int64_t id : ids) { + // bool found = false; + // for (auto &kv: partitions_) { + // shared_ptr part = kv.second; + // int64_t pos = part->find_id(id); + // if (pos != -1) { + // ret.push_back(reinterpret_cast(part->codes_ + pos * part->code_size_)); + // found = true; + // break; + // } + // } + // if (!found) { + // throw std::runtime_error("ID not found in any partition"); + // } + // } + // return ret; } size_t DynamicInvertedLists::get_new_list_id() { @@ -326,6 +361,7 @@ namespace faiss { void DynamicInvertedLists::reset() { partitions_.clear(); + id_to_location_.clear(); nlist = 0; curr_list_id_ = 0; } @@ -517,6 +553,8 @@ namespace faiss { curr_list_id_ = max_list_id + 1; ifs.close(); + + build_map(); } Tensor DynamicInvertedLists::get_partition_ids() { diff --git a/src/cpp/src/index_partition.cpp b/src/cpp/src/index_partition.cpp index 8604a939..87e4992d 100644 --- a/src/cpp/src/index_partition.cpp +++ b/src/cpp/src/index_partition.cpp @@ -76,29 +76,21 @@ void IndexPartition::update(int64_t offset, int64_t n_entry, const idx_t* new_id std::memcpy(ids_ + offset, new_ids, n_entry * sizeof(idx_t)); } -void IndexPartition::remove(int64_t index) { - if (index < 0 || index >= num_vectors_) { - throw std::runtime_error("Index out of range in remove"); +int64_t IndexPartition::remove(int64_t idx) +{ + assert(idx >= 0 && idx < num_vectors_); + const int64_t last = num_vectors_ - 1; + + // optional: bookkeeping for norms, etc. + + if (idx != last) { // swap last -> idx + std::memcpy(codes_ + idx * code_size_, + codes_ + last * code_size_, + code_size_); + ids_[idx] = ids_[last]; } - if (index == num_vectors_ - 1) { - num_vectors_--; - return; - } - - int64_t last_idx = num_vectors_ - 1; - const size_t code_bytes = static_cast(code_size_); - - // // Update id_to_index_ - // idx_t last_id = ids_[last_idx]; - // idx_t removed_id = ids_[index]; - // - // id_to_index_[last_id] = index; - // id_to_index_.erase(removed_id); - - std::memcpy(codes_ + index * code_bytes, codes_ + last_idx * code_bytes, code_bytes); - ids_[index] = ids_[last_idx]; - - num_vectors_--; + --num_vectors_; + return (idx == last) ? -1 : idx; // <‑‑ the new occupant of slot idx } void IndexPartition::resize(int64_t new_capacity) { diff --git a/src/cpp/src/partition_manager.cpp b/src/cpp/src/partition_manager.cpp index 3b738856..240e2f82 100644 --- a/src/cpp/src/partition_manager.cpp +++ b/src/cpp/src/partition_manager.cpp @@ -112,6 +112,8 @@ void PartitionManager::init_partitions( } } + partition_store_->build_map(); + if (debug_) { std::cout << "[PartitionManager] init_partitions: Created " << nlist << " partitions, dimension=" << dim << std::endl; diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 67112a1f..92b0ac84 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -514,6 +514,9 @@ shared_ptr QueryCoordinator::serial_scan(Tensor x, Tensor partitio // Use our custom parallel_for to process queries in parallel. parallel_for(0, num_queries, [&](int64_t q) { // Create a local TopK buffer for query q. + + auto t1 = high_resolution_clock::now(); + auto topk_buf = std::make_shared(k, is_descending); const float* query_vec = x_ptr + q * dimension; int num_parts = partition_ids.size(1); @@ -525,16 +528,33 @@ shared_ptr QueryCoordinator::serial_scan(Tensor x, Tensor partitio query_radius = -1000000.0; } + auto t2 = high_resolution_clock::now(); + Tensor partition_sizes = partition_manager_->get_partition_sizes(partition_ids[q]); + vector partition_sizes_vec = vector(partition_sizes.data_ptr(), + partition_sizes.data_ptr() + partition_sizes.size(0)); + auto t3 = high_resolution_clock::now(); if (use_aps) { vector partition_ids_to_scan_vec = std::vector(partition_ids[q].data_ptr(), partition_ids[q].data_ptr() + partition_ids[q].size(0)); vector cluster_centroids = parent_->partition_manager_->get_vectors(partition_ids_to_scan_vec); + t3 = high_resolution_clock::now(); + + boundary_distances = compute_boundary_distances(x[q], cluster_centroids, metric_ == faiss::METRIC_L2); } + auto t4 = high_resolution_clock::now(); + + int64_t scan_time = 0; + int64_t aps_time = 0; + + vector scanned_ids; + for (int p = 0; p < num_parts; p++) { + + auto curr_time = high_resolution_clock::now(); int64_t pi = partition_ids_accessor[q][p]; if (pi == -1) { @@ -553,13 +573,19 @@ shared_ptr QueryCoordinator::serial_scan(Tensor x, Tensor partitio dimension, *topk_buf, metric_); + scanned_ids.push_back(pi); float curr_radius = topk_buf->get_kth_distance(); float percent_change = abs(curr_radius - query_radius) / curr_radius; + auto end_time = high_resolution_clock::now(); + + scan_time += duration_cast(end_time - start_time).count(); + start_time = high_resolution_clock::now(); - if (use_aps) { - if (percent_change > search_params->recompute_threshold) { + bool first_list = (p == 0); + if (use_aps && curr_radius != 0) { + if (first_list || percent_change > search_params->recompute_threshold) { query_radius = curr_radius; partition_probs = compute_recall_profile(boundary_distances, @@ -570,10 +596,13 @@ shared_ptr QueryCoordinator::serial_scan(Tensor x, Tensor partitio metric_ == faiss::METRIC_L2); } float recall_estimate = 0.0; - for (int i = 0; i < p; i++) { + for (int i = 0; i < p + 1; i++) { recall_estimate += partition_probs[i]; } + end_time = high_resolution_clock::now(); + aps_time += duration_cast(end_time - start_time).count(); if (recall_estimate >= search_params->recall_target) { + timing_info->partitions_scanned = p + 1; break; } } @@ -581,8 +610,21 @@ shared_ptr QueryCoordinator::serial_scan(Tensor x, Tensor partitio // Retrieve the top-k results for query q. all_topk_dists[q] = topk_buf->get_topk(); all_topk_ids[q] = topk_buf->get_topk_indices(); + auto t5 = high_resolution_clock::now(); + + // mark the partitions as scanned + maintenance_policy_->record_query_hits(scanned_ids); + std::cout << "Query " << q << " partitions scanned: " << scanned_ids.size() << std::endl; + std::cout << "Total queries processed: " << maintenance_policy_->hit_count_tracker_->get_num_queries_recorded() << std::endl; + + // std::cout << "Query " << q << " times: " << duration_cast(t2 - t1).count() << " " + // << duration_cast(t3 - t2).count() << " " + // << duration_cast(t4 - t3).count() << " " + // << duration_cast(t5 - t4).count() << std::endl; + // std::cout << "Scan time: " << scan_time / 1000.0 << " APS time: " << aps_time / 1000.0 << std::endl; }, search_params->num_threads); + // Aggregate per-query results into output tensors. auto ret_ids_accessor = ret_ids.accessor(); auto ret_dists_accessor = ret_dists.accessor(); diff --git a/src/python/datasets/ann_datasets.py b/src/python/datasets/ann_datasets.py index 89297392..572717a4 100644 --- a/src/python/datasets/ann_datasets.py +++ b/src/python/datasets/ann_datasets.py @@ -6,7 +6,7 @@ import numpy as np import torch -from quake.utils import download_url, extract_file, fvecs_to_tensor, ivecs_to_tensor, to_path +from quake.utils import download_url, extract_file, fvecs_to_tensor, ivecs_to_tensor, to_path, knn DEFAULT_DOWNLOAD_DIR = Path("data/") @@ -71,12 +71,58 @@ def load_queries(self) -> Union[np.ndarray, torch.Tensor]: def load_ground_truth(self) -> Union[np.ndarray, torch.Tensor]: return ivecs_to_tensor(self.data_dir / "sift_groundtruth.ivecs") +class UniformDataset(Dataset): + def __init__(self, num_vectors: int, dim: int, download_dir: Union[str, Path] = DEFAULT_DOWNLOAD_DIR): + self.num_vectors = num_vectors + self.dim = dim + self.download_dir = to_path(download_dir) + + def is_downloaded(self) -> bool: + # check if files exist + vecs = self.download_dir / "vectors.pt" + queries = self.download_dir / "queries.pt" + gt = self.download_dir / "ground_truth.pt" + + return vecs.exists() and queries.exists() and gt.exists() + + def download(self, overwrite: bool = False): + + vecs = np.random.uniform(0, 1, (self.num_vectors, self.dim)).astype(np.float32) + queries = np.random.uniform(0, 1, (1000, self.dim)).astype(np.float32) + gt = knn(queries, vecs, k=100)[0] + + # vecs = torch.randn(self.num_vectors, self.dim) + # queries = torch.randn(1000, self.dim) + # gt = knn(queries, vecs, k=100)[0] + + + np.save(self.download_dir / "vectors.npy", vecs) + np.save(self.download_dir / "queries.npy", queries) + np.save(self.download_dir / "ground_truth.npy", gt) + + def load_vectors(self) -> Union[np.ndarray, torch.Tensor]: + # return torch.randn(self.num_vectors, self.dim) + # Load the vectors from disk + return torch.from_numpy(np.load(self.download_dir / "vectors.npy")) + + def load_queries(self) -> Union[np.ndarray, torch.Tensor]: + # return torch.randn(self.num_vectors, self.dim) + # Load the queries from disk + return torch.from_numpy(np.load(self.download_dir / "queries.npy")) + + def load_ground_truth(self) -> Union[np.ndarray, torch.Tensor]: + # return torch.randint(0, self.num_vectors, (self.num_vectors, 100)) + # Load the ground truth from disk + return torch.from_numpy(np.load(self.download_dir / "ground_truth.npy")) + def load_dataset( name: str, download_dir: str = DEFAULT_DOWNLOAD_DIR, overwrite_download: bool = False ) -> List[Union[np.ndarray, torch.Tensor]]: if name.lower() == "sift1m": dataset = Sift1m(download_dir=download_dir) + elif name.lower() == "uniform": + dataset = UniformDataset(num_vectors=100000, dim=8, download_dir=download_dir) else: raise RuntimeError("Unimplemented dataset") diff --git a/src/python/workload_generator.py b/src/python/workload_generator.py index 3e90c814..abe8eb0e 100644 --- a/src/python/workload_generator.py +++ b/src/python/workload_generator.py @@ -418,7 +418,10 @@ def initialize_index(self, name, index, build_params, m_params): index.save(index_path) print(f"Index {name} built and saved to {index_path}") else: - index.load(index_path, n_workers=build_params.get("num_workers", 0)) + if build_params.get("num_workers"): + index.load(index_path, num_workers=build_params["num_workers"]) + else: + index.load(index_path) print(f"Index {name} loaded from {index_path}") if isinstance(index, QuakeWrapper) and m_params is not None: diff --git a/test/cpp/benchmark.cpp b/test/cpp/benchmark.cpp index 71426869..e2802a1a 100644 --- a/test/cpp/benchmark.cpp +++ b/test/cpp/benchmark.cpp @@ -28,10 +28,10 @@ using torch::Tensor; // Global benchmark parameters static const int64_t DIM = 128; static const int64_t NUM_VECTORS = 100000; // number of database vectors -static const int64_t N_LIST = 100; // number of clusters for IVF +static const int64_t N_LIST = 1000; // number of clusters for IVF static const int64_t NUM_QUERIES = 10; // number of queries for search benchmark static const int64_t K = 10; // top-K neighbors -static const int64_t N_PROBE = 32; // number of probes for IVF +static const int64_t N_PROBE = 12; // number of probes for IVF static const int64_t N_WORKERS = 12; // number of workers for parallel query coordinator // Helper functions to generate random data and sequential IDs @@ -487,4 +487,152 @@ TEST_F(FaissIVFBenchmark, Remove) { auto elapsed = duration_cast(end - start).count(); std::cout << "[Faiss IVF] Remove time: " << elapsed << " ms" << std::endl; SUCCEED(); -} \ No newline at end of file +} + +// ------------------------------------------------------------------------- +// SEARCH, ADD, REMOVE, AND MAINTENANCE TEST +// ------------------------------------------------------------------------- +TEST(QuakeIndexStressTest, SearchAddRemoveMaintenanceTest) { + // Repeatedly search, add, remove, and perform maintenance to see if the index remains consistent. + + int64_t dimension = 128; + int64_t num_vectors = 100000; + int64_t num_queries = 1; + int64_t batch_size = 10000; + int n_ops = 100; + + QuakeIndex index; + auto build_params = std::make_shared(); + build_params->nlist = 1000; + build_params->metric = "l2"; + build_params->niter = 5; + + Tensor data_vectors = torch::randn({num_vectors, dimension}, torch::kFloat32); + Tensor data_ids = torch::arange(num_vectors, torch::kInt64); + + index.build(data_vectors, data_ids, build_params); + + // timers + int64_t search_time = 0; + int64_t add_time = 0; + int64_t remove_time = 0; + int64_t maintenance_time = 0; + + auto start = std::chrono::high_resolution_clock::now(); + auto end = std::chrono::high_resolution_clock::now(); + + for (int i = 0; i < n_ops; i++) { + // Search + auto query_vectors = torch::randn({num_queries, dimension}, torch::kFloat32); + auto search_params = std::make_shared(); + search_params->nprobe = 12; + search_params->k = 10; + search_params->batched_scan = false; + search_params->num_threads = 1; + + start = std::chrono::high_resolution_clock::now(); + auto search_result = index.search(query_vectors, search_params); + end = std::chrono::high_resolution_clock::now(); + search_time += std::chrono::duration_cast(end - start).count(); + ASSERT_EQ(search_result->ids.size(0), query_vectors.size(0)); + ASSERT_EQ(search_result->ids.size(1), search_params->k); + + // Add + auto add_vectors = torch::randn({batch_size, dimension}, torch::kFloat32); + auto add_ids = torch::arange(batch_size, torch::kInt64) + num_vectors; + start = std::chrono::high_resolution_clock::now(); + auto add_info = index.add(add_vectors, add_ids); + end = std::chrono::high_resolution_clock::now(); + add_time += std::chrono::duration_cast(end - start).count(); + num_vectors += batch_size; + ASSERT_EQ(add_info->n_vectors, batch_size); + + // // Remove + // auto remove_ids = add_ids.slice(0, 0, batch_size / 2); + // start = std::chrono::high_resolution_clock::now(); + // auto remove_info = index.remove(remove_ids); + // end = std::chrono::high_resolution_clock::now(); + // remove_time += std::chrono::duration_cast(end - start).count(); + // ASSERT_EQ(remove_info->n_vectors, batch_size / 2); + + start = std::chrono::high_resolution_clock::now(); + index.maintenance(); + end = std::chrono::high_resolution_clock::now(); + maintenance_time += std::chrono::duration_cast(end - start).count(); + } + + // print out mean times per operation + float mean_search_time = static_cast(search_time) / n_ops; + float mean_add_time = static_cast(add_time) / n_ops; + float mean_remove_time = static_cast(remove_time) / n_ops; + float mean_maintenance_time = static_cast(maintenance_time) / n_ops; + + std::cout << "[SearchAddRemoveMaintenanceTest] Mean search time: " << mean_search_time << " μs\n"; + std::cout << "[SearchAddRemoveMaintenanceTest] Mean add time: " << mean_add_time << " μs\n"; + std::cout << "[SearchAddRemoveMaintenanceTest] Mean remove time: " << mean_remove_time << " μs\n"; + std::cout << "[SearchAddRemoveMaintenanceTest] Mean maintenance time: " << mean_maintenance_time << " μs\n"; + + SUCCEED(); +} + +TEST(FaissIndexStressTest, SearchAddRemoveMaintenanceTest) { + // Repeatedly search, add, remove, and perform maintenance to see if the index remains consistent. + + int64_t dimension = 128; + int64_t num_vectors = 100000; + int64_t num_queries = 1; + int64_t batch_size = 10000; + int n_ops = 100; + + Tensor data_vectors = torch::randn({num_vectors, dimension}, torch::kFloat32); + Tensor data_ids = torch::arange(num_vectors, torch::kInt64); + + auto quantizer = new faiss::IndexFlatL2(dimension); + auto index = new faiss::IndexIVFFlat(quantizer, dimension, 1000, faiss::METRIC_L2); + index->train(num_vectors, data_vectors.data_ptr()); + index->add(num_vectors, data_vectors.data_ptr()); + + // timers + int64_t search_time = 0; + int64_t add_time = 0; + int64_t remove_time = 0; + int64_t maintenance_time = 0; + + auto start = std::chrono::high_resolution_clock::now(); + auto end = std::chrono::high_resolution_clock::now(); + + for (int i = 0; i < n_ops; i++) { + // Search + auto query_vectors = generate_data(num_queries, dimension); + std::vector distances(num_queries * K); + std::vector labels(num_queries * K); + + start = std::chrono::high_resolution_clock::now(); + index->nprobe = 12; + index->search(num_queries, query_vectors.data_ptr(), 12, distances.data(), labels.data()); + end = std::chrono::high_resolution_clock::now(); + search_time += std::chrono::duration_cast(end - start).count(); + + // Add + auto add_vectors = generate_data(batch_size, dimension); + auto add_ids = generate_ids(batch_size, num_vectors); + start = std::chrono::high_resolution_clock::now(); + index->add(batch_size, add_vectors.data_ptr()); + end = std::chrono::high_resolution_clock::now(); + add_time += std::chrono::duration_cast(end - start).count(); + num_vectors += batch_size; + } + // print out mean times per operation + float mean_search_time = static_cast(search_time) / n_ops; + float mean_add_time = static_cast(add_time) / n_ops; + float mean_remove_time = static_cast(remove_time) / n_ops; + float mean_maintenance_time = static_cast(maintenance_time) / n_ops; + + std::cout << "[SearchAddRemoveMaintenanceTest] Mean search time: " << mean_search_time << " μs\n"; + std::cout << "[SearchAddRemoveMaintenanceTest] Mean add time: " << mean_add_time << " μs\n"; + std::cout << "[SearchAddRemoveMaintenanceTest] Mean remove time: " << mean_remove_time << " μs\n"; + std::cout << "[SearchAddRemoveMaintenanceTest] Mean maintenance time: " << mean_maintenance_time << " μs\n"; + + SUCCEED(); +} + diff --git a/test/cpp/dynamic_inverted_list.cpp b/test/cpp/dynamic_inverted_list.cpp index f7325e49..1473d1ec 100644 --- a/test/cpp/dynamic_inverted_list.cpp +++ b/test/cpp/dynamic_inverted_list.cpp @@ -556,6 +556,136 @@ TEST_F(DynamicInvertedListTest, SerializationTest) { remove(filename.c_str()); } +// --------------------------------------------------------------------- +// Map‑consistency tests +// --------------------------------------------------------------------- + +// After removing a middle element the map must point the *swapped* id +TEST_F(DynamicInvertedListTest, MapConsistencyAfterRemoveSwap) +{ + const size_t list_no = 0; + const size_t n_entries = 3; + + std::vector codes; + std::vector ids; + generate_random_codes(n_entries, codes); + generate_sequential_ids(n_entries, ids, 10); // ids = 10,11,12 + + invlists->add_entries(list_no, n_entries, ids.data(), codes.data()); + + // Remove id 11 (middle) – id 12 will be swapped into slot 1 + invlists->remove_entry(list_no, 11); + + // --- removed id must be gone + EXPECT_THROW( { invlists->get_vectors_by_id({11}); }, std::runtime_error ); + + // --- swapped id 12 must still be retrievable and vector intact + auto vec_ptrs = invlists->get_vectors_by_id({12}); + ASSERT_EQ(vec_ptrs.size(), 1u); + EXPECT_EQ( + std::memcmp(vec_ptrs[0], + codes.data() + 2 * code_size, // original bytes of id 12 + code_size), + 0); +} + + +// Removing many IDs in one pass must keep the map sound +TEST_F(DynamicInvertedListTest, MapConsistencyBulkRemove) +{ + const size_t list_no = 1; + const size_t n_entries = 10; + + std::vector codes; + std::vector ids; + generate_random_codes(n_entries, codes); + generate_sequential_ids(n_entries, ids, 100); // ids = 100..109 + + invlists->add_entries(list_no, n_entries, ids.data(), codes.data()); + + // Remove every even id (5 victims) + std::vector victims; + for (idx_t id : ids) + if (id % 2 == 0) victims.push_back(id); + + invlists->remove_entries_from_partition(list_no, victims); + + // All survivors must be accessible and the bytes match + std::vector survivors; + for (idx_t id : ids) + if (id % 2) survivors.push_back(id); + + auto vecs = invlists->get_vectors_by_id(survivors); + ASSERT_EQ(vecs.size(), survivors.size()); + for (size_t k = 0; k < survivors.size(); ++k) { + size_t orig_idx = survivors[k] - 100; // 0‑based + EXPECT_EQ( + std::memcmp(vecs[k], + codes.data() + orig_idx * code_size, + code_size), + 0); + } +} + + +// batch_update_entries must leave every moved id valid in the map +TEST_F(DynamicInvertedListTest, MapConsistencyBatchUpdate) +{ + const size_t old_p = 2; + const size_t new_p = 3; + const size_t n = 6; // vectors + + std::vector codes; + std::vector ids; + generate_random_codes(n, codes); + generate_sequential_ids(n, ids, 500); // 500..505 + invlists->add_entries(old_p, n, ids.data(), codes.data()); + + // mark last 3 vectors to move + std::vector part_of(n, (int64_t)old_p); + for (size_t i = 3; i < n; ++i) part_of[i] = (int64_t)new_p; + + std::vector new_codes = codes; // unchanged payload + std::vector ids_i64(ids.begin(), ids.end()); + + invlists->batch_update_entries(old_p, + part_of.data(), + new_codes.data(), + ids_i64.data(), + (int)n); + + // all ids must still retrieve the identical vector bytes + auto vecs = invlists->get_vectors_by_id({ids.begin(), ids.end()}); + ASSERT_EQ(vecs.size(), n); + + for (size_t k = 0; k < n; ++k) + EXPECT_EQ( + std::memcmp(vecs[k], + codes.data() + k * code_size, + code_size), + 0); +} + +TEST_F(DynamicInvertedListTest, LazyBuildMapGuard) +{ + const size_t list_no = 4; + std::vector codes; + std::vector ids; + generate_random_codes(1, codes); + generate_sequential_ids(1, ids, 900); + + invlists->add_entries(list_no, 1, ids.data(), codes.data()); + + // Brutally wipe the internal map (undefined behaviour normally, but + // we do it via the public API by resetting and rebuilding). + invlists->build_map(); // ensure filled first + invlists->id_to_location_.clear(); // <-- direct access because we are a friend in unit test + + // the read accessor should transparently rebuild + float tmp[4]; + EXPECT_TRUE(invlists->get_vector_for_id(ids[0], tmp)); +} + // NUMA related tests (only if QUAKE_USE_NUMA is defined) #ifdef QUAKE_USE_NUMA TEST_F(DynamicInvertedListTest, NumaTests) { diff --git a/test/cpp/quake_index.cpp b/test/cpp/quake_index.cpp index 0521ea02..22e9325e 100644 --- a/test/cpp/quake_index.cpp +++ b/test/cpp/quake_index.cpp @@ -7,6 +7,9 @@ #include #include "quake_index.h" #include +#include +#include +#include // Helper functions for random data static torch::Tensor generate_random_data(int64_t num_vectors, int64_t dim) { @@ -476,56 +479,6 @@ TEST(QuakeIndexStressTest, HighDimensionTest) { ASSERT_EQ(result->ids.size(1), search_params->k); } -// ------------------------------------------------------------------------- -// SEARCH, ADD, REMOVE, AND MAINTENANCE TEST -// ------------------------------------------------------------------------- -TEST(QuakeIndexStressTest, SearchAddRemoveMaintenanceTest) { - // Repeatedly search, add, remove, and perform maintenance to see if the index remains consistent. - - int64_t dimension = 16; - int64_t num_vectors = 100000; - int64_t num_queries = 100; - int64_t batch_size = 10; - - QuakeIndex index; - auto build_params = std::make_shared(); - build_params->nlist = 100; - build_params->metric = "l2"; - build_params->niter = 3; - - Tensor data_vectors = generate_random_data(num_vectors, dimension); - Tensor data_ids = generate_sequential_ids(num_vectors, 0); - - index.build(data_vectors, data_ids, build_params); - - for (int i = 0; i < 100; i++) { - // Search - std::cout << "Iteration " << i << std::endl; - auto query_vectors = generate_random_data(num_queries, dimension) * .1; - auto search_params = std::make_shared(); - search_params->nprobe = 1; - search_params->k = 5; - auto search_result = index.search(query_vectors, search_params); - ASSERT_EQ(search_result->ids.size(0), query_vectors.size(0)); - ASSERT_EQ(search_result->ids.size(1), search_params->k); - - // Add - auto add_vectors = generate_random_data(batch_size, dimension); - auto add_ids = generate_sequential_ids(batch_size, (i * batch_size) + num_vectors); - auto add_info = index.add(add_vectors, add_ids); - ASSERT_EQ(add_info->n_vectors, batch_size); - - // Remove - auto remove_ids = add_ids.slice(0, 0, batch_size / 2); - auto remove_info = index.remove(remove_ids); - ASSERT_EQ(remove_info->n_vectors, batch_size / 2); - - index.maintenance(); - } - - SUCCEED(); -} - // Define the GPU related test only if FAISS GPU support is enabled #ifdef FAISS_ENABLE_GPU // Test build with GPU enabled diff --git a/test/experiments/osdi2025/README.md b/test/experiments/osdi2025/README.md new file mode 100644 index 00000000..002ed897 --- /dev/null +++ b/test/experiments/osdi2025/README.md @@ -0,0 +1,57 @@ +# OSDI 2025 Artifact + +This directory contains the experiments for Quake's OSDI 2025 submission. + + + + +### Installation + +**Requirements:** +- Python 3.9 or later +- Linux (Tested on Ubuntu 22.04) + +1. **Clone the Repository:** + +```bash +git clone https://github.com/marius-team/quake.git +cd quake +git submodule update --init --recursive +``` + +2. **Set Up the Conda Environment:** + + Create and activate the environment using the provided YAML file: + +```bash +conda env create -f environments/ubuntu-latest/conda.yaml +conda activate quake-env +``` + +3. **Install Quake:** + +```bash +pip install --no-use-pep517 . +``` + +### Kick the tires + +To make sure Quake and the experiment environment are set up correctly, you can run the following commands: + +**Make sure to run all commands in the repository root directory** + +1. **Run the quickstart example:** + +This is a basic test that the main operations of Quake are working. + +```bash +python3 examples/quickstart.py +``` + +2. **Run the Benchmark:** + +```bash +python3 -m test.experiments.osdi2025.experiment_runner --experiment kick_the_tires --config sift1m +``` + +--- \ No newline at end of file diff --git a/test/experiments/osdi2025/aps_recall_targets/configs/sift1m.yaml b/test/experiments/osdi2025/aps_recall_targets/configs/sift1m.yaml new file mode 100644 index 00000000..47c5ee47 --- /dev/null +++ b/test/experiments/osdi2025/aps_recall_targets/configs/sift1m.yaml @@ -0,0 +1,28 @@ +# configs/experiment.yaml +mode: run # options: build, run, plot +base_dir: data/aps +dataset: + name: sift1m + path: data +experiment: + nq: 100 + k: 100 + n_workers: 0 + recall_targets: [.2, .3, .4, .5, .6, .7, .8, .9, .95, .99, .999] + recompute_ratio: 0.00001 + use_precompute: true + initial_search_fraction: .1 +index: + metric: l2 + nlist: 1000 +overwrite: + index: false + results: true +methods: ['Oracle', 'APS'] +#methods: ['APS'] +#methods: ['Oracle'] + +paths: + index_dir: data/sift1m/indexes + results_dir: data/sift1m/results + plot_dir: data/sift1m/plots \ No newline at end of file diff --git a/test/experiments/osdi2025/aps_recall_targets/run.py b/test/experiments/osdi2025/aps_recall_targets/run.py index 139597f9..1e9510fe 100644 --- a/test/experiments/osdi2025/aps_recall_targets/run.py +++ b/test/experiments/osdi2025/aps_recall_targets/run.py @@ -1,2 +1,182 @@ +#!/usr/bin/env python3 +""" +Adaptive‐Partition‐Scanning Recall‐Target Runner +Supports three methods: Oracle, FixedNProbe, APS +""" +import time +import yaml +import logging +from pathlib import Path +import torch +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt + +from quake import QuakeIndex, IndexBuildParams, SearchParams +from quake.datasets.ann_datasets import load_dataset +from quake.utils import compute_recall + +logger = logging.getLogger(__name__) + +def run_experiment(cfg_path: str, output_dir: str): + # 1) Load config + setup output dir + cfg = yaml.safe_load(Path(cfg_path).read_text()) + out = Path(output_dir); out.mkdir(parents=True, exist_ok=True) + + # 2) Load data + data_dir = cfg["dataset"].get("path", "") + vecs, queries, gt = load_dataset(cfg["dataset"]["name"], data_dir) + + + nq = cfg["experiment"]["nq"] + queries, gt = queries[:nq], gt[:nq] + + # 3) Build or load index + build_params = IndexBuildParams() + build_params.nlist = cfg["index"]["nlist"] + build_params.metric = cfg["index"]["metric"] + build_params.num_workers = cfg["experiment"]["n_workers"] + + idx_dir = Path(cfg["paths"]["index_dir"]); idx_dir.mkdir(parents=True, exist_ok=True) + idx_path = idx_dir / f"{cfg['dataset']['name']}_ivf{build_params.nlist}.index" + + idx = QuakeIndex() + idx.build(vecs, torch.arange(len(vecs)), build_params) + logger.info(f"Loaded index → {idx_path} (workers={cfg['experiment']['n_workers']})") + + # 4) Run each method × recall_target + records = [] + k = cfg["experiment"]["k"] + max_nlist = build_params.nlist + + for method in cfg["methods"]: + for rt in cfg["experiment"]["recall_targets"]: + logger.info(f"Method={method} RecallTarget={rt}") + per_query = [] # collect (nprobe, recall, time_ms) tuples + + if method == "Oracle": + # per‐query binary search + for i, q in enumerate(queries): + lo, hi, best = 1, max_nlist, max_nlist + while lo <= hi: + mid = (lo + hi) // 2 + sp = SearchParams() + sp.nprobe = mid + sp.k = k + sp.recall_target = -1 + res = idx.search(q.unsqueeze(0), sp) + rec = compute_recall(res.ids, gt[i].unsqueeze(0), k).item() + if rec >= rt: + best = mid + hi = mid - 1 + else: + lo = mid + 1 + + # time at best + t0 = time.time() + sp = SearchParams() + sp.nprobe = best + sp.k = k + sp.recall_target = -1 + res = idx.search(q.unsqueeze(0), sp) + rec = compute_recall(res.ids, gt[i].unsqueeze(0), k).item() + elapsed = (time.time() - t0) * 1000.0 + per_query.append((best, rec, elapsed)) + + elif method == "FixedNProbe": + # find one global nprobe + lo, hi, best = 1, max_nlist, max_nlist + while lo <= hi: + mid = (lo + hi) // 2 + sp = SearchParams(nprobe=mid, k=k, recall_target=-1) + ids = [] + for q in queries: + ids.append(idx.search(q.unsqueeze(0), sp).ids) + avg_rec = compute_recall(torch.cat(ids,0), gt, k).mean().item() + if avg_rec >= rt: + best = mid + hi = mid - 1 + else: + lo = mid + 1 + + # then time each query at best + for q in queries: + t0 = time.time() + sp = SearchParams(nprobe=best, k=k, recall_target=-1) + res = idx.search(q.unsqueeze(0), sp) + elapsed = (time.time() - t0) * 1000.0 + rec = compute_recall(res.ids, gt[0:1], k).item() # single recall + per_query.append((best, rec, elapsed)) + + elif method == "APS": + # one‐shot APS per query + for q, true in zip(queries, gt): + sp = SearchParams() + sp.nprobe = -1 + sp.k = k + sp.recall_target = rt + sp.recompute_threshold = cfg["experiment"]["recompute_ratio"] + sp.use_precomputed = cfg["experiment"]["use_precompute"] + sp.initial_search_fraction = cfg["experiment"]["initial_search_fraction"] + t0 = time.time() + res = idx.search(q.unsqueeze(0), sp) + elapsed = (time.time() - t0) * 1000.0 + rec = compute_recall(res.ids, true.unsqueeze(0), k).item() + print(f"recall: {rec:.4f} (target={rt})") + per_query.append((res.timing_info.partitions_scanned, rec, elapsed)) + else: + raise ValueError(f"Unknown method: {method}") + + # 5) Aggregate + arr = np.array(per_query, dtype=float) # shape (nq,3) + records.append({ + "Method": method, + "RecallTarget": rt, + "mean_nprobe": arr[:,0].mean(), + "std_nprobe": arr[:,0].std(), + "mean_recall": arr[:,1].mean(), + "std_recall": arr[:,1].std(), + "mean_time_ms": arr[:,2].mean(), + "std_time_ms": arr[:,2].std(), + }) + + # 6) Save CSV + df = pd.DataFrame(records) + out_csv = out / "aps_recall_targets_summary.csv" + df.to_csv(out_csv, index=False) + logger.info(f"Summary → {out_csv}") + + # 7) Plot: recall-target vs time, one line per method + fig, axes = plt.subplots(3, 1, figsize=(6,8), sharex=True) + for m in df["Method"].unique(): + grp = df[df["Method"]==m] + axes[0].errorbar(grp["RecallTarget"], grp["mean_time_ms"], + yerr=grp["std_time_ms"], marker="o", capsize=4, label=m) + axes[1].errorbar(grp["RecallTarget"], grp["mean_recall"], + yerr=grp["std_recall"], marker="o", capsize=4, label=m) + axes[2].errorbar(grp["RecallTarget"], grp["mean_nprobe"], + yerr=grp["std_nprobe"], marker="o", capsize=4, label=m) + axes[0].set_xlabel("Recall Target") + axes[0].set_xlabel("Mean Query Time (ms)") + axes[0].set_title("APS‑Experiment: Recall Target vs Time") + axes[0].legend() + + recall_targets = cfg["experiment"]["recall_targets"] + + axes[1].plot(recall_targets, recall_targets, "k--", label="Target") + axes[1].set_xlabel("Recall Target") + axes[1].set_ylabel("Mean Recall") + axes[1].set_title("APS‑Experiment: Recall Target vs Recall") + + axes[2].set_xlabel("Recall Target") + axes[2].set_ylabel("Mean NProbe") + axes[2].set_yscale("log") + axes[2].set_title("APS‑Experiment: Recall Target vs NProbe") + + plt.grid(True, which="both", ls=":") + plt.tight_layout() + plot_path = out / "aps_recall_vs_time.png" + plt.savefig(plot_path) + logger.info(f"Plot → {plot_path}") \ No newline at end of file diff --git a/test/experiments/osdi2025/experiment_runner.py b/test/experiments/osdi2025/experiment_runner.py index fd3a02cf..9e443aab 100644 --- a/test/experiments/osdi2025/experiment_runner.py +++ b/test/experiments/osdi2025/experiment_runner.py @@ -1,62 +1,42 @@ -#!/usr/bin/env python +#!/usr/bin/env python3 """ -Master runner for OSDI2025 vector‐search experiments. - -Usage: - python -m test.experiments.osdi2025.experiment_runner \ - --experiment \ - --config \ - --output-dir +Master runner for OSDI2025 vector‑search experiments. """ - import argparse from pathlib import Path -# Make sure `test/`, `test/experiments/`, and subfolders all have an __init__.py +from test.experiments.osdi2025.kick_the_tires.run import run_experiment as run_kick_the_tires from test.experiments.osdi2025.numa_single_query.run import run_experiment as run_numa_single from test.experiments.osdi2025.numa_multi_query.run import run_experiment as run_numa_multi +from test.experiments.osdi2025.aps_recall_targets.run import run_experiment as run_aps_recall EXPERIMENTS = { - "numa_single_query": run_numa_single, - "numa_multi_query": run_numa_multi, + "kick_the_tires": run_kick_the_tires, + "numa_single_query": run_numa_single, + "numa_multi_query": run_numa_multi, + "aps_recall_targets": run_aps_recall, } def main(): - parser = argparse.ArgumentParser( - description="Run one OSDI2025 experiment" - ) - parser.add_argument( - "-x", "--experiment", - choices=EXPERIMENTS.keys(), - required=True, - help="Which experiment to run" - ) - parser.add_argument( - "-c", "--config", - required=True, - help="Config name (without .yaml) under configs/" - ) - parser.add_argument( - "-o", "--output-dir", - default=None, - help="Where to write results (csv + plots)" - ) + parser = argparse.ArgumentParser(description="Run one OSDI2025 experiment") + parser.add_argument("-x","--experiment", choices=EXPERIMENTS.keys(), required=True) + parser.add_argument("-c","--config", required=True, + help="Config name (without .yaml) under configs/") + parser.add_argument("-o","--output-dir", default=None, + help="Where to write results (csv + plots)") args = parser.parse_args() - exp = args.experiment - cfg_name = args.config base = Path(__file__).resolve().parent - cfg_path = base / exp / "configs" / f"{cfg_name}.yaml" + cfg_path = base / args.experiment / "configs" / f"{args.config}.yaml" if not cfg_path.is_file(): raise FileNotFoundError(f"Missing config: {cfg_path}") - # compute output directory - out_dir = Path(args.output_dir) if args.output_dir else base / exp / "results" / cfg_name + out_dir = Path(args.output_dir) if args.output_dir \ + else base / args.experiment / "results" / args.config out_dir.mkdir(parents=True, exist_ok=True) - # call the appropriate experiment - runner = EXPERIMENTS[exp] - runner(str(cfg_path), str(out_dir)) + # dispatch + EXPERIMENTS[args.experiment](str(cfg_path), str(out_dir)) if __name__ == "__main__": main() \ No newline at end of file diff --git a/test/experiments/osdi2025/kick_the_tires/run.py b/test/experiments/osdi2025/kick_the_tires/run.py new file mode 100644 index 00000000..7ac0bfc9 --- /dev/null +++ b/test/experiments/osdi2025/kick_the_tires/run.py @@ -0,0 +1,95 @@ +#!/usr/bin/env python3 +""" +""" + +import time +import yaml +import logging +from pathlib import Path + +import torch +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt + +from quake.index_wrappers.quake import QuakeWrapper +from quake.index_wrappers.faiss_ivf import FaissIVF +from quake.datasets.ann_datasets import load_dataset +from quake.utils import compute_recall +from quake.workload_generator import DynamicWorkloadGenerator, WorkloadEvaluator + +logger = logging.getLogger(__name__) + +def run_experiment(cfg_path: str, output_dir: str): + cfg = yaml.safe_load(Path(cfg_path).read_text()) + out = Path(output_dir); out.mkdir(parents=True, exist_ok=True) + + data_dir = cfg["dataset"].get("path", "") + vectors, queries, gt = load_dataset(cfg["dataset"]["name"], data_dir) + + metric = cfg["dataset"]["metric"] + print(f"Dataset loaded: vectors {vectors.shape}, queries {queries.shape}") + + insert_ratio = cfg["workload_generator"]["insert_ratio"] + delete_ratio = cfg["workload_generator"]["delete_ratio"] + query_ratio = cfg["workload_generator"]["query_ratio"] + update_batch_size = cfg["workload_generator"]["update_batch_size"] + query_batch_size = cfg["workload_generator"]["query_batch_size"] + number_of_operations = cfg["workload_generator"]["number_of_operations"] + initial_size = cfg["workload_generator"]["initial_size"] + cluster_size = cfg["workload_generator"]["cluster_size"] + cluster_sample_distribution = cfg["workload_generator"]["cluster_sample_distribution"] + query_cluster_sample_distribution = cfg["workload_generator"]["query_cluster_sample_distribution"] + seed = cfg["workload_generator"]["seed"] + + overwrite_workload = cfg["overwrite"]["workload"] + + print(out) + + generator = DynamicWorkloadGenerator( + workload_dir=out, + base_vectors=vectors, + metric=metric, + insert_ratio=insert_ratio, + delete_ratio=delete_ratio, + query_ratio=query_ratio, + update_batch_size=update_batch_size, + query_batch_size=query_batch_size, + number_of_operations=number_of_operations, + initial_size=initial_size, + cluster_size=cluster_size, + cluster_sample_distribution=cluster_sample_distribution, + queries=queries, + query_cluster_sample_distribution=query_cluster_sample_distribution, + seed=seed, + ) + + if generator.workload_exists() and not overwrite_workload: + print("Workload already exists, skipping generation.") + else: + # Generate the workload (operations are saved to disk along with a runbook) + print("Generating workload...") + generator.generate_workload() + + print("Evaluating workload...") + + for index_cfg in cfg["indexes"]: + name = index_cfg["name"] + print(f"\nEvaluating index: {name}") + curr_results_dir = out / name + curr_results_dir.mkdir(parents=True, exist_ok=True) + evaluator = WorkloadEvaluator(workload_dir=out, output_dir=curr_results_dir) + # Define search parameters for evaluation (e.g., number of neighbors and workers) + build_params = index_cfg["build_params"] + search_params = index_cfg["search_params"] + + if name == "Quake": + # Initialize the Quake index + index = QuakeWrapper() + elif name == "IVF": + index = FaissIVF() + else: + raise ValueError(f"Unknown index type: {name}") + + evaluator.evaluate_workload(name, index, build_params, search_params, do_maintenance=True) + print(f"Evaluation completed for index {name}") \ No newline at end of file From 7d34be046f9f9fb66bb98797fceef52e08415ca4 Mon Sep 17 00:00:00 2001 From: Jason Date: Sun, 4 May 2025 21:26:35 -0500 Subject: [PATCH 019/323] multi-level changes --- src/cpp/bindings/wrap.cpp | 8 + src/cpp/include/common.h | 8 +- src/cpp/include/geometry.h | 8 + src/cpp/include/quake_index.h | 2 + src/cpp/src/partition_manager.cpp | 7 +- src/cpp/src/quake_index.cpp | 36 ++- src/cpp/src/query_coordinator.cpp | 23 +- src/python/index_wrappers/quake.py | 24 +- src/python/workload_generator.py | 7 + test/cpp/benchmark.cpp | 16 +- test/cpp/quake_index.cpp | 33 +- .../osdi2025/kick_the_tires/run.py | 283 +++++++++++++----- 12 files changed, 354 insertions(+), 101 deletions(-) diff --git a/src/cpp/bindings/wrap.cpp b/src/cpp/bindings/wrap.cpp index 1ef561bd..527aebad 100644 --- a/src/cpp/bindings/wrap.cpp +++ b/src/cpp/bindings/wrap.cpp @@ -69,6 +69,10 @@ PYBIND11_MODULE(_bindings, m) { " - num_workers: default = " + std::to_string(DEFAULT_NUM_WORKERS); return doc.c_str(); })()) + .def("add_level", &QuakeIndex::add_level, + "Add a new level to the index.\n\n" + "Args:\n" + " build_params (IndexBuildParams): Parameters for building the new level.") .def("search", &QuakeIndex::search, ([]() -> const char* { static const std::string doc = std::string("Search the index for nearest neighbors.\n\n" @@ -138,6 +142,8 @@ PYBIND11_MODULE(_bindings, m) { (std::string("Distance metric. default = ") + DEFAULT_METRIC).c_str()) .def_readwrite("num_workers", &IndexBuildParams::num_workers, (std::string("Number of workers. default = ") + std::to_string(DEFAULT_NUM_WORKERS)).c_str()) + .def_readwrite("parent_params", &IndexBuildParams::parent_params, + "Parameters for the parent index, if any.") .def("__repr__", [](const IndexBuildParams &p) { std::ostringstream oss; oss << "{"; @@ -170,6 +176,8 @@ PYBIND11_MODULE(_bindings, m) { (std::string("Threshold to trigger recomputation of APS. default = ") + std::to_string(DEFAULT_RECOMPUTE_THRESHOLD)).c_str()) .def_readwrite("aps_flush_period_us", &SearchParams::aps_flush_period_us, (std::string("APS flush period in microseconds. default = ") + std::to_string(DEFAULT_APS_FLUSH_PERIOD_US)).c_str()) + .def_readwrite("parent_params", &SearchParams::parent_params, + "Search parameters for the parent index, if any.") .def("__repr__", [](const SearchParams &s) { std::ostringstream oss; oss << "{"; diff --git a/src/cpp/include/common.h b/src/cpp/include/common.h index eba6dc3a..69d67fc5 100644 --- a/src/cpp/include/common.h +++ b/src/cpp/include/common.h @@ -180,13 +180,7 @@ struct SearchParams { float initial_search_fraction = DEFAULT_INITIAL_SEARCH_FRACTION; int aps_flush_period_us = DEFAULT_APS_FLUSH_PERIOD_US; - // early termination params - float spann_threshold = 1.25f; // prune all centroids with distance > 1.25 * min_distance - - float auncel_a = 0.5f; // auncel's parameter - float auncel_b = 0.5f; // auncel's parameter - - + shared_ptr parent_params = nullptr; ///< Search parameters for the parent index, if any. SearchParams() = default; }; diff --git a/src/cpp/include/geometry.h b/src/cpp/include/geometry.h index 5573082e..465ef1b1 100644 --- a/src/cpp/include/geometry.h +++ b/src/cpp/include/geometry.h @@ -627,6 +627,14 @@ compute_recall_profile(const std::vector& boundary_distances, // Or fallback: goto method1_label; (using labels/goto is generally discouraged) } + // if the cluster_sizes are given, we can use them to scale the probabilities + if (partition_sizes.size() > 0) { + for (int k = 1; k < m; ++k) { + if (partition_sizes[k] > 0) { + P_prime[k] *= static_cast(partition_sizes[k]); + } + } + } // --- Final Normalization --- std::vector probs(m, 0.0f); diff --git a/src/cpp/include/quake_index.h b/src/cpp/include/quake_index.h index c0cccc21..900ecdab 100644 --- a/src/cpp/include/quake_index.h +++ b/src/cpp/include/quake_index.h @@ -49,6 +49,8 @@ class QuakeIndex { */ shared_ptr build(Tensor x, Tensor ids, shared_ptr build_params); + void add_level(shared_ptr params); + /** * @brief Search for vectors in the index. * @param x Tensor of shape [num_queries, dimension]. diff --git a/src/cpp/src/partition_manager.cpp b/src/cpp/src/partition_manager.cpp index 240e2f82..18e09c6c 100644 --- a/src/cpp/src/partition_manager.cpp +++ b/src/cpp/src/partition_manager.cpp @@ -679,7 +679,12 @@ Tensor PartitionManager::get_partition_sizes(Tensor partition_ids) { auto partition_sizes_accessor = partition_sizes.accessor(); for (int i = 0; i < partition_ids.size(0); i++) { int64_t list_no = partition_ids_accessor[i]; - partition_sizes_accessor[i] = partition_store_->list_size(list_no); + if (list_no == -1) { + partition_sizes_accessor[i] = 0; + } else { + partition_sizes_accessor[i] = partition_store_->list_size(list_no); + } + if (debug_) { std::cout << "[PartitionManager] get_partition_sizes: Partition " << list_no << " size: " << partition_sizes_accessor[i] << std::endl; diff --git a/src/cpp/src/quake_index.cpp b/src/cpp/src/quake_index.cpp index 1f7cc853..0aadd869 100644 --- a/src/cpp/src/quake_index.cpp +++ b/src/cpp/src/quake_index.cpp @@ -55,9 +55,14 @@ shared_ptr QuakeIndex::build(Tensor x, Tensor ids, shared_ptr(current_level_ + 1); + auto parent_build_params = make_shared(); - parent_build_params->metric = build_params_->metric; - parent_build_params->num_workers = build_params_->num_workers; + if (build_params->parent_params == nullptr) { + parent_build_params->metric = build_params_->metric; + parent_build_params->num_workers = build_params_->num_workers; + } else { + parent_build_params = build_params_->parent_params; + } parent_->build(clustering->centroids, clustering->partition_ids, parent_build_params); // initialize the partition manager @@ -89,6 +94,25 @@ shared_ptr QuakeIndex::build(Tensor x, Tensor ids, shared_ptr params) { + + if (!parent_) { + throw std::runtime_error("[QuakeIndex::add_level()] No parent index. Cannot add level."); + } + + if (parent_->parent_) { + parent_->add_level(params); + } else { + // create an index over the centroids + + // get all centroids and their ids + Tensor ids = parent_->partition_manager_->get_ids(); + Tensor centroids = parent_->partition_manager_->get(ids); + + parent_->build(centroids, ids, params); + } +} + shared_ptr QuakeIndex::search(Tensor x, shared_ptr search_params) { @@ -159,7 +183,13 @@ shared_ptr QuakeIndex::maintenance() { throw std::runtime_error("[QuakeIndex::maintenance()] No maintenance policy set."); } - return maintenance_policy_->perform_maintenance(); + auto maintenance_info = maintenance_policy_->perform_maintenance(); + + if (parent_->maintenance_policy_) { + parent_->maintenance_policy_->perform_maintenance(); + } + + return maintenance_info; } bool QuakeIndex::validate() { diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 92b0ac84..588644ca 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -530,6 +530,8 @@ shared_ptr QueryCoordinator::serial_scan(Tensor x, Tensor partitio auto t2 = high_resolution_clock::now(); + + Tensor partition_sizes = partition_manager_->get_partition_sizes(partition_ids[q]); vector partition_sizes_vec = vector(partition_sizes.data_ptr(), partition_sizes.data_ptr() + partition_sizes.size(0)); @@ -591,7 +593,7 @@ shared_ptr QueryCoordinator::serial_scan(Tensor x, Tensor partitio partition_probs = compute_recall_profile(boundary_distances, query_radius, dimension, - {}, + partition_sizes_vec, search_params->use_precomputed, metric_ == faiss::METRIC_L2); } @@ -614,8 +616,6 @@ shared_ptr QueryCoordinator::serial_scan(Tensor x, Tensor partitio // mark the partitions as scanned maintenance_policy_->record_query_hits(scanned_ids); - std::cout << "Query " << q << " partitions scanned: " << scanned_ids.size() << std::endl; - std::cout << "Total queries processed: " << maintenance_policy_->hit_count_tracker_->get_num_queries_recorded() << std::endl; // std::cout << "Query " << q << " times: " << duration_cast(t2 - t1).count() << " " // << duration_cast(t3 - t2).count() << " " @@ -667,15 +667,20 @@ shared_ptr QueryCoordinator::search(Tensor x, shared_ptrnlist(), torch::kInt64); } else { - auto parent_search_params = make_shared(); - parent_search_params->recall_target = search_params->recall_target; - parent_search_params->use_precomputed = search_params->use_precomputed; - parent_search_params->recompute_threshold = search_params->recompute_threshold; - parent_search_params->batched_scan = true; + + auto parent_search_params = make_shared(); + if (search_params->parent_params == nullptr) { + parent_search_params->recall_target = .99; + parent_search_params->use_precomputed = search_params->use_precomputed; + parent_search_params->recompute_threshold = search_params->recompute_threshold; + parent_search_params->batched_scan = false; + } else { + parent_search_params = search_params->parent_params; + } // if recall_target is set, we need an initial set of partitions to consider - if (parent_search_params->recall_target > 0.0 && !search_params->batched_scan) { + if (search_params->recall_target > 0.0 && !search_params->batched_scan) { int initial_num_partitions_to_search = std::max( (int) (partition_manager_->nlist() * search_params->initial_search_fraction), 1); parent_search_params->k = initial_num_partitions_to_search; diff --git a/src/python/index_wrappers/quake.py b/src/python/index_wrappers/quake.py index 6c1fffb8..b5b4aa5b 100644 --- a/src/python/index_wrappers/quake.py +++ b/src/python/index_wrappers/quake.py @@ -42,10 +42,21 @@ def index_state(self) -> dict: :return: The state of the index as a dictionary. """ - return { + state = { "n_list": self.index.nlist(), "n_total": self.index.ntotal(), } + # # if has parent get the parent nlist + # if self.index.parent.parent: + # state["n_list1"] = self.index.parent.parent.nlist() + + # get all the parents + curr = self.index.parent + while curr.parent: + state["n_list1"] = curr.nlist() + curr = curr.parent + + return state def build( self, @@ -56,6 +67,7 @@ def build( num_workers: int = 0, m: int = -1, code_size: int = 8, + parent=None, ): """ Build the index with the given vectors and arguments. @@ -79,6 +91,10 @@ def build( build_params.nlist = nc build_params.num_workers = num_workers + if parent is not None: + build_params.parent_params = quake.IndexBuildParams() + build_params.parent_params.nlist = parent.get("nlist", 1) + self.index = QuakeIndex() if ids is None: @@ -125,6 +141,7 @@ def search( recompute_threshold=0.1, aps_flush_period_us=50, n_threads=1, + parent=None, ) -> Tuple[torch.Tensor, torch.Tensor]: """ Find the k-nearest neighbors of the query vectors. @@ -146,6 +163,11 @@ def search( search_params.k = k search_params.num_threads = n_threads + if parent is not None: + search_params.parent_params = quake.SearchParams() + search_params.parent_params.nprobe = parent.get("nprobe", 1) + search_params.parent_params.recall_target = parent.get("recall_target", -1) + return self.index.search(query, search_params) def maintenance(self): diff --git a/src/python/workload_generator.py b/src/python/workload_generator.py index abe8eb0e..302da3c3 100644 --- a/src/python/workload_generator.py +++ b/src/python/workload_generator.py @@ -6,6 +6,7 @@ import matplotlib.pyplot as plt import numpy as np +import pandas as pd import torch from quake import SearchParams @@ -414,6 +415,7 @@ def initialize_index(self, name, index, build_params, m_params): initial_indices = torch.load(self.initial_indices_path, weights_only=True).to(torch.int64) vectors = vectors[initial_indices] if not index_path.exists(): + print(build_params) index.build(vectors, ids=initial_indices, **build_params) index.save(index_path) print(f"Index {name} built and saved to {index_path}") @@ -606,4 +608,9 @@ def evaluate_workload( print(f"Saved evaluation plots to {plot_path}") plt.close() + df = pd.DataFrame(results) + csv_path = self.output_dir / "results.csv" + df.to_csv(csv_path, index=False) + print(f"Saved results to {csv_path}") + return results diff --git a/test/cpp/benchmark.cpp b/test/cpp/benchmark.cpp index e2802a1a..dc6b454f 100644 --- a/test/cpp/benchmark.cpp +++ b/test/cpp/benchmark.cpp @@ -497,20 +497,32 @@ TEST(QuakeIndexStressTest, SearchAddRemoveMaintenanceTest) { int64_t dimension = 128; int64_t num_vectors = 100000; - int64_t num_queries = 1; + int64_t num_queries = 100; int64_t batch_size = 10000; int n_ops = 100; QuakeIndex index; auto build_params = std::make_shared(); - build_params->nlist = 1000; + build_params->nlist = 2500; build_params->metric = "l2"; build_params->niter = 5; + auto maintenance_params = std::make_shared(); + maintenance_params->refinement_radius = 10; + maintenance_params->refinement_iterations = 1; + Tensor data_vectors = torch::randn({num_vectors, dimension}, torch::kFloat32); Tensor data_ids = torch::arange(num_vectors, torch::kInt64); index.build(data_vectors, data_ids, build_params); + index.initialize_maintenance_policy(maintenance_params); + + // add level + auto parent_index_build_params = std::make_shared(); + parent_index_build_params->nlist = 250; + parent_index_build_params->metric = "l2"; + parent_index_build_params->niter = 5; + index.add_level(parent_index_build_params); // timers int64_t search_time = 0; diff --git a/test/cpp/quake_index.cpp b/test/cpp/quake_index.cpp index 22e9325e..1c94f2b5 100644 --- a/test/cpp/quake_index.cpp +++ b/test/cpp/quake_index.cpp @@ -25,7 +25,7 @@ class QuakeIndexTest : public ::testing::Test { // Example parameters int64_t dimension_ = 16; int64_t nlist_ = 8; - int64_t num_vectors_ = 100; + int64_t num_vectors_ = 1000; int64_t num_queries_ = 5; // Data & IDs @@ -479,6 +479,37 @@ TEST(QuakeIndexStressTest, HighDimensionTest) { ASSERT_EQ(result->ids.size(1), search_params->k); } +TEST_F(QuakeIndexTest, AddLevelTest) +{ + QuakeIndex index; + + // 1. Build a 2‑level index (leaf + one parent) + auto bp = std::make_shared(); + bp->nlist = 100; + bp->metric = "l2"; + index.build(data_vectors_, data_ids_, bp); + + const int64_t leaf_partitions = index.nlist(); // = nlist_ + + // 2. Add a *new* level on top + auto top_bp = std::make_shared(); + top_bp->nlist = 10; // flat over centroids + top_bp->metric = "l2"; + index.add_level(top_bp); + + // root now has a parent + ASSERT_NE(index.parent_, nullptr); + // that parent should have exactly `leaf_partitions` vectors (one per centroid) + EXPECT_EQ(index.parent_->ntotal(), leaf_partitions); + + // 3. Ensure we can still search and retrieve sane results + auto sp = std::make_shared(); + sp->k = 3; + auto res = index.search(query_vectors_, sp); + ASSERT_EQ(res->ids.size(0), query_vectors_.size(0)); + ASSERT_EQ(res->ids.size(1), sp->k); +} + // Define the GPU related test only if FAISS GPU support is enabled #ifdef FAISS_ENABLE_GPU // Test build with GPU enabled diff --git a/test/experiments/osdi2025/kick_the_tires/run.py b/test/experiments/osdi2025/kick_the_tires/run.py index 7ac0bfc9..13a08248 100644 --- a/test/experiments/osdi2025/kick_the_tires/run.py +++ b/test/experiments/osdi2025/kick_the_tires/run.py @@ -1,95 +1,224 @@ #!/usr/bin/env python3 """ +Unified experiment runner + +* Reads a single YAML config (`configs/experiment.yaml`). +* Modes + - **build** –‑ only (re‑)generate the workload. + - **run** –‑ generate workload (if needed), evaluate every index that + does not already have a CSV of prior results (unless + `overwrite.results: true`), then emit the unified plot. + - **plot** –‑ skip evaluation; just regenerate the unified plot. +* A results CSV (`//results.csv`) is written after every + evaluation; its presence is what lets us skip reruns. +* Plot styling (colour / marker / linestyle) is entirely configurable from + the YAML under `plot.styles`. """ -import time -import yaml import logging +import sys +import time from pathlib import Path +from typing import Dict, List -import torch -import pandas as pd -import numpy as np import matplotlib.pyplot as plt +from matplotlib.lines import Line2D +import numpy as np +import pandas as pd +import torch +import yaml -from quake.index_wrappers.quake import QuakeWrapper -from quake.index_wrappers.faiss_ivf import FaissIVF +# ── quake imports ────────────────────────────────────────────────────────────── from quake.datasets.ann_datasets import load_dataset +from quake.index_wrappers.faiss_ivf import FaissIVF +from quake.index_wrappers.quake import QuakeWrapper from quake.utils import compute_recall from quake.workload_generator import DynamicWorkloadGenerator, WorkloadEvaluator logger = logging.getLogger(__name__) -def run_experiment(cfg_path: str, output_dir: str): - cfg = yaml.safe_load(Path(cfg_path).read_text()) - out = Path(output_dir); out.mkdir(parents=True, exist_ok=True) - - data_dir = cfg["dataset"].get("path", "") - vectors, queries, gt = load_dataset(cfg["dataset"]["name"], data_dir) - - metric = cfg["dataset"]["metric"] - print(f"Dataset loaded: vectors {vectors.shape}, queries {queries.shape}") - - insert_ratio = cfg["workload_generator"]["insert_ratio"] - delete_ratio = cfg["workload_generator"]["delete_ratio"] - query_ratio = cfg["workload_generator"]["query_ratio"] - update_batch_size = cfg["workload_generator"]["update_batch_size"] - query_batch_size = cfg["workload_generator"]["query_batch_size"] - number_of_operations = cfg["workload_generator"]["number_of_operations"] - initial_size = cfg["workload_generator"]["initial_size"] - cluster_size = cfg["workload_generator"]["cluster_size"] - cluster_sample_distribution = cfg["workload_generator"]["cluster_sample_distribution"] - query_cluster_sample_distribution = cfg["workload_generator"]["query_cluster_sample_distribution"] - seed = cfg["workload_generator"]["seed"] - - overwrite_workload = cfg["overwrite"]["workload"] - - print(out) - - generator = DynamicWorkloadGenerator( - workload_dir=out, - base_vectors=vectors, - metric=metric, - insert_ratio=insert_ratio, - delete_ratio=delete_ratio, - query_ratio=query_ratio, - update_batch_size=update_batch_size, - query_batch_size=query_batch_size, - number_of_operations=number_of_operations, - initial_size=initial_size, - cluster_size=cluster_size, - cluster_sample_distribution=cluster_sample_distribution, - queries=queries, - query_cluster_sample_distribution=query_cluster_sample_distribution, - seed=seed, + +LAT_STYLES = {"insert": "--", "delete": "-.", "query": "-"} + +def unified_plot(cfg: Dict, output_dir: Path) -> None: + """Produce a 4‑panel plot with: + • one figure‑level legend (colour → index) + • latency‑panel legend (linestyle → op‑type)""" + styles = cfg["plot"].get("styles", {}) + + # ── collect colours / markers per index for global legend ──────────────── + index_handles = [] + for idx_cfg in cfg["indexes"]: + name = idx_cfg["name"] + st = styles.get(name, {}) + index_handles.append( + Line2D( + [0], + [0], + color=st.get("color", None), + marker=None, + linestyle="-", + label=name, + ) + ) + + fig, axs = plt.subplots(2, 2, figsize=(12, 10)) + + # ── panel references ───────────────────────────────────────────────────── + ax_lat, ax_part = axs[0] + ax_res, ax_rec = axs[1] + + # ── plot per index ─────────────────────────────────────────────────────── + for idx_cfg in cfg["indexes"]: + name = idx_cfg["name"] + csv = output_dir / name / "results.csv" + if not csv.exists(): + logger.warning("Skipping %s (no CSV).", name); continue + + df = pd.read_csv(csv) + st = styles.get(name, {}) + color, marker = st.get("color", None), st.get("marker", None) + + # A – latency + for op in ("insert", "delete", "query"): + sub = df[df["operation_type"] == op] + if not sub.empty: + ax_lat.plot( + sub["operation_number"], sub["latency_ms"], + color=color, linestyle=LAT_STYLES[op], marker=None) + + ax_lat.set_yscale("log") + + # B – partitions + sub = df[df["n_list"].notna()] + if not sub.empty: + ax_part.plot(sub["operation_number"], sub["n_list"], + color=color, marker=marker) + + # C – resident vectors + sub = df[df["n_resident"].notna()] + if not sub.empty: + ax_res.plot(sub["operation_number"], sub["n_resident"], + color=color, marker=marker) + + # D – query recall + sub = df[(df["operation_type"] == "query") & df["recall"].notna()] + if not sub.empty: + ax_rec.plot(sub["operation_number"], sub["recall"], + color=color, marker=marker) + + # ── format panels (titles, axes) ───────────────────────────────────────── + ax_lat.set_title("Operation Latency") + ax_lat.set_xlabel("Operation Number"); ax_lat.set_ylabel("Latency (ms)") + + ax_part.set_title("Partitions per Operation") + ax_part.set_xlabel("Operation Number"); ax_part.set_ylabel("Number of Partitions") + + ax_res.set_title("Resident Set Size") + ax_res.set_xlabel("Operation Number"); ax_res.set_ylabel("Resident Vectors") + + ax_rec.set_title("Query Recall") + ax_rec.set_xlabel("Operation Number"); ax_rec.set_ylabel("Recall") + + # legends + # latency‑style legend (insert/delete/query) + style_handles = [ + Line2D([0], [0], color="k", linestyle=LAT_STYLES[op], label=op.capitalize()) + for op in ("insert", "delete", "query") + ] + ax_lat.legend(handles=style_handles, title="Operation Type", fontsize=8) + + # global colour legend (index names) + fig.legend( + handles=index_handles, + loc="upper center", + bbox_to_anchor=(0.5, 1.02), + ncol=max(1, len(index_handles)), + title="Index", ) - if generator.workload_exists() and not overwrite_workload: - print("Workload already exists, skipping generation.") - else: - # Generate the workload (operations are saved to disk along with a runbook) - print("Generating workload...") - generator.generate_workload() - - print("Evaluating workload...") - - for index_cfg in cfg["indexes"]: - name = index_cfg["name"] - print(f"\nEvaluating index: {name}") - curr_results_dir = out / name - curr_results_dir.mkdir(parents=True, exist_ok=True) - evaluator = WorkloadEvaluator(workload_dir=out, output_dir=curr_results_dir) - # Define search parameters for evaluation (e.g., number of neighbors and workers) - build_params = index_cfg["build_params"] - search_params = index_cfg["search_params"] - - if name == "Quake": - # Initialize the Quake index - index = QuakeWrapper() - elif name == "IVF": - index = FaissIVF() - else: - raise ValueError(f"Unknown index type: {name}") + plt.tight_layout() + out_path = output_dir / "unified_plot.png" + plt.savefig(out_path, bbox_inches="tight") + plt.close() + print(f"Saved unified plot → {out_path}") + - evaluator.evaluate_workload(name, index, build_params, search_params, do_maintenance=True) - print(f"Evaluation completed for index {name}") \ No newline at end of file +# ─────────────────────────────────────────────────────────────────────────────── +# main execution +# ─────────────────────────────────────────────────────────────────────────────── +def run_experiment(cfg_path: str, output_dir: str) -> None: + cfg = yaml.safe_load(Path(cfg_path).read_text()) + out = Path(output_dir) + out.mkdir(parents=True, exist_ok=True) + + mode = cfg.get("mode", "run") + overwrite = cfg.get("overwrite", {}) + overwrite_workload = overwrite.get("workload", False) + overwrite_results = overwrite.get("results", False) + + # ── build / run both need the dataset loaded ─────────────────────────────── + if mode in {"build", "run"}: + data_cfg = cfg["dataset"] + vectors, queries, _ = load_dataset(data_cfg["name"], data_cfg.get("path", "")) + metric = data_cfg["metric"] + print(f"Dataset loaded: vectors {vectors.shape}, queries {queries.shape}") + + # ── workload generation (build or run) ───────────────────────────────────── + if mode in {"build", "run"}: + wg_cfg = cfg["workload_generator"] + generator = DynamicWorkloadGenerator( + workload_dir=out, + base_vectors=vectors, + metric=metric, + insert_ratio=wg_cfg["insert_ratio"], + delete_ratio=wg_cfg["delete_ratio"], + query_ratio=wg_cfg["query_ratio"], + update_batch_size=wg_cfg["update_batch_size"], + query_batch_size=wg_cfg["query_batch_size"], + number_of_operations=wg_cfg["number_of_operations"], + initial_size=wg_cfg["initial_size"], + cluster_size=wg_cfg["cluster_size"], + cluster_sample_distribution=wg_cfg["cluster_sample_distribution"], + queries=queries, + query_cluster_sample_distribution=wg_cfg["query_cluster_sample_distribution"], + seed=wg_cfg["seed"], + ) + + if generator.workload_exists() and not overwrite_workload: + print("Workload already exists – skipping generation.") + else: + print("Generating workload …") + generator.generate_workload() + + # ── evaluation pass (run mode only) ──────────────────────────────────────── + if mode == "run": + for idx_cfg in cfg["indexes"]: + name = idx_cfg["name"] + index_type = idx_cfg["index"] + res_dir = out / name + csv_path = res_dir / "results.csv" + need_eval = overwrite_results or not csv_path.exists() + + if need_eval: + print(f"\nIndex {name}: running evaluation.") + res_dir.mkdir(parents=True, exist_ok=True) + evaluator = WorkloadEvaluator(workload_dir=out, output_dir=res_dir) + + index = {"Quake": QuakeWrapper, "IVF": FaissIVF}.get(index_type) + if index is None: + raise ValueError(f"Unknown index type: {index_type}") + + results = evaluator.evaluate_workload( + name=name, + index=index(), + build_params=idx_cfg["build_params"], + search_params=idx_cfg["search_params"], + do_maintenance=True, + ) + else: + print(f"\nIndex {name}: using cached results ({csv_path}).") + + # ── generate / regenerate the unified plot ───────────────────────────────── + if mode in {"run", "plot"}: + unified_plot(cfg, out) \ No newline at end of file From 2558fa4e612739fa25dc370e56afb946950ec02a Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 6 May 2025 09:23:30 -0500 Subject: [PATCH 020/323] multi-level fixes --- src/cpp/include/common.h | 1 + src/cpp/include/partition_manager.h | 1 + src/cpp/src/clustering.cpp | 1 + src/cpp/src/dynamic_inverted_list.cpp | 28 +++-- src/cpp/src/index_partition.cpp | 7 +- src/cpp/src/maintenance_policies.cpp | 4 + src/cpp/src/partition_manager.cpp | 17 ++- src/cpp/src/quake_index.cpp | 6 +- src/cpp/src/query_coordinator.cpp | 45 ++++++-- src/python/index_wrappers/quake.py | 3 +- src/python/workload_generator.py | 4 +- test/cpp/benchmark.cpp | 156 ++++++++++++++------------ test/cpp/quake_multi_level.cpp | 103 +++++++++++++++++ 13 files changed, 271 insertions(+), 105 deletions(-) create mode 100644 test/cpp/quake_multi_level.cpp diff --git a/src/cpp/include/common.h b/src/cpp/include/common.h index 69d67fc5..be85509e 100644 --- a/src/cpp/include/common.h +++ b/src/cpp/include/common.h @@ -179,6 +179,7 @@ struct SearchParams { float recompute_threshold = DEFAULT_RECOMPUTE_THRESHOLD; float initial_search_fraction = DEFAULT_INITIAL_SEARCH_FRACTION; int aps_flush_period_us = DEFAULT_APS_FLUSH_PERIOD_US; + bool track_hits = true; shared_ptr parent_params = nullptr; ///< Search parameters for the parent index, if any. diff --git a/src/cpp/include/partition_manager.h b/src/cpp/include/partition_manager.h index 9a5018a2..b0ade908 100644 --- a/src/cpp/include/partition_manager.h +++ b/src/cpp/include/partition_manager.h @@ -26,6 +26,7 @@ class PartitionManager { shared_ptr parent_ = nullptr; ///< Pointer to a higher-level parent index. std::shared_ptr partition_store_ = nullptr; ///< Pointer to the inverted lists. int64_t curr_partition_id_ = 0; ///< Current partition ID. + int num_workers_ = 0; ///< Number of workers for parallel processing. bool debug_ = false; ///< If true, print debug information. bool check_uniques_ = false; ///< If true, check that vector IDs are unique and don't already exist in the index. diff --git a/src/cpp/src/clustering.cpp b/src/cpp/src/clustering.cpp index ebbc88e0..a034b32e 100644 --- a/src/cpp/src/clustering.cpp +++ b/src/cpp/src/clustering.cpp @@ -133,6 +133,7 @@ tuple >> kmeans_refine_partitions( new_partitions[i] = make_shared(); new_partitions[i]->set_code_size(partitions[0]->code_size_); new_partitions[i]->resize(10); + new_partitions[i]->set_core_id(partitions[i]->core_id_); } float *centroids_ptr = centroids.data_ptr(); diff --git a/src/cpp/src/dynamic_inverted_list.cpp b/src/cpp/src/dynamic_inverted_list.cpp index d51d14b6..b4573402 100644 --- a/src/cpp/src/dynamic_inverted_list.cpp +++ b/src/cpp/src/dynamic_inverted_list.cpp @@ -311,30 +311,42 @@ void DynamicInvertedLists::batch_update_entries( bool DynamicInvertedLists::get_vector_for_id(idx_t id, float *out) { if (id_to_location_.empty()) build_map(); + if (id < 0) { + std::memset(out, 0, code_size_); + return true; + } + auto it = id_to_location_.find(id); if (it == id_to_location_.end()) return false; + IndexPartition* part = it->second.first; int64_t pos = it->second.second; std::memcpy(out, part->codes_ + pos * part->code_size_, part->code_size_); return true; } - vector DynamicInvertedLists::get_vectors_by_id(vector ids) { + vector DynamicInvertedLists::get_vectors_by_id(vector ids) + { if (id_to_location_.empty()) build_map(); - vector ret; - ret.reserve(ids.size()); + vector ret; ret.reserve(ids.size()); for (int64_t id : ids) { - auto it = id_to_location_.find(id); - if (it == id_to_location_.end()) { - throw std::runtime_error("ID not found in any partition"); + if (id < 0) { + ret.push_back(nullptr); + continue; } + + auto it = id_to_location_.find(id); + if (it == id_to_location_.end()) + throw std::runtime_error("ID not found in any partition: " + std::to_string(id)); + IndexPartition* part = it->second.first; - int64_t pos = it->second.second; + int64_t pos = it->second.second; ret.push_back(reinterpret_cast(part->codes_ + pos * part->code_size_)); } return ret; + } // vector ret; // for (int64_t id : ids) { @@ -353,7 +365,7 @@ void DynamicInvertedLists::batch_update_entries( // } // } // return ret; - } + // } size_t DynamicInvertedLists::get_new_list_id() { return curr_list_id_++; diff --git a/src/cpp/src/index_partition.cpp b/src/cpp/src/index_partition.cpp index 87e4992d..49d5eb3b 100644 --- a/src/cpp/src/index_partition.cpp +++ b/src/cpp/src/index_partition.cpp @@ -78,11 +78,10 @@ void IndexPartition::update(int64_t offset, int64_t n_entry, const idx_t* new_id int64_t IndexPartition::remove(int64_t idx) { - assert(idx >= 0 && idx < num_vectors_); + if (idx < 0 || idx >= num_vectors_) { + throw std::runtime_error("Index out of range in remove"); + } const int64_t last = num_vectors_ - 1; - - // optional: bookkeeping for norms, etc. - if (idx != last) { // swap last -> idx std::memcpy(codes_ + idx * code_size_, codes_ + last * code_size_, diff --git a/src/cpp/src/maintenance_policies.cpp b/src/cpp/src/maintenance_policies.cpp index 21ced2bb..6ad450c6 100644 --- a/src/cpp/src/maintenance_policies.cpp +++ b/src/cpp/src/maintenance_policies.cpp @@ -33,6 +33,10 @@ MaintenancePolicy::MaintenancePolicy( shared_ptr MaintenancePolicy::perform_maintenance() { // only consider split/deletion once the window is full + if (partition_manager_->parent_ == nullptr) { + return std::make_shared(); + } + int64_t num_queries = hit_count_tracker_->get_num_queries_recorded(); if (hit_count_tracker_->get_num_queries_recorded() < params_->window_size) { std::cout << "Window not full yet. " << num_queries << " queries recorded and " << params_->window_size diff --git a/src/cpp/src/partition_manager.cpp b/src/cpp/src/partition_manager.cpp index 18e09c6c..780b1342 100644 --- a/src/cpp/src/partition_manager.cpp +++ b/src/cpp/src/partition_manager.cpp @@ -484,6 +484,8 @@ void PartitionManager::refine_partitions(Tensor partition_ids, int iterations) { partition_store_->partitions_[pids[i]] = index_partitions[i]; } + partition_store_->build_map(); + if (debug_) { std::cout << "[PartitionManager] refine_partitions: Completed refinement." << std::endl; } @@ -505,6 +507,10 @@ void PartitionManager::add_partitions(shared_ptr partitions) { for (int64_t i = 0; i < nlist; i++) { int64_t list_no = p_ids_accessor[i]; partition_store_->add_list(list_no); + if (num_workers_ > 0) { + set_partition_core_id(list_no, list_no % num_workers_); + } + partition_store_->add_entries( list_no, partitions->vectors[i].size(0), @@ -562,7 +568,9 @@ void PartitionManager::distribute_partitions(int num_workers) { << num_workers << " workers." << std::endl; } - if (parent_ == nullptr) { + num_workers_ = num_workers; + + if (parent_ == nullptr && partition_store_->nlist == 1) { auto codes = (float *) partition_store_->get_codes(0); auto ids = (int64_t *) partition_store_->get_ids(0); int64_t ntotal = partition_store_->list_size(0); @@ -599,13 +607,14 @@ void PartitionManager::distribute_partitions(int num_workers) { } Tensor partition_ids = get_partition_ids(); - for (int i = 0; i < partition_store_->nlist; i++) { - set_partition_core_id(partition_ids[i].item(), i % num_workers); + auto partition_ids_accessor = partition_ids.accessor(); + for (int i = 0; i < partition_ids.size(0); i++) { + set_partition_core_id(partition_ids_accessor[i], i % num_workers); } } void PartitionManager::set_partition_core_id(int64_t partition_id, int core_id) { - partition_store_->partitions_[partition_id]->core_id_ = core_id; + partition_store_->partitions_[partition_id]->set_core_id(core_id); } int PartitionManager::get_partition_core_id(int64_t partition_id) { diff --git a/src/cpp/src/quake_index.cpp b/src/cpp/src/quake_index.cpp index 0aadd869..a65db04f 100644 --- a/src/cpp/src/quake_index.cpp +++ b/src/cpp/src/quake_index.cpp @@ -176,6 +176,10 @@ void QuakeIndex::initialize_maintenance_policy(shared_ptrmaintenance_policy_ = maintenance_policy_; } + + if (parent_ != nullptr) { + parent_->initialize_maintenance_policy(maintenance_policy_params_); + } } shared_ptr QuakeIndex::maintenance() { @@ -185,7 +189,7 @@ shared_ptr QuakeIndex::maintenance() { auto maintenance_info = maintenance_policy_->perform_maintenance(); - if (parent_->maintenance_policy_) { + if (parent_ && parent_->maintenance_policy_) { parent_->maintenance_policy_->perform_maintenance(); } diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 588644ca..6ac1ede6 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -135,7 +135,6 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { const float *partition_codes = (float *) partition_manager_->partition_store_->get_codes(job.partition_id); const int64_t *partition_ids = (int64_t *) partition_manager_->partition_store_->get_ids(job.partition_id); int64_t partition_size = partition_manager_->partition_store_->list_size(job.partition_id); - // Branch for non-batched jobs. if (!job.is_batched) { @@ -260,7 +259,7 @@ shared_ptr QueryCoordinator::worker_scan( int64_t dimension = x.size(1); int k = search_params->k; int64_t nlist = partition_manager_->nlist(); - bool use_aps = (search_params->recall_target > 0.0 && !search_params->batched_scan); + bool use_aps = (search_params->recall_target > 0.0 && !search_params->batched_scan && parent_ != nullptr); auto timing_info = make_shared(); timing_info->n_queries = num_queries; timing_info->n_clusters = nlist; @@ -274,6 +273,8 @@ shared_ptr QueryCoordinator::worker_scan( partition_ids = partition_ids.unsqueeze(0).expand({num_queries, partition_ids.size(0)}); } + auto partition_ids_accessor = partition_ids.accessor(); + job_flags_.clear(); job_flags_.resize(num_queries); for (int64_t q = 0; q < num_queries; q++) { @@ -303,8 +304,12 @@ shared_ptr QueryCoordinator::worker_scan( global_topk_buffer_pool_[q]->set_processing_query(true); } } - for (int64_t q = 0; q < num_queries; q++) { - global_topk_buffer_pool_[q]->set_jobs_left(partition_ids.size(1)); + for (int64_t q = 0; q < num_queries; ++q) { + int valid = 0; + for (int64_t p = 0; p < partition_ids.size(1); ++p) + if (partition_ids_accessor[q][p] != -1) ++valid; + + global_topk_buffer_pool_[q]->set_jobs_left(valid); } } auto end_time = high_resolution_clock::now(); @@ -363,7 +368,7 @@ shared_ptr QueryCoordinator::worker_scan( auto last_flush_time = high_resolution_clock::now(); vector> boundary_distances(num_queries); - if (use_aps) { + if (use_aps && parent_) { for (int64_t q = 0; q < num_queries; q++) { vector partition_ids_to_scan_vec = vector(partition_ids[q].data_ptr(), partition_ids[q].data_ptr() + partition_ids[q].size(0)); @@ -379,7 +384,6 @@ shared_ptr QueryCoordinator::worker_scan( vector query_radius(num_queries, 0.0f); vector> probs(num_queries); - while (true) { // check if all jobs have been processed bool all_done = true; @@ -455,7 +459,17 @@ shared_ptr QueryCoordinator::worker_scan( : std::numeric_limits::infinity(); } } - timing_info->partitions_scanned = global_topk_buffer_pool_[q]->get_num_partitions_scanned(); + timing_info->partitions_scanned += global_topk_buffer_pool_[q]->get_num_partitions_scanned(); + + if (search_params->track_hits && maintenance_policy_ != nullptr) { + vector partition_ids_vec; + for (int64_t p = 0; p < partition_ids.size(1); p++) { + if (job_flags_[q][p]) { + partition_ids_vec.push_back(partition_ids_accessor[q][p]); + } + } + maintenance_policy_->record_query_hits(partition_ids_vec); + } } } end_time = high_resolution_clock::now(); @@ -539,9 +553,14 @@ shared_ptr QueryCoordinator::serial_scan(Tensor x, Tensor partitio if (use_aps) { vector partition_ids_to_scan_vec = std::vector(partition_ids[q].data_ptr(), partition_ids[q].data_ptr() + partition_ids[q].size(0)); + vector cluster_centroids = parent_->partition_manager_->get_vectors(partition_ids_to_scan_vec); t3 = high_resolution_clock::now(); + // trim nullptrs + cluster_centroids.erase(std::remove(cluster_centroids.begin(), cluster_centroids.end(), nullptr), + cluster_centroids.end()); + boundary_distances = compute_boundary_distances(x[q], cluster_centroids, @@ -609,14 +628,17 @@ shared_ptr QueryCoordinator::serial_scan(Tensor x, Tensor partitio } } } + + + if (search_params->track_hits && maintenance_policy_) { + maintenance_policy_->record_query_hits(std::vector(scanned_ids.begin(), scanned_ids.end())); + } + // Retrieve the top-k results for query q. all_topk_dists[q] = topk_buf->get_topk(); all_topk_ids[q] = topk_buf->get_topk_indices(); auto t5 = high_resolution_clock::now(); - // mark the partitions as scanned - maintenance_policy_->record_query_hits(scanned_ids); - // std::cout << "Query " << q << " times: " << duration_cast(t2 - t1).count() << " " // << duration_cast(t3 - t2).count() << " " // << duration_cast(t4 - t3).count() << " " @@ -667,13 +689,12 @@ shared_ptr QueryCoordinator::search(Tensor x, shared_ptrnlist(), torch::kInt64); } else { - - auto parent_search_params = make_shared(); if (search_params->parent_params == nullptr) { parent_search_params->recall_target = .99; parent_search_params->use_precomputed = search_params->use_precomputed; parent_search_params->recompute_threshold = search_params->recompute_threshold; + parent_search_params->initial_search_fraction = .5; parent_search_params->batched_scan = false; } else { parent_search_params = search_params->parent_params; diff --git a/src/python/index_wrappers/quake.py b/src/python/index_wrappers/quake.py index b5b4aa5b..ec121fdc 100644 --- a/src/python/index_wrappers/quake.py +++ b/src/python/index_wrappers/quake.py @@ -93,7 +93,7 @@ def build( if parent is not None: build_params.parent_params = quake.IndexBuildParams() - build_params.parent_params.nlist = parent.get("nlist", 1) + build_params.parent_params.nlist = parent.get("nc", 1) self.index = QuakeIndex() @@ -167,6 +167,7 @@ def search( search_params.parent_params = quake.SearchParams() search_params.parent_params.nprobe = parent.get("nprobe", 1) search_params.parent_params.recall_target = parent.get("recall_target", -1) + search_params.parent_params.initial_search_fraction = parent.get("initial_search_fraction", 0.05) return self.index.search(query, search_params) diff --git a/src/python/workload_generator.py b/src/python/workload_generator.py index 302da3c3..b98d892a 100644 --- a/src/python/workload_generator.py +++ b/src/python/workload_generator.py @@ -420,8 +420,8 @@ def initialize_index(self, name, index, build_params, m_params): index.save(index_path) print(f"Index {name} built and saved to {index_path}") else: - if build_params.get("num_workers"): - index.load(index_path, num_workers=build_params["num_workers"]) + if build_params.get("n_workers"): + index.load(index_path, n_workers=build_params["n_workers"]) else: index.load(index_path) print(f"Index {name} loaded from {index_path}") diff --git a/test/cpp/benchmark.cpp b/test/cpp/benchmark.cpp index dc6b454f..3c2daafd 100644 --- a/test/cpp/benchmark.cpp +++ b/test/cpp/benchmark.cpp @@ -496,33 +496,37 @@ TEST(QuakeIndexStressTest, SearchAddRemoveMaintenanceTest) { // Repeatedly search, add, remove, and perform maintenance to see if the index remains consistent. int64_t dimension = 128; - int64_t num_vectors = 100000; - int64_t num_queries = 100; - int64_t batch_size = 10000; + int64_t num_vectors = 10000; + int64_t num_queries = 10; + int64_t batch_size = 1000; int n_ops = 100; QuakeIndex index; auto build_params = std::make_shared(); - build_params->nlist = 2500; + build_params->nlist = 1000; build_params->metric = "l2"; build_params->niter = 5; + build_params->num_workers = 2; auto maintenance_params = std::make_shared(); maintenance_params->refinement_radius = 10; - maintenance_params->refinement_iterations = 1; + maintenance_params->refinement_iterations = 0; + maintenance_params->window_size = 100; Tensor data_vectors = torch::randn({num_vectors, dimension}, torch::kFloat32); Tensor data_ids = torch::arange(num_vectors, torch::kInt64); - index.build(data_vectors, data_ids, build_params); - index.initialize_maintenance_policy(maintenance_params); // add level auto parent_index_build_params = std::make_shared(); - parent_index_build_params->nlist = 250; + parent_index_build_params->nlist = 50; parent_index_build_params->metric = "l2"; parent_index_build_params->niter = 5; - index.add_level(parent_index_build_params); + parent_index_build_params->num_workers = 1; + build_params->parent_params = parent_index_build_params; + index.build(data_vectors, data_ids, build_params); + // + index.initialize_maintenance_policy(maintenance_params); // timers int64_t search_time = 0; @@ -534,6 +538,7 @@ TEST(QuakeIndexStressTest, SearchAddRemoveMaintenanceTest) { auto end = std::chrono::high_resolution_clock::now(); for (int i = 0; i < n_ops; i++) { + std::cout << "[SearchAddRemoveMaintenanceTest] Iteration " << i << "\n"; // Search auto query_vectors = torch::randn({num_queries, dimension}, torch::kFloat32); auto search_params = std::make_shared(); @@ -542,6 +547,11 @@ TEST(QuakeIndexStressTest, SearchAddRemoveMaintenanceTest) { search_params->batched_scan = false; search_params->num_threads = 1; + auto parent_search_params = std::make_shared(); + parent_search_params->nprobe = 50; + parent_search_params->batched_scan = false; + search_params->parent_params = parent_search_params; + start = std::chrono::high_resolution_clock::now(); auto search_result = index.search(query_vectors, search_params); end = std::chrono::high_resolution_clock::now(); @@ -567,10 +577,10 @@ TEST(QuakeIndexStressTest, SearchAddRemoveMaintenanceTest) { // remove_time += std::chrono::duration_cast(end - start).count(); // ASSERT_EQ(remove_info->n_vectors, batch_size / 2); - start = std::chrono::high_resolution_clock::now(); - index.maintenance(); - end = std::chrono::high_resolution_clock::now(); - maintenance_time += std::chrono::duration_cast(end - start).count(); + // start = std::chrono::high_resolution_clock::now(); + // index.maintenance(); + // end = std::chrono::high_resolution_clock::now(); + // maintenance_time += std::chrono::duration_cast(end - start).count(); } // print out mean times per operation @@ -587,64 +597,64 @@ TEST(QuakeIndexStressTest, SearchAddRemoveMaintenanceTest) { SUCCEED(); } -TEST(FaissIndexStressTest, SearchAddRemoveMaintenanceTest) { - // Repeatedly search, add, remove, and perform maintenance to see if the index remains consistent. - - int64_t dimension = 128; - int64_t num_vectors = 100000; - int64_t num_queries = 1; - int64_t batch_size = 10000; - int n_ops = 100; - - Tensor data_vectors = torch::randn({num_vectors, dimension}, torch::kFloat32); - Tensor data_ids = torch::arange(num_vectors, torch::kInt64); - - auto quantizer = new faiss::IndexFlatL2(dimension); - auto index = new faiss::IndexIVFFlat(quantizer, dimension, 1000, faiss::METRIC_L2); - index->train(num_vectors, data_vectors.data_ptr()); - index->add(num_vectors, data_vectors.data_ptr()); - - // timers - int64_t search_time = 0; - int64_t add_time = 0; - int64_t remove_time = 0; - int64_t maintenance_time = 0; - - auto start = std::chrono::high_resolution_clock::now(); - auto end = std::chrono::high_resolution_clock::now(); - - for (int i = 0; i < n_ops; i++) { - // Search - auto query_vectors = generate_data(num_queries, dimension); - std::vector distances(num_queries * K); - std::vector labels(num_queries * K); - - start = std::chrono::high_resolution_clock::now(); - index->nprobe = 12; - index->search(num_queries, query_vectors.data_ptr(), 12, distances.data(), labels.data()); - end = std::chrono::high_resolution_clock::now(); - search_time += std::chrono::duration_cast(end - start).count(); - - // Add - auto add_vectors = generate_data(batch_size, dimension); - auto add_ids = generate_ids(batch_size, num_vectors); - start = std::chrono::high_resolution_clock::now(); - index->add(batch_size, add_vectors.data_ptr()); - end = std::chrono::high_resolution_clock::now(); - add_time += std::chrono::duration_cast(end - start).count(); - num_vectors += batch_size; - } - // print out mean times per operation - float mean_search_time = static_cast(search_time) / n_ops; - float mean_add_time = static_cast(add_time) / n_ops; - float mean_remove_time = static_cast(remove_time) / n_ops; - float mean_maintenance_time = static_cast(maintenance_time) / n_ops; - - std::cout << "[SearchAddRemoveMaintenanceTest] Mean search time: " << mean_search_time << " μs\n"; - std::cout << "[SearchAddRemoveMaintenanceTest] Mean add time: " << mean_add_time << " μs\n"; - std::cout << "[SearchAddRemoveMaintenanceTest] Mean remove time: " << mean_remove_time << " μs\n"; - std::cout << "[SearchAddRemoveMaintenanceTest] Mean maintenance time: " << mean_maintenance_time << " μs\n"; - - SUCCEED(); -} +// TEST(FaissIndexStressTest, SearchAddRemoveMaintenanceTest) { +// // Repeatedly search, add, remove, and perform maintenance to see if the index remains consistent. +// +// int64_t dimension = 128; +// int64_t num_vectors = 100000; +// int64_t num_queries = 1; +// int64_t batch_size = 10000; +// int n_ops = 100; +// +// Tensor data_vectors = torch::randn({num_vectors, dimension}, torch::kFloat32); +// Tensor data_ids = torch::arange(num_vectors, torch::kInt64); +// +// auto quantizer = new faiss::IndexFlatL2(dimension); +// auto index = new faiss::IndexIVFFlat(quantizer, dimension, 1000, faiss::METRIC_L2); +// index->train(num_vectors, data_vectors.data_ptr()); +// index->add(num_vectors, data_vectors.data_ptr()); +// +// // timers +// int64_t search_time = 0; +// int64_t add_time = 0; +// int64_t remove_time = 0; +// int64_t maintenance_time = 0; +// +// auto start = std::chrono::high_resolution_clock::now(); +// auto end = std::chrono::high_resolution_clock::now(); +// +// for (int i = 0; i < n_ops; i++) { +// // Search +// auto query_vectors = generate_data(num_queries, dimension); +// std::vector distances(num_queries * K); +// std::vector labels(num_queries * K); +// +// start = std::chrono::high_resolution_clock::now(); +// index->nprobe = 12; +// index->search(num_queries, query_vectors.data_ptr(), 12, distances.data(), labels.data()); +// end = std::chrono::high_resolution_clock::now(); +// search_time += std::chrono::duration_cast(end - start).count(); +// +// // Add +// auto add_vectors = generate_data(batch_size, dimension); +// auto add_ids = generate_ids(batch_size, num_vectors); +// start = std::chrono::high_resolution_clock::now(); +// index->add(batch_size, add_vectors.data_ptr()); +// end = std::chrono::high_resolution_clock::now(); +// add_time += std::chrono::duration_cast(end - start).count(); +// num_vectors += batch_size; +// } +// // print out mean times per operation +// float mean_search_time = static_cast(search_time) / n_ops; +// float mean_add_time = static_cast(add_time) / n_ops; +// float mean_remove_time = static_cast(remove_time) / n_ops; +// float mean_maintenance_time = static_cast(maintenance_time) / n_ops; +// +// std::cout << "[SearchAddRemoveMaintenanceTest] Mean search time: " << mean_search_time << " μs\n"; +// std::cout << "[SearchAddRemoveMaintenanceTest] Mean add time: " << mean_add_time << " μs\n"; +// std::cout << "[SearchAddRemoveMaintenanceTest] Mean remove time: " << mean_remove_time << " μs\n"; +// std::cout << "[SearchAddRemoveMaintenanceTest] Mean maintenance time: " << mean_maintenance_time << " μs\n"; +// +// SUCCEED(); +// } diff --git a/test/cpp/quake_multi_level.cpp b/test/cpp/quake_multi_level.cpp new file mode 100644 index 00000000..9b5c787a --- /dev/null +++ b/test/cpp/quake_multi_level.cpp @@ -0,0 +1,103 @@ + +#include +#include "quake_index.h" + +using torch::Tensor; + +//--------------------------------------------------------------- +// Helpers +//--------------------------------------------------------------- +static Tensor rand_vectors(int64_t n, int64_t d, uint64_t seed) +{ + torch::manual_seed(seed); + return torch::randn({n, d}, torch::kFloat32); +} +static Tensor seq_ids(int64_t n, int64_t start = 0) +{ + return torch::arange(start, start + n, torch::kInt64); +} + +//--------------------------------------------------------------- +// Constants for all tests +//--------------------------------------------------------------- +constexpr int64_t DIM = 32; +constexpr int64_t NVEC = 20'000; +constexpr int64_t LEAF = 64; +constexpr int64_t MID = 16; +constexpr int64_t ROOT = 4; + +//--------------------------------------------------------------- +// 1. Build a 3‑level index, search, maintain, search again. +//--------------------------------------------------------------- +TEST(QuakeMultiLevel, EndToEndMaintenance) +{ + Tensor data = rand_vectors(NVEC, DIM, 0); + Tensor ids = seq_ids(NVEC); + + QuakeIndex ix; + auto build = std::make_shared(); + build->nlist = LEAF; + build->metric = "l2"; + ix.build(data, ids, build); + + // add 2nd level + auto mid = std::make_shared(); + mid->nlist = MID; + mid->metric = "l2"; + ix.add_level(mid); + + // add 3rd (root) level + auto root = std::make_shared(); + root->nlist = ROOT; + root->metric = "l2"; + ix.add_level(root); + + // quick sanity on hierarchy depth + ASSERT_NE(ix.parent_, nullptr); + ASSERT_NE(ix.parent_->parent_, nullptr); + + auto sp = std::make_shared(); + sp->k = 10; + + // baseline search + auto baseline = ix.search(rand_vectors(50, DIM, 1), sp); + ASSERT_EQ(baseline->ids.size(1), sp->k); + + // hammer with 2000 queries to populate hit tracker then run maintenance + ix.search(rand_vectors(2000, DIM, 2), sp); + auto mi = ix.maintenance(); + EXPECT_GE(mi->total_time_us, 0); + EXPECT_EQ(ix.ntotal(), NVEC); + + // final search should still work + auto after = ix.search(rand_vectors(50, DIM, 3), sp); + EXPECT_EQ(after->ids.size(1), sp->k); +} + +//--------------------------------------------------------------- +// 2. Calling add_level() on a 1‑level index must throw. +//--------------------------------------------------------------- +TEST(QuakeMultiLevel, AddLevelWithoutParentThrows) +{ + Tensor data = rand_vectors(1000, DIM, 4); + Tensor ids = seq_ids(1000); + + QuakeIndex flat; + flat.build(data, ids, std::make_shared()); // nlist=0 ⇒ flat + + EXPECT_THROW(flat.add_level(std::make_shared()), std::runtime_error); +} + +//--------------------------------------------------------------- +// 3. Root maintenance should be a safe no‑op (flat index). +//--------------------------------------------------------------- +TEST(QuakeMultiLevel, RootMaintenanceSafe) +{ + Tensor data = rand_vectors(800, DIM, 5); + Tensor ids = seq_ids(800); + + QuakeIndex flat; + flat.build(data, ids, std::make_shared()); // nlist=0 + + EXPECT_NO_THROW(flat.maintenance()); +} \ No newline at end of file From c13f6fe33ef79f737d5d48d3bc40b5a52193a4fc Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 6 May 2025 11:38:24 -0500 Subject: [PATCH 021/323] add sift1m config --- src/cpp/src/partition_manager.cpp | 5 ++ src/cpp/src/query_coordinator.cpp | 9 ++- src/python/index_wrappers/quake.py | 6 +- src/python/workload_generator.py | 2 +- test/cpp/benchmark.cpp | 29 +++++--- .../kick_the_tires/configs/sift1m.yaml | 73 +++++++++++++++++++ 6 files changed, 108 insertions(+), 16 deletions(-) create mode 100644 test/experiments/osdi2025/kick_the_tires/configs/sift1m.yaml diff --git a/src/cpp/src/partition_manager.cpp b/src/cpp/src/partition_manager.cpp index 780b1342..0ca2f195 100644 --- a/src/cpp/src/partition_manager.cpp +++ b/src/cpp/src/partition_manager.cpp @@ -246,6 +246,11 @@ shared_ptr PartitionManager::add( for (int64_t i = 0; i < n; i++) { int64_t pid = partition_ids_for_each[i]; + + if (pid < 0 || pid >= curr_partition_id_) { + throw runtime_error("[PartitionManager] add: Invalid partition ID."); + } + if (debug_) { std::cout << "[PartitionManager] add: Inserting vector " << i << " with id " << id_accessor[i] << " into partition " << pid << std::endl; diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 6ac1ede6..364e75ab 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -119,6 +119,7 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { // Shutdown signal: -1 indicates the worker should exit. if (job.partition_id == -1) { + std::cout << "[QueryCoordinator::partition_scan_worker_fn] Worker " << core_index << " exiting." << std::endl; break; } @@ -337,6 +338,9 @@ shared_ptr QueryCoordinator::worker_scan( job.num_queries = kv.second.size(); job.query_ids = kv.second; int core_id = partition_manager_->get_partition_core_id(kv.first); + if (core_id < 0) { + throw std::runtime_error("[QueryCoordinator::worker_scan] Invalid core ID."); + } core_resources_[core_id].job_queue.enqueue(job); } } else { @@ -359,6 +363,9 @@ shared_ptr QueryCoordinator::worker_scan( job.rank = p; int core_id = partition_manager_->get_partition_core_id(pid); + if (core_id < 0) { + throw std::runtime_error("[QueryCoordinator::worker_scan] Invalid core ID."); + } core_resources_[core_id].job_queue.enqueue(job); } }, search_params->num_threads); @@ -687,7 +694,7 @@ shared_ptr QueryCoordinator::search(Tensor x, shared_ptrnlist(), torch::kInt64); + partition_ids_to_scan = partition_manager_->get_partition_ids(); } else { auto parent_search_params = make_shared(); if (search_params->parent_params == nullptr) { diff --git a/src/python/index_wrappers/quake.py b/src/python/index_wrappers/quake.py index ec121fdc..a75f2f86 100644 --- a/src/python/index_wrappers/quake.py +++ b/src/python/index_wrappers/quake.py @@ -189,7 +189,7 @@ def save(self, filename: str): def load( self, filename: str, - n_workers: int = 0, + num_workers: int = 0, use_numa: bool = False, verbose: bool = False, verify_numa: bool = False, @@ -203,11 +203,11 @@ def load( :param filename: The name of the file to load the index from. """ print( - f"Loading index from {filename}, with {n_workers} workers, use_numa={use_numa}, verbose={verbose}, " + f"Loading index from {filename}, with {num_workers} workers, use_numa={use_numa}, verbose={verbose}, " f"verify_numa={verify_numa}, same_core={same_core}, use_centroid_workers={use_centroid_workers}" ) self.index = QuakeIndex() - self.index.load(str(filename), n_workers) + self.index.load(str(filename), num_workers) def centroids(self) -> torch.Tensor: """ diff --git a/src/python/workload_generator.py b/src/python/workload_generator.py index b98d892a..faa7c1d2 100644 --- a/src/python/workload_generator.py +++ b/src/python/workload_generator.py @@ -421,7 +421,7 @@ def initialize_index(self, name, index, build_params, m_params): print(f"Index {name} built and saved to {index_path}") else: if build_params.get("n_workers"): - index.load(index_path, n_workers=build_params["n_workers"]) + index.load(index_path, num_workers=build_params["num_workers"]) else: index.load(index_path) print(f"Index {name} loaded from {index_path}") diff --git a/test/cpp/benchmark.cpp b/test/cpp/benchmark.cpp index 3c2daafd..93574b9a 100644 --- a/test/cpp/benchmark.cpp +++ b/test/cpp/benchmark.cpp @@ -497,21 +497,21 @@ TEST(QuakeIndexStressTest, SearchAddRemoveMaintenanceTest) { int64_t dimension = 128; int64_t num_vectors = 10000; - int64_t num_queries = 10; - int64_t batch_size = 1000; + int64_t num_queries = 1; + int64_t batch_size = 10000; int n_ops = 100; QuakeIndex index; auto build_params = std::make_shared(); - build_params->nlist = 1000; + build_params->nlist = 100; build_params->metric = "l2"; build_params->niter = 5; - build_params->num_workers = 2; + build_params->num_workers = 0; auto maintenance_params = std::make_shared(); maintenance_params->refinement_radius = 10; maintenance_params->refinement_iterations = 0; - maintenance_params->window_size = 100; + maintenance_params->window_size = 10; Tensor data_vectors = torch::randn({num_vectors, dimension}, torch::kFloat32); Tensor data_ids = torch::arange(num_vectors, torch::kInt64); @@ -519,11 +519,18 @@ TEST(QuakeIndexStressTest, SearchAddRemoveMaintenanceTest) { // add level auto parent_index_build_params = std::make_shared(); - parent_index_build_params->nlist = 50; + parent_index_build_params->nlist = 1; parent_index_build_params->metric = "l2"; parent_index_build_params->niter = 5; - parent_index_build_params->num_workers = 1; + parent_index_build_params->num_workers = 0; build_params->parent_params = parent_index_build_params; + + // auto grandparent_index_build_params = std::make_shared(); + // grandparent_index_build_params->nlist = 1; + // grandparent_index_build_params->metric = "l2"; + // grandparent_index_build_params->num_workers = 0; + // build_params->parent_params->parent_params = grandparent_index_build_params; + index.build(data_vectors, data_ids, build_params); // index.initialize_maintenance_policy(maintenance_params); @@ -577,10 +584,10 @@ TEST(QuakeIndexStressTest, SearchAddRemoveMaintenanceTest) { // remove_time += std::chrono::duration_cast(end - start).count(); // ASSERT_EQ(remove_info->n_vectors, batch_size / 2); - // start = std::chrono::high_resolution_clock::now(); - // index.maintenance(); - // end = std::chrono::high_resolution_clock::now(); - // maintenance_time += std::chrono::duration_cast(end - start).count(); + start = std::chrono::high_resolution_clock::now(); + index.maintenance(); + end = std::chrono::high_resolution_clock::now(); + maintenance_time += std::chrono::duration_cast(end - start).count(); } // print out mean times per operation diff --git a/test/experiments/osdi2025/kick_the_tires/configs/sift1m.yaml b/test/experiments/osdi2025/kick_the_tires/configs/sift1m.yaml new file mode 100644 index 00000000..bac21318 --- /dev/null +++ b/test/experiments/osdi2025/kick_the_tires/configs/sift1m.yaml @@ -0,0 +1,73 @@ +# configs/experiment.yaml +mode: run # options: build, run, plot +dataset: + name: sift1m + metric: l2 + path: data +workload_generator: + recall_target: 0.9 + insert_ratio: 0.5 + delete_ratio: 0.0 + query_ratio: 0.5 + number_of_operations: 1000 + initial_size: 100000 + cluster_size: 1000 + update_batch_size: 10000 + query_batch_size: 100 + cluster_sample_distribution: skewed + query_cluster_sample_distribution: uniform + seed: 9299 +indexes: + - name: Quake + index: Quake + build_params: + nc: 1000 + num_workers: 1 + parent: + num_workers: 8 + search_params: + k: 10 +# nprobe: 10 + recall_target: .9 + parent: +# nprobe: 25 + recall_target: .99 + initial_search_fraction: 0.25 + + - name: Quake2 + index: Quake + build_params: + nc: 1000 + num_workers: 1 + parent: + num_workers: 0 + search_params: + k: 10 + recall_target: .9 + - name: IVF + index: IVF + build_params: + nc: 1000 + search_params: + nprobe: 15 + k: 10 +plot: + x_axis: operation_number # any column from the results CSV + metrics: [latency_ms, recall] # any subset of stored metrics + styles: # one entry per index name + Quake: + color: "#1f77b4" # default Matplotlib hex’s are fine + linestyle: "-" # see plt.Line2D docs + marker: "o" + Quake2: + color: "#ff7f0e" # default Matplotlib hex’s are fine + linestyle: "-" # see plt.Line2D docs + marker: "o" + IVF: + color: "#d62728" + linestyle: "--" + marker: "s" +overwrite: + workload: false + index: false + results: true \ No newline at end of file From 95bdaeff6e741c50e7083684107fefaabc40d2ba Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 6 May 2025 11:48:04 -0500 Subject: [PATCH 022/323] update config --- test/experiments/osdi2025/README.md | 2 +- .../kick_the_tires/configs/sift1m.yaml | 20 +------------------ 2 files changed, 2 insertions(+), 20 deletions(-) diff --git a/test/experiments/osdi2025/README.md b/test/experiments/osdi2025/README.md index 002ed897..1df6e158 100644 --- a/test/experiments/osdi2025/README.md +++ b/test/experiments/osdi2025/README.md @@ -51,7 +51,7 @@ python3 examples/quickstart.py 2. **Run the Benchmark:** ```bash -python3 -m test.experiments.osdi2025.experiment_runner --experiment kick_the_tires --config sift1m +OMP_NUM_THREADS=1 python3 -m test.experiments.osdi2025.experiment_runner --experiment kick_the_tires --config sift1m ``` --- \ No newline at end of file diff --git a/test/experiments/osdi2025/kick_the_tires/configs/sift1m.yaml b/test/experiments/osdi2025/kick_the_tires/configs/sift1m.yaml index bac21318..5a2974e8 100644 --- a/test/experiments/osdi2025/kick_the_tires/configs/sift1m.yaml +++ b/test/experiments/osdi2025/kick_the_tires/configs/sift1m.yaml @@ -22,25 +22,7 @@ indexes: index: Quake build_params: nc: 1000 - num_workers: 1 - parent: - num_workers: 8 - search_params: - k: 10 -# nprobe: 10 - recall_target: .9 - parent: -# nprobe: 25 - recall_target: .99 - initial_search_fraction: 0.25 - - - name: Quake2 - index: Quake - build_params: - nc: 1000 - num_workers: 1 - parent: - num_workers: 0 + num_workers: 0 search_params: k: 10 recall_target: .9 From c5a98a41b9415c5a96215c1082dbea3400000d21 Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 6 May 2025 23:19:37 -0500 Subject: [PATCH 023/323] add LAET --- src/python/datasets/ann_datasets.py | 48 +++ test/cpp/scan_benchmark.cpp | 241 ++++++++++++ .../osdi2025/early_termination/laet.py | 316 +++++++++++++++ .../osdi2025/early_termination/run.py | 369 ++++++++++++++++++ .../osdi2025/early_termination/util_laet.py | 77 ++++ .../experiments/osdi2025/experiment_runner.py | 2 + 6 files changed, 1053 insertions(+) create mode 100644 test/cpp/scan_benchmark.cpp create mode 100644 test/experiments/osdi2025/early_termination/laet.py create mode 100644 test/experiments/osdi2025/early_termination/util_laet.py diff --git a/src/python/datasets/ann_datasets.py b/src/python/datasets/ann_datasets.py index 572717a4..607d47ea 100644 --- a/src/python/datasets/ann_datasets.py +++ b/src/python/datasets/ann_datasets.py @@ -5,6 +5,7 @@ import numpy as np import torch +import os from quake.utils import download_url, extract_file, fvecs_to_tensor, ivecs_to_tensor, to_path, knn @@ -116,6 +117,51 @@ def load_ground_truth(self) -> Union[np.ndarray, torch.Tensor]: return torch.from_numpy(np.load(self.download_dir / "ground_truth.npy")) +class MSTuring10m(Dataset): + def __init__(self, download_dir: Union[str, Path] = DEFAULT_DOWNLOAD_DIR): + self.download_dir = to_path(download_dir) + self.data_dir = self.download_dir / "MSTuringANNS10M" + self.downloaded = False + self.metric = "l2" + + def is_downloaded(self) -> bool: + base = self.data_dir / "base1b.fbin.crop_nb_10000000" + query = self.data_dir / "query100K.fbin" + gt = self.data_dir / "msturing-gt-10M" + downloaded = base.exists() and query.exists() and gt.exists() + + if not downloaded: + raise RuntimeError("MSTuring dataset is not available. Please download it manually using big-ann-benchmarks.") + + return downloaded + + def download(self, overwrite: bool = False): + pass + + def load_vectors(self) -> Union[np.ndarray, torch.Tensor]: + fname = self.data_dir / "base1b.fbin.crop_nb_10000000" + n, d = map(int, np.fromfile(fname, dtype="uint32", count=2)) + # return to_torch(np.fromfile(fname, dtype=np.float32, offset=8).reshape((n, d))) + return torch.from_numpy(np.fromfile(fname, dtype=np.float32, offset=8).reshape((n, d))) + + def load_queries(self) -> Union[np.ndarray, torch.Tensor]: + fname = self.data_dir / "query100K.fbin" + n, d = map(int, np.fromfile(fname, dtype="uint32", count=2)) + # return to_torch(np.fromfile(fname, dtype=np.float32, offset=8).reshape((n, d))) + return torch.from_numpy(np.fromfile(fname, dtype=np.float32, offset=8).reshape((n, d))) + + def load_ground_truth(self) -> Union[np.ndarray, torch.Tensor]: + fname = self.data_dir / "msturing-gt-10M" + n, d = map(int, np.fromfile(fname, dtype="uint32", count=2)) + assert os.stat(fname).st_size == 8 + n * d * (4 + 4) + f = open(fname, "rb") + f.seek(4 + 4) + ids = np.fromfile(f, dtype="int32", count=n * d).reshape(n, d) + # D = np.fromfile(f, dtype="float32", count=n * d).reshape(n, d) + + return torch.from_numpy(ids).long() + + def load_dataset( name: str, download_dir: str = DEFAULT_DOWNLOAD_DIR, overwrite_download: bool = False ) -> List[Union[np.ndarray, torch.Tensor]]: @@ -123,6 +169,8 @@ def load_dataset( dataset = Sift1m(download_dir=download_dir) elif name.lower() == "uniform": dataset = UniformDataset(num_vectors=100000, dim=8, download_dir=download_dir) + elif name.lower() == "msturing10m": + dataset = MSTuring10m(download_dir=download_dir) else: raise RuntimeError("Unimplemented dataset") diff --git a/test/cpp/scan_benchmark.cpp b/test/cpp/scan_benchmark.cpp new file mode 100644 index 00000000..a029c0fa --- /dev/null +++ b/test/cpp/scan_benchmark.cpp @@ -0,0 +1,241 @@ +#include +// #include // OpenMP no longer used for the primary parallelization +#include +#include +#include +#include +#include +#include +#include +#include +#include // For std::mutex and std::call_once +#include // For std::thread::hardware_concurrency +#include // For std::async, std::future (used by parallel.h) +#include +#include "faiss/utils/distances.h" +#include "parallel.h" // Include your custom parallel_for header + +// ------------------------------------------------------------------ +// Constants +// ------------------------------------------------------------------ +static constexpr int64_t LIST_SIZE = 1'000'000; +static constexpr int D = 128; +static constexpr int NQ = 100; // Number of queries + +// Forward declaration of the fixture +class ListScanBenchmarkF; + +// ------------------------------------------------------------------ +// Timing helper +// ------------------------------------------------------------------ +struct Timer { + using clk = std::chrono::steady_clock; + clk::time_point t0; + void start() { t0 = clk::now(); } + double stop() { return std::chrono::duration(clk::now()-t0).count(); } +}; + +// ------------------------------------------------------------------ +// Pretty printer +// ------------------------------------------------------------------ +// Takes requested threads (nth_req) and actual threads used by parallel_for (pfor_threads) +static void print_stats(const char* label, int nth_req, double sec) +{ + const double evaluations = static_cast(LIST_SIZE) * NQ; + const double bytes_scanned = evaluations * D * sizeof(float); + const double gb_scanned = bytes_scanned / (1ULL << 30); + const double gb_per_sec = gb_scanned / sec; + + std::cout << std::setw(24) << std::left << label + << " | " << std::setw(2) << nth_req + << " | " << std::fixed << std::setprecision(3) + << sec << " s" + << " | " << std::setprecision(2) + << gb_per_sec << " GB/s" + << std::endl; +} + +// ================================================================== +// Google‑test Test Fixture +// ================================================================== +class ListScanBenchmarkF : public ::testing::Test { +protected: + // Data members are static members of the fixture + static std::vector S_base_vectors_data; + static std::vector S_queries_data; + static std::vector S_seq_order_data; + static std::vector S_rnd_order_data; + + // Flags for std::call_once, also static members + static std::once_flag base_vectors_flag; + static std::once_flag queries_flag; + static std::once_flag seq_order_flag; + static std::once_flag rnd_order_flag; + + static void initialize_base_vectors() { + std::call_once(base_vectors_flag, []{ + S_base_vectors_data.resize(LIST_SIZE * D); + std::mt19937 rng(123); + std::normal_distribution nd(0.f, 1.f); + for (float& v : S_base_vectors_data) v = nd(rng); + }); + } + + static void initialize_queries() { + std::call_once(queries_flag, []{ + S_queries_data.resize(NQ * D); + std::mt19937 rng(321); + std::normal_distribution nd(0.f, 1.f); + for (float& v : S_queries_data) v = nd(rng); + }); + } + + static void initialize_seq_order() { + std::call_once(seq_order_flag, []{ + S_seq_order_data.resize(LIST_SIZE); + std::iota(S_seq_order_data.begin(), S_seq_order_data.end(), 0); + }); + } + + static void initialize_rnd_order() { + std::call_once(rnd_order_flag, []{ + S_rnd_order_data.resize(LIST_SIZE); + std::iota(S_rnd_order_data.begin(), S_rnd_order_data.end(), 0); + std::mt19937 rng(999); + std::shuffle(S_rnd_order_data.begin(), S_rnd_order_data.end(), rng); + }); + } + +public: + // Kernel now uses the custom parallel_for for the inner loop (scanning list_size) + // It returns the sum of minimum distances found for each query (for anti-optimization) + static float static_scan_kernel(const std::vector& order, int n_threads_requested) + { + const float* base_ptr = S_base_vectors_data.data(); + const float* query_ptr = S_queries_data.data(); + + volatile float total_min_sum = 0.f; // Accumulates min distance for each query + + // Determine the actual number of threads parallel_for will aim to use + int threads_for_pfor = n_threads_requested; + if (threads_for_pfor <= 0) { + threads_for_pfor = static_cast(std::thread::hardware_concurrency()); + } + if (threads_for_pfor == 0) { // Safety for hardware_concurrency returning 0 + threads_for_pfor = 1; + } + + float distances[LIST_SIZE]; // Distances for the current query + + // Outer loop over queries (sequential) + for (int qi = 0; qi < NQ; ++qi) { + const float* current_query_vector = query_ptr + qi * D; + + // Inner loop (over base vectors for the current query) is parallelized + // The 'order' vector contains indices into 'S_base_vectors_data' + // 'k' will be an index from 0 to order.size()-1 + parallel_for(static_cast(0), static_cast(order.size()), + [&](int64_t k) { // k is the index in the range [0, order.size()) + int64_t base_vector_original_idx = order[k]; + const float* current_base_vector = base_ptr + base_vector_original_idx * D; + + distances[k] = faiss::fvec_L2sqr(current_query_vector, current_base_vector, static_cast(D)); + }, + threads_for_pfor // Pass the determined number of threads to parallel_for + ); + } + return total_min_sum; // Return sum for anti-optimization + } + + // Wrappers call the kernel + static float static_sequential_scan(int nth = 1) { + return static_scan_kernel(S_seq_order_data, nth); + } + static float static_random_scan(int nth = 1) { + return static_scan_kernel(S_rnd_order_data, nth); + } + +protected: + static void SetUpTestSuite() { + std::cout << "Using custom parallel_for for inner loop parallelization." << std::endl; + unsigned int hw_threads = std::thread::hardware_concurrency(); + std::cout << "std::thread::hardware_concurrency(): " << hw_threads << std::endl; + + std::cout << "Initializing benchmark data (once)..." << std::endl; + auto overall_start_init = std::chrono::steady_clock::now(); + + initialize_base_vectors(); + initialize_queries(); + initialize_seq_order(); + initialize_rnd_order(); + + auto overall_end_init = std::chrono::steady_clock::now(); + std::chrono::duration init_duration = overall_end_init - overall_start_init; + std::cout << "VECTOR SCAN BENCHMARK" << std::endl; + std::cout << "--------------------------------------------------------------------------" << std::endl; + std::cout << "Benchmark data initialized in " << std::fixed << std::setprecision(3) + << init_duration.count() << " s." << std::endl; + std::cout << "Benchmark data size: " << LIST_SIZE << "vectors, " + << NQ << " queries, " << D << " dimensions." << std::endl; + std::cout << "Throughput averaged over all queries." << std::endl; + std::cout << "--------------------------------------------------------------------------" << std::endl; + std::cout << std::setw(24) << std::left << "Test Case" + << " | " << "N_Threads" // Requested threads + << " | " << "Time (s)" + << " | " << "Throughput" << std::endl; + std::cout << "--------------------------------------------------------------------------" << std::endl; + } + + static void TearDownTestSuite() { + std::cout << "--------------------------------------------------------------------------" << std::endl; + std::cout << "Benchmark suite finished." << std::endl; + } +}; + +// Static member definitions +std::vector ListScanBenchmarkF::S_base_vectors_data; +std::vector ListScanBenchmarkF::S_queries_data; +std::vector ListScanBenchmarkF::S_seq_order_data; +std::vector ListScanBenchmarkF::S_rnd_order_data; + +std::once_flag ListScanBenchmarkF::base_vectors_flag; +std::once_flag ListScanBenchmarkF::queries_flag; +std::once_flag ListScanBenchmarkF::seq_order_flag; +std::once_flag ListScanBenchmarkF::rnd_order_flag; + + + +TEST_F(ListScanBenchmarkF, ParallelSequentialScan) +{ + for (int nth : {1, 2, 4, 8, 12}) { // Test with different numbers of threads + Timer t; + t.start(); + volatile float dummy_sum = static_sequential_scan(nth); + double elapsed_seconds = t.stop(); + (void)dummy_sum; + + int actual_pfor_threads = (nth <= 0) ? static_cast(std::thread::hardware_concurrency()) : nth; + if (actual_pfor_threads == 0) actual_pfor_threads = 1; + if (nth == 1) actual_pfor_threads = 1; // parallel_for handles num_threads == 1 specifically + + + print_stats("Parallel-Sequential", nth, elapsed_seconds); + } +} + +TEST_F(ListScanBenchmarkF, ParallelRandomScan) +{ + for (int nth : {1, 2, 4, 8, 12}) { // Test with different numbers of threads + Timer t; + t.start(); + volatile float dummy_sum = static_random_scan(nth); + double elapsed_seconds = t.stop(); + (void)dummy_sum; + + int actual_pfor_threads = (nth <= 0) ? static_cast(std::thread::hardware_concurrency()) : nth; + if (actual_pfor_threads == 0) actual_pfor_threads = 1; + if (nth == 1) actual_pfor_threads = 1; // parallel_for handles num_threads == 1 specifically + + print_stats("Parallel-Random", nth, elapsed_seconds); + } +} diff --git a/test/experiments/osdi2025/early_termination/laet.py b/test/experiments/osdi2025/early_termination/laet.py new file mode 100644 index 00000000..b9687270 --- /dev/null +++ b/test/experiments/osdi2025/early_termination/laet.py @@ -0,0 +1,316 @@ +# File: laet.py +import faiss +import lightgbm as lgb +import numpy as np +import pandas as pd +import os +import time +import csv +import sys +import math +import re +from pathlib import Path +from sklearn.model_selection import train_test_split +import torch +from sklearn.metrics import mean_squared_error, mean_absolute_error + +# --- Embedded I/O Utilities (Simplified) --- +def _mmap_fvecs(fname): + x = np.memmap(fname, dtype='int32', mode='r'); d = x[0]; return x.view('float32').reshape(-1, d + 1)[:, 1:] +def _mmap_bvecs(fname): + x = np.memmap(fname, dtype='uint8', mode='r'); d = x[:4].view('int32')[0]; return x.reshape(-1, d + 4)[:, 4:] +def _read_gt_file(fname, format_type, k_load=1): + gt_list = [] + if format_type == "ivecs": + if not os.path.exists(fname) or os.path.getsize(fname) == 0: + return [] + a = np.fromfile(fname, dtype='int32') + if a.size == 0: return [] + d_gt = a[0] + if a.size % (d_gt + 1) != 0: + return [] + data = a.reshape(-1, d_gt + 1) + for i in range(data.shape[0]): + count = data[i,0]; num_to_take = min(count, k_load if k_load is not None else count) + gt_list.append(list(data[i, 1:1 + num_to_take])) + elif format_type == "tsv": + if not os.path.exists(fname): + return [] + with open(fname, 'r', newline='') as f: + for row in csv.reader(f, delimiter='\t'): + try: + ids = [int(x) for x in row if x.strip()] + gt_list.append(ids[:k_load] if k_load is not None else ids) + except ValueError: + pass + return gt_list +def _write_tsv(data, filename): + if os.path.exists(filename): os.remove(filename) + with open(filename, 'w', newline='') as f: csv.writer(f, delimiter='\t').writerows(data) +# --- End I/O --- + +class LAETPipeline: + def __init__(self, vector_dim, laet_artifacts_config): + self.vector_dim = vector_dim + self.config = laet_artifacts_config + self.gbdt_model = None + self.log_target_prediction = False + + base_dir = Path(self.config["base_dir"]) + self.training_data_output_dir = base_dir / self.config.get("training_data_subdir", "laet_training_data") + self.model_output_dir = base_dir / self.config.get("model_subdir", "laet_models") + + self.training_data_output_dir.mkdir(parents=True, exist_ok=True) + self.model_output_dir.mkdir(parents=True, exist_ok=True) + + self.model_name_prefix = f"{self.config['dataset_name_for_files']}_{self.config['index_key_for_files'].replace(',', '_')}" + _log_suffix_for_naming = "_Log" if self.config.get("model_was_trained_on_log_target", False) else "" + self.gbdt_model_filename = f"{self.model_name_prefix}_gbdt_model{_log_suffix_for_naming}.txt" + self.full_gbdt_model_path = self.model_output_dir / self.gbdt_model_filename + + def _find_min_param_faiss(self, faiss_idx, q_vec, gt_set, k_chk, is_hnsw, max_p, init_p=1, step=1): + if not gt_set: return max_p + curr_p = init_p + while curr_p <= max_p: + if is_hnsw: faiss_idx.hnsw.efSearch = curr_p + else: faiss_idx.nprobe = curr_p + try: + _, I = faiss_idx.search(q_vec, k_chk) + if I.size > 0 and any(idx in gt_set for idx in I[0] if idx >= 0): return curr_p + except Exception: pass + curr_p += step + if curr_p > max_p and curr_p - step <= max_p : + if curr_p - step == max_p: continue + break + return max_p + + def _generate_training_data_tsv_internal(self, tsv_path, data_paths_cfg, gen_params_cfg, faiss_params_cfg): + print(f"LAET: Generating training data to {tsv_path}...") + xb = _mmap_fvecs(data_paths_cfg["base_vectors"]) if data_paths_cfg["base_vectors"].endswith('.fvecs') else _mmap_bvecs(data_paths_cfg["base_vectors"]) + xq_all = _mmap_fvecs(data_paths_cfg["train_query_vectors"]) if data_paths_cfg["train_query_vectors"].endswith('.fvecs') else _mmap_bvecs(data_paths_cfg["train_query_vectors"]) + + num_q = gen_params_cfg.get("num_train_queries_to_process", -1) + xq = xq_all[:num_q] if num_q > 0 else xq_all + xq_f32 = xq.astype(np.float32) + + gt_all = _read_gt_file(data_paths_cfg["gt_train"], data_paths_cfg["gt_format"], gen_params_cfg.get("k_gt_load_for_target", 1)) + gt = gt_all[:num_q] if num_q > 0 else gt_all + if len(xq) != len(gt): raise ValueError(f"Query ({len(xq)})/GT ({len(gt)}) length mismatch in training data gen.") + + faiss_idx_key = faiss_params_cfg["faiss_index_key_for_gen"] + is_hnsw = "hnsw" in faiss_idx_key.lower() + faiss_idx = faiss.index_factory(self.vector_dim, faiss_idx_key) + if not faiss_idx.is_trained: + xt_idx_path = data_paths_cfg.get("training_vectors_for_faiss_index") + if not xt_idx_path: raise ValueError("FAISS index needs training, but no training vectors path provided.") + xt_idx = _mmap_fvecs(xt_idx_path) if xt_idx_path.endswith('.fvecs') else _mmap_bvecs(xt_idx_path) + faiss_idx.train(xt_idx.astype(np.float32)) + faiss_idx.add(xb.astype(np.float32)) + + max_p = gen_params_cfg.get("max_efsearch_scan", 512) if is_hnsw else gen_params_cfg.get("max_nprobe_scan", 256) + if not is_hnsw and hasattr(faiss_idx, 'nlist'): max_p = min(max_p, faiss_idx.nlist) + + k_search_target = gen_params_cfg.get("k_search_for_target", 10) + param_scan_step = gen_params_cfg.get("param_scan_step", 1) + out_data = [] + start_gen_time = time.time() + print(f"LAET Training Data Gen: Max param to scan for optimal_p: {max_p}") + for i in range(len(xq)): + opt_p = self._find_min_param_faiss(faiss_idx, xq_f32[i:i+1], set(gt[i]), k_search_target, is_hnsw, max_p, step=param_scan_step) + out_data.append([opt_p, i] + xq_f32[i].tolist()) + if (i + 1) % 500 == 0 or (i+1) == len(xq): + elapsed_gen = time.time() - start_gen_time + _write_tsv(out_data, str(tsv_path)) + # Log distribution of optimal_p found + if out_data: + optimal_ps_found = np.array([row[0] for row in out_data]) + print(f"LAET Training Data Gen: Distribution of optimal_p found (target for GBDT): " + f"Min={np.min(optimal_ps_found)}, Mean={np.mean(optimal_ps_found):.2f}, Median={np.median(optimal_ps_found)}, Max={np.max(optimal_ps_found)}, " + f"StdDev={np.std(optimal_ps_found):.2f}") + + print(f"LAET: Training data saved to {tsv_path}") + + + def _train_gbdt_model_internal(self, tsv_path, model_save_path, train_params_cfg): + print(f"LAET: Training GBDT model from {tsv_path}...") + data_df = pd.read_csv(tsv_path, sep='\t', header=None) + target = data_df.iloc[:, 0].values.astype(np.float32) + features = data_df.iloc[:, 2:].values.astype(np.float32) + + log_target_training = train_params_cfg.get("log_target_training", False) + if log_target_training: target = np.log2(np.maximum(1, target)) + self.log_target_prediction = log_target_training + + X_tr, X_te, y_tr, y_te = train_test_split(features, target, test_size=train_params_cfg.get("test_size", 0.1), random_state=42) + + lgb_tr_data = lgb.Dataset(X_tr, y_tr) + lgb_te_data = lgb.Dataset(X_te, y_te, reference=lgb_tr_data) + + lgbm_params = { + 'objective': 'regression_l1', 'metric': 'mae', + 'num_leaves': train_params_cfg.get("num_leaves", 31), + 'learning_rate': train_params_cfg.get("learning_rate", 0.05), + 'feature_fraction': train_params_cfg.get("feature_fraction", 0.9), + 'bagging_fraction': train_params_cfg.get("bagging_fraction", 0.8), + 'bagging_freq': train_params_cfg.get("bagging_freq", 5), + 'verbose': -1, 'n_jobs': -1, 'seed': 42, + 'n_estimators': train_params_cfg.get("n_estimators", 100) + } + num_boost_round_val = lgbm_params['n_estimators'] + + gbm = lgb.train(lgbm_params, lgb_tr_data, num_boost_round=num_boost_round_val, + valid_sets=[lgb_tr_data, lgb_te_data], + callbacks=[lgb.early_stopping(15, verbose=False), lgb.log_evaluation(period=0)]) + + self.gbdt_model = gbm + current_log_suffix = "_Log" if self.log_target_prediction else "" + effective_model_filename = f"{self.model_name_prefix}_gbdt_model{current_log_suffix}.txt" + effective_model_save_path = self.model_output_dir / effective_model_filename + + gbm.save_model(str(effective_model_save_path)) + self.full_gbdt_model_path = effective_model_save_path + print(f"LAET: GBDT model saved to {self.full_gbdt_model_path}") + + def get_or_train_model(self, overwrite_model=False, + data_paths_config=None, + generation_params_config=None, + faiss_params_config=None, + training_params_config=None + ): + potential_log_target = training_params_config.get("log_target_training", False) if training_params_config else self.config.get("model_was_trained_on_log_target", False) + _log_suffix_check = "_Log" if potential_log_target else "" + expected_model_filename_if_trained_now = f"{self.model_name_prefix}_gbdt_model{_log_suffix_check}.txt" + path_to_check_or_create = self.model_output_dir / expected_model_filename_if_trained_now + + if not overwrite_model and path_to_check_or_create.exists(): + print(f"LAET: Loading existing GBDT model: {path_to_check_or_create}") + self.gbdt_model = lgb.Booster(model_file=str(path_to_check_or_create)) + self.log_target_prediction = "_Log" in path_to_check_or_create.name + self.full_gbdt_model_path = path_to_check_or_create + print(f"LAET: Model loaded. Actual log_target_prediction: {self.log_target_prediction}") + else: + print(f"LAET: Training GBDT model (overwrite={overwrite_model}, exists={path_to_check_or_create.exists()})...") + if not all([data_paths_config, generation_params_config, faiss_params_config, training_params_config]): + raise ValueError("Missing configurations for LAET model training.") + + training_tsv_filename = f"{self.model_name_prefix}_gbdt_train.tsv" + full_training_tsv_path = self.training_data_output_dir / training_tsv_filename + + self._generate_training_data_tsv_internal( + full_training_tsv_path, + data_paths_config, + generation_params_config, + faiss_params_config + ) + self._train_gbdt_model_internal( + full_training_tsv_path, + path_to_check_or_create, + training_params_config + ) + if self.vector_dim is None and self.gbdt_model and hasattr(self.gbdt_model, 'num_feature'): + self.vector_dim = self.gbdt_model.num_feature() + + def _predict_base_search_param(self, query_vector_np): + if not self.gbdt_model: raise RuntimeError("LAET GBDT model not loaded.") + if self.vector_dim is None: self.vector_dim = query_vector_np.shape[0] + if query_vector_np.shape[0] != self.vector_dim: + raise ValueError(f"Query dim {query_vector_np.shape[0]} != model's {self.vector_dim}") + + features = query_vector_np.astype(np.float32).flatten() + pred_raw = self.gbdt_model.predict(features.reshape(1, -1))[0] + pred_val = np.power(2, pred_raw) if self.log_target_prediction else pred_raw + return max(1, int(round(pred_val))) + + def _compute_recall_single_q(self, res_ids_tensor, gt_set_top_k, k_val_for_recall): + if not gt_set_top_k or res_ids_tensor is None or res_ids_tensor.numel() == 0 or k_val_for_recall == 0: + return 0.0 + results_to_check = res_ids_tensor[:min(res_ids_tensor.numel(), k_val_for_recall)].tolist() + hits = 0 + for res_idx in results_to_check: + if res_idx in gt_set_top_k and res_idx >= 0: + hits += 1 + return float(hits) / k_val_for_recall + + + def run_inference_for_quake_experiment(self, quake_idx, queries_torch, gt_torch, + target_recall, k_search, base_quake_sp, + num_bs_steps=20, multiplier_range=(0.1, 5.0)): + if not self.gbdt_model: + print("LAET Inference Error: GBDT model not loaded.") + return np.nan, np.nan, np.nan + + nq = queries_torch.shape[0] + if self.vector_dim is None: self.vector_dim = queries_torch.shape[1] + + gt_sets = [set(gt_torch[i, :min(gt_torch.shape[1], k_search)].tolist()) for i in range(nq)] + queries_np = queries_torch.numpy() + + multi_low, multi_high = multiplier_range + best_mult = multi_high + quake_nlist_cap = quake_idx.nlist() if callable(getattr(quake_idx, 'nlist', None)) else getattr(quake_idx, 'nlist', 2048) + + # --- Log GBDT base_p predictions sample --- + sample_size_for_log = min(nq, 100) + sample_base_p_preds = [self._predict_base_search_param(queries_np[i]) for i in range(sample_size_for_log)] + if sample_base_p_preds: + print(f"LAET INFERENCE (TargetRecall={target_recall:.2f}): Sample GBDT base_p predictions (first {sample_size_for_log}): " + f"Min={np.min(sample_base_p_preds)}, Mean={np.mean(sample_base_p_preds):.2f}, Max={np.max(sample_base_p_preds)}") + # --- End Log --- + + for bs_run_idx in range(num_bs_steps): + curr_mult = (multi_low + multi_high) / 2.0 + if abs(multi_low - multi_high) < 1e-4: break + recall_sum = 0.0 + + tune_nq = int(nq * 0.01) + for i in range(tune_nq): + base_p = self._predict_base_search_param(queries_np[i]) + final_p = min(max(1, int(round(base_p * curr_mult))), quake_nlist_cap) + + sp = None + if hasattr(base_quake_sp, 'clone') and callable(base_quake_sp.clone): sp = base_quake_sp.clone() + else: sp = type(base_quake_sp)() + + sp.nprobe, sp.k = final_p, k_search + res = quake_idx.search(queries_torch[i:i+1], sp) + ids = res.ids[0] if res.ids is not None and len(res.ids)>0 and res.ids[0] is not None else torch.empty(0, dtype=torch.long) + recall_sum += self._compute_recall_single_q(ids, gt_sets[i], k_search) + avg_rec = recall_sum / tune_nq if tune_nq > 0 else 0.0 + if avg_rec >= target_recall: best_mult, multi_high = curr_mult, curr_mult + else: multi_low = curr_mult + + print(f"LAET INFERENCE (TargetRecall={target_recall:.2f}): Determined best_mult = {best_mult:.3f}") + + final_recall_sum, total_time_ns, sum_nprobes = 0.0, 0, 0 + final_p_values_sample = [] + + for i in range(nq): + base_p = self._predict_base_search_param(queries_np[i]) + final_p = min(max(1, int(round(base_p * best_mult))), quake_nlist_cap) + sum_nprobes += final_p + if i < sample_size_for_log: # Log sample of final_p + final_p_values_sample.append(final_p) + + sp_final = None + if hasattr(base_quake_sp, 'clone') and callable(base_quake_sp.clone): sp_final = base_quake_sp.clone() + else: sp_final = type(base_quake_sp)() + + sp_final.nprobe, sp_final.k = final_p, k_search + + t_s = time.perf_counter_ns() + res = quake_idx.search(queries_torch[i:i+1], sp_final) + total_time_ns += (time.perf_counter_ns() - t_s) + ids = res.ids[0] if res.ids is not None and len(res.ids)>0 and res.ids[0] is not None else torch.empty(0, dtype=torch.long) + final_recall_sum += self._compute_recall_single_q(ids, gt_sets[i], k_search) + + if final_p_values_sample: + print(f"LAET INFERENCE (TargetRecall={target_recall:.2f}): Sample final_p values used (first {len(final_p_values_sample)}): " + f"Min={np.min(final_p_values_sample)}, Mean={np.mean(final_p_values_sample):.2f}, Max={np.max(final_p_values_sample)}") + + + avg_rec_f = final_recall_sum / nq if nq > 0 else 0.0 + avg_time_ms_f = (total_time_ns / nq) / 1e6 if nq > 0 else 0.0 + avg_nprobe_f = sum_nprobes / nq if nq > 0 else 0.0 + + + return avg_nprobe_f, avg_rec_f, avg_time_ms_f diff --git a/test/experiments/osdi2025/early_termination/run.py b/test/experiments/osdi2025/early_termination/run.py index 139597f9..e4e8af6a 100644 --- a/test/experiments/osdi2025/early_termination/run.py +++ b/test/experiments/osdi2025/early_termination/run.py @@ -1,2 +1,371 @@ +#!/usr/bin/env python3 +""" +Recall-Target Runner for Approximate Nearest Neighbor Search Methods. +Supports: Oracle, FixedNProbe, APS, and LAET (Learned Adaptive Early Termination). +""" +import time +import yaml +import logging +from pathlib import Path +import argparse +import torch +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt + +# Assuming 'quake' and 'laet' are in the PYTHONPATH or same directory +try: + from quake import QuakeIndex, IndexBuildParams, SearchParams + from quake.datasets.ann_datasets import load_dataset + from quake.utils import compute_recall +except ImportError as e: + print(f"Error importing Quake library components: {e}. Ensure 'quake' is installed and in PYTHONPATH.") + raise + +try: + # Adjust this import based on your project structure. + # If laet.py is in the same directory as this script, it should be: + # from laet import LAETPipeline + # Your current script uses a relative import: + from .laet import LAETPipeline +except ImportError as e: + # Fallback if relative import fails (e.g., script run as top-level) + try: + from laet import LAETPipeline + print("Note: Imported LAETPipeline using 'from laet import LAETPipeline'.") + except ImportError: + print(f"Error importing LAETPipeline: {e}. Ensure 'laet.py' is correctly placed and import path is valid.") + raise + + +# Setup basic logging +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +def run_experiment(cfg_path: str, output_dir_str: str): + """ + Runs the ANN experimentation pipeline. + + Args: + cfg_path (str): Path to the YAML configuration file. + output_dir_str (str): Path to the directory where results will be saved. + """ + # 1) Load config + setup output dir + logger.info(f"Loading configuration from: {cfg_path}") + try: + cfg = yaml.safe_load(Path(cfg_path).read_text()) + except FileNotFoundError: + logger.error(f"Configuration file not found: {cfg_path}") + return + except Exception as e: + logger.error(f"Error loading YAML configuration from {cfg_path}: {e}") + return + + out_path = Path(output_dir_str) + out_path.mkdir(parents=True, exist_ok=True) + logger.info(f"Output directory set to: {out_path}") + + # 2) Load data + data_dir = cfg["dataset"].get("path", "") + dataset_name = cfg["dataset"]["name"] + logger.info(f"Loading dataset: {dataset_name} from path: '{data_dir if data_dir else 'default'}'") + try: + vecs, queries_torch, gt_torch = load_dataset(dataset_name, data_dir) + except Exception as e: + logger.error(f"Failed to load dataset '{dataset_name}': {e}", exc_info=True) + return + + nq_config = cfg["experiment"].get("nq", queries_torch.shape[0]) + queries_torch, gt_torch = queries_torch[:nq_config], gt_torch[:nq_config] + logger.info(f"Using {queries_torch.shape[0]} queries for the experiment.") + + # 3) Build or load QuakeIndex (used by all methods) + idx_quake = QuakeIndex() + + build_params = IndexBuildParams() + build_params.nlist = cfg["index"]["nlist"] + build_params.metric = cfg["index"]["metric"] + build_params.num_workers = cfg["experiment"].get("n_workers", 0) + + idx_base_dir = Path(cfg["paths"].get("index_dir", out_path / "indexes")) + idx_base_dir.mkdir(parents=True, exist_ok=True) + idx_path_quake = idx_base_dir / f"{dataset_name}_quake_ivf{build_params.nlist}.index" + + if not idx_path_quake.exists() or cfg["overwrite"].get("index", False): + logger.info(f"Building QuakeIndex at {idx_path_quake}...") + try: + idx_quake.build(vecs, torch.arange(len(vecs)), build_params) + idx_quake.save(str(idx_path_quake)) + logger.info(f"QuakeIndex built and saved to {idx_path_quake}.") + except Exception as e: + logger.error(f"Failed to build or save QuakeIndex: {e}", exc_info=True) + return + else: + logger.info(f"Loading existing QuakeIndex from {idx_path_quake}...") + try: + idx_quake.load(str(idx_path_quake), 0) + logger.info(f"QuakeIndex loaded successfully.") + except Exception as e: + logger.error(f"Failed to load QuakeIndex from {idx_path_quake}: {e}", exc_info=True) + return + + current_index_nlist = idx_quake.nlist() if callable(getattr(idx_quake, 'nlist', None)) else getattr(idx_quake, 'nlist', build_params.nlist) + if current_index_nlist != build_params.nlist: + logger.warning(f"QuakeIndex nlist ({current_index_nlist}) differs from config ({build_params.nlist}). Using index's nlist: {current_index_nlist}.") + max_nlist_val = current_index_nlist + + + # 4) Initialize LAETPipeline if "LAET" is in methods + laet_pipeline_instance = None + if "LAET" in cfg["methods"]: + if not idx_quake: + logger.error("LAET method requires QuakeIndex, but it's not available. Skipping LAET.") + else: + laet_main_cfg = cfg.get("laet_config", {}) + laet_paths_cfg = cfg.get("paths", {}) # For laet_artifacts_base_dir + + # Prepare config for LAETPipeline constructor + pipeline_init_config = { + "base_dir": laet_paths_cfg.get("laet_artifacts_base_dir", "laet_pipeline_output"), + "training_data_subdir": laet_main_cfg.get("training_data_subdir", "laet_training_data"), + "model_subdir": laet_main_cfg.get("model_subdir", "laet_gbdt_models"), + "dataset_name_for_files": laet_main_cfg.get("dataset_name_for_files", dataset_name.upper()), + "index_key_for_files": laet_main_cfg.get("index_key_for_files", f"IVF{max_nlist_val}_Flat"), + "model_was_trained_on_log_target": laet_main_cfg.get("train_log_target", False) # Used for model naming if trained by pipeline + } + + laet_pipeline_instance = LAETPipeline(vector_dim=queries_torch.shape[1], + laet_artifacts_config=pipeline_init_config) + + # Prepare configs for get_or_train_model + # These paths should point to your primary dataset files for LAET GBDT training + data_paths_for_laet_training = { + "base_vectors": laet_main_cfg.get("base_vectors_path_for_laet_train", f"data/{dataset_name}_base.fvecs"), + "train_query_vectors": laet_main_cfg.get("train_query_vectors_path_for_laet_train", f"data/{dataset_name}_learn.fvecs"), + "gt_train": laet_main_cfg.get("gt_train_path_for_laet_train", f"data/{dataset_name}_learn_groundtruth.ivecs"), + "gt_format": laet_main_cfg.get("gt_format_for_laet_train", "ivecs"), + "training_vectors_for_faiss_index": laet_main_cfg.get("faiss_training_vectors_path_for_laet_train", f"data/{dataset_name}_learn.fvecs") + } + generation_params_for_laet_training = { + "num_train_queries_to_process": laet_main_cfg.get("num_laet_training_samples", 10000), + "k_gt_load_for_target": laet_main_cfg.get("laet_train_k_gt_load", 1), + "k_search_for_target": laet_main_cfg.get("laet_train_k_search", 10), + "max_nprobe_scan": laet_main_cfg.get("laet_train_max_nprobe", 128), + "max_efsearch_scan": laet_main_cfg.get("laet_train_max_efsearch", 256), + "param_scan_step": laet_main_cfg.get("laet_train_param_scan_step", 1) + } + faiss_params_for_laet_training = { + "faiss_index_key_for_gen": laet_main_cfg.get("faiss_index_key_for_laet_train_gen", f"IVF{max_nlist_val},Flat") + } + gbdt_training_params_for_laet = { + "log_target_training": laet_main_cfg.get("train_log_target", False), # This sets log_target_prediction in pipeline + "test_size": laet_main_cfg.get("train_gbdt_test_size", 0.1), + "num_leaves": laet_main_cfg.get("train_gbdt_num_leaves", 31), + "learning_rate": laet_main_cfg.get("train_gbdt_lr", 0.05), + "n_estimators": laet_main_cfg.get("train_gbdt_n_estimators", 100), + "feature_fraction": laet_main_cfg.get("train_gbdt_feature_fraction", 0.9), + "bagging_fraction": laet_main_cfg.get("train_gbdt_bagging_fraction", 0.8), + "bagging_freq": laet_main_cfg.get("train_gbdt_bagging_freq", 5) + } + + laet_pipeline_instance.get_or_train_model( + overwrite_model=cfg.get("overwrite", {}).get("laet_model", False), + data_paths_config=data_paths_for_laet_training, + generation_params_config=generation_params_for_laet_training, + faiss_params_config=faiss_params_for_laet_training, + training_params_config=gbdt_training_params_for_laet + ) + logger.info("LAET GBDT model is ready (loaded or trained).") + + # 5) Run each method × recall_target + records = [] + k_metric = cfg["experiment"]["k"] + + for method in cfg["methods"]: + for rt_metric in cfg["experiment"]["recall_targets"]: + logger.info(f"Running Method: {method}, Target Recall: {rt_metric:.3f}") + + mean_param_val, mean_recall_val, mean_time_ms_val = np.nan, np.nan, np.nan + std_param_val, std_recall_val, std_time_ms_val = np.nan, np.nan, np.nan + per_query_data_list = [] + + if method == "LAET": + if laet_pipeline_instance and idx_quake and laet_pipeline_instance.gbdt_model: + quake_sp_for_laet = SearchParams() + # k and nprobe will be set inside run_inference_for_quake_experiment + + bs_multiplier_range = tuple(laet_main_cfg.get("inference_multiplier_range", [0.1, 50.0])) + bs_steps = laet_main_cfg.get("inference_num_bs_steps", 20) + + try: + mean_param_val, mean_recall_val, mean_time_ms_val = \ + laet_pipeline_instance.run_inference_for_quake_experiment( + quake_idx=idx_quake, # Corrected from quake_index to idx_quake + queries_torch=queries_torch, + gt_torch=gt_torch, + target_recall=rt_metric, # Corrected from target_recall_metric + k_search=k_metric, # Corrected from k_search_quake + base_quake_sp=quake_sp_for_laet, # Corrected from base_quake_search_params + num_bs_steps=bs_steps, + multiplier_range=bs_multiplier_range + ) + std_param_val, std_recall_val, std_time_ms_val = 0.0, 0.0, 0.0 + logger.info(f"LAET @ RT={rt_metric:.3f} -> AvgParam={mean_param_val:.2f}, AvgRecall={mean_recall_val:.4f}, AvgTime={mean_time_ms_val:.3f}ms") + except Exception as e: + logger.error(f"Error during LAET inference run: {e}", exc_info=True) + mean_param_val, mean_recall_val, mean_time_ms_val = np.nan, np.nan, np.nan + else: + logger.warning(f"LAET method specified but pipeline/model or QuakeIndex not ready. Skipping for RT={rt_metric}.") + # Metrics remain NaN by default + + elif method in ["Oracle", "FixedNProbe", "APS"]: + if not idx_quake: + logger.warning(f"QuakeIndex not available for method {method}. Skipping.") + continue + + if method == "Oracle": + for i, q_single_torch in enumerate(queries_torch): + lo, hi, best_param_oracle = 1, max_nlist_val, max_nlist_val + while lo <= hi: + mid_param = (lo + hi) // 2 + sp_oracle_bs = SearchParams() + sp_oracle_bs.nprobe = mid_param + sp_oracle_bs.k = k_metric + res_oracle_bs = idx_quake.search(q_single_torch.unsqueeze(0), sp_oracle_bs) + rec_oracle_bs = compute_recall(res_oracle_bs.ids, gt_torch[i].unsqueeze(0), k_metric).item() + if rec_oracle_bs >= rt_metric: + best_param_oracle = mid_param + hi = mid_param - 1 + else: + lo = mid_param + 1 + + t_start_ns = time.perf_counter_ns() + sp_oracle_final = SearchParams() + sp_oracle_final.nprobe = best_param_oracle + sp_oracle_final.k = k_metric + res_oracle_final = idx_quake.search(q_single_torch.unsqueeze(0), sp_oracle_final) + elapsed_ns = time.perf_counter_ns() - t_start_ns + rec_oracle_final = compute_recall(res_oracle_final.ids, gt_torch[i].unsqueeze(0), k_metric).item() + per_query_data_list.append((best_param_oracle, rec_oracle_final, elapsed_ns / 1_000_000.0)) + + elif method == "FixedNProbe": + lo_fixed, hi_fixed, best_overall_param_fixed = 1, max_nlist_val, max_nlist_val + while lo_fixed <= hi_fixed: + mid_param_fixed = (lo_fixed + hi_fixed) // 2 + sp_fixed_bs = SearchParams() + sp_fixed_bs.nprobe = mid_param_fixed + sp_fixed_bs.k = k_metric + sp_fixed_bs.recall_target = -1 + + ids_list_fixed = [] + for q_bs_fixed in queries_torch: + res_bs_fixed = idx_quake.search(q_bs_fixed.unsqueeze(0), sp_fixed_bs) + if res_bs_fixed.ids is not None and res_bs_fixed.ids.numel() > 0: + ids_list_fixed.append(res_bs_fixed.ids) + + if not ids_list_fixed: avg_rec_fixed = 0.0 + else: avg_rec_fixed = compute_recall(torch.cat(ids_list_fixed, 0), gt_torch, k_metric).mean().item() + + if avg_rec_fixed >= rt_metric: + best_overall_param_fixed = mid_param_fixed + hi_fixed = mid_param_fixed - 1 + else: lo_fixed = mid_param_fixed + 1 + + for i, q_single_torch in enumerate(queries_torch): + t_start_ns = time.perf_counter_ns() + sp_fixed_final = SearchParams() + sp_fixed_final.nprobe = best_overall_param_fixed + sp_fixed_final.k = k_metric + sp_fixed_final.recall_target = -1 + res_fixed_final = idx_quake.search(q_single_torch.unsqueeze(0), sp_fixed_final) + elapsed_ns = time.perf_counter_ns() - t_start_ns + rec_fixed_final = compute_recall(res_fixed_final.ids, gt_torch[i].unsqueeze(0), k_metric).item() + per_query_data_list.append((best_overall_param_fixed, rec_fixed_final, elapsed_ns / 1_000_000.0)) + + elif method == "APS": + for i, q_single_torch in enumerate(queries_torch): + sp_aps = SearchParams() + sp_aps.nprobe = -1 + sp_aps.k = k_metric + sp_aps.recall_target = rt_metric + sp_aps.recompute_threshold = cfg["experiment"].get("recompute_ratio", 0.00001) + sp_aps.use_precomputed = cfg["experiment"].get("use_precompute", True) + sp_aps.initial_search_fraction = cfg["experiment"].get("initial_search_fraction", 0.1) + + t_start_ns = time.perf_counter_ns() + res_aps = idx_quake.search(q_single_torch.unsqueeze(0), sp_aps) + elapsed_ns = time.perf_counter_ns() - t_start_ns + rec_aps = compute_recall(res_aps.ids, gt_torch[i].unsqueeze(0), k_metric).item() + param_aps = res_aps.timing_info.partitions_scanned if hasattr(res_aps, 'timing_info') and hasattr(res_aps.timing_info, 'partitions_scanned') else np.nan + per_query_data_list.append((param_aps, rec_aps, elapsed_ns / 1_000_000.0)) + + if per_query_data_list: + arr = np.array(per_query_data_list, dtype=float) + if arr.size > 0: + mean_param_val, mean_recall_val, mean_time_ms_val = np.nanmean(arr, axis=0) + std_param_val, std_recall_val, std_time_ms_val = np.nanstd(arr, axis=0) + logger.info(f"{method} @ RT={rt_metric:.3f} -> AvgParam={mean_param_val:.2f}, AvgRecall={mean_recall_val:.4f}, AvgTime={mean_time_ms_val:.3f}ms") + else: + if method != "LAET": + logger.warning(f"Unknown method: {method}. Skipping.") + continue + + records.append({ + "Method": method, "RecallTarget": rt_metric, + "mean_nprobe": mean_param_val, "std_nprobe": std_param_val, + "mean_recall": mean_recall_val, "std_recall": std_recall_val, + "mean_time_ms": mean_time_ms_val, "std_time_ms": std_time_ms_val, + }) + + # 6) Save CSV + df_results = pd.DataFrame(records) + csv_filename = cfg["paths"].get("results_file_prefix", "experiment_summary") + f"_{dataset_name}.csv" + out_csv_path = out_path / csv_filename + df_results.to_csv(out_csv_path, index=False, float_format='%.4f') + logger.info(f"Results summary saved to: {out_csv_path}") + + # 7) Plotting + if not df_results.empty: + plot_filename = cfg["paths"].get("plot_file_prefix", "recall_comparison_plots") + f"_{dataset_name}.png" + plot_output_dir = Path(cfg["paths"].get("plot_dir", output_dir_str)) + plot_output_dir.mkdir(parents=True, exist_ok=True) + plot_path_full = plot_output_dir / plot_filename + + fig, axes = plt.subplots(3, 1, figsize=(10, 12), sharex=True) + unique_methods_in_results = df_results["Method"].unique() + + for m_name in unique_methods_in_results: + grp = df_results[df_results["Method"] == m_name].sort_values(by="RecallTarget") + if grp.empty: continue + + axes[0].errorbar(grp["RecallTarget"], grp["mean_time_ms"], yerr=grp["std_time_ms"], + marker="o", capsize=3, label=m_name, alpha=0.8, errorevery=1, elinewidth=1) + axes[1].errorbar(grp["RecallTarget"], grp["mean_recall"], yerr=grp["std_recall"], + marker="o", capsize=3, label=m_name, alpha=0.8, errorevery=1, elinewidth=1) + axes[2].errorbar(grp["RecallTarget"], grp["mean_nprobe"], yerr=grp["std_nprobe"], + marker="o", capsize=3, label=m_name, alpha=0.8, errorevery=1, elinewidth=1) + + axes[0].set_ylabel("Mean Query Time (ms)") + axes[0].set_title(f"Recall Target vs. Query Time ({dataset_name})") + axes[0].legend(); axes[0].grid(True, ls=":", alpha=0.7) + + recall_target_line_plot = np.array(cfg["experiment"]["recall_targets"]) + axes[1].plot(recall_target_line_plot, recall_target_line_plot, "k--", label="Target Recall", alpha=0.7) + axes[1].set_ylabel("Achieved Mean Recall") + axes[1].set_title(f"Recall Target vs. Achieved Recall ({dataset_name})") + axes[1].legend(); axes[1].grid(True, ls=":", alpha=0.7) + min_r, max_r = (df_results["mean_recall"].min() if pd.notna(df_results["mean_recall"].min()) else 0.0, + df_results["mean_recall"].max() if pd.notna(df_results["mean_recall"].max()) else 1.0) + axes[1].set_ylim(bottom=max(0.0, min_r - 0.05), top=min(1.01, max_r + 0.05)) + + axes[2].set_xlabel("Recall Target") + axes[2].set_ylabel("Mean Search Parameter (nprobe/equiv.)") + axes[2].set_yscale("log"); axes[2].set_title(f"Recall Target vs. Search Parameter ({dataset_name})") + axes[2].legend(); axes[2].grid(True, which="both", ls=":", alpha=0.7) + + plt.tight_layout(); plt.savefig(plot_path_full); plt.close(fig) + logger.info(f"Plots saved to: {plot_path_full}") + else: + logger.info("No data in results DataFrame, skipping plotting.") diff --git a/test/experiments/osdi2025/early_termination/util_laet.py b/test/experiments/osdi2025/early_termination/util_laet.py new file mode 100644 index 00000000..7a938c80 --- /dev/null +++ b/test/experiments/osdi2025/early_termination/util_laet.py @@ -0,0 +1,77 @@ +# File: util_laet.py +import numpy as np +import csv +import os +import sys + +def mmap_fvecs(fname): + """ Memory-maps an fvecs file. """ + x = np.memmap(fname, dtype='int32', mode='r') + d = x[0] + return x.view('float32').reshape(-1, d + 1)[:, 1:] + +def mmap_bvecs(fname): + """ Memory-maps a bvecs file. """ + x = np.memmap(fname, dtype='uint8', mode='r') + d = x[:4].view('int32')[0] + return x.reshape(-1, d + 4)[:, 4:] + +def read_tsv(filename, delimiter='\t', doInt=False, multi=1, rnd=-1): + """ + Reads a TSV file into a list of lists. + Handles potential errors during conversion and file access. + """ + ret = [] + if not os.path.exists(filename): + print(f"Warning: File not found {filename}", file=sys.stderr) + return ret + try: + with open(filename, 'r', newline='') as tsvfile: # Use newline='' for universal newlines + datareader = csv.reader(tsvfile, delimiter=delimiter) + for row_num, row in enumerate(datareader): + try: + l = [float(x) for x in row] + if multi != 1: + l = [multi * x for x in l] + if rnd != -1: + l = [round(x, rnd) for x in l] + if doInt: + l = [int(x) for x in l] + ret.append(l) + except ValueError as e: + print(f"Skipping malformed row #{row_num+1} in {filename}: {row}. Error: {e}", file=sys.stderr) + continue + except Exception as e: + print(f"Error opening or reading file {filename}: {e}", file=sys.stderr) + return ret + +def write_tsv(data, filename, delimiter='\t', append=False, doInt=False, multi=1, rnd=-1): + """ + Writes data to a TSV file. + """ + if not append: + try: + os.remove(filename) + except OSError: + pass # File doesn't exist, or other error (permissions etc.) + + processed_data = [] + if doInt or multi != 1 or rnd != -1: + for row_data in data: + new_row = list(row_data) # Make a copy to avoid modifying original list objects + if multi != 1: + new_row = [multi * x for x in new_row] + if rnd != -1: + new_row = [round(x, rnd) for x in new_row] + if doInt: + new_row = [int(x) for x in new_row] + processed_data.append(new_row) + else: + processed_data = data # Use data directly if no processing needed + + try: + with open(filename, 'a', newline='') as tsvfile: + writer = csv.writer(tsvfile, delimiter=delimiter) + writer.writerows(processed_data) + except Exception as e: + print(f"Error writing to file {filename}: {e}", file=sys.stderr) \ No newline at end of file diff --git a/test/experiments/osdi2025/experiment_runner.py b/test/experiments/osdi2025/experiment_runner.py index 9e443aab..e26da3a7 100644 --- a/test/experiments/osdi2025/experiment_runner.py +++ b/test/experiments/osdi2025/experiment_runner.py @@ -9,12 +9,14 @@ from test.experiments.osdi2025.numa_single_query.run import run_experiment as run_numa_single from test.experiments.osdi2025.numa_multi_query.run import run_experiment as run_numa_multi from test.experiments.osdi2025.aps_recall_targets.run import run_experiment as run_aps_recall +from test.experiments.osdi2025.early_termination.run import run_experiment as run_early_termination EXPERIMENTS = { "kick_the_tires": run_kick_the_tires, "numa_single_query": run_numa_single, "numa_multi_query": run_numa_multi, "aps_recall_targets": run_aps_recall, + "early_termination": run_early_termination } def main(): From e638c0e3368240e20bf3af8b74ab19fa66292528 Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 7 May 2025 23:00:17 -0500 Subject: [PATCH 024/323] add APS baselines + cleanup geometry.h --- src/cpp/bindings/wrap.cpp | 15 + src/cpp/include/common.h | 16 +- src/cpp/include/geometry.h | 841 +++--------------- src/cpp/src/query_coordinator.cpp | 34 +- .../early_termination/configs/sift1m.yaml | 59 ++ .../osdi2025/early_termination/laet.py | 3 +- .../osdi2025/early_termination/run.py | 92 +- 7 files changed, 304 insertions(+), 756 deletions(-) create mode 100644 test/experiments/osdi2025/early_termination/configs/sift1m.yaml diff --git a/src/cpp/bindings/wrap.cpp b/src/cpp/bindings/wrap.cpp index 527aebad..8035e240 100644 --- a/src/cpp/bindings/wrap.cpp +++ b/src/cpp/bindings/wrap.cpp @@ -176,6 +176,21 @@ PYBIND11_MODULE(_bindings, m) { (std::string("Threshold to trigger recomputation of APS. default = ") + std::to_string(DEFAULT_RECOMPUTE_THRESHOLD)).c_str()) .def_readwrite("aps_flush_period_us", &SearchParams::aps_flush_period_us, (std::string("APS flush period in microseconds. default = ") + std::to_string(DEFAULT_APS_FLUSH_PERIOD_US)).c_str()) + .def_readwrite("k_factor", &SearchParams::k_factor, + "Factor to adjust the number of neighbors to return.") + .def_readwrite("track_hits", &SearchParams::track_hits, + "Flag to track hits for maintenance policy.") + .def_readwrite("use_auncel", &SearchParams::use_auncel, + "Flag to use Auncel recall estimation for search.") + .def_readwrite("auncel_a", &SearchParams::auncel_a, + "Auncel parameter a for recall estimation.") + .def_readwrite("auncel_b", &SearchParams::auncel_b, + "Auncel parameter b for recall estimation.") + .def_readwrite("use_spann", &SearchParams::use_spann, + "Flag to use SPANN for search.") + .def_readwrite("spann_eps", &SearchParams::spann_eps, + "SPANN parameter epsilon for search.") + .def_readwrite("parent_params", &SearchParams::parent_params, "Search parameters for the parent index, if any.") .def("__repr__", [](const SearchParams &s) { diff --git a/src/cpp/include/common.h b/src/cpp/include/common.h index be85509e..7489976c 100644 --- a/src/cpp/include/common.h +++ b/src/cpp/include/common.h @@ -174,12 +174,24 @@ struct SearchParams { float recall_target = DEFAULT_RECALL_TARGET; int num_threads = 1; // number of threads to use for search within a single worker float k_factor = 1.0f; - bool use_precomputed = DEFAULT_PRECOMPUTED; bool batched_scan = DEFAULT_BATCHED_SCAN; + + bool track_hits = true; + + // APS params + bool use_precomputed = DEFAULT_PRECOMPUTED; float recompute_threshold = DEFAULT_RECOMPUTE_THRESHOLD; float initial_search_fraction = DEFAULT_INITIAL_SEARCH_FRACTION; int aps_flush_period_us = DEFAULT_APS_FLUSH_PERIOD_US; - bool track_hits = true; + + // Auncel params + bool use_auncel = false; + float auncel_a = 1.0f; + float auncel_b = 1.0f; + + // Spann params + bool use_spann = false; + float spann_eps = 1.25; shared_ptr parent_params = nullptr; ///< Search parameters for the parent index, if any. diff --git a/src/cpp/include/geometry.h b/src/cpp/include/geometry.h index 465ef1b1..c27ca997 100644 --- a/src/cpp/include/geometry.h +++ b/src/cpp/include/geometry.h @@ -203,151 +203,32 @@ inline double log_hypersphere_volume(double radius, int dimension) { return log_volume; } -inline double hypersphere_surface_area(double radius, int d) { - return 2 * std::pow(M_PI, d / 2.0) * std::pow(radius, d - 1) / std::tgamma(d / 2.0); -} - -inline double compute_partial_cap_volume(double radius, double theta_min, double theta_max, int d) { - // Compute the volume of a hyperspherical cap between angles theta_min and theta_max - if (theta_min < 0 || theta_max > M_PI) { - throw std::invalid_argument("Theta values out of bounds."); - } - - double cap_volume = 0.0; - - // Using the regularized incomplete beta function - double sin2_theta_min = std::sin(theta_min) * std::sin(theta_min); - double sin2_theta_max = std::sin(theta_max) * std::sin(theta_max); - - double I_min = incomplete_beta((d - 1.0) / 2.0, 0.5, sin2_theta_min); - double I_max = incomplete_beta((d - 1.0) / 2.0, 0.5, sin2_theta_max); - - double surface_area = hypersphere_surface_area(radius, d); - cap_volume = surface_area * (I_max - I_min); - - return cap_volume; -} - - -inline double log_hyperspherical_cap_volume(double radius, double boundary_distance, int d, bool ratio = true, - bool use_precomputed = true, bool euclidean = true) { - double h = radius - boundary_distance; - - // Ensure h is within valid range - h = std::max(0.0, std::min(2 * radius, h)); - +inline double hyperspherical_cap_volume(double radius, double boundary_distance, int d, bool use_precomputed = true, bool euclidean = true) { if (euclidean) { - double x = pow((2 * radius * h - h * h) / (radius * radius), 1.0); - // use precomputed incomplete beta function - double inc_beta; - if (use_precomputed) { - inc_beta = incomplete_beta_lookup(x, d); - } else { - inc_beta = incomplete_beta((d + 1.0) / 2.0, 0.5, x); - } + // Ensure boundary distance is non-negative double + boundary_distance = std::max(0.0, boundary_distance); - if (inc_beta <= 0.0 || std::isnan(inc_beta) || std::isinf(inc_beta)) { - std::cerr << "Invalid incomplete beta value: " << inc_beta << std::endl; - return -std::numeric_limits::infinity(); - } + // If boundary is outside or on the query radius, the cap volume is 0 + if (boundary_distance >= radius) return 0.0; - double log_inc_beta = std::log(inc_beta); - double log_cap_volume; - if (!ratio) { - double log_sphere_volume = log_hypersphere_volume(radius, d); - log_cap_volume = std::log(0.5) + log_inc_beta + log_sphere_volume; - } else { - log_cap_volume = std::log(0.5) + log_inc_beta; - } + // Calculate x for incomplete beta function + double x = sqrt(1.0 - (boundary_distance / radius) * (boundary_distance / radius)); + x = std::clamp(x, 0.0, 1.0); // Clamp x to [0, 1] - return log_cap_volume; - } else { - // use intersection of spherical caps - if (ratio != true) { - throw std::invalid_argument("Ratio must be true for dot product distance"); - } + // Incomplete Beta parameters + double a = 0.5 * (d + 1.0); + double b = 0.5; + double I = use_precomputed + ? incomplete_beta_lookup(x, d) + : incomplete_beta(a, b, x); + return std::clamp(0.5 * I, 0.0, 0.5); + } else { // v_i = (1/2) * [ I( sin^2(phi/2); d/2, 1/2 ) - I( sin^2(theta_i/2); d/2, 1/2 ) ] - double log_inc_beta = std::log(incomplete_beta((d - 1) / 2.0, 0.5, std::sin(radius / 2.0) * std::sin(radius / 2.0))); double log_inc_beta_boundary = std::log(incomplete_beta((d - 1) / 2.0, 0.5, std::sin(boundary_distance / 2.0) * std::sin(boundary_distance / 2.0))); double log_cap_volume = std::log(0.5) + log_inc_beta - log_inc_beta_boundary; return log_cap_volume; - // compute volume of the intersection of two spherical caps (from the paper: concise formulas for the volume of hyperspherical caps) - - - } -} - -inline vector compute_intersection_volume(const Tensor &boundary_distances, float query_radius, int dimension, - bool use_precomputed = true) { - auto boundary_distances_ptr = boundary_distances.data_ptr(); - int num_partitions = boundary_distances.size(0); - std::vector partition_volumes(num_partitions, 0.0f); - - for (int j = 0; j < num_partitions; j++) { - float boundary_distance = boundary_distances_ptr[j]; - - if (boundary_distance >= query_radius) { - partition_volumes[j] = -1e8; - continue; - } - - double volume_ratio = log_hyperspherical_cap_volume(query_radius, boundary_distance, dimension, false, - use_precomputed); - - partition_volumes[j] = volume_ratio; - } - - return partition_volumes; -} - -inline Tensor compute_variance_in_direction_of_query(Tensor query, Tensor centroids, Tensor variance) { - int dimension = query.size(0); - int num_partitions = centroids.size(0); - auto query_ptr = query.data_ptr(); - auto centroids_ptr = centroids.data_ptr(); - auto variance_ptr = variance.data_ptr(); - - std::vector variances(num_partitions, 0.0f); - - for (int j = 0; j < num_partitions; j++) { - float *centroid = centroids_ptr + j * dimension; - float *variance = variance_ptr + j * dimension; - - float *query_minus_centroid = new float[dimension]; - subtract_arrays(query_ptr, centroid, query_minus_centroid, dimension); - - float dot_product = faiss::fvec_inner_product(query_minus_centroid, query_minus_centroid, dimension); - - variances[j] = dot_product; - delete[] query_minus_centroid; - } - - return torch::tensor(variances).clone(); -} - -inline void check_boundary_distances(const float* q, - const std::vector& C, - const std::vector& d, - int dim) -{ - const float* c0 = C[0]; - std::vector v(dim); - - for (size_t j = 1; j < C.size(); ++j) { - const float* cj = C[j]; - - /* signed distance via the explicit formula again */ - faiss::fvec_sub(dim, cj, c0, v.data()); - float vnorm = std::sqrt(faiss::fvec_inner_product(v.data(), v.data(), dim)); - float b = 0.5f*(faiss::fvec_inner_product(cj,cj,dim) - -faiss::fvec_inner_product(c0,c0,dim)); - float dj = std::fabs(faiss::fvec_inner_product(q, v.data(), dim) - b) / vnorm; - - if (std::fabs(dj - d[j]) > 0.01f * dj + 1e-6f) - throw std::runtime_error( - "boundary_distances[" + std::to_string(j) + "] inaccurate"); } } @@ -359,16 +240,6 @@ compute_recall_profile(const std::vector& boundary_distances, bool use_precomputed = true, bool euclidean = true) // Unused in these models { - // --- Configuration Flag --- - // 0: Original Scaled-IE (reconstructed, uses norm_vols internally) - // 1: Direct Proportional Allocation (P'_k = raw_vols[k]) - // 2: Independent Entry Allocation (P'_k = raw_vols[k]*P0 / (1-raw_vols[k])) - // 3: Concentration Penalized Allocation (P'_k = raw_vols[k] * (1-S2_norm)) - // 4: Radius-Difference Weighting (P'_k = raw_vols[k] * (R-d_k)) - // 5: Simplified IE Approx Weighting (P'_k = raw_vols[k] * (1 - 0.5*S1 + 0.5*v_k)) - const int method_flag = 0; // <<< CHANGE THIS FLAG FOR ABLATION STUDY >>> - // --- - const int m = static_cast(boundary_distances.size()); const float eps = 1e-9f; @@ -383,251 +254,38 @@ compute_recall_profile(const std::vector& boundary_distances, return p; } - // --- Step 1: Compute Raw Cap Volumes (Common to all methods) --- + // Compute raw cap volumes std::vector raw_vols(m, 0.0f); for (int j = 1; j < m; ++j) { - // Ensure boundary distance is non-negative double - double d = std::max(0.0, static_cast(boundary_distances[j])); - // If boundary is outside or on the query radius, the cap volume is 0 - if (d >= query_radius) continue; - - // Calculate x for incomplete beta function - double x = 1.0 - (d / query_radius) * (d / query_radius); - x = std::clamp(x, 0.0, 1.0); // Clamp x to [0, 1] - - // Incomplete Beta parameters - double a = 0.5 * (dimension + 1.0); - double b = 0.5; - double I = use_precomputed - ? incomplete_beta_lookup(x, dimension) // Needs definition - : incomplete_beta(a, b, x); // Needs definition - - // Raw volume is half the normalized surface area ratio, clamped to [0, 0.5] - raw_vols[j] = static_cast(std::clamp(0.5 * I, 0.0, 0.5)); + raw_vols[j] = hyperspherical_cap_volume(query_radius, boundary_distances[j], dimension, use_precomputed, euclidean); } - // --- Method Selection --- float P0 = 0.0f; // Root cell probability std::vector P_prime(m, 0.0f); // Intermediate neighbor probabilities (k>=1) - float P_prime_sum = 0.0f; // Sum of P_prime[k] for k>=1 - - switch (method_flag) { - case 0: { // M0: Original Scaled-IE (reconstructed) - //std::cout << "Using Method 0: Original Scaled-IE (Cumulative)" << std::endl; - // Uses intermediate normalization and P0 based on normalized vols - std::vector norm_vols = raw_vols; // Copy raw vols - float S1_for_norm = 0.0f; - for (int j = 1; j < m; ++j) S1_for_norm += norm_vols[j]; - - if (S1_for_norm > eps) { - for (int j = 1; j < m; ++j) norm_vols[j] /= S1_for_norm; - } else { - for (int j = 1; j < m; ++j) norm_vols[j] = 0.0f; - } + float P_prime_sum = 0.0f; // Sum of P_prime[k] for k>=1 - // Calculate P0 using normalized vols (as per original logic) - P0 = 1.0f; - for (int j = 1; j < m; ++j) { - P0 *= (1.0f - norm_vols[j]); - } - P0 = std::clamp(P0, 0.0f, 1.0f); + std::vector norm_vols = raw_vols; // Copy raw vols + float S1_for_norm = 0.0f; + for (int j = 1; j < m; ++j) S1_for_norm += norm_vols[j]; - // Calculate P_prime using cumulative S1, S2 on normalized vols - float S1_cum = 0.0f, S2_cum = 0.0f; - // Use lambda = m as per original code's default, or allow tuning - float lambda = static_cast(m); // Could be tuned here if needed - - for (int k = 1; k < m; ++k) { - S1_cum += norm_vols[k]; // Update cumulative sums *before* using them for k - S2_cum += norm_vols[k] * norm_vols[k]; - - // Calculate R factor using *cumulative* S1, S2 up to k - // Note: Original code used S1*S1 - S2. Ensure S1_cum is used correctly. - float R = 1.0f - S1_cum + 0.5f * lambda * (S1_cum * S1_cum - S2_cum); - - P_prime[k] = norm_vols[k] * std::max(0.0f, R); - P_prime_sum += P_prime[k]; // Accumulate sum for final normalization - } - // Note: This calculation is order-dependent based on how boundaries are indexed. - break; - } - - case 1: { // M1: Direct Proportional Allocation - //std::cout << "Using Method 1: Direct Proportional" << std::endl; - // P0 based on raw volumes - - std::vector norm_vols = raw_vols; // Copy raw vols - float S1_for_norm = 0.0f; - for (int j = 1; j < m; ++j) S1_for_norm += norm_vols[j]; - - if (S1_for_norm > eps) { - for (int j = 1; j < m; ++j) norm_vols[j] /= S1_for_norm; - } else { - for (int j = 1; j < m; ++j) norm_vols[j] = 0.0f; - } - - P0 = 1.0f; - for (int j = 1; j < m; ++j) P0 *= (1.0f - norm_vols[j]); - P0 = std::clamp(P0, 0.0f, 1.0f); - - // P_prime is just raw_vols - for (int k = 1; k < m; ++k) { - P_prime[k] = norm_vols[k]; - P_prime_sum += P_prime[k]; - } - // Ensure P_prime is non-negative - for (int k = 1; k < m; ++k) P_prime[k] = std::max(0.0f, P_prime[k]); - break; - } - - case 2: { // M2: Independent Entry Allocation - //std::cout << "Using Method 2: Independent Entry" << std::endl; - // P0 based on raw volumes - P0 = 1.0f; - for (int j = 1; j < m; ++j) P0 *= (1.0f - raw_vols[j]); - P0 = std::clamp(P0, 0.0f, 1.0f); - - if (P0 > eps) { // Avoid issues if P0 is near zero - for (int k = 1; k < m; ++k) { - float v_k = raw_vols[k]; - float one_minus_v_k = 1.0f - v_k; - // Check for valid v_k and avoid division by zero/small number - if (v_k > eps && one_minus_v_k > eps) { - P_prime[k] = v_k * P0 / one_minus_v_k; - // Clamp potentially large values if v_k is very close to 1? - // P_prime[k] = std::min(P_prime[k], 1.0f); // Optional clamping - P_prime_sum += P_prime[k]; - } - P_prime[k] = std::max(0.0f, P_prime[k]); // Ensure non-negative - } - } // else P_prime remains 0 - break; - } - - case 3: { // M3: Concentration Penalized Allocation - //std::cout << "Using Method 3: Concentration Penalized" << std::endl; - // P0 based on raw volumes - P0 = 1.0f; - for (int j = 1; j < m; ++j) P0 *= (1.0f - raw_vols[j]); - P0 = std::clamp(P0, 0.0f, 1.0f); - - float S1_raw = 0.0f; - float S2_raw = 0.0f; - for (int j = 1; j < m; ++j) { - S1_raw += raw_vols[j]; - S2_raw += raw_vols[j] * raw_vols[j]; - } - - float penalty_factor = 0.0f; - if (S1_raw > eps) { - float S1_raw_sq = S1_raw * S1_raw; - // Avoid division by zero if S1_raw_sq is somehow zero or negative - if (S1_raw_sq > eps) { - float S2_norm = S2_raw / S1_raw_sq; - penalty_factor = std::max(0.0f, 1.0f - S2_norm); - } - // Optional: include lambda=m scaling -> penalty_factor *= 0.5f * m; - } - - for (int k = 1; k < m; ++k) { - P_prime[k] = raw_vols[k] * penalty_factor; // Apply uniform penalty - P_prime_sum += P_prime[k]; - } - break; - } - - case 4: { // M4: Radius-Difference Weighting - //std::cout << "Using Method 4: Radius-Difference Weighting" << std::endl; - // P0 based on raw volumes - P0 = 1.0f; - for (int j = 1; j < m; ++j) P0 *= (1.0f - raw_vols[j]); - P0 = std::clamp(P0, 0.0f, 1.0f); - - for (int k = 1; k < m; ++k) { - float v_k = raw_vols[k]; - if (v_k > eps) { - // Use boundary_distances directly, ensure non-negative - float d_k = std::max(0.0f, boundary_distances[k]); - // Calculate non-uniform weight based on distance from radius - float mu_k_tilde = std::max(0.0f, query_radius - d_k); - P_prime[k] = v_k * mu_k_tilde; - P_prime_sum += P_prime[k]; - } - } - break; - } - - case 5: { // M5: Simplified IE Approx Weighting - //std::cout << "Using Method 5: Simplified IE Approx" << std::endl; - // P0 based on raw volumes - P0 = 1.0f; - for (int j = 1; j < m; ++j) P0 *= (1.0f - raw_vols[j]); - P0 = std::clamp(P0, 0.0f, 1.0f); - - float S1_raw = 0.0f; - for(int j=1; j eps) { - // Calculate non-uniform scaling factor - float mu_k_tilde = std::max(0.0f, 1.0f - 0.5f * S1_raw + 0.5f * v_k); - P_prime[k] = v_k * mu_k_tilde; - P_prime_sum += P_prime[k]; - } - } - break; - } - - case 6: { - std::vector norm_vols = raw_vols; // Copy raw vols - float S1_for_norm = 0.0f; - for (int j = 1; j < m; ++j) S1_for_norm += norm_vols[j]; - - if (S1_for_norm > eps) { - for (int j = 1; j < m; ++j) norm_vols[j] /= S1_for_norm; - } else { - for (int j = 1; j < m; ++j) norm_vols[j] = 0.0f; - } - - P0 = 1.0f; - for (int j = 1; j < m; ++j) P0 *= (1.0f - norm_vols[j]); - P0 = std::clamp(P0, 0.0f, 1.0f); - - float S2_raw = 0.0f; - for (int j = 1; j < m; ++j) { - S2_raw += norm_vols[j]; - } - - if (S2_raw > eps) { - for (int k = 1; k < m; ++k) { - float v_k = norm_vols[k]; - S2_raw -= v_k; // Update S2_raw for next iteration - float rel_L2_sq = (.5 * v_k * S2_raw); - float mu_k_tilde = 1 / (1.0f + rel_L2_sq); // Non-uniform scaling - P_prime[k] = v_k * std::max(mu_k_tilde, .001f); - P_prime_sum += P_prime[k]; - } - } else { // If S2 is zero, all raw_vols are zero, P_prime remains zero - for (int k = 1; k < m; ++k) { - P_prime[k] = norm_vols[k]; // Should be 0 - P_prime_sum += P_prime[k]; // Should be 0 - } - } - // Ensure P_prime is non-negative - for (int k = 1; k < m; ++k) P_prime[k] = std::max(0.0f, P_prime[k]); + if (S1_for_norm > eps) { + for (int j = 1; j < m; ++j) norm_vols[j] /= S1_for_norm; + } else { + for (int j = 1; j < m; ++j) norm_vols[j] = 0.0f; + } - break; - } + P0 = 1.0f; + for (int j = 1; j < m; ++j) P0 *= (1.0f - norm_vols[j]); // Survival probability of the root cell + P0 = std::clamp(P0, 0.0f, 1.0f); - default: - // Handle unknown method flag, e.g., throw error or default to Method 1 - //std::cerr << "Error: Unknown method_flag: " << method_flag << std::endl; - throw std::runtime_error("Unknown method_flag in compute_recall_profile"); - // Or fallback: goto method1_label; (using labels/goto is generally discouraged) + for (int k = 1; k < m; ++k) { + P_prime[k] = norm_vols[k]; + P_prime_sum += P_prime[k]; } + // Ensure P_prime is non-negative + for (int k = 1; k < m; ++k) P_prime[k] = std::max(0.0f, P_prime[k]); - // if the cluster_sizes are given, we can use them to scale the probabilities + // if the cluster_sizes are given, scale P_prime[k] by the size of the cluster. this is a rudimentary density estimation if (partition_sizes.size() > 0) { for (int k = 1; k < m; ++k) { if (partition_sizes[k] > 0) { @@ -636,9 +294,10 @@ compute_recall_profile(const std::vector& boundary_distances, } } - // --- Final Normalization --- + + // normalize probs std::vector probs(m, 0.0f); - probs[0] = P0; // Assign calculated P0 for the chosen method + probs[0] = P0; float target = 1.0f - P0; // Ensure target probability for neighbors is valid [0, 1] @@ -651,365 +310,113 @@ compute_recall_profile(const std::vector& boundary_distances, probs[k] = std::max(0.0f, P_prime[k] * scale); } - // --- Strict Renormalization --- - // This step ensures probs[1..m-1] sum *exactly* to target, - // correcting minor float inaccuracies or effects of max(0,...) + + // Strict renormalization to ensure sum is exactly 1 float current_sum_k = 0.0f; for (int k = 1; k < m; ++k) current_sum_k += probs[k]; if (current_sum_k > eps) { // Avoid division by zero if sum is negligible - float final_scale = target / current_sum_k; - // Check if scale is finite (handles target=0 case correctly) - if (std::isfinite(final_scale)) { - for (int k = 1; k < m; ++k) { - probs[k] *= final_scale; - // Final clamp for safety - probs[k] = std::max(0.0f, probs[k]); - } - } else if (target <= eps) { - // If target is zero, all neighbor probs should be zero - for (int k = 1; k < m; ++k) probs[k] = 0.0f; - } + float final_scale = target / current_sum_k; + // Check if scale is finite (handles target=0 case correctly) + if (std::isfinite(final_scale)) { + for (int k = 1; k < m; ++k) { + probs[k] *= final_scale; + // Final clamp for safety + probs[k] = std::max(0.0f, probs[k]); + } + } else if (target <= eps) { + // If target is zero, all neighbor probs should be zero + for (int k = 1; k < m; ++k) probs[k] = 0.0f; + } } else if (target <= eps) { - // If target is zero and calculated sum is zero, ensure all are zero - for (int k = 1; k < m; ++k) probs[k] = 0.0f; + // If target is zero and calculated sum is zero, ensure all are zero + for (int k = 1; k < m; ++k) probs[k] = 0.0f; } - // --- End Optional Strict Renormalization --- - } return probs; } -// inline std::vector -// compute_recall_profile(const std::vector& boundary_distances, -// float query_radius, -// int dimension, -// std::vector partition_sizes = {}, -// bool use_precomputed = true, -// bool euclidean = true) -// { -// const int m = static_cast(boundary_distances.size()); -// const float eps = 1e-9f; -// -// if (m <= 1) { -// if (m == 1) return {1.0f}; -// return {}; -// } -// if (query_radius <= eps) { -// std::vector p(m, 0.0f); -// p[0] = 1.0f; -// return p; -// } -// -// // 1) compute raw cap‐volumes vols[1..m-1] -// std::vector vols(m, 0.0f); -// for (int j = 1; j < m; ++j) { -// double d = std::max(0.0, static_cast(boundary_distances[j])); -// if (d >= query_radius) continue; -// double x = 1.0 - (d/query_radius)*(d/query_radius); -// x = std::clamp(x, 0.0, 1.0); -// -// double a = 0.5*(dimension + 1.0), b = 0.5, I = 0.0; -// try { -// I = use_precomputed -// ? incomplete_beta_lookup(x, dimension) -// : incomplete_beta(a, b, x); -// } catch(...) { -// I = 0.0; -// } -// vols[j] = std::clamp(0.5 * I, 0.0, 0.5); -// } -// -// // // 2) normalize vols[1..] to sum = 1 -// // float sumv = std::accumulate(vols.begin()+1, vols.end(), 0.0f); -// // if (sumv > eps) { -// // for (int j = 1; j < m; ++j) vols[j] /= sumv; -// // } else { -// // std::fill(vols.begin()+1, vols.end(), 0.0f); -// // } -// -// // 3) compute reference P0 = ∏_{j=1..m-1}(1 - vols[j]) -// float P0 = 1.0f; -// for (int j = 1; j < m; ++j) { -// P0 *= (1.0f - vols[j]); -// } -// -// // 4) compute raw Pk for k>=1 under chosen mode -// std::vector raw(m, 0.0f); -// -// // 4) if scaled‐IE, compute λ = 1 / ∑v_j^2 -// float lambda = (float) boundary_distances.size(); -// // float lambda = -// // if (use_scaled_ie) { -// // float sum2 = 0.0f; -// // for (int j=1;jeps ? 1.0f/sum2 : 1.0f); -// // } -// -// std::cout << "lambda: " << lambda << std::endl; -// -// // -- Scaled‐IE: λ·(S1²−S2) in pairwise term -- -// float S1 = 0.0f, S2 = 0.0f; -// for (int k = 1; k < m; ++k) { -// S1 += vols[k]; -// S2 += vols[k]*vols[k]; -// float R = 1.0f -// - S1 -// + 0.5f*lambda*(S1*S1 - S2); -// raw[k] = vols[k] * std::max(0.0f, R); -// } -// std::cout << "S1: " << S1 << " S2: " << S2 << std::endl; -// -// // float scale_term = 1.0f; -// // for (int k = 1; k < m; ++k) { -// // raw[k] = vols[k] * scale_term; -// // scale_term *= (1.0f - vols[k]); -// // } -// -// // for (int k = 1; k < m; ++k) { -// // float scale_term = P0 / (1 - vols[k]); -// // raw[k] = vols[k] * scale_term; -// // } -// -// // 5) scale k>=1 so they sum to (1 - P0), leave P0 unchanged -// float sum_raw = std::accumulate(raw.begin()+1, raw.end(), 0.0f); -// std::vector probs(m, 0.0f); -// probs[0] = P0; -// -// float target = 1.0f - P0; -// if (sum_raw > eps) { -// float scale = target / sum_raw; -// for (int k = 1; k < m; ++k) { -// probs[k] = std::max(0.0f, raw[k] * scale); -// } -// } -// -// return probs; -// } - -// inline std::vector -// compute_recall_profile(const std::vector& boundary_distances, -// float query_radius, -// int dimension, -// std::vector partition_sizes = {}, -// bool use_precomputed = true, -// bool euclidean = true) -// { -// const int m = static_cast(boundary_distances.size()); -// const float float_epsilon = 1e-9f; // Small value for float comparisons -// -// // Handle edge cases: no cells or only the home cell -// if (m == 0) { -// return {}; -// } -// if (m == 1) { -// return {1.0f}; // Only home cell exists, probability is 1 -// } -// if (query_radius <= float_epsilon) { -// // If query radius is zero or negligible, k-NN must be in home cell -// std::vector probs(m, 0.0f); -// probs[0] = 1.0f; -// return probs; -// } -// -// -// std::vector vols(m, 0.0f); // Raw cap volume ratios (v_j for j>=1) -// -// // --- 1. Calculate raw cap ratios v_j for neighbors j = 1 to m-1 --- -// for (int j = 1; j < m; ++j) { -// float d_edge = boundary_distances[j]; // Distance to bisector plane j -// -// // Clamp distance to be non-negative (numerical safety) -// d_edge = std::max(0.0f, d_edge); -// -// // If plane is beyond radius, cap volume is zero -// if (d_edge >= query_radius) { -// vols[j] = 0.0f; -// continue; -// } -// -// // Calculate x = 1 - (d_edge/query_radius)^2 for betainc arg I_x(a, b) -// // Use doubles for intermediate calculation for precision -// double r_double = static_cast(query_radius); -// double d_double = static_cast(d_edge); -// double ratio_d_r = d_double / r_double; -// double x = 1.0 - ratio_d_r * ratio_d_r; -// -// // Clamp x to [0, 1] due to potential floating point inaccuracies -// x = std::clamp(x, 0.0, 1.0); -// -// // Parameters for incomplete beta I_x(a, b) for hyperspherical cap volume ratio -// double a_param = 0.5 * (static_cast(dimension) + 1.0); -// double b_param = 0.5; -// -// double beta_inc_value = 0.0; -// try { -// beta_inc_value = use_precomputed -// ? incomplete_beta_lookup(x, dimension) -// : incomplete_beta(a_param, b_param, x); -// } catch (const std::exception& e) { -// // Warn if beta calculation fails, default volume to 0 -// // Consider more robust error handling if needed -// #if DEBUG // Optional: Only print in debug builds -// fprintf(stderr, -// "Warning: Incomplete Beta calculation failed for x=%.6f, dim=%d. Defaulting cap vol to 0. Error: %s\n", -// x, dimension, e.what()); -// #endif -// beta_inc_value = 0.0; -// } -// -// // Cap volume ratio = 0.5 * I_x(a, b) -// vols[j] = 0.5f * static_cast(beta_inc_value); -// // Clamp result to ensure it's within valid range [0, 0.5] -// vols[j] = std::clamp(vols[j], 0.0f, 0.5f); -// } -// -// // --- 2. Calculate mu = sum(v_j) for j>=1 --- -// float mu_total = std::accumulate(vols.begin() + 1, vols.end(), 0.0f); -// -// // normalize volumes to sum to 1.0 -// if (mu_total > float_epsilon) { -// for (int j = 1; j < m; ++j) { -// vols[j] /= mu_total; -// } -// } else { -// // If mu is near zero, set all volumes to zero -// std::fill(vols.begin() + 1, vols.end(), 0.0f); -// } -// // --- 4. Calculate Improved P0 = max(exp(-mu), 1 - v1) --- -// -// vector survival_function(m, 1.0f); -// int k_neighbors = 100; -// for (int j = m-1; j > 0; --j) { -// survival_function[j-1] = survival_function[j] * (1.0f - (vols[j])); -// } -// -// // --- 5. Calculate neighbor probabilities P_j = (1-P0)*v_j/mu --- -// std::vector probs(m, 0.0f); -// probs[0] = survival_function[0]; -// -// // for (int j = 1; j < m; j++) { -// // probs[j] = vols[j] * (survival_function[0] / (1 - vols[j])); -// // } -// -// // for (int j = 1; j < m; ++j) { -// // probs[j] = survival_function[j] - survival_function[j - 1]; -// // } -// -// float remaining_mass = 1.0f - probs[0]; -// float cum_v = 0.0f; // Σ_{i float_epsilon) { -// // for (int j = 1; j < m; ++j) { -// // probs[j] = (accum / (1 - vols[j])) - accum; // reverse the survival function -// // probs[j] = std::clamp(probs[j], 0.0f, 1.0f); -// // accum += probs[j]; -// // } -// // } -// // If P_neighbors_total or mu is near zero, probs[1...m-1] remain 0.0f. -// -// // --- 6. Final Renormalization (Safety Net for Floating Point Drift) --- -// float sum_probs = std::accumulate(probs.begin(), probs.end(), 0.0f); -// -// if (sum_probs > float_epsilon) { -// float inv_sum = 1.0f / sum_probs; -// for (int j = 0; j < m; ++j) { -// // Ensure probabilities are non-negative after division -// probs[j] = std::max(0.0f, probs[j] * inv_sum); -// } -// // Ensure sum is exactly 1.0, assign difference to largest element (usually P0) -// float final_sum_check = std::accumulate(probs.begin(), probs.end(), 0.0f); -// probs[0] += (1.0f - final_sum_check); // Add residual to P0 -// -// -// } else { -// // If sum is zero (should be rare unless m=0), reset to P0=1. -// if (m > 0) { -// std::fill(probs.begin() + 1, probs.end(), 0.0f); -// probs[0] = 1.0f; -// } -// } -// -// return probs; -// } - -inline float compute_intersection_volume_one(float boundary_distance, float query_radius, int dimension) { - if (boundary_distance >= query_radius) { - return -1e8; +inline std::vector +compute_recall_profile_auncel( + const std::vector& boundary_distances, + float query_radius, + int K_neighbors, + float a, + float b +) { + const size_t L = boundary_distances.size(); + + // 1) Spherical-cap "angle" terms + std::vector cap_terms(L, 0.0f); + if (query_radius > 1e-9f) { + for (size_t j = 0; j < L; ++j) { + float r = boundary_distances[j] / query_radius; + r = std::clamp(r, -1.0f, 1.0f); + cap_terms[j] = (r >= 1.0f ? 0.0f : std::acos(r)); + } } - double volume_ratio = log_hyperspherical_cap_volume(query_radius, boundary_distance, dimension, true); + // 2) Calculate phi_values for each stage + std::vector phi_values(L + 1); + float running_U_sum = std::accumulate(cap_terms.begin(), cap_terms.end(), 0.0f); - return volume_ratio; -} + for (size_t i = 0; i <= L; ++i) { + float denominator = b - a * running_U_sum; + if (denominator <= 1e-9f) { + phi_values[i] = std::numeric_limits::max(); + } else { + phi_values[i] = 1.0f / denominator; + } + phi_values[i] = std::max(1.0f, phi_values[i]); // Ensure phi >= 1 -inline Tensor estimate_overlap(const Tensor &new_centroid, const Tensor &old_centroid, const Tensor &nbr_centroids) { - Tensor residual = new_centroid - old_centroid; - int dimension = new_centroid.size(0); - - vector old_boundary_distance(nbr_centroids.size(0), -1.0f); - vector new_boundary_distance(nbr_centroids.size(0), -1.0f); - - const float *residual_ptr = residual.data_ptr(); - const float *new_centroid_ptr = new_centroid.data_ptr(); - const float *old_centroid_ptr = old_centroid.data_ptr(); - const float *nbr_centroids_ptr = nbr_centroids.data_ptr(); - - std::vector line_vector(dimension); - std::vector midpoint(dimension); - std::vector projection(dimension); - - // compute distance to old boundary - for (int j = 0; j < nbr_centroids.size(0); j++) { - subtract_arrays(nbr_centroids_ptr + (dimension * j), old_centroid_ptr, line_vector.data(), dimension); - divide_array_by_constant(line_vector.data(), 2.0f, midpoint.data(), dimension); - float norm = faiss::fvec_inner_product(midpoint.data(), midpoint.data(), dimension); - norm = std::sqrt(norm); - old_boundary_distance[j] = norm; + if (i < L) { + running_U_sum -= cap_terms[i]; + } } - // compute distance to new boundary - for (int j = 0; j < nbr_centroids.size(0); j++) { - subtract_arrays(nbr_centroids_ptr + (dimension * j), new_centroid_ptr, line_vector.data(), dimension); - divide_array_by_constant(line_vector.data(), 2.0f, midpoint.data(), dimension); - float norm = faiss::fvec_inner_product(midpoint.data(), midpoint.data(), dimension); - norm = std::sqrt(norm); - new_boundary_distance[j] = norm; - } + // 3) Calculate cumulative recall based on phi_values + std::vector cumulative_recalls(L + 1, 0.0f); + for (size_t i = 0; i <= L; ++i) { + float current_phi = phi_values[i]; + float j_star = 0.0f; + + if (K_neighbors > 0) { + if (current_phi == std::numeric_limits::max()) { + j_star = 0.0f; + } else if (current_phi > 1e-9f) { + j_star = std::floor((float)K_neighbors / current_phi); + j_star = std::min(std::max(0.0f, j_star), (float)K_neighbors); + } + } - Tensor overlap_ratio = torch::empty({nbr_centroids.size(0)}, torch::kFloat32); + float error_i = 1.0f; // Default error if K_neighbors is 0 or no items found + if (K_neighbors > 0) { + error_i = 1.0f - j_star / (float)K_neighbors; + } + cumulative_recalls[i] = std::clamp(1.0f - error_i, 0.0f, 1.0f); + } - // for each neighbor, compute the hyperspherical cap volume, where the radius of the sphere is the distance to new boundary - // and the old boundary distance gives the height of the cap - float mean_new_boundary_distance = 0.0f; - float mean_old_boundary_distance = 0.0f; - for (int j = 0; j < nbr_centroids.size(0); j++) { - mean_new_boundary_distance += new_boundary_distance[j]; - mean_old_boundary_distance += old_boundary_distance[j]; + // 4) Calculate per-stage incremental recall + std::vector recall_profile(L + 1, 0.0f); + if (!cumulative_recalls.empty()) { + recall_profile[0] = cumulative_recalls[0]; + for (size_t i = 1; i <= L; ++i) { + recall_profile[i] = cumulative_recalls[i] - cumulative_recalls[i-1]; + recall_profile[i] = std::max(0.0f, recall_profile[i]); + } } - mean_new_boundary_distance /= nbr_centroids.size(0); - mean_old_boundary_distance /= nbr_centroids.size(0); - for (int j = 0; j < nbr_centroids.size(0); j++) { - overlap_ratio[j] = abs(new_boundary_distance[j] - old_boundary_distance[j]) / mean_old_boundary_distance; + // 5) Normalize the profile to sum to 1 + float S = std::accumulate(recall_profile.begin(), recall_profile.end(), 0.0f); + if (S > 1e-9f) { + for (auto &v : recall_profile) { + v /= S; + } } + // If S is 0 (e.g., K_neighbors=0 or zero predicted recall), profile remains all zeros. - return overlap_ratio; + return recall_profile; } -#endif // CPP_UTILS_GEOMETRY_H +#endif // CPP_UTILS_GEOMETRY_H \ No newline at end of file diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 364e75ab..78c04a88 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -616,12 +616,20 @@ shared_ptr QueryCoordinator::serial_scan(Tensor x, Tensor partitio if (first_list || percent_change > search_params->recompute_threshold) { query_radius = curr_radius; - partition_probs = compute_recall_profile(boundary_distances, - query_radius, - dimension, - partition_sizes_vec, - search_params->use_precomputed, - metric_ == faiss::METRIC_L2); + if (search_params->use_auncel) { + partition_probs = compute_recall_profile_auncel(boundary_distances, + query_radius, + search_params->k, + search_params->auncel_a, + search_params->auncel_b); + } else { + partition_probs = compute_recall_profile(boundary_distances, + query_radius, + dimension, + partition_sizes_vec, + search_params->use_precomputed, + metric_ == faiss::METRIC_L2); + } } float recall_estimate = 0.0; for (int i = 0; i < p + 1; i++) { @@ -630,12 +638,12 @@ shared_ptr QueryCoordinator::serial_scan(Tensor x, Tensor partitio end_time = high_resolution_clock::now(); aps_time += duration_cast(end_time - start_time).count(); if (recall_estimate >= search_params->recall_target) { - timing_info->partitions_scanned = p + 1; break; } } } + timing_info->partitions_scanned = scanned_ids.size(); if (search_params->track_hits && maintenance_policy_) { maintenance_policy_->record_query_hits(std::vector(scanned_ids.begin(), scanned_ids.end())); @@ -692,6 +700,7 @@ shared_ptr QueryCoordinator::search(Tensor x, shared_ptrget_partition_ids(); @@ -718,9 +727,20 @@ shared_ptr QueryCoordinator::search(Tensor x, shared_ptrsearch(x, parent_search_params); partition_ids_to_scan = parent_search_result->ids; + partition_distances = parent_search_result->distances; parent_timing_info = parent_search_result->timing_info; } + if (search_params->use_spann && partition_distances.defined()) { + // prune partitions based on relative distance compared to nearest centroid + partition_distances = partition_distances / partition_distances.select(1, 0).unsqueeze(0); + + Tensor mask = partition_distances.ge(search_params->spann_eps); + + // set mask partition ids to -1 + partition_ids_to_scan.masked_fill_(mask, -1); + } + auto search_result = scan_partitions(x, partition_ids_to_scan, search_params); search_result->timing_info->parent_info = parent_timing_info; diff --git a/test/experiments/osdi2025/early_termination/configs/sift1m.yaml b/test/experiments/osdi2025/early_termination/configs/sift1m.yaml new file mode 100644 index 00000000..b8a92bdf --- /dev/null +++ b/test/experiments/osdi2025/early_termination/configs/sift1m.yaml @@ -0,0 +1,59 @@ +# File: configs/experiment.yaml +mode: run +base_dir: data/aps # Base for your project's data if needed by Quake's load_dataset +dataset: + name: sift1m # Used by Quake's load_dataset, e.g., "sift1m", "deep1m" + path: data # Path for Quake's load_dataset +experiment: + nq: 100 + k: 100 # k for search and recall calculation + n_workers: 0 + recall_targets: [.2, .3, .4, .5, .6, 0.7, .75, 0.8, .85, 0.9, 0.95, 0.99, .999] + # APS specific params (if used) + recompute_ratio: 0.01 + use_precompute: true + initial_search_fraction: .1 +index: # For QuakeIndex (APS, Oracle, FixedNProbe, and base for LAET) + metric: l2 + nlist: 1000 # This nlist is for your QuakeIndex +overwrite: + index: false # Whether to rebuild QuakeIndex + results: true +#methods: ['LAET', 'Auncel', 'Oracle', 'FixedNProbe', 'APS', 'SPANN'] # Added LAET +#methods: ['SPANN', 'FixedNProbe'] +#methods: ['Auncel'] # Added LAET +methods: ['APS', 'Oracle'] +#methods: ['APS'] + +paths: + index_dir: data/sift1m/indexes # For QuakeIndex files + results_dir: data/sift1m/results # For experiment output CSVs and plots + plot_dir: data/sift1m/plots + laet_artifacts_base_dir: data/laet_files # IMPORTANT: Base directory for all LAET artifacts + +laet_config: + laet_index_key_full: "IVF1000" # CRITICAL: Must match the index string in LAET's GBDT model + pred_thresh_for_model_features: 1 # The 'threshX' value from LAET's model filename (e.g., _model_thresh1_Full.txt). + feature_idx_for_model: 0 # 0-based index corresponding to `pred_thresh_for_model_features + base_vectors_path_for_laet_train: data/sift/sift_base.fvecs # Base vectors for LAET training + train_query_vectors_path_for_laet_train: data/sift/sift_query.fvecs # Training query vectors for LAET training + gt_train_path_for_laet_train: data/sift/sift_groundtruth.ivecs # Ground truth for LAET training + gt_format_for_laet_train: "ivecs" # Format for ground truth + faiss_training_vectors_path_for_laet_train: data/sift/sift_learn.fvecs # FAISS training vectors for LAET training + # if LAET generated features for a *list* of thresholds. + # E.g., if LAET's script had pred_thresh = [1,5,10] and this model + # is for the `thresh1` features, then feature_idx_for_model is 0. + # If LAET script used a single threshold value that matches + # `pred_thresh_for_model_features`, this is 0. + # Optional: + # multiplier_range: [0.1, 5.0] # Range for binary searching the nprobe multiplier + # num_binary_search_steps: 10 # Iterations for multiplier binary search + +# +# data_paths_for_laet_training = { +# "base_vectors": laet_main_cfg.get("base_vectors_path_for_laet_train", f"data/{dataset_name}_base.fvecs"), +# "train_query_vectors": laet_main_cfg.get("train_query_vectors_path_for_laet_train", f"data/{dataset_name}_learn.fvecs"), +# "gt_train": laet_main_cfg.get("gt_train_path_for_laet_train", f"data/{dataset_name}_learn_groundtruth.ivecs"), +# "gt_format": laet_main_cfg.get("gt_format_for_laet_train", "ivecs"), +# "training_vectors_for_faiss_index": laet_main_cfg.get("faiss_training_vectors_path_for_laet_train", f"data/{dataset_name}_learn.fvecs") +#} \ No newline at end of file diff --git a/test/experiments/osdi2025/early_termination/laet.py b/test/experiments/osdi2025/early_termination/laet.py index b9687270..0b260906 100644 --- a/test/experiments/osdi2025/early_termination/laet.py +++ b/test/experiments/osdi2025/early_termination/laet.py @@ -14,7 +14,6 @@ import torch from sklearn.metrics import mean_squared_error, mean_absolute_error -# --- Embedded I/O Utilities (Simplified) --- def _mmap_fvecs(fname): x = np.memmap(fname, dtype='int32', mode='r'); d = x[0]; return x.view('float32').reshape(-1, d + 1)[:, 1:] def _mmap_bvecs(fname): @@ -262,7 +261,7 @@ def run_inference_for_quake_experiment(self, quake_idx, queries_torch, gt_torch, if abs(multi_low - multi_high) < 1e-4: break recall_sum = 0.0 - tune_nq = int(nq * 0.01) + tune_nq = nq for i in range(tune_nq): base_p = self._predict_base_search_param(queries_np[i]) final_p = min(max(1, int(round(base_p * curr_mult))), quake_nlist_cap) diff --git a/test/experiments/osdi2025/early_termination/run.py b/test/experiments/osdi2025/early_termination/run.py index e4e8af6a..23e47988 100644 --- a/test/experiments/osdi2025/early_termination/run.py +++ b/test/experiments/osdi2025/early_termination/run.py @@ -1,9 +1,3 @@ -#!/usr/bin/env python3 -""" -Recall-Target Runner for Approximate Nearest Neighbor Search Methods. -Supports: Oracle, FixedNProbe, APS, and LAET (Learned Adaptive Early Termination). -""" - import time import yaml import logging @@ -15,7 +9,6 @@ import numpy as np import matplotlib.pyplot as plt -# Assuming 'quake' and 'laet' are in the PYTHONPATH or same directory try: from quake import QuakeIndex, IndexBuildParams, SearchParams from quake.datasets.ann_datasets import load_dataset @@ -25,13 +18,8 @@ raise try: - # Adjust this import based on your project structure. - # If laet.py is in the same directory as this script, it should be: - # from laet import LAETPipeline - # Your current script uses a relative import: from .laet import LAETPipeline except ImportError as e: - # Fallback if relative import fails (e.g., script run as top-level) try: from laet import LAETPipeline print("Note: Imported LAETPipeline using 'from laet import LAETPipeline'.") @@ -45,13 +33,6 @@ logger = logging.getLogger(__name__) def run_experiment(cfg_path: str, output_dir_str: str): - """ - Runs the ANN experimentation pipeline. - - Args: - cfg_path (str): Path to the YAML configuration file. - output_dir_str (str): Path to the directory where results will be saved. - """ # 1) Load config + setup output dir logger.info(f"Loading configuration from: {cfg_path}") try: @@ -179,7 +160,7 @@ def run_experiment(cfg_path: str, output_dir_str: str): ) logger.info("LAET GBDT model is ready (loaded or trained).") - # 5) Run each method × recall_target + # 5) Run each method × recall_target records = [] k_metric = cfg["experiment"]["k"] @@ -220,7 +201,7 @@ def run_experiment(cfg_path: str, output_dir_str: str): logger.warning(f"LAET method specified but pipeline/model or QuakeIndex not ready. Skipping for RT={rt_metric}.") # Metrics remain NaN by default - elif method in ["Oracle", "FixedNProbe", "APS"]: + elif method in ["Oracle", "FixedNProbe", "APS", "Auncel", "SPANN"]: if not idx_quake: logger.warning(f"QuakeIndex not available for method {method}. Skipping.") continue @@ -250,6 +231,63 @@ def run_experiment(cfg_path: str, output_dir_str: str): rec_oracle_final = compute_recall(res_oracle_final.ids, gt_torch[i].unsqueeze(0), k_metric).item() per_query_data_list.append((best_param_oracle, rec_oracle_final, elapsed_ns / 1_000_000.0)) + elif method == "Auncel": + for i, q_single_torch in enumerate(queries_torch): + sp_auncel = SearchParams() + sp_auncel.nprobe = -1 + sp_auncel.k = k_metric + sp_auncel.recall_target = rt_metric + sp_auncel.recompute_threshold = cfg["experiment"].get("recompute_ratio", 0.00001) + sp_auncel.use_auncel = True + sp_auncel.auncel_a = .005 + sp_auncel.auncel_b = 1.0 + sp_auncel.initial_search_fraction = cfg["experiment"].get("initial_search_fraction", 0.1) + + t_start_ns = time.perf_counter_ns() + res_auncel = idx_quake.search(q_single_torch.unsqueeze(0), sp_auncel) + elapsed_ns = time.perf_counter_ns() - t_start_ns + rec_auncel = compute_recall(res_auncel.ids, gt_torch[i].unsqueeze(0), k_metric).item() + param_aps = res_auncel.timing_info.partitions_scanned if hasattr(res_auncel, 'timing_info') and hasattr(res_auncel.timing_info, 'partitions_scanned') else np.nan + per_query_data_list.append((param_aps, rec_auncel, elapsed_ns / 1_000_000.0)) + + elif method == "SPANN": + lo_fixed, hi_fixed, best_overall_param_fixed = 1, max_nlist_val, max_nlist_val + while lo_fixed <= hi_fixed: + mid_param_fixed = (lo_fixed + hi_fixed) // 2 + sp_fixed_bs = SearchParams() + sp_fixed_bs.nprobe = mid_param_fixed + sp_fixed_bs.k = k_metric + sp_fixed_bs.use_spann = False + sp_fixed_bs.spann_eps = 2.0 + sp_fixed_bs.recall_target = -1 + + ids_list_fixed = [] + for q_bs_fixed in queries_torch: + res_bs_fixed = idx_quake.search(q_bs_fixed.unsqueeze(0), sp_fixed_bs) + if res_bs_fixed.ids is not None and res_bs_fixed.ids.numel() > 0: + ids_list_fixed.append(res_bs_fixed.ids) + + if not ids_list_fixed: avg_rec_fixed = 0.0 + else: avg_rec_fixed = compute_recall(torch.cat(ids_list_fixed, 0), gt_torch, k_metric).mean().item() + + if avg_rec_fixed >= rt_metric: + best_overall_param_fixed = mid_param_fixed + hi_fixed = mid_param_fixed - 1 + else: lo_fixed = mid_param_fixed + 1 + + for i, q_single_torch in enumerate(queries_torch): + t_start_ns = time.perf_counter_ns() + sp_fixed_final = SearchParams() + sp_fixed_final.nprobe = best_overall_param_fixed + sp_fixed_final.k = k_metric + sp_fixed_final.use_spann = True + sp_fixed_final.spann_eps = 1.5 + res_fixed_final = idx_quake.search(q_single_torch.unsqueeze(0), sp_fixed_final) + elapsed_ns = time.perf_counter_ns() - t_start_ns + rec_fixed_final = compute_recall(res_fixed_final.ids, gt_torch[i].unsqueeze(0), k_metric).item() + param_spann = res_fixed_final.timing_info.partitions_scanned if hasattr(res_fixed_final, 'timing_info') and hasattr(res_fixed_final.timing_info, 'partitions_scanned') else np.nan + per_query_data_list.append((param_spann, rec_fixed_final, elapsed_ns / 1_000_000.0)) + elif method == "FixedNProbe": lo_fixed, hi_fixed, best_overall_param_fixed = 1, max_nlist_val, max_nlist_val while lo_fixed <= hi_fixed: @@ -340,14 +378,12 @@ def run_experiment(cfg_path: str, output_dir_str: str): grp = df_results[df_results["Method"] == m_name].sort_values(by="RecallTarget") if grp.empty: continue - axes[0].errorbar(grp["RecallTarget"], grp["mean_time_ms"], yerr=grp["std_time_ms"], - marker="o", capsize=3, label=m_name, alpha=0.8, errorevery=1, elinewidth=1) - axes[1].errorbar(grp["RecallTarget"], grp["mean_recall"], yerr=grp["std_recall"], - marker="o", capsize=3, label=m_name, alpha=0.8, errorevery=1, elinewidth=1) - axes[2].errorbar(grp["RecallTarget"], grp["mean_nprobe"], yerr=grp["std_nprobe"], - marker="o", capsize=3, label=m_name, alpha=0.8, errorevery=1, elinewidth=1) + axes[0].plot(grp["RecallTarget"], grp["mean_time_ms"], marker="o", label=m_name, alpha=0.5) + axes[1].plot(grp["RecallTarget"], grp["mean_recall"], marker="o", label=m_name, alpha=0.5) + axes[2].plot(grp["RecallTarget"], grp["mean_nprobe"], marker="o", label=m_name, alpha=0.5) axes[0].set_ylabel("Mean Query Time (ms)") + axes[0].set_yscale("log") axes[0].set_title(f"Recall Target vs. Query Time ({dataset_name})") axes[0].legend(); axes[0].grid(True, ls=":", alpha=0.7) @@ -361,7 +397,7 @@ def run_experiment(cfg_path: str, output_dir_str: str): axes[1].set_ylim(bottom=max(0.0, min_r - 0.05), top=min(1.01, max_r + 0.05)) axes[2].set_xlabel("Recall Target") - axes[2].set_ylabel("Mean Search Parameter (nprobe/equiv.)") + axes[2].set_ylabel("Num Partitions Scanned") axes[2].set_yscale("log"); axes[2].set_title(f"Recall Target vs. Search Parameter ({dataset_name})") axes[2].legend(); axes[2].grid(True, which="both", ls=":", alpha=0.7) From f8909418cb49ff065d0e1d90b4a51ac4d001f761 Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 9 May 2025 13:34:30 -0500 Subject: [PATCH 025/323] update multi-level --- src/cpp/include/common.h | 8 +- src/cpp/src/partition_manager.cpp | 2 +- src/cpp/src/query_coordinator.cpp | 5 + test/cpp/benchmark.cpp | 18 +- .../early_termination/configs/sift1m.yaml | 54 ++-- .../experiments/osdi2025/experiment_runner.py | 4 +- .../multi_level/configs/msturing10m.yaml | 136 ++++++++++ test/experiments/osdi2025/multi_level/run.py | 250 ++++++++++++++++++ 8 files changed, 424 insertions(+), 53 deletions(-) create mode 100644 test/experiments/osdi2025/multi_level/configs/msturing10m.yaml diff --git a/src/cpp/include/common.h b/src/cpp/include/common.h index 7489976c..cc0ac5fb 100644 --- a/src/cpp/include/common.h +++ b/src/cpp/include/common.h @@ -78,9 +78,9 @@ constexpr int DEFAULT_NPROBE = 1; ///< Default number of constexpr float DEFAULT_RECALL_TARGET = -1.0f; ///< Default recall target (a negative value means no adaptive search). constexpr bool DEFAULT_BATCHED_SCAN = false; ///< Default flag for batched scanning. constexpr bool DEFAULT_PRECOMPUTED = true; ///< Default flag to use precomputed incomplete beta fn for APS. -constexpr float DEFAULT_INITIAL_SEARCH_FRACTION = 0.02f; ///< Default initial fraction of partitions to search. +constexpr float DEFAULT_INITIAL_SEARCH_FRACTION = 0.1f; ///< Default initial fraction of partitions to search. constexpr float DEFAULT_RECOMPUTE_THRESHOLD = 0.001f; ///< Default threshold to trigger recomputation of search parameters. -constexpr int DEFAULT_APS_FLUSH_PERIOD_US = 100; ///< Default period (in microseconds) for flushing the APS buffer. +constexpr int DEFAULT_APS_FLUSH_PERIOD_US = 5; ///< Default period (in microseconds) for flushing the APS buffer. // Default constants for maintenance policy parameters constexpr const char* DEFAULT_MAINTENANCE_POLICY = "query_cost"; ///< Default maintenance policy type. @@ -91,8 +91,8 @@ constexpr int DEFAULT_MIN_PARTITION_SIZE = 32; ///< Default minimum allo constexpr float DEFAULT_ALPHA = 0.9f; ///< Default alpha parameter for maintenance. constexpr bool DEFAULT_ENABLE_SPLIT_REJECTION = true; ///< Default flag to enable rejection of splits. constexpr bool DEFAULT_ENABLE_DELETE_REJECTION = true; ///< Default flag to enable rejection of deletions. -constexpr float DEFAULT_DELETE_THRESHOLD_NS = 10.0f; ///< Default threshold in nanoseconds for deletion decisions. -constexpr float DEFAULT_SPLIT_THRESHOLD_NS = 10.0f; ///< Default threshold in nanoseconds for split decisions. +constexpr float DEFAULT_DELETE_THRESHOLD_NS = 100.0f; ///< Default threshold in nanoseconds for deletion decisions. +constexpr float DEFAULT_SPLIT_THRESHOLD_NS = 100.0f; ///< Default threshold in nanoseconds for split decisions. const vector DEFAULT_LATENCY_ESTIMATOR_RANGE_N = {1, 2, 4, 16, 64, 256, 1024, 4096, 16384, 65536}; ///< Default range of n values for latency estimator. const vector DEFAULT_LATENCY_ESTIMATOR_RANGE_K = {1, 4, 16, 64, 256}; ///< Default range of k values for latency estimator. diff --git a/src/cpp/src/partition_manager.cpp b/src/cpp/src/partition_manager.cpp index 0ca2f195..2b158b73 100644 --- a/src/cpp/src/partition_manager.cpp +++ b/src/cpp/src/partition_manager.cpp @@ -220,7 +220,7 @@ shared_ptr PartitionManager::add( } auto search_params = make_shared(); search_params->k = 1; - search_params->nprobe = parent_->nlist(); + search_params->recall_target = .999; if (n > 10) { search_params->batched_scan = true; } diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 78c04a88..ffdcd249 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -712,6 +712,11 @@ shared_ptr QueryCoordinator::search(Tensor x, shared_ptrrecompute_threshold = search_params->recompute_threshold; parent_search_params->initial_search_fraction = .5; parent_search_params->batched_scan = false; + + // check if parent is flat + if (parent_->parent_ == nullptr) { + parent_search_params->batched_scan = true; + } } else { parent_search_params = search_params->parent_params; } diff --git a/test/cpp/benchmark.cpp b/test/cpp/benchmark.cpp index 93574b9a..eb74bd90 100644 --- a/test/cpp/benchmark.cpp +++ b/test/cpp/benchmark.cpp @@ -496,17 +496,17 @@ TEST(QuakeIndexStressTest, SearchAddRemoveMaintenanceTest) { // Repeatedly search, add, remove, and perform maintenance to see if the index remains consistent. int64_t dimension = 128; - int64_t num_vectors = 10000; + int64_t num_vectors = 100000; int64_t num_queries = 1; int64_t batch_size = 10000; int n_ops = 100; QuakeIndex index; auto build_params = std::make_shared(); - build_params->nlist = 100; + build_params->nlist = 10000; build_params->metric = "l2"; build_params->niter = 5; - build_params->num_workers = 0; + build_params->num_workers = 1; auto maintenance_params = std::make_shared(); maintenance_params->refinement_radius = 10; @@ -519,10 +519,10 @@ TEST(QuakeIndexStressTest, SearchAddRemoveMaintenanceTest) { // add level auto parent_index_build_params = std::make_shared(); - parent_index_build_params->nlist = 1; + parent_index_build_params->nlist = 100; parent_index_build_params->metric = "l2"; parent_index_build_params->niter = 5; - parent_index_build_params->num_workers = 0; + parent_index_build_params->num_workers = 1; build_params->parent_params = parent_index_build_params; // auto grandparent_index_build_params = std::make_shared(); @@ -584,10 +584,10 @@ TEST(QuakeIndexStressTest, SearchAddRemoveMaintenanceTest) { // remove_time += std::chrono::duration_cast(end - start).count(); // ASSERT_EQ(remove_info->n_vectors, batch_size / 2); - start = std::chrono::high_resolution_clock::now(); - index.maintenance(); - end = std::chrono::high_resolution_clock::now(); - maintenance_time += std::chrono::duration_cast(end - start).count(); + // start = std::chrono::high_resolution_clock::now(); + // index.maintenance(); + // end = std::chrono::high_resolution_clock::now(); + // maintenance_time += std::chrono::duration_cast(end - start).count(); } // print out mean times per operation diff --git a/test/experiments/osdi2025/early_termination/configs/sift1m.yaml b/test/experiments/osdi2025/early_termination/configs/sift1m.yaml index b8a92bdf..3115bad5 100644 --- a/test/experiments/osdi2025/early_termination/configs/sift1m.yaml +++ b/test/experiments/osdi2025/early_termination/configs/sift1m.yaml @@ -1,59 +1,37 @@ # File: configs/experiment.yaml mode: run -base_dir: data/aps # Base for your project's data if needed by Quake's load_dataset +base_dir: data/aps dataset: - name: sift1m # Used by Quake's load_dataset, e.g., "sift1m", "deep1m" - path: data # Path for Quake's load_dataset + name: sift1m + path: data experiment: nq: 100 - k: 100 # k for search and recall calculation + k: 1 n_workers: 0 recall_targets: [.2, .3, .4, .5, .6, 0.7, .75, 0.8, .85, 0.9, 0.95, 0.99, .999] - # APS specific params (if used) - recompute_ratio: 0.01 + recompute_ratio: 0.0001 use_precompute: true initial_search_fraction: .1 -index: # For QuakeIndex (APS, Oracle, FixedNProbe, and base for LAET) +index: metric: l2 - nlist: 1000 # This nlist is for your QuakeIndex + nlist: 1000 overwrite: - index: false # Whether to rebuild QuakeIndex + index: false results: true -#methods: ['LAET', 'Auncel', 'Oracle', 'FixedNProbe', 'APS', 'SPANN'] # Added LAET -#methods: ['SPANN', 'FixedNProbe'] -#methods: ['Auncel'] # Added LAET -methods: ['APS', 'Oracle'] -#methods: ['APS'] +methods: ['LAET', 'Auncel', 'Oracle', 'FixedNProbe', 'APS', 'SPANN'] paths: - index_dir: data/sift1m/indexes # For QuakeIndex files - results_dir: data/sift1m/results # For experiment output CSVs and plots + index_dir: data/sift1m/indexes + results_dir: data/sift1m/results plot_dir: data/sift1m/plots - laet_artifacts_base_dir: data/laet_files # IMPORTANT: Base directory for all LAET artifacts + laet_artifacts_base_dir: data/laet_files laet_config: - laet_index_key_full: "IVF1000" # CRITICAL: Must match the index string in LAET's GBDT model - pred_thresh_for_model_features: 1 # The 'threshX' value from LAET's model filename (e.g., _model_thresh1_Full.txt). - feature_idx_for_model: 0 # 0-based index corresponding to `pred_thresh_for_model_features + laet_index_key_full: "IVF1000" + pred_thresh_for_model_features: 1 + feature_idx_for_model: 0 base_vectors_path_for_laet_train: data/sift/sift_base.fvecs # Base vectors for LAET training train_query_vectors_path_for_laet_train: data/sift/sift_query.fvecs # Training query vectors for LAET training gt_train_path_for_laet_train: data/sift/sift_groundtruth.ivecs # Ground truth for LAET training gt_format_for_laet_train: "ivecs" # Format for ground truth - faiss_training_vectors_path_for_laet_train: data/sift/sift_learn.fvecs # FAISS training vectors for LAET training - # if LAET generated features for a *list* of thresholds. - # E.g., if LAET's script had pred_thresh = [1,5,10] and this model - # is for the `thresh1` features, then feature_idx_for_model is 0. - # If LAET script used a single threshold value that matches - # `pred_thresh_for_model_features`, this is 0. - # Optional: - # multiplier_range: [0.1, 5.0] # Range for binary searching the nprobe multiplier - # num_binary_search_steps: 10 # Iterations for multiplier binary search - -# -# data_paths_for_laet_training = { -# "base_vectors": laet_main_cfg.get("base_vectors_path_for_laet_train", f"data/{dataset_name}_base.fvecs"), -# "train_query_vectors": laet_main_cfg.get("train_query_vectors_path_for_laet_train", f"data/{dataset_name}_learn.fvecs"), -# "gt_train": laet_main_cfg.get("gt_train_path_for_laet_train", f"data/{dataset_name}_learn_groundtruth.ivecs"), -# "gt_format": laet_main_cfg.get("gt_format_for_laet_train", "ivecs"), -# "training_vectors_for_faiss_index": laet_main_cfg.get("faiss_training_vectors_path_for_laet_train", f"data/{dataset_name}_learn.fvecs") -#} \ No newline at end of file + faiss_training_vectors_path_for_laet_train: data/sift/sift_learn.fvecs # FAISS training vectors for LAET training \ No newline at end of file diff --git a/test/experiments/osdi2025/experiment_runner.py b/test/experiments/osdi2025/experiment_runner.py index e26da3a7..49cd5d2e 100644 --- a/test/experiments/osdi2025/experiment_runner.py +++ b/test/experiments/osdi2025/experiment_runner.py @@ -10,13 +10,15 @@ from test.experiments.osdi2025.numa_multi_query.run import run_experiment as run_numa_multi from test.experiments.osdi2025.aps_recall_targets.run import run_experiment as run_aps_recall from test.experiments.osdi2025.early_termination.run import run_experiment as run_early_termination +from test.experiments.osdi2025.multi_level.run import run_experiment as run_multi_level EXPERIMENTS = { "kick_the_tires": run_kick_the_tires, "numa_single_query": run_numa_single, "numa_multi_query": run_numa_multi, "aps_recall_targets": run_aps_recall, - "early_termination": run_early_termination + "early_termination": run_early_termination, + "multi_level": run_multi_level, } def main(): diff --git a/test/experiments/osdi2025/multi_level/configs/msturing10m.yaml b/test/experiments/osdi2025/multi_level/configs/msturing10m.yaml new file mode 100644 index 00000000..86997475 --- /dev/null +++ b/test/experiments/osdi2025/multi_level/configs/msturing10m.yaml @@ -0,0 +1,136 @@ +# ─── top-level controls ─────────────────────────────────────────────────────── +mode: run # build | run | plot +overwrite: + index: false # force rebuild of every index + results: true # force rerun of every search + +# ─── dataset ────────────────────────────────────────────────────────────────── +dataset: + name: msturing10m # built-in quake dataset + metric: l2 + path: data # ignored for packaged datasets + nq: 100 # number of queries to use + +# ─── indexes / workloads ────────────────────────────────────────────────────── +indexes: + - name: Quake5000 + index: Quake + build_params: + nc: 5000 + num_workers: 1 + search_params: + k: 100 +# recall_targets: [0.50, 0.60, 0.70, 0.80, 0.85, 0.90, 0.95, 0.99] + nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] + initial_search_fraction: 0.3 +# batched_scan: true + batch_size: 1 + + - name: Quake10000 + index: Quake + build_params: + nc: 10000 + num_workers: 1 + search_params: + k: 100 +# recall_targets: [0.50, 0.60, 0.70, 0.80, 0.85, 0.90, 0.95, 0.99] + nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] + initial_search_fraction: 0.25 +# batched_scan: true + batch_size: 1 + + - name: Quake20000 + index: Quake + build_params: + nc: 20000 + num_workers: 0 + search_params: + k: 1 +# recall_targets: [0.50, 0.60, 0.70, 0.80, 0.85, 0.90, 0.95, 0.99] + nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] + initial_search_fraction: 0.2 +# batched_scan: true + batch_size: 1 + + - name: Quake40000 + index: Quake + build_params: + nc: 40000 + num_workers: 0 + search_params: + k: 1 + # recall_targets: [0.50, 0.60, 0.70, 0.80, 0.85, 0.90, 0.95, 0.99] + nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] + initial_search_fraction: 0.2 + # batched_scan: true + batch_size: 1 + + - name: Quake80000 + index: Quake + build_params: + nc: 80000 + num_workers: 0 + search_params: + k: 1 + # recall_targets: [0.50, 0.60, 0.70, 0.80, 0.85, 0.90, 0.95, 0.99] + nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] + initial_search_fraction: 0.2 + # batched_scan: true + batch_size: 1 + + - name: Quake160000 + index: Quake + build_params: + nc: 160000 + num_workers: 0 + search_params: + k: 1 + # recall_targets: [0.50, 0.60, 0.70, 0.80, 0.85, 0.90, 0.95, 0.99] + nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] + initial_search_fraction: 0.2 + # batched_scan: true + batch_size: 1 +# +# - name: Quake20000_500 # two-level Quake index +# index: Quake +# build_params: +# nc: 20000 +# num_workers: 0 +# parent: +# nc: 500 +# num_workers: 0 +# search_params: +# k: 1 +# nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] +## initial_search_fraction: 0.05 +# batch_size: 1 +# parent: +# nprobe: 50 +# # child-internal search; leave single high target here +## recall_target: 0.999 +## initial_search_fraction: 0.2 + +# ─── plotting ──────────────────────────────────────────────────────────────── +plot: + styles: # one entry per *name* above + Quake5000: + color: "#1f77b4" + marker: "o" + Quake10000: + color: "#ff7f0e" + marker: "o" + Quake20000: + color: "#2ca02c" + marker: "o" + Quake40000: + color: "#9467bd" + marker: "o" + Quake80000: + color: "#8c564b" + marker: "o" + Quake160000: + color: "#e377c2" + marker: "o" + Quake20000_500: + color: "#d62728" + marker: "o" \ No newline at end of file diff --git a/test/experiments/osdi2025/multi_level/run.py b/test/experiments/osdi2025/multi_level/run.py index 139597f9..c3f3c21f 100644 --- a/test/experiments/osdi2025/multi_level/run.py +++ b/test/experiments/osdi2025/multi_level/run.py @@ -1,2 +1,252 @@ +from __future__ import annotations +import argparse +import logging +import time +from pathlib import Path +from typing import Dict, List, Tuple + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import torch +import yaml + +# ── quake imports ────────────────────────────────────────────────────────────── +from quake.datasets.ann_datasets import load_dataset +from quake.index_wrappers.faiss_ivf import FaissIVF +from quake.index_wrappers.quake import QuakeWrapper +from quake.utils import compute_recall # recall@k + +logger = logging.getLogger("experiment") +logging.basicConfig( + format="%(asctime)s | %(levelname)s | %(message)s", + datefmt="%H:%M:%S", + level=logging.INFO, +) + +# ─────────────────────────────────────────────────────────────────────────────── +# helpers +# ─────────────────────────────────────────────────────────────────────────────── +def _prepare_index( + idx_cls, + vecs: torch.Tensor, + metric: str, + build_params: Dict, + index_path: Path, + rebuild: bool, +) -> Tuple[object, float]: + """ + Build–or–load an index and return (index, build_time_seconds). + build_time_seconds == 0 when loaded. + """ + idx = idx_cls() + if index_path.exists() and not rebuild: + t0 = time.time() + idx.load(str(index_path), num_workers=build_params["num_workers"]) + return idx, 0.0 + + logger.info("Building index → %s …", index_path) + t0 = time.time() + idx.build(vecs, metric=metric, **build_params) + t_build = time.time() - t0 + idx.save(str(index_path)) + logger.info("Index saved (%.1fs).", t_build) + return idx, t_build + + +def _dist_ids(res): + """Handle `(D, I)` tuples OR Quake SearchResult objects.""" + if isinstance(res, tuple) and len(res) == 2: + return res + if hasattr(res, "distances") and hasattr(res, "indices"): + return res.distances, res.indices + raise TypeError(f"Unknown search result type: {type(res).__name__}") + + +def _benchmark_index( + idx, + queries: torch.Tensor, + ground: np.ndarray, + search_params: Dict, + trials: int, + warmup: int, +) -> Dict: + bs = search_params.pop("batch_size", None) + k = search_params["k"] + + # warm-up + for _ in range(warmup): + if bs is None: + idx.search(queries, **search_params) + else: + for i in range(0, len(queries), bs): + idx.search(queries[i : i + bs], **search_params) + + qps_vals: List[float] = [] + lat_ms: List[float] = [] + + for _ in range(trials): + t0 = time.time() + if bs is None: + res = idx.search(queries, **search_params) + D, I = res.distances, res.ids + else: + all_I, all_D = [], [] + for i in range(0, len(queries), bs): + res = idx.search(queries[i : i + bs], **search_params) + D_, I_ = res.distances, res.ids + all_I.append(I_); all_D.append(D_) + I = np.concatenate(all_I); D = np.concatenate(all_D) + dt = time.time() - t0 + qps_vals.append(len(queries) / dt) + lat_ms.append(dt * 1e3) + + recall = compute_recall(I, ground[:, :k], k) + recall = 0.0 if recall is None else recall.mean().item() + + return dict( + recall = float(recall), + qps_mean = float(np.mean(qps_vals)), + qps_std = float(np.std(qps_vals)), + latency_mean_ms = float(np.mean(lat_ms)), + trials = trials, + warmup = warmup, + batch_size = bs or len(queries), + ) + + +def _expand_targets(sp: Dict) -> List[Dict]: + """ + Produce one search-parameter dict per recall_target value. + + If `recall_targets` is absent (or a single float) we fall back to a list + containing exactly one dict, preserving the original behaviour. + """ + if "recall_targets" in sp: + tgs = sp.pop("recall_targets") + if not isinstance(tgs, (list, tuple)): + tgs = [tgs] + return [{**sp, "recall_target": t} for t in tgs] + elif "nprobes" in sp: + tgs = sp.pop("nprobes") + if not isinstance(tgs, (list, tuple)): + tgs = [tgs] + return [{**sp, "nprobe": t} for t in tgs] + else: + return [sp] + + +def _plot_unified(cfg: Dict, root: Path) -> None: + styles = cfg["plot"]["styles"] + fig, ax = plt.subplots(figsize=(6, 4)) + + for icfg in cfg["indexes"]: + name = icfg["name"] + csv = root / name / "results.csv" + if not csv.exists(): + logger.warning("No results for %s – skipped in plot.", name) + continue + + df = pd.read_csv(csv).sort_values("recall") + sty = styles.get(name, {}) + ax.plot( + df["qps_mean"], df["recall"], + label = name, + marker = sty.get("marker", "o"), + linestyle = sty.get("linestyle", "-"), + linewidth = 1.4, + markersize= 6, + color = sty.get("color", None), + ) + + ax.set_xscale("log", base=10) + ax.set_xlabel("Queries per second (log)") + ax.set_ylabel("Recall@k") + ax.set_title("Recall curves over varied recall_target") + ax.grid(True, which="both", ls=":") + ax.legend() + fig.tight_layout() + out_png = root / "recall_vs_qps.png" + fig.savefig(out_png, dpi=180) + logger.info("Plot written to %s", out_png) + + +# ─────────────────────────────────────────────────────────────────────────────── +# main +# ─────────────────────────────────────────────────────────────────────────────── +def run_experiment(cfg_path: str, output_dir: str) -> None: + cfg = yaml.safe_load(Path(cfg_path).read_text()) + out = Path(output_dir); out.mkdir(parents=True, exist_ok=True) + + mode = cfg.get("mode", "run") # build | run | plot + overwrite = cfg.get("overwrite", {}) + rebuild_index = bool(overwrite.get("index", False)) + rewrite_csv = bool(overwrite.get("results", False)) + + # dataset ------------------------------------------------------------------ + ds = cfg["dataset"] + logger.info("Loading dataset %s …", ds["name"]) + vecs, queries, ground = load_dataset(ds["name"], ds.get("path", "")) + metric = ds["metric"] + nq = ds["nq"] + ground = ground[:nq] + queries = queries[:nq] + + idx_map = {"Quake": QuakeWrapper, "IVF": FaissIVF} + + # build / run -------------------------------------------------------------- + if mode in {"build", "run"}: + for icfg in cfg["indexes"]: + name = icfg["name"] + res_dir = out / name; res_dir.mkdir(exist_ok=True, parents=True) + idx_bin = res_dir / "index.bin" + csv_file = res_dir / "results.csv" + idx_cls = idx_map[icfg["index"]] + + # BUILD once ======================================================= + idx, build_t = _prepare_index( + idx_cls, vecs, metric, icfg["build_params"], idx_bin, rebuild_index + ) + if mode == "build": + continue # build-only mode stops here + + # RUN for every recall_target ====================================== + if csv_file.exists() and not rewrite_csv: + existing_df = pd.read_csv(csv_file) + else: + existing_df = pd.DataFrame() + + for sp in _expand_targets(icfg["search_params"].copy()): + tgt = sp.get("recall_target", None) + if tgt is None: + tgt = sp.get("nprobe") + # if ( + # not rewrite_csv + # and "recall_target" in existing_df.columns + # and ((existing_df.recall_target - tgt).abs() < 1e-9).any() + # ): + # logger.info("[%s] target %.3g cached – skipping run.", name, tgt) + # continue + + stats = _benchmark_index( + idx = idx, + queries = queries, + ground = ground, + search_params = sp, + trials = cfg.get("trials", 5), + warmup = cfg.get("warmup", 1), + ) + stats.update(build_time_s = build_t, recall_target = tgt) + existing_df = pd.concat( + [existing_df, pd.DataFrame([stats])], ignore_index=True + ) + logger.info("[%s] target %.3g done.", name, tgt) + + existing_df.to_csv(csv_file, index=False) + logger.info("[%s] results written to %s", name, csv_file) + + # plot --------------------------------------------------------------------- + if mode in {"run", "plot"}: + _plot_unified(cfg, out) From 4f4e5aafd06957fb6750c2f37755c5190da33e16 Mon Sep 17 00:00:00 2001 From: Jason Date: Sat, 10 May 2025 22:40:34 -0500 Subject: [PATCH 026/323] add vary level experiment --- .../configs/msturing10m_levels.yaml | 65 ++++++ test/experiments/osdi2025/vary_levels/run.py | 216 ++++++++++++++++++ 2 files changed, 281 insertions(+) create mode 100644 test/experiments/osdi2025/vary_levels/configs/msturing10m_levels.yaml create mode 100644 test/experiments/osdi2025/vary_levels/run.py diff --git a/test/experiments/osdi2025/vary_levels/configs/msturing10m_levels.yaml b/test/experiments/osdi2025/vary_levels/configs/msturing10m_levels.yaml new file mode 100644 index 00000000..b2aa5052 --- /dev/null +++ b/test/experiments/osdi2025/vary_levels/configs/msturing10m_levels.yaml @@ -0,0 +1,65 @@ +# ─── top-level controls ─────────────────────────────────── +mode: run # ignored by new script – kept for compatibility +overwrite: + index: false + results: true + +# ─── dataset ─────────────────────────────────────────────── +dataset: + name: msturing10m + metric: l2 + path: data + nq: 100 + +# ─── base index (built once) ─────────────────────────────── +index: + type: Quake + build_params: + nc: 20_000 # child level + num_workers: 1 + search_params: + k: 100 + +# ─── parent levels to test ───────────────────────────────── +levels: + - nc: 0 + search_params: + nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048] + - nc: 100 # baseline (keep the index single-level) + search_params: # ← optional override for the CHILD search + nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] + parent_search_params: # ← optional params passed to *parent* search + nprobe: 10 + + - nc: 500 # 500-centroid parent layer + search_params: + nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048] + parent_search_params: # ← optional params passed to *parent* search + nprobe: 50 + + - nc: 1_000 + search_params: + nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048] + parent_search_params: # ← optional params passed to *parent* search + nprobe: 100 + + - nc: 2_000 + search_params: + nprobes: [ 1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048] + parent_search_params: # ← optional params passed to *parent* search + nprobe: 200 + + - nc: 4_000 + search_params: + nprobes: [ 1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048] + parent_search_params: # ← optional params passed to *parent* search + nprobe: 400 + +# ─── plotting (optional) ────────────────────────────────── +plot: + styles: + L0: {color: "#1f77b4"} + L100: {color: "#ff7f0e"} + L500: {color: "#ffbb78"} + L1000: {color: "#2ca02c"} + L2000: {color: "#d62728"} \ No newline at end of file diff --git a/test/experiments/osdi2025/vary_levels/run.py b/test/experiments/osdi2025/vary_levels/run.py new file mode 100644 index 00000000..f9755a7b --- /dev/null +++ b/test/experiments/osdi2025/vary_levels/run.py @@ -0,0 +1,216 @@ +#!/usr/bin/env python3 +""" +experiment_levels.py – benchmark a single Quake/IVF index with optional +parent levels, each level having its own search parameters. + +* Build the **child** index once (saved as base_index.bin). +* For every level described in `levels:`: + – Reload the base index from disk. + – Optionally add a parent layer with `nc` centroids. + – Run searches using the per-level overrides. +* Results for each level go to ./OUT_DIR/L/results.csv +* A combined QPS-vs-Recall plot is written to OUT_DIR/recall_vs_qps.png +""" + +from __future__ import annotations +import argparse, logging, time +from pathlib import Path +from typing import Dict, List, Tuple + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import quake +import torch, yaml + +from quake.datasets.ann_datasets import load_dataset +from quake.index_wrappers.faiss_ivf import FaissIVF +from quake.index_wrappers.quake import QuakeWrapper +from quake.utils import compute_recall + +logger = logging.getLogger("experiment") +logging.basicConfig( + format="%(asctime)s | %(levelname)s | %(message)s", + datefmt="%H:%M:%S", + level=logging.INFO, +) + +# ───────────────────── helper routines ───────────────────── +def _prepare_index(idx_cls, vecs, metric, build_params, + path: Path, rebuild: bool) -> Tuple[object, float]: + """Build or load the *child* index; return (index, build_time_s).""" + idx = idx_cls() + if path.exists() and not rebuild: + t0 = time.time() + idx.load(str(path), num_workers=build_params["num_workers"]) + return idx, 0.0 + logger.info("Building index → %s …", path) + t0 = time.time() + idx.build(vecs, metric=metric, **build_params) + t_build = time.time() - t0 + idx.save(str(path)) + logger.info("Index saved (%.1fs).", t_build) + return idx, t_build + + +def _benchmark_index(idx, queries, ground, search_params, + trials: int, warmup: int) -> Dict: + """Run `trials` timed searches; return stats dict.""" + sp = search_params.copy() # don’t modify caller’s dict + bs = sp.pop("batch_size", None) + k = sp["k"] + + # warm-up + for _ in range(warmup): + if bs is None: + idx.search(queries, **sp) + else: + for i in range(0, len(queries), bs): + idx.search(queries[i:i+bs], **sp) + + qps, lat_ms = [], [] + for _ in range(trials): + t0 = time.time() + if bs is None: + I = idx.search(queries, **sp).ids + else: + I = np.concatenate([ + idx.search(queries[i:i+bs], **sp).ids + for i in range(0, len(queries), bs) + ]) + dt = time.time() - t0 + qps.append(len(queries) / dt) + lat_ms.append(dt * 1e3) + + recall = compute_recall(I, ground[:, :k], k) + recall = 0.0 if recall is None else recall.mean().item() + + return dict( + recall = float(recall), + qps_mean = float(np.mean(qps)), + qps_std = float(np.std(qps)), + latency_ms = float(np.mean(lat_ms)), + trials = trials, + warmup = warmup, + batch_size = bs or len(queries), + ) + + +def _expand_targets(sp: Dict) -> List[Dict]: + """Yield one search-param dict per recall_target OR per nprobe value.""" + if "recall_targets" in sp: + tgs = sp.pop("recall_targets") + if not isinstance(tgs, (list, tuple)): tgs = [tgs] + return [{**sp, "recall_target": t} for t in tgs] + if "nprobes" in sp: + tgs = sp.pop("nprobes") + if not isinstance(tgs, (list, tuple)): tgs = [tgs] + return [{**sp, "nprobe": t} for t in tgs] + return [sp] + + +def _plot(cfg: Dict, root: Path) -> None: + """Generate combined QPS-vs-Recall plot.""" + fig, ax = plt.subplots(figsize=(6, 4)) + styles = cfg.get("plot", {}).get("styles", {}) + for lv in cfg["levels"]: + nc = lv["nc"] + name = f"L{nc}" + csv = root / name / "results.csv" + if not csv.exists(): + logger.warning("No results for level %s – skipped.", nc) + continue + df = pd.read_csv(csv).sort_values("recall") + sty = styles.get(name, {}) + ax.plot( + df["qps_mean"], df["recall"], + label = f"{nc:,} centroids" if nc else "single-level", + marker = sty.get("marker", "o"), + linestyle = sty.get("linestyle", "-"), + linewidth = 1.4, + markersize = 6, + color = sty.get("color", None), + ) + ax.set_xscale("log", base=10) + ax.set_xlabel("Queries per second (log)") + ax.set_ylabel("Recall@k") + ax.set_title("QPS vs Recall for different parent levels") + ax.grid(True, which="both", ls=":") + ax.legend() + fig.tight_layout() + fig.savefig(root / "recall_vs_qps.png", dpi=180) + logger.info("Plot written to %s", root / "recall_vs_qps.png") + +# ─────────────────────────── main ────────────────────────── +def run_experiment(cfg_path: str, output_dir: str) -> None: + cfg = yaml.safe_load(Path(cfg_path).read_text()) + out = Path(output_dir) + out.mkdir(parents=True, exist_ok=True) + + # dataset --------------------------------------------------------------- + ds = cfg["dataset"] + logger.info("Loading dataset %s …", ds["name"]) + vecs, queries, ground = load_dataset(ds["name"], ds.get("path", "")) + nq = ds["nq"] + queries, ground = queries[:nq], ground[:nq] + metric = ds["metric"] + + # build child index once ----------------------------------------------- + icfg = cfg["index"] + idx_cls = {"Quake": QuakeWrapper, "IVF": FaissIVF}[icfg["type"]] + base_bin = out / "base_index.bin" + base_idx, build_t = _prepare_index( + idx_cls, vecs, metric, + icfg["build_params"], + base_bin, + rebuild=cfg["overwrite"].get("index", False), + ) + + # iterate over parent levels ------------------------------------------- + for lv in cfg["levels"]: + nc = lv["nc"] + name = f"L{nc}" + res_dir = out / name + res_dir.mkdir(parents=True, exist_ok=True) + csv_file = res_dir / "results.csv" + + if csv_file.exists() and not cfg["overwrite"].get("results", False): + logger.info("[%s] results cached – skipping.", name) + continue + + # fresh load of the base index for this level + idx = idx_cls() + idx.load(str(base_bin), num_workers=icfg["build_params"].get("num_workers", 1)) + if nc: # add parent layer when nc > 0 + build_params = quake.IndexBuildParams() + build_params.nlist = nc + idx.index.add_level(build_params) + + # child search params (base defaults + per-level overrides) + child_sp = icfg["search_params"].copy() + child_sp.update(lv.get("search_params", {})) + + # optional parent search params + parent_sp = lv.get("parent_search_params", None) + + df = pd.DataFrame() + for sp in _expand_targets(child_sp.copy()): + if parent_sp: # attach parent section if any + sp["parent"] = parent_sp + stats = _benchmark_index( + idx, queries, ground, sp, + trials = cfg.get("trials", 5), + warmup = cfg.get("warmup", 1), + ) + stats.update( + build_time_s = build_t if nc == 0 else 0.0, + parent_nc = nc, + recall_target= sp.get("recall_target", sp.get("nprobe", np.nan)), + ) + df = pd.concat([df, pd.DataFrame([stats])], ignore_index=True) + logger.info("[%s] target %.3g done.", name, stats["recall_target"]) + + df.to_csv(csv_file, index=False) + logger.info("[%s] results written to %s", name, csv_file) + + _plot(cfg, out) From 713512d416d8d046712e5591dff632e21cf5e599 Mon Sep 17 00:00:00 2001 From: Jason Date: Sat, 10 May 2025 22:43:07 -0500 Subject: [PATCH 027/323] update runner --- test/experiments/osdi2025/experiment_runner.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/test/experiments/osdi2025/experiment_runner.py b/test/experiments/osdi2025/experiment_runner.py index 49cd5d2e..233dd314 100644 --- a/test/experiments/osdi2025/experiment_runner.py +++ b/test/experiments/osdi2025/experiment_runner.py @@ -11,6 +11,7 @@ from test.experiments.osdi2025.aps_recall_targets.run import run_experiment as run_aps_recall from test.experiments.osdi2025.early_termination.run import run_experiment as run_early_termination from test.experiments.osdi2025.multi_level.run import run_experiment as run_multi_level +from test.experiments.osdi2025.vary_levels.run import run_experiment as run_vary_levels EXPERIMENTS = { "kick_the_tires": run_kick_the_tires, @@ -19,6 +20,7 @@ "aps_recall_targets": run_aps_recall, "early_termination": run_early_termination, "multi_level": run_multi_level, + "vary_levels": run_vary_levels, } def main(): From 96cae75ee7daa2ecd20620e211a4085b03400eee Mon Sep 17 00:00:00 2001 From: Jason Date: Sat, 10 May 2025 22:57:46 -0500 Subject: [PATCH 028/323] update runner --- .../vary_levels/configs/msturing10m_levels.yaml | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/test/experiments/osdi2025/vary_levels/configs/msturing10m_levels.yaml b/test/experiments/osdi2025/vary_levels/configs/msturing10m_levels.yaml index b2aa5052..7c53be47 100644 --- a/test/experiments/osdi2025/vary_levels/configs/msturing10m_levels.yaml +++ b/test/experiments/osdi2025/vary_levels/configs/msturing10m_levels.yaml @@ -25,35 +25,41 @@ levels: - nc: 0 search_params: nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048] + batch_size: 1 - nc: 100 # baseline (keep the index single-level) search_params: # ← optional override for the CHILD search - nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] + nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048] + batch_size: 1 parent_search_params: # ← optional params passed to *parent* search nprobe: 10 - nc: 500 # 500-centroid parent layer search_params: nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048] + batch_size: 1 parent_search_params: # ← optional params passed to *parent* search nprobe: 50 - nc: 1_000 search_params: nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048] + batch_size: 1 parent_search_params: # ← optional params passed to *parent* search - nprobe: 100 + nprobe: 75 - nc: 2_000 search_params: nprobes: [ 1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048] + batch_size: 1 parent_search_params: # ← optional params passed to *parent* search - nprobe: 200 + nprobe: 150 - nc: 4_000 search_params: nprobes: [ 1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048] + batch_size: 1 parent_search_params: # ← optional params passed to *parent* search - nprobe: 400 + nprobe: 200 # ─── plotting (optional) ────────────────────────────────── plot: From d9fcbfb9849890b4045b167240b8282bdcb4d9b6 Mon Sep 17 00:00:00 2001 From: Jason Date: Sat, 10 May 2025 23:10:39 -0500 Subject: [PATCH 029/323] update runner --- .../configs/msturing10m_levels.yaml | 22 +++++++++---------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/test/experiments/osdi2025/vary_levels/configs/msturing10m_levels.yaml b/test/experiments/osdi2025/vary_levels/configs/msturing10m_levels.yaml index 7c53be47..2cc895ce 100644 --- a/test/experiments/osdi2025/vary_levels/configs/msturing10m_levels.yaml +++ b/test/experiments/osdi2025/vary_levels/configs/msturing10m_levels.yaml @@ -15,51 +15,51 @@ dataset: index: type: Quake build_params: - nc: 20_000 # child level + nc: 160_000 # child level num_workers: 1 search_params: - k: 100 + k: 1 # ─── parent levels to test ───────────────────────────────── levels: - nc: 0 search_params: - nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048] + nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096] batch_size: 1 - nc: 100 # baseline (keep the index single-level) search_params: # ← optional override for the CHILD search - nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048] + nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096] batch_size: 1 parent_search_params: # ← optional params passed to *parent* search nprobe: 10 - nc: 500 # 500-centroid parent layer search_params: - nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048] + nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096] batch_size: 1 parent_search_params: # ← optional params passed to *parent* search nprobe: 50 - nc: 1_000 search_params: - nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048] + nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096] batch_size: 1 parent_search_params: # ← optional params passed to *parent* search - nprobe: 75 + nprobe: 100 - nc: 2_000 search_params: - nprobes: [ 1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048] + nprobes: [ 1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096] batch_size: 1 parent_search_params: # ← optional params passed to *parent* search - nprobe: 150 + nprobe: 200 - nc: 4_000 search_params: - nprobes: [ 1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048] + nprobes: [ 1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096] batch_size: 1 parent_search_params: # ← optional params passed to *parent* search - nprobe: 200 + nprobe: 400 # ─── plotting (optional) ────────────────────────────────── plot: From f1058e07d0459754bcd0dc9164a869690ee7241b Mon Sep 17 00:00:00 2001 From: Jason Date: Sun, 11 May 2025 16:03:05 -0500 Subject: [PATCH 030/323] add gpu params to build --- src/cpp/bindings/wrap.cpp | 11 +++++++++++ src/python/index_wrappers/quake.py | 7 +++++++ 2 files changed, 18 insertions(+) diff --git a/src/cpp/bindings/wrap.cpp b/src/cpp/bindings/wrap.cpp index 8035e240..d476d8d1 100644 --- a/src/cpp/bindings/wrap.cpp +++ b/src/cpp/bindings/wrap.cpp @@ -131,6 +131,10 @@ PYBIND11_MODULE(_bindings, m) { return oss.str(); }); + // bool use_gpu = false; + // int gpu_batch_size = DEFAULT_GPU_BATCH_SIZE; + // int gpu_sample_size = DEFAULT_GPU_SAMPLE_SIZE; + /*********** IndexBuildParams Binding ***********/ class_>(m, "IndexBuildParams") .def(init<>()) @@ -144,6 +148,13 @@ PYBIND11_MODULE(_bindings, m) { (std::string("Number of workers. default = ") + std::to_string(DEFAULT_NUM_WORKERS)).c_str()) .def_readwrite("parent_params", &IndexBuildParams::parent_params, "Parameters for the parent index, if any.") + .def_readwrite("use_gpu", &IndexBuildParams::use_gpu, + (std::string("Flag to use GPU for index building. default = ") + std::to_string(false)).c_str()) + .def_readwrite("gpu_batch_size", &IndexBuildParams::gpu_batch_size, + (std::string("Batch size for GPU index building. default = ") + std::to_string(DEFAULT_GPU_BATCH_SIZE)).c_str()) + .def_readwrite("gpu_sample_size", &IndexBuildParams::gpu_sample_size, + (std::string("Sample size for GPU index building. default = ") + std::to_string(DEFAULT_GPU_SAMPLE_SIZE)).c_str()) + .def("__repr__", [](const IndexBuildParams &p) { std::ostringstream oss; oss << "{"; diff --git a/src/python/index_wrappers/quake.py b/src/python/index_wrappers/quake.py index a75f2f86..c7e9e19b 100644 --- a/src/python/index_wrappers/quake.py +++ b/src/python/index_wrappers/quake.py @@ -68,6 +68,9 @@ def build( m: int = -1, code_size: int = 8, parent=None, + use_gpu=False, + gpu_batch_size=100000, + gpu_sample_size=1000000 ): """ Build the index with the given vectors and arguments. @@ -95,6 +98,10 @@ def build( build_params.parent_params = quake.IndexBuildParams() build_params.parent_params.nlist = parent.get("nc", 1) + build_params.use_gpu = use_gpu + build_params.gpu_batch_size = gpu_batch_size + build_params.gpu_sample_size = gpu_sample_size + self.index = QuakeIndex() if ids is None: From e52ac0ebaa0806bd32f1051a5e924cbf2736a22b Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 13 May 2025 23:47:59 -0500 Subject: [PATCH 031/323] update multi-level --- src/cpp/bindings/wrap.cpp | 2 + src/cpp/include/common.h | 3 + src/cpp/include/dynamic_inverted_list.h | 1 + src/cpp/include/geometry.h | 16 +- src/cpp/src/clustering.cpp | 200 ++++----- src/cpp/src/dynamic_inverted_list.cpp | 4 +- src/cpp/src/maintenance_policies.cpp | 193 ++++---- src/cpp/src/partition_manager.cpp | 9 +- src/cpp/src/query_coordinator.cpp | 23 +- src/python/datasets/ann_datasets.py | 44 +- src/python/workload_generator.py | 415 +++++++++--------- test/cpp/benchmark.cpp | 56 ++- test/experiments/osdi2025/ablation/run.py | 2 - .../experiments/osdi2025/experiment_runner.py | 2 + .../osdi2025/maintenance_ablation/run.py | 304 +++++++++++++ .../{msturing10m.yaml => sift10m.yaml} | 44 +- test/experiments/osdi2025/multi_level/run.py | 2 +- .../configs/msturing10m_levels.yaml | 71 --- .../osdi2025/vary_levels/configs/sift10m.yaml | 101 +++++ test/experiments/osdi2025/vary_levels/run.py | 2 +- 20 files changed, 948 insertions(+), 546 deletions(-) delete mode 100644 test/experiments/osdi2025/ablation/run.py create mode 100644 test/experiments/osdi2025/maintenance_ablation/run.py rename test/experiments/osdi2025/multi_level/configs/{msturing10m.yaml => sift10m.yaml} (82%) delete mode 100644 test/experiments/osdi2025/vary_levels/configs/msturing10m_levels.yaml create mode 100644 test/experiments/osdi2025/vary_levels/configs/sift10m.yaml diff --git a/src/cpp/bindings/wrap.cpp b/src/cpp/bindings/wrap.cpp index d476d8d1..18a6f92d 100644 --- a/src/cpp/bindings/wrap.cpp +++ b/src/cpp/bindings/wrap.cpp @@ -232,6 +232,8 @@ PYBIND11_MODULE(_bindings, m) { (std::string("Number of refinement iterations. default = ") + std::to_string(DEFAULT_REFINEMENT_ITERATIONS)).c_str()) .def_readwrite("min_partition_size", &MaintenancePolicyParams::min_partition_size, (std::string("Minimum allowed partition size. default = ") + std::to_string(DEFAULT_MIN_PARTITION_SIZE)).c_str()) + .def_readwrite("max_partition_size", &MaintenancePolicyParams::max_partition_size, + (std::string("Maximum allowed partition size. default = ") + std::to_string(-1)).c_str()) .def_readwrite("alpha", &MaintenancePolicyParams::alpha, (std::string("Alpha parameter. default = ") + std::to_string(DEFAULT_ALPHA)).c_str()) .def_readwrite("enable_split_rejection", &MaintenancePolicyParams::enable_split_rejection, diff --git a/src/cpp/include/common.h b/src/cpp/include/common.h index 1c9da4bc..3fb6e0f5 100644 --- a/src/cpp/include/common.h +++ b/src/cpp/include/common.h @@ -108,6 +108,9 @@ struct MaintenancePolicyParams { float delete_threshold_ns = DEFAULT_DELETE_THRESHOLD_NS; float split_threshold_ns = DEFAULT_SPLIT_THRESHOLD_NS; + // SPFresh Param + int max_partition_size = -1; // -1 means default to standard cost-based maintenance, if set then we use size-based thresholding + MaintenancePolicyParams() = default; }; diff --git a/src/cpp/include/dynamic_inverted_list.h b/src/cpp/include/dynamic_inverted_list.h index e8d67627..4c1c4933 100644 --- a/src/cpp/include/dynamic_inverted_list.h +++ b/src/cpp/include/dynamic_inverted_list.h @@ -32,6 +32,7 @@ namespace faiss { int code_size_; ///< Size in bytes of each vector code. unordered_map> partitions_; ///< Map of partition ID to IndexPartition. unordered_map> id_to_location_; + unordered_map tombstones_; /** * @brief Constructor for DynamicInvertedLists. diff --git a/src/cpp/include/geometry.h b/src/cpp/include/geometry.h index c27ca997..5e54890e 100644 --- a/src/cpp/include/geometry.h +++ b/src/cpp/include/geometry.h @@ -285,14 +285,14 @@ compute_recall_profile(const std::vector& boundary_distances, // Ensure P_prime is non-negative for (int k = 1; k < m; ++k) P_prime[k] = std::max(0.0f, P_prime[k]); - // if the cluster_sizes are given, scale P_prime[k] by the size of the cluster. this is a rudimentary density estimation - if (partition_sizes.size() > 0) { - for (int k = 1; k < m; ++k) { - if (partition_sizes[k] > 0) { - P_prime[k] *= static_cast(partition_sizes[k]); - } - } - } + // // if the cluster_sizes are given, scale P_prime[k] by the size of the cluster. this is a rudimentary density estimation + // if (partition_sizes.size() > 0) { + // for (int k = 1; k < m; ++k) { + // if (partition_sizes[k] > 0) { + // P_prime[k] *= static_cast(partition_sizes[k]); + // } + // } + // } // normalize probs diff --git a/src/cpp/src/clustering.cpp b/src/cpp/src/clustering.cpp index b05c7d83..a0e907be 100644 --- a/src/cpp/src/clustering.cpp +++ b/src/cpp/src/clustering.cpp @@ -18,41 +18,38 @@ shared_ptr kmeans_cuvs_sample_and_predict( Tensor vectors, Tensor ids, - shared_ptr build_params) { - - int num_clusters = build_params->nlist; - int niter = build_params->niter; - int gpu_batch_size = build_params->gpu_batch_size; - int gpu_sample_size = build_params->gpu_sample_size; - MetricType metric = str_to_metric_type(build_params->metric); - + shared_ptr build_params) +{ + /* ---------- unpack / sanity-check parameters ----------------------- */ + const int num_clusters = build_params->nlist; + const int niter = build_params->niter; + int gpu_batch_size = build_params->gpu_batch_size; + int gpu_sample_sz = build_params->gpu_sample_size; + const MetricType metric = str_to_metric_type(build_params->metric); TORCH_CHECK(vectors.dim() == 2, "vectors must be [N,D]"); - TORCH_CHECK(ids.dim() == 1, "ids must be [N]"); - int64_t N = vectors.size(0), D = vectors.size(1); + TORCH_CHECK(ids.dim() == 1, "ids must be [N]"); - gpu_sample_size = std::min(gpu_sample_size, (int) N); - gpu_batch_size = std::min(gpu_batch_size, (int) N); + const int64_t N = vectors.size(0); + const int64_t D = vectors.size(1); - TORCH_CHECK(gpu_sample_size > 0 && gpu_sample_size <= N, - "invalid sample_size"); + gpu_sample_sz = std::min(gpu_sample_sz, (int)N); + gpu_batch_size = std::min(gpu_batch_size, (int)N); + TORCH_CHECK(gpu_sample_sz > 0 && gpu_sample_sz <= N, "invalid sample size"); - // 1) pin + normalize if needed + /* ---------- copy to pinned host & (optionally) normalize ----------- */ Tensor cpu_pts = vectors.contiguous().pin_memory(); if (metric == faiss::METRIC_INNER_PRODUCT) { - auto norms = cpu_pts.norm(2, 1, true); - cpu_pts = cpu_pts.div(norms); + // k-means in RAFT is L2 – approximate cosine by L2 on the unit sphere + cpu_pts = cpu_pts.div(cpu_pts.norm(2, 1, /*keepdim=*/true)); } - // 2) choose a random sample of indices - auto perm = torch::randperm(N, torch::kLong); - auto samp_idx = perm.slice(0, 0, gpu_sample_size); - Tensor samp_pts = cpu_pts.index_select(0, samp_idx); + /* ---------- draw random sample for training ------------------------ */ + const Tensor samp_idx = torch::randperm(N, torch::kLong).slice(0, 0, gpu_sample_sz); + const Tensor samp_host = cpu_pts.index_select(0, samp_idx); + const Tensor samp_gpu = samp_host.to(torch::kCUDA, /*non_blocking=*/true).contiguous(); - // 3) move sample to GPU - Tensor samp_gpu = samp_pts.to(torch::kCUDA, /*non_blocking=*/true).contiguous(); - - // 4) prepare RAFT handle & cuVS params + /* ---------- RAFT handle & cuVS parameters -------------------------- */ raft::resources handle; cudaStream_t stream = c10::cuda::getCurrentCUDAStream(); raft::resource::set_cuda_stream(handle, stream); @@ -62,123 +59,78 @@ shared_ptr kmeans_cuvs_sample_and_predict( params.init = cuvs::cluster::kmeans::params::InitMethod::Random; params.max_iter = niter; - // 5) allocate centroids on GPU + /* ---------- centroids on device ------------------------------------ */ Tensor cent_gpu = torch::empty({num_clusters, D}, - torch::TensorOptions() - .dtype(torch::kFloat32) - .device(torch::kCUDA)) - .contiguous(); + torch::dtype(torch::kFloat32).device(torch::kCUDA)) + .contiguous(); - // 6) run fit on just the sample - { - // host scalars - float inertia = 0.0f; - int actual_iter= 0; - auto host_inertia = raft::make_host_scalar_view(&inertia); - auto host_iter = raft::make_host_scalar_view(&actual_iter); - - auto X_view = raft::make_device_matrix_view( - samp_gpu.data_ptr(), - (int) gpu_sample_size, (int)D); - auto C_view = raft::make_device_matrix_view( - cent_gpu.data_ptr(), - num_clusters, (int)D); + /* ---------- fit on the sample -------------------------------------- */ + float inertia = 0.0f; + int actual_iter = 0; cuvs::cluster::kmeans::fit( - handle, params, - X_view, - std::nullopt, - C_view, - host_inertia, - host_iter + handle, params, + raft::make_device_matrix_view(samp_gpu.data_ptr(), + gpu_sample_sz, (int)D), + std::nullopt, + raft::make_device_matrix_view(cent_gpu.data_ptr(), + num_clusters, (int)D), + raft::make_host_scalar_view(&inertia), + raft::make_host_scalar_view(&actual_iter) ); - } - Tensor all_labels = torch::empty({N}, torch::kLong); - - auto predict_fn = [&](Tensor batch_cpu, int64_t off) { - int64_t bs = batch_cpu.size(0); - - // allocate exactly bs labels on the GPU - Tensor labels32 = torch::empty( - {bs}, - torch::TensorOptions() - .dtype(torch::kInt32) - .device(torch::kCUDA)); - - Tensor batch_gpu = batch_cpu.to(torch::kCUDA, /*NB=*/true) - .contiguous(); - - auto Xv = raft::make_device_matrix_view( - batch_gpu.data_ptr(), - (int)bs, (int)D); - auto Lv = raft::make_device_vector_view( - labels32.data_ptr(), (int)bs); - - float pred_inertia = 0.0f; - auto host_pred = raft::make_host_scalar_view(&pred_inertia); - - cuvs::cluster::kmeans::predict( - handle, params, - Xv, - std::nullopt, - raft::make_device_matrix_view( - cent_gpu.data_ptr(), - num_clusters, (int)D), - Lv, - false, - host_pred - ); + /* ---------- predict every point, in order, exactly once ------------ */ + Tensor all_labels = torch::empty({N}, torch::kLong); // on CPU - // now safe to copy back exactly bs elements - all_labels.narrow(0, off, bs) - .copy_( - labels32.to(torch::kLong) - .to(torch::kCPU) - ); + auto run_predict = [&](Tensor batch_host, int64_t dst_off) + { + const int64_t bs = batch_host.size(0); + + Tensor batch_gpu = batch_host.to(torch::kCUDA, /*non_blocking=*/true) + .contiguous(); + Tensor lbl_gpu32 = torch::empty({bs}, + torch::dtype(torch::kInt32).device(torch::kCUDA)); + + cuvs::cluster::kmeans::predict( + handle, params, + raft::make_device_matrix_view(batch_gpu.data_ptr(), + bs, (int)D), + std::nullopt, + raft::make_device_matrix_view(cent_gpu.data_ptr(), + num_clusters, (int)D), + raft::make_device_vector_view(lbl_gpu32.data_ptr(), bs), + /*verbosity=*/false, + raft::make_host_scalar_view(nullptr) // we don't need the inertia + ); + + all_labels.narrow(0, dst_off, bs) + .copy_(lbl_gpu32.to(torch::kLong).cpu(), /*non_blocking=*/false); }; - // predict the sample slice - predict_fn(samp_pts, /*off=*/0); - - // predict the rest for (int64_t off = 0; off < N; off += gpu_batch_size) { - int64_t bs = std::min(gpu_batch_size, N - off); - if (off < gpu_sample_size) { - if (off + bs <= gpu_sample_size) { - continue; - } else { - int64_t overlap = gpu_sample_size - off; - Tensor rest_chunk = cpu_pts.slice(0, off + overlap, off + bs); - predict_fn(rest_chunk, /*off=*/off + overlap); - continue; - } - } - Tensor rest_chunk = cpu_pts.slice(0, off, off + bs); - predict_fn(rest_chunk, /*off=*/off); + const int64_t bs = std::min(gpu_batch_size, N - off); + run_predict(cpu_pts.slice(0, off, off + bs), off); } - // 8) group on CPU - Tensor sorted_lbl, sorted_idx; - std::tie(sorted_lbl, sorted_idx) = torch::sort(all_labels); - Tensor sorted_vecs = vectors.index_select(0, sorted_idx); - Tensor sorted_ids = ids.index_select(0, sorted_idx); - - Tensor counts = torch::bincount(sorted_lbl, /*weights=*/{}, num_clusters); - auto cnt_cpu = counts.to(torch::kCPU); - std::vector split_sizes( - cnt_cpu.data_ptr(), - cnt_cpu.data_ptr() + num_clusters - ); - auto cluster_vecs = torch::split(sorted_vecs, split_sizes, 0); - auto cluster_ids = torch::split(sorted_ids, split_sizes, 0); + /* ---------- group vectors/ids by cluster on CPU -------------------- */ + Tensor lbl_sorted, idx_sorted; + std::tie(lbl_sorted, idx_sorted) = torch::sort(all_labels); + Tensor vecs_sorted = vectors.index_select(0, idx_sorted); + Tensor ids_sorted = ids.index_select(0, idx_sorted); + Tensor counts = torch::bincount(lbl_sorted, /*weights=*/{}, num_clusters); + std::vector split_sz(counts.data_ptr(), + counts.data_ptr() + num_clusters); + + std::vector cluster_vecs = torch::split(vecs_sorted, split_sz, 0); + std::vector cluster_ids = torch::split(ids_sorted, split_sz, 0); + + /* ---------- package result ---------------------------------------- */ auto out = std::make_shared(); out->centroids = cent_gpu.cpu().contiguous(); out->partition_ids = torch::arange(num_clusters, torch::kLong); out->vectors = std::move(cluster_vecs); out->vector_ids = std::move(cluster_ids); - return out; } #endif diff --git a/src/cpp/src/dynamic_inverted_list.cpp b/src/cpp/src/dynamic_inverted_list.cpp index b4573402..2e82ca97 100644 --- a/src/cpp/src/dynamic_inverted_list.cpp +++ b/src/cpp/src/dynamic_inverted_list.cpp @@ -68,7 +68,8 @@ namespace faiss { size_t DynamicInvertedLists::list_size(size_t list_no) const { auto it = partitions_.find(list_no); if (it == partitions_.end()) { - throw std::runtime_error("List does not exist in list_size"); + string err_message = "List " + std::to_string(list_no) + " does not exist in list_size"; + throw std::runtime_error(err_message); } return static_cast(it->second->num_vectors_); } @@ -278,7 +279,6 @@ void DynamicInvertedLists::batch_update_entries( void DynamicInvertedLists::remove_list(size_t list_no) { auto it = partitions_.find(list_no); if (it == partitions_.end()) { - // Already doesn't exist return; } diff --git a/src/cpp/src/maintenance_policies.cpp b/src/cpp/src/maintenance_policies.cpp index 6ad450c6..2d357e68 100644 --- a/src/cpp/src/maintenance_policies.cpp +++ b/src/cpp/src/maintenance_policies.cpp @@ -33,103 +33,117 @@ MaintenancePolicy::MaintenancePolicy( shared_ptr MaintenancePolicy::perform_maintenance() { // only consider split/deletion once the window is full + auto start_total = steady_clock::now(); if (partition_manager_->parent_ == nullptr) { return std::make_shared(); } - int64_t num_queries = hit_count_tracker_->get_num_queries_recorded(); - if (hit_count_tracker_->get_num_queries_recorded() < params_->window_size) { - std::cout << "Window not full yet. " << num_queries << " queries recorded and " << params_->window_size - << " queries required." << std::endl; - return std::make_shared(); - } - - auto start_total = steady_clock::now(); - // STEP 1: Aggregate hit counts from the HitCountTracker. - vector > per_query_hits = hit_count_tracker_->get_per_query_hits(); - unordered_map aggregated_hits; - for (const auto &query_hits: per_query_hits) { - for (int64_t pid: query_hits) { - aggregated_hits[pid]++; - } - } + vector partitions_to_delete; + vector partitions_to_split; Tensor all_partition_ids_tens = partition_manager_->get_partition_ids(); vector all_partition_ids = vector(all_partition_ids_tens.data_ptr(), all_partition_ids_tens.data_ptr() + all_partition_ids_tens.size(0)); - // STEP 2: Use cost estimation to decide which partitions to delete or split. - int total_partitions = partition_manager_->nlist(); - float current_scan_fraction = hit_count_tracker_->get_current_scan_fraction(); - vector partitions_to_delete; - vector partitions_to_split; + if (params_->max_partition_size != -1) { + for (const auto &partition_id: all_partition_ids) { + int partition_size = partition_manager_->get_partition_size(partition_id); - int avg_partition_size = partition_manager_->ntotal() / total_partitions; - for (const auto &partition_id: all_partition_ids) { - // Get hit count and hit rate for the partition. - int hit_count = aggregated_hits[partition_id]; - float hit_rate = static_cast(hit_count) / static_cast(params_->window_size); - int partition_size = partition_manager_->get_partition_size(partition_id); - - // Deletion decision. - float delete_delta = cost_estimator_->compute_delete_delta( - partition_size, hit_rate, total_partitions, current_scan_fraction, avg_partition_size); - - if (delete_delta < -params_->delete_threshold_ns) { - - if (params_->enable_delete_rejection && partition_size > params_->min_partition_size) { - // check the assignments of the partitions to be deleted. - auto search_params = make_shared(); - search_params->k = 2; // get the top 2 partitions, ignore the first one as it is the partition itself - search_params->batched_scan = true; - float *partition_vectors = (float *) partition_manager_->partition_store_->partitions_[partition_id]->codes_; - Tensor part_vecs = torch::from_blob(partition_vectors, {(int64_t) partition_manager_->partition_store_->list_size(partition_id), - partition_manager_->d()}, torch::kFloat32); - auto res = partition_manager_->parent_->search(part_vecs, search_params); - - Tensor reassign_ids = res->ids.flatten(); - - // remove the partition itself - reassign_ids = reassign_ids.masked_select(reassign_ids != partition_id); - - // Get A) the unique partitions, B) the number reassigned, C) the size of the partitions, D) hit rates of the partitions - Tensor uniques; - Tensor counts; - std::tie(uniques, std::ignore, counts) = torch::_unique2(reassign_ids, true, false, true); - Tensor part_sizes = partition_manager_->get_partition_sizes(uniques); - - // convert to vectors - vector reassign_id_vec = vector(uniques.data_ptr(), uniques.data_ptr() + uniques.size(0)); - - vector reassign_sizes = vector(part_sizes.data_ptr(), - part_sizes.data_ptr() + part_sizes.size(0)); - vector reassign_counts = vector(counts.data_ptr(), - counts.data_ptr() + counts.size(0)); - vector hit_rates; - for (int64_t reassign_id: reassign_id_vec) { - hit_rates.push_back(static_cast(aggregated_hits[reassign_id]) / static_cast(params_->window_size)); - } + if (partition_size > params_->max_partition_size) { + partitions_to_split.emplace_back(partition_id); + } else if (partition_size < params_->min_partition_size) { + partitions_to_delete.emplace_back(partition_id); + } + } + + } else { + int64_t num_queries = hit_count_tracker_->get_num_queries_recorded(); + if (hit_count_tracker_->get_num_queries_recorded() < params_->window_size) { + std::cout << "Window not full yet. " << num_queries << " queries recorded and " << params_->window_size + << " queries required." << std::endl; + return std::make_shared(); + } - float delta = cost_estimator_->compute_delete_delta_w_reassign(partition_manager_->get_partition_size(partition_id), - static_cast(aggregated_hits[partition_id]) / static_cast(params_->window_size), - total_partitions, - reassign_counts, - reassign_sizes, - hit_rates); + // STEP 1: Aggregate hit counts from the HitCountTracker. + vector > per_query_hits = hit_count_tracker_->get_per_query_hits(); + unordered_map aggregated_hits; + for (const auto &query_hits: per_query_hits) { + for (int64_t pid: query_hits) { + aggregated_hits[pid]++; + } + } - if (delta < -params_->delete_threshold_ns) { + // STEP 2: Use cost estimation to decide which partitions to delete or split. + int total_partitions = partition_manager_->nlist(); + float current_scan_fraction = hit_count_tracker_->get_current_scan_fraction(); + + int avg_partition_size = partition_manager_->ntotal() / total_partitions; + for (const auto &partition_id: all_partition_ids) { + // Get hit count and hit rate for the partition. + int hit_count = aggregated_hits[partition_id]; + float hit_rate = static_cast(hit_count) / static_cast(params_->window_size); + int partition_size = partition_manager_->get_partition_size(partition_id); + + // Deletion decision. + float delete_delta = cost_estimator_->compute_delete_delta( + partition_size, hit_rate, total_partitions, current_scan_fraction, avg_partition_size); + + if (delete_delta < -params_->delete_threshold_ns) { + + if (params_->enable_delete_rejection && partition_size > params_->min_partition_size) { + // check the assignments of the partitions to be deleted. + auto search_params = make_shared(); + search_params->k = 2; // get the top 2 partitions, ignore the first one as it is the partition itself + search_params->batched_scan = true; + float *partition_vectors = (float *) partition_manager_->partition_store_->partitions_[partition_id]->codes_; + Tensor part_vecs = torch::from_blob(partition_vectors, {(int64_t) partition_manager_->partition_store_->list_size(partition_id), + partition_manager_->d()}, torch::kFloat32); + auto res = partition_manager_->parent_->search(part_vecs, search_params); + + Tensor reassign_ids = res->ids.flatten(); + + // remove the partition itself + reassign_ids = reassign_ids.masked_select(reassign_ids != partition_id); + + // Get A) the unique partitions, B) the number reassigned, C) the size of the partitions, D) hit rates of the partitions + Tensor uniques; + Tensor counts; + std::tie(uniques, std::ignore, counts) = torch::_unique2(reassign_ids, true, false, true); + Tensor part_sizes = partition_manager_->get_partition_sizes(uniques); + + // convert to vectors + vector reassign_id_vec = vector(uniques.data_ptr(), uniques.data_ptr() + uniques.size(0)); + + vector reassign_sizes = vector(part_sizes.data_ptr(), + part_sizes.data_ptr() + part_sizes.size(0)); + vector reassign_counts = vector(counts.data_ptr(), + counts.data_ptr() + counts.size(0)); + vector hit_rates; + for (int64_t reassign_id: reassign_id_vec) { + hit_rates.push_back(static_cast(aggregated_hits[reassign_id]) / static_cast(params_->window_size)); + } + + float delta = cost_estimator_->compute_delete_delta_w_reassign(partition_manager_->get_partition_size(partition_id), + static_cast(aggregated_hits[partition_id]) / static_cast(params_->window_size), + total_partitions, + reassign_counts, + reassign_sizes, + hit_rates); + + if (delta < -params_->delete_threshold_ns) { + partitions_to_delete.push_back(partition_id); + } + } else { partitions_to_delete.push_back(partition_id); } } else { - partitions_to_delete.push_back(partition_id); - } - } else { - if (partition_size > params_->min_partition_size) { - float split_delta = cost_estimator_->compute_split_delta( - partition_size, hit_rate, total_partitions); - if (split_delta < -params_->split_threshold_ns) { - partitions_to_split.push_back(partition_id); + if (partition_size > params_->min_partition_size) { + float split_delta = cost_estimator_->compute_split_delta( + partition_size, hit_rate, total_partitions); + if (split_delta < -params_->split_threshold_ns) { + partitions_to_split.push_back(partition_id); + } } } } @@ -154,7 +168,6 @@ shared_ptr MaintenancePolicy::perform_maintenance() { auto start_split = steady_clock::now(); shared_ptr split_partitions; if (partitions_to_split_tens.numel() > 0) { - // split the partitions into two split_partitions = partition_manager_->split_partitions(partitions_to_split_tens); @@ -171,12 +184,26 @@ shared_ptr MaintenancePolicy::perform_maintenance() { } auto end_total = steady_clock::now(); - // STEP 6: Fill in timing details. + // STEP 6: Clean up any empty partitions + vector empty_ids = {}; + for (auto pair : partition_manager_->partition_store_->partitions_) { + if (pair.second->num_vectors_ <= 0) { + empty_ids.emplace_back(pair.first); + } + } + if (empty_ids.size() > 0) { + partition_manager_->delete_partitions(torch::from_blob(empty_ids.data(), {static_cast(empty_ids.size())}, torch::kInt64)); + } + + // STEP 7: Fill in timing details. shared_ptr timing_info = std::make_shared(); timing_info->delete_time_us = duration_cast(end_delete - start_delete).count(); timing_info->split_time_us = duration_cast(end_split - start_split).count(); timing_info->total_time_us = duration_cast(end_total - start_total).count(); + timing_info->n_splits = static_cast(partitions_to_split.size()); + timing_info->n_deletes = static_cast(partitions_to_delete.size()); + return timing_info; } diff --git a/src/cpp/src/partition_manager.cpp b/src/cpp/src/partition_manager.cpp index ef43bd91..5445e377 100644 --- a/src/cpp/src/partition_manager.cpp +++ b/src/cpp/src/partition_manager.cpp @@ -201,7 +201,12 @@ shared_ptr PartitionManager::add( // Determine partition assignments for each vector. vector partition_ids_for_each(n, -1); if (parent_ == nullptr) { - partition_ids_for_each.assign(n, 0); + // round robin assign + int64_t num_parts = nlist(); + for (int64_t i = 0; i < partition_ids_for_each.size(); i++) { + partition_ids_for_each[i] = i % num_parts; + } + if (debug_) { std::cout << "[PartitionManager] add: No parent index; assigning all vectors to partition 0." << std::endl; } @@ -414,7 +419,7 @@ shared_ptr PartitionManager::split_partitions(const Tensor &partitio split_vectors.reserve(total_new_partitions); split_ids.reserve(total_new_partitions); - shared_ptr clustering = select_partitions(partition_ids); + shared_ptr clustering = select_partitions(partition_ids, true); shared_ptr build_params = make_shared(); build_params->nlist = num_splits; diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index ffdcd249..5483a4c7 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -135,7 +135,27 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { // Retrieve partition data. const float *partition_codes = (float *) partition_manager_->partition_store_->get_codes(job.partition_id); const int64_t *partition_ids = (int64_t *) partition_manager_->partition_store_->get_ids(job.partition_id); - int64_t partition_size = partition_manager_->partition_store_->list_size(job.partition_id); + + // skip partition if it is not found (workaround) + int64_t partition_size = 0; + try { + partition_size = partition_manager_->partition_store_->list_size(job.partition_id); + } catch (const std::runtime_error &e) { + std::cerr << "[QueryCoordinator::partition_scan_worker_fn] Partition " << job.partition_id << " not found." << std::endl; + continue; + } + + // skip partition if it is empty + if (partition_size == 0) { + std::cerr << "[QueryCoordinator::partition_scan_worker_fn] Partition " << job.partition_id << " is empty." << std::endl; + // call dummy batch add to decrement topk_buffer jobs counter. #TODO remove jobs counter from topk_buffer + for (int64_t q = 0; q < job.num_queries; q++) { + int64_t global_q = job.query_ids[q]; + global_topk_buffer_pool_[global_q]->batch_add(nullptr, nullptr, 0); + } + continue; + } + // Branch for non-batched jobs. if (!job.is_batched) { @@ -593,7 +613,6 @@ shared_ptr QueryCoordinator::serial_scan(Tensor x, Tensor partitio float *list_vectors = (float *) partition_manager_->partition_store_->get_codes(pi); int64_t *list_ids = (int64_t *) partition_manager_->partition_store_->get_ids(pi); int64_t list_size = partition_manager_->partition_store_->list_size(pi); - scan_list(query_vec, list_vectors, list_ids, diff --git a/src/python/datasets/ann_datasets.py b/src/python/datasets/ann_datasets.py index 607d47ea..0d37bcfe 100644 --- a/src/python/datasets/ann_datasets.py +++ b/src/python/datasets/ann_datasets.py @@ -161,6 +161,46 @@ def load_ground_truth(self) -> Union[np.ndarray, torch.Tensor]: return torch.from_numpy(ids).long() +class Sift10m(Dataset): + def __init__(self, download_dir: Union[str, Path] = DEFAULT_DOWNLOAD_DIR): + self.download_dir = to_path(download_dir) + self.data_dir = self.download_dir / "sift10m" + self.downloaded = False + self.metric = "l2" + + def is_downloaded(self) -> bool: + base = self.data_dir / "base.1B.u8bin.crop_nb_10000000" + query = self.data_dir / "query.public.10K.u8bin" + gt = self.data_dir / "bigann-10M" + return base.exists() and query.exists() and gt.exists() + + def download(self, overwrite: bool = False): + pass + + def load_vectors(self) -> Union[np.ndarray, torch.Tensor]: + fname = self.data_dir / "base.1B.u8bin.crop_nb_10000000" + n, d = map(int, np.fromfile(fname, dtype="uint32", count=2)) + # return to_torch(np.fromfile(fname, dtype=np.uint8, offset=8).reshape((n, d))) + return torch.from_numpy(np.fromfile(fname, dtype=np.uint8, offset=8).reshape((n, d))).to(torch.float32) + + def load_queries(self) -> Union[np.ndarray, torch.Tensor]: + fname = self.data_dir / "query.public.10K.u8bin" + n, d = map(int, np.fromfile(fname, dtype="uint32", count=2)) + # return to_torch(np.fromfile(fname, dtype=np.uint8, offset=8).reshape((n, d))) + return torch.from_numpy(np.fromfile(fname, dtype=np.uint8, offset=8).reshape((n, d))).to(torch.float32) + + def load_ground_truth(self) -> Union[np.ndarray, torch.Tensor]: + fname = self.data_dir / "bigann-10M" + n, d = map(int, np.fromfile(fname, dtype="uint32", count=2)) + assert os.stat(fname).st_size == 8 + n * d * (4 + 4) + f = open(fname, "rb") + f.seek(4 + 4) + ids = np.fromfile(f, dtype="int32", count=n * d).reshape(n, d) + # D = np.fromfile(f, dtype="float32", count=n * d).reshape(n, d) + + return torch.from_numpy(ids).long() + + def load_dataset( name: str, download_dir: str = DEFAULT_DOWNLOAD_DIR, overwrite_download: bool = False @@ -171,8 +211,10 @@ def load_dataset( dataset = UniformDataset(num_vectors=100000, dim=8, download_dir=download_dir) elif name.lower() == "msturing10m": dataset = MSTuring10m(download_dir=download_dir) + elif name.lower() == "sift10m": + dataset = Sift10m(download_dir=download_dir) else: - raise RuntimeError("Unimplemented dataset") + raise RuntimeError("Unimplemented dataset + " + name) dataset.download(overwrite=overwrite_download) return dataset.load() diff --git a/src/python/workload_generator.py b/src/python/workload_generator.py index faa7c1d2..f390aad7 100644 --- a/src/python/workload_generator.py +++ b/src/python/workload_generator.py @@ -2,11 +2,12 @@ import time from abc import ABC, abstractmethod from pathlib import Path -from typing import Optional, Union +from typing import Optional, Union, Dict, List import matplotlib.pyplot as plt import numpy as np import pandas as pd +import quake import torch from quake import SearchParams @@ -387,230 +388,230 @@ def generate_workload(self): class WorkloadEvaluator: - """ - Evaluates a generated workload on a given index and produces summary statistics and plots. - """ - - def __init__( - self, - workload_dir: Union[str, Path], - output_dir: Union[str, Path], - base_vectors_path: Optional[Union[str, Path]] = None, - ): - self.workload_dir = to_path(workload_dir) - self.output_dir = to_path(output_dir) - self.runbook_path = self.workload_dir / "runbook.json" - self.operations_dir = self.workload_dir / "operations" - self.initial_indices_path = self.workload_dir / "initial_indices.pt" - self.base_vectors_path = ( - to_path(base_vectors_path) if base_vectors_path else self.workload_dir / "base_vectors.pt" - ) - self.runbook = None - - def initialize_index(self, name, index, build_params, m_params): - index_dir = self.workload_dir / "init_indexes" - index_dir.mkdir(parents=True, exist_ok=True) - index_path = index_dir / f"{name}.index" - vectors = torch.load(self.base_vectors_path, weights_only=True).to(torch.float32) - initial_indices = torch.load(self.initial_indices_path, weights_only=True).to(torch.int64) - vectors = vectors[initial_indices] - if not index_path.exists(): - print(build_params) - index.build(vectors, ids=initial_indices, **build_params) - index.save(index_path) - print(f"Index {name} built and saved to {index_path}") + # --------------------------------------------------------------------- + def __init__(self, + workload_dir: Union[str, Path], + output_dir: Union[str, Path], + base_vectors_path: Optional[Union[str, Path]] = None): + + self.workload_dir = to_path(workload_dir) + self.output_dir = to_path(output_dir) + self.runbook_path = self.workload_dir / "runbook.json" + self.ops_dir = self.workload_dir / "operations" + self.init_ids_path = self.workload_dir / "initial_indices.pt" + self.base_vec_path = (to_path(base_vectors_path) + if base_vectors_path + else self.workload_dir / "base_vectors.pt") + + # --------------------------------------------------------------------- + def _init_index(self, name: str, wrapper, build_params: Dict, + m_params: Optional[Dict]): + + idx_dir = self.workload_dir / "init_indexes" + idx_dir.mkdir(parents=True, exist_ok=True) + idx_file = idx_dir / f"{name}.index" + + vecs = torch.load(self.base_vec_path, weights_only=True).float() + init_ids = torch.load(self.init_ids_path, weights_only=True).long() + vecs_init = vecs[init_ids] + + if not idx_file.exists(): + print(f"[{name}] building base index …") + wrapper.build(vecs_init, ids=init_ids, **build_params) + wrapper.save(idx_file) + print(f"[{name}] stored → {idx_file}") else: - if build_params.get("n_workers"): - index.load(index_path, num_workers=build_params["num_workers"]) - else: - index.load(index_path) - print(f"Index {name} loaded from {index_path}") + wrapper.load(idx_file, + num_workers=build_params.get("num_workers", 0)) + print(f"[{name}] loaded ← {idx_file}") + + if isinstance(wrapper, QuakeWrapper) and m_params: + mp = quake.MaintenancePolicyParams() + for k, v in m_params.items(): + setattr(mp, k, v) + wrapper.index.initialize_maintenance_policy(mp) + print(f"[{name}] maintenance policy: {m_params}") + + return wrapper + + # --------------------------------------------------------------------- + def evaluate_workload(self, *, + name: str, + index, + build_params: Dict, + search_params: Dict, + do_maintenance: bool = False, + m_params: Optional[Dict] = None, + batch: bool = False) -> List[Dict]: - if isinstance(index, QuakeWrapper) and m_params is not None: - index.index.initialize_maintenance_policy(m_params) - print(f"Maintenance policy initialized: {m_params}") + self.output_dir.mkdir(parents=True, exist_ok=True) + index = self._init_index(name, index, build_params, m_params) - return index + runbook = json.load(open(self.runbook_path)) + base_vecs = torch.load(self.base_vec_path, weights_only=True).float() + queries = (base_vecs if runbook["parameters"]["sample_queries"] + else torch.load(self.workload_dir / "query_vectors.pt", + weights_only=True).float()) - def evaluate_workload( - self, name, index, build_params, search_params, do_maintenance=False, m_params=None, batch=False - ): - """ - Evaluate the workload on the index. At the end a summary is printed and a multi-panel plot is saved. - """ - # validate search_params - assert "k" in search_params, "search_params must contain 'k' for number of neighbors" - - # --- Load Workload and Index --- - base_vectors = torch.load(self.base_vectors_path, weights_only=True).to(torch.float32) - initial_indices = torch.load(self.initial_indices_path, weights_only=True).to(torch.int64) - - index = self.initialize_index(name, index, build_params, m_params) - - self.runbook = json.load(open(self.runbook_path, "r")) - query_vectors = ( - base_vectors - if self.runbook["parameters"]["sample_queries"] - else torch.load(self.workload_dir / "query_vectors.pt", weights_only=True) - ) - query_vectors = query_vectors.to(torch.float32) - start_time = time.time() - init_time = time.time() - start_time - self.runbook["initialize"]["time"] = init_time - - # --- Run Operations --- - curr_ids = initial_indices - curr_vectors = base_vectors[curr_ids] results = [] - for operation_id, operation in self.runbook["operations"].items(): - print(f"Operation {operation_id}/{len(self.runbook['operations'])}...") - operation_id_int = int(operation_id) - operation_type = operation["type"] - operation_ids = torch.load(self.operations_dir / f"{operation_id}.pt", weights_only=True) - if operation_type == "insert": - curr_ids = torch.cat([curr_ids, operation_ids]) - curr_vectors = torch.cat([curr_vectors, base_vectors[operation_ids]]) - start_time = time.time() - index.add(base_vectors[operation_ids], ids=operation_ids, num_threads=16) - op_time = time.time() - start_time - mean_recall = None - elif operation_type == "delete": - start_time = time.time() - index.remove(operation_ids) - op_time = time.time() - start_time - mean_recall = None - elif operation_type == "query": - gt_ids = torch.load(self.operations_dir / f"{operation_id}_gt_ids.pt", weights_only=True) - # gt_dist = torch.load(self.operations_dir / f"{operation_id}_gt_dists.pt", weights_only=True) - queries = query_vectors[operation_ids] - - start_time = time.time() + totals = dict(query=0., insert=0., delete=0., maintain=0.) + + print(f"─ Evaluating workload on {name} ({len(runbook['operations'])} ops) ─") + for op_id, op in runbook["operations"].items(): + op_no = int(op_id) + op_type = op["type"] + ids = torch.load(self.ops_dir / f"{op_id}.pt", + weights_only=True) + + print(f"[{name}] op {op_no:4d} | {op_type:<6} | {len(ids):6d} ids", end="", flush=True) + + # ----- perform the operation ---------------------------------- + if op_type == "insert": + t0 = time.perf_counter() + index.add(base_vecs[ids], ids=ids) + latency_ms = (time.perf_counter() - t0) * 1e3 + recall = None + elif op_type == "delete": + t0 = time.perf_counter() + index.remove(ids) + latency_ms = (time.perf_counter() - t0) * 1e3 + recall = None + elif op_type == "query": + qs = queries[ids] + t0 = time.perf_counter() if batch: - search_result = index.search(queries, **search_params) - pred_ids = search_result.ids - timing_infos = [search_result.timing_info] + sr = index.search(qs, **search_params) + pred_ids = sr.ids else: - Is, Ds, timing_infos = [], [], [] - - for query in queries: - query = query.unsqueeze(0) - search_result = index.search(query, **search_params) - Is.append(search_result.ids) - Ds.append(search_result.distances) - timing_infos.append(search_result.timing_info) - pred_ids = torch.cat(Is) - op_time = time.time() - start_time - - recalls = compute_recall(pred_ids, gt_ids, search_params["k"]) - mean_recall = recalls.mean().item() - self.runbook["operations"][operation_id]["recall"] = mean_recall - total_time = sum([ti.total_time_ns for ti in timing_infos]) - mean_time = total_time / len(timing_infos) - print(f"Query Time: {mean_time:.2f} ns, Recall: {mean_recall:.2f}") + pred_ids = torch.cat([ + index.search(q.unsqueeze(0), **search_params).ids + for q in qs + ]) + latency_ms = (time.perf_counter() - t0) * 1e3 + gt_ids = torch.load(self.ops_dir / f"{op_id}_gt_ids.pt", + weights_only=True) + recall = compute_recall(pred_ids, gt_ids, + search_params["k"]).mean().item() + op["recall"] = recall + else: + raise ValueError(op_type) + totals[op_type] += latency_ms + + # ----- maintenance ------------------------------------------- + maint_ms = nsplits = ndeletes = 0 if do_maintenance: - print("Running maintenance...") - index.maintenance() - - n_resident = operation.get("n_resident", None) - index_state = index.index_state() - result = { - "operation_number": operation_id_int, - "operation_type": operation_type, - "latency_ms": op_time * 1000, - "recall": mean_recall, - "n_resident": n_resident, + mi = index.maintenance() + maint_ms = mi.total_time_us / 1e3 + nsplits = mi.n_splits + ndeletes = mi.n_deletes + totals["maintain"] += maint_ms + + print(f" | lat {latency_ms:8.2f} ms" + f" | maint {maint_ms:7.2f} ms" + f" | splits {nsplits:4d}" + f" | dels {ndeletes:4d}" + f"{' | rec {:.3f}'.format(recall) if recall is not None else ''}") + + # ----- store row --------------------------------------------- + row = { + "operation_number" : op_no, + "operation_type" : op_type, + "latency_ms" : latency_ms, + "maintenance_time_ms": maint_ms, + "n_splits" : nsplits, + "n_deletes" : ndeletes, + "recall" : recall, + "n_resident" : op.get("n_resident"), } - result.update(index_state) - result.update(search_params) - results.append(result) - - # --- Print Evaluation Summary --- - # Gather per-operation metrics - latencies_insert = [r["latency_ms"] for r in results if r["operation_type"] == "insert"] - op_nums_insert = [r["operation_number"] for r in results if r["operation_type"] == "insert"] - latencies_delete = [r["latency_ms"] for r in results if r["operation_type"] == "delete"] - op_nums_delete = [r["operation_number"] for r in results if r["operation_type"] == "delete"] - latencies_query = [r["latency_ms"] for r in results if r["operation_type"] == "query"] - op_nums_query = [r["operation_number"] for r in results if r["operation_type"] == "query"] - query_recalls = [r["recall"] for r in results if r["operation_type"] == "query" and r["recall"] is not None] - n_vectors = [r["n_resident"] for r in results if r["n_resident"] is not None] - - avg_latency_insert = np.mean(latencies_insert) if latencies_insert else None - avg_latency_delete = np.mean(latencies_delete) if latencies_delete else None - avg_latency_query = np.mean(latencies_query) if latencies_query else None - avg_query_recall = np.mean(query_recalls) if query_recalls else None - - print("\nWorkload Evaluation Summary:") - if avg_latency_insert is not None: - print(f"Average Insert Latency: {avg_latency_insert:.2f} ms") - if avg_latency_delete is not None: - print(f"Average Delete Latency: {avg_latency_delete:.2f} ms") - if avg_latency_query is not None: - print(f"Average Query Latency: {avg_latency_query:.2f} ms") - if avg_query_recall is not None: - print(f"Average Query Recall: {avg_query_recall:.2f}") - - # --- Generate Multi-Panel Plot --- + row.update(index.index_state()) # keeps existing behaviour + row.update(search_params) # 〃 + results.append(row) + + # ---- original four-panel figure (unchanged) --------------------- + df = pd.DataFrame(results) + self._four_panel_plot(df) + + # ---- cumulative-time bar chart --------------------------------- + self._time_breakdown_plot(totals, name) + + # ---- CSV -------------------------------------------------------- + df.to_csv(self.output_dir / "results.csv", index=False) + print(f"Results → {self.output_dir / 'results.csv'}") + return results + + # --------------------------------------------------------------------- + def _four_panel_plot(self, df: pd.DataFrame): + """ + Re-implementation of the original four-panel plot – byte-for-byte + identical file name (*evaluation_plots.png*). + """ + lat_insert = df[df.operation_type == "insert"] + lat_delete = df[df.operation_type == "delete"] + lat_query = df[df.operation_type == "query" ] + fig, axs = plt.subplots(2, 2, figsize=(12, 10)) - # Plot A: Latency per operation type + + # A: latency ax = axs[0, 0] - if op_nums_insert: - ax.plot(op_nums_insert, latencies_insert, label="Insert", marker="o") - if op_nums_delete: - ax.plot(op_nums_delete, latencies_delete, label="Delete", marker="s") - if op_nums_query: - ax.plot(op_nums_query, latencies_query, label="Query", marker="^") - ax.set_xlabel("Operation Number") - ax.set_ylabel("Latency (ms)") - ax.set_title("Operation Latency") - ax.legend() - # Plot B: Number of partitions per operation (if available) + if not lat_insert.empty: + ax.plot(lat_insert.operation_number, lat_insert.latency_ms, + label="Insert", marker="o") + if not lat_delete.empty: + ax.plot(lat_delete.operation_number, lat_delete.latency_ms, + label="Delete", marker="s") + if not lat_query.empty: + ax.plot(lat_query.operation_number, lat_query.latency_ms, + label="Query", marker="^") + ax.set(xlabel="Operation Number", ylabel="Latency (ms)", + title="Operation Latency"); ax.legend() + + # B: partitions + part = df[df.n_list.notna()] ax = axs[0, 1] - partitions = [r["n_list"] for r in results if r["n_list"] is not None] - op_nums_part = [r["operation_number"] for r in results if r["n_list"] is not None] - if partitions: - ax.plot(op_nums_part, partitions, marker="o") - ax.set_xlabel("Operation Number") - ax.set_ylabel("Number of Partitions") - ax.set_title("Partitions per Operation") + if not part.empty: + ax.plot(part.operation_number, part.n_list, marker="o") + ax.set(xlabel="Operation Number", ylabel="Number of Partitions", + title="Partitions per Operation") else: - ax.text(0.5, 0.5, "No partition info", ha="center", va="center") - ax.axis("off") - # Plot C: Resident set size per operation + ax.text(0.5, 0.5, "No partition info", + ha="center", va="center"); ax.axis("off") + + # C: resident set size + res = df[df.n_resident.notna()] ax = axs[1, 0] - if n_vectors: - op_nums_vect = [r["operation_number"] for r in results if r["n_resident"] is not None] - ax.plot(op_nums_vect, n_vectors, marker="o") - ax.set_xlabel("Operation Number") - ax.set_ylabel("Resident Vectors") - ax.set_title("Resident Set Size") + if not res.empty: + ax.plot(res.operation_number, res.n_resident, marker="o") + ax.set(xlabel="Operation Number", ylabel="Resident Vectors", + title="Resident Set Size") else: - ax.text(0.5, 0.5, "No resident set info", ha="center", va="center") - ax.axis("off") - # Plot D: Query recall per query operation + ax.text(0.5, 0.5, "No resident set info", + ha="center", va="center"); ax.axis("off") + + # D: recall + rec = df[(df.operation_type == "query") & df.recall.notna()] ax = axs[1, 1] - if op_nums_query and query_recalls: - ax.plot(op_nums_query, query_recalls, marker="o") - ax.set_xlabel("Operation Number") - ax.set_ylabel("Query Recall") - ax.set_title("Query Recall") + if not rec.empty: + ax.plot(rec.operation_number, rec.recall, marker="o") + ax.set(xlabel="Operation Number", ylabel="Query Recall", + title="Query Recall") else: - ax.text(0.5, 0.5, "No query recall info", ha="center", va="center") - ax.axis("off") - plt.tight_layout() - - # create output directory if it doesn't exist - self.output_dir.mkdir(parents=True, exist_ok=True) + ax.text(0.5, 0.5, "No query recall info", + ha="center", va="center"); ax.axis("off") - plot_path = self.output_dir / "evaluation_plots.png" - plt.savefig(plot_path) - print(f"Saved evaluation plots to {plot_path}") + plt.tight_layout() + plt.savefig(self.output_dir / "evaluation_plots.png") plt.close() - df = pd.DataFrame(results) - csv_path = self.output_dir / "results.csv" - df.to_csv(csv_path, index=False) - print(f"Saved results to {csv_path}") - - return results + # --------------------------------------------------------------------- + def _time_breakdown_plot(self, totals: Dict[str, float], title: str): + plt.figure(figsize=(6, 4)) + bars = [totals["query"], totals["insert"], + totals["delete"], totals["maintain"], + sum(totals.values())] + plt.bar(["Query", "Insert", "Delete", "Maintain", "Total"], bars) + plt.ylabel("Cumulative time (ms)") + plt.title(f"Time budget – {title}") + plt.tight_layout() + plt.savefig(self.output_dir / "time_breakdown.png", dpi=150) + plt.close() \ No newline at end of file diff --git a/test/cpp/benchmark.cpp b/test/cpp/benchmark.cpp index eb74bd90..f5280166 100644 --- a/test/cpp/benchmark.cpp +++ b/test/cpp/benchmark.cpp @@ -496,22 +496,26 @@ TEST(QuakeIndexStressTest, SearchAddRemoveMaintenanceTest) { // Repeatedly search, add, remove, and perform maintenance to see if the index remains consistent. int64_t dimension = 128; - int64_t num_vectors = 100000; - int64_t num_queries = 1; + int64_t num_vectors = 10000; + int64_t num_queries = 100; int64_t batch_size = 10000; int n_ops = 100; QuakeIndex index; auto build_params = std::make_shared(); - build_params->nlist = 10000; + build_params->nlist = 100; build_params->metric = "l2"; build_params->niter = 5; build_params->num_workers = 1; auto maintenance_params = std::make_shared(); - maintenance_params->refinement_radius = 10; + maintenance_params->refinement_radius = 0; maintenance_params->refinement_iterations = 0; - maintenance_params->window_size = 10; + maintenance_params->split_threshold_ns = 1; + maintenance_params->delete_threshold_ns = 1; + maintenance_params->enable_delete_rejection = false; + maintenance_params->enable_split_rejection = false; + maintenance_params->window_size = 1000; Tensor data_vectors = torch::randn({num_vectors, dimension}, torch::kFloat32); Tensor data_ids = torch::arange(num_vectors, torch::kInt64); @@ -523,7 +527,7 @@ TEST(QuakeIndexStressTest, SearchAddRemoveMaintenanceTest) { parent_index_build_params->metric = "l2"; parent_index_build_params->niter = 5; parent_index_build_params->num_workers = 1; - build_params->parent_params = parent_index_build_params; + // build_params->parent_params = parent_index_build_params; // auto grandparent_index_build_params = std::make_shared(); // grandparent_index_build_params->nlist = 1; @@ -544,10 +548,15 @@ TEST(QuakeIndexStressTest, SearchAddRemoveMaintenanceTest) { auto start = std::chrono::high_resolution_clock::now(); auto end = std::chrono::high_resolution_clock::now(); + int64_t num_deleted = 0; + + Tensor origin = torch::zeros(dimension); + Tensor drift = .1 * torch::ones(dimension); + for (int i = 0; i < n_ops; i++) { std::cout << "[SearchAddRemoveMaintenanceTest] Iteration " << i << "\n"; // Search - auto query_vectors = torch::randn({num_queries, dimension}, torch::kFloat32); + auto query_vectors = torch::randn({num_queries, dimension}, torch::kFloat32) + origin; auto search_params = std::make_shared(); search_params->nprobe = 12; search_params->k = 10; @@ -567,7 +576,8 @@ TEST(QuakeIndexStressTest, SearchAddRemoveMaintenanceTest) { ASSERT_EQ(search_result->ids.size(1), search_params->k); // Add - auto add_vectors = torch::randn({batch_size, dimension}, torch::kFloat32); + auto add_vectors = torch::randn({batch_size, dimension}, torch::kFloat32) + origin; + origin += drift; auto add_ids = torch::arange(batch_size, torch::kInt64) + num_vectors; start = std::chrono::high_resolution_clock::now(); auto add_info = index.add(add_vectors, add_ids); @@ -576,18 +586,24 @@ TEST(QuakeIndexStressTest, SearchAddRemoveMaintenanceTest) { num_vectors += batch_size; ASSERT_EQ(add_info->n_vectors, batch_size); - // // Remove - // auto remove_ids = add_ids.slice(0, 0, batch_size / 2); - // start = std::chrono::high_resolution_clock::now(); - // auto remove_info = index.remove(remove_ids); - // end = std::chrono::high_resolution_clock::now(); - // remove_time += std::chrono::duration_cast(end - start).count(); - // ASSERT_EQ(remove_info->n_vectors, batch_size / 2); - - // start = std::chrono::high_resolution_clock::now(); - // index.maintenance(); - // end = std::chrono::high_resolution_clock::now(); - // maintenance_time += std::chrono::duration_cast(end - start).count(); + // Remove + auto remove_ids = torch::arange(batch_size) + num_deleted; + num_deleted += batch_size; + start = std::chrono::high_resolution_clock::now(); + auto remove_info = index.remove(remove_ids); + end = std::chrono::high_resolution_clock::now(); + remove_time += std::chrono::duration_cast(end - start).count(); + ASSERT_EQ(remove_info->n_vectors, batch_size); + + start = std::chrono::high_resolution_clock::now(); + auto timing_info = index.maintenance(); + end = std::chrono::high_resolution_clock::now(); + maintenance_time += std::chrono::duration_cast(end - start).count(); + + std::cout << "n_splits=" << timing_info->n_splits << ", n_deletes=" << timing_info->n_deletes + << ", delete_time=" << timing_info->delete_time_us << " μs" + << ", split_time=" << timing_info->split_time_us << " μs" + << ", total_time=" << timing_info->total_time_us << " μs\n"; } // print out mean times per operation diff --git a/test/experiments/osdi2025/ablation/run.py b/test/experiments/osdi2025/ablation/run.py deleted file mode 100644 index 139597f9..00000000 --- a/test/experiments/osdi2025/ablation/run.py +++ /dev/null @@ -1,2 +0,0 @@ - - diff --git a/test/experiments/osdi2025/experiment_runner.py b/test/experiments/osdi2025/experiment_runner.py index 233dd314..b51b8725 100644 --- a/test/experiments/osdi2025/experiment_runner.py +++ b/test/experiments/osdi2025/experiment_runner.py @@ -12,6 +12,7 @@ from test.experiments.osdi2025.early_termination.run import run_experiment as run_early_termination from test.experiments.osdi2025.multi_level.run import run_experiment as run_multi_level from test.experiments.osdi2025.vary_levels.run import run_experiment as run_vary_levels +from test.experiments.osdi2025.maintenance_ablation.run import run_experiment as run_ablation EXPERIMENTS = { "kick_the_tires": run_kick_the_tires, @@ -21,6 +22,7 @@ "early_termination": run_early_termination, "multi_level": run_multi_level, "vary_levels": run_vary_levels, + "maintenance_ablation": run_ablation, } def main(): diff --git a/test/experiments/osdi2025/maintenance_ablation/run.py b/test/experiments/osdi2025/maintenance_ablation/run.py new file mode 100644 index 00000000..70e40db5 --- /dev/null +++ b/test/experiments/osdi2025/maintenance_ablation/run.py @@ -0,0 +1,304 @@ +#!/usr/bin/env python3 +""" +Quake maintenance–ablation runner +───────────────────────────────── +• Builds / runs / plots exactly like the original “kick-the-tires” script. +• Per-operation CSV rows now contain + n_splits n_deletes maintenance_time_ms + (written by WorkloadEvaluator below). +• After all indices finish, this script also produces + //time_breakdown.png + – a stacked-bar chart of cumulative time spent in + Search / Insert / Delete / Maintenance / Total. +""" +from __future__ import annotations +import argparse, logging +from pathlib import Path +from typing import Dict, List +import matplotlib.pyplot as plt +from matplotlib.lines import Line2D +from matplotlib.cm import get_cmap +import numpy as np, pandas as pd, yaml + +# quake wrappers ---------------------------------------------------------------- +from quake.datasets.ann_datasets import load_dataset +from quake.index_wrappers.faiss_ivf import FaissIVF +from quake.index_wrappers.quake import QuakeWrapper +from quake.workload_generator import DynamicWorkloadGenerator, WorkloadEvaluator + +logging.basicConfig(level=logging.INFO, format="%(asctime)s | %(levelname)s | %(message)s") +log = logging.getLogger(__name__) + + +OP_STYLE = { + "query" : dict(ls="-", marker="o", mfc="none", ms=4, lw=1.2), + "insert" : dict(ls="-", marker="s", mfc="none", ms=4, lw=1.2), + "delete" : dict(ls="-", marker="^", mfc="none", ms=4, lw=1.2), + "maintain": dict(ls="None", marker="X", mfc="black", ms=5), # points only +} + +LAT_OPS = ["query", "insert", "delete", "maintain"] +IDX_LAT_Q, IDX_LAT_I, IDX_LAT_D, IDX_LAT_M = 0, 1, 2, 3 +IDX_PART, IDX_RES, IDX_REC, IDX_TOT, IDX_SPL = 4, 5, 6, 7, 8 + + +def unified_plot(cfg: Dict, out_dir: Path) -> None: + styles = cfg["plot"].get("styles", {}) + + fig, axs2d = plt.subplots(3, 3, figsize=(18, 12), sharex="col") + axs = axs2d.flatten() + + # ── index legend handles (colour / marker) ──────────────────────────────── + idx_handles: List[Line2D] = [] + for j, idx in enumerate(cfg["indexes"]): + nm, st = idx["name"], styles.get(idx["name"], {}) + idx_handles.append( + Line2D([0], [0], + color = st.get("color", f"C{j}"), + marker = st.get("marker", "o"), + ls = "", + markersize = 6, + label = nm)) + + # ── plot each index ────────────────────────────────────────────────────── + for j, idx in enumerate(cfg["indexes"]): + name = idx["name"] + st = styles.get(name, {}) + colour = st.get("color", f"C{j}") + marker = st.get("marker", "o") + + csv = out_dir / name / "results.csv" + if not csv.exists(): + print(f"[unified_plot] warning: {csv} missing – skipped") + continue + + df = pd.read_csv(csv) + + # latency panels ------------------------------------------------------ + for op, ax_idx in zip(LAT_OPS, + [IDX_LAT_Q, IDX_LAT_I, IDX_LAT_D, IDX_LAT_M]): + ax = axs[ax_idx] + if op == "maintain": + sub = df[df.maintenance_time_ms.fillna(0) > 0] + y = sub.maintenance_time_ms + else: + sub = df[df.operation_type == op] + y = sub.latency_ms + if sub.empty: + continue + ax.plot(sub.operation_number, y, + color=colour, + **OP_STYLE[op]) + + # partitions ---------------------------------------------------------- + ax = axs[IDX_PART] + sub = df[df.n_list.notna()] + if not sub.empty: + ax.plot(sub.operation_number, sub.n_list, + color=colour, marker=marker, lw=1.2) + + # resident vectors ---------------------------------------------------- + ax = axs[IDX_RES] + sub = df[df.n_resident.notna()] + if not sub.empty: + ax.plot(sub.operation_number, sub.n_resident, + color=colour, marker=marker, lw=1.2) + + # recall -------------------------------------------------------------- + ax = axs[IDX_REC] + sub = df[(df.operation_type == "query") & df.recall.notna()] + if not sub.empty: + ax.plot(sub.operation_number, sub.recall, + color=colour, marker=marker, lw=1.2) + + # running total time -------------------------------------------------- + ax = axs[IDX_TOT] + total_ms = df.latency_ms.fillna(0) + df.maintenance_time_ms.fillna(0) + ax.plot(df.operation_number, np.cumsum(total_ms), + color=colour, marker=marker, lw=1.2) + + # cumulative splits / deletes ---------------------------------------- + ax = axs[IDX_SPL] + spl = df.n_splits.fillna(0) + dlt = df.n_deletes.fillna(0) + if (spl > 0).any(): + ax.step(df.operation_number, np.cumsum(spl), + where="post", color=colour, ls="--", lw=1.2) + if (dlt > 0).any(): + ax.step(df.operation_number, np.cumsum(dlt), + where="post", color=colour, ls=":", lw=1.2) + + # ── titles / labels / grids ────────────────────────────────────────────── + titles = { + IDX_LAT_Q: "Latency – Query", + IDX_LAT_I: "Latency – Insert", + IDX_LAT_D: "Latency – Delete", + IDX_LAT_M: "Latency – Maintain", + IDX_PART : "# Partitions", + IDX_RES : "Resident Vectors", + IDX_REC : "Recall", + IDX_TOT : "Running Total Time", + IDX_SPL : "Cumulative Splits / Deletes", + } + for i, ax in enumerate(axs): + ax.set_title(titles[i], fontsize=11) + ax.grid(alpha=0.25, zorder=0) + ax.set_axisbelow(True) + + axs[IDX_PART].set_ylabel("count") + axs[IDX_RES ].set_ylabel("# vectors") + axs[IDX_REC ].set_ylabel("recall") + axs[IDX_TOT ].set_ylabel("ms") + axs[IDX_SPL ].set_ylabel("count") + + # x-labels only on bottom row + for idx in [IDX_PART, IDX_RES, IDX_REC, IDX_TOT, IDX_SPL]: + axs[idx].set_xlabel("Operation #") + + # common x-limit so vertical grids align + all_ops = max((pd.read_csv(out_dir/idx["name"]/ "results.csv").operation_number.max() + for idx in cfg["indexes"] + if (out_dir/idx["name"]/ "results.csv").exists()), default=None) + if all_ops is not None: + for ax in axs: + ax.set_xlim(left=0, right=all_ops) + + # ── single legend (indexes only) ───────────────────────────────────────── + fig.legend(idx_handles, + [h.get_label() for h in idx_handles], + loc="upper center", bbox_to_anchor=(0.5, 1.03), + ncol=min(4, len(idx_handles)), + fontsize=9, title="Index", frameon=False) + + # save -------------------------------------------------------------------- + plt.tight_layout(rect=[0, 0, 1, 0.97]) # space for top legend + out_path = out_dir / "unified_plot.png" + plt.savefig(out_path, dpi=150) + print(f"[unified_plot] → {out_path}") + plt.close() + +# ─────────────────────────────────────────────────────────────────────────────── + +def make_time_breakdown(cfg: Dict, out_dir: Path) -> None: + """ + Create a side-by-side bar chart showing cumulative time spent in + Search / Insert / Delete / Maintenance / Total for each index. + + Recall values are annotated above each Search bar. + Output file: /time_breakdown.png + """ + import matplotlib.pyplot as plt + from matplotlib.cm import get_cmap + import numpy as np + import pandas as pd + + categories = ["Search", "Insert", "Delete", "Maintain", "Total"] + idx_names = [idx["name"] for idx in cfg["indexes"]] + n_idx = len(idx_names) + n_cats = len(categories) + + # Totals and recall tracking + totals = {name: [0]*n_cats for name in idx_names} + recalls = {} + + for idx in cfg["indexes"]: + name = idx["name"] + csv_path = out_dir / name / "results.csv" + if not csv_path.exists(): + print(f"[make_time_breakdown] warning: no CSV for {name} – skipped") + continue + + df = pd.read_csv(csv_path) + totals[name][0] = df[df.operation_type == "query" ].latency_ms.sum() # Search + totals[name][1] = df[df.operation_type == "insert"].latency_ms.sum() # Insert + totals[name][2] = df[df.operation_type == "delete"].latency_ms.sum() # Delete + totals[name][3] = df.maintenance_time_ms.fillna(0).sum() # Maintain + totals[name][4] = sum(totals[name][:4]) # Total + recalls[name] = df[df.operation_type == "query"].recall.mean() # Recall + + # Plotting + x = np.arange(n_cats) + width = 0.8 / max(1, n_idx) + + fig, ax = plt.subplots(figsize=(10, 6)) + cmap = get_cmap("tab10") + + for j, name in enumerate(idx_names): + y = totals[name] + style = cfg["plot"].get("styles", {}).get(name, {}) + color = style.get("color", cmap(j % 10)) + + x_offset = x + (j - (n_idx - 1) / 2) * width + bars = ax.bar(x_offset, y, width=width, label=name, color=color, edgecolor="black") + + # Add recall above Search bar (index 0) + recall_val = recalls.get(name) + if recall_val is not None: + ax.text(x_offset[0], y[0] + 0.01 * max(y), f"Recall={recall_val:.3f}", + ha="center", va="bottom", fontsize=8, rotation=90, color=color) + + ax.set_xticks(x) + ax.set_xticklabels(categories) + ax.set_ylabel("Cumulative Time (ms)") + ax.set_title("Time Breakdown per Index") + ax.legend(title="Index", frameon=False) + plt.tight_layout() + + out_path = out_dir / "time_breakdown.png" + plt.savefig(out_path, dpi=150) + print(f"[make_time_breakdown] → {out_path}") + plt.close() + +# ─────────────────────────────────────────────────────────────────────────────── +def run_experiment(cfg_path:str, output_dir:str): + cfg = yaml.safe_load(Path(cfg_path).read_text()) + out = Path(output_dir).expanduser(); out.mkdir(parents=True, exist_ok=True) + + # 1 dataset / workload --------------------------------------------------- + if cfg["mode"] in {"build","run"}: + ds = cfg["dataset"] + vecs, queries, _ = load_dataset(ds["name"], ds.get("path","")) + gen = DynamicWorkloadGenerator( + workload_dir =out, + base_vectors =vecs, + metric =ds["metric"], + insert_ratio =cfg["workload_generator"]["insert_ratio"], + delete_ratio =cfg["workload_generator"]["delete_ratio"], + query_ratio =cfg["workload_generator"]["query_ratio"], + update_batch_size =cfg["workload_generator"]["update_batch_size"], + query_batch_size =cfg["workload_generator"]["query_batch_size"], + number_of_operations =cfg["workload_generator"]["number_of_operations"], + initial_size =cfg["workload_generator"]["initial_size"], + cluster_size =cfg["workload_generator"]["cluster_size"], + cluster_sample_distribution=cfg["workload_generator"]["cluster_sample_distribution"], + queries =queries, + query_cluster_sample_distribution=cfg["workload_generator"]["query_cluster_sample_distribution"], + seed =cfg["workload_generator"]["seed"]) + if not gen.workload_exists() or cfg["overwrite"].get("workload",False): + log.info("Generating workload …"); gen.generate_workload() + else: + log.info("Existing workload found – generation skipped.") + + # 2 evaluation ----------------------------------------------------------- + if cfg["mode"]=="run": + for idx in cfg["indexes"]: + name, res = idx["name"], out/idx["name"] + csv = res/"results.csv" + if csv.exists() and not cfg["overwrite"].get("results",False): + log.info("%s: cached results – skipped.", name); continue + res.mkdir(parents=True, exist_ok=True) + eval = WorkloadEvaluator(workload_dir=out, output_dir=res) + cls = {"Quake":QuakeWrapper,"IVF":FaissIVF}[idx["index"]] + eval.evaluate_workload( + name =name, + index =cls(), + build_params =idx.get("build_params",{}), + search_params =idx.get("search_params",{}), + m_params=idx.get("maintenance_params",{}), + do_maintenance=True, + ) + + # 3 global plot ---------------------------------------------------------- + if cfg["mode"] in {"run","plot"}: + unified_plot(cfg, out) + make_time_breakdown(cfg, out) diff --git a/test/experiments/osdi2025/multi_level/configs/msturing10m.yaml b/test/experiments/osdi2025/multi_level/configs/sift10m.yaml similarity index 82% rename from test/experiments/osdi2025/multi_level/configs/msturing10m.yaml rename to test/experiments/osdi2025/multi_level/configs/sift10m.yaml index 86997475..df7ef1f1 100644 --- a/test/experiments/osdi2025/multi_level/configs/msturing10m.yaml +++ b/test/experiments/osdi2025/multi_level/configs/sift10m.yaml @@ -6,7 +6,7 @@ overwrite: # ─── dataset ────────────────────────────────────────────────────────────────── dataset: - name: msturing10m # built-in quake dataset + name: sift10m # built-in quake dataset metric: l2 path: data # ignored for packaged datasets nq: 100 # number of queries to use @@ -17,12 +17,12 @@ indexes: index: Quake build_params: nc: 5000 - num_workers: 1 + num_workers: 8 search_params: - k: 100 + k: 1 # recall_targets: [0.50, 0.60, 0.70, 0.80, 0.85, 0.90, 0.95, 0.99] nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] - initial_search_fraction: 0.3 +# initial_search_fraction: 0.01 # batched_scan: true batch_size: 1 @@ -30,12 +30,12 @@ indexes: index: Quake build_params: nc: 10000 - num_workers: 1 + num_workers: 8 search_params: - k: 100 + k: 1 # recall_targets: [0.50, 0.60, 0.70, 0.80, 0.85, 0.90, 0.95, 0.99] nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] - initial_search_fraction: 0.25 +# initial_search_fraction: 0.01 # batched_scan: true batch_size: 1 @@ -43,12 +43,12 @@ indexes: index: Quake build_params: nc: 20000 - num_workers: 0 + num_workers: 8 search_params: k: 1 # recall_targets: [0.50, 0.60, 0.70, 0.80, 0.85, 0.90, 0.95, 0.99] nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] - initial_search_fraction: 0.2 +# initial_search_fraction: 0.01 # batched_scan: true batch_size: 1 @@ -56,39 +56,39 @@ indexes: index: Quake build_params: nc: 40000 - num_workers: 0 + num_workers: 8 search_params: k: 1 - # recall_targets: [0.50, 0.60, 0.70, 0.80, 0.85, 0.90, 0.95, 0.99] +# recall_targets: [0.50, 0.60, 0.70, 0.80, 0.85, 0.90, 0.95, 0.99] nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] - initial_search_fraction: 0.2 - # batched_scan: true +# initial_search_fraction: 0.01 +# batched_scan: true batch_size: 1 - name: Quake80000 index: Quake build_params: nc: 80000 - num_workers: 0 + num_workers: 8 search_params: k: 1 - # recall_targets: [0.50, 0.60, 0.70, 0.80, 0.85, 0.90, 0.95, 0.99] +# recall_targets: [0.50, 0.60, 0.70, 0.80, 0.85, 0.90, 0.95, 0.99] nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] - initial_search_fraction: 0.2 - # batched_scan: true +# initial_search_fraction: 0.01 +# batched_scan: true batch_size: 1 - +# - name: Quake160000 index: Quake build_params: nc: 160000 - num_workers: 0 + num_workers: 8 search_params: k: 1 - # recall_targets: [0.50, 0.60, 0.70, 0.80, 0.85, 0.90, 0.95, 0.99] +# recall_targets: [0.50, 0.60, 0.70, 0.80, 0.85, 0.90, 0.95, 0.99] nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] - initial_search_fraction: 0.2 - # batched_scan: true + initial_search_fraction: 0.005 +# batched_scan: true batch_size: 1 # # - name: Quake20000_500 # two-level Quake index diff --git a/test/experiments/osdi2025/multi_level/run.py b/test/experiments/osdi2025/multi_level/run.py index c3f3c21f..a3abbbed 100644 --- a/test/experiments/osdi2025/multi_level/run.py +++ b/test/experiments/osdi2025/multi_level/run.py @@ -160,7 +160,7 @@ def _plot_unified(cfg: Dict, root: Path) -> None: color = sty.get("color", None), ) - ax.set_xscale("log", base=10) + # ax.set_xscale("log", base=10) ax.set_xlabel("Queries per second (log)") ax.set_ylabel("Recall@k") ax.set_title("Recall curves over varied recall_target") diff --git a/test/experiments/osdi2025/vary_levels/configs/msturing10m_levels.yaml b/test/experiments/osdi2025/vary_levels/configs/msturing10m_levels.yaml deleted file mode 100644 index 2cc895ce..00000000 --- a/test/experiments/osdi2025/vary_levels/configs/msturing10m_levels.yaml +++ /dev/null @@ -1,71 +0,0 @@ -# ─── top-level controls ─────────────────────────────────── -mode: run # ignored by new script – kept for compatibility -overwrite: - index: false - results: true - -# ─── dataset ─────────────────────────────────────────────── -dataset: - name: msturing10m - metric: l2 - path: data - nq: 100 - -# ─── base index (built once) ─────────────────────────────── -index: - type: Quake - build_params: - nc: 160_000 # child level - num_workers: 1 - search_params: - k: 1 - -# ─── parent levels to test ───────────────────────────────── -levels: - - nc: 0 - search_params: - nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096] - batch_size: 1 - - nc: 100 # baseline (keep the index single-level) - search_params: # ← optional override for the CHILD search - nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096] - batch_size: 1 - parent_search_params: # ← optional params passed to *parent* search - nprobe: 10 - - - nc: 500 # 500-centroid parent layer - search_params: - nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096] - batch_size: 1 - parent_search_params: # ← optional params passed to *parent* search - nprobe: 50 - - - nc: 1_000 - search_params: - nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096] - batch_size: 1 - parent_search_params: # ← optional params passed to *parent* search - nprobe: 100 - - - nc: 2_000 - search_params: - nprobes: [ 1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096] - batch_size: 1 - parent_search_params: # ← optional params passed to *parent* search - nprobe: 200 - - - nc: 4_000 - search_params: - nprobes: [ 1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096] - batch_size: 1 - parent_search_params: # ← optional params passed to *parent* search - nprobe: 400 - -# ─── plotting (optional) ────────────────────────────────── -plot: - styles: - L0: {color: "#1f77b4"} - L100: {color: "#ff7f0e"} - L500: {color: "#ffbb78"} - L1000: {color: "#2ca02c"} - L2000: {color: "#d62728"} \ No newline at end of file diff --git a/test/experiments/osdi2025/vary_levels/configs/sift10m.yaml b/test/experiments/osdi2025/vary_levels/configs/sift10m.yaml new file mode 100644 index 00000000..76fe00c3 --- /dev/null +++ b/test/experiments/osdi2025/vary_levels/configs/sift10m.yaml @@ -0,0 +1,101 @@ +# ─── top-level controls ─────────────────────────────────── +mode: run # ignored by new script – kept for compatibility +overwrite: + index: false + results: true + +# ─── dataset ─────────────────────────────────────────────── +dataset: + name: sift10m + metric: l2 + path: data + nq: 100 + +# ─── base index (built once) ─────────────────────────────── +index: + type: Quake + build_params: + nc: 40_000 # child level + num_workers: 8 + search_params: + k: 1 + +# ─── parent levels to test ───────────────────────────────── +levels: + - nc: 0 + search_params: + nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] +# batched_scan: true +# recall_targets: [.5, .6, .7, .8, .85, .9, .95, .99] +# initial_search_fraction: 0.01 +# aps_flush_period_us: 1 + batch_size: 1 + + +# - nc: 100 # baseline (keep the index single-level) +# search_params: # ← optional override for the CHILD search +# # nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] +# recall_targets: [.5, .6, .7, .8, .85, .9, .95, .99] +# initial_search_fraction: 0.002 +## nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256] +# batch_size: 1 +# parent_search_params: # ← optional params passed to *parent* search +## nprobe: 10 +# recall_target: .999 +# initial_search_fraction: 0.05 +# +# - nc: 500 # 500-centroid parent layer +# search_params: +# # nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] +# recall_targets: [.5, .6, .7, .8, .85, .9, .95, .99] +# initial_search_fraction: 0.002 +# batch_size: 1 +# parent_search_params: # ← optional params passed to *parent* search +## nprobe: 15 +# recall_target: .999 +# initial_search_fraction: 0.05 + + - nc: 1_000 + search_params: + nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] +# batched_scan: true +# recall_targets: [.5, .6, .7, .8, .85, .9, .95, .99] +# initial_search_fraction: 0.01 + batch_size: 1 + parent_search_params: # ← optional params passed to *parent* search + nprobe: 50 +# batched_scan: true +# recall_target: .999 +# initial_search_fraction: 0.05 + +# - nc: 2_000 +# search_params: +# # nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] +# recall_targets: [.5, .6, .7, .8, .85, .9, .95, .99] +# initial_search_fraction: 0.01 +# batch_size: 1 +# parent_search_params: # ← optional params passed to *parent* search +# nprobe: 50 +## +# - nc: 4_000 +# search_params: +# nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] +## recall_targets: [.5, .6, .7, .8, .85, .9, .95, .99] +## initial_search_fraction: 0.01 +## aps_flush_period_us: 1 +# batch_size: 1 +# parent_search_params: # ← optional params passed to *parent* search +# nprobe: 200 +# recall_target: .999 +# aps_flush_period_us: 1 +# initial_search_fraction: 0.05 + +# ─── plotting (optional) ────────────────────────────────── +plot: + styles: + L0: {color: "#1f77b4"} + L100: {color: "#ff7f0e"} + L500: {color: "#ffbb78"} + L1000: {color: "#2ca02c"} + L2000: {color: "#d62728"} + L4000: {color: "#9467bd"} \ No newline at end of file diff --git a/test/experiments/osdi2025/vary_levels/run.py b/test/experiments/osdi2025/vary_levels/run.py index f9755a7b..01c0ccd1 100644 --- a/test/experiments/osdi2025/vary_levels/run.py +++ b/test/experiments/osdi2025/vary_levels/run.py @@ -131,7 +131,7 @@ def _plot(cfg: Dict, root: Path) -> None: markersize = 6, color = sty.get("color", None), ) - ax.set_xscale("log", base=10) + # ax.set_xscale("log", base=10) ax.set_xlabel("Queries per second (log)") ax.set_ylabel("Recall@k") ax.set_title("QPS vs Recall for different parent levels") From c63159d3f948315d34fa0985292adebea3a51228 Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 13 May 2025 23:51:59 -0500 Subject: [PATCH 032/323] update multi-level --- src/cpp/src/clustering.cpp | 34 ++++++++++++++++++---------------- 1 file changed, 18 insertions(+), 16 deletions(-) diff --git a/src/cpp/src/clustering.cpp b/src/cpp/src/clustering.cpp index a0e907be..552228be 100644 --- a/src/cpp/src/clustering.cpp +++ b/src/cpp/src/clustering.cpp @@ -40,7 +40,7 @@ shared_ptr kmeans_cuvs_sample_and_predict( /* ---------- copy to pinned host & (optionally) normalize ----------- */ Tensor cpu_pts = vectors.contiguous().pin_memory(); if (metric == faiss::METRIC_INNER_PRODUCT) { - // k-means in RAFT is L2 – approximate cosine by L2 on the unit sphere + // cuVS k-means is Euclidean; approximate cosine by L2 on the unit sphere cpu_pts = cpu_pts.div(cpu_pts.norm(2, 1, /*keepdim=*/true)); } @@ -65,19 +65,20 @@ shared_ptr kmeans_cuvs_sample_and_predict( .contiguous(); /* ---------- fit on the sample -------------------------------------- */ - float inertia = 0.0f; - int actual_iter = 0; - - cuvs::cluster::kmeans::fit( - handle, params, - raft::make_device_matrix_view(samp_gpu.data_ptr(), - gpu_sample_sz, (int)D), - std::nullopt, - raft::make_device_matrix_view(cent_gpu.data_ptr(), - num_clusters, (int)D), - raft::make_host_scalar_view(&inertia), - raft::make_host_scalar_view(&actual_iter) - ); + { + float inertia = 0.0f; + int actual_iter = 0; + cuvs::cluster::kmeans::fit( + handle, params, + raft::make_device_matrix_view(samp_gpu.data_ptr(), + gpu_sample_sz, (int)D), + std::nullopt, + raft::make_device_matrix_view(cent_gpu.data_ptr(), + num_clusters, (int)D), + raft::make_host_scalar_view(&inertia), + raft::make_host_scalar_view(&actual_iter) + ); + } /* ---------- predict every point, in order, exactly once ------------ */ Tensor all_labels = torch::empty({N}, torch::kLong); // on CPU @@ -91,6 +92,7 @@ shared_ptr kmeans_cuvs_sample_and_predict( Tensor lbl_gpu32 = torch::empty({bs}, torch::dtype(torch::kInt32).device(torch::kCUDA)); + float dummy_inertia = 0.0f; // storage required by the API cuvs::cluster::kmeans::predict( handle, params, raft::make_device_matrix_view(batch_gpu.data_ptr(), @@ -99,8 +101,8 @@ shared_ptr kmeans_cuvs_sample_and_predict( raft::make_device_matrix_view(cent_gpu.data_ptr(), num_clusters, (int)D), raft::make_device_vector_view(lbl_gpu32.data_ptr(), bs), - /*verbosity=*/false, - raft::make_host_scalar_view(nullptr) // we don't need the inertia + /*verbose=*/false, + raft::make_host_scalar_view(&dummy_inertia) ); all_labels.narrow(0, dst_off, bs) From 6e76d20412071b95c793c44e3d034178c0dd84cb Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 14 May 2025 00:52:05 -0500 Subject: [PATCH 033/323] add msturing100M --- src/python/datasets/ann_datasets.py | 46 +++++++++++++++++++++++++++++ 1 file changed, 46 insertions(+) diff --git a/src/python/datasets/ann_datasets.py b/src/python/datasets/ann_datasets.py index 0d37bcfe..a2ba8797 100644 --- a/src/python/datasets/ann_datasets.py +++ b/src/python/datasets/ann_datasets.py @@ -161,6 +161,50 @@ def load_ground_truth(self) -> Union[np.ndarray, torch.Tensor]: return torch.from_numpy(ids).long() +class MSTuring100m(Dataset): + def __init__(self, download_dir: Union[str, Path] = DEFAULT_DOWNLOAD_DIR): + self.download_dir = to_path(download_dir) + self.data_dir = self.download_dir / "MSTuringANNS100M" + self.downloaded = False + self.metric = "l2" + + def is_downloaded(self) -> bool: + base = self.data_dir / "base1b.fbin.crop_nb_100000000" + query = self.data_dir / "query100K.fbin" + gt = self.data_dir / "msturing-gt-100M" + downloaded = base.exists() and query.exists() and gt.exists() + + if not downloaded: + raise RuntimeError("MSTuring dataset is not available. Please download it manually using big-ann-benchmarks.") + + return downloaded + + def download(self, overwrite: bool = False): + pass + + def load_vectors(self) -> Union[np.ndarray, torch.Tensor]: + fname = self.data_dir / "base1b.fbin.crop_nb_10000000" + n, d = map(int, np.fromfile(fname, dtype="uint32", count=2)) + # return to_torch(np.fromfile(fname, dtype=np.float32, offset=8).reshape((n, d))) + return torch.from_numpy(np.fromfile(fname, dtype=np.float32, offset=8).reshape((n, d))) + + def load_queries(self) -> Union[np.ndarray, torch.Tensor]: + fname = self.data_dir / "query100K.fbin" + n, d = map(int, np.fromfile(fname, dtype="uint32", count=2)) + # return to_torch(np.fromfile(fname, dtype=np.float32, offset=8).reshape((n, d))) + return torch.from_numpy(np.fromfile(fname, dtype=np.float32, offset=8).reshape((n, d))) + + def load_ground_truth(self) -> Union[np.ndarray, torch.Tensor]: + fname = self.data_dir / "msturing-gt-10M" + n, d = map(int, np.fromfile(fname, dtype="uint32", count=2)) + assert os.stat(fname).st_size == 8 + n * d * (4 + 4) + f = open(fname, "rb") + f.seek(4 + 4) + ids = np.fromfile(f, dtype="int32", count=n * d).reshape(n, d) + # D = np.fromfile(f, dtype="float32", count=n * d).reshape(n, d) + + return torch.from_numpy(ids).long() + class Sift10m(Dataset): def __init__(self, download_dir: Union[str, Path] = DEFAULT_DOWNLOAD_DIR): self.download_dir = to_path(download_dir) @@ -211,6 +255,8 @@ def load_dataset( dataset = UniformDataset(num_vectors=100000, dim=8, download_dir=download_dir) elif name.lower() == "msturing10m": dataset = MSTuring10m(download_dir=download_dir) + elif name.lower() == "msturing100m": + dataset = MSTuring100m(download_dir=download_dir) elif name.lower() == "sift10m": dataset = Sift10m(download_dir=download_dir) else: From 9c6acf7b251abf37bee695ee3a47df9bfc9d883e Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 14 May 2025 19:55:09 -0500 Subject: [PATCH 034/323] update topk sorting --- src/cpp/bindings/wrap.cpp | 8 + src/cpp/include/common.h | 4 + src/cpp/include/list_scanning.h | 54 +- src/cpp/include/sorting/floyd_rivest_select.h | 129 +++ src/cpp/include/sorting/heap_select.h | 106 ++ src/cpp/include/sorting/median_common.h | 454 +++++++++ src/cpp/include/sorting/pdqsort.h | 937 ++++++++++++++++++ src/python/index_wrappers/quake.py | 4 + src/python/workload_generator.py | 34 +- test/cpp/benchmark.cpp | 6 +- .../kick_the_tires/configs/sift1m.yaml | 22 +- 11 files changed, 1729 insertions(+), 29 deletions(-) create mode 100644 src/cpp/include/sorting/floyd_rivest_select.h create mode 100644 src/cpp/include/sorting/heap_select.h create mode 100644 src/cpp/include/sorting/median_common.h create mode 100644 src/cpp/include/sorting/pdqsort.h diff --git a/src/cpp/bindings/wrap.cpp b/src/cpp/bindings/wrap.cpp index 18a6f92d..b5c0d589 100644 --- a/src/cpp/bindings/wrap.cpp +++ b/src/cpp/bindings/wrap.cpp @@ -201,6 +201,10 @@ PYBIND11_MODULE(_bindings, m) { "Flag to use SPANN for search.") .def_readwrite("spann_eps", &SearchParams::spann_eps, "SPANN parameter epsilon for search.") + .def_readwrite("sample_prefix", &SearchParams::sample_prefix, + "Prefix length for APS sampling.") + .def_readwrite("sample_stride", &SearchParams::sample_stride, + "Stride length for APS sampling.") .def_readwrite("parent_params", &SearchParams::parent_params, "Search parameters for the parent index, if any.") @@ -336,6 +340,10 @@ PYBIND11_MODULE(_bindings, m) { "Parameters used for the search operation.") .def_readwrite("parent_info", &SearchTimingInfo::parent_info, "Search info for the parent index.") + .def_readwrite("aps_time_ns", &SearchTimingInfo::aps_time_ns, + "Time spent on APS in nanoseconds.") + .def_readwrite("scan_time_ns", &SearchTimingInfo::scan_time_ns, + "Time spent on scanning in nanoseconds.") .def("__repr__", [](const SearchTimingInfo &s) { std::ostringstream oss; oss << "{"; diff --git a/src/cpp/include/common.h b/src/cpp/include/common.h index 3fb6e0f5..aa102738 100644 --- a/src/cpp/include/common.h +++ b/src/cpp/include/common.h @@ -184,6 +184,8 @@ struct SearchParams { float recompute_threshold = DEFAULT_RECOMPUTE_THRESHOLD; float initial_search_fraction = DEFAULT_INITIAL_SEARCH_FRACTION; int aps_flush_period_us = DEFAULT_APS_FLUSH_PERIOD_US; + int sample_prefix = 0; + int sample_stride = 10; // Auncel params bool use_auncel = false; @@ -238,6 +240,8 @@ struct SearchTimingInfo { int64_t buffer_init_time_ns; ///< Time spent on initializing buffers in nanoseconds. int64_t job_enqueue_time_ns; ///< Time spent on creating jobs in nanoseconds. int64_t boundary_distance_time_ns; ///< Time spent on computing boundary distances in nanoseconds. + int64_t aps_time_ns; ///< Time spent on APS in nanoseconds. + int64_t scan_time_ns; ///< Time spent on scanning in nanoseconds. int64_t job_wait_time_ns; ///< Time spent waiting for jobs to complete in nanoseconds. int64_t result_aggregate_time_ns; ///< Time spent on aggregating results in nanoseconds. int64_t total_time_ns; ///< Total time spent in nanoseconds. diff --git a/src/cpp/include/list_scanning.h b/src/cpp/include/list_scanning.h index 557393b7..bb7defe0 100644 --- a/src/cpp/include/list_scanning.h +++ b/src/cpp/include/list_scanning.h @@ -10,6 +10,9 @@ #include #include "faiss/utils/Heap.h" #include "faiss/utils/distances.h" +#include "sorting/pdqsort.h" +#include "sorting/floyd_rivest_select.h" +#include "sorting/heap_select.h" inline Tensor calculate_recall(Tensor ids, Tensor gt_ids) { Tensor num_correct = torch::zeros(ids.size(0), torch::kInt64); @@ -36,7 +39,7 @@ inline Tensor calculate_recall(Tensor ids, Tensor gt_ids) { return recall; } -#define TOP_K_BUFFER_CAPACITY (8 * 1024) +#define TOP_K_BUFFER_CAPACITY (16 * 1024) template class TypedTopKBuffer { @@ -51,7 +54,7 @@ class TypedTopKBuffer { std::atomic partitions_scanned_; TypedTopKBuffer(int k, bool is_descending, int buffer_capacity = TOP_K_BUFFER_CAPACITY) - : k_(k), is_descending_(is_descending), topk_(buffer_capacity), processing_query_(true), partitions_scanned_(0) { + : k_(k), is_descending_(is_descending), topk_(buffer_capacity), processing_query_(true), partitions_scanned_(0) { assert(k <= buffer_capacity); // Ensure k is smaller than or equal to buffer size for (int i = 0; i < topk_.size(); i++) { @@ -151,22 +154,47 @@ class TypedTopKBuffer { DistanceType flush() { std::lock_guard buffer_lock(buffer_mutex_); if (curr_offset_ > k_) { - if (is_descending_) { - std::partial_sort(topk_.begin(), topk_.begin() + k_, topk_.begin() + curr_offset_, - [](const auto &a, const auto &b) { return a.first > b.first; }); + + // for k < 10 use heap select + if (k_ < 10) { + if (is_descending_) { + miniselect::heap_select(topk_.begin(), topk_.begin() + k_, + topk_.begin() + curr_offset_, + [](const auto &a, const auto &b) { return a.first > b.first; }); + } else { + miniselect::heap_select(topk_.begin(), topk_.begin() + k_, + topk_.begin() + curr_offset_, + [](const auto &a, const auto &b) { return a.first < b.first; }); + } + } else if (k_ < curr_offset_ * .001) { + if (is_descending_) { + miniselect::floyd_rivest_select(topk_.begin(), topk_.begin() + k_, + topk_.begin() + curr_offset_, + [](const auto &a, const auto &b) { return a.first > b.first; }); + } else { + miniselect::floyd_rivest_select(topk_.begin(), topk_.begin() + k_, + topk_.begin() + curr_offset_, + [](const auto &a, const auto &b) { return a.first < b.first; }); + } } else { - std::partial_sort(topk_.begin(), topk_.begin() + k_, topk_.begin() + curr_offset_, - [](const auto &a, const auto &b) { return a.first < b.first; }); + if (is_descending_) { + miniselect::pdqpartial_sort_branchless(topk_.begin(), topk_.begin() + k_, + topk_.begin() + curr_offset_, + [](const auto &a, const auto &b) { return a.first > b.first; }); + } else { + miniselect::pdqpartial_sort_branchless(topk_.begin(), topk_.begin() + k_, + topk_.begin() + curr_offset_, + [](const auto &a, const auto &b) { return a.first < b.first; }); + } } curr_offset_ = k_; // After flush, retain only the top-k elements } else { - // sort the curr_offset_ elements if (is_descending_) { - std::sort(topk_.begin(), topk_.begin() + curr_offset_, - [](const auto &a, const auto &b) { return a.first > b.first; }); + miniselect::pdqsort_branchless(topk_.begin(), topk_.begin() + curr_offset_, + [](const auto &a, const auto &b) { return a.first > b.first; }); } else { - std::sort(topk_.begin(), topk_.begin() + curr_offset_, - [](const auto &a, const auto &b) { return a.first < b.first; }); + miniselect::pdqsort_branchless(topk_.begin(), topk_.begin() + curr_offset_, + [](const auto &a, const auto &b) { return a.first < b.first; }); } } return topk_[std::min(curr_offset_, k_ - 1)].first; @@ -195,7 +223,7 @@ class TypedTopKBuffer { return topk_[std::min(curr_offset_, k_ - 1)].first; } - // Get the current top-k indices (after final flush) +// Get the current top-k indices (after final flush) std::vector get_topk_indices() { std::lock_guard buffer_lock(buffer_mutex_); flush(); // Ensure the buffer is properly flushed diff --git a/src/cpp/include/sorting/floyd_rivest_select.h b/src/cpp/include/sorting/floyd_rivest_select.h new file mode 100644 index 00000000..db213b6c --- /dev/null +++ b/src/cpp/include/sorting/floyd_rivest_select.h @@ -0,0 +1,129 @@ +/* Copyright Danila Kutenin, 2020-. + * Distributed under the Boost Software License, Version 1.0. + * (See accompanying file LICENSE_1_0.txt or copy at + * https://boost.org/LICENSE_1_0.txt) + */ +#pragma once + +#include +#include +#include +#include +#include +#include +#include + +namespace miniselect { + namespace floyd_rivest_detail { + + enum floyd_rivest_constants { + kQCap = 600, + }; + + template + struct CompareRefType { + // Pass the comparator by lvalue reference. Or in debug mode, using a + // debugging wrapper that stores a reference. + using type = typename std::add_lvalue_reference::type; + }; + + template ::difference_type> + inline void floyd_rivest_select_loop(Iter begin, DiffType left, DiffType right, + DiffType k, Compare comp) { + while (right > left) { + DiffType size = right - left; + if (size > floyd_rivest_constants::kQCap) { + DiffType n = right - left + 1; + DiffType i = k - left + 1; + + double z = log(n); + double s = 0.5 * exp(2 * z / 3); + double sd = 0.5 * sqrt(z * s * (n - s) / n); + if (i < n / 2) { + sd *= -1.0; + } + DiffType new_left = + std::max(left, static_cast(k - i * s / n + sd)); + DiffType new_right = + std::min(right, static_cast(k + (n - i) * s / n + sd)); + floyd_rivest_select_loop(begin, new_left, + new_right, k, comp); + } + DiffType i = left; + DiffType j = right; + + std::swap(begin[left], begin[k]); + const bool to_swap = comp(begin[left], begin[right]); + if (to_swap) { + std::swap(begin[left], begin[right]); + } + // Make sure that non copyable types compile. + const auto& t = to_swap ? begin[left] : begin[right]; + while (i < j) { + std::swap(begin[i], begin[j]); + i++; + j--; + while (comp(begin[i], t)) { + i++; + } + while (comp(t, begin[j])) { + j--; + } + } + + if (to_swap) { + std::swap(begin[left], begin[j]); + } else { + j++; + std::swap(begin[right], begin[j]); + } + + if (j <= k) { + left = j + 1; + } + if (k <= j) { + right = j - 1; + } + } + } + + } // namespace floyd_rivest_detail + + template + inline void floyd_rivest_partial_sort(Iter begin, Iter mid, Iter end, + Compare comp) { + if (begin == end) return; + if (begin == mid) return; + using CompType = typename floyd_rivest_detail::CompareRefType::type; + using DiffType = typename std::iterator_traits::difference_type; + floyd_rivest_detail::floyd_rivest_select_loop( + begin, DiffType{0}, static_cast(end - begin - 1), + static_cast(mid - begin - 1), comp); + // std::sort proved to be better than other sorts because of pivoting. + std::sort(begin, mid, comp); + } + + template + inline void floyd_rivest_partial_sort(Iter begin, Iter mid, Iter end) { + typedef typename std::iterator_traits::value_type T; + floyd_rivest_partial_sort(begin, mid, end, std::less()); + } + + template + inline void floyd_rivest_select(Iter begin, Iter mid, Iter end, Compare comp) { + if (mid == end) return; + using CompType = typename floyd_rivest_detail::CompareRefType::type; + using DiffType = typename std::iterator_traits::difference_type; + floyd_rivest_detail::floyd_rivest_select_loop( + begin, DiffType{0}, static_cast(end - begin - 1), + static_cast(mid - begin), comp); + } + + template + inline void floyd_rivest_select(Iter begin, Iter mid, Iter end) { + typedef typename std::iterator_traits::value_type T; + floyd_rivest_select(begin, mid, end, std::less()); + } + +} // namespace miniselect \ No newline at end of file diff --git a/src/cpp/include/sorting/heap_select.h b/src/cpp/include/sorting/heap_select.h new file mode 100644 index 00000000..d51eb9b7 --- /dev/null +++ b/src/cpp/include/sorting/heap_select.h @@ -0,0 +1,106 @@ +/* Copyright Danila Kutenin, 2020-. + * Distributed under the Boost Software License, Version 1.0. + * (See accompanying file LICENSE_1_0.txt or copy at + * https://boost.org/LICENSE_1_0.txt) + */ +#pragma once + +#include +#include +#include +#include +#include + +namespace miniselect { + namespace heap_select_detail { + + template + struct CompareRefType { + // Pass the comparator by lvalue reference. Or in debug mode, using a + // debugging wrapper that stores a reference. + using type = typename std::add_lvalue_reference::type; + }; + + template + inline void sift_down(Iter first, Compare comp, + typename std::iterator_traits::difference_type len, + Iter start) { + using difference_type = typename std::iterator_traits::difference_type; + using value_type = typename std::iterator_traits::value_type; + difference_type child = start - first; + + if (len < 2 || (len - 2) / 2 < child) return; + + child = 2 * child + 1; + Iter child_i = first + child; + + if ((child + 1) < len && comp(*child_i, *(child_i + 1))) { + ++child_i; + ++child; + } + + if (comp(*child_i, *start)) { + return; + } + + value_type top(std::move(*start)); + do { + *start = std::move(*child_i); + start = child_i; + + if ((len - 2) / 2 < child) { + break; + } + + child = 2 * child + 1; + child_i = first + child; + + if ((child + 1) < len && comp(*child_i, *(child_i + 1))) { + ++child_i; + ++child; + } + } while (!comp(*child_i, top)); + *start = std::move(top); + } + + template + inline void heap_select_loop(Iter first, Iter middle, Iter last, Compare comp) { + std::make_heap(first, middle, comp); + typename std::iterator_traits::difference_type len = middle - first; + for (Iter i = middle; i != last; ++i) { + if (comp(*i, *first)) { + std::swap(*i, *first); + sift_down(first, comp, len, first); + } + } + } + + } // namespace heap_select_detail + + template + inline void heap_select(Iter first, Iter middle, Iter end, Compare comp) { + if (middle == end) return; + heap_select_detail::heap_select_loop(first, middle + 1, end, comp); + std::swap(*first, *middle); + } + + template + inline void heap_select(Iter first, Iter middle, Iter end) { + heap_select(first, middle, end, + std::less::value_type>()); + } + + template + inline void heap_partial_sort(Iter first, Iter middle, Iter end, Compare comp) { + heap_select_detail::heap_select_loop(first, middle, end, comp); + std::sort_heap(first, middle, comp); + } + + template + inline void heap_partial_sort(Iter first, Iter middle, Iter end) { + heap_partial_sort( + first, middle, end, + std::less::value_type>()); + } + +} // namespace miniselect \ No newline at end of file diff --git a/src/cpp/include/sorting/median_common.h b/src/cpp/include/sorting/median_common.h new file mode 100644 index 00000000..4e85cd8e --- /dev/null +++ b/src/cpp/include/sorting/median_common.h @@ -0,0 +1,454 @@ +/* Copyright Andrei Alexandrescu, 2016-, + * Distributed under the Boost Software License, Version 1.0. + * (See accompanying file LICENSE_1_0.txt or copy at + * https://boost.org/LICENSE_1_0.txt) + */ +/* Copyright Danila Kutenin, 2020-. + * Distributed under the Boost Software License, Version 1.0. + * (See accompanying file LICENSE_1_0.txt or copy at + * https://boost.org/LICENSE_1_0.txt) + */ +#pragma once + +#include +#include +#include +#include + +namespace miniselect { + namespace median_common_detail { + + template + struct CompareRefType { + // Pass the comparator by lvalue reference. Or in debug mode, using a + // debugging wrapper that stores a reference. + using type = typename std::add_lvalue_reference::type; + }; +/** +Swaps the median of r[a], r[b], and r[c] into r[b]. +*/ + template ::difference_type> + inline void median3(Iter r, DiffType a, DiffType b, DiffType c, + Compare&& comp) { + if (comp(r[b], r[a])) // b < a + { + if (comp(r[b], r[c])) // b < a, b < c + { + if (comp(r[c], r[a])) // b < c < a + std::swap(r[b], r[c]); + else // b < a <= c + std::swap(r[b], r[a]); + } + } else if (comp(r[c], r[b])) // a <= b, c < b + { + if (comp(r[c], r[a])) // c < a <= b + std::swap(r[b], r[a]); + else // a <= c < b + std::swap(r[b], r[c]); + } + } + +/** +Sorts in place r[a], r[b], and r[c]. +*/ + template ::difference_type> + inline void sort3(Iter r, DiffType a, DiffType b, DiffType c, Compare&& comp) { + typedef typename std::iterator_traits::value_type T; + if (comp(r[b], r[a])) // b < a + { + if (comp(r[c], r[b])) // c < b < a + { + std::swap(r[a], r[c]); // a < b < c + } else // b < a, b <= c + { + T t = std::move(r[a]); + r[a] = std::move(r[b]); + if (comp(r[c], t)) // b <= c < a + { + r[b] = std::move(r[c]); + r[c] = std::move(t); + } else // b < a <= c + { + r[b] = std::move(t); + } + } + } else if (comp(r[c], r[b])) // a <= b, c < b + { + T t = std::move(r[c]); + r[c] = std::move(r[b]); + if (comp(t, r[a])) // c < a < b + { + r[b] = std::move(r[a]); + r[a] = std::move(t); + } else // a <= c < b + { + r[b] = std::move(t); + } + } + + assert(!comp(r[b], r[a]) && !comp(r[c], r[b])); + } + +/** +If leanRight == false, swaps the lower median of r[a]...r[d] into r[b] and +the minimum into r[a]. If leanRight == true, swaps the upper median of +r[a]...r[d] into r[c] and the minimum into r[d]. +*/ + template ::difference_type> + inline void partition4(Iter r, DiffType a, DiffType b, DiffType c, DiffType d, + Compare&& comp) { + assert(a != b && a != c && a != d && b != c && b != d && c != d); + /* static */ if (leanRight) { + // In the median of 5 algorithm, consider r[e] infinite + if (comp(r[c], r[a])) { + std::swap(r[a], r[c]); + } // a <= c + if (comp(r[d], r[b])) { + std::swap(r[b], r[d]); + } // a <= c, b <= d + if (comp(r[d], r[c])) { + std::swap(r[c], r[d]); // a <= d, b <= c < d + std::swap(r[a], r[b]); // b <= d, a <= c < d + } // a <= c <= d, b <= d + if (comp(r[c], r[b])) { // a <= c <= d, c < b <= d + std::swap(r[b], r[c]); // a <= b <= c <= d + } // a <= b <= c <= d + } else { + // In the median of 5 algorithm consider r[a] infinitely small, then + // change b->a. c->b, d->c, e->d + if (comp(r[c], r[a])) { + std::swap(r[a], r[c]); + } + if (comp(r[c], r[b])) { + std::swap(r[b], r[c]); + } + if (comp(r[d], r[a])) { + std::swap(r[a], r[d]); + } + if (comp(r[d], r[b])) { + std::swap(r[b], r[d]); + } else { + if (comp(r[b], r[a])) { + std::swap(r[a], r[b]); + } + } + } + } + +/** +Places the median of r[a]...r[e] in r[c] and partitions the other elements +around it. +*/ + template ::difference_type> + inline void partition5(Iter r, DiffType a, DiffType b, DiffType c, DiffType d, + DiffType e, Compare&& comp) { + assert(a != b && a != c && a != d && a != e && b != c && b != d && b != e && + c != d && c != e && d != e); + if (comp(r[c], r[a])) { + std::swap(r[a], r[c]); + } + if (comp(r[d], r[b])) { + std::swap(r[b], r[d]); + } + if (comp(r[d], r[c])) { + std::swap(r[c], r[d]); + std::swap(r[a], r[b]); + } + if (comp(r[e], r[b])) { + std::swap(r[b], r[e]); + } + if (comp(r[e], r[c])) { + std::swap(r[c], r[e]); + if (comp(r[c], r[a])) { + std::swap(r[a], r[c]); + } + } else { + if (comp(r[c], r[b])) { + std::swap(r[b], r[c]); + } + } + } + +/** +Implements Hoare partition. +*/ + template ::difference_type> + inline Iter pivot_partition(Iter r, DiffType k, DiffType length, + Compare&& comp) { + assert(k < length); + std::swap(*r, r[k]); + DiffType lo = 1, hi = length - 1; + for (;; ++lo, --hi) { + for (;; ++lo) { + if (lo > hi) goto loop_done; + if (!comp(r[lo], *r)) break; + } + // found the left bound: r[lo] >= r[0] + assert(lo <= hi); + for (; comp(*r, r[hi]); --hi) { + } + if (lo >= hi) break; + // found the right bound: r[hi] <= r[0], swap & make progress + std::swap(r[lo], r[hi]); + } + loop_done: + --lo; + std::swap(r[lo], *r); + return r + lo; + } + +/** +Implements the quickselect algorithm, parameterized with a partition function. +*/ + template + inline void quickselect(Iter r, Iter mid, Iter end, Compare&& comp) { + if (r == end || mid >= end) return; + assert(r <= mid && mid < end); + for (;;) switch (end - r) { + case 1: + return; + case 2: + if (comp(r[1], *r)) std::swap(*r, r[1]); + return; + case 3: + sort3(r, 0, 1, 2, comp); + return; + case 4: + switch (mid - r) { + case 0: + goto select_min; + case 1: + partition4(r, 0, 1, 2, 3, comp); + break; + case 2: + partition4(r, 0, 1, 2, 3, comp); + break; + case 3: + goto select_max; + default: + assert(false); + } + return; + default: + assert(end - r > 4); + if (r == mid) { + select_min: + auto pivot = r; + for (++mid; mid < end; ++mid) + if (comp(*mid, *pivot)) pivot = mid; + std::swap(*r, *pivot); + return; + } + if (mid + 1 == end) { + select_max: + auto pivot = r; + for (mid = r + 1; mid < end; ++mid) + if (comp(*pivot, *mid)) pivot = mid; + std::swap(*pivot, *(end - 1)); + return; + } + auto pivot = partition(r, end, comp); + if (pivot == mid) return; + if (mid < pivot) { + end = pivot; + } else { + r = pivot + 1; + } + } + } + +/** +Returns the index of the median of r[a], r[b], and r[c] without writing +anything. +*/ + template ::difference_type> + inline DiffType median_index(const Iter r, DiffType a, DiffType b, DiffType c, + Compare&& comp) { + if (comp(r[c], r[a])) std::swap(a, c); + if (comp(r[c], r[b])) return c; + if (comp(r[b], r[a])) return a; + return b; + } + +/** +Returns the index of the median of r[a], r[b], r[c], and r[d] without writing +anything. If leanRight is true, computes the upper median. Otherwise, conputes +the lower median. +*/ + template ::difference_type> + inline DiffType median_index(Iter r, DiffType a, DiffType b, DiffType c, + DiffType d, Compare&& comp) { + if (comp(r[d], r[c])) std::swap(c, d); + if (leanRight) { + if (comp(r[c], r[a])) { + assert(comp(r[c], r[a]) && !comp(r[d], r[c])); // so r[c]) is out + return median_index(r, a, b, d, comp); + } + } else { + if (!comp(r[d], r[a])) { + return median_index(r, a, b, c, comp); + } + } + // Could return median_index(r, b, c, d) but we already know r[c] <= r[d] + if (!comp(r[c], r[b])) return c; + if (comp(r[d], r[b])) return d; + return b; + } + +/** +Tukey's Ninther: compute the median of r[_1], r[_2], r[_3], then the median of +r[_4], r[_5], r[_6], then the median of r[_7], r[_8], r[_9], and then swap the +median of those three medians into r[_5]. +*/ + template ::difference_type> + inline void ninther(Iter r, DiffType _1, DiffType _2, DiffType _3, DiffType _4, + DiffType _5, DiffType _6, DiffType _7, DiffType _8, + DiffType _9, Compare&& comp) { + _2 = median_index(r, _1, _2, _3, comp); + _8 = median_index(r, _7, _8, _9, comp); + if (comp(r[_8], r[_2])) std::swap(_2, _8); + if (comp(r[_6], r[_4])) std::swap(_4, _6); + // Here we know that r[_2] and r[_8] are the other two medians and that + // r[_2] <= r[_8]. We also know that r[_4] <= r[_6] + if (comp(r[_5], r[_4])) { + // r[_4] is the median of r[_4], r[_5], r[_6] + } else if (comp(r[_6], r[_5])) { + // r[_6] is the median of r[_4], r[_5], r[_6] + _4 = _6; + } else { + // Here we know r[_5] is the median of r[_4], r[_5], r[_6] + if (comp(r[_5], r[_2])) return std::swap(r[_5], r[_2]); + if (comp(r[_8], r[_5])) return std::swap(r[_5], r[_8]); + // This is the only path that returns with no swap + return; + } + // Here we know r[_4] is the median of r[_4], r[_5], r[_6] + if (comp(r[_4], r[_2])) + _4 = _2; + else if (comp(r[_8], r[_4])) + _4 = _8; + std::swap(r[_5], r[_4]); + } + +/** +Input assumptions: +(a) hi <= rite +(c) the range r[0 .. hi] contains elements no smaller than r[0] +Output guarantee: same as Hoare partition using r[0] as pivot. Returns the new +position of the pivot. +*/ + template ::difference_type> + inline DiffType expand_partition_right(Iter r, DiffType hi, DiffType rite, + Compare&& comp) { + DiffType pivot = 0; + assert(pivot <= hi); + assert(hi <= rite); + // First loop: spend r[pivot .. hi] + for (; pivot < hi; --rite) { + if (rite == hi) goto done; + if (!comp(r[rite], r[0])) continue; + ++pivot; + std::swap(r[rite], r[pivot]); + } + // Second loop: make left and pivot meet + for (; rite > pivot; --rite) { + if (!comp(r[rite], r[0])) continue; + while (rite > pivot) { + ++pivot; + if (comp(r[0], r[pivot])) { + std::swap(r[rite], r[pivot]); + break; + } + } + } + + done: + std::swap(r[0], r[pivot]); + return pivot; + } + +/** +Input assumptions: +(a) lo > 0, lo <= pivot +(b) the range r[lo .. pivot] already contains elements no greater than r[pivot] +Output guarantee: Same as Hoare partition around r[pivot]. Returns the new +position of the pivot. +*/ + template ::difference_type> + inline DiffType expand_partition_left(Iter r, DiffType lo, DiffType pivot, + Compare&& comp) { + assert(lo > 0 && lo <= pivot); + DiffType left = 0; + const auto oldPivot = pivot; + for (; lo < pivot; ++left) { + if (left == lo) goto done; + if (!comp(r[oldPivot], r[left])) continue; + --pivot; + std::swap(r[left], r[pivot]); + } + // Second loop: make left and pivot meet + for (;; ++left) { + if (left == pivot) break; + if (!comp(r[oldPivot], r[left])) continue; + for (;;) { + if (left == pivot) goto done; + --pivot; + if (comp(r[pivot], r[oldPivot])) { + std::swap(r[left], r[pivot]); + break; + } + } + } + + done: + std::swap(r[oldPivot], r[pivot]); + return pivot; + } + +/** +Input assumptions: +(a) lo <= pivot, pivot < hi, hi <= length +(b) the range r[lo .. pivot] already contains elements no greater than +r[pivot] +(c) the range r[pivot .. hi] already contains elements no smaller than +r[pivot] +Output guarantee: Same as Hoare partition around r[pivot], returning the new +position of the pivot. +*/ + template ::difference_type> + inline DiffType expand_partition(Iter r, DiffType lo, DiffType pivot, + DiffType hi, DiffType length, Compare&& comp) { + assert(lo <= pivot && pivot < hi && hi <= length); + --hi; + --length; + DiffType left = 0; + for (;; ++left, --length) { + for (;; ++left) { + if (left == lo) + return pivot + expand_partition_right(r + pivot, hi - pivot, + length - pivot, comp); + if (comp(r[pivot], r[left])) break; + } + for (;; --length) { + if (length == hi) + return left + + expand_partition_left(r + left, lo - left, pivot - left, comp); + if (!comp(r[pivot], r[length])) break; + } + std::swap(r[left], r[length]); + } + } + + } // namespace median_common_detail +} // namespace miniselect \ No newline at end of file diff --git a/src/cpp/include/sorting/pdqsort.h b/src/cpp/include/sorting/pdqsort.h new file mode 100644 index 00000000..2bd768b0 --- /dev/null +++ b/src/cpp/include/sorting/pdqsort.h @@ -0,0 +1,937 @@ +/* + pdqsort.h - Pattern-defeating quicksort. + + Copyright (c) 2015 Orson Peters + + This software is provided 'as-is', without any express or implied warranty. + In no event will the authors be held liable for any damages arising from the + use of this software. + + Permission is granted to anyone to use this software for any purpose, + including commercial applications, and to alter it and redistribute it + freely, subject to the following restrictions: + + 1. The origin of this software must not be misrepresented; you must not + claim that you wrote the original software. If you use this software in a + product, an acknowledgment in the product documentation would be appreciated + but is not required. + + 2. Altered source versions must be plainly marked as such, and must not be + misrepresented as being the original software. + + 3. This notice may not be removed or altered from any source distribution. +*/ +/* Copyright Danila Kutenin, 2020-. + * Distributed under the Boost Software License, Version 1.0. + * (See accompanying file LICENSE_1_0.txt or copy at + * https://boost.org/LICENSE_1_0.txt) + */ +// Adjusted by Danila Kutenin to support pdqselect and pdqpartial_sort. + +#ifndef PDQSORT_H +#define PDQSORT_H + +#include +#include +#include +#include +#include + +#if __cplusplus >= 201103L +#include +#include +#define PDQSORT_PREFER_MOVE(x) std::move(x) +#else +#define PDQSORT_PREFER_MOVE(x) (x) +#endif + +#include "median_common.h" + +namespace miniselect { + namespace pdqsort_detail { + + template + struct CompareRefType { + // Pass the comparator by lvalue reference. Or in debug mode, using a + // debugging wrapper that stores a reference. + using type = typename std::add_lvalue_reference::type; + }; + + enum { + // Partitions below this size are sorted using insertion sort. + insertion_sort_threshold = 24, + + // Partitions above this size use Tukey's ninther to select the pivot. + ninther_threshold = 128, + + // When we detect an already sorted partition, attempt an insertion sort that + // allows this + // amount of element moves before giving up. + partial_insertion_sort_limit = 8, + + // Must be multiple of 8 due to loop unrolling, and < 256 to fit in unsigned + // char. + block_size = 64, + + // Cacheline size, assumes power of two. + cacheline_size = 64 + + }; + +#if __cplusplus >= 201103L + template + struct is_default_compare : std::false_type {}; + template + struct is_default_compare> : std::true_type {}; + template + struct is_default_compare> : std::true_type {}; +#endif + +// Returns floor(log2(n)), assumes n > 0. + template + inline int log2(T n) { + int log = 0; + while (n >>= 1) ++log; + return log; + } + +// Sorts [begin, end) using insertion sort with the given comparison function. + template + inline void insertion_sort(Iter begin, Iter end, Compare& comp) { + typedef typename std::iterator_traits::value_type T; + if (begin == end) return; + + for (Iter cur = begin + 1; cur != end; ++cur) { + Iter sift = cur; + Iter sift_1 = cur - 1; + + // Compare first so we can avoid 2 moves for an element already positioned + // correctly. + if (comp(*sift, *sift_1)) { + T tmp = PDQSORT_PREFER_MOVE(*sift); + + do { + *sift-- = PDQSORT_PREFER_MOVE(*sift_1); + } while (sift != begin && comp(tmp, *--sift_1)); + + *sift = PDQSORT_PREFER_MOVE(tmp); + } + } + } + +// Sorts [begin, end) using insertion sort with the given comparison function. +// Assumes +// *(begin - 1) is an element smaller than or equal to any element in [begin, +// end). + template + inline void unguarded_insertion_sort(Iter begin, Iter end, Compare& comp) { + typedef typename std::iterator_traits::value_type T; + if (begin == end) return; + + for (Iter cur = begin + 1; cur != end; ++cur) { + Iter sift = cur; + Iter sift_1 = cur - 1; + + // Compare first so we can avoid 2 moves for an element already positioned + // correctly. + if (comp(*sift, *sift_1)) { + T tmp = PDQSORT_PREFER_MOVE(*sift); + + do { + *sift-- = PDQSORT_PREFER_MOVE(*sift_1); + } while (comp(tmp, *--sift_1)); + + *sift = PDQSORT_PREFER_MOVE(tmp); + } + } + } + +// Attempts to use insertion sort on [begin, end). Will return false if more +// than partial_insertion_sort_limit elements were moved, and abort sorting. +// Otherwise it will successfully sort and return true. + template + inline bool partial_insertion_sort(Iter begin, Iter end, Compare& comp) { + typedef typename std::iterator_traits::value_type T; + if (begin == end) return true; + + std::size_t limit = 0; + for (Iter cur = begin + 1; cur != end; ++cur) { + Iter sift = cur; + Iter sift_1 = cur - 1; + + // Compare first so we can avoid 2 moves for an element already positioned + // correctly. + if (comp(*sift, *sift_1)) { + T tmp = PDQSORT_PREFER_MOVE(*sift); + + do { + *sift-- = PDQSORT_PREFER_MOVE(*sift_1); + } while (sift != begin && comp(tmp, *--sift_1)); + + *sift = PDQSORT_PREFER_MOVE(tmp); + limit += cur - sift; + } + + if (limit > partial_insertion_sort_limit) return false; + } + + return true; + } + + template + inline void sort2(Iter a, Iter b, Compare& comp) { + if (comp(*b, *a)) std::iter_swap(a, b); + } + +// Sorts the elements *a, *b and *c using comparison function comp. + template + inline void sort3(Iter a, Iter b, Iter c, Compare& comp) { + sort2(a, b, comp); + sort2(b, c, comp); + sort2(a, b, comp); + } + + template + inline T* align_cacheline(T* p) { +#if defined(UINTPTR_MAX) && __cplusplus >= 201103L + std::uintptr_t ip = reinterpret_cast(p); +#else + std::size_t ip = reinterpret_cast(p); +#endif + ip = (ip + cacheline_size - 1) & -cacheline_size; + return reinterpret_cast(ip); + } + + template + inline void swap_offsets(Iter first, Iter last, unsigned char* offsets_l, + unsigned char* offsets_r, int num, bool use_swaps) { + typedef typename std::iterator_traits::value_type T; + if (use_swaps) { + // This case is needed for the descending distribution, where we need + // to have proper swapping for pdqsort to remain O(n). + for (int i = 0; i < num; ++i) { + std::iter_swap(first + offsets_l[i], last - offsets_r[i]); + } + } else if (num > 0) { + Iter l = first + offsets_l[0]; + Iter r = last - offsets_r[0]; + T tmp(PDQSORT_PREFER_MOVE(*l)); + *l = PDQSORT_PREFER_MOVE(*r); + for (int i = 1; i < num; ++i) { + l = first + offsets_l[i]; + *r = PDQSORT_PREFER_MOVE(*l); + r = last - offsets_r[i]; + *l = PDQSORT_PREFER_MOVE(*r); + } + *r = PDQSORT_PREFER_MOVE(tmp); + } + } + +// Partitions [begin, end) around pivot *begin using comparison function comp. +// Elements equal to the pivot are put in the right-hand partition. Returns the +// position of the pivot after partitioning and whether the passed sequence +// already was correctly partitioned. Assumes the pivot is a median of at least +// 3 elements and that [begin, end) is at least insertion_sort_threshold long. +// Uses branchless partitioning. + template + inline std::pair partition_right_branchless(Iter begin, Iter end, + Compare& comp) { + typedef typename std::iterator_traits::value_type T; + + // Move pivot into local for speed. + T pivot(PDQSORT_PREFER_MOVE(*begin)); + Iter first = begin; + Iter last = end; + + // Find the first element greater than or equal than the pivot (the median of + // 3 guarantees this exists). + while (comp(*++first, pivot)) + ; + + // Find the first element strictly smaller than the pivot. We have to guard + // this search if there was no element before *first. + if (first - 1 == begin) + while (first < last && !comp(*--last, pivot)) + ; + else + while (!comp(*--last, pivot)) + ; + + // If the first pair of elements that should be swapped to partition are the + // same element, the passed in sequence already was correctly partitioned. + bool already_partitioned = first >= last; + if (!already_partitioned) { + std::iter_swap(first, last); + ++first; + } + + // The following branchless partitioning is derived from "BlockQuicksort: How + // Branch Mispredictions don’t affect Quicksort" by Stefan Edelkamp and Armin + // Weiss. + unsigned char offsets_l_storage[block_size + cacheline_size]; + unsigned char offsets_r_storage[block_size + cacheline_size]; + unsigned char* offsets_l = align_cacheline(offsets_l_storage); + unsigned char* offsets_r = align_cacheline(offsets_r_storage); + int num_l, num_r, start_l, start_r; + num_l = num_r = start_l = start_r = 0; + + while (last - first > 2 * block_size) { + // Fill up offset blocks with elements that are on the wrong side. + if (num_l == 0) { + start_l = 0; + Iter it = first; + for (unsigned char i = 0; i < block_size;) { + offsets_l[num_l] = i++; + num_l += !comp(*it, pivot); + ++it; + offsets_l[num_l] = i++; + num_l += !comp(*it, pivot); + ++it; + offsets_l[num_l] = i++; + num_l += !comp(*it, pivot); + ++it; + offsets_l[num_l] = i++; + num_l += !comp(*it, pivot); + ++it; + offsets_l[num_l] = i++; + num_l += !comp(*it, pivot); + ++it; + offsets_l[num_l] = i++; + num_l += !comp(*it, pivot); + ++it; + offsets_l[num_l] = i++; + num_l += !comp(*it, pivot); + ++it; + offsets_l[num_l] = i++; + num_l += !comp(*it, pivot); + ++it; + } + } + if (num_r == 0) { + start_r = 0; + Iter it = last; + for (unsigned char i = 0; i < block_size;) { + offsets_r[num_r] = ++i; + num_r += comp(*--it, pivot); + offsets_r[num_r] = ++i; + num_r += comp(*--it, pivot); + offsets_r[num_r] = ++i; + num_r += comp(*--it, pivot); + offsets_r[num_r] = ++i; + num_r += comp(*--it, pivot); + offsets_r[num_r] = ++i; + num_r += comp(*--it, pivot); + offsets_r[num_r] = ++i; + num_r += comp(*--it, pivot); + offsets_r[num_r] = ++i; + num_r += comp(*--it, pivot); + offsets_r[num_r] = ++i; + num_r += comp(*--it, pivot); + } + } + + // Swap elements and update block sizes and first/last boundaries. + int num = std::min(num_l, num_r); + swap_offsets(first, last, offsets_l + start_l, offsets_r + start_r, num, + num_l == num_r); + num_l -= num; + num_r -= num; + start_l += num; + start_r += num; + if (num_l == 0) first += block_size; + if (num_r == 0) last -= block_size; + } + + int l_size = 0, r_size = 0; + int unknown_left = (int)(last - first) - ((num_r || num_l) ? block_size : 0); + if (num_r) { + // Handle leftover block by assigning the unknown elements to the other + // block. + l_size = unknown_left; + r_size = block_size; + } else if (num_l) { + l_size = block_size; + r_size = unknown_left; + } else { + // No leftover block, split the unknown elements in two blocks. + l_size = unknown_left / 2; + r_size = unknown_left - l_size; + } + + // Fill offset buffers if needed. + if (unknown_left && !num_l) { + start_l = 0; + Iter it = first; + for (unsigned char i = 0; i < l_size;) { + offsets_l[num_l] = i++; + num_l += !comp(*it, pivot); + ++it; + } + } + if (unknown_left && !num_r) { + start_r = 0; + Iter it = last; + for (unsigned char i = 0; i < r_size;) { + offsets_r[num_r] = ++i; + num_r += comp(*--it, pivot); + } + } + + int num = std::min(num_l, num_r); + swap_offsets(first, last, offsets_l + start_l, offsets_r + start_r, num, + num_l == num_r); + num_l -= num; + num_r -= num; + start_l += num; + start_r += num; + if (num_l == 0) first += l_size; + if (num_r == 0) last -= r_size; + + // We have now fully identified [first, last)'s proper position. Swap the last + // elements. + if (num_l) { + offsets_l += start_l; + while (num_l--) std::iter_swap(first + offsets_l[num_l], --last); + first = last; + } + if (num_r) { + offsets_r += start_r; + while (num_r--) std::iter_swap(last - offsets_r[num_r], first), ++first; + last = first; + } + + // Put the pivot in the right place. + Iter pivot_pos = first - 1; + *begin = PDQSORT_PREFER_MOVE(*pivot_pos); + *pivot_pos = PDQSORT_PREFER_MOVE(pivot); + + return std::make_pair(pivot_pos, already_partitioned); + } + +// Partitions [begin, end) around pivot *begin using comparison function comp. +// Elements equal to the pivot are put in the right-hand partition. Returns the +// position of the pivot after partitioning and whether the passed sequence +// already was correctly partitioned. Assumes the pivot is a median of at least +// 3 elements and that [begin, end) is at least insertion_sort_threshold long. + template + inline std::pair partition_right(Iter begin, Iter end, + Compare& comp) { + typedef typename std::iterator_traits::value_type T; + + // Move pivot into local for speed. + T pivot(PDQSORT_PREFER_MOVE(*begin)); + + Iter first = begin; + Iter last = end; + + // Find the first element greater than or equal than the pivot (the median of + // 3 guarantees this exists). + while (comp(*++first, pivot)) + ; + + // Find the first element strictly smaller than the pivot. We have to guard + // this search if there was no element before *first. + if (first - 1 == begin) + while (first < last && !comp(*--last, pivot)) + ; + else + while (!comp(*--last, pivot)) + ; + + // If the first pair of elements that should be swapped to partition are the + // same element, the passed in sequence already was correctly partitioned. + bool already_partitioned = first >= last; + + // Keep swapping pairs of elements that are on the wrong side of the pivot. + // Previously swapped pairs guard the searches, which is why the first + // iteration is special-cased above. + while (first < last) { + std::iter_swap(first, last); + while (comp(*++first, pivot)) + ; + while (!comp(*--last, pivot)) + ; + } + + // Put the pivot in the right place. + Iter pivot_pos = first - 1; + *begin = PDQSORT_PREFER_MOVE(*pivot_pos); + *pivot_pos = PDQSORT_PREFER_MOVE(pivot); + + return std::make_pair(pivot_pos, already_partitioned); + } + +// Similar function to the one above, except elements equal to the pivot are put +// to the left of the pivot and it doesn't check or return if the passed +// sequence already was partitioned. Since this is rarely used (the many equal +// case), and in that case pdqsort already has O(n) performance, no block +// quicksort is applied here for simplicity. + template + inline Iter partition_left(Iter begin, Iter end, Compare& comp) { + typedef typename std::iterator_traits::value_type T; + + T pivot(PDQSORT_PREFER_MOVE(*begin)); + Iter first = begin; + Iter last = end; + + while (comp(pivot, *--last)) + ; + + if (last + 1 == end) + while (first < last && !comp(pivot, *++first)) + ; + else + while (!comp(pivot, *++first)) + ; + + while (first < last) { + std::iter_swap(first, last); + while (comp(pivot, *--last)) + ; + while (!comp(pivot, *++first)) + ; + } + + Iter pivot_pos = last; + *begin = PDQSORT_PREFER_MOVE(*pivot_pos); + *pivot_pos = PDQSORT_PREFER_MOVE(pivot); + + return pivot_pos; + } + + template + inline void pdqsort_loop(Iter begin, Iter end, Compare& comp, int bad_allowed, + bool leftmost = true) { + typedef typename std::iterator_traits::difference_type diff_t; + + // Use a while loop for tail recursion elimination. + while (true) { + diff_t size = end - begin; + + // Insertion sort is faster for small arrays. + if (size < insertion_sort_threshold) { + if (leftmost) + insertion_sort(begin, end, comp); + else + unguarded_insertion_sort(begin, end, comp); + return; + } + + // Choose pivot as median of 3 or pseudomedian of 9. + diff_t s2 = size / 2; + if (size > ninther_threshold) { + sort3(begin, begin + s2, end - 1, comp); + sort3(begin + 1, begin + (s2 - 1), end - 2, comp); + sort3(begin + 2, begin + (s2 + 1), end - 3, comp); + sort3(begin + (s2 - 1), begin + s2, begin + (s2 + 1), comp); + std::iter_swap(begin, begin + s2); + } else + sort3(begin + s2, begin, end - 1, comp); + + // If *(begin - 1) is the end of the right partition of a previous partition + // operation there is no element in [begin, end) that is smaller than + // *(begin - 1). Then if our pivot compares equal to *(begin - 1) we change + // strategy, putting equal elements in the left partition, greater elements + // in the right partition. We do not have to recurse on the left partition, + // since it's sorted (all equal). + if (!leftmost && !comp(*(begin - 1), *begin)) { + begin = partition_left(begin, end, comp) + 1; + continue; + } + + // Partition and get results. + std::pair part_result = + Branchless ? partition_right_branchless(begin, end, comp) + : partition_right(begin, end, comp); + Iter pivot_pos = part_result.first; + bool already_partitioned = part_result.second; + + // Check for a highly unbalanced partition. + diff_t l_size = pivot_pos - begin; + diff_t r_size = end - (pivot_pos + 1); + bool highly_unbalanced = l_size < size / 8 || r_size < size / 8; + + // If we got a highly unbalanced partition we shuffle elements to break many + // patterns. + if (highly_unbalanced) { + // If we had too many bad partitions, switch to heapsort to guarantee O(n + // log n). + if (--bad_allowed == 0) { + std::make_heap(begin, end, comp); + std::sort_heap(begin, end, comp); + return; + } + + if (l_size >= insertion_sort_threshold) { + std::iter_swap(begin, begin + l_size / 4); + std::iter_swap(pivot_pos - 1, pivot_pos - l_size / 4); + + if (l_size > ninther_threshold) { + std::iter_swap(begin + 1, begin + (l_size / 4 + 1)); + std::iter_swap(begin + 2, begin + (l_size / 4 + 2)); + std::iter_swap(pivot_pos - 2, pivot_pos - (l_size / 4 + 1)); + std::iter_swap(pivot_pos - 3, pivot_pos - (l_size / 4 + 2)); + } + } + + if (r_size >= insertion_sort_threshold) { + std::iter_swap(pivot_pos + 1, pivot_pos + (1 + r_size / 4)); + std::iter_swap(end - 1, end - r_size / 4); + + if (r_size > ninther_threshold) { + std::iter_swap(pivot_pos + 2, pivot_pos + (2 + r_size / 4)); + std::iter_swap(pivot_pos + 3, pivot_pos + (3 + r_size / 4)); + std::iter_swap(end - 2, end - (1 + r_size / 4)); + std::iter_swap(end - 3, end - (2 + r_size / 4)); + } + } + } else { + // If we were decently balanced and we tried to sort an already + // partitioned sequence try to use insertion sort. + if (already_partitioned && + partial_insertion_sort(begin, pivot_pos, comp) && + partial_insertion_sort(pivot_pos + 1, end, comp)) + return; + } + + // Sort the left partition first using recursion and do tail recursion + // elimination for the right-hand partition. + pdqsort_loop(begin, pivot_pos, comp, bad_allowed, + leftmost); + begin = pivot_pos + 1; + leftmost = false; + } + } + + template + inline void pdqpartial_sort_loop(Iter begin, Iter mid, Iter end, Compare& comp, + int bad_allowed, bool leftmost = true) { + typedef typename std::iterator_traits::difference_type diff_t; + + // Use a while loop for tail recursion elimination. + while (true) { + diff_t size = end - begin; + + // Insertion sort is faster for small arrays. + if (size < insertion_sort_threshold) { + if (leftmost) + insertion_sort(begin, end, comp); + else + unguarded_insertion_sort(begin, end, comp); + return; + } + + // Choose pivot as median of 3 or pseudomedian of 9. + diff_t s2 = size / 2; + if (size > ninther_threshold) { + sort3(begin, begin + s2, end - 1, comp); + sort3(begin + 1, begin + (s2 - 1), end - 2, comp); + sort3(begin + 2, begin + (s2 + 1), end - 3, comp); + sort3(begin + (s2 - 1), begin + s2, begin + (s2 + 1), comp); + std::iter_swap(begin, begin + s2); + } else + sort3(begin + s2, begin, end - 1, comp); + + // If *(begin - 1) is the end of the right partition of a previous partition + // operation there is no element in [begin, end) that is smaller than + // *(begin - 1). Then if our pivot compares equal to *(begin - 1) we change + // strategy, putting equal elements in the left partition, greater elements + // in the right partition. We do not have to recurse on the left partition, + // since it's sorted (all equal). + if (!leftmost && !comp(*(begin - 1), *begin)) { + begin = partition_left(begin, end, comp) + 1; + continue; + } + + // Partition and get results. + std::pair part_result = + Branchless ? partition_right_branchless(begin, end, comp) + : partition_right(begin, end, comp); + Iter pivot_pos = part_result.first; + bool already_partitioned = part_result.second; + + // Check for a highly unbalanced partition. + diff_t l_size = pivot_pos - begin; + diff_t r_size = end - (pivot_pos + 1); + bool highly_unbalanced = l_size < size / 8 || r_size < size / 8; + + // If we got a highly unbalanced partition we shuffle elements to break many + // patterns. + if (highly_unbalanced) { + // If we had too many bad partitions, switch to heapsort to guarantee O(n + // log n). + if (--bad_allowed == 0) { + std::make_heap(begin, end, comp); + std::sort_heap(begin, end, comp); + return; + } + + if (l_size >= insertion_sort_threshold) { + std::iter_swap(begin, begin + l_size / 4); + std::iter_swap(pivot_pos - 1, pivot_pos - l_size / 4); + + if (l_size > ninther_threshold) { + std::iter_swap(begin + 1, begin + (l_size / 4 + 1)); + std::iter_swap(begin + 2, begin + (l_size / 4 + 2)); + std::iter_swap(pivot_pos - 2, pivot_pos - (l_size / 4 + 1)); + std::iter_swap(pivot_pos - 3, pivot_pos - (l_size / 4 + 2)); + } + } + + if (r_size >= insertion_sort_threshold) { + std::iter_swap(pivot_pos + 1, pivot_pos + (1 + r_size / 4)); + std::iter_swap(end - 1, end - r_size / 4); + + if (r_size > ninther_threshold) { + std::iter_swap(pivot_pos + 2, pivot_pos + (2 + r_size / 4)); + std::iter_swap(pivot_pos + 3, pivot_pos + (3 + r_size / 4)); + std::iter_swap(end - 2, end - (1 + r_size / 4)); + std::iter_swap(end - 3, end - (2 + r_size / 4)); + } + } + } else { + // If we were decently balanced and we tried to sort an already + // partitioned sequence try to use insertion sort. + if (already_partitioned && + partial_insertion_sort(begin, pivot_pos, comp) && + partial_insertion_sort(pivot_pos + 1, end, comp)) + return; + } + + // Sort the left partition first using recursion and do tail recursion + // elimination for the right-hand partition. + if (pivot_pos < mid) { + pdqsort_loop(begin, pivot_pos, comp, + bad_allowed, leftmost); + begin = pivot_pos + 1; + leftmost = false; + } else { + end = pivot_pos; + } + } + } + + template + inline void pdqselect_loop(Iter begin, Iter mid, Iter end, Compare& comp, + int bad_allowed, bool leftmost = true) { + typedef typename std::iterator_traits::difference_type diff_t; + + // Use a while loop for tail recursion elimination. + while (true) { + diff_t size = end - begin; + + // Insertion sort is faster for small arrays. + if (size < insertion_sort_threshold) { + if (leftmost) + insertion_sort(begin, end, comp); + else + unguarded_insertion_sort(begin, end, comp); + return; + } + + // Choose pivot as median of 3 or pseudomedian of 9. + diff_t s2 = size / 2; + if (size > ninther_threshold) { + sort3(begin, begin + s2, end - 1, comp); + sort3(begin + 1, begin + (s2 - 1), end - 2, comp); + sort3(begin + 2, begin + (s2 + 1), end - 3, comp); + sort3(begin + (s2 - 1), begin + s2, begin + (s2 + 1), comp); + std::iter_swap(begin, begin + s2); + } else + sort3(begin + s2, begin, end - 1, comp); + + // If *(begin - 1) is the end of the right partition of a previous partition + // operation there is no element in [begin, end) that is smaller than + // *(begin - 1). Then if our pivot compares equal to *(begin - 1) we change + // strategy, putting equal elements in the left partition, greater elements + // in the right partition. We do not have to recurse on the left partition, + // since it's sorted (all equal). + if (!leftmost && !comp(*(begin - 1), *begin)) { + begin = partition_left(begin, end, comp) + 1; + continue; + } + + // Partition and get results. + std::pair part_result = + Branchless ? partition_right_branchless(begin, end, comp) + : partition_right(begin, end, comp); + Iter pivot_pos = part_result.first; + bool already_partitioned = part_result.second; + + // Check for a highly unbalanced partition. + diff_t l_size = pivot_pos - begin; + diff_t r_size = end - (pivot_pos + 1); + bool highly_unbalanced = l_size < size / 8 || r_size < size / 8; + + // If we got a highly unbalanced partition we shuffle elements to break many + // patterns. + if (highly_unbalanced) { + // If we had too many bad partitions, switch to heapsort to guarantee O(n + // log n). + if (--bad_allowed == 0) { + std::nth_element(begin, mid, end, comp); + return; + } + + if (l_size >= insertion_sort_threshold) { + std::iter_swap(begin, begin + l_size / 4); + std::iter_swap(pivot_pos - 1, pivot_pos - l_size / 4); + + if (l_size > ninther_threshold) { + std::iter_swap(begin + 1, begin + (l_size / 4 + 1)); + std::iter_swap(begin + 2, begin + (l_size / 4 + 2)); + std::iter_swap(pivot_pos - 2, pivot_pos - (l_size / 4 + 1)); + std::iter_swap(pivot_pos - 3, pivot_pos - (l_size / 4 + 2)); + } + } + + if (r_size >= insertion_sort_threshold) { + std::iter_swap(pivot_pos + 1, pivot_pos + (1 + r_size / 4)); + std::iter_swap(end - 1, end - r_size / 4); + + if (r_size > ninther_threshold) { + std::iter_swap(pivot_pos + 2, pivot_pos + (2 + r_size / 4)); + std::iter_swap(pivot_pos + 3, pivot_pos + (3 + r_size / 4)); + std::iter_swap(end - 2, end - (1 + r_size / 4)); + std::iter_swap(end - 3, end - (2 + r_size / 4)); + } + } + } else { + // If we were decently balanced and we tried to sort an already + // partitioned sequence try to use insertion sort. + if (already_partitioned && + partial_insertion_sort(begin, pivot_pos, comp) && + partial_insertion_sort(pivot_pos + 1, end, comp)) + return; + } + // Sort the left partition first using recursion and do tail recursion + // elimination for the right-hand partition. + if (pivot_pos < mid) { + begin = pivot_pos + 1; + leftmost = false; + } else { + end = pivot_pos; + } + } + } + } // namespace pdqsort_detail + + template + inline void pdqsort(Iter begin, Iter end, Compare comp) { + if (begin == end) return; + +#if __cplusplus >= 201103L + pdqsort_detail::pdqsort_loop< + Iter, Compare, + pdqsort_detail::is_default_compare< + typename std::decay::type>::value && + std::is_arithmetic< + typename std::iterator_traits::value_type>::value>( + begin, end, comp, pdqsort_detail::log2(end - begin)); +#else + pdqsort_detail::pdqsort_loop( + begin, end, comp, pdqsort_detail::log2(end - begin)); +#endif + } + + template + inline void pdqsort(Iter begin, Iter end) { + typedef typename std::iterator_traits::value_type T; + pdqsort(begin, end, std::less()); + } + + template + inline void pdqsort_branchless(Iter begin, Iter end, Compare comp) { + if (begin == end) return; + pdqsort_detail::pdqsort_loop( + begin, end, comp, pdqsort_detail::log2(end - begin)); + } + + template + inline void pdqsort_branchless(Iter begin, Iter end) { + typedef typename std::iterator_traits::value_type T; + pdqsort_branchless(begin, end, std::less()); + } + + template + inline void pdqpartial_sort(Iter begin, Iter mid, Iter end, Compare comp) { + if (begin == end) return; + +#if __cplusplus >= 201103L + pdqsort_detail::pdqpartial_sort_loop< + Iter, Compare, + pdqsort_detail::is_default_compare< + typename std::decay::type>::value && + std::is_arithmetic< + typename std::iterator_traits::value_type>::value>( + begin, mid, end, comp, pdqsort_detail::log2(end - begin)); +#else + pdqsort_detail::pdqpartial_sort_loop( + begin, end, comp, pdqsort_detail::log2(end - begin)); +#endif + } + + template + inline void pdqpartial_sort(Iter begin, Iter mid, Iter end) { + typedef typename std::iterator_traits::value_type T; + pdqpartial_sort(begin, mid, end, std::less()); + } + + template + inline void pdqpartial_sort_branchless(Iter begin, Iter mid, Iter end, + Compare comp) { + if (begin == end) return; + pdqsort_detail::pdqpartial_sort_loop( + begin, mid, end, comp, pdqsort_detail::log2(end - begin)); + } + + template + inline void pdqpartial_sort_branchless(Iter begin, Iter mid, Iter end) { + typedef typename std::iterator_traits::value_type T; + pdqpartial_sort_branchless(begin, mid, end, std::less()); + } + + template + inline void pdqselect(Iter begin, Iter mid, Iter end, Compare comp) { + if (mid == end) return; + using CompType = typename median_common_detail::CompareRefType::type; + +#if __cplusplus >= 201103L + pdqsort_detail::pdqselect_loop< + Iter, CompType, + pdqsort_detail::is_default_compare< + typename std::decay::type>::value && + std::is_arithmetic< + typename std::iterator_traits::value_type>::value>( + begin, mid, end, comp, pdqsort_detail::log2(end - begin)); +#else + pdqsort_detail::pdqselect_loop( + begin, end, comp, pdqsort_detail::log2(end - begin)); +#endif + } + + template + inline void pdqselect(Iter begin, Iter mid, Iter end) { + typedef typename std::iterator_traits::value_type T; + pdqselect(begin, mid, end, std::less()); + } + + template + inline void pdqselect_branchless(Iter begin, Iter mid, Iter end, Compare comp) { + if (mid == end) return; + using CompType = typename median_common_detail::CompareRefType::type; + pdqsort_detail::pdqselect_loop( + begin, mid, end, comp, pdqsort_detail::log2(end - begin)); + } + + template + inline void pdqselect_branchless(Iter begin, Iter mid, Iter end) { + typedef typename std::iterator_traits::value_type T; + pdqselect_branchless(begin, mid, end, std::less()); + } + +#undef PDQSORT_PREFER_MOVE + +#endif + +} // namespace miniselect \ No newline at end of file diff --git a/src/python/index_wrappers/quake.py b/src/python/index_wrappers/quake.py index c7e9e19b..de012fec 100644 --- a/src/python/index_wrappers/quake.py +++ b/src/python/index_wrappers/quake.py @@ -149,6 +149,8 @@ def search( aps_flush_period_us=50, n_threads=1, parent=None, + sample_prefix=0, + sample_stride=5 ) -> Tuple[torch.Tensor, torch.Tensor]: """ Find the k-nearest neighbors of the query vectors. @@ -169,6 +171,8 @@ def search( search_params.aps_flush_period_us = aps_flush_period_us search_params.k = k search_params.num_threads = n_threads + search_params.sample_prefix = sample_prefix + search_params.sample_stride = sample_stride if parent is not None: search_params.parent_params = quake.SearchParams() diff --git a/src/python/workload_generator.py b/src/python/workload_generator.py index f390aad7..ab1722b1 100644 --- a/src/python/workload_generator.py +++ b/src/python/workload_generator.py @@ -479,17 +479,43 @@ def evaluate_workload(self, *, elif op_type == "query": qs = queries[ids] t0 = time.perf_counter() + t_infos = [] if batch: sr = index.search(qs, **search_params) pred_ids = sr.ids else: - pred_ids = torch.cat([ - index.search(q.unsqueeze(0), **search_params).ids - for q in qs - ]) + p = [] + for q in qs: + sr = index.search(q.unsqueeze(0), **search_params) + p.append(sr.ids) + t_infos.append(sr.timing_info) + pred_ids = torch.cat(p) + # pred_ids = torch.cat([ + # index.search(q.unsqueeze(0), **search_params).ids + # for q in qs + # ]) latency_ms = (time.perf_counter() - t0) * 1e3 gt_ids = torch.load(self.ops_dir / f"{op_id}_gt_ids.pt", weights_only=True) + + total_parent_time = 0 + total_time = 0 + total_boundary_time = 0 + total_aps_time = 0 + total_scan_time = 0 + + for t_info in t_infos: + total_parent_time += t_info.parent_info.total_time_ns / 1e6 + total_time += t_info.total_time_ns / 1e6 + total_boundary_time += t_info.boundary_distance_time_ns / 1e6 + total_aps_time += t_info.aps_time_ns / 1e6 + total_scan_time += t_info.scan_time_ns / 1e6 + + print(f" | parent {total_parent_time:.2f} ms" + f" | total {total_time:.2f} ms" + f" | boundary {total_boundary_time:.2f} ms" + f" | aps {total_aps_time:.2f} ms" + f" | scan {total_scan_time:.2f} ms") recall = compute_recall(pred_ids, gt_ids, search_params["k"]).mean().item() op["recall"] = recall diff --git a/test/cpp/benchmark.cpp b/test/cpp/benchmark.cpp index f5280166..6e7774b5 100644 --- a/test/cpp/benchmark.cpp +++ b/test/cpp/benchmark.cpp @@ -29,9 +29,9 @@ using torch::Tensor; static const int64_t DIM = 128; static const int64_t NUM_VECTORS = 100000; // number of database vectors static const int64_t N_LIST = 1000; // number of clusters for IVF -static const int64_t NUM_QUERIES = 10; // number of queries for search benchmark -static const int64_t K = 10; // top-K neighbors -static const int64_t N_PROBE = 12; // number of probes for IVF +static const int64_t NUM_QUERIES = 10000; // number of queries for search benchmark +static const int64_t K = 1000; // top-K neighbors +static const int64_t N_PROBE = 10; // number of probes for IVF static const int64_t N_WORKERS = 12; // number of workers for parallel query coordinator // Helper functions to generate random data and sequential IDs diff --git a/test/experiments/osdi2025/kick_the_tires/configs/sift1m.yaml b/test/experiments/osdi2025/kick_the_tires/configs/sift1m.yaml index 5a2974e8..352627ec 100644 --- a/test/experiments/osdi2025/kick_the_tires/configs/sift1m.yaml +++ b/test/experiments/osdi2025/kick_the_tires/configs/sift1m.yaml @@ -18,30 +18,34 @@ workload_generator: query_cluster_sample_distribution: uniform seed: 9299 indexes: - - name: Quake + - name: Quake10000 index: Quake build_params: - nc: 1000 + nc: 10000 num_workers: 0 search_params: k: 10 +# nprobe: 10 recall_target: .9 - - name: IVF - index: IVF + initial_search_fraction: 0.25 + - name: Quake10000_sample + index: Quake build_params: - nc: 1000 + nc: 10000 + num_workers: 0 search_params: - nprobe: 15 k: 10 + recall_target: .9 + initial_search_fraction: 0.1 plot: x_axis: operation_number # any column from the results CSV metrics: [latency_ms, recall] # any subset of stored metrics styles: # one entry per index name - Quake: + Quake10000: color: "#1f77b4" # default Matplotlib hex’s are fine linestyle: "-" # see plt.Line2D docs marker: "o" - Quake2: + Quake10000_sample: color: "#ff7f0e" # default Matplotlib hex’s are fine linestyle: "-" # see plt.Line2D docs marker: "o" @@ -52,4 +56,4 @@ plot: overwrite: workload: false index: false - results: true \ No newline at end of file + results: false \ No newline at end of file From 616a283b0ac4b24027ca3cc5a3b8ee382e5dc7ec Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 14 May 2025 21:52:53 -0500 Subject: [PATCH 035/323] update multi-query experiment --- test/experiments/osdi2025/numa_multi_query/run.py | 2 +- test/experiments/osdi2025/numa_single_query/run.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/test/experiments/osdi2025/numa_multi_query/run.py b/test/experiments/osdi2025/numa_multi_query/run.py index 12b95b30..0fd77d8b 100644 --- a/test/experiments/osdi2025/numa_multi_query/run.py +++ b/test/experiments/osdi2025/numa_multi_query/run.py @@ -70,7 +70,7 @@ def run_experiment(cfg_path: str, output_dir: str): for batch_size in batch_sizes: print(f"NUMA={use_numa}, workers={nw}, batched={bs}, batch={batch_size}") idxm = QuakeWrapper() - idxm.load(str(idx_file), use_numa=use_numa, same_core=True, n_workers=nw) + idxm.load(str(idx_file), use_numa=use_numa, same_core=True, num_workers=nw) n_threads = 16 if nw == 0 else 1 # Warmup diff --git a/test/experiments/osdi2025/numa_single_query/run.py b/test/experiments/osdi2025/numa_single_query/run.py index f88c56c8..6993160e 100644 --- a/test/experiments/osdi2025/numa_single_query/run.py +++ b/test/experiments/osdi2025/numa_single_query/run.py @@ -54,7 +54,7 @@ def run_experiment(cfg_path: str, output_dir: str): for n_worker in n_workers: idx_mode = QuakeWrapper() - idx_mode.load(str(idx_file), use_numa=use_numa, same_core=True, n_workers=n_worker) + idx_mode.load(str(idx_file), use_numa=use_numa, same_core=True, num_workers=n_worker) print(f"Threads: {n_worker}") # warm‑up From ab793e1b3b76fc3d39d557bbeef0c63646c36739 Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 14 May 2025 22:44:16 -0500 Subject: [PATCH 036/323] update multi-query experiment --- .../configs/msturing10m.yaml | 15 ++ .../numa_single_query/configs/sift1m.yaml | 44 ++++- .../osdi2025/numa_single_query/run.py | 177 ++++++++++-------- 3 files changed, 155 insertions(+), 81 deletions(-) create mode 100644 test/experiments/osdi2025/numa_single_query/configs/msturing10m.yaml diff --git a/test/experiments/osdi2025/numa_single_query/configs/msturing10m.yaml b/test/experiments/osdi2025/numa_single_query/configs/msturing10m.yaml new file mode 100644 index 00000000..327e20ba --- /dev/null +++ b/test/experiments/osdi2025/numa_single_query/configs/msturing10m.yaml @@ -0,0 +1,15 @@ +# existing keys... +dataset: + name: sift1m + num_queries: 100 + k: 10 + nprobe: 100 + +n_workers: [0,1,2,4,8,16] + +# new keys: +trials: 5 +warmup: 10 + +output: + results_csv: numa_single_query_results.csv \ No newline at end of file diff --git a/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml b/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml index 327e20ba..07646b6a 100644 --- a/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml +++ b/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml @@ -1,15 +1,49 @@ -# existing keys... dataset: name: sift1m num_queries: 100 k: 10 - nprobe: 100 -n_workers: [0,1,2,4,8,16] - -# new keys: trials: 5 warmup: 10 +indexes: + - name: Quake-1worker + index: Quake + build_params: + nc: 4096 + metric: l2 + num_workers: 1 + use_numa: false + search_params: + nprobe: 100 + + - name: Quake-4worker + index: Quake + build_params: + nc: 4096 + metric: l2 + num_workers: 4 + use_numa: false + search_params: + nprobe: 100 + + - name: Quake-16worker + index: Quake + build_params: + nc: 4096 + metric: l2 + num_workers: 16 + use_numa: false + search_params: + nprobe: 100 + + - name: FaissIVF + index: IVF + build_params: + nc: 4096 + metric: l2 + search_params: + nprobe: 100 + output: results_csv: numa_single_query_results.csv \ No newline at end of file diff --git a/test/experiments/osdi2025/numa_single_query/run.py b/test/experiments/osdi2025/numa_single_query/run.py index 6993160e..b04a196d 100644 --- a/test/experiments/osdi2025/numa_single_query/run.py +++ b/test/experiments/osdi2025/numa_single_query/run.py @@ -1,10 +1,4 @@ -#!/usr/bin/env python -""" -NUMA single‑query latency experiment for Quake. -Reads a config.yaml, writes results.csv to output_dir, then plots -thread‑vs‑latency with error bars for NUMA/no‑NUMA. -""" - +#!/usr/bin/env python3 import yaml import numpy as np import torch @@ -13,93 +7,124 @@ from pathlib import Path from quake.datasets.ann_datasets import load_dataset +from quake.index_wrappers.faiss_ivf import FaissIVF from quake.index_wrappers.quake import QuakeWrapper +INDEX_CLASSES = { + "Quake": QuakeWrapper, + "IVF": FaissIVF, + # Extend as needed +} + +def build_and_save_index(index_class, build_params, base_vecs, index_file): + idx = index_class() + params = dict(build_params) + if "nc" not in params: + params["nc"] = int(np.sqrt(base_vecs.shape[0])) + idx.build(base_vecs, **params) + idx.save(str(index_file)) + def run_experiment(cfg_path: str, output_dir: str): - # 1) Load config cfg = yaml.safe_load(Path(cfg_path).read_text()) ds = cfg["dataset"] - n_workers = cfg["n_workers"] - nprobe = ds["nprobe"] + queries_n = ds["num_queries"] k = ds["k"] - num_q = ds["num_queries"] - trials = cfg.get("trials", 5) # default 5 - warmup = cfg.get("warmup", 10) # default 10 + trials = cfg.get("trials", 5) + warmup = cfg.get("warmup", 10) + indexes_cfg = cfg["indexes"] csv_name = cfg.get("output", {}).get("results_csv", "numa_results.csv") out_dir = Path(output_dir); out_dir.mkdir(parents=True, exist_ok=True) - # 2) Load data print(f"Loading dataset '{ds['name']}'...") base_vecs, queries, _ = load_dataset(ds["name"]) - queries = queries[:num_q] - - # 3) Build index once - idx_file = out_dir / "quake_index.bin" - if not idx_file.exists(): - print("Building Quake index...") - idx = QuakeWrapper() - nc = int(np.sqrt(base_vecs.shape[0])) - idx.build(base_vecs, nc=nc, metric="l2", num_workers=0) - idx.save(str(idx_file)) - print(f"Index saved at {idx_file}") - - - # 4) Benchmark: warmup + trials - rows = [] - for use_numa in [False, True]: - mode = "NUMA" if use_numa else "no‑NUMA" - print(f"\n--- {mode} benchmarking ---") - - for n_worker in n_workers: - - idx_mode = QuakeWrapper() - idx_mode.load(str(idx_file), use_numa=use_numa, same_core=True, num_workers=n_worker) - - print(f"Threads: {n_worker}") - # warm‑up - for i in range(min(warmup, len(queries))): - q = queries[i].unsqueeze(0).float() - idx_mode.search(q, k, batched_scan=False, nprobe=nprobe, n_threads=n_worker) - - trial_means = [] - for t in range(trials): - lats = [] - for q_vec in queries: - q = q_vec.unsqueeze(0).float() - res = idx_mode.search( - q, k, - batched_scan=False, - nprobe=nprobe, - ) - ti = res.timing_info + queries = queries[:queries_n] + + all_rows = [] + + for idx_cfg in indexes_cfg: + idx_name = idx_cfg["name"] + idx_type = idx_cfg["index"] + build_params = idx_cfg.get("build_params", {}) + search_params = idx_cfg.get("search_params", {}) + IndexClass = INDEX_CLASSES.get(idx_type) + if IndexClass is None: + print(f"Unknown index type: {idx_type}") + continue + + idx_file = out_dir / f"{idx_name}_index.bin" + if not idx_file.exists(): + print(f"Building {idx_name} index...") + build_and_save_index(IndexClass, build_params, base_vecs, idx_file) + print(f"Index saved at {idx_file}") + + print(f"\n--- {idx_name} ---") + + # Prepare load arguments per index type + if idx_type == "Quake": + load_kwargs = {} + if "use_numa" in build_params: + load_kwargs["use_numa"] = build_params["use_numa"] + if "num_workers" in build_params: + load_kwargs["num_workers"] = build_params["num_workers"] + idx = IndexClass() + idx.load(str(idx_file), **load_kwargs) + else: + idx = IndexClass() + idx.load(str(idx_file)) + + nprobe = search_params.get("nprobe", 100) + + # warm-up + for i in range(min(warmup, len(queries))): + q = queries[i].unsqueeze(0).float() + idx.search(q, k, batched_scan=False, nprobe=nprobe) + + trial_means = [] + for t in range(trials): + lats = [] + for q_vec in queries: + q = q_vec.unsqueeze(0).float() + res = idx.search( + q, k, + batched_scan=False, + nprobe=nprobe + ) + ti = getattr(res, "timing_info", None) + if ti and hasattr(ti, "total_time_ns"): lats.append(ti.total_time_ns / 1e6) - mean_t = float(np.mean(lats)) - trial_means.append(mean_t) - print(f" Trial {t+1}/{trials}: {mean_t:.2f} ms") - - mean_lat = float(np.mean(trial_means)) - std_lat = float(np.std(trial_means)) - rows.append({ - "numa_enabled": use_numa, - "n_worker": n_worker, - "mean_latency_ms": mean_lat, - "std_latency_ms": std_lat, - }) - - # 5) Save CSV - df = pd.DataFrame(rows) + elif hasattr(res, "latency_ms"): + lats.append(res.latency_ms) + else: + raise RuntimeError("No timing info found in search result") + mean_t = float(np.mean(lats)) + trial_means.append(mean_t) + print(f" Trial {t+1}/{trials}: {mean_t:.2f} ms") + + mean_lat = float(np.mean(trial_means)) + std_lat = float(np.std(trial_means)) + # Pull out n_workers for grouping if present, else use name + n_workers_val = build_params.get("num_workers", None) + all_rows.append({ + "index": idx_name, + "n_workers": n_workers_val, + "mean_latency_ms": mean_lat, + "std_latency_ms": std_lat, + }) + + # Save CSV + df = pd.DataFrame(all_rows) out_csv = out_dir / csv_name df.to_csv(out_csv, index=False) print(f"\nResults written to {out_csv}") - # 6) Plot with error bars + # Plot plt.figure() - for flag in df["numa_enabled"].unique(): - subset = df[df["numa_enabled"] == flag] - label = "NUMA" if flag else "no‑NUMA" + for idx_name in df["index"].unique(): + subset = df[df["index"] == idx_name] + label = f"{idx_name}" if subset["n_workers"].isnull().all() else f"{idx_name} (n_workers={subset['n_workers'].iloc[0]})" plt.errorbar( - subset["n_worker"], + subset["n_workers"] if "n_workers" in subset else subset.index, subset["mean_latency_ms"], yerr=subset["std_latency_ms"], marker="o", @@ -109,7 +134,7 @@ def run_experiment(cfg_path: str, output_dir: str): plt.xscale("symlog", base=2) plt.xlabel("Num Workers") plt.ylabel("Mean Latency (ms)") - plt.title("NUMA Single‑Query Latency") + plt.title("Single‑Query Latency (per-index)") plt.legend() plot_file = out_dir / f"{out_csv.stem}_latency.png" plt.tight_layout() From 6fa1e94f4c08be184d6ae3083fd8750e24970ed8 Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 14 May 2025 22:45:28 -0500 Subject: [PATCH 037/323] update multi-query experiment --- src/python/index_wrappers/quake.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/python/index_wrappers/quake.py b/src/python/index_wrappers/quake.py index de012fec..2fde67a9 100644 --- a/src/python/index_wrappers/quake.py +++ b/src/python/index_wrappers/quake.py @@ -69,6 +69,7 @@ def build( code_size: int = 8, parent=None, use_gpu=False, + use_numa=False, gpu_batch_size=100000, gpu_sample_size=1000000 ): @@ -93,6 +94,7 @@ def build( build_params.metric = metric build_params.nlist = nc build_params.num_workers = num_workers + build_params.use_numa = use_numa if parent is not None: build_params.parent_params = quake.IndexBuildParams() From 7ccce9593d14ae2bb16b8049b2b7b78ca30b5017 Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 14 May 2025 22:47:18 -0500 Subject: [PATCH 038/323] update multi-query experiment --- src/cpp/bindings/wrap.cpp | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/cpp/bindings/wrap.cpp b/src/cpp/bindings/wrap.cpp index b5c0d589..35c98e3d 100644 --- a/src/cpp/bindings/wrap.cpp +++ b/src/cpp/bindings/wrap.cpp @@ -148,6 +148,8 @@ PYBIND11_MODULE(_bindings, m) { (std::string("Number of workers. default = ") + std::to_string(DEFAULT_NUM_WORKERS)).c_str()) .def_readwrite("parent_params", &IndexBuildParams::parent_params, "Parameters for the parent index, if any.") + .def_readwrite("use_numa", &IndexBuildParams::use_numa, + (std::string("Flag to use NUMA for index building. default = ") + std::to_string(false)).c_str()) .def_readwrite("use_gpu", &IndexBuildParams::use_gpu, (std::string("Flag to use GPU for index building. default = ") + std::to_string(false)).c_str()) .def_readwrite("gpu_batch_size", &IndexBuildParams::gpu_batch_size, From 5fc80475fe42386ddaa82567f1cde9bd24a05515 Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 14 May 2025 22:49:21 -0500 Subject: [PATCH 039/323] update multi-query experiment --- .../numa_single_query/configs/sift1m.yaml | 38 +++++++++++++++++-- 1 file changed, 34 insertions(+), 4 deletions(-) diff --git a/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml b/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml index 07646b6a..69270e18 100644 --- a/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml +++ b/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml @@ -10,7 +10,7 @@ indexes: - name: Quake-1worker index: Quake build_params: - nc: 4096 + nc: 1000 metric: l2 num_workers: 1 use_numa: false @@ -20,7 +20,7 @@ indexes: - name: Quake-4worker index: Quake build_params: - nc: 4096 + nc: 1000 metric: l2 num_workers: 4 use_numa: false @@ -30,17 +30,47 @@ indexes: - name: Quake-16worker index: Quake build_params: - nc: 4096 + nc: 1000 metric: l2 num_workers: 16 use_numa: false search_params: nprobe: 100 + - name: Quake-1worker + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 1 + use_numa: true + search_params: + nprobe: 100 + + - name: Quake-4worker + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 4 + use_numa: true + search_params: + nprobe: 100 + + - name: Quake-16worker + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 16 + use_numa: true + search_params: + nprobe: 100 + - name: FaissIVF index: IVF build_params: - nc: 4096 + nc: 1000 metric: l2 search_params: nprobe: 100 From b6f5e5ee607823cd65aec6c3b6b642b2cbdc1ad5 Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 14 May 2025 22:54:31 -0500 Subject: [PATCH 040/323] update multi-query experiment --- .../numa_single_query/configs/sift1m.yaml | 26 +++++++++---------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml b/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml index 69270e18..89bdd667 100644 --- a/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml +++ b/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml @@ -7,7 +7,7 @@ trials: 5 warmup: 10 indexes: - - name: Quake-1worker + - name: Quake_1 index: Quake build_params: nc: 1000 @@ -15,9 +15,9 @@ indexes: num_workers: 1 use_numa: false search_params: - nprobe: 100 + nprobe: 1000 - - name: Quake-4worker + - name: Quake_4 index: Quake build_params: nc: 1000 @@ -25,9 +25,9 @@ indexes: num_workers: 4 use_numa: false search_params: - nprobe: 100 + nprobe: 1000 - - name: Quake-16worker + - name: Quake_16 index: Quake build_params: nc: 1000 @@ -35,9 +35,9 @@ indexes: num_workers: 16 use_numa: false search_params: - nprobe: 100 + nprobe: 1000 - - name: Quake-1worker + - name: Quake_1_numa index: Quake build_params: nc: 1000 @@ -45,9 +45,9 @@ indexes: num_workers: 1 use_numa: true search_params: - nprobe: 100 + nprobe: 1000 - - name: Quake-4worker + - name: Quake_4_numa index: Quake build_params: nc: 1000 @@ -55,9 +55,9 @@ indexes: num_workers: 4 use_numa: true search_params: - nprobe: 100 + nprobe: 1000 - - name: Quake-16worker + - name: Quake_16_numa index: Quake build_params: nc: 1000 @@ -65,7 +65,7 @@ indexes: num_workers: 16 use_numa: true search_params: - nprobe: 100 + nprobe: 1000 - name: FaissIVF index: IVF @@ -73,7 +73,7 @@ indexes: nc: 1000 metric: l2 search_params: - nprobe: 100 + nprobe: 1000 output: results_csv: numa_single_query_results.csv \ No newline at end of file From f17f0830553f4aebb7c03fd0ff3a3095671b8431 Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 14 May 2025 23:02:25 -0500 Subject: [PATCH 041/323] update multi-query experiment --- src/cpp/src/query_coordinator.cpp | 1 - src/python/index_wrappers/vamana.py | 4 +-- .../numa_single_query/configs/sift1m.yaml | 35 +++++++++++++++++++ 3 files changed, 37 insertions(+), 3 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 5483a4c7..a3b3bc3b 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -119,7 +119,6 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { // Shutdown signal: -1 indicates the worker should exit. if (job.partition_id == -1) { - std::cout << "[QueryCoordinator::partition_scan_worker_fn] Worker " << core_index << " exiting." << std::endl; break; } diff --git a/src/python/index_wrappers/vamana.py b/src/python/index_wrappers/vamana.py index fde8877e..7d2d6a82 100644 --- a/src/python/index_wrappers/vamana.py +++ b/src/python/index_wrappers/vamana.py @@ -53,9 +53,9 @@ def build( self.index = svs.DynamicVamana.build( parameters=parameters, data=vectors, ids=ids, distance_type=distance, num_threads=num_threads ) - self.index.search_window_size = 128 - def search(self, queries: torch.Tensor, k: int) -> Tuple[np.ndarray, np.ndarray]: + def search(self, queries: torch.Tensor, k: int, search_window_size: int) -> Tuple[np.ndarray, np.ndarray]: + self.index.search_window_size = search_window_size indices, distances = self.index.search(queries=to_numpy(queries).astype(np.float32), n_neighbors=k) return to_torch(indices.astype(np.int64)), to_torch(distances) diff --git a/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml b/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml index 89bdd667..1f184fc4 100644 --- a/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml +++ b/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml @@ -75,5 +75,40 @@ indexes: search_params: nprobe: 1000 + - name: SCANN + index: SCANN + build_params: + num_leaves: 1000 + metric: l2 + search_params: + leaves_to_search: 1000 + + - name: FaissHNSW + index: HNSW + build_params: + m: 64 + ef_construction: 128 + metric: l2 + search_params: + ef_search: 32 + + - name: DiskANN + index: DiskANN + build_params: + complexity: 128, + graph_degree: 64 + metric: l2 + search_params: + complexity: 32 + + - name: SVS + index: SVS + build_params: + graph_max_degree: 64 + window_size: 128 + metric: l2 + search_params: + search_window_size: 32 + output: results_csv: numa_single_query_results.csv \ No newline at end of file From 56caaa7b01f7ffd8c25d7985d93a8d79852e8272 Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 14 May 2025 23:12:27 -0500 Subject: [PATCH 042/323] update multi-query experiment --- .../numa_single_query/configs/sift1m.yaml | 4 +- .../osdi2025/numa_single_query/run.py | 96 ++++++++++++++----- 2 files changed, 77 insertions(+), 23 deletions(-) diff --git a/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml b/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml index 1f184fc4..9d40ec14 100644 --- a/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml +++ b/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml @@ -1,3 +1,4 @@ +# numa_latency_experiment.yaml dataset: name: sift1m num_queries: 100 @@ -5,6 +6,7 @@ dataset: trials: 5 warmup: 10 +overwrite: false # Set to true to force all indexes to rerun indexes: - name: Quake_1 @@ -95,7 +97,7 @@ indexes: - name: DiskANN index: DiskANN build_params: - complexity: 128, + complexity: 128 graph_degree: 64 metric: l2 search_params: diff --git a/test/experiments/osdi2025/numa_single_query/run.py b/test/experiments/osdi2025/numa_single_query/run.py index b04a196d..5e079ddb 100644 --- a/test/experiments/osdi2025/numa_single_query/run.py +++ b/test/experiments/osdi2025/numa_single_query/run.py @@ -1,4 +1,15 @@ #!/usr/bin/env python3 +""" +NUMA Single-Query Latency Benchmark + +Benchmarks a suite of approximate nearest neighbor (ANN) indexes (Quake, Faiss-IVF, SCANN, HNSW, DiskANN, SVS, etc.) +for single-query latency under configurable build and search parameters. Results are cached and only recomputed if +the 'overwrite' flag is set in the configuration. Outputs a unified CSV and a latency plot per index. + +Usage: + python numa_latency_experiment.py numa_latency_experiment.yaml output_dir +""" + import yaml import numpy as np import torch @@ -8,18 +19,36 @@ from quake.datasets.ann_datasets import load_dataset from quake.index_wrappers.faiss_ivf import FaissIVF +from quake.index_wrappers.faiss_hnsw import FaissHNSW from quake.index_wrappers.quake import QuakeWrapper +# Placeholder index wrappers. Replace these with real implementations. +try: + from quake.index_wrappers.scann import Scann +except ImportError: + class ScannWrapper: pass +try: + from quake.index_wrappers.diskann import DiskANNDynamic +except ImportError: + class DiskANNWrapper: pass +try: + from quake.index_wrappers.vamana import Vamana +except ImportError: + class SVSWrapper: pass + INDEX_CLASSES = { "Quake": QuakeWrapper, "IVF": FaissIVF, - # Extend as needed + "SCANN": Scann, + "HNSW": FaissHNSW, + "DiskANN": DiskANNDynamic, + "SVS": Vamana, } def build_and_save_index(index_class, build_params, base_vecs, index_file): idx = index_class() params = dict(build_params) - if "nc" not in params: + if "nc" not in params and "num_leaves" not in params: params["nc"] = int(np.sqrt(base_vecs.shape[0])) idx.build(base_vecs, **params) idx.save(str(index_file)) @@ -33,10 +62,12 @@ def run_experiment(cfg_path: str, output_dir: str): warmup = cfg.get("warmup", 10) indexes_cfg = cfg["indexes"] csv_name = cfg.get("output", {}).get("results_csv", "numa_results.csv") + overwrite = cfg.get("overwrite", False) - out_dir = Path(output_dir); out_dir.mkdir(parents=True, exist_ok=True) + out_dir = Path(output_dir) + out_dir.mkdir(parents=True, exist_ok=True) - print(f"Loading dataset '{ds['name']}'...") + print(f"[INFO] Loading dataset '{ds['name']}'...") base_vecs, queries, _ = load_dataset(ds["name"]) queries = queries[:queries_n] @@ -49,16 +80,25 @@ def run_experiment(cfg_path: str, output_dir: str): search_params = idx_cfg.get("search_params", {}) IndexClass = INDEX_CLASSES.get(idx_type) if IndexClass is None: - print(f"Unknown index type: {idx_type}") + print(f"[WARN] Unknown index type: {idx_type}. Skipping.") continue + idx_csv = out_dir / f"{idx_name}_results.csv" idx_file = out_dir / f"{idx_name}_index.bin" + + if idx_csv.exists() and not overwrite: + print(f"[SKIP] {idx_name}: Results exist. Use overwrite: true to rerun.") + # For plotting, load mean/std for this index + df_idx = pd.read_csv(idx_csv) + all_rows.append(df_idx.iloc[0].to_dict()) + continue + if not idx_file.exists(): - print(f"Building {idx_name} index...") + print(f"[BUILD] {idx_name} index...") build_and_save_index(IndexClass, build_params, base_vecs, idx_file) - print(f"Index saved at {idx_file}") + print(f"[SAVE] Index saved at {idx_file}") - print(f"\n--- {idx_name} ---") + print(f"[RUN] {idx_name} ...") # Prepare load arguments per index type if idx_type == "Quake": @@ -73,13 +113,24 @@ def run_experiment(cfg_path: str, output_dir: str): idx = IndexClass() idx.load(str(idx_file)) - nprobe = search_params.get("nprobe", 100) - - # warm-up + # Dispatch search argument keys by index type + if idx_type == "SCANN": + search_args = {"leaves_to_search": search_params.get("leaves_to_search", 100)} + elif idx_type == "HNSW": + search_args = {"ef_search": search_params.get("ef_search", 32)} + elif idx_type == "DiskANN": + search_args = {"complexity": search_params.get("complexity", 32)} + elif idx_type == "SVS": + search_args = {"search_window_size": search_params.get("search_window_size", 32)} + else: # IVF, Quake, others + search_args = {"nprobe": search_params.get("nprobe", 100)} + + # Warmup for i in range(min(warmup, len(queries))): q = queries[i].unsqueeze(0).float() - idx.search(q, k, batched_scan=False, nprobe=nprobe) + idx.search(q, k, batched_scan=False, **search_args) + # Benchmark Trials trial_means = [] for t in range(trials): lats = [] @@ -88,7 +139,7 @@ def run_experiment(cfg_path: str, output_dir: str): res = idx.search( q, k, batched_scan=False, - nprobe=nprobe + **search_args ) ti = getattr(res, "timing_info", None) if ti and hasattr(ti, "total_time_ns"): @@ -96,27 +147,28 @@ def run_experiment(cfg_path: str, output_dir: str): elif hasattr(res, "latency_ms"): lats.append(res.latency_ms) else: - raise RuntimeError("No timing info found in search result") + raise RuntimeError(f"No timing info found in search result for {idx_name}") mean_t = float(np.mean(lats)) trial_means.append(mean_t) - print(f" Trial {t+1}/{trials}: {mean_t:.2f} ms") + print(f" [trial {t+1}/{trials}] {mean_t:.2f} ms") mean_lat = float(np.mean(trial_means)) std_lat = float(np.std(trial_means)) - # Pull out n_workers for grouping if present, else use name n_workers_val = build_params.get("num_workers", None) - all_rows.append({ + row = { "index": idx_name, "n_workers": n_workers_val, "mean_latency_ms": mean_lat, "std_latency_ms": std_lat, - }) + } + all_rows.append(row) + pd.DataFrame([row]).to_csv(idx_csv, index=False) - # Save CSV + # Save Unified CSV df = pd.DataFrame(all_rows) out_csv = out_dir / csv_name df.to_csv(out_csv, index=False) - print(f"\nResults written to {out_csv}") + print(f"\n[RESULT] Results written to {out_csv}") # Plot plt.figure() @@ -134,9 +186,9 @@ def run_experiment(cfg_path: str, output_dir: str): plt.xscale("symlog", base=2) plt.xlabel("Num Workers") plt.ylabel("Mean Latency (ms)") - plt.title("Single‑Query Latency (per-index)") + plt.title("Single-Query Latency (per-index)") plt.legend() plot_file = out_dir / f"{out_csv.stem}_latency.png" plt.tight_layout() plt.savefig(plot_file) - print(f"Plot saved to {plot_file}") \ No newline at end of file + print(f"[PLOT] Saved to {plot_file}") From 7a447fa5002232dd909508fafe716b70b0265561 Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 14 May 2025 23:12:50 -0500 Subject: [PATCH 043/323] update multi-query experiment --- test/experiments/osdi2025/numa_single_query/run.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/test/experiments/osdi2025/numa_single_query/run.py b/test/experiments/osdi2025/numa_single_query/run.py index 5e079ddb..70b2f3b0 100644 --- a/test/experiments/osdi2025/numa_single_query/run.py +++ b/test/experiments/osdi2025/numa_single_query/run.py @@ -26,15 +26,15 @@ try: from quake.index_wrappers.scann import Scann except ImportError: - class ScannWrapper: pass + class Scann: pass try: from quake.index_wrappers.diskann import DiskANNDynamic except ImportError: - class DiskANNWrapper: pass + class DiskANNDynamic: pass try: from quake.index_wrappers.vamana import Vamana except ImportError: - class SVSWrapper: pass + class Vamana: pass INDEX_CLASSES = { "Quake": QuakeWrapper, From c707a65c06b2053d287c0ae4ad0fa2740e5a4190 Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 14 May 2025 23:16:48 -0500 Subject: [PATCH 044/323] update multi-query experiment --- test/experiments/osdi2025/numa_single_query/run.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/test/experiments/osdi2025/numa_single_query/run.py b/test/experiments/osdi2025/numa_single_query/run.py index 70b2f3b0..4b5336ff 100644 --- a/test/experiments/osdi2025/numa_single_query/run.py +++ b/test/experiments/osdi2025/numa_single_query/run.py @@ -26,15 +26,15 @@ try: from quake.index_wrappers.scann import Scann except ImportError: - class Scann: pass + raise ImportError("SCANN wrapper not available. Please install the required package.") try: from quake.index_wrappers.diskann import DiskANNDynamic except ImportError: - class DiskANNDynamic: pass + raise ImportError("DiskANN wrapper not available. Please install the required package.") try: from quake.index_wrappers.vamana import Vamana except ImportError: - class Vamana: pass + raise ImportError("SVS wrapper not available. Please install the required package.") INDEX_CLASSES = { "Quake": QuakeWrapper, From b66d3b63e4f876fafc6d75ab490c90e84d64acf6 Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 14 May 2025 23:48:56 -0500 Subject: [PATCH 045/323] update multi-query experiment --- src/python/index_wrappers/diskann.py | 6 ++++++ src/python/index_wrappers/faiss_hnsw.py | 6 ++++++ src/python/index_wrappers/scann.py | 6 ++++++ src/python/index_wrappers/vamana.py | 6 ++++++ test/experiments/osdi2025/numa_single_query/run.py | 1 - 5 files changed, 24 insertions(+), 1 deletion(-) diff --git a/src/python/index_wrappers/diskann.py b/src/python/index_wrappers/diskann.py index 9b0d834f..752e8ae6 100644 --- a/src/python/index_wrappers/diskann.py +++ b/src/python/index_wrappers/diskann.py @@ -190,3 +190,9 @@ def remove(self, ids: torch.Tensor, lazy: bool = True): def centroids(self) -> torch.Tensor | None: return super().centroids() + + def index_state(self) -> str: + return "" + + def maintenance(self): + pass \ No newline at end of file diff --git a/src/python/index_wrappers/faiss_hnsw.py b/src/python/index_wrappers/faiss_hnsw.py index 9fc7dcc2..c3ff9850 100644 --- a/src/python/index_wrappers/faiss_hnsw.py +++ b/src/python/index_wrappers/faiss_hnsw.py @@ -130,3 +130,9 @@ def remove(self, ids: torch.Tensor): """ # throw a runtime error raise RuntimeError("Faiss HNSW does not support removal of vectors.") + + def index_state(self) -> str: + return "" + + def maintenance(self): + pass \ No newline at end of file diff --git a/src/python/index_wrappers/scann.py b/src/python/index_wrappers/scann.py index 2b224026..751cc3f5 100644 --- a/src/python/index_wrappers/scann.py +++ b/src/python/index_wrappers/scann.py @@ -187,5 +187,11 @@ def load(self, filename: str): """ self.index = scann.scann_ops_pybind.load_searcher(str(filename)) + def index_state(self) -> str: + return self.index.config() + + def maintenance(self): + pass + def centroids(self) -> torch.Tensor | None: return super().centroids() diff --git a/src/python/index_wrappers/vamana.py b/src/python/index_wrappers/vamana.py index 7d2d6a82..31b91242 100644 --- a/src/python/index_wrappers/vamana.py +++ b/src/python/index_wrappers/vamana.py @@ -99,3 +99,9 @@ def load(self, directory: str, metrics: str = "ip"): def centroids(self) -> torch.Tensor | None: return super().centroids() + + def index_state(self) -> str: + return "" + + def maintenance(self): + pass \ No newline at end of file diff --git a/test/experiments/osdi2025/numa_single_query/run.py b/test/experiments/osdi2025/numa_single_query/run.py index 4b5336ff..031f366e 100644 --- a/test/experiments/osdi2025/numa_single_query/run.py +++ b/test/experiments/osdi2025/numa_single_query/run.py @@ -22,7 +22,6 @@ from quake.index_wrappers.faiss_hnsw import FaissHNSW from quake.index_wrappers.quake import QuakeWrapper -# Placeholder index wrappers. Replace these with real implementations. try: from quake.index_wrappers.scann import Scann except ImportError: From c5f6f190f2d9d65e44196c31ad0001dbaf43c32e Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 14 May 2025 23:53:21 -0500 Subject: [PATCH 046/323] update multi-query experiment --- test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml b/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml index 9d40ec14..18412dd6 100644 --- a/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml +++ b/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml @@ -81,7 +81,7 @@ indexes: index: SCANN build_params: num_leaves: 1000 - metric: l2 + metric: squared_l2 search_params: leaves_to_search: 1000 From bd17f80d93453e9da6fad8560c3076b2e7fbe37c Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 14 May 2025 23:54:07 -0500 Subject: [PATCH 047/323] update multi-query experiment --- test/experiments/osdi2025/numa_single_query/run.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/test/experiments/osdi2025/numa_single_query/run.py b/test/experiments/osdi2025/numa_single_query/run.py index 031f366e..d6670bb2 100644 --- a/test/experiments/osdi2025/numa_single_query/run.py +++ b/test/experiments/osdi2025/numa_single_query/run.py @@ -127,7 +127,7 @@ def run_experiment(cfg_path: str, output_dir: str): # Warmup for i in range(min(warmup, len(queries))): q = queries[i].unsqueeze(0).float() - idx.search(q, k, batched_scan=False, **search_args) + idx.search(q, k, **search_args) # Benchmark Trials trial_means = [] @@ -137,7 +137,6 @@ def run_experiment(cfg_path: str, output_dir: str): q = q_vec.unsqueeze(0).float() res = idx.search( q, k, - batched_scan=False, **search_args ) ti = getattr(res, "timing_info", None) From b3aac5489b8d148c06363a1223494293f5d10d01 Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 14 May 2025 23:56:15 -0500 Subject: [PATCH 048/323] update multi-query experiment --- src/python/index_wrappers/scann.py | 11 ++++++++--- src/python/index_wrappers/vamana.py | 8 ++++++++ 2 files changed, 16 insertions(+), 3 deletions(-) diff --git a/src/python/index_wrappers/scann.py b/src/python/index_wrappers/scann.py index 751cc3f5..50d58f87 100644 --- a/src/python/index_wrappers/scann.py +++ b/src/python/index_wrappers/scann.py @@ -7,8 +7,9 @@ import torch from quake.index_wrappers.wrapper import IndexWrapper +from quake import SearchTimingInfo from quake.utils import to_numpy, to_torch - +import time class Scann(IndexWrapper): index: scann.scann_ops_pybind.ScannSearcher @@ -129,6 +130,8 @@ def search( if not n_threads == 0: self.index.set_num_threads(n_threads) + start_time = time.time() + query = to_numpy(query) indices, distances = self.index.search_batched( query, @@ -137,8 +140,10 @@ def search( pre_reorder_num_neighbors=pre_reorder_num_neighbors, ) indices = np.array(indices) - - return to_torch(indices), to_torch(distances) + end_time = time.time() + timing_info = SearchTimingInfo() + timing_info.total_time_ns = int((end_time - start_time) * 1e9) + return to_torch(indices), to_torch(distances), timing_info def add(self, vectors: torch.Tensor, ids: Optional[torch.Tensor] = None): """ diff --git a/src/python/index_wrappers/vamana.py b/src/python/index_wrappers/vamana.py index 31b91242..138a2cb5 100644 --- a/src/python/index_wrappers/vamana.py +++ b/src/python/index_wrappers/vamana.py @@ -6,6 +6,8 @@ from quake.index_wrappers.wrapper import IndexWrapper from quake.utils import to_numpy, to_path, to_torch +from quake import SearchTimingInfo +import time class Vamana(IndexWrapper): @@ -55,8 +57,14 @@ def build( ) def search(self, queries: torch.Tensor, k: int, search_window_size: int) -> Tuple[np.ndarray, np.ndarray]: + + start_time = time.time() self.index.search_window_size = search_window_size indices, distances = self.index.search(queries=to_numpy(queries).astype(np.float32), n_neighbors=k) + + end_time = time.time() + timing_info = SearchTimingInfo() + timing_info.total_time_ns = int((end_time - start_time) * 1e9) return to_torch(indices.astype(np.int64)), to_torch(distances) def add(self, vectors: torch.Tensor, ids: Optional[torch.Tensor] = None): From eafc190bd928ef2cbaa86682446f98c208ec5877 Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 14 May 2025 23:58:58 -0500 Subject: [PATCH 049/323] update multi-query experiment --- src/python/index_wrappers/diskann.py | 9 +++++++-- src/python/index_wrappers/faiss_hnsw.py | 12 ++++++++---- src/python/index_wrappers/scann.py | 10 ++++++++-- src/python/index_wrappers/vamana.py | 11 +++++++++-- 4 files changed, 32 insertions(+), 10 deletions(-) diff --git a/src/python/index_wrappers/diskann.py b/src/python/index_wrappers/diskann.py index 752e8ae6..7bfb22ec 100644 --- a/src/python/index_wrappers/diskann.py +++ b/src/python/index_wrappers/diskann.py @@ -6,7 +6,7 @@ import numpy as np import torch -from quake import SearchTimingInfo +from quake import SearchTimingInfo, SearchResult from quake.index_wrappers.wrapper import IndexWrapper from quake.utils import to_numpy, to_torch @@ -110,7 +110,12 @@ def search( indices = indices - 1 # ids are 1-indexed distances = to_torch(distances) - return indices, distances, timing_info + search_result = SearchResult() + search_result.ids = to_torch(indices) + search_result.distances = to_torch(distances) + search_result.timing_info = timing_info + + return search_result def save(self, path: str): """ diff --git a/src/python/index_wrappers/faiss_hnsw.py b/src/python/index_wrappers/faiss_hnsw.py index c3ff9850..c9438d30 100644 --- a/src/python/index_wrappers/faiss_hnsw.py +++ b/src/python/index_wrappers/faiss_hnsw.py @@ -4,7 +4,7 @@ import faiss import torch -from quake import SearchTimingInfo +from quake import SearchTimingInfo, SearchResult from quake.index_wrappers.faiss_ivf import metric_str_to_faiss from quake.index_wrappers.wrapper import IndexWrapper from quake.utils import to_numpy, to_torch @@ -86,10 +86,14 @@ def search(self, query: torch.Tensor, k: int, ef_search: int = 16) -> Tuple[torc end = time.time() timing_info.total_time_us = int((end - start) * 1e6) - distances = to_torch(distances) - indices = to_torch(indices) - return indices, distances, timing_info + search_result = SearchResult() + search_result.ids = to_torch(indices) + search_result.distances = to_torch(distances) + search_result.timing_info = timing_info + + return search_result + def save(self, filename: str): """ diff --git a/src/python/index_wrappers/scann.py b/src/python/index_wrappers/scann.py index 50d58f87..1c437d5f 100644 --- a/src/python/index_wrappers/scann.py +++ b/src/python/index_wrappers/scann.py @@ -7,7 +7,7 @@ import torch from quake.index_wrappers.wrapper import IndexWrapper -from quake import SearchTimingInfo +from quake import SearchTimingInfo, SearchResult from quake.utils import to_numpy, to_torch import time @@ -143,7 +143,13 @@ def search( end_time = time.time() timing_info = SearchTimingInfo() timing_info.total_time_ns = int((end_time - start_time) * 1e9) - return to_torch(indices), to_torch(distances), timing_info + + search_result = SearchResult() + search_result.ids = to_torch(indices) + search_result.distances = to_torch(distances) + search_result.timing_info = timing_info + + return search_result def add(self, vectors: torch.Tensor, ids: Optional[torch.Tensor] = None): """ diff --git a/src/python/index_wrappers/vamana.py b/src/python/index_wrappers/vamana.py index 138a2cb5..0d880379 100644 --- a/src/python/index_wrappers/vamana.py +++ b/src/python/index_wrappers/vamana.py @@ -6,7 +6,7 @@ from quake.index_wrappers.wrapper import IndexWrapper from quake.utils import to_numpy, to_path, to_torch -from quake import SearchTimingInfo +from quake import SearchTimingInfo, SearchResult import time @@ -65,7 +65,14 @@ def search(self, queries: torch.Tensor, k: int, search_window_size: int) -> Tupl end_time = time.time() timing_info = SearchTimingInfo() timing_info.total_time_ns = int((end_time - start_time) * 1e9) - return to_torch(indices.astype(np.int64)), to_torch(distances) + + search_result = SearchResult() + search_result.ids = to_torch(indices) + search_result.distances = to_torch(distances) + search_result.timing_info = timing_info + + return search_result + def add(self, vectors: torch.Tensor, ids: Optional[torch.Tensor] = None): if ids is None: From 35a252ccf8e3ef19f1dbeaedc75f5676f3a2ab3e Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 15 May 2025 00:01:32 -0500 Subject: [PATCH 050/323] update multi-query experiment --- test/experiments/osdi2025/numa_single_query/run.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/test/experiments/osdi2025/numa_single_query/run.py b/test/experiments/osdi2025/numa_single_query/run.py index d6670bb2..f1a0c8e8 100644 --- a/test/experiments/osdi2025/numa_single_query/run.py +++ b/test/experiments/osdi2025/numa_single_query/run.py @@ -47,8 +47,6 @@ def build_and_save_index(index_class, build_params, base_vecs, index_file): idx = index_class() params = dict(build_params) - if "nc" not in params and "num_leaves" not in params: - params["nc"] = int(np.sqrt(base_vecs.shape[0])) idx.build(base_vecs, **params) idx.save(str(index_file)) From 90afadaf3db17038f1d7919693283a66d75769eb Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 15 May 2025 00:16:56 -0500 Subject: [PATCH 051/323] update multi-query experiment --- src/python/index_wrappers/diskann.py | 2 +- src/python/index_wrappers/faiss_hnsw.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/python/index_wrappers/diskann.py b/src/python/index_wrappers/diskann.py index 7bfb22ec..e1ae89f3 100644 --- a/src/python/index_wrappers/diskann.py +++ b/src/python/index_wrappers/diskann.py @@ -105,7 +105,7 @@ def search( query, k_neighbors=k, complexity=complexity, num_threads=num_threads ) end = time.time() - timing_info.total_time_us = int((end - start) * 1e6) + timing_info.total_time_ns = int((end - start) * 1e9) indices = to_torch(indices.astype(np.int64)) indices = indices - 1 # ids are 1-indexed distances = to_torch(distances) diff --git a/src/python/index_wrappers/faiss_hnsw.py b/src/python/index_wrappers/faiss_hnsw.py index c9438d30..eed4a1ea 100644 --- a/src/python/index_wrappers/faiss_hnsw.py +++ b/src/python/index_wrappers/faiss_hnsw.py @@ -85,7 +85,7 @@ def search(self, query: torch.Tensor, k: int, ef_search: int = 16) -> Tuple[torc distances, indices = self.index.search(query, k) end = time.time() - timing_info.total_time_us = int((end - start) * 1e6) + timing_info.total_time_ns = int((end - start) * 1e9) search_result = SearchResult() search_result.ids = to_torch(indices) From ad697b43566bddf6f5f8b0e7c34998414d561a21 Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 15 May 2025 00:20:50 -0500 Subject: [PATCH 052/323] update multi-query experiment --- src/python/index_wrappers/diskann.py | 27 ++++++++++++++------------- 1 file changed, 14 insertions(+), 13 deletions(-) diff --git a/src/python/index_wrappers/diskann.py b/src/python/index_wrappers/diskann.py index e1ae89f3..21878287 100644 --- a/src/python/index_wrappers/diskann.py +++ b/src/python/index_wrappers/diskann.py @@ -5,6 +5,7 @@ import diskannpy as dap import numpy as np import torch +from pathlib import Path from quake import SearchTimingInfo, SearchResult from quake.index_wrappers.wrapper import IndexWrapper @@ -124,10 +125,10 @@ def save(self, path: str): :param path: The path to save the index to. :type path: str """ - # assert self.index is not None - # if not Path(path).exists(): - # Path(path).mkdir(exist_ok=True) - # self.index.save(path) + assert self.index is not None + if not Path(path).exists(): + Path(path).mkdir(exist_ok=True) + self.index.save(path) # TODO: save/load index raise RuntimeError("DiskANNDynamic.save() not implemented") @@ -146,16 +147,16 @@ def load( :param path: The path to load the index from. :type path: str """ - # self.index = dap.DynamicMemoryIndex.from_file( - # index_directory=path, - # max_vectors=max_vectors, - # complexity=complexity, - # graph_degree=graph_degree, - # num_threads=num_threads, - # ) + self.index = dap.DynamicMemoryIndex.from_file( + index_directory=path, + max_vectors=max_vectors, + complexity=complexity, + graph_degree=graph_degree, + num_threads=num_threads, + ) - # TODO: save/load index - raise RuntimeError("DiskANNDynamic.save() not implemented") + # # TODO: save/load index + # raise RuntimeError("DiskANNDynamic.save() not implemented") def add(self, vectors: torch.Tensor, ids: torch.Tensor, num_threads: int = 0): """ From 9f08841dc97f1c17876d3339499d046ec7085176 Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 15 May 2025 00:28:32 -0500 Subject: [PATCH 053/323] update multi-query experiment --- src/python/index_wrappers/diskann.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/src/python/index_wrappers/diskann.py b/src/python/index_wrappers/diskann.py index 21878287..7cef67fd 100644 --- a/src/python/index_wrappers/diskann.py +++ b/src/python/index_wrappers/diskann.py @@ -130,9 +130,6 @@ def save(self, path: str): Path(path).mkdir(exist_ok=True) self.index.save(path) - # TODO: save/load index - raise RuntimeError("DiskANNDynamic.save() not implemented") - def load( self, path: str, From c3aa79a61d35cc80d2ec5d6002f090cf23280b9d Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 15 May 2025 00:36:21 -0500 Subject: [PATCH 054/323] update multi-query experiment --- test/experiments/osdi2025/numa_single_query/run.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/test/experiments/osdi2025/numa_single_query/run.py b/test/experiments/osdi2025/numa_single_query/run.py index f1a0c8e8..f349983a 100644 --- a/test/experiments/osdi2025/numa_single_query/run.py +++ b/test/experiments/osdi2025/numa_single_query/run.py @@ -106,6 +106,9 @@ def run_experiment(cfg_path: str, output_dir: str): load_kwargs["num_workers"] = build_params["num_workers"] idx = IndexClass() idx.load(str(idx_file), **load_kwargs) + elif idx_type == "DiskANN": + idx = IndexClass() + idx.load(str(idx_file), **build_params) else: idx = IndexClass() idx.load(str(idx_file)) From 5a1a438b9a282abf9cc15a7095944267188e6553 Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 15 May 2025 00:37:38 -0500 Subject: [PATCH 055/323] update multi-query experiment --- test/experiments/osdi2025/numa_single_query/run.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/test/experiments/osdi2025/numa_single_query/run.py b/test/experiments/osdi2025/numa_single_query/run.py index f349983a..0807e083 100644 --- a/test/experiments/osdi2025/numa_single_query/run.py +++ b/test/experiments/osdi2025/numa_single_query/run.py @@ -108,6 +108,8 @@ def run_experiment(cfg_path: str, output_dir: str): idx.load(str(idx_file), **load_kwargs) elif idx_type == "DiskANN": idx = IndexClass() + # remove metric from build_params + build_params.pop("metric", None) idx.load(str(idx_file), **build_params) else: idx = IndexClass() From 6ef5ac9aa3049e5dcf867bb69e01949d18801807 Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 15 May 2025 00:38:28 -0500 Subject: [PATCH 056/323] update multi-query experiment --- test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml b/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml index 18412dd6..46ee1db9 100644 --- a/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml +++ b/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml @@ -107,6 +107,7 @@ indexes: index: SVS build_params: graph_max_degree: 64 + alpha: 1.1 window_size: 128 metric: l2 search_params: From f6445e705f3b75af52a45e6708b72ad9243f6344 Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 15 May 2025 00:44:16 -0500 Subject: [PATCH 057/323] update multi-query experiment --- .../osdi2025/numa_single_query/run.py | 99 ++++++++++++++++--- 1 file changed, 83 insertions(+), 16 deletions(-) diff --git a/test/experiments/osdi2025/numa_single_query/run.py b/test/experiments/osdi2025/numa_single_query/run.py index 0807e083..96c720ec 100644 --- a/test/experiments/osdi2025/numa_single_query/run.py +++ b/test/experiments/osdi2025/numa_single_query/run.py @@ -1,10 +1,11 @@ #!/usr/bin/env python3 """ -NUMA Single-Query Latency Benchmark +NUMA Single-Query Latency and Recall Benchmark -Benchmarks a suite of approximate nearest neighbor (ANN) indexes (Quake, Faiss-IVF, SCANN, HNSW, DiskANN, SVS, etc.) -for single-query latency under configurable build and search parameters. Results are cached and only recomputed if -the 'overwrite' flag is set in the configuration. Outputs a unified CSV and a latency plot per index. +Benchmarks a suite of ANN indexes (Quake, Faiss-IVF, SCANN, HNSW, DiskANN, SVS, etc.) +for single-query latency and recall@k under configurable build and search parameters. +Results are cached and only recomputed if the 'overwrite' flag is set in the configuration. +Outputs a unified CSV and a latency plot per index. Usage: python numa_latency_experiment.py numa_latency_experiment.yaml output_dir @@ -44,6 +45,32 @@ "SVS": Vamana, } +def extract_indices(res): + """Helper to extract predicted indices from result.""" + if hasattr(res, "I"): + return res.I + if hasattr(res, "indices"): + return res.indices + raise RuntimeError("Cannot extract predicted indices from search result.") + +def compute_recall_at_k(pred, gt, k): + """ + pred: [num_queries, k] predicted indices + gt: [num_queries, ...] ground truth indices + Returns recall@k + """ + # Faiss GT may be shape [num_queries, k'] or [num_queries] + if gt.ndim == 1: + gt = gt[:, None] + matches = 0 + num = pred.shape[0] + for i in range(num): + pred_set = set(pred[i]) + gt_set = set(gt[i]) + matches += len(pred_set & gt_set) + recall = matches / (num * min(k, gt.shape[1])) + return recall + def build_and_save_index(index_class, build_params, base_vecs, index_file): idx = index_class() params = dict(build_params) @@ -65,8 +92,9 @@ def run_experiment(cfg_path: str, output_dir: str): out_dir.mkdir(parents=True, exist_ok=True) print(f"[INFO] Loading dataset '{ds['name']}'...") - base_vecs, queries, _ = load_dataset(ds["name"]) + base_vecs, queries, gt = load_dataset(ds["name"]) queries = queries[:queries_n] + gt = gt[:queries_n] if gt is not None else None all_rows = [] @@ -85,7 +113,6 @@ def run_experiment(cfg_path: str, output_dir: str): if idx_csv.exists() and not overwrite: print(f"[SKIP] {idx_name}: Results exist. Use overwrite: true to rerun.") - # For plotting, load mean/std for this index df_idx = pd.read_csv(idx_csv) all_rows.append(df_idx.iloc[0].to_dict()) continue @@ -97,7 +124,6 @@ def run_experiment(cfg_path: str, output_dir: str): print(f"[RUN] {idx_name} ...") - # Prepare load arguments per index type if idx_type == "Quake": load_kwargs = {} if "use_numa" in build_params: @@ -115,7 +141,6 @@ def run_experiment(cfg_path: str, output_dir: str): idx = IndexClass() idx.load(str(idx_file)) - # Dispatch search argument keys by index type if idx_type == "SCANN": search_args = {"leaves_to_search": search_params.get("leaves_to_search", 100)} elif idx_type == "HNSW": @@ -132,16 +157,16 @@ def run_experiment(cfg_path: str, output_dir: str): q = queries[i].unsqueeze(0).float() idx.search(q, k, **search_args) - # Benchmark Trials + # Benchmark Trials (and collect recall for first trial) trial_means = [] + trial_recalls = [] for t in range(trials): lats = [] - for q_vec in queries: + if t == 0 and gt is not None: + all_preds = [] + for qi, q_vec in enumerate(queries): q = q_vec.unsqueeze(0).float() - res = idx.search( - q, k, - **search_args - ) + res = idx.search(q, k, **search_args) ti = getattr(res, "timing_info", None) if ti and hasattr(ti, "total_time_ns"): lats.append(ti.total_time_ns / 1e6) @@ -149,18 +174,38 @@ def run_experiment(cfg_path: str, output_dir: str): lats.append(res.latency_ms) else: raise RuntimeError(f"No timing info found in search result for {idx_name}") + # Collect predicted indices for recall in trial 0 + if t == 0 and gt is not None: + try: + pred = extract_indices(res) + if isinstance(pred, torch.Tensor): + pred = pred.cpu().numpy() + elif not isinstance(pred, np.ndarray): + pred = np.asarray(pred) + all_preds.append(pred[0] if pred.ndim > 1 else pred) + except Exception: + # Defensive fallback: don't break script if recall fails + all_preds.append(np.full((k,), -1, dtype=np.int64)) mean_t = float(np.mean(lats)) trial_means.append(mean_t) - print(f" [trial {t+1}/{trials}] {mean_t:.2f} ms") + if t == 0 and gt is not None: + preds_arr = np.stack(all_preds, axis=0) + recall = compute_recall_at_k(preds_arr, gt, k) + trial_recalls.append(recall) + print(f" [trial {t+1}/{trials}] {mean_t:.2f} ms | recall@{k}: {recall:.4f}") + else: + print(f" [trial {t+1}/{trials}] {mean_t:.2f} ms") mean_lat = float(np.mean(trial_means)) std_lat = float(np.std(trial_means)) + mean_recall = float(trial_recalls[0]) if trial_recalls else np.nan n_workers_val = build_params.get("num_workers", None) row = { "index": idx_name, "n_workers": n_workers_val, "mean_latency_ms": mean_lat, "std_latency_ms": std_lat, + f"recall_at_{k}": mean_recall, } all_rows.append(row) pd.DataFrame([row]).to_csv(idx_csv, index=False) @@ -171,7 +216,7 @@ def run_experiment(cfg_path: str, output_dir: str): df.to_csv(out_csv, index=False) print(f"\n[RESULT] Results written to {out_csv}") - # Plot + # Plot Latency plt.figure() for idx_name in df["index"].unique(): subset = df[df["index"] == idx_name] @@ -193,3 +238,25 @@ def run_experiment(cfg_path: str, output_dir: str): plt.tight_layout() plt.savefig(plot_file) print(f"[PLOT] Saved to {plot_file}") + + # Plot Recall + if f"recall_at_{k}" in df.columns: + plt.figure() + for idx_name in df["index"].unique(): + subset = df[df["index"] == idx_name] + label = f"{idx_name}" if subset["n_workers"].isnull().all() else f"{idx_name} (n_workers={subset['n_workers'].iloc[0]})" + plt.plot( + subset["n_workers"] if "n_workers" in subset else subset.index, + subset[f"recall_at_{k}"], + marker="o", + label=label + ) + plt.xscale("symlog", base=2) + plt.xlabel("Num Workers") + plt.ylabel(f"Recall@{k}") + plt.title(f"Recall@{k} (per-index)") + plt.legend() + plot_file = out_dir / f"{out_csv.stem}_recall.png" + plt.tight_layout() + plt.savefig(plot_file) + print(f"[PLOT] Recall plot saved to {plot_file}") From ab223d37106a67751afa5454d99525aa14dc549f Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 15 May 2025 01:02:18 -0500 Subject: [PATCH 058/323] update multi-query experiment --- .../experiments/osdi2025/numa_single_query/run.py | 15 ++++----------- 1 file changed, 4 insertions(+), 11 deletions(-) diff --git a/test/experiments/osdi2025/numa_single_query/run.py b/test/experiments/osdi2025/numa_single_query/run.py index 96c720ec..1375c627 100644 --- a/test/experiments/osdi2025/numa_single_query/run.py +++ b/test/experiments/osdi2025/numa_single_query/run.py @@ -176,20 +176,13 @@ def run_experiment(cfg_path: str, output_dir: str): raise RuntimeError(f"No timing info found in search result for {idx_name}") # Collect predicted indices for recall in trial 0 if t == 0 and gt is not None: - try: - pred = extract_indices(res) - if isinstance(pred, torch.Tensor): - pred = pred.cpu().numpy() - elif not isinstance(pred, np.ndarray): - pred = np.asarray(pred) - all_preds.append(pred[0] if pred.ndim > 1 else pred) - except Exception: - # Defensive fallback: don't break script if recall fails - all_preds.append(np.full((k,), -1, dtype=np.int64)) + pred = extract_indices(res) # [1, k] + all_preds.append(pred[0]) # get the [k] mean_t = float(np.mean(lats)) trial_means.append(mean_t) if t == 0 and gt is not None: - preds_arr = np.stack(all_preds, axis=0) + preds_arr = np.stack(all_preds, axis=0) # [num_queries, k] + print(f"[DEBUG] preds_arr shape: {preds_arr.shape}, gt shape: {gt.shape}") recall = compute_recall_at_k(preds_arr, gt, k) trial_recalls.append(recall) print(f" [trial {t+1}/{trials}] {mean_t:.2f} ms | recall@{k}: {recall:.4f}") From 1dfce55605987e22bef4e66d735657ec26bd22cb Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 15 May 2025 01:02:59 -0500 Subject: [PATCH 059/323] update multi-query experiment --- test/experiments/osdi2025/numa_single_query/run.py | 10 +--------- 1 file changed, 1 insertion(+), 9 deletions(-) diff --git a/test/experiments/osdi2025/numa_single_query/run.py b/test/experiments/osdi2025/numa_single_query/run.py index 1375c627..c82fb12a 100644 --- a/test/experiments/osdi2025/numa_single_query/run.py +++ b/test/experiments/osdi2025/numa_single_query/run.py @@ -45,14 +45,6 @@ "SVS": Vamana, } -def extract_indices(res): - """Helper to extract predicted indices from result.""" - if hasattr(res, "I"): - return res.I - if hasattr(res, "indices"): - return res.indices - raise RuntimeError("Cannot extract predicted indices from search result.") - def compute_recall_at_k(pred, gt, k): """ pred: [num_queries, k] predicted indices @@ -176,7 +168,7 @@ def run_experiment(cfg_path: str, output_dir: str): raise RuntimeError(f"No timing info found in search result for {idx_name}") # Collect predicted indices for recall in trial 0 if t == 0 and gt is not None: - pred = extract_indices(res) # [1, k] + pred = res.ids # [1, k] all_preds.append(pred[0]) # get the [k] mean_t = float(np.mean(lats)) trial_means.append(mean_t) From eac904e702cbfad04c6018724b2aac325ae71acf Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 15 May 2025 01:03:52 -0500 Subject: [PATCH 060/323] update multi-query experiment --- test/experiments/osdi2025/numa_single_query/run.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/test/experiments/osdi2025/numa_single_query/run.py b/test/experiments/osdi2025/numa_single_query/run.py index c82fb12a..4b17105f 100644 --- a/test/experiments/osdi2025/numa_single_query/run.py +++ b/test/experiments/osdi2025/numa_single_query/run.py @@ -52,6 +52,8 @@ def compute_recall_at_k(pred, gt, k): Returns recall@k """ # Faiss GT may be shape [num_queries, k'] or [num_queries] + print(pred) + print(gt) if gt.ndim == 1: gt = gt[:, None] matches = 0 From 1255f0ab137cbb311cfc3b7349a3ea172e487066 Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 15 May 2025 01:05:01 -0500 Subject: [PATCH 061/323] update multi-query experiment --- test/experiments/osdi2025/numa_single_query/run.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/experiments/osdi2025/numa_single_query/run.py b/test/experiments/osdi2025/numa_single_query/run.py index 4b17105f..ce4a9b7a 100644 --- a/test/experiments/osdi2025/numa_single_query/run.py +++ b/test/experiments/osdi2025/numa_single_query/run.py @@ -170,7 +170,7 @@ def run_experiment(cfg_path: str, output_dir: str): raise RuntimeError(f"No timing info found in search result for {idx_name}") # Collect predicted indices for recall in trial 0 if t == 0 and gt is not None: - pred = res.ids # [1, k] + pred = res.ids.numpy() # [1, k] all_preds.append(pred[0]) # get the [k] mean_t = float(np.mean(lats)) trial_means.append(mean_t) From 7b42c66fc4c8940a3419c5552d143d81810c382a Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 15 May 2025 01:05:36 -0500 Subject: [PATCH 062/323] update multi-query experiment --- test/experiments/osdi2025/numa_single_query/run.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/experiments/osdi2025/numa_single_query/run.py b/test/experiments/osdi2025/numa_single_query/run.py index ce4a9b7a..61acf310 100644 --- a/test/experiments/osdi2025/numa_single_query/run.py +++ b/test/experiments/osdi2025/numa_single_query/run.py @@ -177,7 +177,7 @@ def run_experiment(cfg_path: str, output_dir: str): if t == 0 and gt is not None: preds_arr = np.stack(all_preds, axis=0) # [num_queries, k] print(f"[DEBUG] preds_arr shape: {preds_arr.shape}, gt shape: {gt.shape}") - recall = compute_recall_at_k(preds_arr, gt, k) + recall = compute_recall_at_k(preds_arr, gt.numpy(), k) trial_recalls.append(recall) print(f" [trial {t+1}/{trials}] {mean_t:.2f} ms | recall@{k}: {recall:.4f}") else: From 1cc8d522e6be78dbaf1a08b98d27b5f62d1501b7 Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 15 May 2025 01:06:12 -0500 Subject: [PATCH 063/323] update multi-query experiment --- test/experiments/osdi2025/numa_single_query/run.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/test/experiments/osdi2025/numa_single_query/run.py b/test/experiments/osdi2025/numa_single_query/run.py index 61acf310..d97ddbdf 100644 --- a/test/experiments/osdi2025/numa_single_query/run.py +++ b/test/experiments/osdi2025/numa_single_query/run.py @@ -51,9 +51,6 @@ def compute_recall_at_k(pred, gt, k): gt: [num_queries, ...] ground truth indices Returns recall@k """ - # Faiss GT may be shape [num_queries, k'] or [num_queries] - print(pred) - print(gt) if gt.ndim == 1: gt = gt[:, None] matches = 0 From d70bd2543f7c6923e79d7ebf1f2aed1e5b865620 Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 15 May 2025 01:07:40 -0500 Subject: [PATCH 064/323] update multi-query experiment --- .../numa_single_query/configs/sift1m.yaml | 2 +- .../osdi2025/numa_single_query/run.py | 24 ++++--------------- 2 files changed, 5 insertions(+), 21 deletions(-) diff --git a/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml b/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml index 46ee1db9..589ccb80 100644 --- a/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml +++ b/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml @@ -2,7 +2,7 @@ dataset: name: sift1m num_queries: 100 - k: 10 + k: 100 trials: 5 warmup: 10 diff --git a/test/experiments/osdi2025/numa_single_query/run.py b/test/experiments/osdi2025/numa_single_query/run.py index d97ddbdf..2c59f663 100644 --- a/test/experiments/osdi2025/numa_single_query/run.py +++ b/test/experiments/osdi2025/numa_single_query/run.py @@ -17,6 +17,7 @@ import pandas as pd import matplotlib.pyplot as plt from pathlib import Path +from quake.utils import compute_recall from quake.datasets.ann_datasets import load_dataset from quake.index_wrappers.faiss_ivf import FaissIVF @@ -45,23 +46,6 @@ "SVS": Vamana, } -def compute_recall_at_k(pred, gt, k): - """ - pred: [num_queries, k] predicted indices - gt: [num_queries, ...] ground truth indices - Returns recall@k - """ - if gt.ndim == 1: - gt = gt[:, None] - matches = 0 - num = pred.shape[0] - for i in range(num): - pred_set = set(pred[i]) - gt_set = set(gt[i]) - matches += len(pred_set & gt_set) - recall = matches / (num * min(k, gt.shape[1])) - return recall - def build_and_save_index(index_class, build_params, base_vecs, index_file): idx = index_class() params = dict(build_params) @@ -167,14 +151,14 @@ def run_experiment(cfg_path: str, output_dir: str): raise RuntimeError(f"No timing info found in search result for {idx_name}") # Collect predicted indices for recall in trial 0 if t == 0 and gt is not None: - pred = res.ids.numpy() # [1, k] + pred = res.ids # [1, k] all_preds.append(pred[0]) # get the [k] mean_t = float(np.mean(lats)) trial_means.append(mean_t) if t == 0 and gt is not None: - preds_arr = np.stack(all_preds, axis=0) # [num_queries, k] + preds_arr = torch.stack(all_preds) # [num_queries, k] print(f"[DEBUG] preds_arr shape: {preds_arr.shape}, gt shape: {gt.shape}") - recall = compute_recall_at_k(preds_arr, gt.numpy(), k) + recall = compute_recall(preds_arr, gt, k) trial_recalls.append(recall) print(f" [trial {t+1}/{trials}] {mean_t:.2f} ms | recall@{k}: {recall:.4f}") else: From bfd604a9a4660dbcede95cdf15478060937ec33b Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 15 May 2025 01:09:28 -0500 Subject: [PATCH 065/323] update multi-query experiment --- test/experiments/osdi2025/numa_single_query/run.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/experiments/osdi2025/numa_single_query/run.py b/test/experiments/osdi2025/numa_single_query/run.py index 2c59f663..95dea09d 100644 --- a/test/experiments/osdi2025/numa_single_query/run.py +++ b/test/experiments/osdi2025/numa_single_query/run.py @@ -158,7 +158,7 @@ def run_experiment(cfg_path: str, output_dir: str): if t == 0 and gt is not None: preds_arr = torch.stack(all_preds) # [num_queries, k] print(f"[DEBUG] preds_arr shape: {preds_arr.shape}, gt shape: {gt.shape}") - recall = compute_recall(preds_arr, gt, k) + recall = float(compute_recall(preds_arr, gt, k).mean()) trial_recalls.append(recall) print(f" [trial {t+1}/{trials}] {mean_t:.2f} ms | recall@{k}: {recall:.4f}") else: From 6cbf125b48dc488d318ef572ad6e7e951086284c Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 15 May 2025 01:22:08 -0500 Subject: [PATCH 066/323] update multi-query experiment --- src/python/index_wrappers/diskann.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/src/python/index_wrappers/diskann.py b/src/python/index_wrappers/diskann.py index 7cef67fd..3f268f98 100644 --- a/src/python/index_wrappers/diskann.py +++ b/src/python/index_wrappers/diskann.py @@ -102,9 +102,15 @@ def search( timing_info = SearchTimingInfo() start = time.time() - indices, distances = self.index.batch_search( - query, k_neighbors=k, complexity=complexity, num_threads=num_threads - ) + if query.shape[0] == 1: + indices, distances = self.index.search( + query, k_neighbors=k, complexity=complexity, num_threads=num_threads + ) + else: + # batch search + indices, distances = self.index.batch_search( + query, k_neighbors=k, complexity=complexity, num_threads=num_threads + ) end = time.time() timing_info.total_time_ns = int((end - start) * 1e9) indices = to_torch(indices.astype(np.int64)) From ec1ddbff3c53b63b8be6f63e37836ed73f5b2ef4 Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 15 May 2025 01:24:08 -0500 Subject: [PATCH 067/323] update multi-query experiment --- src/python/index_wrappers/diskann.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/src/python/index_wrappers/diskann.py b/src/python/index_wrappers/diskann.py index 3f268f98..0c58792c 100644 --- a/src/python/index_wrappers/diskann.py +++ b/src/python/index_wrappers/diskann.py @@ -104,8 +104,7 @@ def search( start = time.time() if query.shape[0] == 1: indices, distances = self.index.search( - query, k_neighbors=k, complexity=complexity, num_threads=num_threads - ) + query, k_neighbors=k, complexity=complexity) else: # batch search indices, distances = self.index.batch_search( From 1621a093986e2d1121f9840f9106e11302974f7d Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 15 May 2025 01:26:38 -0500 Subject: [PATCH 068/323] update multi-query experiment --- src/python/index_wrappers/diskann.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/python/index_wrappers/diskann.py b/src/python/index_wrappers/diskann.py index 0c58792c..340e6e86 100644 --- a/src/python/index_wrappers/diskann.py +++ b/src/python/index_wrappers/diskann.py @@ -104,7 +104,9 @@ def search( start = time.time() if query.shape[0] == 1: indices, distances = self.index.search( - query, k_neighbors=k, complexity=complexity) + query.flatten(), k_neighbors=k, complexity=complexity) + indices = indices.reshape(1, -1) + distances = distances.reshape(1, -1) else: # batch search indices, distances = self.index.batch_search( From b49d517e93956d8a960b5d2e95b81ceed99c2d9d Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 15 May 2025 02:08:09 -0500 Subject: [PATCH 069/323] update multi-query experiment --- .../numa_multi_query/configs/sift1m.yaml | 127 +++++- .../osdi2025/numa_multi_query/run.py | 384 ++++++++++-------- 2 files changed, 335 insertions(+), 176 deletions(-) diff --git a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml index b6b5b768..067cfd52 100644 --- a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml +++ b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml @@ -1,18 +1,123 @@ -# existing keys... +# numa_latency_experiment.yaml dataset: name: sift1m num_queries: 10000 - k: 10 - nprobe: 10 + k: 100 -#n_workers: [0,1,2,4,8,16] -#batch_sizes: [1, 10, 100, 100] -n_workers: [1, 2, 4, 8, 16] -batch_sizes: [1, 10, 100, 1000, 10000] +trials: 3 +warmup: 1 +overwrite: false # Set to true to force all indexes to rerun -# new keys: -trials: 5 -warmup: 10 +indexes: + - name: Quake_1 + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 1 + use_numa: false + search_params: + nprobe: 15 + batched_scan: true + + - name: Quake_4 + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 4 + use_numa: false + search_params: + nprobe: 15 + batched_scan: true + + - name: Quake_16 + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 16 + use_numa: false + search_params: + nprobe: 15 + batched_scan: true + + - name: Quake_1_numa + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 1 + use_numa: true + search_params: + nprobe: 15 + batched_scan: true + + - name: Quake_4_numa + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 4 + use_numa: true + search_params: + nprobe: 15 + batched_scan: true + + - name: Quake_16_numa + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 16 + use_numa: true + search_params: + nprobe: 15 + batched_scan: true + + - name: FaissIVF + index: IVF + build_params: + nc: 1000 + metric: l2 + search_params: + nprobe: 15 + + - name: SCANN + index: SCANN + build_params: + num_leaves: 1000 + metric: squared_l2 + search_params: + leaves_to_search: 15 + + - name: FaissHNSW + index: HNSW + build_params: + m: 64 + ef_construction: 128 + metric: l2 + search_params: + ef_search: 24 + + - name: DiskANN + index: DiskANN + build_params: + complexity: 128 + graph_degree: 64 + metric: l2 + search_params: + complexity: 16 + + - name: SVS + index: SVS + build_params: + graph_max_degree: 64 + alpha: 1.1 + window_size: 128 + metric: l2 + search_params: + search_window_size: 24 output: - results_csv: numa_multi_query_results.csv \ No newline at end of file + results_csv: numa_single_query_results.csv \ No newline at end of file diff --git a/test/experiments/osdi2025/numa_multi_query/run.py b/test/experiments/osdi2025/numa_multi_query/run.py index 0fd77d8b..e16c7ee5 100644 --- a/test/experiments/osdi2025/numa_multi_query/run.py +++ b/test/experiments/osdi2025/numa_multi_query/run.py @@ -1,186 +1,240 @@ #!/usr/bin/env python3 """ -NUMA multi‐query QPS experiment for Quake. +NUMA Batch-Query Latency and Recall Benchmark -Sweeps NUMA on/off, worker counts, batched_scan on/off, and batch sizes; -records mean/std QPS and produces a simplified batched_scan QPS plot. +Benchmarks a suite of ANN indexes (Quake, Faiss-IVF, SCANN, HNSW, DiskANN, SVS, etc.) +for batch-query latency and recall@k under configurable build and search parameters. +Results are cached and only recomputed if the 'overwrite' flag is set in the configuration. +Outputs a unified CSV and a latency plot per index. + +Usage: + python numa_latency_batch_experiment.py numa_latency_experiment.yaml output_dir """ import yaml -import time import numpy as np import torch import pandas as pd import matplotlib.pyplot as plt from pathlib import Path +from time import time +from quake.utils import compute_recall from quake.datasets.ann_datasets import load_dataset +from quake.index_wrappers.faiss_ivf import FaissIVF +from quake.index_wrappers.faiss_hnsw import FaissHNSW from quake.index_wrappers.quake import QuakeWrapper +try: + from quake.index_wrappers.scann import Scann +except ImportError: + raise ImportError("SCANN wrapper not available. Please install the required package.") +try: + from quake.index_wrappers.diskann import DiskANNDynamic +except ImportError: + raise ImportError("DiskANN wrapper not available. Please install the required package.") +try: + from quake.index_wrappers.vamana import Vamana +except ImportError: + raise ImportError("SVS wrapper not available. Please install the required package.") + +INDEX_CLASSES = { + "Quake": QuakeWrapper, + "IVF": FaissIVF, + "SCANN": Scann, + "HNSW": FaissHNSW, + "DiskANN": DiskANNDynamic, + "SVS": Vamana, +} + +def build_and_save_index(index_class, build_params, base_vecs, index_file): + idx = index_class() + params = dict(build_params) + idx.build(base_vecs, **params) + idx.save(str(index_file)) + +def extract_ids(res): + if hasattr(res, "ids"): + arr = res.ids + elif hasattr(res, "I"): + arr = res.I + elif hasattr(res, "indices"): + arr = res.indices + else: + raise RuntimeError("Cannot extract predicted indices from search result.") + if isinstance(arr, torch.Tensor): + arr = arr.cpu().numpy() + elif not isinstance(arr, np.ndarray): + arr = np.asarray(arr) + return arr + def run_experiment(cfg_path: str, output_dir: str): - cfg = yaml.safe_load(Path(cfg_path).read_text()) - out = Path(output_dir) - out.mkdir(exist_ok=True, parents=True) - csv_name = cfg.get("output", {}).get("results_csv", "results.csv") - out_csv = out / csv_name - - # check if the results already exist - if not Path(out_csv).exists(): - - # Load config - ds = cfg["dataset"] - n_workers = cfg["n_workers"] - batched_opts= cfg.get("batch_options", [False, True]) - batch_sizes = cfg.get("batch_sizes", [ds["num_queries"]]) - num_q = ds["num_queries"] - k = ds["k"] - nprobe = ds["nprobe"] - trials = cfg.get("trials", 5) - warmup = cfg.get("warmup", 2) - - - # Load data - print(f"Loading {ds['name']} ({num_q} queries)…") - base_vecs, queries_data, _ = load_dataset(ds["name"]) - queries = ( - torch.from_numpy(queries_data[:num_q]).float() - if isinstance(queries_data, np.ndarray) - else queries_data[:num_q].float() - ) + cfg = yaml.safe_load(Path(cfg_path).read_text()) + ds = cfg["dataset"] + queries_n = ds["num_queries"] + k = ds["k"] + trials = cfg.get("trials", 5) + warmup = cfg.get("warmup", 10) + indexes_cfg = cfg["indexes"] + csv_name = cfg.get("output", {}).get("results_csv", "numa_results.csv") + overwrite = cfg.get("overwrite", False) + + out_dir = Path(output_dir) + out_dir.mkdir(parents=True, exist_ok=True) + + print(f"[INFO] Loading dataset '{ds['name']}'...") + base_vecs, queries, gt = load_dataset(ds["name"]) + queries = queries[:queries_n] + gt = gt[:queries_n] if gt is not None else None + + all_rows = [] + + for idx_cfg in indexes_cfg: + idx_name = idx_cfg["name"] + idx_type = idx_cfg["index"] + build_params = idx_cfg.get("build_params", {}) + search_params = idx_cfg.get("search_params", {}) + IndexClass = INDEX_CLASSES.get(idx_type) + if IndexClass is None: + print(f"[WARN] Unknown index type: {idx_type}. Skipping.") + continue + + idx_csv = out_dir / f"{idx_name}_results.csv" + idx_file = out_dir / f"{idx_name}_index.bin" + + if idx_csv.exists() and not overwrite: + print(f"[SKIP] {idx_name}: Results exist. Use overwrite: true to rerun.") + df_idx = pd.read_csv(idx_csv) + all_rows.append(df_idx.iloc[0].to_dict()) + continue - # Build index if needed - idx_file = out / "quake_index.bin" if not idx_file.exists(): - print("Building index…") - builder = QuakeWrapper() - vecs = ( - torch.from_numpy(base_vecs).float() - if isinstance(base_vecs, np.ndarray) - else base_vecs.float() - ) - nc = int(np.sqrt(vecs.shape[0])) - builder.build(vecs, nc=nc, metric="l2", num_workers=0) - builder.save(str(idx_file)) - print("Index saved.") - - records = [] - for use_numa in [False]: - for nw in n_workers: - for bs in batched_opts: - for batch_size in batch_sizes: - print(f"NUMA={use_numa}, workers={nw}, batched={bs}, batch={batch_size}") - idxm = QuakeWrapper() - idxm.load(str(idx_file), use_numa=use_numa, same_core=True, num_workers=nw) - n_threads = 16 if nw == 0 else 1 - - # Warmup - for _ in range(min(warmup,1)): - if bs: - for i in range(0, num_q, batch_size): - idxm.search(queries[i:i+batch_size], k, - batched_scan=bs, - nprobe=nprobe, - n_threads=n_threads) - else: - idxm.search(queries, k, - batched_scan=bs, - nprobe=nprobe, - n_threads=n_threads) - - # Trials → QPS - qps_vals = [] - for t in range(trials): - start = time.time() - if bs: - for i in range(0, num_q, batch_size): - idxm.search(queries[i:i+batch_size], k, - batched_scan=bs, - nprobe=nprobe, - n_threads=n_threads) - else: - idxm.search(queries, k, - batched_scan=bs, - nprobe=nprobe, - n_threads=n_threads) - elapsed = time.time() - start - qps_vals.append(num_q / elapsed) - print(f" Trial {t+1}/{trials}: {qps_vals[-1]:.1f} QPS") - - records.append({ - "numa_enabled": use_numa, - "n_worker": nw, - "batched_scan": bs, - "batch_size": batch_size, - "mean_qps": np.mean(qps_vals), - "std_qps": np.std(qps_vals), - }) - - # Save CSV - df = pd.DataFrame(records) - df.to_csv(out_csv, index=False) - print(f"Results → {out_csv}") - - df = pd.read_csv(out_csv) - - # Simplified batched_scan QPS plot - fig, ax = plt.subplots(1, 1, figsize=(12, 4)) - for bsz in sorted(df["batch_size"].unique()): - for bs in (False, True): - grp = df[(df["batch_size"] == bsz) & (df["batched_scan"] == bs)] - if grp.empty: - continue - linestyle = '-' if not bs else '--' - label = f"batch={bsz}" + ("" if not bs else " batched") - ax.plot( - grp["n_worker"], - grp["mean_qps"], - marker='o', - linestyle=linestyle, - label=label - ) - ax.set_xscale("symlog", base=2) - ax.set_yscale("log", base=2) - ax.set_xlabel("Workers") - ax.set_ylabel("QPS") - ax.legend(title="Config", fontsize="small") - - fig.suptitle("QPS vs. Workers (solid=regular, dashed=batched)") - fig.tight_layout(rect=[0, 0, 1, 0.92]) - plot_path = out / f"{out_csv.stem}_combined_qps.png" - fig.savefig(plot_path) - print(f"Combined plot saved to {plot_path}") - - # 7) QPS vs. batch size for 1 and 16 workers, solid=unbatched, dashed=batched - fig2, ax2 = plt.subplots(1, 1, figsize=(6, 4)) - - # choose which workers to show - workers = [1, 16] - # matplotlib will auto‑cycle colors for each worker - for w in workers: - for bs, ls in [(False, "-"), (True, "--")]: - sub = df[(df["n_worker"] == w) & (df["batched_scan"] == bs)] - if sub.empty: - continue - sub = sub.sort_values("batch_size") - label = f"{w} worker{'s' if w>1 else ''}" + (" (batched)" if bs else "") - ax2.errorbar( - sub["batch_size"], - sub["mean_qps"], - yerr=sub["std_qps"], + print(f"[BUILD] {idx_name} index...") + build_and_save_index(IndexClass, build_params, base_vecs, idx_file) + print(f"[SAVE] Index saved at {idx_file}") + + print(f"[RUN] {idx_name} ...") + + if idx_type == "Quake": + load_kwargs = {} + if "use_numa" in build_params: + load_kwargs["use_numa"] = build_params["use_numa"] + if "num_workers" in build_params: + load_kwargs["num_workers"] = build_params["num_workers"] + idx = IndexClass() + idx.load(str(idx_file), **load_kwargs) + elif idx_type == "DiskANN": + idx = IndexClass() + build_params.pop("metric", None) + idx.load(str(idx_file), **build_params) + else: + idx = IndexClass() + idx.load(str(idx_file)) + + if idx_type == "SCANN": + search_args = {"leaves_to_search": search_params.get("leaves_to_search", 100)} + elif idx_type == "HNSW": + search_args = {"ef_search": search_params.get("ef_search", 32)} + elif idx_type == "DiskANN": + search_args = {"complexity": search_params.get("complexity", 32)} + elif idx_type == "SVS": + search_args = {"search_window_size": search_params.get("search_window_size", 32)} + else: # IVF, Quake, others + search_args = {"nprobe": search_params.get("nprobe", 100)} + + # Warmup (single batch, no timing) + for i in range(min(warmup, 1)): + idx.search(queries, k, **search_args) + + # Benchmark Trials (latency and recall) + trial_means = [] + trial_recalls = [] + for t in range(trials): + t0 = time() + res = idx.search(queries, k, **search_args) + t1 = time() + mean_t = ((t1 - t0) * 1e3) / queries.shape[0] # ms per query + trial_means.append(mean_t) + + preds_arr = extract_ids(res) + if preds_arr.shape[0] != queries.shape[0]: + raise RuntimeError(f"Pred shape {preds_arr.shape} doesn't match queries {queries.shape}") + + if gt is not None: + recall = float(compute_recall(preds_arr, gt, k).mean()) + trial_recalls.append(recall) + print(f" [trial {t+1}/{trials}] {mean_t:.2f} ms | recall@{k}: {recall:.4f}") + else: + print(f" [trial {t+1}/{trials}] {mean_t:.2f} ms") + + mean_lat = float(np.mean(trial_means)) + std_lat = float(np.std(trial_means)) + mean_recall = float(np.mean(trial_recalls)) if trial_recalls else np.nan + n_workers_val = build_params.get("num_workers", None) + row = { + "index": idx_name, + "n_workers": n_workers_val, + "mean_latency_ms": mean_lat, + "std_latency_ms": std_lat, + f"recall_at_{k}": mean_recall, + } + all_rows.append(row) + pd.DataFrame([row]).to_csv(idx_csv, index=False) + + # Save Unified CSV + df = pd.DataFrame(all_rows) + out_csv = out_dir / csv_name + df.to_csv(out_csv, index=False) + print(f"\n[RESULT] Results written to {out_csv}") + + # Plot Latency + plt.figure() + for idx_name in df["index"].unique(): + subset = df[df["index"] == idx_name] + label = f"{idx_name}" if subset["n_workers"].isnull().all() else f"{idx_name} (n_workers={subset['n_workers'].iloc[0]})" + plt.errorbar( + subset["n_workers"] if "n_workers" in subset else subset.index, + subset["mean_latency_ms"], + yerr=subset["std_latency_ms"], + marker="o", + capsize=5, + label=label + ) + plt.xscale("symlog", base=2) + plt.xlabel("Num Workers") + plt.ylabel("Mean Latency (ms)") + plt.title("Batch Query Latency (per-index)") + plt.legend() + plot_file = out_dir / f"{out_csv.stem}_latency.png" + plt.tight_layout() + plt.savefig(plot_file) + print(f"[PLOT] Saved to {plot_file}") + + # Plot Recall + if f"recall_at_{k}" in df.columns: + plt.figure() + for idx_name in df["index"].unique(): + subset = df[df["index"] == idx_name] + label = f"{idx_name}" if subset["n_workers"].isnull().all() else f"{idx_name} (n_workers={subset['n_workers'].iloc[0]})" + plt.plot( + subset["n_workers"] if "n_workers" in subset else subset.index, + subset[f"recall_at_{k}"], marker="o", - linestyle=ls, - capsize=4, label=label ) - - ax2.set_xscale("symlog", base=2) - ax2.set_yscale("log", base=2) - ax2.set_xlabel("Batch Size") - ax2.set_ylabel("QPS") - ax2.set_title("QPS vs. Batch Size\n(solid=unbatched, dashed=batched)") - ax2.grid(True, which="both", ls=":") - ax2.legend(title="Configuration", fontsize="small", ncol=1) - fig2.tight_layout() - plot2_path = out / f"{out_csv.stem}_qps_vs_batchsize_full.png" - fig2.savefig(plot2_path) - print(f"Full batch‑size QPS plot saved to {plot2_path}") \ No newline at end of file + plt.xscale("symlog", base=2) + plt.xlabel("Num Workers") + plt.ylabel(f"Recall@{k}") + plt.title(f"Recall@{k} (per-index)") + plt.legend() + plot_file = out_dir / f"{out_csv.stem}_recall.png" + plt.tight_layout() + plt.savefig(plot_file) + print(f"[PLOT] Recall plot saved to {plot_file}") + +if __name__ == "__main__": + import sys + if len(sys.argv) != 3: + print(f"Usage: {sys.argv[0]} ") + sys.exit(1) + run_experiment(sys.argv[1], sys.argv[2]) \ No newline at end of file From a6d28199142c1a8221003d0611d594d4e1e178b9 Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 15 May 2025 02:46:32 -0500 Subject: [PATCH 070/323] update multi-query --- .../osdi2025/numa_multi_query/run.py | 84 ++++++++++--------- 1 file changed, 45 insertions(+), 39 deletions(-) diff --git a/test/experiments/osdi2025/numa_multi_query/run.py b/test/experiments/osdi2025/numa_multi_query/run.py index e16c7ee5..e9d7a735 100644 --- a/test/experiments/osdi2025/numa_multi_query/run.py +++ b/test/experiments/osdi2025/numa_multi_query/run.py @@ -25,26 +25,26 @@ from quake.index_wrappers.faiss_hnsw import FaissHNSW from quake.index_wrappers.quake import QuakeWrapper -try: - from quake.index_wrappers.scann import Scann -except ImportError: - raise ImportError("SCANN wrapper not available. Please install the required package.") -try: - from quake.index_wrappers.diskann import DiskANNDynamic -except ImportError: - raise ImportError("DiskANN wrapper not available. Please install the required package.") -try: - from quake.index_wrappers.vamana import Vamana -except ImportError: - raise ImportError("SVS wrapper not available. Please install the required package.") +# try: +# from quake.index_wrappers.scann import Scann +# except ImportError: +# raise ImportError("SCANN wrapper not available. Please install the required package.") +# try: +# from quake.index_wrappers.diskann import DiskANNDynamic +# except ImportError: +# raise ImportError("DiskANN wrapper not available. Please install the required package.") +# try: +# from quake.index_wrappers.vamana import Vamana +# except ImportError: +# raise ImportError("SVS wrapper not available. Please install the required package.") INDEX_CLASSES = { "Quake": QuakeWrapper, "IVF": FaissIVF, - "SCANN": Scann, + # "SCANN": Scann, "HNSW": FaissHNSW, - "DiskANN": DiskANNDynamic, - "SVS": Vamana, + # "DiskANN": DiskANNDynamic, + # "SVS": Vamana, } def build_and_save_index(index_class, build_params, base_vecs, index_file): @@ -87,12 +87,29 @@ def run_experiment(cfg_path: str, output_dir: str): queries = queries[:queries_n] gt = gt[:queries_n] if gt is not None else None + # --- Build the Quake base index ONCE --- + quake_base_params = None + quake_base_index_file = out_dir / "Quake_base_index.bin" + # Find the build params for the first Quake config for base index + for idx_cfg in indexes_cfg: + if idx_cfg["index"] == "Quake": + quake_base_params = dict(idx_cfg.get("build_params", {})) + # Remove any runtime/load-only params + quake_base_params.pop("num_workers", None) + quake_base_params.pop("use_numa", None) + break + if quake_base_params is not None: + if not quake_base_index_file.exists(): + print(f"[BUILD] Quake (shared) base index ...") + build_and_save_index(QuakeWrapper, quake_base_params, base_vecs, quake_base_index_file) + print(f"[SAVE] Quake base index saved at {quake_base_index_file}") + all_rows = [] for idx_cfg in indexes_cfg: idx_name = idx_cfg["name"] idx_type = idx_cfg["index"] - build_params = idx_cfg.get("build_params", {}) + build_params = dict(idx_cfg.get("build_params", {})) search_params = idx_cfg.get("search_params", {}) IndexClass = INDEX_CLASSES.get(idx_type) if IndexClass is None: @@ -100,7 +117,11 @@ def run_experiment(cfg_path: str, output_dir: str): continue idx_csv = out_dir / f"{idx_name}_results.csv" - idx_file = out_dir / f"{idx_name}_index.bin" + # Quake uses the shared base index; others get their own file + if idx_type == "Quake": + idx_file = quake_base_index_file + else: + idx_file = out_dir / f"{idx_name}_index.bin" if idx_csv.exists() and not overwrite: print(f"[SKIP] {idx_name}: Results exist. Use overwrite: true to rerun.") @@ -108,7 +129,7 @@ def run_experiment(cfg_path: str, output_dir: str): all_rows.append(df_idx.iloc[0].to_dict()) continue - if not idx_file.exists(): + if idx_type != "Quake" and not idx_file.exists(): print(f"[BUILD] {idx_name} index...") build_and_save_index(IndexClass, build_params, base_vecs, idx_file) print(f"[SAVE] Index saved at {idx_file}") @@ -116,12 +137,13 @@ def run_experiment(cfg_path: str, output_dir: str): print(f"[RUN] {idx_name} ...") if idx_type == "Quake": + # Only use load-time params for Quake load_kwargs = {} if "use_numa" in build_params: load_kwargs["use_numa"] = build_params["use_numa"] if "num_workers" in build_params: load_kwargs["num_workers"] = build_params["num_workers"] - idx = IndexClass() + idx = QuakeWrapper() idx.load(str(idx_file), **load_kwargs) elif idx_type == "DiskANN": idx = IndexClass() @@ -131,27 +153,18 @@ def run_experiment(cfg_path: str, output_dir: str): idx = IndexClass() idx.load(str(idx_file)) - if idx_type == "SCANN": - search_args = {"leaves_to_search": search_params.get("leaves_to_search", 100)} - elif idx_type == "HNSW": - search_args = {"ef_search": search_params.get("ef_search", 32)} - elif idx_type == "DiskANN": - search_args = {"complexity": search_params.get("complexity", 32)} - elif idx_type == "SVS": - search_args = {"search_window_size": search_params.get("search_window_size", 32)} - else: # IVF, Quake, others - search_args = {"nprobe": search_params.get("nprobe", 100)} + print(search_params) # Warmup (single batch, no timing) for i in range(min(warmup, 1)): - idx.search(queries, k, **search_args) + idx.search(queries, k, **search_params) # Benchmark Trials (latency and recall) trial_means = [] trial_recalls = [] for t in range(trials): t0 = time() - res = idx.search(queries, k, **search_args) + res = idx.search(queries, k, **search_params) t1 = time() mean_t = ((t1 - t0) * 1e3) / queries.shape[0] # ms per query trial_means.append(mean_t) @@ -230,11 +243,4 @@ def run_experiment(cfg_path: str, output_dir: str): plot_file = out_dir / f"{out_csv.stem}_recall.png" plt.tight_layout() plt.savefig(plot_file) - print(f"[PLOT] Recall plot saved to {plot_file}") - -if __name__ == "__main__": - import sys - if len(sys.argv) != 3: - print(f"Usage: {sys.argv[0]} ") - sys.exit(1) - run_experiment(sys.argv[1], sys.argv[2]) \ No newline at end of file + print(f"[PLOT] Recall plot saved to {plot_file}") \ No newline at end of file From 1696247aaf42dd87a60216bfd9f67cb82453e3d4 Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 15 May 2025 02:47:08 -0500 Subject: [PATCH 071/323] update multi-query --- .../numa_multi_query/configs/sift1m.yaml | 130 ++++++++++-------- 1 file changed, 76 insertions(+), 54 deletions(-) diff --git a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml index 067cfd52..ecaeb901 100644 --- a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml +++ b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml @@ -2,13 +2,24 @@ dataset: name: sift1m num_queries: 10000 - k: 100 + k: 1 trials: 3 warmup: 1 -overwrite: false # Set to true to force all indexes to rerun +overwrite: true # Set to true to force all indexes to rerun indexes: + - name: Quake_0 + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 0 + use_numa: false + search_params: + nprobe: 20 + batched_scan: true + - name: Quake_1 index: Quake build_params: @@ -17,7 +28,7 @@ indexes: num_workers: 1 use_numa: false search_params: - nprobe: 15 + nprobe: 20 batched_scan: true - name: Quake_4 @@ -28,7 +39,7 @@ indexes: num_workers: 4 use_numa: false search_params: - nprobe: 15 + nprobe: 20 batched_scan: true - name: Quake_16 @@ -39,9 +50,20 @@ indexes: num_workers: 16 use_numa: false search_params: - nprobe: 15 + nprobe: 20 batched_scan: true + - name: Quake_0_numa + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 0 + use_numa: true + search_params: + nprobe: 20 + batched_scan: false + - name: Quake_1_numa index: Quake build_params: @@ -50,8 +72,8 @@ indexes: num_workers: 1 use_numa: true search_params: - nprobe: 15 - batched_scan: true + nprobe: 20 + batched_scan: false - name: Quake_4_numa index: Quake @@ -61,8 +83,8 @@ indexes: num_workers: 4 use_numa: true search_params: - nprobe: 15 - batched_scan: true + nprobe: 20 + batched_scan: false - name: Quake_16_numa index: Quake @@ -72,52 +94,52 @@ indexes: num_workers: 16 use_numa: true search_params: - nprobe: 15 - batched_scan: true + nprobe: 20 + batched_scan: false - - name: FaissIVF - index: IVF - build_params: - nc: 1000 - metric: l2 - search_params: - nprobe: 15 - - - name: SCANN - index: SCANN - build_params: - num_leaves: 1000 - metric: squared_l2 - search_params: - leaves_to_search: 15 - - - name: FaissHNSW - index: HNSW - build_params: - m: 64 - ef_construction: 128 - metric: l2 - search_params: - ef_search: 24 - - - name: DiskANN - index: DiskANN - build_params: - complexity: 128 - graph_degree: 64 - metric: l2 - search_params: - complexity: 16 - - - name: SVS - index: SVS - build_params: - graph_max_degree: 64 - alpha: 1.1 - window_size: 128 - metric: l2 - search_params: - search_window_size: 24 +# - name: FaissIVF +# index: IVF +# build_params: +# nc: 1000 +# metric: l2 +# search_params: +# nprobe: 15 +# +# - name: SCANN +# index: SCANN +# build_params: +# num_leaves: 1000 +# metric: squared_l2 +# search_params: +# leaves_to_search: 15 +# +# - name: FaissHNSW +# index: HNSW +# build_params: +# m: 64 +# ef_construction: 128 +# metric: l2 +# search_params: +# ef_search: 24 +# +# - name: DiskANN +# index: DiskANN +# build_params: +# complexity: 128 +# graph_degree: 64 +# metric: l2 +# search_params: +# complexity: 16 +# +# - name: SVS +# index: SVS +# build_params: +# graph_max_degree: 64 +# alpha: 1.1 +# window_size: 128 +# metric: l2 +# search_params: +# search_window_size: 24 output: results_csv: numa_single_query_results.csv \ No newline at end of file From 65ae621fa3a6e3ed3ca1a0f1f1d508e66906c7e5 Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 15 May 2025 03:06:43 -0500 Subject: [PATCH 072/323] update batched scan --- src/cpp/include/list_scanning.h | 139 +++++++++++++----- test/cpp/benchmark.cpp | 6 +- .../experiments/osdi2025/experiment_runner.py | 4 +- .../numa_multi_query/configs/sift1m.yaml | 2 +- 4 files changed, 107 insertions(+), 44 deletions(-) diff --git a/src/cpp/include/list_scanning.h b/src/cpp/include/list_scanning.h index bb7defe0..48c5475c 100644 --- a/src/cpp/include/list_scanning.h +++ b/src/cpp/include/list_scanning.h @@ -343,60 +343,123 @@ inline void scan_list(const float *query_vec, } } -inline void batched_scan_list(const float *query_vecs, - const float *list_vecs, - const int64_t *list_ids, - int num_queries, - int list_size, - int dim, - vector> &topk_buffers, - MetricType metric = faiss::METRIC_L2) { +inline void batched_scan_list( + const float *query_vecs, + const float *list_vecs, + const int64_t *list_ids, + int num_queries, + int list_size, + int dim, + vector> &topk_buffers, + MetricType metric = faiss::METRIC_L2) +{ if (list_size == 0 || list_vecs == nullptr) { - // No list vectors to process; return; } - // Ensure k does not exceed list_size - int k = topk_buffers[0]->k_; - int k_max = std::min(k, list_size); + // Wrap raw arrays in torch Tensors, no copy + torch::Tensor query = torch::from_blob((void*)query_vecs, {num_queries, dim}, torch::kFloat32); + torch::Tensor list = torch::from_blob((void*)list_vecs, {list_size, dim}, torch::kFloat32); - int64_t *labels = (int64_t *) malloc(num_queries * k_max * sizeof(int64_t)); - float *distances = (float *) malloc(num_queries * k_max * sizeof(float)); - - if (metric == faiss::METRIC_INNER_PRODUCT) { - faiss::float_minheap_array_t res = {size_t(num_queries), size_t(k_max), labels, distances}; - faiss::knn_inner_product(query_vecs, list_vecs, dim, num_queries, list_size, &res, nullptr); - } else if (metric == faiss::METRIC_L2) { - faiss::float_maxheap_array_t res = {size_t(num_queries), size_t(k_max), labels, distances}; - faiss::knn_L2sqr(query_vecs, list_vecs, dim, num_queries, list_size, &res, nullptr, nullptr); + torch::Tensor distances; + if (metric == faiss::METRIC_L2) { + // Returns [num_queries, list_size], each entry is the Euclidean distance + distances = torch::cdist(query, list, 2.0); + } else if (metric == faiss::METRIC_INNER_PRODUCT) { + // [num_queries, list_size], each entry is dot product + distances = torch::matmul(query, list.t()); } else { throw std::runtime_error("Metric type not supported"); } - // map the labels to the actual list_ids - if (list_ids != nullptr) { - for (int i = 0; i < num_queries; i++) { - for (int j = 0; j < k_max; j++) { - labels[i * k_max + j] = list_ids[labels[i * k_max + j]]; + // For each query, push all list vectors and their distances into TopkBuffer + for (int i = 0; i < num_queries; ++i) { + auto row = distances[i]; + std::vector dists(list_size); + std::vector ids(list_size); + + if (metric == faiss::METRIC_L2) { + // L2: real distances + auto row_acc = row.accessor(); + for (int j = 0; j < list_size; ++j) { + dists[j] = row_acc[j]; + } + } else { + // IP: negative to mimic Faiss's heap convention if your buffer expects minimal distance + auto row_acc = row.accessor(); + for (int j = 0; j < list_size; ++j) { + dists[j] = row_acc[j]; } } - } - // if the metric is l2, convert the distances to sqrt - if (metric == faiss::METRIC_L2) { - for (int i = 0; i < num_queries * k_max; i++) { - distances[i] = sqrt(distances[i]); + if (list_ids) { + for (int j = 0; j < list_size; ++j) { + ids[j] = list_ids[j]; + } + } else { + for (int j = 0; j < list_size; ++j) { + ids[j] = j; + } } - } - // add distances to the topk buffers - for (int i = 0; i < num_queries; i++) { - topk_buffers[i]->batch_add(distances + i * k_max, labels + i * k_max, k_max); + topk_buffers[i]->batch_add(dists.data(), ids.data(), list_size); } - - free(labels); - free(distances); } +//inline void batched_scan_list(const float *query_vecs, +// const float *list_vecs, +// const int64_t *list_ids, +// int num_queries, +// int list_size, +// int dim, +// vector> &topk_buffers, +// MetricType metric = faiss::METRIC_L2) { +// if (list_size == 0 || list_vecs == nullptr) { +// // No list vectors to process; +// return; +// } +// +// // Ensure k does not exceed list_size +// int k = topk_buffers[0]->k_; +// int k_max = std::min(k, list_size); +// +// int64_t *labels = (int64_t *) malloc(num_queries * k_max * sizeof(int64_t)); +// float *distances = (float *) malloc(num_queries * k_max * sizeof(float)); +// +// if (metric == faiss::METRIC_INNER_PRODUCT) { +// faiss::float_minheap_array_t res = {size_t(num_queries), size_t(k_max), labels, distances}; +// faiss::knn_inner_product(query_vecs, list_vecs, dim, num_queries, list_size, &res, nullptr); +// } else if (metric == faiss::METRIC_L2) { +// faiss::float_maxheap_array_t res = {size_t(num_queries), size_t(k_max), labels, distances}; +// faiss::knn_L2sqr(query_vecs, list_vecs, dim, num_queries, list_size, &res, nullptr, nullptr); +// } else { +// throw std::runtime_error("Metric type not supported"); +// } +// +// // map the labels to the actual list_ids +// if (list_ids != nullptr) { +// for (int i = 0; i < num_queries; i++) { +// for (int j = 0; j < k_max; j++) { +// labels[i * k_max + j] = list_ids[labels[i * k_max + j]]; +// } +// } +// } +// +// // if the metric is l2, convert the distances to sqrt +// if (metric == faiss::METRIC_L2) { +// for (int i = 0; i < num_queries * k_max; i++) { +// distances[i] = sqrt(distances[i]); +// } +// } +// +// // add distances to the topk buffers +// for (int i = 0; i < num_queries; i++) { +// topk_buffers[i]->batch_add(distances + i * k_max, labels + i * k_max, k_max); +// } +// +// free(labels); +// free(distances); +//} + // } #endif //LIST_SCANNING_H diff --git a/test/cpp/benchmark.cpp b/test/cpp/benchmark.cpp index 6e7774b5..5bb3646b 100644 --- a/test/cpp/benchmark.cpp +++ b/test/cpp/benchmark.cpp @@ -27,11 +27,11 @@ using torch::Tensor; // Global benchmark parameters static const int64_t DIM = 128; -static const int64_t NUM_VECTORS = 100000; // number of database vectors +static const int64_t NUM_VECTORS = 1000000; // number of database vectors static const int64_t N_LIST = 1000; // number of clusters for IVF static const int64_t NUM_QUERIES = 10000; // number of queries for search benchmark -static const int64_t K = 1000; // top-K neighbors -static const int64_t N_PROBE = 10; // number of probes for IVF +static const int64_t K = 100; // top-K neighbors +static const int64_t N_PROBE = 20; // number of probes for IVF static const int64_t N_WORKERS = 12; // number of workers for parallel query coordinator // Helper functions to generate random data and sequential IDs diff --git a/test/experiments/osdi2025/experiment_runner.py b/test/experiments/osdi2025/experiment_runner.py index b51b8725..b9142976 100644 --- a/test/experiments/osdi2025/experiment_runner.py +++ b/test/experiments/osdi2025/experiment_runner.py @@ -6,7 +6,7 @@ from pathlib import Path from test.experiments.osdi2025.kick_the_tires.run import run_experiment as run_kick_the_tires -from test.experiments.osdi2025.numa_single_query.run import run_experiment as run_numa_single +# from test.experiments.osdi2025.numa_single_query.run import run_experiment as run_numa_single from test.experiments.osdi2025.numa_multi_query.run import run_experiment as run_numa_multi from test.experiments.osdi2025.aps_recall_targets.run import run_experiment as run_aps_recall from test.experiments.osdi2025.early_termination.run import run_experiment as run_early_termination @@ -16,7 +16,7 @@ EXPERIMENTS = { "kick_the_tires": run_kick_the_tires, - "numa_single_query": run_numa_single, + # "numa_single_query": run_numa_single, "numa_multi_query": run_numa_multi, "aps_recall_targets": run_aps_recall, "early_termination": run_early_termination, diff --git a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml index ecaeb901..79a69316 100644 --- a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml +++ b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml @@ -2,7 +2,7 @@ dataset: name: sift1m num_queries: 10000 - k: 1 + k: 100 trials: 3 warmup: 1 From afc39969e98cc13fc6d0b50867366c17b8f08500 Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 15 May 2025 11:55:40 -0500 Subject: [PATCH 073/323] scan tweaks --- src/cpp/include/list_scanning.h | 20 ++++--------- src/cpp/src/query_coordinator.cpp | 50 +++++++++++++++++++++---------- 2 files changed, 40 insertions(+), 30 deletions(-) diff --git a/src/cpp/include/list_scanning.h b/src/cpp/include/list_scanning.h index 48c5475c..573f0b4e 100644 --- a/src/cpp/include/list_scanning.h +++ b/src/cpp/include/list_scanning.h @@ -358,8 +358,8 @@ inline void batched_scan_list( } // Wrap raw arrays in torch Tensors, no copy - torch::Tensor query = torch::from_blob((void*)query_vecs, {num_queries, dim}, torch::kFloat32); - torch::Tensor list = torch::from_blob((void*)list_vecs, {list_size, dim}, torch::kFloat32); + Tensor query = torch::from_blob((void*)query_vecs, {num_queries, dim}, torch::kFloat32); + Tensor list = torch::from_blob((void*)list_vecs, {list_size, dim}, torch::kFloat32); torch::Tensor distances; if (metric == faiss::METRIC_L2) { @@ -373,23 +373,13 @@ inline void batched_scan_list( } // For each query, push all list vectors and their distances into TopkBuffer + auto distances_acc = distances.accessor(); for (int i = 0; i < num_queries; ++i) { - auto row = distances[i]; std::vector dists(list_size); std::vector ids(list_size); - if (metric == faiss::METRIC_L2) { - // L2: real distances - auto row_acc = row.accessor(); - for (int j = 0; j < list_size; ++j) { - dists[j] = row_acc[j]; - } - } else { - // IP: negative to mimic Faiss's heap convention if your buffer expects minimal distance - auto row_acc = row.accessor(); - for (int j = 0; j < list_size; ++j) { - dists[j] = row_acc[j]; - } + for (int j = 0; j < list_size; ++j) { + dists[j] = distances_acc[i][j]; } if (list_ids) { diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index a3b3bc3b..dc5c628a 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -103,8 +103,6 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { std::cout << "[QueryCoordinator::partition_scan_worker_fn] Failed to set thread affinity on core " << core_index << std::endl; } - - while (true) { ScanJob job; @@ -197,11 +195,12 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { throw std::runtime_error("[partition_scan_worker_fn] Invalid batched job."); } - // Allocate a thread-local query buffer if needed. if (res.local_query_buffer.size() < partition_manager_->d() * sizeof(float) * job.num_queries) { res.local_query_buffer.resize(partition_manager_->d() * sizeof(float) * job.num_queries); } + auto s1 = std::chrono::high_resolution_clock::now(); + int64_t d = partition_manager_->d(); std::vector query_subset(job.num_queries * d); for (int i = 0; i < job.num_queries; i++) { @@ -210,7 +209,7 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { job.query_vector + global_q * d, d * sizeof(float)); } - // Then copy query_subset into your local buffer if needed: + if(memcpy(res.local_query_buffer.data(), query_subset.data(), query_subset.size() * sizeof(float)) == nullptr) { @@ -230,6 +229,8 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { } } + auto s2 = std::chrono::high_resolution_clock::now(); + // Process the batched job. batched_scan_list((float *) res.local_query_buffer.data(), partition_codes, @@ -240,18 +241,29 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { res.topk_buffer_pool, metric_); - vector> topk_list(job.num_queries); - vector> topk_indices_list(job.num_queries); - for (int64_t q = 0; q < job.num_queries; q++) { - topk_list[q] = res.topk_buffer_pool[q]->get_topk(); - topk_indices_list[q] = res.topk_buffer_pool[q]->get_topk_indices(); - } + auto s3 = std::chrono::high_resolution_clock::now(); for (int64_t q = 0; q < job.num_queries; q++) { int64_t global_q = job.query_ids[q]; - int n_results = topk_indices_list[q].size(); - global_topk_buffer_pool_[global_q]->batch_add(topk_list[q].data(), topk_indices_list[q].data(), n_results); + + vector topk = res.topk_buffer_pool[q]->get_topk(); + vector topk_indices = res.topk_buffer_pool[q]->get_topk_indices(); + int n_results = topk_indices.size(); + + global_topk_buffer_pool_[global_q]->batch_add(topk.data(), topk_indices.data(), n_results); } + + auto s4 = std::chrono::high_resolution_clock::now(); + + // print out timings for worker + auto wait_time = std::chrono::duration_cast(s2 - s1).count(); + auto process_time = std::chrono::duration_cast(s3 - s2).count(); + auto merge_time = std::chrono::duration_cast(s4 - s3).count(); + +// std::cout << "[QueryCoordinator::partition_scan_worker_fn] Worker " << core_index +// << " wait_time: " << wait_time / 1e6 << " ms, " +// << "process_time: " << process_time / 1e6 << " ms, " +// << "merge_time: " << merge_time / 1e6 << " ms" << std::endl; } auto job_process_end = std::chrono::high_resolution_clock::now(); job_process_time_ns += std::chrono::duration_cast(job_process_end - job_process_start).count(); @@ -414,7 +426,12 @@ shared_ptr QueryCoordinator::worker_scan( // check if all jobs have been processed bool all_done = true; for (int64_t q = 0; q < num_queries; q++) { - all_done = all_done && (global_topk_buffer_pool_[q]->jobs_left_ == 0); + if (global_topk_buffer_pool_[q]->jobs_left_ == 0) { + // flush the topk buffer (if needed) + global_topk_buffer_pool_[q]->flush(); + } else { + all_done = false; + } } if (all_done) { @@ -456,7 +473,7 @@ shared_ptr QueryCoordinator::worker_scan( } last_flush_time = high_resolution_clock::now(); } - std::this_thread::sleep_for(microseconds(1)); + std::this_thread::sleep_for(microseconds(5)); } end_time = high_resolution_clock::now(); timing_info->job_wait_time_ns = @@ -466,7 +483,10 @@ shared_ptr QueryCoordinator::worker_scan( start_time = high_resolution_clock::now(); auto topk_ids = torch::full({num_queries, k}, -1, torch::kInt64); auto topk_dists = torch::full({num_queries, k}, - std::numeric_limits::infinity(), torch::kFloat32); + (metric_ == faiss::METRIC_INNER_PRODUCT) + ? -std::numeric_limits::infinity() + : std::numeric_limits::infinity(), + torch::kFloat32); auto ids_accessor = topk_ids.accessor(); auto dists_accessor = topk_dists.accessor(); { From 2a86be1cc9e170445be4670cb00dea34624eba1c Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 15 May 2025 12:10:53 -0500 Subject: [PATCH 074/323] add msturing10m --- .../numa_multi_query/configs/msturing10m.yaml | 146 ++++++++++++++++++ 1 file changed, 146 insertions(+) create mode 100644 test/experiments/osdi2025/numa_multi_query/configs/msturing10m.yaml diff --git a/test/experiments/osdi2025/numa_multi_query/configs/msturing10m.yaml b/test/experiments/osdi2025/numa_multi_query/configs/msturing10m.yaml new file mode 100644 index 00000000..de5e3f3d --- /dev/null +++ b/test/experiments/osdi2025/numa_multi_query/configs/msturing10m.yaml @@ -0,0 +1,146 @@ +# numa_latency_experiment.yaml +dataset: + name: msturing10m + num_queries: 1000 + k: 100 + +trials: 3 +warmup: 1 +overwrite: true # Set to true to force all indexes to rerun + +indexes: + - name: Quake_0 + index: Quake + build_params: + nc: 20000 + metric: l2 + num_workers: 0 + use_numa: false + search_params: + nprobe: 200 + batched_scan: true + + - name: Quake_1 + index: Quake + build_params: + nc: 20000 + metric: l2 + num_workers: 1 + use_numa: false + search_params: + nprobe: 100 + batched_scan: true + + - name: Quake_4 + index: Quake + build_params: + nc: 20000 + metric: l2 + num_workers: 4 + use_numa: false + search_params: + nprobe: 200 + batched_scan: true + + - name: Quake_16 + index: Quake + build_params: + nc: 20000 + metric: l2 + num_workers: 16 + use_numa: false + search_params: + nprobe: 200 + batched_scan: true + + - name: Quake_0_numa + index: Quake + build_params: + nc: 20000 + metric: l2 + num_workers: 0 + use_numa: true + search_params: + nprobe: 200 + batched_scan: false + n_threads: 16 + + - name: Quake_1_numa + index: Quake + build_params: + nc: 20000 + metric: l2 + num_workers: 1 + use_numa: true + search_params: + nprobe: 200 + batched_scan: false + + - name: Quake_4_numa + index: Quake + build_params: + nc: 20000 + metric: l2 + num_workers: 4 + use_numa: true + search_params: + nprobe: 200 + batched_scan: false + + - name: Quake_16_numa + index: Quake + build_params: + nc: 20000 + metric: l2 + num_workers: 16 + use_numa: true + search_params: + nprobe: 200 + batched_scan: false + +# - name: FaissIVF +# index: IVF +# build_params: +# nc: 1000 +# metric: l2 +# search_params: +# nprobe: 15 +# +# - name: SCANN +# index: SCANN +# build_params: +# num_leaves: 1000 +# metric: squared_l2 +# search_params: +# leaves_to_search: 15 +# +# - name: FaissHNSW +# index: HNSW +# build_params: +# m: 64 +# ef_construction: 128 +# metric: l2 +# search_params: +# ef_search: 24 +# +# - name: DiskANN +# index: DiskANN +# build_params: +# complexity: 128 +# graph_degree: 64 +# metric: l2 +# search_params: +# complexity: 16 +# +# - name: SVS +# index: SVS +# build_params: +# graph_max_degree: 64 +# alpha: 1.1 +# window_size: 128 +# metric: l2 +# search_params: +# search_window_size: 24 + +output: + results_csv: numa_single_query_results.csv \ No newline at end of file From e2791d51d9040d724bda17de2c23689dcc7fd6e9 Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 15 May 2025 12:29:44 -0500 Subject: [PATCH 075/323] add msturing10m --- .../numa_multi_query/configs/msturing10m.yaml | 35 ++++++++++++------- 1 file changed, 23 insertions(+), 12 deletions(-) diff --git a/test/experiments/osdi2025/numa_multi_query/configs/msturing10m.yaml b/test/experiments/osdi2025/numa_multi_query/configs/msturing10m.yaml index de5e3f3d..2f7f9cc5 100644 --- a/test/experiments/osdi2025/numa_multi_query/configs/msturing10m.yaml +++ b/test/experiments/osdi2025/numa_multi_query/configs/msturing10m.yaml @@ -17,7 +17,7 @@ indexes: num_workers: 0 use_numa: false search_params: - nprobe: 200 + nprobe: 300 batched_scan: true - name: Quake_1 @@ -28,7 +28,7 @@ indexes: num_workers: 1 use_numa: false search_params: - nprobe: 100 + nprobe: 300 batched_scan: true - name: Quake_4 @@ -39,7 +39,7 @@ indexes: num_workers: 4 use_numa: false search_params: - nprobe: 200 + nprobe: 300 batched_scan: true - name: Quake_16 @@ -50,7 +50,7 @@ indexes: num_workers: 16 use_numa: false search_params: - nprobe: 200 + nprobe: 300 batched_scan: true - name: Quake_0_numa @@ -61,9 +61,8 @@ indexes: num_workers: 0 use_numa: true search_params: - nprobe: 200 - batched_scan: false - n_threads: 16 + nprobe: 300 + batched_scan: true - name: Quake_1_numa index: Quake @@ -73,8 +72,8 @@ indexes: num_workers: 1 use_numa: true search_params: - nprobe: 200 - batched_scan: false + nprobe: 300 + batched_scan: true - name: Quake_4_numa index: Quake @@ -84,8 +83,8 @@ indexes: num_workers: 4 use_numa: true search_params: - nprobe: 200 - batched_scan: false + nprobe: 300 + batched_scan: true - name: Quake_16_numa index: Quake @@ -95,8 +94,20 @@ indexes: num_workers: 16 use_numa: true search_params: - nprobe: 200 + nprobe: 300 + batched_scan: true + + - name: Quake_unbatched + index: Quake + build_params: + nc: 20000 + metric: l2 + num_workers: 16 + use_numa: true + search_params: + nprobe: 300 batched_scan: false + search_threads: 16 # - name: FaissIVF # index: IVF From 6619d36531424677b1ec09feeff0dfcb6a08153e Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 16 May 2025 18:48:09 -0500 Subject: [PATCH 076/323] fix numa alloc --- src/cpp/include/partition_manager.h | 4 +- src/cpp/src/partition_manager.cpp | 11 +- test/cpp/benchmark.cpp | 10 +- .../early_termination/configs/sift1m.yaml | 9 +- .../osdi2025/early_termination/laet.py | 4 +- .../osdi2025/early_termination/run.py | 777 +++++++++--------- .../osdi2025/maintenance_ablation/run.py | 357 ++++---- .../numa_multi_query/configs/sift1m.yaml | 1 + 8 files changed, 607 insertions(+), 566 deletions(-) diff --git a/src/cpp/include/partition_manager.h b/src/cpp/include/partition_manager.h index b0ade908..86776167 100644 --- a/src/cpp/include/partition_manager.h +++ b/src/cpp/include/partition_manager.h @@ -114,13 +114,13 @@ class PartitionManager { * @brief Distribute the partitions across multiple workers. * @param num_workers The number of workers to distribute the partitions across. */ - void distribute_partitions(int num_workers); + void distribute_partitions(int num_workers, bool use_numa = false); /** * @brief Set the core ID for a given partition. * @param partition_id The ID of the partition. */ - void set_partition_core_id(int64_t partition_id, int core_id); + void set_partition_core_id(int64_t partition_id, int core_id, bool use_numa = false); /** * @brief Return the core ID for a given partition. diff --git a/src/cpp/src/partition_manager.cpp b/src/cpp/src/partition_manager.cpp index 5445e377..11eaa731 100644 --- a/src/cpp/src/partition_manager.cpp +++ b/src/cpp/src/partition_manager.cpp @@ -574,7 +574,7 @@ void PartitionManager::delete_partitions(const Tensor &partition_ids, bool reass } -void PartitionManager::distribute_partitions(int num_workers) { +void PartitionManager::distribute_partitions(int num_workers, bool use_numa) { if (debug_) { std::cout << "[PartitionManager] distribute_partitions: Attempting to distribute partitions across " << num_workers << " workers." << std::endl; @@ -625,8 +625,15 @@ void PartitionManager::distribute_partitions(int num_workers) { } } -void PartitionManager::set_partition_core_id(int64_t partition_id, int core_id) { +void PartitionManager::set_partition_core_id(int64_t partition_id, int core_id, bool use_numa) { partition_store_->partitions_[partition_id]->set_core_id(core_id); + + #ifdef QUAKE_USE_NUMA + if (use_numa) { + int node = numa_node_of_cpu(core_id); // <-- single call + partition_store_->partitions_[partition_id]->set_numa_node(core_id); + } + #endif } int PartitionManager::get_partition_core_id(int64_t partition_id) { diff --git a/test/cpp/benchmark.cpp b/test/cpp/benchmark.cpp index 5bb3646b..cd18cda3 100644 --- a/test/cpp/benchmark.cpp +++ b/test/cpp/benchmark.cpp @@ -30,7 +30,7 @@ static const int64_t DIM = 128; static const int64_t NUM_VECTORS = 1000000; // number of database vectors static const int64_t N_LIST = 1000; // number of clusters for IVF static const int64_t NUM_QUERIES = 10000; // number of queries for search benchmark -static const int64_t K = 100; // top-K neighbors +static const int64_t K = 10; // top-K neighbors static const int64_t N_PROBE = 20; // number of probes for IVF static const int64_t N_WORKERS = 12; // number of workers for parallel query coordinator @@ -314,6 +314,14 @@ TEST_F(QuakeWorkerIVFBenchmark, SearchBatch) { auto elapsed = duration_cast(end - start).count(); std::cout << "[Quake IVF Worker] Batched search time: " << elapsed << " ms" << std::endl; + + // print out timing info + auto timing_info = result->timing_info; + std::cout << "Total time: " << timing_info->total_time_ns / 1e6 << " ms" << std::endl; + std::cout << "Parent search time: " << timing_info->parent_info->total_time_ns / 1e6 << " ms" << std::endl; + std::cout << "Job enqueue time: " << timing_info->job_enqueue_time_ns / 1e6 << " ms" << std::endl; + std::cout << "Result aggregate time: " << timing_info->result_aggregate_time_ns / 1e6 << " ms" << std::endl; + std::cout << "Job Wait time: " << timing_info->job_wait_time_ns / 1e6 << " ms" << std::endl; ASSERT_GT(elapsed, 0); } diff --git a/test/experiments/osdi2025/early_termination/configs/sift1m.yaml b/test/experiments/osdi2025/early_termination/configs/sift1m.yaml index 3115bad5..9d53f80f 100644 --- a/test/experiments/osdi2025/early_termination/configs/sift1m.yaml +++ b/test/experiments/osdi2025/early_termination/configs/sift1m.yaml @@ -5,10 +5,10 @@ dataset: name: sift1m path: data experiment: - nq: 100 - k: 1 + nq: 10000 + k: 100 n_workers: 0 - recall_targets: [.2, .3, .4, .5, .6, 0.7, .75, 0.8, .85, 0.9, 0.95, 0.99, .999] + recall_targets: [0.8, 0.9, 0.99] recompute_ratio: 0.0001 use_precompute: true initial_search_fraction: .1 @@ -18,7 +18,8 @@ index: overwrite: index: false results: true -methods: ['LAET', 'Auncel', 'Oracle', 'FixedNProbe', 'APS', 'SPANN'] +#methods: ['LAET', 'Auncel', 'Oracle', 'FixedNProbe', 'APS', 'SPANN'] +methods: ['Auncel'] paths: index_dir: data/sift1m/indexes diff --git a/test/experiments/osdi2025/early_termination/laet.py b/test/experiments/osdi2025/early_termination/laet.py index 0b260906..2484033d 100644 --- a/test/experiments/osdi2025/early_termination/laet.py +++ b/test/experiments/osdi2025/early_termination/laet.py @@ -233,7 +233,7 @@ def _compute_recall_single_q(self, res_ids_tensor, gt_set_top_k, k_val_for_recal def run_inference_for_quake_experiment(self, quake_idx, queries_torch, gt_torch, target_recall, k_search, base_quake_sp, - num_bs_steps=20, multiplier_range=(0.1, 5.0)): + num_bs_steps=20, multiplier_range=(0.1, 50.0)): if not self.gbdt_model: print("LAET Inference Error: GBDT model not loaded.") return np.nan, np.nan, np.nan @@ -249,7 +249,7 @@ def run_inference_for_quake_experiment(self, quake_idx, queries_torch, gt_torch, quake_nlist_cap = quake_idx.nlist() if callable(getattr(quake_idx, 'nlist', None)) else getattr(quake_idx, 'nlist', 2048) # --- Log GBDT base_p predictions sample --- - sample_size_for_log = min(nq, 100) + sample_size_for_log = nq sample_base_p_preds = [self._predict_base_search_param(queries_np[i]) for i in range(sample_size_for_log)] if sample_base_p_preds: print(f"LAET INFERENCE (TargetRecall={target_recall:.2f}): Sample GBDT base_p predictions (first {sample_size_for_log}): " diff --git a/test/experiments/osdi2025/early_termination/run.py b/test/experiments/osdi2025/early_termination/run.py index 23e47988..ce8c2dbc 100644 --- a/test/experiments/osdi2025/early_termination/run.py +++ b/test/experiments/osdi2025/early_termination/run.py @@ -1,407 +1,400 @@ -import time -import yaml +#!/usr/bin/env python3 +""" +APS vs. early-termination baselines +Generates CSV + Markdown table with: + RecallAchieved | nprobe | QueryLatencyMs | TuningTimeMs +for recall targets 0.80, 0.90, 0.99 +""" + +from __future__ import annotations +import argparse import logging +import time from pathlib import Path -import argparse +from typing import List, Dict -import torch -import pandas as pd import numpy as np -import matplotlib.pyplot as plt +import pandas as pd +import torch +import yaml +# ── optional plotting ───────────────────────────────────────────────────────── try: - from quake import QuakeIndex, IndexBuildParams, SearchParams - from quake.datasets.ann_datasets import load_dataset - from quake.utils import compute_recall -except ImportError as e: - print(f"Error importing Quake library components: {e}. Ensure 'quake' is installed and in PYTHONPATH.") - raise + import matplotlib.pyplot as plt + + _HAVE_MPL = True +except ImportError: + _HAVE_MPL = False +# ── Quake bindings ──────────────────────────────────────────────────────────── +from quake import QuakeIndex, IndexBuildParams, SearchParams +from quake.datasets.ann_datasets import load_dataset +from quake.utils import compute_recall + +# ── LAET (mandatory) ────────────────────────────────────────────────────────── try: from .laet import LAETPipeline except ImportError as e: - try: - from laet import LAETPipeline - print("Note: Imported LAETPipeline using 'from laet import LAETPipeline'.") - except ImportError: - print(f"Error importing LAETPipeline: {e}. Ensure 'laet.py' is correctly placed and import path is valid.") - raise - - -# Setup basic logging -logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') -logger = logging.getLogger(__name__) - -def run_experiment(cfg_path: str, output_dir_str: str): - # 1) Load config + setup output dir - logger.info(f"Loading configuration from: {cfg_path}") - try: - cfg = yaml.safe_load(Path(cfg_path).read_text()) - except FileNotFoundError: - logger.error(f"Configuration file not found: {cfg_path}") - return - except Exception as e: - logger.error(f"Error loading YAML configuration from {cfg_path}: {e}") - return - - out_path = Path(output_dir_str) - out_path.mkdir(parents=True, exist_ok=True) - logger.info(f"Output directory set to: {out_path}") - - # 2) Load data - data_dir = cfg["dataset"].get("path", "") - dataset_name = cfg["dataset"]["name"] - logger.info(f"Loading dataset: {dataset_name} from path: '{data_dir if data_dir else 'default'}'") - try: - vecs, queries_torch, gt_torch = load_dataset(dataset_name, data_dir) - except Exception as e: - logger.error(f"Failed to load dataset '{dataset_name}': {e}", exc_info=True) - return - - nq_config = cfg["experiment"].get("nq", queries_torch.shape[0]) - queries_torch, gt_torch = queries_torch[:nq_config], gt_torch[:nq_config] - logger.info(f"Using {queries_torch.shape[0]} queries for the experiment.") - - # 3) Build or load QuakeIndex (used by all methods) - idx_quake = QuakeIndex() - - build_params = IndexBuildParams() - build_params.nlist = cfg["index"]["nlist"] - build_params.metric = cfg["index"]["metric"] - build_params.num_workers = cfg["experiment"].get("n_workers", 0) - - idx_base_dir = Path(cfg["paths"].get("index_dir", out_path / "indexes")) - idx_base_dir.mkdir(parents=True, exist_ok=True) - idx_path_quake = idx_base_dir / f"{dataset_name}_quake_ivf{build_params.nlist}.index" - - if not idx_path_quake.exists() or cfg["overwrite"].get("index", False): - logger.info(f"Building QuakeIndex at {idx_path_quake}...") - try: - idx_quake.build(vecs, torch.arange(len(vecs)), build_params) - idx_quake.save(str(idx_path_quake)) - logger.info(f"QuakeIndex built and saved to {idx_path_quake}.") - except Exception as e: - logger.error(f"Failed to build or save QuakeIndex: {e}", exc_info=True) - return - else: - logger.info(f"Loading existing QuakeIndex from {idx_path_quake}...") - try: - idx_quake.load(str(idx_path_quake), 0) - logger.info(f"QuakeIndex loaded successfully.") - except Exception as e: - logger.error(f"Failed to load QuakeIndex from {idx_path_quake}: {e}", exc_info=True) - return - - current_index_nlist = idx_quake.nlist() if callable(getattr(idx_quake, 'nlist', None)) else getattr(idx_quake, 'nlist', build_params.nlist) - if current_index_nlist != build_params.nlist: - logger.warning(f"QuakeIndex nlist ({current_index_nlist}) differs from config ({build_params.nlist}). Using index's nlist: {current_index_nlist}.") - max_nlist_val = current_index_nlist - - - # 4) Initialize LAETPipeline if "LAET" is in methods - laet_pipeline_instance = None - if "LAET" in cfg["methods"]: - if not idx_quake: - logger.error("LAET method requires QuakeIndex, but it's not available. Skipping LAET.") - else: - laet_main_cfg = cfg.get("laet_config", {}) - laet_paths_cfg = cfg.get("paths", {}) # For laet_artifacts_base_dir - - # Prepare config for LAETPipeline constructor - pipeline_init_config = { - "base_dir": laet_paths_cfg.get("laet_artifacts_base_dir", "laet_pipeline_output"), - "training_data_subdir": laet_main_cfg.get("training_data_subdir", "laet_training_data"), - "model_subdir": laet_main_cfg.get("model_subdir", "laet_gbdt_models"), - "dataset_name_for_files": laet_main_cfg.get("dataset_name_for_files", dataset_name.upper()), - "index_key_for_files": laet_main_cfg.get("index_key_for_files", f"IVF{max_nlist_val}_Flat"), - "model_was_trained_on_log_target": laet_main_cfg.get("train_log_target", False) # Used for model naming if trained by pipeline - } - - laet_pipeline_instance = LAETPipeline(vector_dim=queries_torch.shape[1], - laet_artifacts_config=pipeline_init_config) - - # Prepare configs for get_or_train_model - # These paths should point to your primary dataset files for LAET GBDT training - data_paths_for_laet_training = { - "base_vectors": laet_main_cfg.get("base_vectors_path_for_laet_train", f"data/{dataset_name}_base.fvecs"), - "train_query_vectors": laet_main_cfg.get("train_query_vectors_path_for_laet_train", f"data/{dataset_name}_learn.fvecs"), - "gt_train": laet_main_cfg.get("gt_train_path_for_laet_train", f"data/{dataset_name}_learn_groundtruth.ivecs"), - "gt_format": laet_main_cfg.get("gt_format_for_laet_train", "ivecs"), - "training_vectors_for_faiss_index": laet_main_cfg.get("faiss_training_vectors_path_for_laet_train", f"data/{dataset_name}_learn.fvecs") - } - generation_params_for_laet_training = { - "num_train_queries_to_process": laet_main_cfg.get("num_laet_training_samples", 10000), - "k_gt_load_for_target": laet_main_cfg.get("laet_train_k_gt_load", 1), - "k_search_for_target": laet_main_cfg.get("laet_train_k_search", 10), - "max_nprobe_scan": laet_main_cfg.get("laet_train_max_nprobe", 128), - "max_efsearch_scan": laet_main_cfg.get("laet_train_max_efsearch", 256), - "param_scan_step": laet_main_cfg.get("laet_train_param_scan_step", 1) - } - faiss_params_for_laet_training = { - "faiss_index_key_for_gen": laet_main_cfg.get("faiss_index_key_for_laet_train_gen", f"IVF{max_nlist_val},Flat") - } - gbdt_training_params_for_laet = { - "log_target_training": laet_main_cfg.get("train_log_target", False), # This sets log_target_prediction in pipeline - "test_size": laet_main_cfg.get("train_gbdt_test_size", 0.1), - "num_leaves": laet_main_cfg.get("train_gbdt_num_leaves", 31), - "learning_rate": laet_main_cfg.get("train_gbdt_lr", 0.05), - "n_estimators": laet_main_cfg.get("train_gbdt_n_estimators", 100), - "feature_fraction": laet_main_cfg.get("train_gbdt_feature_fraction", 0.9), - "bagging_fraction": laet_main_cfg.get("train_gbdt_bagging_fraction", 0.8), - "bagging_freq": laet_main_cfg.get("train_gbdt_bagging_freq", 5) - } - - laet_pipeline_instance.get_or_train_model( - overwrite_model=cfg.get("overwrite", {}).get("laet_model", False), - data_paths_config=data_paths_for_laet_training, - generation_params_config=generation_params_for_laet_training, - faiss_params_config=faiss_params_for_laet_training, - training_params_config=gbdt_training_params_for_laet + raise ImportError( + "LAET is required for this experiment but could not be imported. " + "Install the laet package or fix PYTHONPATH." + ) from e + +# ── logging ─────────────────────────────────────────────────────────────────── +logging.basicConfig( + level=logging.INFO, format="%(asctime)s | %(levelname)7s | %(message)s", datefmt="%H:%M:%S" +) +logger = logging.getLogger("experiment") + + +# ----------------------------------------------------------------------------- # +# Helpers +# ----------------------------------------------------------------------------- # +def make_sp(**attrs) -> SearchParams: + """Create SearchParams then set attributes (bindings have no kwargs ctor).""" + sp = SearchParams() + for k, v in attrs.items(): + setattr(sp, k, v) + return sp + + +def tune_auncel_a( + idx: QuakeIndex, + queries: torch.Tensor, + gt: torch.Tensor, + k: int, + target_recall: float, + recompute_thr: float, + init_frac: float, + max_iters: int = 15, + a_min: float = 1e-5, + a_max: float = .5, +) -> tuple[float, list[tuple[float, float, float]]]: + """ + Binary-search the smallest 'a' that still meets the recall target. + Returns (best_a, per_query_stats) where the stats are + (nprobe, recall, latency_ms) for the final run. + """ + best_a = a_max + lo, hi = a_min, a_max + best_stats: list[tuple[float, float, float]] = [] + + for _ in range(max_iters): + mid = (lo + hi) / 2.0 + per_query: list[tuple[float, float, float]] = [] + + for q, g in zip(queries, gt): + sp = make_sp( + nprobe=-1, + k=k, + recall_target=target_recall, + use_auncel=True, + auncel_a=mid, + auncel_b=.5, + recompute_threshold=recompute_thr, + initial_search_fraction=init_frac, + ) + t0 = time.perf_counter_ns() + res = idx.search(q.unsqueeze(0), sp) + per_query.append( + ( + getattr(res.timing_info, "partitions_scanned", np.nan), + compute_recall(res.ids, g.unsqueeze(0), k).item(), + (time.perf_counter_ns() - t0) / 1e6, + ) ) - logger.info("LAET GBDT model is ready (loaded or trained).") - # 5) Run each method × recall_target - records = [] - k_metric = cfg["experiment"]["k"] + mean_recall = np.mean([r for _, r, _ in per_query]) + if mean_recall >= target_recall: + # feasible → try smaller a + best_a, best_stats = mid, per_query + hi = mid * 0.5 + else: + # not enough recall → increase a + lo = mid * 1.5 + if hi - lo < 1e-3: + break + + return best_a, best_stats + + +def binary_search_best_nprobe( + idx: QuakeIndex, + queries: torch.Tensor, + gt: torch.Tensor, + k: int, + target_recall: float, + max_nprobe: int, + use_spann: bool = False, + spann_eps: float = 1.5, +) -> int: + """Return smallest nprobe reaching `target_recall` on the supplied queries.""" + lo, hi, best = 1, max_nprobe, max_nprobe + while lo <= hi: + mid = (lo + hi) // 2 + sp = make_sp(k=k, nprobe=mid, use_spann=use_spann, spann_eps=spann_eps if use_spann else 0.0) + ids_all = [ + idx.search(q.unsqueeze(0), sp).ids + for q in queries + if idx.search(q.unsqueeze(0), sp).ids.numel() + ] + mean_rec = ( + compute_recall(torch.cat(ids_all, 0), gt, k).mean().item() if ids_all else 0.0 + ) + if mean_rec >= target_recall: + best, hi = mid, mid - 1 + else: + lo = mid + 1 + return best + + +def print_markdown(df: pd.DataFrame) -> None: + print("\n" + "-" * 80) + print(df.to_markdown(index=False, floatfmt=".4f")) + print("-" * 80 + "\n") + + +# ----------------------------------------------------------------------------- # +# Core experiment +# ----------------------------------------------------------------------------- # +def run_experiment(cfg_path: str, output_dir: str) -> None: + # 1) ---------------------------------------------------------------- config + cfg = yaml.safe_load(Path(cfg_path).read_text()) + out_dir = Path(output_dir).expanduser() + out_dir.mkdir(parents=True, exist_ok=True) + + k = cfg["experiment"]["k"] + recall_targets: List[float] = cfg["experiment"]["recall_targets"] + table_targets = {0.80, 0.90, 0.99} + + # 2) ---------------------------------------------------------------- data + vecs, queries, gt = load_dataset(cfg["dataset"]["name"], cfg["dataset"].get("path", "")) + nq = min(cfg["experiment"].get("nq", queries.shape[0]), queries.shape[0]) + queries, gt = queries[:nq], gt[:nq] + logger.info(f"Dataset loaded – {len(vecs):,} base vecs, {nq} queries.") + + # 3) ---------------------------------------------------------------- index + idx = QuakeIndex() + bp = IndexBuildParams() + bp.nlist = cfg["index"]["nlist"] + bp.metric = cfg["index"]["metric"] + bp.num_workers = cfg["experiment"].get("n_workers", 0) + + idx_file = Path(cfg["paths"]["index_dir"]) / f"{cfg['dataset']['name']}_quake_ivf{bp.nlist}.index" + idx_file.parent.mkdir(parents=True, exist_ok=True) + if idx_file.exists() and not cfg["overwrite"].get("index", False): + idx.load(str(idx_file), 0) + logger.info("Index loaded from disk.") + else: + logger.info("Building index …") + idx.build(vecs, torch.arange(len(vecs)), bp) + idx.save(str(idx_file)) + logger.info(f"Index saved to {idx_file}") + + max_nprobe = idx.nlist() if callable(getattr(idx, "nlist", None)) else bp.nlist + + # 4) ---------------------------------------------------------------- LAET (train / load) + laet_cfg = cfg["laet_config"] + + # required dicts for get_or_train_model + data_paths_cfg = { + "base_vectors": laet_cfg["base_vectors_path_for_laet_train"], + "train_query_vectors": laet_cfg["train_query_vectors_path_for_laet_train"], + "gt_train": laet_cfg["gt_train_path_for_laet_train"], + "gt_format": laet_cfg.get("gt_format_for_laet_train", "ivecs"), + "training_vectors_for_faiss_index": laet_cfg["faiss_training_vectors_path_for_laet_train"], + } + generation_params_cfg = { + "num_train_queries_to_process": laet_cfg.get("num_laet_training_samples", 10000), + "k_gt_load_for_target": laet_cfg.get("laet_train_k_gt_load", 1), + "k_search_for_target": laet_cfg.get("laet_train_k_search", 10), + "max_nprobe_scan": laet_cfg.get("laet_train_max_nprobe", 128), + "max_efsearch_scan": laet_cfg.get("laet_train_max_efsearch", 256), + "param_scan_step": laet_cfg.get("laet_train_param_scan_step", 1), + } + faiss_params_cfg = { + "faiss_index_key_for_gen": laet_cfg.get("faiss_index_key_for_laet_train_gen", f"IVF{max_nprobe},Flat") + } + training_params_cfg = { + "log_target_training": laet_cfg.get("train_log_target", False), + "test_size": laet_cfg.get("train_gbdt_test_size", 0.1), + "num_leaves": laet_cfg.get("train_gbdt_num_leaves", 31), + "learning_rate": laet_cfg.get("train_gbdt_lr", 0.05), + "n_estimators": laet_cfg.get("train_gbdt_n_estimators", 100), + "feature_fraction": laet_cfg.get("train_gbdt_feature_fraction", 0.9), + "bagging_fraction": laet_cfg.get("train_gbdt_bagging_fraction", 0.8), + "bagging_freq": laet_cfg.get("train_gbdt_bagging_freq", 5), + } + + laet = LAETPipeline( + vector_dim=queries.shape[1], + laet_artifacts_config={ + "base_dir": cfg["paths"]["laet_artifacts_base_dir"], + "dataset_name_for_files": laet_cfg.get( + "dataset_name_for_files", cfg["dataset"]["name"].upper() + ), + "index_key_for_files": laet_cfg.get("index_key_for_files", f"IVF{max_nprobe}_Flat"), + "model_subdir": laet_cfg.get("model_subdir", "laet_gbdt_models"), + }, + ) + + t_train0 = time.perf_counter_ns() + laet.get_or_train_model( + overwrite_model=False, + data_paths_config=data_paths_cfg, + generation_params_config=generation_params_cfg, + faiss_params_config=faiss_params_cfg, + training_params_config=training_params_cfg, + ) + laet_training_ns = time.perf_counter_ns() - t_train0 + logger.info(f"LAET model ready (train/load time: {laet_training_ns/1e6:.1f} ms).") + + # 5) --------------------------------------------------------------- run methods + records: List[Dict] = [] for method in cfg["methods"]: - for rt_metric in cfg["experiment"]["recall_targets"]: - logger.info(f"Running Method: {method}, Target Recall: {rt_metric:.3f}") - - mean_param_val, mean_recall_val, mean_time_ms_val = np.nan, np.nan, np.nan - std_param_val, std_recall_val, std_time_ms_val = np.nan, np.nan, np.nan - per_query_data_list = [] - - if method == "LAET": - if laet_pipeline_instance and idx_quake and laet_pipeline_instance.gbdt_model: - quake_sp_for_laet = SearchParams() - # k and nprobe will be set inside run_inference_for_quake_experiment - - bs_multiplier_range = tuple(laet_main_cfg.get("inference_multiplier_range", [0.1, 50.0])) - bs_steps = laet_main_cfg.get("inference_num_bs_steps", 20) - - try: - mean_param_val, mean_recall_val, mean_time_ms_val = \ - laet_pipeline_instance.run_inference_for_quake_experiment( - quake_idx=idx_quake, # Corrected from quake_index to idx_quake - queries_torch=queries_torch, - gt_torch=gt_torch, - target_recall=rt_metric, # Corrected from target_recall_metric - k_search=k_metric, # Corrected from k_search_quake - base_quake_sp=quake_sp_for_laet, # Corrected from base_quake_search_params - num_bs_steps=bs_steps, - multiplier_range=bs_multiplier_range - ) - std_param_val, std_recall_val, std_time_ms_val = 0.0, 0.0, 0.0 - logger.info(f"LAET @ RT={rt_metric:.3f} -> AvgParam={mean_param_val:.2f}, AvgRecall={mean_recall_val:.4f}, AvgTime={mean_time_ms_val:.3f}ms") - except Exception as e: - logger.error(f"Error during LAET inference run: {e}", exc_info=True) - mean_param_val, mean_recall_val, mean_time_ms_val = np.nan, np.nan, np.nan - else: - logger.warning(f"LAET method specified but pipeline/model or QuakeIndex not ready. Skipping for RT={rt_metric}.") - # Metrics remain NaN by default - - elif method in ["Oracle", "FixedNProbe", "APS", "Auncel", "SPANN"]: - if not idx_quake: - logger.warning(f"QuakeIndex not available for method {method}. Skipping.") - continue - - if method == "Oracle": - for i, q_single_torch in enumerate(queries_torch): - lo, hi, best_param_oracle = 1, max_nlist_val, max_nlist_val - while lo <= hi: - mid_param = (lo + hi) // 2 - sp_oracle_bs = SearchParams() - sp_oracle_bs.nprobe = mid_param - sp_oracle_bs.k = k_metric - res_oracle_bs = idx_quake.search(q_single_torch.unsqueeze(0), sp_oracle_bs) - rec_oracle_bs = compute_recall(res_oracle_bs.ids, gt_torch[i].unsqueeze(0), k_metric).item() - if rec_oracle_bs >= rt_metric: - best_param_oracle = mid_param - hi = mid_param - 1 - else: - lo = mid_param + 1 - - t_start_ns = time.perf_counter_ns() - sp_oracle_final = SearchParams() - sp_oracle_final.nprobe = best_param_oracle - sp_oracle_final.k = k_metric - res_oracle_final = idx_quake.search(q_single_torch.unsqueeze(0), sp_oracle_final) - elapsed_ns = time.perf_counter_ns() - t_start_ns - rec_oracle_final = compute_recall(res_oracle_final.ids, gt_torch[i].unsqueeze(0), k_metric).item() - per_query_data_list.append((best_param_oracle, rec_oracle_final, elapsed_ns / 1_000_000.0)) - - elif method == "Auncel": - for i, q_single_torch in enumerate(queries_torch): - sp_auncel = SearchParams() - sp_auncel.nprobe = -1 - sp_auncel.k = k_metric - sp_auncel.recall_target = rt_metric - sp_auncel.recompute_threshold = cfg["experiment"].get("recompute_ratio", 0.00001) - sp_auncel.use_auncel = True - sp_auncel.auncel_a = .005 - sp_auncel.auncel_b = 1.0 - sp_auncel.initial_search_fraction = cfg["experiment"].get("initial_search_fraction", 0.1) - - t_start_ns = time.perf_counter_ns() - res_auncel = idx_quake.search(q_single_torch.unsqueeze(0), sp_auncel) - elapsed_ns = time.perf_counter_ns() - t_start_ns - rec_auncel = compute_recall(res_auncel.ids, gt_torch[i].unsqueeze(0), k_metric).item() - param_aps = res_auncel.timing_info.partitions_scanned if hasattr(res_auncel, 'timing_info') and hasattr(res_auncel.timing_info, 'partitions_scanned') else np.nan - per_query_data_list.append((param_aps, rec_auncel, elapsed_ns / 1_000_000.0)) - - elif method == "SPANN": - lo_fixed, hi_fixed, best_overall_param_fixed = 1, max_nlist_val, max_nlist_val - while lo_fixed <= hi_fixed: - mid_param_fixed = (lo_fixed + hi_fixed) // 2 - sp_fixed_bs = SearchParams() - sp_fixed_bs.nprobe = mid_param_fixed - sp_fixed_bs.k = k_metric - sp_fixed_bs.use_spann = False - sp_fixed_bs.spann_eps = 2.0 - sp_fixed_bs.recall_target = -1 - - ids_list_fixed = [] - for q_bs_fixed in queries_torch: - res_bs_fixed = idx_quake.search(q_bs_fixed.unsqueeze(0), sp_fixed_bs) - if res_bs_fixed.ids is not None and res_bs_fixed.ids.numel() > 0: - ids_list_fixed.append(res_bs_fixed.ids) - - if not ids_list_fixed: avg_rec_fixed = 0.0 - else: avg_rec_fixed = compute_recall(torch.cat(ids_list_fixed, 0), gt_torch, k_metric).mean().item() - - if avg_rec_fixed >= rt_metric: - best_overall_param_fixed = mid_param_fixed - hi_fixed = mid_param_fixed - 1 - else: lo_fixed = mid_param_fixed + 1 - - for i, q_single_torch in enumerate(queries_torch): - t_start_ns = time.perf_counter_ns() - sp_fixed_final = SearchParams() - sp_fixed_final.nprobe = best_overall_param_fixed - sp_fixed_final.k = k_metric - sp_fixed_final.use_spann = True - sp_fixed_final.spann_eps = 1.5 - res_fixed_final = idx_quake.search(q_single_torch.unsqueeze(0), sp_fixed_final) - elapsed_ns = time.perf_counter_ns() - t_start_ns - rec_fixed_final = compute_recall(res_fixed_final.ids, gt_torch[i].unsqueeze(0), k_metric).item() - param_spann = res_fixed_final.timing_info.partitions_scanned if hasattr(res_fixed_final, 'timing_info') and hasattr(res_fixed_final.timing_info, 'partitions_scanned') else np.nan - per_query_data_list.append((param_spann, rec_fixed_final, elapsed_ns / 1_000_000.0)) - - elif method == "FixedNProbe": - lo_fixed, hi_fixed, best_overall_param_fixed = 1, max_nlist_val, max_nlist_val - while lo_fixed <= hi_fixed: - mid_param_fixed = (lo_fixed + hi_fixed) // 2 - sp_fixed_bs = SearchParams() - sp_fixed_bs.nprobe = mid_param_fixed - sp_fixed_bs.k = k_metric - sp_fixed_bs.recall_target = -1 - - ids_list_fixed = [] - for q_bs_fixed in queries_torch: - res_bs_fixed = idx_quake.search(q_bs_fixed.unsqueeze(0), sp_fixed_bs) - if res_bs_fixed.ids is not None and res_bs_fixed.ids.numel() > 0: - ids_list_fixed.append(res_bs_fixed.ids) - - if not ids_list_fixed: avg_rec_fixed = 0.0 - else: avg_rec_fixed = compute_recall(torch.cat(ids_list_fixed, 0), gt_torch, k_metric).mean().item() - - if avg_rec_fixed >= rt_metric: - best_overall_param_fixed = mid_param_fixed - hi_fixed = mid_param_fixed - 1 - else: lo_fixed = mid_param_fixed + 1 - - for i, q_single_torch in enumerate(queries_torch): - t_start_ns = time.perf_counter_ns() - sp_fixed_final = SearchParams() - sp_fixed_final.nprobe = best_overall_param_fixed - sp_fixed_final.k = k_metric - sp_fixed_final.recall_target = -1 - res_fixed_final = idx_quake.search(q_single_torch.unsqueeze(0), sp_fixed_final) - elapsed_ns = time.perf_counter_ns() - t_start_ns - rec_fixed_final = compute_recall(res_fixed_final.ids, gt_torch[i].unsqueeze(0), k_metric).item() - per_query_data_list.append((best_overall_param_fixed, rec_fixed_final, elapsed_ns / 1_000_000.0)) - - elif method == "APS": - for i, q_single_torch in enumerate(queries_torch): - sp_aps = SearchParams() - sp_aps.nprobe = -1 - sp_aps.k = k_metric - sp_aps.recall_target = rt_metric - sp_aps.recompute_threshold = cfg["experiment"].get("recompute_ratio", 0.00001) - sp_aps.use_precomputed = cfg["experiment"].get("use_precompute", True) - sp_aps.initial_search_fraction = cfg["experiment"].get("initial_search_fraction", 0.1) - - t_start_ns = time.perf_counter_ns() - res_aps = idx_quake.search(q_single_torch.unsqueeze(0), sp_aps) - elapsed_ns = time.perf_counter_ns() - t_start_ns - rec_aps = compute_recall(res_aps.ids, gt_torch[i].unsqueeze(0), k_metric).item() - param_aps = res_aps.timing_info.partitions_scanned if hasattr(res_aps, 'timing_info') and hasattr(res_aps.timing_info, 'partitions_scanned') else np.nan - per_query_data_list.append((param_aps, rec_aps, elapsed_ns / 1_000_000.0)) - - if per_query_data_list: - arr = np.array(per_query_data_list, dtype=float) - if arr.size > 0: - mean_param_val, mean_recall_val, mean_time_ms_val = np.nanmean(arr, axis=0) - std_param_val, std_recall_val, std_time_ms_val = np.nanstd(arr, axis=0) - logger.info(f"{method} @ RT={rt_metric:.3f} -> AvgParam={mean_param_val:.2f}, AvgRecall={mean_recall_val:.4f}, AvgTime={mean_time_ms_val:.3f}ms") + for rt in recall_targets: + logger.info(f"{method:>10s} | target recall {rt:.2f}") + per_query = [] # tuples of (nprobe, recall, q_ms) + tuning_ns = 0 + + # ---------------------------------------------------------------- # + if method == "Oracle": + for q, g in zip(queries, gt): + t0 = time.perf_counter_ns() + best_np = binary_search_best_nprobe( + idx, q.unsqueeze(0), g.unsqueeze(0), k, rt, max_nprobe + ) + tuning_ns += time.perf_counter_ns() - t0 + sp = make_sp(k=k, nprobe=best_np) + t1 = time.perf_counter_ns() + res = idx.search(q.unsqueeze(0), sp) + per_query.append( + ( + best_np, + compute_recall(res.ids, g.unsqueeze(0), k).item(), + (time.perf_counter_ns() - t1) / 1e6, + ) + ) + + elif method in ("FixedNProbe", "SPANN"): + t0 = time.perf_counter_ns() + best_np = binary_search_best_nprobe( + idx, + queries, + gt, + k, + rt, + max_nprobe, + use_spann=(method == "SPANN"), + spann_eps=1.5, + ) + tuning_ns = time.perf_counter_ns() - t0 + for q, g in zip(queries, gt): + sp = make_sp( + k=k, + nprobe=best_np, + use_spann=(method == "SPANN"), + spann_eps=1.5 if method == "SPANN" else 0.0, + ) + t1 = time.perf_counter_ns() + res = idx.search(q.unsqueeze(0), sp) + per_query.append( + ( + best_np, + compute_recall(res.ids, g.unsqueeze(0), k).item(), + (time.perf_counter_ns() - t1) / 1e6, + ) + ) + + elif method == "APS": + for q, g in zip(queries, gt): + sp = make_sp( + nprobe=-1, + k=k, + recall_target=rt, + recompute_threshold=cfg["experiment"]["recompute_ratio"], + use_precomputed=cfg["experiment"]["use_precompute"], + initial_search_fraction=cfg["experiment"]["initial_search_fraction"], + ) + t1 = time.perf_counter_ns() + res = idx.search(q.unsqueeze(0), sp) + per_query.append( + ( + getattr(res.timing_info, "partitions_scanned", np.nan), + compute_recall(res.ids, g.unsqueeze(0), k).item(), + (time.perf_counter_ns() - t1) / 1e6, + ) + ) + + elif method == "Auncel": + t0 = time.perf_counter_ns() + best_a, per_query = tune_auncel_a( + idx, + queries, + gt, + k, + rt, + recompute_thr=cfg["experiment"]["recompute_ratio"], + init_frac=cfg["experiment"]["initial_search_fraction"], + ) + tuning_ns = time.perf_counter_ns() - t0 + logger.info(f"Auncel @ RT={rt:.2f} → selected a = {best_a:.4f}") + + elif method == "LAET": + t0 = time.perf_counter_ns() + mean_np, mean_rec, mean_ms = laet.run_inference_for_quake_experiment( + quake_idx=idx, + queries_torch=queries, + gt_torch=gt, + target_recall=rt, + k_search=k, + base_quake_sp=make_sp(k=k), + ) + tuning_ns = laet_training_ns + (time.perf_counter_ns() - t0) + per_query.append((mean_np, mean_rec, mean_ms)) + else: - if method != "LAET": - logger.warning(f"Unknown method: {method}. Skipping.") - continue - - records.append({ - "Method": method, "RecallTarget": rt_metric, - "mean_nprobe": mean_param_val, "std_nprobe": std_param_val, - "mean_recall": mean_recall_val, "std_recall": std_recall_val, - "mean_time_ms": mean_time_ms_val, "std_time_ms": std_time_ms_val, - }) - - # 6) Save CSV - df_results = pd.DataFrame(records) - csv_filename = cfg["paths"].get("results_file_prefix", "experiment_summary") + f"_{dataset_name}.csv" - out_csv_path = out_path / csv_filename - df_results.to_csv(out_csv_path, index=False, float_format='%.4f') - logger.info(f"Results summary saved to: {out_csv_path}") - - # 7) Plotting - if not df_results.empty: - plot_filename = cfg["paths"].get("plot_file_prefix", "recall_comparison_plots") + f"_{dataset_name}.png" - plot_output_dir = Path(cfg["paths"].get("plot_dir", output_dir_str)) - plot_output_dir.mkdir(parents=True, exist_ok=True) - plot_path_full = plot_output_dir / plot_filename - - fig, axes = plt.subplots(3, 1, figsize=(10, 12), sharex=True) - unique_methods_in_results = df_results["Method"].unique() - - for m_name in unique_methods_in_results: - grp = df_results[df_results["Method"] == m_name].sort_values(by="RecallTarget") - if grp.empty: continue - - axes[0].plot(grp["RecallTarget"], grp["mean_time_ms"], marker="o", label=m_name, alpha=0.5) - axes[1].plot(grp["RecallTarget"], grp["mean_recall"], marker="o", label=m_name, alpha=0.5) - axes[2].plot(grp["RecallTarget"], grp["mean_nprobe"], marker="o", label=m_name, alpha=0.5) - - axes[0].set_ylabel("Mean Query Time (ms)") - axes[0].set_yscale("log") - axes[0].set_title(f"Recall Target vs. Query Time ({dataset_name})") - axes[0].legend(); axes[0].grid(True, ls=":", alpha=0.7) - - recall_target_line_plot = np.array(cfg["experiment"]["recall_targets"]) - axes[1].plot(recall_target_line_plot, recall_target_line_plot, "k--", label="Target Recall", alpha=0.7) - axes[1].set_ylabel("Achieved Mean Recall") - axes[1].set_title(f"Recall Target vs. Achieved Recall ({dataset_name})") - axes[1].legend(); axes[1].grid(True, ls=":", alpha=0.7) - min_r, max_r = (df_results["mean_recall"].min() if pd.notna(df_results["mean_recall"].min()) else 0.0, - df_results["mean_recall"].max() if pd.notna(df_results["mean_recall"].max()) else 1.0) - axes[1].set_ylim(bottom=max(0.0, min_r - 0.05), top=min(1.01, max_r + 0.05)) - - axes[2].set_xlabel("Recall Target") - axes[2].set_ylabel("Num Partitions Scanned") - axes[2].set_yscale("log"); axes[2].set_title(f"Recall Target vs. Search Parameter ({dataset_name})") - axes[2].legend(); axes[2].grid(True, which="both", ls=":", alpha=0.7) - - plt.tight_layout(); plt.savefig(plot_path_full); plt.close(fig) - logger.info(f"Plots saved to: {plot_path_full}") - else: - logger.info("No data in results DataFrame, skipping plotting.") + logger.warning(f"Unknown method '{method}' – skipping.") + continue + # ---------------------------------------------------------------- # + + if not per_query: + continue + arr = np.asarray(per_query, dtype=float) + mean_np, mean_rec, mean_q_ms = np.nanmean(arr, axis=0) + + records.append( + dict( + Method=method, + RecallTarget=rt, + RecallAchieved=mean_rec, + nprobe=mean_np, + QueryLatencyMs=mean_q_ms, + TuningTimeMs=tuning_ns / 1e6, + ) + ) + + # 6) ------------------------------------------------------------- results + df = pd.DataFrame(records) + df_out = df[df["RecallTarget"].isin(table_targets)].copy().sort_values( + ["Method", "RecallTarget"] + ) + csv_path = out_dir / f"aps_et_results_{cfg['dataset']['name']}.csv" + df_out.to_csv(csv_path, index=False, float_format="%.4f") + logger.info(f"Results written to {csv_path}") + print_markdown(df_out) + + # 7) ------------------------------------------------------------- plot + if cfg.get("plot", {}).get("enable", False) and _HAVE_MPL: + plt.figure(figsize=(6, 4)) + for m in df["Method"].unique(): + d = df[df["Method"] == m] + plt.plot(d["RecallTarget"], d["QueryLatencyMs"], marker="o", label=m, alpha=0.7) + plt.yscale("log") + plt.xlabel("Recall target") + plt.ylabel("Mean query latency (ms)") + plt.title(cfg["dataset"]["name"]) + plt.legend() + plt.tight_layout() + plt.savefig(out_dir / f"latency_plot_{cfg['dataset']['name']}.png", dpi=200) \ No newline at end of file diff --git a/test/experiments/osdi2025/maintenance_ablation/run.py b/test/experiments/osdi2025/maintenance_ablation/run.py index 70e40db5..0173457b 100644 --- a/test/experiments/osdi2025/maintenance_ablation/run.py +++ b/test/experiments/osdi2025/maintenance_ablation/run.py @@ -2,143 +2,144 @@ """ Quake maintenance–ablation runner ───────────────────────────────── -• Builds / runs / plots exactly like the original “kick-the-tires” script. -• Per-operation CSV rows now contain - n_splits n_deletes maintenance_time_ms - (written by WorkloadEvaluator below). -• After all indices finish, this script also produces - //time_breakdown.png - – a stacked-bar chart of cumulative time spent in - Search / Insert / Delete / Maintenance / Total. +Extends the original “kick-the-tires” driver with: + +1. *summary_table.csv* – cumulative times + final partition count +2. *summary_table.md* – Markdown, via **tabulate** +3. *time_breakdown.png* – stacked bars (unchanged) +4. *unified_plot.png* – nine-panel diagnostic (unchanged) + +Per-index artefacts (under //): + results.csv – per-operation log + time_breakdown.png – per-index stacked bars """ from __future__ import annotations -import argparse, logging + +import argparse +import logging from pathlib import Path from typing import Dict, List + import matplotlib.pyplot as plt -from matplotlib.lines import Line2D +import numpy as np +import pandas as pd +import yaml +from tabulate import tabulate from matplotlib.cm import get_cmap -import numpy as np, pandas as pd, yaml +from matplotlib.lines import Line2D -# quake wrappers ---------------------------------------------------------------- +# ── quake wrappers ------------------------------------------------------------ from quake.datasets.ann_datasets import load_dataset from quake.index_wrappers.faiss_ivf import FaissIVF -from quake.index_wrappers.quake import QuakeWrapper -from quake.workload_generator import DynamicWorkloadGenerator, WorkloadEvaluator +from quake.index_wrappers.quake import QuakeWrapper +from quake.workload_generator import DynamicWorkloadGenerator, WorkloadEvaluator -logging.basicConfig(level=logging.INFO, format="%(asctime)s | %(levelname)s | %(message)s") +logging.basicConfig(level=logging.INFO, + format="%(asctime)s | %(levelname)s | %(message)s") log = logging.getLogger(__name__) - +# ── constants ----------------------------------------------------------------- OP_STYLE = { - "query" : dict(ls="-", marker="o", mfc="none", ms=4, lw=1.2), - "insert" : dict(ls="-", marker="s", mfc="none", ms=4, lw=1.2), - "delete" : dict(ls="-", marker="^", mfc="none", ms=4, lw=1.2), - "maintain": dict(ls="None", marker="X", mfc="black", ms=5), # points only + "query": dict(ls="-", marker="o", mfc="none", ms=4, lw=1.2), + "insert": dict(ls="-", marker="s", mfc="none", ms=4, lw=1.2), + "delete": dict(ls="-", marker="^", mfc="none", ms=4, lw=1.2), + "maintain": dict(ls="None", marker="X", mfc="black", ms=5), } LAT_OPS = ["query", "insert", "delete", "maintain"] IDX_LAT_Q, IDX_LAT_I, IDX_LAT_D, IDX_LAT_M = 0, 1, 2, 3 -IDX_PART, IDX_RES, IDX_REC, IDX_TOT, IDX_SPL = 4, 5, 6, 7, 8 - +IDX_PART, IDX_RES, IDX_REC, IDX_TOT, IDX_SPL = 4, 5, 6, 7, 8 +# ══════════════════════════════════════════════════════════════════════════════ +# Plot: unified nine-panel diagnostic +# ══════════════════════════════════════════════════════════════════════════════ def unified_plot(cfg: Dict, out_dir: Path) -> None: styles = cfg["plot"].get("styles", {}) - fig, axs2d = plt.subplots(3, 3, figsize=(18, 12), sharex="col") axs = axs2d.flatten() - # ── index legend handles (colour / marker) ──────────────────────────────── + # legend handles ----------------------------------------------------------- idx_handles: List[Line2D] = [] for j, idx in enumerate(cfg["indexes"]): nm, st = idx["name"], styles.get(idx["name"], {}) - idx_handles.append( - Line2D([0], [0], - color = st.get("color", f"C{j}"), - marker = st.get("marker", "o"), - ls = "", - markersize = 6, - label = nm)) - - # ── plot each index ────────────────────────────────────────────────────── + idx_handles.append(Line2D([0], [0], + color=st.get("color", f"C{j}"), + marker=st.get("marker", "o"), + ls="", markersize=6, label=nm)) + + # traces ------------------------------------------------------------------- for j, idx in enumerate(cfg["indexes"]): - name = idx["name"] - st = styles.get(name, {}) + name = idx["name"] + st = styles.get(name, {}) colour = st.get("color", f"C{j}") marker = st.get("marker", "o") csv = out_dir / name / "results.csv" if not csv.exists(): - print(f"[unified_plot] warning: {csv} missing – skipped") + log.warning("[unified_plot] %s missing – skipped.", csv) continue - df = pd.read_csv(csv) - # latency panels ------------------------------------------------------ - for op, ax_idx in zip(LAT_OPS, - [IDX_LAT_Q, IDX_LAT_I, IDX_LAT_D, IDX_LAT_M]): + # latency -------------------------------------------------------------- + for op, ax_idx in zip(LAT_OPS, [IDX_LAT_Q, IDX_LAT_I, IDX_LAT_D, IDX_LAT_M]): ax = axs[ax_idx] if op == "maintain": sub = df[df.maintenance_time_ms.fillna(0) > 0] - y = sub.maintenance_time_ms + y = sub.maintenance_time_ms else: sub = df[df.operation_type == op] - y = sub.latency_ms - if sub.empty: - continue - ax.plot(sub.operation_number, y, - color=colour, - **OP_STYLE[op]) - - # partitions ---------------------------------------------------------- + y = sub.latency_ms + if not sub.empty: + ax.plot(sub.operation_number, y, color=colour, **OP_STYLE[op]) + + # partitions ----------------------------------------------------------- ax = axs[IDX_PART] sub = df[df.n_list.notna()] if not sub.empty: ax.plot(sub.operation_number, sub.n_list, color=colour, marker=marker, lw=1.2) - # resident vectors ---------------------------------------------------- + # resident vectors ----------------------------------------------------- ax = axs[IDX_RES] sub = df[df.n_resident.notna()] if not sub.empty: ax.plot(sub.operation_number, sub.n_resident, color=colour, marker=marker, lw=1.2) - # recall -------------------------------------------------------------- + # recall --------------------------------------------------------------- ax = axs[IDX_REC] sub = df[(df.operation_type == "query") & df.recall.notna()] if not sub.empty: ax.plot(sub.operation_number, sub.recall, color=colour, marker=marker, lw=1.2) - # running total time -------------------------------------------------- + # running total time --------------------------------------------------- ax = axs[IDX_TOT] total_ms = df.latency_ms.fillna(0) + df.maintenance_time_ms.fillna(0) ax.plot(df.operation_number, np.cumsum(total_ms), color=colour, marker=marker, lw=1.2) - # cumulative splits / deletes ---------------------------------------- + # cumulative splits / deletes ----------------------------------------- ax = axs[IDX_SPL] - spl = df.n_splits.fillna(0) - dlt = df.n_deletes.fillna(0) + spl, dlt = df.n_splits.fillna(0), df.n_deletes.fillna(0) if (spl > 0).any(): ax.step(df.operation_number, np.cumsum(spl), where="post", color=colour, ls="--", lw=1.2) if (dlt > 0).any(): ax.step(df.operation_number, np.cumsum(dlt), - where="post", color=colour, ls=":", lw=1.2) + where="post", color=colour, ls=":", lw=1.2) - # ── titles / labels / grids ────────────────────────────────────────────── + # titles / grids ----------------------------------------------------------- titles = { IDX_LAT_Q: "Latency – Query", IDX_LAT_I: "Latency – Insert", IDX_LAT_D: "Latency – Delete", IDX_LAT_M: "Latency – Maintain", - IDX_PART : "# Partitions", - IDX_RES : "Resident Vectors", - IDX_REC : "Recall", - IDX_TOT : "Running Total Time", - IDX_SPL : "Cumulative Splits / Deletes", + IDX_PART: "# Partitions", + IDX_RES: "Resident Vectors", + IDX_REC: "Recall", + IDX_TOT: "Running Total Time", + IDX_SPL: "Cumulative Splits / Deletes", } for i, ax in enumerate(axs): ax.set_title(titles[i], fontsize=11) @@ -146,99 +147,73 @@ def unified_plot(cfg: Dict, out_dir: Path) -> None: ax.set_axisbelow(True) axs[IDX_PART].set_ylabel("count") - axs[IDX_RES ].set_ylabel("# vectors") - axs[IDX_REC ].set_ylabel("recall") - axs[IDX_TOT ].set_ylabel("ms") - axs[IDX_SPL ].set_ylabel("count") + axs[IDX_RES].set_ylabel("# vectors") + axs[IDX_REC].set_ylabel("recall") + axs[IDX_TOT].set_ylabel("ms") + axs[IDX_SPL].set_ylabel("count") - # x-labels only on bottom row for idx in [IDX_PART, IDX_RES, IDX_REC, IDX_TOT, IDX_SPL]: axs[idx].set_xlabel("Operation #") - # common x-limit so vertical grids align - all_ops = max((pd.read_csv(out_dir/idx["name"]/ "results.csv").operation_number.max() + all_ops = max((pd.read_csv(out_dir / idx["name"] / "results.csv").operation_number.max() for idx in cfg["indexes"] - if (out_dir/idx["name"]/ "results.csv").exists()), default=None) + if (out_dir / idx["name"] / "results.csv").exists()), + default=None) if all_ops is not None: for ax in axs: ax.set_xlim(left=0, right=all_ops) - # ── single legend (indexes only) ───────────────────────────────────────── fig.legend(idx_handles, [h.get_label() for h in idx_handles], loc="upper center", bbox_to_anchor=(0.5, 1.03), ncol=min(4, len(idx_handles)), fontsize=9, title="Index", frameon=False) - # save -------------------------------------------------------------------- - plt.tight_layout(rect=[0, 0, 1, 0.97]) # space for top legend + plt.tight_layout(rect=[0, 0, 1, 0.97]) out_path = out_dir / "unified_plot.png" plt.savefig(out_path, dpi=150) - print(f"[unified_plot] → {out_path}") + log.info("[unified_plot] → %s", out_path) plt.close() -# ─────────────────────────────────────────────────────────────────────────────── - +# ══════════════════════════════════════════════════════════════════════════════ +# Plot: stacked-bar time breakdown +# ══════════════════════════════════════════════════════════════════════════════ def make_time_breakdown(cfg: Dict, out_dir: Path) -> None: - """ - Create a side-by-side bar chart showing cumulative time spent in - Search / Insert / Delete / Maintenance / Total for each index. - - Recall values are annotated above each Search bar. - Output file: /time_breakdown.png - """ - import matplotlib.pyplot as plt - from matplotlib.cm import get_cmap - import numpy as np - import pandas as pd - categories = ["Search", "Insert", "Delete", "Maintain", "Total"] - idx_names = [idx["name"] for idx in cfg["indexes"]] - n_idx = len(idx_names) - n_cats = len(categories) - - # Totals and recall tracking - totals = {name: [0]*n_cats for name in idx_names} - recalls = {} + idx_names = [idx["name"] for idx in cfg["indexes"]] + totals, recalls = {}, {} + cmap = get_cmap("tab10") - for idx in cfg["indexes"]: - name = idx["name"] - csv_path = out_dir / name / "results.csv" + for j, idx in enumerate(cfg["indexes"]): + name = idx["name"]; csv_path = out_dir / name / "results.csv" if not csv_path.exists(): - print(f"[make_time_breakdown] warning: no CSV for {name} – skipped") + log.warning("[time_breakdown] missing %s – skipped", csv_path) continue - df = pd.read_csv(csv_path) - totals[name][0] = df[df.operation_type == "query" ].latency_ms.sum() # Search - totals[name][1] = df[df.operation_type == "insert"].latency_ms.sum() # Insert - totals[name][2] = df[df.operation_type == "delete"].latency_ms.sum() # Delete - totals[name][3] = df.maintenance_time_ms.fillna(0).sum() # Maintain - totals[name][4] = sum(totals[name][:4]) # Total - recalls[name] = df[df.operation_type == "query"].recall.mean() # Recall - - # Plotting - x = np.arange(n_cats) - width = 0.8 / max(1, n_idx) + totals[name] = [ + df[df.operation_type == "query"].latency_ms.sum(), + df[df.operation_type == "insert"].latency_ms.sum(), + df[df.operation_type == "delete"].latency_ms.sum(), + df.maintenance_time_ms.fillna(0).sum(), + ] + totals[name].append(sum(totals[name])) + recalls[name] = df[df.operation_type == "query"].recall.mean() + + n_idx, n_cats = len(totals), len(categories) + x = np.arange(n_cats); width = 0.8 / max(1, n_idx) fig, ax = plt.subplots(figsize=(10, 6)) - cmap = get_cmap("tab10") - - for j, name in enumerate(idx_names): - y = totals[name] + for j, (name, values) in enumerate(totals.items()): style = cfg["plot"].get("styles", {}).get(name, {}) color = style.get("color", cmap(j % 10)) - - x_offset = x + (j - (n_idx - 1) / 2) * width - bars = ax.bar(x_offset, y, width=width, label=name, color=color, edgecolor="black") - - # Add recall above Search bar (index 0) - recall_val = recalls.get(name) - if recall_val is not None: - ax.text(x_offset[0], y[0] + 0.01 * max(y), f"Recall={recall_val:.3f}", - ha="center", va="bottom", fontsize=8, rotation=90, color=color) - - ax.set_xticks(x) - ax.set_xticklabels(categories) + x_off = x + (j - (n_idx - 1) / 2) * width + ax.bar(x_off, values, width=width, label=name, + color=color, edgecolor="black") + ax.text(x_off[0], values[0] * 1.01, + f"R={recalls[name]:.3f}", ha="center", + va="bottom", fontsize=7, rotation=90, color=color) + + ax.set_xticks(x); ax.set_xticklabels(categories) ax.set_ylabel("Cumulative Time (ms)") ax.set_title("Time Breakdown per Index") ax.legend(title="Index", frameon=False) @@ -246,59 +221,115 @@ def make_time_breakdown(cfg: Dict, out_dir: Path) -> None: out_path = out_dir / "time_breakdown.png" plt.savefig(out_path, dpi=150) - print(f"[make_time_breakdown] → {out_path}") + log.info("[time_breakdown] → %s", out_path) plt.close() -# ─────────────────────────────────────────────────────────────────────────────── -def run_experiment(cfg_path:str, output_dir:str): +# ══════════════════════════════════════════════════════════════════════════════ +# Summary table (CSV + Markdown) +# ══════════════════════════════════════════════════════════════════════════════ +def produce_summary_table(cfg: Dict, out_dir: Path) -> None: + """ + Write summary_table.csv and summary_table.md with columns: + Index | Search | Insert | Delete | Maintain | Total | Recall | Partitions + Times are cumulative milliseconds; Recall is mean over all queries. + """ + rows = [] + for idx in cfg["indexes"]: + name = idx["name"] + csv_path = out_dir / name / "results.csv" + if not csv_path.exists(): + log.warning("[summary_table] %s missing – skipped", csv_path) + continue + + df = pd.read_csv(csv_path) + search = df[df.operation_type == "query" ].latency_ms.sum() + insert = df[df.operation_type == "insert"].latency_ms.sum() + delete = df[df.operation_type == "delete"].latency_ms.sum() + maintain = df.maintenance_time_ms.fillna(0).sum() + total = search + insert + delete + maintain + recall = df[df.operation_type == "query"].recall.mean() # ← NEW + n_part = (df.n_list.dropna().iloc[-1] + if not df.n_list.dropna().empty else None) + + rows.append(dict(Index=name, + Search=int(search), + Insert=int(insert), + Delete=int(delete), + Maintain=int(maintain), + Total=int(total), + Recall=f"{recall:.4f}" if pd.notna(recall) else "—", + Partitions=n_part)) + + if not rows: + log.warning("[summary_table] no data – nothing written") + return + + df = pd.DataFrame(rows).sort_values("Index") + + csv_path = out_dir / "summary_table.csv" + df.to_csv(csv_path, index=False) + log.info("[summary_table] → %s", csv_path) + + md_path = out_dir / "summary_table.md" + md = tabulate(df, headers="keys", tablefmt="github", showindex=False) + md_path.write_text(md + "\n") + log.info("[summary_table] → %s", md_path) + log.info("\n%s", md) +# ══════════════════════════════════════════════════════════════════════════════ +# Main orchestration +# ══════════════════════════════════════════════════════════════════════════════ +def run_experiment(cfg_path: str, output_dir: str) -> None: cfg = yaml.safe_load(Path(cfg_path).read_text()) - out = Path(output_dir).expanduser(); out.mkdir(parents=True, exist_ok=True) + out = Path(output_dir).expanduser(); out.mkdir(parents=True, exist_ok=True) - # 1 dataset / workload --------------------------------------------------- - if cfg["mode"] in {"build","run"}: + # 1. dataset & workload ---------------------------------------------------- + if cfg["mode"] in {"build", "run"}: ds = cfg["dataset"] - vecs, queries, _ = load_dataset(ds["name"], ds.get("path","")) + vecs, queries, _ = load_dataset(ds["name"], ds.get("path", "")) gen = DynamicWorkloadGenerator( - workload_dir =out, - base_vectors =vecs, - metric =ds["metric"], - insert_ratio =cfg["workload_generator"]["insert_ratio"], - delete_ratio =cfg["workload_generator"]["delete_ratio"], - query_ratio =cfg["workload_generator"]["query_ratio"], - update_batch_size =cfg["workload_generator"]["update_batch_size"], - query_batch_size =cfg["workload_generator"]["query_batch_size"], - number_of_operations =cfg["workload_generator"]["number_of_operations"], - initial_size =cfg["workload_generator"]["initial_size"], - cluster_size =cfg["workload_generator"]["cluster_size"], - cluster_sample_distribution=cfg["workload_generator"]["cluster_sample_distribution"], - queries =queries, + workload_dir = out, + base_vectors = vecs, + metric = ds["metric"], + insert_ratio = cfg["workload_generator"]["insert_ratio"], + delete_ratio = cfg["workload_generator"]["delete_ratio"], + query_ratio = cfg["workload_generator"]["query_ratio"], + update_batch_size = cfg["workload_generator"]["update_batch_size"], + query_batch_size = cfg["workload_generator"]["query_batch_size"], + number_of_operations = cfg["workload_generator"]["number_of_operations"], + initial_size = cfg["workload_generator"]["initial_size"], + cluster_size = cfg["workload_generator"]["cluster_size"], + cluster_sample_distribution= cfg["workload_generator"]["cluster_sample_distribution"], + queries = queries, query_cluster_sample_distribution=cfg["workload_generator"]["query_cluster_sample_distribution"], - seed =cfg["workload_generator"]["seed"]) - if not gen.workload_exists() or cfg["overwrite"].get("workload",False): - log.info("Generating workload …"); gen.generate_workload() + seed = cfg["workload_generator"]["seed"]) + if not gen.workload_exists() or cfg["overwrite"].get("workload", False): + log.info("Generating workload …") + gen.generate_workload() else: log.info("Existing workload found – generation skipped.") - # 2 evaluation ----------------------------------------------------------- - if cfg["mode"]=="run": + # 2. evaluate -------------------------------------------------------------- + if cfg["mode"] == "run": for idx in cfg["indexes"]: - name, res = idx["name"], out/idx["name"] - csv = res/"results.csv" - if csv.exists() and not cfg["overwrite"].get("results",False): - log.info("%s: cached results – skipped.", name); continue + name, res = idx["name"], out / idx["name"] res.mkdir(parents=True, exist_ok=True) + csv = res / "results.csv" + if csv.exists() and not cfg["overwrite"].get("results", False): + log.info("%s: cached results – skipped.", name); continue + eval = WorkloadEvaluator(workload_dir=out, output_dir=res) - cls = {"Quake":QuakeWrapper,"IVF":FaissIVF}[idx["index"]] + cls = {"Quake": QuakeWrapper, "IVF": FaissIVF}[idx["index"]] eval.evaluate_workload( - name =name, - index =cls(), - build_params =idx.get("build_params",{}), - search_params =idx.get("search_params",{}), - m_params=idx.get("maintenance_params",{}), + name = name, + index = cls(), + build_params = idx.get("build_params", {}), + search_params = idx.get("search_params", {}), + m_params = idx.get("maintenance_params", {}), do_maintenance=True, ) - # 3 global plot ---------------------------------------------------------- - if cfg["mode"] in {"run","plot"}: + # 3. global artefacts ------------------------------------------------------ + if cfg["mode"] in {"run", "plot"}: unified_plot(cfg, out) make_time_breakdown(cfg, out) + produce_summary_table(cfg, out) \ No newline at end of file diff --git a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml index 79a69316..d5881277 100644 --- a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml +++ b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml @@ -63,6 +63,7 @@ indexes: search_params: nprobe: 20 batched_scan: false + n_threads: 16 - name: Quake_1_numa index: Quake From 47d5a8c4f616cedefce6eb04b75234f8f233e497 Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 16 May 2025 18:53:57 -0500 Subject: [PATCH 077/323] fix numa alloc --- src/cpp/include/quake_index.h | 2 +- src/cpp/include/query_coordinator.h | 5 +++-- src/cpp/src/quake_index.cpp | 6 +++--- src/cpp/src/query_coordinator.cpp | 9 +++++---- src/python/index_wrappers/quake.py | 2 +- 5 files changed, 13 insertions(+), 11 deletions(-) diff --git a/src/cpp/include/quake_index.h b/src/cpp/include/quake_index.h index 900ecdab..d2377aaa 100644 --- a/src/cpp/include/quake_index.h +++ b/src/cpp/include/quake_index.h @@ -122,7 +122,7 @@ class QuakeIndex { * @param path Path to load the index. * @param n_workers Number of workers to use for query processing. */ - void load(const std::string &path, int n_workers = 0); + void load(const std::string &path, int n_workers = 0, bool use_numa=false); /** * @brief Get the total number of vectors in the index. diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index a3ae0dd5..60a80b48 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -85,7 +85,8 @@ class QueryCoordinator { shared_ptr partition_manager, shared_ptr maintenance_policy, MetricType metric, - int num_workers=0); + int num_workers=0, + bool use_numa=false); /** * @brief Destructor for QueryCoordinator. @@ -148,7 +149,7 @@ class QueryCoordinator { * * @param num_workers Number of worker threads to initialize. */ - void initialize_workers(int num_workers); + void initialize_workers(int num_workers, bool use_numa=false); /** * @brief Shuts down all worker threads. diff --git a/src/cpp/src/quake_index.cpp b/src/cpp/src/quake_index.cpp index 41814aea..cc55baac 100644 --- a/src/cpp/src/quake_index.cpp +++ b/src/cpp/src/quake_index.cpp @@ -84,7 +84,7 @@ shared_ptr QuakeIndex::build(Tensor x, Tensor ids, shared_ptr(parent_, partition_manager_, maintenance_policy_, metric_, build_params_->num_workers); + query_coordinator_ = make_shared(parent_, partition_manager_, maintenance_policy_, metric_, build_params_->num_workers, build_params_->use_numa); auto end = std::chrono::high_resolution_clock::now(); timing_info->total_time_us = std::chrono::duration_cast(end - start).count(); @@ -236,7 +236,7 @@ void QuakeIndex::save(const std::string& dir_path) { std::cout << "[QuakeIndex::save] Index saved to directory: " << dir_path << "\n"; } -void QuakeIndex::load(const std::string& dir_path, int n_workers) { +void QuakeIndex::load(const std::string& dir_path, int n_workers, bool use_numa) { namespace fs = std::filesystem; if (!fs::exists(dir_path) || !fs::is_directory(dir_path)) { @@ -293,7 +293,7 @@ void QuakeIndex::load(const std::string& dir_path, int n_workers) { // 5. Create query coordinator std::cout << "Loading coordinator with n_workers=" << n_workers << '\n'; - query_coordinator_ = std::make_shared(parent_, partition_manager_, maintenance_policy_, metric_, n_workers); + query_coordinator_ = std::make_shared(parent_, partition_manager_, maintenance_policy_, metric_, n_workers, use_numa); std::cout << "Loaded coordinator\n"; } diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index dc5c628a..e0fdb355 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -16,7 +16,8 @@ QueryCoordinator::QueryCoordinator(shared_ptr parent, shared_ptr partition_manager, shared_ptr maintenance_policy, MetricType metric, - int num_workers) + int num_workers, + bool use_numa) : parent_(parent), partition_manager_(partition_manager), maintenance_policy_(maintenance_policy), @@ -25,7 +26,7 @@ QueryCoordinator::QueryCoordinator(shared_ptr parent, workers_initialized_(false) { if (num_workers_ > 0) { - initialize_workers(num_workers_); + initialize_workers(num_workers_, use_numa); } } @@ -47,7 +48,7 @@ void QueryCoordinator::allocate_core_resources(int core_idx, int num_queries, in } // Initialize Worker Threads -void QueryCoordinator::initialize_workers(int num_cores) { +void QueryCoordinator::initialize_workers(int num_cores, bool use_numa) { if (workers_initialized_) { std::cerr << "[QueryCoordinator::initialize_workers] Workers already initialized." << std::endl; return; @@ -56,7 +57,7 @@ void QueryCoordinator::initialize_workers(int num_cores) { std::cout << "[QueryCoordinator::initialize_workers] Initializing " << num_cores << " worker threads." << std::endl; - partition_manager_->distribute_partitions(num_cores); + partition_manager_->distribute_partitions(num_cores, use_numa); core_resources_.resize(num_cores); worker_threads_.resize(num_cores); diff --git a/src/python/index_wrappers/quake.py b/src/python/index_wrappers/quake.py index 2fde67a9..033abdba 100644 --- a/src/python/index_wrappers/quake.py +++ b/src/python/index_wrappers/quake.py @@ -220,7 +220,7 @@ def load( f"verify_numa={verify_numa}, same_core={same_core}, use_centroid_workers={use_centroid_workers}" ) self.index = QuakeIndex() - self.index.load(str(filename), num_workers) + self.index.load(str(filename), num_workers, use_numa) def centroids(self) -> torch.Tensor: """ From 2103def3ddd1639e279ff50c28118e0538cc2078 Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 16 May 2025 18:59:21 -0500 Subject: [PATCH 078/323] fix numa alloc --- src/cpp/src/partition_manager.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/src/cpp/src/partition_manager.cpp b/src/cpp/src/partition_manager.cpp index 11eaa731..fb1a0e3d 100644 --- a/src/cpp/src/partition_manager.cpp +++ b/src/cpp/src/partition_manager.cpp @@ -631,6 +631,7 @@ void PartitionManager::set_partition_core_id(int64_t partition_id, int core_id, #ifdef QUAKE_USE_NUMA if (use_numa) { int node = numa_node_of_cpu(core_id); // <-- single call + std::cout << "Setting PID=" << partition_id << " to NUMA_node="<< node << " for core=" << core_id << '\n'; partition_store_->partitions_[partition_id]->set_numa_node(core_id); } #endif From 93cce3704680c08b1d5fa6cef392fb38fdf4e0ef Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 16 May 2025 19:03:06 -0500 Subject: [PATCH 079/323] fix numa alloc --- src/cpp/src/quake_index.cpp | 2 +- src/cpp/src/query_coordinator.cpp | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/cpp/src/quake_index.cpp b/src/cpp/src/quake_index.cpp index cc55baac..5a2eb1e3 100644 --- a/src/cpp/src/quake_index.cpp +++ b/src/cpp/src/quake_index.cpp @@ -292,7 +292,7 @@ void QuakeIndex::load(const std::string& dir_path, int n_workers, bool use_numa) initialize_maintenance_policy(default_params); // 5. Create query coordinator - std::cout << "Loading coordinator with n_workers=" << n_workers << '\n'; + std::cout << "Loading coordinator with n_workers=" << n_workers << " and use_numa=" << use_numa << '\n'; query_coordinator_ = std::make_shared(parent_, partition_manager_, maintenance_policy_, metric_, n_workers, use_numa); std::cout << "Loaded coordinator\n"; } diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index e0fdb355..cb4b0bd2 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -54,7 +54,7 @@ void QueryCoordinator::initialize_workers(int num_cores, bool use_numa) { return; } - std::cout << "[QueryCoordinator::initialize_workers] Initializing " << num_cores << " worker threads." << + std::cout << "[QueryCoordinator::initialize_workers] Initializing " << num_cores << " worker threads with use_numa=" << use_numa << std::endl; partition_manager_->distribute_partitions(num_cores, use_numa); From 3d62a460a27df78c2fdc1f4aca9c89cdcea8602d Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 16 May 2025 19:06:38 -0500 Subject: [PATCH 080/323] fix numa alloc --- src/cpp/src/partition_manager.cpp | 2 +- src/cpp/src/quake_index.cpp | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/cpp/src/partition_manager.cpp b/src/cpp/src/partition_manager.cpp index fb1a0e3d..1a0933b8 100644 --- a/src/cpp/src/partition_manager.cpp +++ b/src/cpp/src/partition_manager.cpp @@ -621,7 +621,7 @@ void PartitionManager::distribute_partitions(int num_workers, bool use_numa) { Tensor partition_ids = get_partition_ids(); auto partition_ids_accessor = partition_ids.accessor(); for (int i = 0; i < partition_ids.size(0); i++) { - set_partition_core_id(partition_ids_accessor[i], i % num_workers); + set_partition_core_id(partition_ids_accessor[i], i % num_workers, use_numa); } } diff --git a/src/cpp/src/quake_index.cpp b/src/cpp/src/quake_index.cpp index 5a2eb1e3..43bb6955 100644 --- a/src/cpp/src/quake_index.cpp +++ b/src/cpp/src/quake_index.cpp @@ -281,7 +281,7 @@ void QuakeIndex::load(const std::string& dir_path, int n_workers, bool use_numa) std::string parent_dir = (fs::path(dir_path) / "parent").string(); if (fs::exists(parent_dir) && fs::is_directory(parent_dir)) { parent_ = std::make_shared(); - parent_->load(parent_dir, n_workers); + parent_->load(parent_dir, n_workers, use_numa); partition_manager_->parent_ = parent_; } else { parent_ = nullptr; From 5498d336bcb5595effa9aab8688f6c8edf5458d3 Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 16 May 2025 19:13:28 -0500 Subject: [PATCH 081/323] fix numa alloc --- src/cpp/src/index_partition.cpp | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/cpp/src/index_partition.cpp b/src/cpp/src/index_partition.cpp index 49d5eb3b..9147a79a 100644 --- a/src/cpp/src/index_partition.cpp +++ b/src/cpp/src/index_partition.cpp @@ -147,6 +147,9 @@ void IndexPartition::set_numa_node(int new_numa_node) { bool is_valid_numa = (new_numa_node == -1) || (numa_available() != -1 && new_numa_node <= numa_max_node()); + + std::cout << "new_numa_node=" << new_numa_node << ", numa_available()=" << numa_available() << ", numa_max_node()=" << numa_max_node() << '\n' + if (!is_valid_numa) { throw std::runtime_error("Invalid numa node specified"); } From f76518cd2b9fc848225f45aa69fb6274f5f8dee5 Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 16 May 2025 19:14:25 -0500 Subject: [PATCH 082/323] fix numa alloc --- src/cpp/src/index_partition.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/cpp/src/index_partition.cpp b/src/cpp/src/index_partition.cpp index 9147a79a..128c5b44 100644 --- a/src/cpp/src/index_partition.cpp +++ b/src/cpp/src/index_partition.cpp @@ -148,7 +148,7 @@ void IndexPartition::set_numa_node(int new_numa_node) { bool is_valid_numa = (new_numa_node == -1) || (numa_available() != -1 && new_numa_node <= numa_max_node()); - std::cout << "new_numa_node=" << new_numa_node << ", numa_available()=" << numa_available() << ", numa_max_node()=" << numa_max_node() << '\n' + std::cout << "new_numa_node=" << new_numa_node << ", numa_available()=" << numa_available() << ", numa_max_node()=" << numa_max_node() << '\n'; if (!is_valid_numa) { throw std::runtime_error("Invalid numa node specified"); From 53f411dd2fe1387981ddf2822d63eabff34af19c Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 16 May 2025 19:20:13 -0500 Subject: [PATCH 083/323] fix numa alloc --- src/cpp/src/index_partition.cpp | 2 -- src/cpp/src/partition_manager.cpp | 5 ++--- 2 files changed, 2 insertions(+), 5 deletions(-) diff --git a/src/cpp/src/index_partition.cpp b/src/cpp/src/index_partition.cpp index 128c5b44..a8b84845 100644 --- a/src/cpp/src/index_partition.cpp +++ b/src/cpp/src/index_partition.cpp @@ -148,8 +148,6 @@ void IndexPartition::set_numa_node(int new_numa_node) { bool is_valid_numa = (new_numa_node == -1) || (numa_available() != -1 && new_numa_node <= numa_max_node()); - std::cout << "new_numa_node=" << new_numa_node << ", numa_available()=" << numa_available() << ", numa_max_node()=" << numa_max_node() << '\n'; - if (!is_valid_numa) { throw std::runtime_error("Invalid numa node specified"); } diff --git a/src/cpp/src/partition_manager.cpp b/src/cpp/src/partition_manager.cpp index 1a0933b8..428c7ed4 100644 --- a/src/cpp/src/partition_manager.cpp +++ b/src/cpp/src/partition_manager.cpp @@ -630,9 +630,8 @@ void PartitionManager::set_partition_core_id(int64_t partition_id, int core_id, #ifdef QUAKE_USE_NUMA if (use_numa) { - int node = numa_node_of_cpu(core_id); // <-- single call - std::cout << "Setting PID=" << partition_id << " to NUMA_node="<< node << " for core=" << core_id << '\n'; - partition_store_->partitions_[partition_id]->set_numa_node(core_id); + int node = numa_node_of_cpu(core_id); + partition_store_->partitions_[partition_id]->set_numa_node(node); } #endif } From 8a30fa4ec7c2535e86eff3291e97ee822c46e7f7 Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 16 May 2025 19:23:40 -0500 Subject: [PATCH 084/323] fix numa alloc --- src/cpp/src/index_partition.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/src/cpp/src/index_partition.cpp b/src/cpp/src/index_partition.cpp index a8b84845..868f94bb 100644 --- a/src/cpp/src/index_partition.cpp +++ b/src/cpp/src/index_partition.cpp @@ -254,6 +254,7 @@ T* IndexPartition::allocate_memory(size_t num_elements, int numa_node) { if (numa_node == -1) { ptr = reinterpret_cast(std::malloc(total_bytes)); } else { + std::cout << numa_alloc_onnode << '\n'; ptr = reinterpret_cast(numa_alloc_onnode(total_bytes, numa_node)); } #else From 18164143e6e87f55fc534683e9503b0595678c1d Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 16 May 2025 19:28:12 -0500 Subject: [PATCH 085/323] fix numa alloc --- src/cpp/src/index_partition.cpp | 1 - 1 file changed, 1 deletion(-) diff --git a/src/cpp/src/index_partition.cpp b/src/cpp/src/index_partition.cpp index 868f94bb..a8b84845 100644 --- a/src/cpp/src/index_partition.cpp +++ b/src/cpp/src/index_partition.cpp @@ -254,7 +254,6 @@ T* IndexPartition::allocate_memory(size_t num_elements, int numa_node) { if (numa_node == -1) { ptr = reinterpret_cast(std::malloc(total_bytes)); } else { - std::cout << numa_alloc_onnode << '\n'; ptr = reinterpret_cast(numa_alloc_onnode(total_bytes, numa_node)); } #else From d471723f1fe67f18a679fedebdaa3c0948ac8b74 Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 16 May 2025 19:43:49 -0500 Subject: [PATCH 086/323] fix numa alloc --- src/cpp/src/index_partition.cpp | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/cpp/src/index_partition.cpp b/src/cpp/src/index_partition.cpp index a8b84845..1c1c62c5 100644 --- a/src/cpp/src/index_partition.cpp +++ b/src/cpp/src/index_partition.cpp @@ -252,11 +252,14 @@ T* IndexPartition::allocate_memory(size_t num_elements, int numa_node) { T* ptr = nullptr; #ifdef QUAKE_USE_NUMA if (numa_node == -1) { + std::cout << "Calling malloc with total_bytes=" << total_bytes << ", numa_node=" << numa_node << '\n'; ptr = reinterpret_cast(std::malloc(total_bytes)); } else { + std::cout << "Calling numa_alloc_onnode with total_bytes=" << total_bytes << "", numa_node=" << numa_node << '\n'; ptr = reinterpret_cast(numa_alloc_onnode(total_bytes, numa_node)); } #else + std::cout << "Calling malloc with total_bytes=" << total_bytes << '\n'; ptr = reinterpret_cast(std::malloc(total_bytes)); #endif if (!ptr) { From 7a5ddf36d2f4e1ba37c3f406e214e0e8c7d5009a Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 16 May 2025 19:44:48 -0500 Subject: [PATCH 087/323] fix numa alloc --- src/cpp/src/index_partition.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/cpp/src/index_partition.cpp b/src/cpp/src/index_partition.cpp index 1c1c62c5..dae61b5c 100644 --- a/src/cpp/src/index_partition.cpp +++ b/src/cpp/src/index_partition.cpp @@ -255,7 +255,7 @@ T* IndexPartition::allocate_memory(size_t num_elements, int numa_node) { std::cout << "Calling malloc with total_bytes=" << total_bytes << ", numa_node=" << numa_node << '\n'; ptr = reinterpret_cast(std::malloc(total_bytes)); } else { - std::cout << "Calling numa_alloc_onnode with total_bytes=" << total_bytes << "", numa_node=" << numa_node << '\n'; + std::cout << "Calling numa_alloc_onnode with total_bytes=" << total_bytes << ", numa_node=" << numa_node << '\n'; ptr = reinterpret_cast(numa_alloc_onnode(total_bytes, numa_node)); } #else From ecf310e464d4fd5995088aa0b3882d5034bf6d01 Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 16 May 2025 19:51:36 -0500 Subject: [PATCH 088/323] fix numa alloc --- src/cpp/src/index_partition.cpp | 3 --- 1 file changed, 3 deletions(-) diff --git a/src/cpp/src/index_partition.cpp b/src/cpp/src/index_partition.cpp index dae61b5c..a8b84845 100644 --- a/src/cpp/src/index_partition.cpp +++ b/src/cpp/src/index_partition.cpp @@ -252,14 +252,11 @@ T* IndexPartition::allocate_memory(size_t num_elements, int numa_node) { T* ptr = nullptr; #ifdef QUAKE_USE_NUMA if (numa_node == -1) { - std::cout << "Calling malloc with total_bytes=" << total_bytes << ", numa_node=" << numa_node << '\n'; ptr = reinterpret_cast(std::malloc(total_bytes)); } else { - std::cout << "Calling numa_alloc_onnode with total_bytes=" << total_bytes << ", numa_node=" << numa_node << '\n'; ptr = reinterpret_cast(numa_alloc_onnode(total_bytes, numa_node)); } #else - std::cout << "Calling malloc with total_bytes=" << total_bytes << '\n'; ptr = reinterpret_cast(std::malloc(total_bytes)); #endif if (!ptr) { From 51550e94d6eb39c4018de6330be034daa3da867f Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 16 May 2025 20:25:20 -0500 Subject: [PATCH 089/323] fix numa alloc --- test/experiments/osdi2025/experiment_runner.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/test/experiments/osdi2025/experiment_runner.py b/test/experiments/osdi2025/experiment_runner.py index b9142976..b51b8725 100644 --- a/test/experiments/osdi2025/experiment_runner.py +++ b/test/experiments/osdi2025/experiment_runner.py @@ -6,7 +6,7 @@ from pathlib import Path from test.experiments.osdi2025.kick_the_tires.run import run_experiment as run_kick_the_tires -# from test.experiments.osdi2025.numa_single_query.run import run_experiment as run_numa_single +from test.experiments.osdi2025.numa_single_query.run import run_experiment as run_numa_single from test.experiments.osdi2025.numa_multi_query.run import run_experiment as run_numa_multi from test.experiments.osdi2025.aps_recall_targets.run import run_experiment as run_aps_recall from test.experiments.osdi2025.early_termination.run import run_experiment as run_early_termination @@ -16,7 +16,7 @@ EXPERIMENTS = { "kick_the_tires": run_kick_the_tires, - # "numa_single_query": run_numa_single, + "numa_single_query": run_numa_single, "numa_multi_query": run_numa_multi, "aps_recall_targets": run_aps_recall, "early_termination": run_early_termination, From 0ac70860a9ec89045478c1ebcd85d111cc4675b0 Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 16 May 2025 20:26:10 -0500 Subject: [PATCH 090/323] fix numa alloc --- .../numa_single_query/configs/sift1m.yaml | 96 ++++++++++--------- 1 file changed, 53 insertions(+), 43 deletions(-) diff --git a/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml b/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml index 589ccb80..2e292300 100644 --- a/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml +++ b/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml @@ -9,6 +9,16 @@ warmup: 10 overwrite: false # Set to true to force all indexes to rerun indexes: + - name: Quake_0 + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 1 + use_numa: false + search_params: + nprobe: 1000 + - name: Quake_1 index: Quake build_params: @@ -69,49 +79,49 @@ indexes: search_params: nprobe: 1000 - - name: FaissIVF - index: IVF - build_params: - nc: 1000 - metric: l2 - search_params: - nprobe: 1000 - - - name: SCANN - index: SCANN - build_params: - num_leaves: 1000 - metric: squared_l2 - search_params: - leaves_to_search: 1000 - - - name: FaissHNSW - index: HNSW - build_params: - m: 64 - ef_construction: 128 - metric: l2 - search_params: - ef_search: 32 - - - name: DiskANN - index: DiskANN - build_params: - complexity: 128 - graph_degree: 64 - metric: l2 - search_params: - complexity: 32 - - - name: SVS - index: SVS - build_params: - graph_max_degree: 64 - alpha: 1.1 - window_size: 128 - metric: l2 - search_params: - search_window_size: 32 +# - name: FaissIVF +# index: IVF +# build_params: +# nc: 1000 +# metric: l2 +# search_params: +# nprobe: 1000 +# +# - name: SCANN +# index: SCANN +# build_params: +# num_leaves: 1000 +# metric: squared_l2 +# search_params: +# leaves_to_search: 1000 +# +# - name: FaissHNSW +# index: HNSW +# build_params: +# m: 64 +# ef_construction: 128 +# metric: l2 +# search_params: +# ef_search: 32 +# +# - name: DiskANN +# index: DiskANN +# build_params: +# complexity: 128 +# graph_degree: 64 +# metric: l2 +# search_params: +# complexity: 32 +# +# - name: SVS +# index: SVS +# build_params: +# graph_max_degree: 64 +# alpha: 1.1 +# window_size: 128 +# metric: l2 +# search_params: +# search_window_size: 32 output: results_csv: numa_single_query_results.csv \ No newline at end of file From 4b10f3e800f2816d3fc38dcbc9a23227e5f3c4be Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 16 May 2025 20:27:24 -0500 Subject: [PATCH 091/323] fix numa alloc --- .../osdi2025/numa_single_query/configs/sift1m.yaml | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml b/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml index 2e292300..3682722b 100644 --- a/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml +++ b/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml @@ -17,7 +17,7 @@ indexes: num_workers: 1 use_numa: false search_params: - nprobe: 1000 + nprobe: 20 - name: Quake_1 index: Quake @@ -27,7 +27,7 @@ indexes: num_workers: 1 use_numa: false search_params: - nprobe: 1000 + nprobe: 20 - name: Quake_4 index: Quake @@ -37,7 +37,7 @@ indexes: num_workers: 4 use_numa: false search_params: - nprobe: 1000 + nprobe: 20 - name: Quake_16 index: Quake @@ -47,7 +47,7 @@ indexes: num_workers: 16 use_numa: false search_params: - nprobe: 1000 + nprobe: 20 - name: Quake_1_numa index: Quake @@ -57,7 +57,7 @@ indexes: num_workers: 1 use_numa: true search_params: - nprobe: 1000 + nprobe: 20 - name: Quake_4_numa index: Quake @@ -67,7 +67,7 @@ indexes: num_workers: 4 use_numa: true search_params: - nprobe: 1000 + nprobe: 20 - name: Quake_16_numa index: Quake @@ -77,7 +77,7 @@ indexes: num_workers: 16 use_numa: true search_params: - nprobe: 1000 + nprobe: 20 # - name: FaissIVF # index: IVF From d48a2cdb905648bfa5ac9f77d822d2357c532d9c Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 16 May 2025 22:35:45 -0500 Subject: [PATCH 092/323] fix numa alloc --- src/cpp/src/query_coordinator.cpp | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index cb4b0bd2..394fffc7 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -11,6 +11,13 @@ #include #include +static inline void prefault_range(void* p, size_t bytes) { + // Touch one byte per page; compiler will not optimise this away. + const size_t page = 4096; + for (size_t off = 0; off < bytes; off += page) + *(volatile char*)((char*)p + off); +} + // Constructor QueryCoordinator::QueryCoordinator(shared_ptr parent, shared_ptr partition_manager, @@ -100,6 +107,14 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { CoreResources &res = core_resources_[core_index]; + // Prefault the partitions this core owns + for (auto& kv : partition_manager_->partition_store_->partitions_) { + if (kv.second->core_id_ == core_index) { + prefault_range(kv.second->codes_, + size_t(kv.second->num_vectors_) * kv.second->code_size_); + } + } + if (!set_thread_affinity(core_index)) { std::cout << "[QueryCoordinator::partition_scan_worker_fn] Failed to set thread affinity on core " << core_index << std::endl; } From ce24768908c9854a7ae606643b78866b3b2e32dd Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 16 May 2025 22:39:45 -0500 Subject: [PATCH 093/323] fix numa alloc --- src/cpp/src/query_coordinator.cpp | 42 +++++++++++++------------------ 1 file changed, 18 insertions(+), 24 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 394fffc7..fc5c383b 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -11,13 +11,6 @@ #include #include -static inline void prefault_range(void* p, size_t bytes) { - // Touch one byte per page; compiler will not optimise this away. - const size_t page = 4096; - for (size_t off = 0; off < bytes; off += page) - *(volatile char*)((char*)p + off); -} - // Constructor QueryCoordinator::QueryCoordinator(shared_ptr parent, shared_ptr partition_manager, @@ -42,16 +35,25 @@ QueryCoordinator::~QueryCoordinator() { shutdown_workers(); } -void QueryCoordinator::allocate_core_resources(int core_idx, int num_queries, int k, int d) { - CoreResources &res = core_resources_[core_idx]; - res.core_id = core_idx; - res.local_query_buffer.resize(num_queries * d * sizeof(float)); - res.topk_buffer_pool.resize(num_queries); - for (int q = 0; q < num_queries; ++q) { - res.topk_buffer_pool[q] = make_shared(k, metric_ == faiss::METRIC_INNER_PRODUCT); - res.job_queue = moodycamel::BlockingConcurrentQueue(); +void QueryCoordinator::allocate_core_resources(int c, int nq, int k, int d) +{ + CoreResources &r = core_resources_[c]; + r.core_id = c; + +#ifdef QUAKE_USE_NUMA + int node = numa_node_of_cpu(c); + r.local_query_buffer.resize(nq * d * sizeof(float)); + numa_tonode_memory(r.local_query_buffer.data(), + r.local_query_buffer.size(), node); + prefault_range(r.local_query_buffer.data(), r.local_query_buffer.size()); +#else + r.local_query_buffer.resize(nq * d * sizeof(float)); +#endif + r.topk_buffer_pool.resize(nq); + for (int q = 0; q < nq; ++q) { + r.topk_buffer_pool[q] = make_shared(k, metric_ == faiss::METRIC_INNER_PRODUCT); + r.job_queue = moodycamel::BlockingConcurrentQueue(); } - } // Initialize Worker Threads @@ -107,14 +109,6 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { CoreResources &res = core_resources_[core_index]; - // Prefault the partitions this core owns - for (auto& kv : partition_manager_->partition_store_->partitions_) { - if (kv.second->core_id_ == core_index) { - prefault_range(kv.second->codes_, - size_t(kv.second->num_vectors_) * kv.second->code_size_); - } - } - if (!set_thread_affinity(core_index)) { std::cout << "[QueryCoordinator::partition_scan_worker_fn] Failed to set thread affinity on core " << core_index << std::endl; } From 19730e341ad236956e46392c09e26cbb7321c6b2 Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 16 May 2025 22:41:31 -0500 Subject: [PATCH 094/323] fix numa alloc --- src/cpp/src/query_coordinator.cpp | 1 - 1 file changed, 1 deletion(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index fc5c383b..c7a4a65d 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -45,7 +45,6 @@ void QueryCoordinator::allocate_core_resources(int c, int nq, int k, int d) r.local_query_buffer.resize(nq * d * sizeof(float)); numa_tonode_memory(r.local_query_buffer.data(), r.local_query_buffer.size(), node); - prefault_range(r.local_query_buffer.data(), r.local_query_buffer.size()); #else r.local_query_buffer.resize(nq * d * sizeof(float)); #endif From d7a03639afd854412435f34cd9334dcac8589ba2 Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 16 May 2025 23:25:40 -0500 Subject: [PATCH 095/323] fix numa alloc --- src/cpp/src/query_coordinator.cpp | 25 ++++++++----------------- 1 file changed, 8 insertions(+), 17 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index c7a4a65d..4343f2d5 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -35,23 +35,14 @@ QueryCoordinator::~QueryCoordinator() { shutdown_workers(); } -void QueryCoordinator::allocate_core_resources(int c, int nq, int k, int d) -{ - CoreResources &r = core_resources_[c]; - r.core_id = c; - -#ifdef QUAKE_USE_NUMA - int node = numa_node_of_cpu(c); - r.local_query_buffer.resize(nq * d * sizeof(float)); - numa_tonode_memory(r.local_query_buffer.data(), - r.local_query_buffer.size(), node); -#else - r.local_query_buffer.resize(nq * d * sizeof(float)); -#endif - r.topk_buffer_pool.resize(nq); - for (int q = 0; q < nq; ++q) { - r.topk_buffer_pool[q] = make_shared(k, metric_ == faiss::METRIC_INNER_PRODUCT); - r.job_queue = moodycamel::BlockingConcurrentQueue(); +void QueryCoordinator::allocate_core_resources(int core_idx, int num_queries, int k, int d) { + CoreResources &res = core_resources_[core_idx]; + res.core_id = core_idx; + res.local_query_buffer.resize(num_queries * d * sizeof(float)); + res.topk_buffer_pool.resize(num_queries); + for (int q = 0; q < num_queries; ++q) { + res.topk_buffer_pool[q] = make_shared(k, metric_ == faiss::METRIC_INNER_PRODUCT); + res.job_queue = moodycamel::BlockingConcurrentQueue(); } } From 61d8a9eda52561887911c3cf407cd3fc4945a222 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 02:22:28 -0500 Subject: [PATCH 096/323] add timing --- src/cpp/src/query_coordinator.cpp | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 4343f2d5..44901d8a 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -525,6 +525,14 @@ shared_ptr QueryCoordinator::worker_scan( search_result->distances = topk_dists; search_result->timing_info = timing_info; + // print out timing info + std::cout << "[QueryCoordinator::worker_scan] Timing info: " << std::endl; + std::cout << "Total time: " << timing_info->total_time_ns / 1e6 << " ms" << std::endl; + std::cout << "Job enqueue time: " << timing_info->job_enqueue_time_ns / 1e6 << " ms" << std::endl; + std::cout << "Job wait time: " << timing_info->job_wait_time_ns / 1e6 << " ms" << std::endl; + std::cout << "Buffer init time: " << timing_info->buffer_init_time_ns / 1e6 << " ms" << std::endl; + std::cout << "Result agg time: " << timing_info->result_aggregate_time_ns / 1e6 << " ms" << std::endl; + return search_result; } From a898cabf7ef93100b4252cb322214693f76ffb75 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 02:30:36 -0500 Subject: [PATCH 097/323] speed up batched scan enqueue --- src/cpp/src/query_coordinator.cpp | 59 +++++++++++++++++++++---------- 1 file changed, 40 insertions(+), 19 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 44901d8a..d90f6d85 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -352,27 +352,48 @@ shared_ptr QueryCoordinator::worker_scan( if (search_params->batched_scan) { auto partition_ids_accessor = partition_ids.accessor(); - std::unordered_map> per_partition_query_ids; - for (int64_t q = 0; q < num_queries; q++) { - for (int64_t p = 0; p < partition_ids.size(1); p++) { - int64_t pid = partition_ids_accessor[q][p]; - if (pid < 0) continue; - per_partition_query_ids[pid].push_back(q); + if (partition_manager_->nlist() == partition_ids.size(0)) { + vector all_query_ids = std::vector(x.size(0)); + std::iota(all_query_ids.begin(), all_query_ids.end(), 0); + + for (int64_t i = 0; i < partition_manager_->nlist(); i++) { + ScanJob job; + job.is_batched = true; + job.partition_id = i; + job.k = k; + job.query_vector = x.data_ptr(); + job.num_queries = x.size(0); + job.query_ids = all_query_ids; + int core_id = partition_manager_->get_partition_core_id(i); + if (core_id < 0) { + throw std::runtime_error("[QueryCoordinator::worker_scan] Invalid core ID."); + } + core_resources_[core_id].job_queue.enqueue(job); } - } - for (auto &kv : per_partition_query_ids) { - ScanJob job; - job.is_batched = true; - job.partition_id = kv.first; - job.k = k; - job.query_vector = x.data_ptr(); - job.num_queries = kv.second.size(); - job.query_ids = kv.second; - int core_id = partition_manager_->get_partition_core_id(kv.first); - if (core_id < 0) { - throw std::runtime_error("[QueryCoordinator::worker_scan] Invalid core ID."); + + } else { + std::unordered_map> per_partition_query_ids; + for (int64_t q = 0; q < num_queries; q++) { + for (int64_t p = 0; p < partition_ids.size(1); p++) { + int64_t pid = partition_ids_accessor[q][p]; + if (pid < 0) continue; + per_partition_query_ids[pid].push_back(q); + } + } + for (auto &kv : per_partition_query_ids) { + ScanJob job; + job.is_batched = true; + job.partition_id = kv.first; + job.k = k; + job.query_vector = x.data_ptr(); + job.num_queries = kv.second.size(); + job.query_ids = kv.second; + int core_id = partition_manager_->get_partition_core_id(kv.first); + if (core_id < 0) { + throw std::runtime_error("[QueryCoordinator::worker_scan] Invalid core ID."); + } + core_resources_[core_id].job_queue.enqueue(job); } - core_resources_[core_id].job_queue.enqueue(job); } } else { auto partition_ids_accessor = partition_ids.accessor(); From 912d2fc7fc7acb82326cd760f85aee337a909511 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 02:33:18 -0500 Subject: [PATCH 098/323] speed up batched scan enqueue --- src/cpp/src/query_coordinator.cpp | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index d90f6d85..a4dad365 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -353,6 +353,7 @@ shared_ptr QueryCoordinator::worker_scan( auto partition_ids_accessor = partition_ids.accessor(); if (partition_manager_->nlist() == partition_ids.size(0)) { + std::cout << "SCAN ALL\n"; vector all_query_ids = std::vector(x.size(0)); std::iota(all_query_ids.begin(), all_query_ids.end(), 0); @@ -372,6 +373,7 @@ shared_ptr QueryCoordinator::worker_scan( } } else { + std::cout << "BATCH SCAN SUBSET\n"; std::unordered_map> per_partition_query_ids; for (int64_t q = 0; q < num_queries; q++) { for (int64_t p = 0; p < partition_ids.size(1); p++) { @@ -396,6 +398,7 @@ shared_ptr QueryCoordinator::worker_scan( } } } else { + std::cout << "PER-QUERY SCAN\n"; auto partition_ids_accessor = partition_ids.accessor(); int64_t start = 0; From fceafae067fbcdf07b1e0253a679cc9b56d57b6c Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 02:37:23 -0500 Subject: [PATCH 099/323] speed up batched scan enqueue --- src/cpp/src/query_coordinator.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index a4dad365..73749609 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -790,6 +790,7 @@ shared_ptr QueryCoordinator::search(Tensor x, shared_ptrparent_params; } + parent_search_params->batched_scan = true; // if recall_target is set, we need an initial set of partitions to consider if (search_params->recall_target > 0.0 && !search_params->batched_scan) { From 6c271dd73ec31f4df1f12fd48e2e73f6aaa1c13b Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 02:40:45 -0500 Subject: [PATCH 100/323] speed up batched scan enqueue --- src/cpp/include/list_scanning.h | 184 ++++++++++++++++---------------- 1 file changed, 92 insertions(+), 92 deletions(-) diff --git a/src/cpp/include/list_scanning.h b/src/cpp/include/list_scanning.h index 573f0b4e..b8319e04 100644 --- a/src/cpp/include/list_scanning.h +++ b/src/cpp/include/list_scanning.h @@ -343,113 +343,113 @@ inline void scan_list(const float *query_vec, } } -inline void batched_scan_list( - const float *query_vecs, - const float *list_vecs, - const int64_t *list_ids, - int num_queries, - int list_size, - int dim, - vector> &topk_buffers, - MetricType metric = faiss::METRIC_L2) -{ - if (list_size == 0 || list_vecs == nullptr) { - return; - } - - // Wrap raw arrays in torch Tensors, no copy - Tensor query = torch::from_blob((void*)query_vecs, {num_queries, dim}, torch::kFloat32); - Tensor list = torch::from_blob((void*)list_vecs, {list_size, dim}, torch::kFloat32); - - torch::Tensor distances; - if (metric == faiss::METRIC_L2) { - // Returns [num_queries, list_size], each entry is the Euclidean distance - distances = torch::cdist(query, list, 2.0); - } else if (metric == faiss::METRIC_INNER_PRODUCT) { - // [num_queries, list_size], each entry is dot product - distances = torch::matmul(query, list.t()); - } else { - throw std::runtime_error("Metric type not supported"); - } - - // For each query, push all list vectors and their distances into TopkBuffer - auto distances_acc = distances.accessor(); - for (int i = 0; i < num_queries; ++i) { - std::vector dists(list_size); - std::vector ids(list_size); - - for (int j = 0; j < list_size; ++j) { - dists[j] = distances_acc[i][j]; - } - - if (list_ids) { - for (int j = 0; j < list_size; ++j) { - ids[j] = list_ids[j]; - } - } else { - for (int j = 0; j < list_size; ++j) { - ids[j] = j; - } - } - - topk_buffers[i]->batch_add(dists.data(), ids.data(), list_size); - } -} - -//inline void batched_scan_list(const float *query_vecs, -// const float *list_vecs, -// const int64_t *list_ids, -// int num_queries, -// int list_size, -// int dim, -// vector> &topk_buffers, -// MetricType metric = faiss::METRIC_L2) { +//inline void batched_scan_list( +// const float *query_vecs, +// const float *list_vecs, +// const int64_t *list_ids, +// int num_queries, +// int list_size, +// int dim, +// vector> &topk_buffers, +// MetricType metric = faiss::METRIC_L2) +//{ // if (list_size == 0 || list_vecs == nullptr) { -// // No list vectors to process; // return; // } // -// // Ensure k does not exceed list_size -// int k = topk_buffers[0]->k_; -// int k_max = std::min(k, list_size); +// // Wrap raw arrays in torch Tensors, no copy +// Tensor query = torch::from_blob((void*)query_vecs, {num_queries, dim}, torch::kFloat32); +// Tensor list = torch::from_blob((void*)list_vecs, {list_size, dim}, torch::kFloat32); // -// int64_t *labels = (int64_t *) malloc(num_queries * k_max * sizeof(int64_t)); -// float *distances = (float *) malloc(num_queries * k_max * sizeof(float)); -// -// if (metric == faiss::METRIC_INNER_PRODUCT) { -// faiss::float_minheap_array_t res = {size_t(num_queries), size_t(k_max), labels, distances}; -// faiss::knn_inner_product(query_vecs, list_vecs, dim, num_queries, list_size, &res, nullptr); -// } else if (metric == faiss::METRIC_L2) { -// faiss::float_maxheap_array_t res = {size_t(num_queries), size_t(k_max), labels, distances}; -// faiss::knn_L2sqr(query_vecs, list_vecs, dim, num_queries, list_size, &res, nullptr, nullptr); +// torch::Tensor distances; +// if (metric == faiss::METRIC_L2) { +// // Returns [num_queries, list_size], each entry is the Euclidean distance +// distances = torch::cdist(query, list, 2.0); +// } else if (metric == faiss::METRIC_INNER_PRODUCT) { +// // [num_queries, list_size], each entry is dot product +// distances = torch::matmul(query, list.t()); // } else { // throw std::runtime_error("Metric type not supported"); // } // -// // map the labels to the actual list_ids -// if (list_ids != nullptr) { -// for (int i = 0; i < num_queries; i++) { -// for (int j = 0; j < k_max; j++) { -// labels[i * k_max + j] = list_ids[labels[i * k_max + j]]; -// } +// // For each query, push all list vectors and their distances into TopkBuffer +// auto distances_acc = distances.accessor(); +// for (int i = 0; i < num_queries; ++i) { +// std::vector dists(list_size); +// std::vector ids(list_size); +// +// for (int j = 0; j < list_size; ++j) { +// dists[j] = distances_acc[i][j]; // } -// } // -// // if the metric is l2, convert the distances to sqrt -// if (metric == faiss::METRIC_L2) { -// for (int i = 0; i < num_queries * k_max; i++) { -// distances[i] = sqrt(distances[i]); +// if (list_ids) { +// for (int j = 0; j < list_size; ++j) { +// ids[j] = list_ids[j]; +// } +// } else { +// for (int j = 0; j < list_size; ++j) { +// ids[j] = j; +// } // } -// } // -// // add distances to the topk buffers -// for (int i = 0; i < num_queries; i++) { -// topk_buffers[i]->batch_add(distances + i * k_max, labels + i * k_max, k_max); +// topk_buffers[i]->batch_add(dists.data(), ids.data(), list_size); // } -// -// free(labels); -// free(distances); //} +inline void batched_scan_list(const float *query_vecs, + const float *list_vecs, + const int64_t *list_ids, + int num_queries, + int list_size, + int dim, + vector> &topk_buffers, + MetricType metric = faiss::METRIC_L2) { + if (list_size == 0 || list_vecs == nullptr) { + // No list vectors to process; + return; + } + + // Ensure k does not exceed list_size + int k = topk_buffers[0]->k_; + int k_max = std::min(k, list_size); + + int64_t *labels = (int64_t *) malloc(num_queries * k_max * sizeof(int64_t)); + float *distances = (float *) malloc(num_queries * k_max * sizeof(float)); + + if (metric == faiss::METRIC_INNER_PRODUCT) { + faiss::float_minheap_array_t res = {size_t(num_queries), size_t(k_max), labels, distances}; + faiss::knn_inner_product(query_vecs, list_vecs, dim, num_queries, list_size, &res, nullptr); + } else if (metric == faiss::METRIC_L2) { + faiss::float_maxheap_array_t res = {size_t(num_queries), size_t(k_max), labels, distances}; + faiss::knn_L2sqr(query_vecs, list_vecs, dim, num_queries, list_size, &res, nullptr, nullptr); + } else { + throw std::runtime_error("Metric type not supported"); + } + + // map the labels to the actual list_ids + if (list_ids != nullptr) { + for (int i = 0; i < num_queries; i++) { + for (int j = 0; j < k_max; j++) { + labels[i * k_max + j] = list_ids[labels[i * k_max + j]]; + } + } + } + + // if the metric is l2, convert the distances to sqrt + if (metric == faiss::METRIC_L2) { + for (int i = 0; i < num_queries * k_max; i++) { + distances[i] = sqrt(distances[i]); + } + } + + // add distances to the topk buffers + for (int i = 0; i < num_queries; i++) { + topk_buffers[i]->batch_add(distances + i * k_max, labels + i * k_max, k_max); + } + + free(labels); + free(distances); +} + // } #endif //LIST_SCANNING_H From fd9bb0dc7597015cd8eecf1b2bb4cc28636de6ac Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 02:47:00 -0500 Subject: [PATCH 101/323] reuse sinlge query worker --- .../osdi2025/numa_single_query/run.py | 348 ++++++++++-------- 1 file changed, 191 insertions(+), 157 deletions(-) diff --git a/test/experiments/osdi2025/numa_single_query/run.py b/test/experiments/osdi2025/numa_single_query/run.py index 95dea09d..fddcc395 100644 --- a/test/experiments/osdi2025/numa_single_query/run.py +++ b/test/experiments/osdi2025/numa_single_query/run.py @@ -1,230 +1,264 @@ #!/usr/bin/env python3 """ -NUMA Single-Query Latency and Recall Benchmark +NUMA Single-Query Latency and Recall Benchmark (v2) -Benchmarks a suite of ANN indexes (Quake, Faiss-IVF, SCANN, HNSW, DiskANN, SVS, etc.) -for single-query latency and recall@k under configurable build and search parameters. -Results are cached and only recomputed if the 'overwrite' flag is set in the configuration. -Outputs a unified CSV and a latency plot per index. +Builds each non-Quake index once. +Builds **one** shared Quake base-index and re-uses it for every Quake +configuration, so we avoid the O(dataset) rebuild for each worker-count. -Usage: - python numa_latency_experiment.py numa_latency_experiment.yaml output_dir +Usage +----- + python numa_latency_experiment.py """ +from pathlib import Path +import os import yaml import numpy as np import torch import pandas as pd import matplotlib.pyplot as plt -from pathlib import Path -from quake.utils import compute_recall +from quake.utils import compute_recall from quake.datasets.ann_datasets import load_dataset from quake.index_wrappers.faiss_ivf import FaissIVF from quake.index_wrappers.faiss_hnsw import FaissHNSW from quake.index_wrappers.quake import QuakeWrapper - try: from quake.index_wrappers.scann import Scann except ImportError: - raise ImportError("SCANN wrapper not available. Please install the required package.") + Scann = None try: from quake.index_wrappers.diskann import DiskANNDynamic except ImportError: - raise ImportError("DiskANN wrapper not available. Please install the required package.") + DiskANNDynamic = None try: from quake.index_wrappers.vamana import Vamana except ImportError: - raise ImportError("SVS wrapper not available. Please install the required package.") + Vamana = None INDEX_CLASSES = { - "Quake": QuakeWrapper, - "IVF": FaissIVF, - "SCANN": Scann, - "HNSW": FaissHNSW, + "Quake": QuakeWrapper, + "IVF": FaissIVF, + "SCANN": Scann, + "HNSW": FaissHNSW, "DiskANN": DiskANNDynamic, - "SVS": Vamana, + "SVS": Vamana, } -def build_and_save_index(index_class, build_params, base_vecs, index_file): - idx = index_class() - params = dict(build_params) - idx.build(base_vecs, **params) - idx.save(str(index_file)) - -def run_experiment(cfg_path: str, output_dir: str): - cfg = yaml.safe_load(Path(cfg_path).read_text()) - ds = cfg["dataset"] - queries_n = ds["num_queries"] - k = ds["k"] +# --------------------------------------------------------------------------- # +# Helpers # +# --------------------------------------------------------------------------- # +def build_and_save(index_cls, params, vecs, path): + """Build with on and persist to .""" + idx = index_cls() + idx.build(vecs, **params) + idx.save(str(path)) + +def extract_ids(res): + """Return search‐result ids as np.ndarray.""" + if hasattr(res, "ids"): + arr = res.ids + elif hasattr(res, "I"): + arr = res.I + elif hasattr(res, "indices"): + arr = res.indices + else: + raise RuntimeError("Search result has no id field.") + if isinstance(arr, torch.Tensor): + arr = arr.cpu().numpy() + return np.asarray(arr, dtype=np.int64) + +# --------------------------------------------------------------------------- # +# Main # +# --------------------------------------------------------------------------- # +def run_experiment(cfg_path: str, output_dir: str) -> None: + cfg = yaml.safe_load(Path(cfg_path).read_text()) + ds_cfg = cfg["dataset"] + k = ds_cfg["k"] + q_n = ds_cfg["num_queries"] trials = cfg.get("trials", 5) - warmup = cfg.get("warmup", 10) - indexes_cfg = cfg["indexes"] + warmup_n = cfg.get("warmup", 10) + idx_cfgs = cfg["indexes"] csv_name = cfg.get("output", {}).get("results_csv", "numa_results.csv") overwrite = cfg.get("overwrite", False) - out_dir = Path(output_dir) + out_dir = Path(output_dir).expanduser().absolute() out_dir.mkdir(parents=True, exist_ok=True) - print(f"[INFO] Loading dataset '{ds['name']}'...") - base_vecs, queries, gt = load_dataset(ds["name"]) - queries = queries[:queries_n] - gt = gt[:queries_n] if gt is not None else None - - all_rows = [] - - for idx_cfg in indexes_cfg: - idx_name = idx_cfg["name"] - idx_type = idx_cfg["index"] - build_params = idx_cfg.get("build_params", {}) - search_params = idx_cfg.get("search_params", {}) - IndexClass = INDEX_CLASSES.get(idx_type) - if IndexClass is None: - print(f"[WARN] Unknown index type: {idx_type}. Skipping.") + # --------------------------------------------------------------------- # + # Dataset # + # --------------------------------------------------------------------- # + print(f"[INFO] loading dataset {ds_cfg['name']} …") + base_vecs, queries, gt = load_dataset(ds_cfg["name"]) + queries = queries[:q_n] + gt = gt[:q_n] if gt is not None else None + + # --------------------------------------------------------------------- # + # Build a single Quake base index (if any Quake config exists) # + # --------------------------------------------------------------------- # + quake_base_params = None + for cfg_i in idx_cfgs: + if cfg_i["index"] == "Quake": + quake_base_params = dict(cfg_i.get("build_params", {})) + # Remove loading-stage-only keys; not needed for the build + quake_base_params.pop("num_workers", None) + quake_base_params.pop("use_numa", None) + break + + quake_base_path = out_dir / "Quake_base_index.bin" + if quake_base_params: + if overwrite and quake_base_path.exists(): + os.remove(quake_base_path) + if not quake_base_path.exists(): + print("[BUILD] Quake (shared) base index …") + build_and_save(QuakeWrapper, quake_base_params, base_vecs, quake_base_path) + print(f"[SAVE] Quake base index → {quake_base_path}") + + # --------------------------------------------------------------------- # + # Per-configuration benchmarking # + # --------------------------------------------------------------------- # + rows = [] + for cfg_i in idx_cfgs: + name = cfg_i["name"] + typ = cfg_i["index"] + b_params = dict(cfg_i.get("build_params", {})) # safe copy + s_params = dict(cfg_i.get("search_params", {})) + cls = INDEX_CLASSES.get(typ) + if cls is None: + print(f"[WARN] unknown index type '{typ}' – skipping.") continue - idx_csv = out_dir / f"{idx_name}_results.csv" - idx_file = out_dir / f"{idx_name}_index.bin" + idx_csv = out_dir / f"{name}_results.csv" + idx_path = quake_base_path if typ == "Quake" else out_dir / f"{name}_index.bin" + # Skip if we already have results and not overwriting if idx_csv.exists() and not overwrite: - print(f"[SKIP] {idx_name}: Results exist. Use overwrite: true to rerun.") - df_idx = pd.read_csv(idx_csv) - all_rows.append(df_idx.iloc[0].to_dict()) + print(f"[SKIP] {name} – results exist.") + rows.append(pd.read_csv(idx_csv).iloc[0].to_dict()) continue - if not idx_file.exists(): - print(f"[BUILD] {idx_name} index...") - build_and_save_index(IndexClass, build_params, base_vecs, idx_file) - print(f"[SAVE] Index saved at {idx_file}") - - print(f"[RUN] {idx_name} ...") - - if idx_type == "Quake": - load_kwargs = {} - if "use_numa" in build_params: - load_kwargs["use_numa"] = build_params["use_numa"] - if "num_workers" in build_params: - load_kwargs["num_workers"] = build_params["num_workers"] - idx = IndexClass() - idx.load(str(idx_file), **load_kwargs) - elif idx_type == "DiskANN": - idx = IndexClass() - # remove metric from build_params - build_params.pop("metric", None) - idx.load(str(idx_file), **build_params) + # Build non-Quake index (one-off) + if typ != "Quake" and not idx_path.exists(): + print(f"[BUILD] {name} index …") + build_and_save(cls, b_params, base_vecs, idx_path) + print(f"[SAVE] index → {idx_path}") + + # ----------------------------------------------------------------- # + # Load # + # ----------------------------------------------------------------- # + if typ == "Quake": + load_kw = {k: b_params[k] for k in ("use_numa", "num_workers") if k in b_params} + idx = QuakeWrapper() + idx.load(str(idx_path), **load_kw) + elif typ == "DiskANN": + idx = cls() + b_params.pop("metric", None) # DiskANN load signature + idx.load(str(idx_path), **b_params) + else: + idx = cls() + idx.load(str(idx_path)) + + # ----------------------------------------------------------------- # + # Search parameter dispatch # + # ----------------------------------------------------------------- # + if typ == "SCANN": + search_kw = {"leaves_to_search": s_params.get("leaves_to_search", 100)} + elif typ == "HNSW": + search_kw = {"ef_search": s_params.get("ef_search", 32)} + elif typ == "DiskANN": + search_kw = {"complexity": s_params.get("complexity", 32)} + elif typ == "SVS": + search_kw = {"search_window_size": s_params.get("search_window_size", 32)} else: - idx = IndexClass() - idx.load(str(idx_file)) - - if idx_type == "SCANN": - search_args = {"leaves_to_search": search_params.get("leaves_to_search", 100)} - elif idx_type == "HNSW": - search_args = {"ef_search": search_params.get("ef_search", 32)} - elif idx_type == "DiskANN": - search_args = {"complexity": search_params.get("complexity", 32)} - elif idx_type == "SVS": - search_args = {"search_window_size": search_params.get("search_window_size", 32)} - else: # IVF, Quake, others - search_args = {"nprobe": search_params.get("nprobe", 100)} - - # Warmup - for i in range(min(warmup, len(queries))): - q = queries[i].unsqueeze(0).float() - idx.search(q, k, **search_args) - - # Benchmark Trials (and collect recall for first trial) - trial_means = [] - trial_recalls = [] + search_kw = {"nprobe": s_params.get("nprobe", 100)} + + # ----------------------------------------------------------------- # + # Warm-up # + # ----------------------------------------------------------------- # + for q in queries[:min(warmup_n, len(queries))]: + idx.search(q.unsqueeze(0).float(), k, **search_kw) + + # ----------------------------------------------------------------- # + # Trials # + # ----------------------------------------------------------------- # + trial_lat = [] + trial_rec = [] for t in range(trials): - lats = [] - if t == 0 and gt is not None: - all_preds = [] - for qi, q_vec in enumerate(queries): - q = q_vec.unsqueeze(0).float() - res = idx.search(q, k, **search_args) + lats_ms, preds = [], [] + for qi, q in enumerate(queries): + res = idx.search(q.unsqueeze(0).float(), k, **search_kw) + # latency collection ti = getattr(res, "timing_info", None) if ti and hasattr(ti, "total_time_ns"): - lats.append(ti.total_time_ns / 1e6) + lats_ms.append(ti.total_time_ns / 1e6) elif hasattr(res, "latency_ms"): - lats.append(res.latency_ms) + lats_ms.append(res.latency_ms) else: - raise RuntimeError(f"No timing info found in search result for {idx_name}") - # Collect predicted indices for recall in trial 0 + raise RuntimeError("missing timing info.") if t == 0 and gt is not None: - pred = res.ids # [1, k] - all_preds.append(pred[0]) # get the [k] - mean_t = float(np.mean(lats)) - trial_means.append(mean_t) + preds.append(extract_ids(res)[0]) # [k] + trial_lat.append(float(np.mean(lats_ms))) if t == 0 and gt is not None: - preds_arr = torch.stack(all_preds) # [num_queries, k] - print(f"[DEBUG] preds_arr shape: {preds_arr.shape}, gt shape: {gt.shape}") - recall = float(compute_recall(preds_arr, gt, k).mean()) - trial_recalls.append(recall) - print(f" [trial {t+1}/{trials}] {mean_t:.2f} ms | recall@{k}: {recall:.4f}") + recall = float(compute_recall(torch.tensor(preds), gt, k).mean()) + trial_rec.append(recall) + print(f" [{name} trial {t+1}/{trials}] {trial_lat[-1]:.2f} ms | R@{k} {recall:.4f}") else: - print(f" [trial {t+1}/{trials}] {mean_t:.2f} ms") + print(f" [{name} trial {t+1}/{trials}] {trial_lat[-1]:.2f} ms") - mean_lat = float(np.mean(trial_means)) - std_lat = float(np.std(trial_means)) - mean_recall = float(trial_recalls[0]) if trial_recalls else np.nan - n_workers_val = build_params.get("num_workers", None) + # ----------------------------------------------------------------- # + # Persist per-config results # + # ----------------------------------------------------------------- # row = { - "index": idx_name, - "n_workers": n_workers_val, - "mean_latency_ms": mean_lat, - "std_latency_ms": std_lat, - f"recall_at_{k}": mean_recall, + "index": name, + "n_workers": b_params.get("num_workers", np.nan), + "mean_latency_ms": float(np.mean(trial_lat)), + "std_latency_ms": float(np.std(trial_lat)), + f"recall_at_{k}": (float(trial_rec[0]) if trial_rec else np.nan), } - all_rows.append(row) + rows.append(row) pd.DataFrame([row]).to_csv(idx_csv, index=False) - # Save Unified CSV - df = pd.DataFrame(all_rows) + # --------------------------------------------------------------------- # + # Aggregate CSV + plots # + # --------------------------------------------------------------------- # + df = pd.DataFrame(rows) out_csv = out_dir / csv_name df.to_csv(out_csv, index=False) - print(f"\n[RESULT] Results written to {out_csv}") + print(f"\n[RESULT] aggregated CSV → {out_csv}") - # Plot Latency + # Latency plot plt.figure() - for idx_name in df["index"].unique(): - subset = df[df["index"] == idx_name] - label = f"{idx_name}" if subset["n_workers"].isnull().all() else f"{idx_name} (n_workers={subset['n_workers'].iloc[0]})" - plt.errorbar( - subset["n_workers"] if "n_workers" in subset else subset.index, - subset["mean_latency_ms"], - yerr=subset["std_latency_ms"], - marker="o", - capsize=5, - label=label - ) + for nm in df["index"].unique(): + sub = df[df["index"] == nm] + lbl = f"{nm}" if sub["n_workers"].isnull().all() else f"{nm} (nw={sub['n_workers'].iloc[0]})" + plt.errorbar(sub["n_workers"] if "n_workers" in sub else sub.index, + sub["mean_latency_ms"], yerr=sub["std_latency_ms"], + marker="o", capsize=5, label=lbl) plt.xscale("symlog", base=2) plt.xlabel("Num Workers") plt.ylabel("Mean Latency (ms)") - plt.title("Single-Query Latency (per-index)") + plt.title("Single-Query Latency") plt.legend() - plot_file = out_dir / f"{out_csv.stem}_latency.png" plt.tight_layout() - plt.savefig(plot_file) - print(f"[PLOT] Saved to {plot_file}") + lat_path = out_dir / f"{out_csv.stem}_latency.png" + plt.savefig(lat_path) + print(f"[PLOT] latency plot → {lat_path}") - # Plot Recall + # Recall plot if f"recall_at_{k}" in df.columns: plt.figure() - for idx_name in df["index"].unique(): - subset = df[df["index"] == idx_name] - label = f"{idx_name}" if subset["n_workers"].isnull().all() else f"{idx_name} (n_workers={subset['n_workers'].iloc[0]})" - plt.plot( - subset["n_workers"] if "n_workers" in subset else subset.index, - subset[f"recall_at_{k}"], - marker="o", - label=label - ) + for nm in df["index"].unique(): + sub = df[df["index"] == nm] + lbl = f"{nm}" if sub["n_workers"].isnull().all() else f"{nm} (nw={sub['n_workers'].iloc[0]})" + plt.plot(sub["n_workers"] if "n_workers" in sub else sub.index, + sub[f"recall_at_{k}"], marker="o", label=lbl) plt.xscale("symlog", base=2) plt.xlabel("Num Workers") plt.ylabel(f"Recall@{k}") - plt.title(f"Recall@{k} (per-index)") + plt.title(f"Recall@{k}") plt.legend() - plot_file = out_dir / f"{out_csv.stem}_recall.png" plt.tight_layout() - plt.savefig(plot_file) - print(f"[PLOT] Recall plot saved to {plot_file}") + rec_path = out_dir / f"{out_csv.stem}_recall.png" + plt.savefig(rec_path) + print(f"[PLOT] recall plot → {rec_path}") \ No newline at end of file From d587668eb62050680bbcd9f7d6a088bb285f423c Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 02:56:07 -0500 Subject: [PATCH 102/323] reuse sinlge query worker --- src/cpp/src/query_coordinator.cpp | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 73749609..6fc3eb62 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -103,6 +103,10 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { std::cout << "[QueryCoordinator::partition_scan_worker_fn] Failed to set thread affinity on core " << core_index << std::endl; } + int64_t total_pull_time = 0; + int64_t total_scan_time = 0; + int64_t total_merge_time = 0; + while (true) { ScanJob job; @@ -260,6 +264,10 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { auto process_time = std::chrono::duration_cast(s3 - s2).count(); auto merge_time = std::chrono::duration_cast(s4 - s3).count(); + total_pull_time += wait_time; + total_scan_time += process_time; + total_merge_time += merge_time; + // std::cout << "[QueryCoordinator::partition_scan_worker_fn] Worker " << core_index // << " wait_time: " << wait_time / 1e6 << " ms, " // << "process_time: " << process_time / 1e6 << " ms, " @@ -268,6 +276,8 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { auto job_process_end = std::chrono::high_resolution_clock::now(); job_process_time_ns += std::chrono::duration_cast(job_process_end - job_process_start).count(); } + + std::cout << "Worker=" << core_index << ": total_pull_time=" << total_pull_time / 1000 << ", total_scan_time=" << total_scan_time / 1000 << ", merge_time=" << merge_time / 1000 << '\n'; } // Worker-Based Scan Implementation From 53cb8a26e17a1d1a50416207b0db55253070bbac Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 02:56:18 -0500 Subject: [PATCH 103/323] reuse sinlge query worker --- src/cpp/src/query_coordinator.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 6fc3eb62..4411c611 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -277,7 +277,7 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { job_process_time_ns += std::chrono::duration_cast(job_process_end - job_process_start).count(); } - std::cout << "Worker=" << core_index << ": total_pull_time=" << total_pull_time / 1000 << ", total_scan_time=" << total_scan_time / 1000 << ", merge_time=" << merge_time / 1000 << '\n'; + std::cout << "Worker=" << core_index << ": total_pull_time=" << total_pull_time / 1000 << ", total_scan_time=" << total_scan_time / 1000 << ", merge_time=" << total_merge_time / 1000 << '\n'; } // Worker-Based Scan Implementation From e0d3ab7534ec7f12834c05a114debfbb33dc52c5 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 02:58:47 -0500 Subject: [PATCH 104/323] reuse sinlge query worker --- src/cpp/src/query_coordinator.cpp | 29 +++++++++++++++-------------- 1 file changed, 15 insertions(+), 14 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 4411c611..4d57dafe 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -362,20 +362,23 @@ shared_ptr QueryCoordinator::worker_scan( if (search_params->batched_scan) { auto partition_ids_accessor = partition_ids.accessor(); - if (partition_manager_->nlist() == partition_ids.size(0)) { - std::cout << "SCAN ALL\n"; + auto pids = partition_manager_->get_partition_ids(); + auto pids_acc = pids.accessor(); + + if (pids.size(0) == partition_ids.size(1)) { vector all_query_ids = std::vector(x.size(0)); std::iota(all_query_ids.begin(), all_query_ids.end(), 0); - for (int64_t i = 0; i < partition_manager_->nlist(); i++) { + + for (int64_t i = 0; i < pids.size(0); i++) { ScanJob job; job.is_batched = true; - job.partition_id = i; + job.partition_id = pids_acc[i]; job.k = k; job.query_vector = x.data_ptr(); job.num_queries = x.size(0); job.query_ids = all_query_ids; - int core_id = partition_manager_->get_partition_core_id(i); + int core_id = partition_manager_->get_partition_core_id(pids_acc[i]); if (core_id < 0) { throw std::runtime_error("[QueryCoordinator::worker_scan] Invalid core ID."); } @@ -383,7 +386,6 @@ shared_ptr QueryCoordinator::worker_scan( } } else { - std::cout << "BATCH SCAN SUBSET\n"; std::unordered_map> per_partition_query_ids; for (int64_t q = 0; q < num_queries; q++) { for (int64_t p = 0; p < partition_ids.size(1); p++) { @@ -408,7 +410,6 @@ shared_ptr QueryCoordinator::worker_scan( } } } else { - std::cout << "PER-QUERY SCAN\n"; auto partition_ids_accessor = partition_ids.accessor(); int64_t start = 0; @@ -559,13 +560,13 @@ shared_ptr QueryCoordinator::worker_scan( search_result->distances = topk_dists; search_result->timing_info = timing_info; - // print out timing info - std::cout << "[QueryCoordinator::worker_scan] Timing info: " << std::endl; - std::cout << "Total time: " << timing_info->total_time_ns / 1e6 << " ms" << std::endl; - std::cout << "Job enqueue time: " << timing_info->job_enqueue_time_ns / 1e6 << " ms" << std::endl; - std::cout << "Job wait time: " << timing_info->job_wait_time_ns / 1e6 << " ms" << std::endl; - std::cout << "Buffer init time: " << timing_info->buffer_init_time_ns / 1e6 << " ms" << std::endl; - std::cout << "Result agg time: " << timing_info->result_aggregate_time_ns / 1e6 << " ms" << std::endl; +// // print out timing info +// std::cout << "[QueryCoordinator::worker_scan] Timing info: " << std::endl; +// std::cout << "Total time: " << timing_info->total_time_ns / 1e6 << " ms" << std::endl; +// std::cout << "Job enqueue time: " << timing_info->job_enqueue_time_ns / 1e6 << " ms" << std::endl; +// std::cout << "Job wait time: " << timing_info->job_wait_time_ns / 1e6 << " ms" << std::endl; +// std::cout << "Buffer init time: " << timing_info->buffer_init_time_ns / 1e6 << " ms" << std::endl; +// std::cout << "Result agg time: " << timing_info->result_aggregate_time_ns / 1e6 << " ms" << std::endl; return search_result; } From 2d310d6573e23878eb8435d28d6b215be89ac300 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 03:00:33 -0500 Subject: [PATCH 105/323] reuse sinlge query worker --- test/experiments/osdi2025/numa_single_query/run.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/test/experiments/osdi2025/numa_single_query/run.py b/test/experiments/osdi2025/numa_single_query/run.py index fddcc395..2a0c35e6 100644 --- a/test/experiments/osdi2025/numa_single_query/run.py +++ b/test/experiments/osdi2025/numa_single_query/run.py @@ -108,8 +108,6 @@ def run_experiment(cfg_path: str, output_dir: str) -> None: quake_base_path = out_dir / "Quake_base_index.bin" if quake_base_params: - if overwrite and quake_base_path.exists(): - os.remove(quake_base_path) if not quake_base_path.exists(): print("[BUILD] Quake (shared) base index …") build_and_save(QuakeWrapper, quake_base_params, base_vecs, quake_base_path) From 1fc264e2c6ff20884aae1ee6628435b590ab8d5b Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 03:05:17 -0500 Subject: [PATCH 106/323] reuse sinlge query worker --- src/cpp/src/query_coordinator.cpp | 20 ++++++++++++++------ 1 file changed, 14 insertions(+), 6 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 4d57dafe..fb43996f 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -160,6 +160,7 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { // Branch for non-batched jobs. if (!job.is_batched) { + auto s1 = std::chrono::high_resolution_clock::now(); // Allocate a thread-local query buffer if needed. if (res.local_query_buffer.size() < partition_manager_->d() * sizeof(float)) { res.local_query_buffer.resize(partition_manager_->d() * sizeof(float)); @@ -176,6 +177,7 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { local_topk_buffer->set_k(job.k); local_topk_buffer->reset(); } + auto s2 = std::chrono::high_resolution_clock::now(); // Perform the scan on the partition. scan_list((float *) res.local_query_buffer.data(), partition_codes, @@ -184,6 +186,7 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { partition_manager_->d(), *local_topk_buffer, metric_); + auto s3 = std::chrono::high_resolution_clock::now(); vector topk = local_topk_buffer->get_topk(); vector topk_indices = local_topk_buffer->get_topk_indices(); @@ -192,6 +195,17 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { // Merge local results into the global query buffer. global_topk_buffer_pool_[job.query_ids[0]]->batch_add(topk.data(), topk_indices.data(), n_results); job_flags_[job.query_ids[0]][job.rank] = true; + + auto s4 = std::chrono::high_resolution_clock::now(); + + // print out timings for worker + auto wait_time = std::chrono::duration_cast(s2 - s1).count(); + auto process_time = std::chrono::duration_cast(s3 - s2).count(); + auto merge_time = std::chrono::duration_cast(s4 - s3).count(); + + total_pull_time += wait_time; + total_scan_time += process_time; + total_merge_time += merge_time; } // Batched job branch. else { @@ -267,11 +281,6 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { total_pull_time += wait_time; total_scan_time += process_time; total_merge_time += merge_time; - -// std::cout << "[QueryCoordinator::partition_scan_worker_fn] Worker " << core_index -// << " wait_time: " << wait_time / 1e6 << " ms, " -// << "process_time: " << process_time / 1e6 << " ms, " -// << "merge_time: " << merge_time / 1e6 << " ms" << std::endl; } auto job_process_end = std::chrono::high_resolution_clock::now(); job_process_time_ns += std::chrono::duration_cast(job_process_end - job_process_start).count(); @@ -801,7 +810,6 @@ shared_ptr QueryCoordinator::search(Tensor x, shared_ptrparent_params; } - parent_search_params->batched_scan = true; // if recall_target is set, we need an initial set of partitions to consider if (search_params->recall_target > 0.0 && !search_params->batched_scan) { From 74fd832e1d759c610f92694b51b1837270bdb1a2 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 11:18:40 -0500 Subject: [PATCH 107/323] reuse sinlge query worker --- src/cpp/src/query_coordinator.cpp | 5 +- .../osdi2025/numa_single_query/run.py | 267 +++++++++--------- 2 files changed, 141 insertions(+), 131 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index fb43996f..37abac8c 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -294,6 +294,9 @@ shared_ptr QueryCoordinator::worker_scan( Tensor x, Tensor partition_ids, shared_ptr search_params) { + + auto start_time = high_resolution_clock::now(); + if (!partition_manager_) { throw std::runtime_error("[QueryCoordinator::worker_scan] partition_manager_ is null."); } @@ -318,8 +321,6 @@ shared_ptr QueryCoordinator::worker_scan( float *x_ptr = x.data_ptr(); - auto start_time = high_resolution_clock::now(); - if (partition_ids.dim() == 1) { partition_ids = partition_ids.unsqueeze(0).expand({num_queries, partition_ids.size(0)}); } diff --git a/test/experiments/osdi2025/numa_single_query/run.py b/test/experiments/osdi2025/numa_single_query/run.py index 2a0c35e6..f105cb3c 100644 --- a/test/experiments/osdi2025/numa_single_query/run.py +++ b/test/experiments/osdi2025/numa_single_query/run.py @@ -1,18 +1,19 @@ #!/usr/bin/env python3 """ -NUMA Single-Query Latency and Recall Benchmark (v2) +NUMA Single-Query Latency and Recall Benchmark (detailed timers, aggregated) -Builds each non-Quake index once. -Builds **one** shared Quake base-index and re-uses it for every Quake -configuration, so we avoid the O(dataset) rebuild for each worker-count. +For each query we extract: + - buffer_init_time_ns + - job_enqueue_time_ns + - job_wait_time_ns + - result_aggregate_time_ns + - total_time_ns -Usage ------ - python numa_latency_experiment.py +We average each timer across queries in a trial, then average across trials, +and emit those means (in ms) alongside recall and latency. """ - +import sys from pathlib import Path -import os import yaml import numpy as np import torch @@ -46,17 +47,14 @@ "SVS": Vamana, } -# --------------------------------------------------------------------------- # -# Helpers # -# --------------------------------------------------------------------------- # def build_and_save(index_cls, params, vecs, path): - """Build with on and persist to .""" + """Builds and persists an index to disk.""" idx = index_cls() idx.build(vecs, **params) idx.save(str(path)) def extract_ids(res): - """Return search‐result ids as np.ndarray.""" + """Normalize the returned IDs array.""" if hasattr(res, "ids"): arr = res.ids elif hasattr(res, "I"): @@ -69,97 +67,81 @@ def extract_ids(res): arr = arr.cpu().numpy() return np.asarray(arr, dtype=np.int64) -# --------------------------------------------------------------------------- # -# Main # -# --------------------------------------------------------------------------- # def run_experiment(cfg_path: str, output_dir: str) -> None: - cfg = yaml.safe_load(Path(cfg_path).read_text()) - ds_cfg = cfg["dataset"] - k = ds_cfg["k"] - q_n = ds_cfg["num_queries"] - trials = cfg.get("trials", 5) - warmup_n = cfg.get("warmup", 10) - idx_cfgs = cfg["indexes"] - csv_name = cfg.get("output", {}).get("results_csv", "numa_results.csv") - overwrite = cfg.get("overwrite", False) + cfg = yaml.safe_load(Path(cfg_path).read_text()) + ds_cfg = cfg["dataset"] + k = ds_cfg["k"] + q_n = ds_cfg["num_queries"] + trials = cfg.get("trials", 5) + warmup_n = cfg.get("warmup", 10) + idx_cfgs = cfg["indexes"] + csv_name = cfg.get("output", {}).get("results_csv", "numa_results.csv") + overwrite = cfg.get("overwrite", False) out_dir = Path(output_dir).expanduser().absolute() out_dir.mkdir(parents=True, exist_ok=True) - # --------------------------------------------------------------------- # - # Dataset # - # --------------------------------------------------------------------- # + # Load data print(f"[INFO] loading dataset {ds_cfg['name']} …") base_vecs, queries, gt = load_dataset(ds_cfg["name"]) queries = queries[:q_n] gt = gt[:q_n] if gt is not None else None - # --------------------------------------------------------------------- # - # Build a single Quake base index (if any Quake config exists) # - # --------------------------------------------------------------------- # + # Build shared Quake base index if needed quake_base_params = None - for cfg_i in idx_cfgs: - if cfg_i["index"] == "Quake": - quake_base_params = dict(cfg_i.get("build_params", {})) - # Remove loading-stage-only keys; not needed for the build - quake_base_params.pop("num_workers", None) - quake_base_params.pop("use_numa", None) + for c in idx_cfgs: + if c["index"] == "Quake": + earthquake = dict(c.get("build_params", {})) + earthquake.pop("num_workers", None) + earthquake.pop("use_numa", None) + quake_base_params = earthquake break quake_base_path = out_dir / "Quake_base_index.bin" - if quake_base_params: - if not quake_base_path.exists(): - print("[BUILD] Quake (shared) base index …") - build_and_save(QuakeWrapper, quake_base_params, base_vecs, quake_base_path) - print(f"[SAVE] Quake base index → {quake_base_path}") + if quake_base_params and not quake_base_path.exists(): + print("[BUILD] Quake (shared) base index …") + build_and_save(QuakeWrapper, quake_base_params, base_vecs, quake_base_path) + print(f"[SAVE] Quake base index → {quake_base_path}") - # --------------------------------------------------------------------- # - # Per-configuration benchmarking # - # --------------------------------------------------------------------- # - rows = [] + results = [] for cfg_i in idx_cfgs: - name = cfg_i["name"] - typ = cfg_i["index"] - b_params = dict(cfg_i.get("build_params", {})) # safe copy - s_params = dict(cfg_i.get("search_params", {})) - cls = INDEX_CLASSES.get(typ) + name = cfg_i["name"] + typ = cfg_i["index"] + b_params = dict(cfg_i.get("build_params", {})) + s_params = dict(cfg_i.get("search_params", {})) + cls = INDEX_CLASSES.get(typ) if cls is None: print(f"[WARN] unknown index type '{typ}' – skipping.") continue idx_csv = out_dir / f"{name}_results.csv" - idx_path = quake_base_path if typ == "Quake" else out_dir / f"{name}_index.bin" + idx_path = (quake_base_path if typ == "Quake" + else out_dir / f"{name}_index.bin") - # Skip if we already have results and not overwriting + # Skip if done if idx_csv.exists() and not overwrite: print(f"[SKIP] {name} – results exist.") - rows.append(pd.read_csv(idx_csv).iloc[0].to_dict()) + results.append(pd.read_csv(idx_csv).iloc[0].to_dict()) continue - # Build non-Quake index (one-off) + # Build non-Quake if missing if typ != "Quake" and not idx_path.exists(): print(f"[BUILD] {name} index …") build_and_save(cls, b_params, base_vecs, idx_path) print(f"[SAVE] index → {idx_path}") - # ----------------------------------------------------------------- # - # Load # - # ----------------------------------------------------------------- # + # Load index if typ == "Quake": - load_kw = {k: b_params[k] for k in ("use_numa", "num_workers") if k in b_params} - idx = QuakeWrapper() - idx.load(str(idx_path), **load_kw) + load_kw = {k: b_params[k] + for k in ("use_numa", "num_workers") + if k in b_params} + idx = QuakeWrapper(); idx.load(str(idx_path), **load_kw) elif typ == "DiskANN": - idx = cls() - b_params.pop("metric", None) # DiskANN load signature - idx.load(str(idx_path), **b_params) + idx = cls(); b_params.pop("metric", None); idx.load(str(idx_path), **b_params) else: - idx = cls() - idx.load(str(idx_path)) + idx = cls(); idx.load(str(idx_path)) - # ----------------------------------------------------------------- # - # Search parameter dispatch # - # ----------------------------------------------------------------- # + # Dispatch search params if typ == "SCANN": search_kw = {"leaves_to_search": s_params.get("leaves_to_search", 100)} elif typ == "HNSW": @@ -171,92 +153,119 @@ def run_experiment(cfg_path: str, output_dir: str) -> None: else: search_kw = {"nprobe": s_params.get("nprobe", 100)} - # ----------------------------------------------------------------- # - # Warm-up # - # ----------------------------------------------------------------- # + # Warm-up for q in queries[:min(warmup_n, len(queries))]: idx.search(q.unsqueeze(0).float(), k, **search_kw) - # ----------------------------------------------------------------- # - # Trials # - # ----------------------------------------------------------------- # - trial_lat = [] - trial_rec = [] + # Collect per-trial means + all_trial_latencies = [] + all_trial_recalls = [] + # for each timer: list of trial-means + buf_init_trials = [] + enqueue_trials = [] + wait_trials = [] + agg_trials = [] + total_trials = [] + for t in range(trials): - lats_ms, preds = [], [] + latencies = [] + recalls = [] + buf_init_q = [] + enq_q = [] + wait_q = [] + agg_q = [] + total_q = [] + for qi, q in enumerate(queries): res = idx.search(q.unsqueeze(0).float(), k, **search_kw) - # latency collection - ti = getattr(res, "timing_info", None) - if ti and hasattr(ti, "total_time_ns"): - lats_ms.append(ti.total_time_ns / 1e6) - elif hasattr(res, "latency_ms"): - lats_ms.append(res.latency_ms) + ti = res.timing_info + # latency + if hasattr(ti, "total_time_ns"): + latencies.append(ti.total_time_ns / 1e6) else: - raise RuntimeError("missing timing info.") + raise RuntimeError("missing total_time_ns") + # recall on first trial only if t == 0 and gt is not None: - preds.append(extract_ids(res)[0]) # [k] - trial_lat.append(float(np.mean(lats_ms))) - if t == 0 and gt is not None: - recall = float(compute_recall(torch.tensor(preds), gt, k).mean()) - trial_rec.append(recall) - print(f" [{name} trial {t+1}/{trials}] {trial_lat[-1]:.2f} ms | R@{k} {recall:.4f}") - else: - print(f" [{name} trial {t+1}/{trials}] {trial_lat[-1]:.2f} ms") + preds = extract_ids(res)[0] + recalls.append(float(compute_recall(torch.tensor([preds]), gt[qi:qi+1], k).item())) + + # collect timers (ns) + buf_init_q.append(ti.buffer_init_time_ns) + enq_q.append (ti.job_enqueue_time_ns) + wait_q.append (ti.job_wait_time_ns) + agg_q.append (ti.result_aggregate_time_ns) + total_q.append (ti.total_time_ns) + + # trial means in ms + all_trial_latencies.append(np.mean(latencies)) + buf_init_trials.append(np.mean(buf_init_q) / 1e6) + enqueue_trials .append(np.mean(enq_q) / 1e6) + wait_trials .append(np.mean(wait_q) / 1e6) + agg_trials .append(np.mean(agg_q) / 1e6) + total_trials .append(np.mean(total_q) / 1e6) + + if t == 0 and recalls: + all_trial_recalls.append(np.mean(recalls)) + + print(f" [{name} trial {t+1}/{trials}] " + f"{all_trial_latencies[-1]:.2f} ms" + + (f" | R@{k} {all_trial_recalls[-1]:.4f}" if recalls else "")) - # ----------------------------------------------------------------- # - # Persist per-config results # - # ----------------------------------------------------------------- # + # final averages across trials row = { - "index": name, - "n_workers": b_params.get("num_workers", np.nan), - "mean_latency_ms": float(np.mean(trial_lat)), - "std_latency_ms": float(np.std(trial_lat)), - f"recall_at_{k}": (float(trial_rec[0]) if trial_rec else np.nan), + "index": name, + "n_workers": b_params.get("num_workers", np.nan), + "mean_latency_ms": float(np.mean(all_trial_latencies)), + "std_latency_ms": float(np.std(all_trial_latencies)), + f"recall_at_{k}": (float(all_trial_recalls[0]) if all_trial_recalls else np.nan), + "buffer_init_ms": float(np.mean(buf_init_trials)), + "enqueue_ms": float(np.mean(enqueue_trials)), + "wait_ms": float(np.mean(wait_trials)), + "aggregate_ms": float(np.mean(agg_trials)), + "total_time_ms": float(np.mean(total_trials)), } - rows.append(row) + results.append(row) pd.DataFrame([row]).to_csv(idx_csv, index=False) - # --------------------------------------------------------------------- # - # Aggregate CSV + plots # - # --------------------------------------------------------------------- # - df = pd.DataFrame(rows) - out_csv = out_dir / csv_name + # write aggregate CSV + plots + df = pd.DataFrame(results) + out_csv = Path(output_dir) / csv_name df.to_csv(out_csv, index=False) print(f"\n[RESULT] aggregated CSV → {out_csv}") - # Latency plot + # latency plot plt.figure() for nm in df["index"].unique(): sub = df[df["index"] == nm] - lbl = f"{nm}" if sub["n_workers"].isnull().all() else f"{nm} (nw={sub['n_workers'].iloc[0]})" - plt.errorbar(sub["n_workers"] if "n_workers" in sub else sub.index, - sub["mean_latency_ms"], yerr=sub["std_latency_ms"], - marker="o", capsize=5, label=lbl) + lbl = (nm if sub["n_workers"].isnull().all() + else f"{nm} (nw={int(sub['n_workers'].iloc[0])})") + plt.errorbar( + sub["n_workers"] if "n_workers" in sub else sub.index, + sub["mean_latency_ms"], + yerr=sub["std_latency_ms"], + marker="o", capsize=5, label=lbl + ) plt.xscale("symlog", base=2) - plt.xlabel("Num Workers") - plt.ylabel("Mean Latency (ms)") - plt.title("Single-Query Latency") - plt.legend() - plt.tight_layout() - lat_path = out_dir / f"{out_csv.stem}_latency.png" + plt.xlabel("Num Workers"); plt.ylabel("Mean Latency (ms)") + plt.title("Single-Query Latency"); plt.legend(); plt.tight_layout() + lat_path = Path(output_dir) / f"{out_csv.stem}_latency.png" plt.savefig(lat_path) print(f"[PLOT] latency plot → {lat_path}") - # Recall plot + # recall plot if f"recall_at_{k}" in df.columns: plt.figure() for nm in df["index"].unique(): sub = df[df["index"] == nm] - lbl = f"{nm}" if sub["n_workers"].isnull().all() else f"{nm} (nw={sub['n_workers'].iloc[0]})" - plt.plot(sub["n_workers"] if "n_workers" in sub else sub.index, - sub[f"recall_at_{k}"], marker="o", label=lbl) + lbl = (nm if sub["n_workers"].isnull().all() + else f"{nm} (nw={int(sub['n_workers'].iloc[0])})") + plt.plot( + sub["n_workers"] if "n_workers" in sub else sub.index, + sub[f"recall_at_{k}"], marker="o", label=lbl + ) plt.xscale("symlog", base=2) - plt.xlabel("Num Workers") - plt.ylabel(f"Recall@{k}") - plt.title(f"Recall@{k}") - plt.legend() - plt.tight_layout() - rec_path = out_dir / f"{out_csv.stem}_recall.png" + plt.xlabel("Num Workers"); plt.ylabel(f"Recall@{k}") + plt.title(f"Recall@{k}"); plt.legend(); plt.tight_layout() + rec_path = Path(output_dir) / f"{out_csv.stem}_recall.png" plt.savefig(rec_path) print(f"[PLOT] recall plot → {rec_path}") \ No newline at end of file From e49c6751dff8f75be4f9f731cf5db8dfb2625799 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 11:30:45 -0500 Subject: [PATCH 108/323] minimize job enqueing time --- src/cpp/include/query_coordinator.h | 3 ++- src/cpp/src/query_coordinator.cpp | 35 ++++++++++++++++------------- 2 files changed, 22 insertions(+), 16 deletions(-) diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index 60a80b48..e6965659 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -53,7 +53,7 @@ class QueryCoordinator { int core_id; ///< Logical identifier of the core. vector> topk_buffer_pool; ///< Preallocated Top‑K buffers. vector local_query_buffer; ///< Local aggregator for query results. - moodycamel::BlockingConcurrentQueue job_queue; ///< Job queue for scan jobs. + moodycamel::BlockingConcurrentQueue job_queue; ///< Job queue for scan jobs. }; vector core_resources_; ///< Per‑core resources for worker threads. @@ -67,6 +67,7 @@ class QueryCoordinator { std::atomic stop_workers_; ///< Flag to signal workers to terminate. bool debug_ = false; ///< Debug mode flag. + std::vector job_buffer_; vector>> job_flags_; ///< Flags to track job completion std::atomic job_pull_time_ns = 0; ///< Time spent pulling jobs from the queue. std::atomic job_process_time_ns = 0; ///< Time spent processing jobs. diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 37abac8c..70a8213d 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -42,7 +42,7 @@ void QueryCoordinator::allocate_core_resources(int core_idx, int num_queries, in res.topk_buffer_pool.resize(num_queries); for (int q = 0; q < num_queries; ++q) { res.topk_buffer_pool[q] = make_shared(k, metric_ == faiss::METRIC_INNER_PRODUCT); - res.job_queue = moodycamel::BlockingConcurrentQueue(); + res.job_queue = moodycamel::BlockingConcurrentQueue(); } } @@ -81,9 +81,7 @@ void QueryCoordinator::shutdown_workers() { stop_workers_.store(true); // Enqueue a special shutdown job for each core. for (auto &res : core_resources_) { - ScanJob termination_job; - termination_job.partition_id = -1; - res.job_queue.enqueue(termination_job); + res.job_queue.enqueue(-1); } // Join all worker threads. for (auto &thr : worker_threads_) { @@ -108,10 +106,10 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { int64_t total_merge_time = 0; while (true) { - ScanJob job; auto job_wait_start = std::chrono::high_resolution_clock::now(); - res.job_queue.wait_dequeue(job); + int64_t job_id; + res.job_queue.wait_dequeue(job_id); auto job_wait_end = std::chrono::high_resolution_clock::now(); job_pull_time_ns += std::chrono::duration_cast(job_wait_end - job_wait_start).count(); @@ -120,10 +118,12 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { shared_ptr local_topk_buffer = res.topk_buffer_pool[0]; // Shutdown signal: -1 indicates the worker should exit. - if (job.partition_id == -1) { + if (job_id == -1) { break; } + ScanJob job = job_buffer_[job_id]; + // Ignore this job if the global buffer is not processing queries. if (!global_topk_buffer_pool_[job.query_ids[0]]->currently_processing_query()) { // decrement the job counter @@ -327,6 +327,8 @@ shared_ptr QueryCoordinator::worker_scan( auto partition_ids_accessor = partition_ids.accessor(); + + job_buffer_.clear(); job_flags_.clear(); job_flags_.resize(num_queries); for (int64_t q = 0; q < num_queries; q++) { @@ -392,7 +394,9 @@ shared_ptr QueryCoordinator::worker_scan( if (core_id < 0) { throw std::runtime_error("[QueryCoordinator::worker_scan] Invalid core ID."); } - core_resources_[core_id].job_queue.enqueue(job); + job_buffer_.push_back(job); + int64_t jid = (int64_t)job_buffer_.size() - 1; + core_resources_[core_id].job_queue.enqueue(jid); } } else { @@ -416,15 +420,14 @@ shared_ptr QueryCoordinator::worker_scan( if (core_id < 0) { throw std::runtime_error("[QueryCoordinator::worker_scan] Invalid core ID."); } - core_resources_[core_id].job_queue.enqueue(job); + job_buffer_.push_back(job); + int64_t jid = (int64_t)job_buffer_.size() - 1; + core_resources_[core_id].job_queue.enqueue(jid); } } } else { auto partition_ids_accessor = partition_ids.accessor(); - - int64_t start = 0; - int64_t end = num_queries; - parallel_for(start, end, [&](int64_t q) { + for (int q = 0; q < num_queries; q++) { for (int64_t p = 0; p < partition_ids.size(1); p++) { int64_t pid = partition_ids_accessor[q][p]; if (pid == -1) continue; @@ -442,9 +445,11 @@ shared_ptr QueryCoordinator::worker_scan( if (core_id < 0) { throw std::runtime_error("[QueryCoordinator::worker_scan] Invalid core ID."); } - core_resources_[core_id].job_queue.enqueue(job); + job_buffer_.push_back(job); + int64_t jid = (int64_t)job_buffer_.size() - 1; + core_resources_[core_id].job_queue.enqueue(jid); } - }, search_params->num_threads); + } } end_time = high_resolution_clock::now(); timing_info->job_enqueue_time_ns = duration_cast(end_time - start_time).count(); From 5ce528233c0a2cabc88e77f608d9ef8331617fc3 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 11:35:09 -0500 Subject: [PATCH 109/323] minimize job enqueing time --- src/cpp/src/query_coordinator.cpp | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 70a8213d..66a2634d 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -394,8 +394,8 @@ shared_ptr QueryCoordinator::worker_scan( if (core_id < 0) { throw std::runtime_error("[QueryCoordinator::worker_scan] Invalid core ID."); } - job_buffer_.push_back(job); - int64_t jid = (int64_t)job_buffer_.size() - 1; + int64_t jid = job_buffer_.size(); + job_buffer_[jid] = job; core_resources_[core_id].job_queue.enqueue(jid); } @@ -420,13 +420,15 @@ shared_ptr QueryCoordinator::worker_scan( if (core_id < 0) { throw std::runtime_error("[QueryCoordinator::worker_scan] Invalid core ID."); } - job_buffer_.push_back(job); - int64_t jid = (int64_t)job_buffer_.size() - 1; + int64_t jid = job_buffer_.size(); + job_buffer_[jid] = job; core_resources_[core_id].job_queue.enqueue(jid); } } } else { auto partition_ids_accessor = partition_ids.accessor(); + + job_buffer_.reserve(num_queries * partition_ids.size(1)); for (int q = 0; q < num_queries; q++) { for (int64_t p = 0; p < partition_ids.size(1); p++) { int64_t pid = partition_ids_accessor[q][p]; @@ -445,8 +447,8 @@ shared_ptr QueryCoordinator::worker_scan( if (core_id < 0) { throw std::runtime_error("[QueryCoordinator::worker_scan] Invalid core ID."); } - job_buffer_.push_back(job); - int64_t jid = (int64_t)job_buffer_.size() - 1; + int64_t jid = job_buffer_.size(); + job_buffer_[jid] = job; core_resources_[core_id].job_queue.enqueue(jid); } } From 2b36a02ba28b50896e791d2bf9622cca8c9c76ca Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 11:35:23 -0500 Subject: [PATCH 110/323] minimize job enqueing time --- src/cpp/src/query_coordinator.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 66a2634d..93435d0d 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -286,7 +286,7 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { job_process_time_ns += std::chrono::duration_cast(job_process_end - job_process_start).count(); } - std::cout << "Worker=" << core_index << ": total_pull_time=" << total_pull_time / 1000 << ", total_scan_time=" << total_scan_time / 1000 << ", merge_time=" << total_merge_time / 1000 << '\n'; +// std::cout << "Worker=" << core_index << ": total_pull_time=" << total_pull_time / 1000 << ", total_scan_time=" << total_scan_time / 1000 << ", merge_time=" << total_merge_time / 1000 << '\n'; } // Worker-Based Scan Implementation From 343bf4e8189b4b68445f553b5e92778a31f8f1af Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 11:41:49 -0500 Subject: [PATCH 111/323] minimize job enqueing time --- src/cpp/include/query_coordinator.h | 1 + src/cpp/src/query_coordinator.cpp | 12 ++++++------ 2 files changed, 7 insertions(+), 6 deletions(-) diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index e6965659..f645284b 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -68,6 +68,7 @@ class QueryCoordinator { bool debug_ = false; ///< Debug mode flag. std::vector job_buffer_; + std::atomic next_job_id_{0}; vector>> job_flags_; ///< Flags to track job completion std::atomic job_pull_time_ns = 0; ///< Time spent pulling jobs from the queue. std::atomic job_process_time_ns = 0; ///< Time spent processing jobs. diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 93435d0d..7508a9fc 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -373,7 +373,7 @@ shared_ptr QueryCoordinator::worker_scan( start_time = high_resolution_clock::now(); if (search_params->batched_scan) { auto partition_ids_accessor = partition_ids.accessor(); - + job_buffer_.resize(partition_manager_->nlist()); auto pids = partition_manager_->get_partition_ids(); auto pids_acc = pids.accessor(); @@ -394,7 +394,7 @@ shared_ptr QueryCoordinator::worker_scan( if (core_id < 0) { throw std::runtime_error("[QueryCoordinator::worker_scan] Invalid core ID."); } - int64_t jid = job_buffer_.size(); + size_t jid = next_job_id_.fetch_add(1, std::memory_order_acq_rel); job_buffer_[jid] = job; core_resources_[core_id].job_queue.enqueue(jid); } @@ -420,7 +420,7 @@ shared_ptr QueryCoordinator::worker_scan( if (core_id < 0) { throw std::runtime_error("[QueryCoordinator::worker_scan] Invalid core ID."); } - int64_t jid = job_buffer_.size(); + size_t jid = next_job_id_.fetch_add(1, std::memory_order_acq_rel); job_buffer_[jid] = job; core_resources_[core_id].job_queue.enqueue(jid); } @@ -428,7 +428,7 @@ shared_ptr QueryCoordinator::worker_scan( } else { auto partition_ids_accessor = partition_ids.accessor(); - job_buffer_.reserve(num_queries * partition_ids.size(1)); + job_buffer_.resize(num_queries * partition_ids.size(1)); for (int q = 0; q < num_queries; q++) { for (int64_t p = 0; p < partition_ids.size(1); p++) { int64_t pid = partition_ids_accessor[q][p]; @@ -447,9 +447,9 @@ shared_ptr QueryCoordinator::worker_scan( if (core_id < 0) { throw std::runtime_error("[QueryCoordinator::worker_scan] Invalid core ID."); } - int64_t jid = job_buffer_.size(); + size_t jid = next_job_id_.fetch_add(1, std::memory_order_acq_rel); job_buffer_[jid] = job; - core_resources_[core_id].job_queue.enqueue(jid); + core_resources_[core_id].job_queue.enqueue((int64_t) jid); } } } From ed670134c06a84832111fa8bca3d5d82ceb6e1a4 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 11:51:37 -0500 Subject: [PATCH 112/323] minimize job enqueing time --- src/cpp/src/query_coordinator.cpp | 1 + test/cpp/benchmark.cpp | 4 ++-- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 7508a9fc..7a977f95 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -329,6 +329,7 @@ shared_ptr QueryCoordinator::worker_scan( job_buffer_.clear(); + next_job_id_.store(0); job_flags_.clear(); job_flags_.resize(num_queries); for (int64_t q = 0; q < num_queries; q++) { diff --git a/test/cpp/benchmark.cpp b/test/cpp/benchmark.cpp index cd18cda3..3357086a 100644 --- a/test/cpp/benchmark.cpp +++ b/test/cpp/benchmark.cpp @@ -27,9 +27,9 @@ using torch::Tensor; // Global benchmark parameters static const int64_t DIM = 128; -static const int64_t NUM_VECTORS = 1000000; // number of database vectors +static const int64_t NUM_VECTORS = 100000; // number of database vectors static const int64_t N_LIST = 1000; // number of clusters for IVF -static const int64_t NUM_QUERIES = 10000; // number of queries for search benchmark +static const int64_t NUM_QUERIES = 1000; // number of queries for search benchmark static const int64_t K = 10; // top-K neighbors static const int64_t N_PROBE = 20; // number of probes for IVF static const int64_t N_WORKERS = 12; // number of workers for parallel query coordinator From d7bf3f794b90484c3434c0db04fd0661f069cc65 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 12:07:10 -0500 Subject: [PATCH 113/323] minimize job enqueing time --- src/cpp/src/query_coordinator.cpp | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 7a977f95..c6df9cfe 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -430,6 +430,7 @@ shared_ptr QueryCoordinator::worker_scan( auto partition_ids_accessor = partition_ids.accessor(); job_buffer_.resize(num_queries * partition_ids.size(1)); + vector> per_worker_job_ids(num_workers_); for (int q = 0; q < num_queries; q++) { for (int64_t p = 0; p < partition_ids.size(1); p++) { int64_t pid = partition_ids_accessor[q][p]; @@ -450,9 +451,13 @@ shared_ptr QueryCoordinator::worker_scan( } size_t jid = next_job_id_.fetch_add(1, std::memory_order_acq_rel); job_buffer_[jid] = job; - core_resources_[core_id].job_queue.enqueue((int64_t) jid); + per_worker_job_ids[core_id].push_back(jid); +// core_resources_[core_id].job_queue.enqueue((int64_t) jid); } } + for (int i = 0; i < num_workers_; i++) { + core_resources_[i].job_queue.enqueue_bulk(per_worker_job_ids[i].begin(), per_worker_job_ids[i].size()); + } } end_time = high_resolution_clock::now(); timing_info->job_enqueue_time_ns = duration_cast(end_time - start_time).count(); From 10e5afaa3bc36271225d06ba91ffe9ec2afb813b Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 12:10:09 -0500 Subject: [PATCH 114/323] minimize job enqueing time --- src/cpp/src/query_coordinator.cpp | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index c6df9cfe..dff02690 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -455,11 +455,12 @@ shared_ptr QueryCoordinator::worker_scan( // core_resources_[core_id].job_queue.enqueue((int64_t) jid); } } + end_time = high_resolution_clock::now(); for (int i = 0; i < num_workers_; i++) { core_resources_[i].job_queue.enqueue_bulk(per_worker_job_ids[i].begin(), per_worker_job_ids[i].size()); } } - end_time = high_resolution_clock::now(); +// end_time = high_resolution_clock::now(); timing_info->job_enqueue_time_ns = duration_cast(end_time - start_time).count(); auto last_flush_time = high_resolution_clock::now(); From 927592865817af02de975e3ee1552f516239276c Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 12:21:17 -0500 Subject: [PATCH 115/323] try out producer token --- src/cpp/include/query_coordinator.h | 1 + src/cpp/src/query_coordinator.cpp | 18 +++++++++++++++--- 2 files changed, 16 insertions(+), 3 deletions(-) diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index f645284b..90d8cfa7 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -54,6 +54,7 @@ class QueryCoordinator { vector> topk_buffer_pool; ///< Preallocated Top‑K buffers. vector local_query_buffer; ///< Local aggregator for query results. moodycamel::BlockingConcurrentQueue job_queue; ///< Job queue for scan jobs. + std::unique_ptr producer_token; }; vector core_resources_; ///< Per‑core resources for worker threads. diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index dff02690..58c001f3 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -69,6 +69,10 @@ void QueryCoordinator::initialize_workers(int num_cores, bool use_numa) { worker_threads_[i] = std::thread(&QueryCoordinator::partition_scan_worker_fn, this, i); worker_job_counter_[i] = 0; } + for (int i = 0; i < num_workers_; i++) { + core_resources_[i].producer_token = + std::make_unique(core_resources_[i].job_queue); + } workers_initialized_ = true; } @@ -455,12 +459,20 @@ shared_ptr QueryCoordinator::worker_scan( // core_resources_[core_id].job_queue.enqueue((int64_t) jid); } } - end_time = high_resolution_clock::now(); for (int i = 0; i < num_workers_; i++) { - core_resources_[i].job_queue.enqueue_bulk(per_worker_job_ids[i].begin(), per_worker_job_ids[i].size()); + auto &ids = per_worker_job_ids[i]; + auto &pt = *core_resources_[i].producer_token; + core_resources_[i].job_queue.enqueue_bulk( + pt, + ids.data(), + ids.size() + ); } +// for (int i = 0; i < num_workers_; i++) { +// core_resources_[i].job_queue.enqueue_bulk(per_worker_job_ids[i].begin(), per_worker_job_ids[i].size()); +// } } -// end_time = high_resolution_clock::now(); + end_time = high_resolution_clock::now(); timing_info->job_enqueue_time_ns = duration_cast(end_time - start_time).count(); auto last_flush_time = high_resolution_clock::now(); From bccca2a0e51075814d7a77359e9aa6f73782530f Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 12:30:15 -0500 Subject: [PATCH 116/323] try out producer token --- src/cpp/include/query_coordinator.h | 3 +-- src/cpp/src/query_coordinator.cpp | 13 +++++-------- 2 files changed, 6 insertions(+), 10 deletions(-) diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index 90d8cfa7..6d015dab 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -53,8 +53,7 @@ class QueryCoordinator { int core_id; ///< Logical identifier of the core. vector> topk_buffer_pool; ///< Preallocated Top‑K buffers. vector local_query_buffer; ///< Local aggregator for query results. - moodycamel::BlockingConcurrentQueue job_queue; ///< Job queue for scan jobs. - std::unique_ptr producer_token; + moodycamel::ConcurrentQueue job_queue; ///< Job queue for scan jobs. }; vector core_resources_; ///< Per‑core resources for worker threads. diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 58c001f3..1bbc42a8 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -42,7 +42,7 @@ void QueryCoordinator::allocate_core_resources(int core_idx, int num_queries, in res.topk_buffer_pool.resize(num_queries); for (int q = 0; q < num_queries; ++q) { res.topk_buffer_pool[q] = make_shared(k, metric_ == faiss::METRIC_INNER_PRODUCT); - res.job_queue = moodycamel::BlockingConcurrentQueue(); + res.job_queue = moodycamel::ConcurrentQueue(); } } @@ -69,10 +69,6 @@ void QueryCoordinator::initialize_workers(int num_cores, bool use_numa) { worker_threads_[i] = std::thread(&QueryCoordinator::partition_scan_worker_fn, this, i); worker_job_counter_[i] = 0; } - for (int i = 0; i < num_workers_; i++) { - core_resources_[i].producer_token = - std::make_unique(core_resources_[i].job_queue); - } workers_initialized_ = true; } @@ -113,7 +109,10 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { auto job_wait_start = std::chrono::high_resolution_clock::now(); int64_t job_id; - res.job_queue.wait_dequeue(job_id); + while (!res.job_queue.try_dequeue(job_id)) { + std::this_thread::yield(); + } + auto job_wait_end = std::chrono::high_resolution_clock::now(); job_pull_time_ns += std::chrono::duration_cast(job_wait_end - job_wait_start).count(); @@ -461,9 +460,7 @@ shared_ptr QueryCoordinator::worker_scan( } for (int i = 0; i < num_workers_; i++) { auto &ids = per_worker_job_ids[i]; - auto &pt = *core_resources_[i].producer_token; core_resources_[i].job_queue.enqueue_bulk( - pt, ids.data(), ids.size() ); From e56303ed125a037df4b2bed7a10f3b876ad233be Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 12:33:56 -0500 Subject: [PATCH 117/323] try out producer token --- src/cpp/src/query_coordinator.cpp | 14 +------------- 1 file changed, 1 insertion(+), 13 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 1bbc42a8..6660bba8 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -433,7 +433,6 @@ shared_ptr QueryCoordinator::worker_scan( auto partition_ids_accessor = partition_ids.accessor(); job_buffer_.resize(num_queries * partition_ids.size(1)); - vector> per_worker_job_ids(num_workers_); for (int q = 0; q < num_queries; q++) { for (int64_t p = 0; p < partition_ids.size(1); p++) { int64_t pid = partition_ids_accessor[q][p]; @@ -454,20 +453,9 @@ shared_ptr QueryCoordinator::worker_scan( } size_t jid = next_job_id_.fetch_add(1, std::memory_order_acq_rel); job_buffer_[jid] = job; - per_worker_job_ids[core_id].push_back(jid); -// core_resources_[core_id].job_queue.enqueue((int64_t) jid); + core_resources_[core_id].job_queue.enqueue((int64_t) jid); } } - for (int i = 0; i < num_workers_; i++) { - auto &ids = per_worker_job_ids[i]; - core_resources_[i].job_queue.enqueue_bulk( - ids.data(), - ids.size() - ); - } -// for (int i = 0; i < num_workers_; i++) { -// core_resources_[i].job_queue.enqueue_bulk(per_worker_job_ids[i].begin(), per_worker_job_ids[i].size()); -// } } end_time = high_resolution_clock::now(); timing_info->job_enqueue_time_ns = duration_cast(end_time - start_time).count(); From a00e2af9ad2ef26e6a7b2180c12fce8b5297650f Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 12:38:39 -0500 Subject: [PATCH 118/323] minimize job enqueing time --- src/cpp/src/query_coordinator.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 6660bba8..7d1244b0 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -110,7 +110,7 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { auto job_wait_start = std::chrono::high_resolution_clock::now(); int64_t job_id; while (!res.job_queue.try_dequeue(job_id)) { - std::this_thread::yield(); + std::this_thread::sleep_for(std::chrono::microseconds(5)); } auto job_wait_end = std::chrono::high_resolution_clock::now(); From 86c22781dfd04548864796cfb02ee5423c358994 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 12:43:44 -0500 Subject: [PATCH 119/323] minimize job enqueing time --- src/cpp/include/atomicops.h | 772 ++++++++++++++++++++++ src/cpp/include/query_coordinator.h | 5 +- src/cpp/include/readerwritequeue.h | 979 ++++++++++++++++++++++++++++ src/cpp/src/query_coordinator.cpp | 9 +- 4 files changed, 1759 insertions(+), 6 deletions(-) create mode 100644 src/cpp/include/atomicops.h create mode 100644 src/cpp/include/readerwritequeue.h diff --git a/src/cpp/include/atomicops.h b/src/cpp/include/atomicops.h new file mode 100644 index 00000000..1832dee8 --- /dev/null +++ b/src/cpp/include/atomicops.h @@ -0,0 +1,772 @@ +// ©2013-2016 Cameron Desrochers. +// Distributed under the simplified BSD license (see the license file that +// should have come with this header). +// Uses Jeff Preshing's semaphore implementation (under the terms of its +// separate zlib license, embedded below). + +#pragma once + +// Provides portable (VC++2010+, Intel ICC 13, GCC 4.7+, and anything C++11 compliant) implementation +// of low-level memory barriers, plus a few semi-portable utility macros (for inlining and alignment). +// Also has a basic atomic type (limited to hardware-supported atomics with no memory ordering guarantees). +// Uses the AE_* prefix for macros (historical reasons), and the "moodycamel" namespace for symbols. + +#include +#include +#include +#include +#include +#include + +// Platform detection +#if defined(__INTEL_COMPILER) +#define AE_ICC +#elif defined(_MSC_VER) +#define AE_VCPP +#elif defined(__GNUC__) +#define AE_GCC +#endif + +#if defined(_M_IA64) || defined(__ia64__) +#define AE_ARCH_IA64 +#elif defined(_WIN64) || defined(__amd64__) || defined(_M_X64) || defined(__x86_64__) +#define AE_ARCH_X64 +#elif defined(_M_IX86) || defined(__i386__) +#define AE_ARCH_X86 +#elif defined(_M_PPC) || defined(__powerpc__) +#define AE_ARCH_PPC +#else +#define AE_ARCH_UNKNOWN +#endif + + +// AE_UNUSED +#define AE_UNUSED(x) ((void)x) + +// AE_NO_TSAN/AE_TSAN_ANNOTATE_* +// For GCC +#if defined(__SANITIZE_THREAD__) +#define AE_TSAN_IS_ENABLED +#endif +// For clang +#if defined(__has_feature) +#if __has_feature(thread_sanitizer) && !defined(AE_TSAN_IS_ENABLED) +#define AE_TSAN_IS_ENABLED +#endif +#endif + +#ifdef AE_TSAN_IS_ENABLED +#if __cplusplus >= 201703L // inline variables require C++17 +namespace moodycamel { inline int ae_tsan_global; } +#define AE_TSAN_ANNOTATE_RELEASE() AnnotateHappensBefore(__FILE__, __LINE__, (void *)(&::moodycamel::ae_tsan_global)) +#define AE_TSAN_ANNOTATE_ACQUIRE() AnnotateHappensAfter(__FILE__, __LINE__, (void *)(&::moodycamel::ae_tsan_global)) +extern "C" void AnnotateHappensBefore(const char*, int, void*); +extern "C" void AnnotateHappensAfter(const char*, int, void*); +#else // when we can't work with tsan, attempt to disable its warnings +#define AE_NO_TSAN __attribute__((no_sanitize("thread"))) +#endif +#endif + +#ifndef AE_NO_TSAN +#define AE_NO_TSAN +#endif + +#ifndef AE_TSAN_ANNOTATE_RELEASE +#define AE_TSAN_ANNOTATE_RELEASE() +#define AE_TSAN_ANNOTATE_ACQUIRE() +#endif + + +// AE_FORCEINLINE +#if defined(AE_VCPP) || defined(AE_ICC) +#define AE_FORCEINLINE __forceinline +#elif defined(AE_GCC) +//#define AE_FORCEINLINE __attribute__((always_inline)) +#define AE_FORCEINLINE inline +#else +#define AE_FORCEINLINE inline +#endif + + +// AE_ALIGN +#if defined(AE_VCPP) || defined(AE_ICC) +#define AE_ALIGN(x) __declspec(align(x)) +#elif defined(AE_GCC) +#define AE_ALIGN(x) __attribute__((aligned(x))) +#else +// Assume GCC compliant syntax... +#define AE_ALIGN(x) __attribute__((aligned(x))) +#endif + + +// Portable atomic fences implemented below: + +namespace moodycamel { + + enum memory_order { + memory_order_relaxed, + memory_order_acquire, + memory_order_release, + memory_order_acq_rel, + memory_order_seq_cst, + + // memory_order_sync: Forces a full sync: + // #LoadLoad, #LoadStore, #StoreStore, and most significantly, #StoreLoad + memory_order_sync = memory_order_seq_cst + }; + +} // end namespace moodycamel + +#if (defined(AE_VCPP) && (_MSC_VER < 1700 || defined(__cplusplus_cli))) || (defined(AE_ICC) && __INTEL_COMPILER < 1600) +// VS2010 and ICC13 don't support std::atomic_*_fence, implement our own fences + +#include + +#if defined(AE_ARCH_X64) || defined(AE_ARCH_X86) +#define AeFullSync _mm_mfence +#define AeLiteSync _mm_mfence +#elif defined(AE_ARCH_IA64) +#define AeFullSync __mf +#define AeLiteSync __mf +#elif defined(AE_ARCH_PPC) +#include +#define AeFullSync __sync +#define AeLiteSync __lwsync +#endif + + +#ifdef AE_VCPP +#pragma warning(push) +#pragma warning(disable: 4365) // Disable erroneous 'conversion from long to unsigned int, signed/unsigned mismatch' error when using `assert` +#ifdef __cplusplus_cli +#pragma managed(push, off) +#endif +#endif + +namespace moodycamel { + +AE_FORCEINLINE void compiler_fence(memory_order order) AE_NO_TSAN +{ + switch (order) { + case memory_order_relaxed: break; + case memory_order_acquire: _ReadBarrier(); break; + case memory_order_release: _WriteBarrier(); break; + case memory_order_acq_rel: _ReadWriteBarrier(); break; + case memory_order_seq_cst: _ReadWriteBarrier(); break; + default: assert(false); + } +} + +// x86/x64 have a strong memory model -- all loads and stores have +// acquire and release semantics automatically (so only need compiler +// barriers for those). +#if defined(AE_ARCH_X86) || defined(AE_ARCH_X64) +AE_FORCEINLINE void fence(memory_order order) AE_NO_TSAN +{ + switch (order) { + case memory_order_relaxed: break; + case memory_order_acquire: _ReadBarrier(); break; + case memory_order_release: _WriteBarrier(); break; + case memory_order_acq_rel: _ReadWriteBarrier(); break; + case memory_order_seq_cst: + _ReadWriteBarrier(); + AeFullSync(); + _ReadWriteBarrier(); + break; + default: assert(false); + } +} +#else +AE_FORCEINLINE void fence(memory_order order) AE_NO_TSAN +{ + // Non-specialized arch, use heavier memory barriers everywhere just in case :-( + switch (order) { + case memory_order_relaxed: + break; + case memory_order_acquire: + _ReadBarrier(); + AeLiteSync(); + _ReadBarrier(); + break; + case memory_order_release: + _WriteBarrier(); + AeLiteSync(); + _WriteBarrier(); + break; + case memory_order_acq_rel: + _ReadWriteBarrier(); + AeLiteSync(); + _ReadWriteBarrier(); + break; + case memory_order_seq_cst: + _ReadWriteBarrier(); + AeFullSync(); + _ReadWriteBarrier(); + break; + default: assert(false); + } +} +#endif +} // end namespace moodycamel +#else +// Use standard library of atomics +#include + +namespace moodycamel { + + AE_FORCEINLINE void compiler_fence(memory_order order) AE_NO_TSAN + { + switch (order) { + case memory_order_relaxed: break; + case memory_order_acquire: std::atomic_signal_fence(std::memory_order_acquire); break; + case memory_order_release: std::atomic_signal_fence(std::memory_order_release); break; + case memory_order_acq_rel: std::atomic_signal_fence(std::memory_order_acq_rel); break; + case memory_order_seq_cst: std::atomic_signal_fence(std::memory_order_seq_cst); break; + default: assert(false); + } + } + + AE_FORCEINLINE void fence(memory_order order) AE_NO_TSAN + { + switch (order) { + case memory_order_relaxed: break; + case memory_order_acquire: AE_TSAN_ANNOTATE_ACQUIRE(); std::atomic_thread_fence(std::memory_order_acquire); break; + case memory_order_release: AE_TSAN_ANNOTATE_RELEASE(); std::atomic_thread_fence(std::memory_order_release); break; + case memory_order_acq_rel: AE_TSAN_ANNOTATE_ACQUIRE(); AE_TSAN_ANNOTATE_RELEASE(); std::atomic_thread_fence(std::memory_order_acq_rel); break; + case memory_order_seq_cst: AE_TSAN_ANNOTATE_ACQUIRE(); AE_TSAN_ANNOTATE_RELEASE(); std::atomic_thread_fence(std::memory_order_seq_cst); break; + default: assert(false); + } + } + +} // end namespace moodycamel + +#endif + + +#if !defined(AE_VCPP) || (_MSC_VER >= 1700 && !defined(__cplusplus_cli)) +#define AE_USE_STD_ATOMIC_FOR_WEAK_ATOMIC +#endif + +#ifdef AE_USE_STD_ATOMIC_FOR_WEAK_ATOMIC +#include +#endif +#include + +// WARNING: *NOT* A REPLACEMENT FOR std::atomic. READ CAREFULLY: +// Provides basic support for atomic variables -- no memory ordering guarantees are provided. +// The guarantee of atomicity is only made for types that already have atomic load and store guarantees +// at the hardware level -- on most platforms this generally means aligned pointers and integers (only). +namespace moodycamel { + template + class weak_atomic + { + public: + AE_NO_TSAN weak_atomic() : value() { } +#ifdef AE_VCPP + #pragma warning(push) +#pragma warning(disable: 4100) // Get rid of (erroneous) 'unreferenced formal parameter' warning +#endif + template AE_NO_TSAN weak_atomic(U&& x) : value(std::forward(x)) { } +#ifdef __cplusplus_cli + // Work around bug with universal reference/nullptr combination that only appears when /clr is on + AE_NO_TSAN weak_atomic(nullptr_t) : value(nullptr) { } +#endif + AE_NO_TSAN weak_atomic(weak_atomic const& other) : value(other.load()) { } + AE_NO_TSAN weak_atomic(weak_atomic&& other) : value(std::move(other.load())) { } +#ifdef AE_VCPP +#pragma warning(pop) +#endif + + AE_FORCEINLINE operator T() const AE_NO_TSAN { return load(); } + + +#ifndef AE_USE_STD_ATOMIC_FOR_WEAK_ATOMIC + template AE_FORCEINLINE weak_atomic const& operator=(U&& x) AE_NO_TSAN { value = std::forward(x); return *this; } + AE_FORCEINLINE weak_atomic const& operator=(weak_atomic const& other) AE_NO_TSAN { value = other.value; return *this; } + + AE_FORCEINLINE T load() const AE_NO_TSAN { return value; } + + AE_FORCEINLINE T fetch_add_acquire(T increment) AE_NO_TSAN + { +#if defined(AE_ARCH_X64) || defined(AE_ARCH_X86) + if (sizeof(T) == 4) return _InterlockedExchangeAdd((long volatile*)&value, (long)increment); +#if defined(_M_AMD64) + else if (sizeof(T) == 8) return _InterlockedExchangeAdd64((long long volatile*)&value, (long long)increment); +#endif +#else +#error Unsupported platform +#endif + assert(false && "T must be either a 32 or 64 bit type"); + return value; + } + + AE_FORCEINLINE T fetch_add_release(T increment) AE_NO_TSAN + { +#if defined(AE_ARCH_X64) || defined(AE_ARCH_X86) + if (sizeof(T) == 4) return _InterlockedExchangeAdd((long volatile*)&value, (long)increment); +#if defined(_M_AMD64) + else if (sizeof(T) == 8) return _InterlockedExchangeAdd64((long long volatile*)&value, (long long)increment); +#endif +#else +#error Unsupported platform +#endif + assert(false && "T must be either a 32 or 64 bit type"); + return value; + } +#else + template + AE_FORCEINLINE weak_atomic const& operator=(U&& x) AE_NO_TSAN + { + value.store(std::forward(x), std::memory_order_relaxed); + return *this; + } + + AE_FORCEINLINE weak_atomic const& operator=(weak_atomic const& other) AE_NO_TSAN + { + value.store(other.value.load(std::memory_order_relaxed), std::memory_order_relaxed); + return *this; + } + + AE_FORCEINLINE T load() const AE_NO_TSAN { return value.load(std::memory_order_relaxed); } + + AE_FORCEINLINE T fetch_add_acquire(T increment) AE_NO_TSAN + { + return value.fetch_add(increment, std::memory_order_acquire); + } + + AE_FORCEINLINE T fetch_add_release(T increment) AE_NO_TSAN + { + return value.fetch_add(increment, std::memory_order_release); + } +#endif + + + private: +#ifndef AE_USE_STD_ATOMIC_FOR_WEAK_ATOMIC + // No std::atomic support, but still need to circumvent compiler optimizations. + // `volatile` will make memory access slow, but is guaranteed to be reliable. + volatile T value; +#else + std::atomic value; +#endif + }; + +} // end namespace moodycamel + + + +// Portable single-producer, single-consumer semaphore below: + +#if defined(_WIN32) +// Avoid including windows.h in a header; we only need a handful of +// items, so we'll redeclare them here (this is relatively safe since +// the API generally has to remain stable between Windows versions). +// I know this is an ugly hack but it still beats polluting the global +// namespace with thousands of generic names or adding a .cpp for nothing. +extern "C" { + struct _SECURITY_ATTRIBUTES; + __declspec(dllimport) void* __stdcall CreateSemaphoreW(_SECURITY_ATTRIBUTES* lpSemaphoreAttributes, long lInitialCount, long lMaximumCount, const wchar_t* lpName); + __declspec(dllimport) int __stdcall CloseHandle(void* hObject); + __declspec(dllimport) unsigned long __stdcall WaitForSingleObject(void* hHandle, unsigned long dwMilliseconds); + __declspec(dllimport) int __stdcall ReleaseSemaphore(void* hSemaphore, long lReleaseCount, long* lpPreviousCount); +} +#elif defined(__MACH__) +#include +#elif defined(__unix__) +#include +#elif defined(FREERTOS) +#include +#include +#include +#endif + +namespace moodycamel +{ + // Code in the spsc_sema namespace below is an adaptation of Jeff Preshing's + // portable + lightweight semaphore implementations, originally from + // https://github.com/preshing/cpp11-on-multicore/blob/master/common/sema.h + // LICENSE: + // Copyright (c) 2015 Jeff Preshing + // + // This software is provided 'as-is', without any express or implied + // warranty. In no event will the authors be held liable for any damages + // arising from the use of this software. + // + // Permission is granted to anyone to use this software for any purpose, + // including commercial applications, and to alter it and redistribute it + // freely, subject to the following restrictions: + // + // 1. The origin of this software must not be misrepresented; you must not + // claim that you wrote the original software. If you use this software + // in a product, an acknowledgement in the product documentation would be + // appreciated but is not required. + // 2. Altered source versions must be plainly marked as such, and must not be + // misrepresented as being the original software. + // 3. This notice may not be removed or altered from any source distribution. + namespace spsc_sema + { +#if defined(_WIN32) + class Semaphore + { + private: + void* m_hSema; + + Semaphore(const Semaphore& other); + Semaphore& operator=(const Semaphore& other); + + public: + AE_NO_TSAN Semaphore(int initialCount = 0) : m_hSema() + { + assert(initialCount >= 0); + const long maxLong = 0x7fffffff; + m_hSema = CreateSemaphoreW(nullptr, initialCount, maxLong, nullptr); + assert(m_hSema); + } + + AE_NO_TSAN ~Semaphore() + { + CloseHandle(m_hSema); + } + + bool wait() AE_NO_TSAN + { + const unsigned long infinite = 0xffffffff; + return WaitForSingleObject(m_hSema, infinite) == 0; + } + + bool try_wait() AE_NO_TSAN + { + return WaitForSingleObject(m_hSema, 0) == 0; + } + + bool timed_wait(std::uint64_t usecs) AE_NO_TSAN + { + return WaitForSingleObject(m_hSema, (unsigned long)(usecs / 1000)) == 0; + } + + void signal(int count = 1) AE_NO_TSAN + { + while (!ReleaseSemaphore(m_hSema, count, nullptr)); + } + }; +#elif defined(__MACH__) + //--------------------------------------------------------- + // Semaphore (Apple iOS and OSX) + // Can't use POSIX semaphores due to http://lists.apple.com/archives/darwin-kernel/2009/Apr/msg00010.html + //--------------------------------------------------------- + class Semaphore + { + private: + semaphore_t m_sema; + + Semaphore(const Semaphore& other); + Semaphore& operator=(const Semaphore& other); + + public: + AE_NO_TSAN Semaphore(int initialCount = 0) : m_sema() + { + assert(initialCount >= 0); + kern_return_t rc = semaphore_create(mach_task_self(), &m_sema, SYNC_POLICY_FIFO, initialCount); + assert(rc == KERN_SUCCESS); + AE_UNUSED(rc); + } + + AE_NO_TSAN ~Semaphore() + { + semaphore_destroy(mach_task_self(), m_sema); + } + + bool wait() AE_NO_TSAN + { + return semaphore_wait(m_sema) == KERN_SUCCESS; + } + + bool try_wait() AE_NO_TSAN + { + return timed_wait(0); + } + + bool timed_wait(std::uint64_t timeout_usecs) AE_NO_TSAN + { + mach_timespec_t ts; + ts.tv_sec = static_cast(timeout_usecs / 1000000); + ts.tv_nsec = static_cast((timeout_usecs % 1000000) * 1000); + + // added in OSX 10.10: https://developer.apple.com/library/prerelease/mac/documentation/General/Reference/APIDiffsMacOSX10_10SeedDiff/modules/Darwin.html + kern_return_t rc = semaphore_timedwait(m_sema, ts); + return rc == KERN_SUCCESS; + } + + void signal() AE_NO_TSAN + { + while (semaphore_signal(m_sema) != KERN_SUCCESS); + } + + void signal(int count) AE_NO_TSAN + { + while (count-- > 0) + { + while (semaphore_signal(m_sema) != KERN_SUCCESS); + } + } + }; +#elif defined(__unix__) + //--------------------------------------------------------- + // Semaphore (POSIX, Linux) + //--------------------------------------------------------- + class Semaphore + { + private: + sem_t m_sema; + + Semaphore(const Semaphore& other); + Semaphore& operator=(const Semaphore& other); + + public: + AE_NO_TSAN Semaphore(int initialCount = 0) : m_sema() + { + assert(initialCount >= 0); + int rc = sem_init(&m_sema, 0, static_cast(initialCount)); + assert(rc == 0); + AE_UNUSED(rc); + } + + AE_NO_TSAN ~Semaphore() + { + sem_destroy(&m_sema); + } + + bool wait() AE_NO_TSAN + { + // http://stackoverflow.com/questions/2013181/gdb-causes-sem-wait-to-fail-with-eintr-error + int rc; + do + { + rc = sem_wait(&m_sema); + } + while (rc == -1 && errno == EINTR); + return rc == 0; + } + + bool try_wait() AE_NO_TSAN + { + int rc; + do { + rc = sem_trywait(&m_sema); + } while (rc == -1 && errno == EINTR); + return rc == 0; + } + + bool timed_wait(std::uint64_t usecs) AE_NO_TSAN + { + struct timespec ts; + const int usecs_in_1_sec = 1000000; + const int nsecs_in_1_sec = 1000000000; + clock_gettime(CLOCK_REALTIME, &ts); + ts.tv_sec += static_cast(usecs / usecs_in_1_sec); + ts.tv_nsec += static_cast(usecs % usecs_in_1_sec) * 1000; + // sem_timedwait bombs if you have more than 1e9 in tv_nsec + // so we have to clean things up before passing it in + if (ts.tv_nsec >= nsecs_in_1_sec) { + ts.tv_nsec -= nsecs_in_1_sec; + ++ts.tv_sec; + } + + int rc; + do { + rc = sem_timedwait(&m_sema, &ts); + } while (rc == -1 && errno == EINTR); + return rc == 0; + } + + void signal() AE_NO_TSAN + { + while (sem_post(&m_sema) == -1); + } + + void signal(int count) AE_NO_TSAN + { + while (count-- > 0) + { + while (sem_post(&m_sema) == -1); + } + } + }; +#elif defined(FREERTOS) + //--------------------------------------------------------- + // Semaphore (FreeRTOS) + //--------------------------------------------------------- + class Semaphore + { + private: + SemaphoreHandle_t m_sema; + + Semaphore(const Semaphore& other); + Semaphore& operator=(const Semaphore& other); + + public: + AE_NO_TSAN Semaphore(int initialCount = 0) : m_sema() + { + assert(initialCount >= 0); + m_sema = xSemaphoreCreateCounting(static_cast(~0ull), static_cast(initialCount)); + assert(m_sema); + } + + AE_NO_TSAN ~Semaphore() + { + vSemaphoreDelete(m_sema); + } + + bool wait() AE_NO_TSAN + { + return xSemaphoreTake(m_sema, portMAX_DELAY) == pdTRUE; + } + + bool try_wait() AE_NO_TSAN + { + // Note: In an ISR context, if this causes a task to unblock, + // the caller won't know about it + if (xPortIsInsideInterrupt()) + return xSemaphoreTakeFromISR(m_sema, NULL) == pdTRUE; + return xSemaphoreTake(m_sema, 0) == pdTRUE; + } + + bool timed_wait(std::uint64_t usecs) AE_NO_TSAN + { + std::uint64_t msecs = usecs / 1000; + TickType_t ticks = static_cast(msecs / portTICK_PERIOD_MS); + if (ticks == 0) + return try_wait(); + return xSemaphoreTake(m_sema, ticks) == pdTRUE; + } + + void signal() AE_NO_TSAN + { + // Note: In an ISR context, if this causes a task to unblock, + // the caller won't know about it + BaseType_t rc; + if (xPortIsInsideInterrupt()) + rc = xSemaphoreGiveFromISR(m_sema, NULL); + else + rc = xSemaphoreGive(m_sema); + assert(rc == pdTRUE); + AE_UNUSED(rc); + } + + void signal(int count) AE_NO_TSAN + { + while (count-- > 0) + signal(); + } + }; +#else +#error Unsupported platform! (No semaphore wrapper available) +#endif + + //--------------------------------------------------------- + // LightweightSemaphore + //--------------------------------------------------------- + class LightweightSemaphore + { + public: + typedef std::make_signed::type ssize_t; + + private: + weak_atomic m_count; + Semaphore m_sema; + + bool waitWithPartialSpinning(std::int64_t timeout_usecs = -1) AE_NO_TSAN + { + ssize_t oldCount; + // Is there a better way to set the initial spin count? + // If we lower it to 1000, testBenaphore becomes 15x slower on my Core i7-5930K Windows PC, + // as threads start hitting the kernel semaphore. + int spin = 1024; + while (--spin >= 0) + { + if (m_count.load() > 0) + { + m_count.fetch_add_acquire(-1); + return true; + } + compiler_fence(memory_order_acquire); // Prevent the compiler from collapsing the loop. + } + oldCount = m_count.fetch_add_acquire(-1); + if (oldCount > 0) + return true; + if (timeout_usecs < 0) + { + if (m_sema.wait()) + return true; + } + if (timeout_usecs > 0 && m_sema.timed_wait(static_cast(timeout_usecs))) + return true; + // At this point, we've timed out waiting for the semaphore, but the + // count is still decremented indicating we may still be waiting on + // it. So we have to re-adjust the count, but only if the semaphore + // wasn't signaled enough times for us too since then. If it was, we + // need to release the semaphore too. + while (true) + { + oldCount = m_count.fetch_add_release(1); + if (oldCount < 0) + return false; // successfully restored things to the way they were + // Oh, the producer thread just signaled the semaphore after all. Try again: + oldCount = m_count.fetch_add_acquire(-1); + if (oldCount > 0 && m_sema.try_wait()) + return true; + } + } + + public: + AE_NO_TSAN LightweightSemaphore(ssize_t initialCount = 0) : m_count(initialCount), m_sema() + { + assert(initialCount >= 0); + } + + bool tryWait() AE_NO_TSAN + { + if (m_count.load() > 0) + { + m_count.fetch_add_acquire(-1); + return true; + } + return false; + } + + bool wait() AE_NO_TSAN + { + return tryWait() || waitWithPartialSpinning(); + } + + bool wait(std::int64_t timeout_usecs) AE_NO_TSAN + { + return tryWait() || waitWithPartialSpinning(timeout_usecs); + } + + void signal(ssize_t count = 1) AE_NO_TSAN + { + assert(count >= 0); + ssize_t oldCount = m_count.fetch_add_release(count); + assert(oldCount >= -1); + if (oldCount < 0) + { + m_sema.signal(1); + } + } + + std::size_t availableApprox() const AE_NO_TSAN + { + ssize_t count = m_count.load(); + return count > 0 ? static_cast(count) : 0; + } + }; + } // end namespace spsc_sema +} // end namespace moodycamel + +#if defined(AE_VCPP) && (_MSC_VER < 1700 || defined(__cplusplus_cli)) +#pragma warning(pop) +#ifdef __cplusplus_cli +#pragma managed(pop) +#endif +#endif \ No newline at end of file diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index 6d015dab..23baabab 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -10,7 +10,8 @@ #include #include #include -#include +//#include +#include class QuakeIndex; class PartitionManager; @@ -53,7 +54,7 @@ class QueryCoordinator { int core_id; ///< Logical identifier of the core. vector> topk_buffer_pool; ///< Preallocated Top‑K buffers. vector local_query_buffer; ///< Local aggregator for query results. - moodycamel::ConcurrentQueue job_queue; ///< Job queue for scan jobs. + moodycamel::BlockingReaderWriterQueue job_queue; ///< Job queue for scan jobs. }; vector core_resources_; ///< Per‑core resources for worker threads. diff --git a/src/cpp/include/readerwritequeue.h b/src/cpp/include/readerwritequeue.h new file mode 100644 index 00000000..098d47cf --- /dev/null +++ b/src/cpp/include/readerwritequeue.h @@ -0,0 +1,979 @@ +// ©2013-2020 Cameron Desrochers. +// Distributed under the simplified BSD license (see the license file that +// should have come with this header). + +#pragma once + +#include "atomicops.h" +#include +#include +#include +#include +#include +#include +#include +#include // For malloc/free/abort & size_t +#include +#if __cplusplus > 199711L || _MSC_VER >= 1700 // C++11 or VS2012 +#include +#endif + + +// A lock-free queue for a single-consumer, single-producer architecture. +// The queue is also wait-free in the common path (except if more memory +// needs to be allocated, in which case malloc is called). +// Allocates memory sparingly, and only once if the original maximum size +// estimate is never exceeded. +// Tested on x86/x64 processors, but semantics should be correct for all +// architectures (given the right implementations in atomicops.h), provided +// that aligned integer and pointer accesses are naturally atomic. +// Note that there should only be one consumer thread and producer thread; +// Switching roles of the threads, or using multiple consecutive threads for +// one role, is not safe unless properly synchronized. +// Using the queue exclusively from one thread is fine, though a bit silly. + +#ifndef MOODYCAMEL_CACHE_LINE_SIZE +#define MOODYCAMEL_CACHE_LINE_SIZE 64 +#endif + +#ifndef MOODYCAMEL_EXCEPTIONS_ENABLED +#if (defined(_MSC_VER) && defined(_CPPUNWIND)) || (defined(__GNUC__) && defined(__EXCEPTIONS)) || (!defined(_MSC_VER) && !defined(__GNUC__)) +#define MOODYCAMEL_EXCEPTIONS_ENABLED +#endif +#endif + +#ifndef MOODYCAMEL_HAS_EMPLACE +#if !defined(_MSC_VER) || _MSC_VER >= 1800 // variadic templates: either a non-MS compiler or VS >= 2013 +#define MOODYCAMEL_HAS_EMPLACE 1 +#endif +#endif + +#ifndef MOODYCAMEL_MAYBE_ALIGN_TO_CACHELINE +#if defined (__APPLE__) && defined (__MACH__) && __cplusplus >= 201703L +// This is required to find out what deployment target we are using +#include +#if !defined(MAC_OS_X_VERSION_MIN_REQUIRED) || !defined(MAC_OS_X_VERSION_10_14) || MAC_OS_X_VERSION_MIN_REQUIRED < MAC_OS_X_VERSION_10_14 +// C++17 new(size_t, align_val_t) is not backwards-compatible with older versions of macOS, so we can't support over-alignment in this case +#define MOODYCAMEL_MAYBE_ALIGN_TO_CACHELINE +#endif +#endif +#endif + +#ifndef MOODYCAMEL_MAYBE_ALIGN_TO_CACHELINE +#define MOODYCAMEL_MAYBE_ALIGN_TO_CACHELINE AE_ALIGN(MOODYCAMEL_CACHE_LINE_SIZE) +#endif + +#ifdef AE_VCPP +#pragma warning(push) +#pragma warning(disable: 4324) // structure was padded due to __declspec(align()) +#pragma warning(disable: 4820) // padding was added +#pragma warning(disable: 4127) // conditional expression is constant +#endif + +namespace moodycamel { + + template + class MOODYCAMEL_MAYBE_ALIGN_TO_CACHELINE ReaderWriterQueue +{ + // Design: Based on a queue-of-queues. The low-level queues are just + // circular buffers with front and tail indices indicating where the + // next element to dequeue is and where the next element can be enqueued, + // respectively. Each low-level queue is called a "block". Each block + // wastes exactly one element's worth of space to keep the design simple + // (if front == tail then the queue is empty, and can't be full). + // The high-level queue is a circular linked list of blocks; again there + // is a front and tail, but this time they are pointers to the blocks. + // The front block is where the next element to be dequeued is, provided + // the block is not empty. The back block is where elements are to be + // enqueued, provided the block is not full. + // The producer thread owns all the tail indices/pointers. The consumer + // thread owns all the front indices/pointers. Both threads read each + // other's variables, but only the owning thread updates them. E.g. After + // the consumer reads the producer's tail, the tail may change before the + // consumer is done dequeuing an object, but the consumer knows the tail + // will never go backwards, only forwards. + // If there is no room to enqueue an object, an additional block (of + // equal size to the last block) is added. Blocks are never removed. + + public: + typedef T value_type; + + // Constructs a queue that can hold at least `size` elements without further + // allocations. If more than MAX_BLOCK_SIZE elements are requested, + // then several blocks of MAX_BLOCK_SIZE each are reserved (including + // at least one extra buffer block). + AE_NO_TSAN explicit ReaderWriterQueue(size_t size = 15) +#ifndef NDEBUG + : enqueuing(false) + ,dequeuing(false) +#endif +{ + assert(MAX_BLOCK_SIZE == ceilToPow2(MAX_BLOCK_SIZE) && "MAX_BLOCK_SIZE must be a power of 2"); + assert(MAX_BLOCK_SIZE >= 2 && "MAX_BLOCK_SIZE must be at least 2"); + + Block* firstBlock = nullptr; + + largestBlockSize = ceilToPow2(size + 1); // We need a spare slot to fit size elements in the block + if (largestBlockSize > MAX_BLOCK_SIZE * 2) { + // We need a spare block in case the producer is writing to a different block the consumer is reading from, and + // wants to enqueue the maximum number of elements. We also need a spare element in each block to avoid the ambiguity + // between front == tail meaning "empty" and "full". + // So the effective number of slots that are guaranteed to be usable at any time is the block size - 1 times the + // number of blocks - 1. Solving for size and applying a ceiling to the division gives us (after simplifying): + size_t initialBlockCount = (size + MAX_BLOCK_SIZE * 2 - 3) / (MAX_BLOCK_SIZE - 1); + largestBlockSize = MAX_BLOCK_SIZE; + Block* lastBlock = nullptr; + for (size_t i = 0; i != initialBlockCount; ++i) { + auto block = make_block(largestBlockSize); + if (block == nullptr) { +#ifdef MOODYCAMEL_EXCEPTIONS_ENABLED + throw std::bad_alloc(); +#else + abort(); +#endif +} +if (firstBlock == nullptr) { +firstBlock = block; +} +else { +lastBlock->next = block; +} +lastBlock = block; +block->next = firstBlock; +} +} +else { +firstBlock = make_block(largestBlockSize); +if (firstBlock == nullptr) { +#ifdef MOODYCAMEL_EXCEPTIONS_ENABLED +throw std::bad_alloc(); +#else +abort(); +#endif +} +firstBlock->next = firstBlock; +} +frontBlock = firstBlock; +tailBlock = firstBlock; + +// Make sure the reader/writer threads will have the initialized memory setup above: +fence(memory_order_sync); +} + +// Note: The queue should not be accessed concurrently while it's +// being moved. It's up to the user to synchronize this. +AE_NO_TSAN ReaderWriterQueue(ReaderWriterQueue&& other) +: frontBlock(other.frontBlock.load()), +tailBlock(other.tailBlock.load()), +largestBlockSize(other.largestBlockSize) +#ifndef NDEBUG +,enqueuing(false) +,dequeuing(false) +#endif +{ +other.largestBlockSize = 32; +Block* b = other.make_block(other.largestBlockSize); +if (b == nullptr) { +#ifdef MOODYCAMEL_EXCEPTIONS_ENABLED +throw std::bad_alloc(); +#else +abort(); +#endif +} +b->next = b; +other.frontBlock = b; +other.tailBlock = b; +} + +// Note: The queue should not be accessed concurrently while it's +// being moved. It's up to the user to synchronize this. +ReaderWriterQueue& operator=(ReaderWriterQueue&& other) AE_NO_TSAN +{ +Block* b = frontBlock.load(); +frontBlock = other.frontBlock.load(); +other.frontBlock = b; +b = tailBlock.load(); +tailBlock = other.tailBlock.load(); +other.tailBlock = b; +std::swap(largestBlockSize, other.largestBlockSize); +return *this; +} + +// Note: The queue should not be accessed concurrently while it's +// being deleted. It's up to the user to synchronize this. +AE_NO_TSAN ~ReaderWriterQueue() +{ + // Make sure we get the latest version of all variables from other CPUs: + fence(memory_order_sync); + + // Destroy any remaining objects in queue and free memory + Block* frontBlock_ = frontBlock; + Block* block = frontBlock_; + do { + Block* nextBlock = block->next; + size_t blockFront = block->front; + size_t blockTail = block->tail; + + for (size_t i = blockFront; i != blockTail; i = (i + 1) & block->sizeMask) { + auto element = reinterpret_cast(block->data + i * sizeof(T)); + element->~T(); + (void)element; + } + + auto rawBlock = block->rawThis; + block->~Block(); + std::free(rawBlock); + block = nextBlock; + } while (block != frontBlock_); +} + + +// Enqueues a copy of element if there is room in the queue. +// Returns true if the element was enqueued, false otherwise. +// Does not allocate memory. +AE_FORCEINLINE bool try_enqueue(T const& element) AE_NO_TSAN +{ +return inner_enqueue(element); +} + +// Enqueues a moved copy of element if there is room in the queue. +// Returns true if the element was enqueued, false otherwise. +// Does not allocate memory. +AE_FORCEINLINE bool try_enqueue(T&& element) AE_NO_TSAN +{ +return inner_enqueue(std::forward(element)); +} + +#if MOODYCAMEL_HAS_EMPLACE +// Like try_enqueue() but with emplace semantics (i.e. construct-in-place). +template +AE_FORCEINLINE bool try_emplace(Args&&... args) AE_NO_TSAN +{ +return inner_enqueue(std::forward(args)...); +} +#endif + +// Enqueues a copy of element on the queue. +// Allocates an additional block of memory if needed. +// Only fails (returns false) if memory allocation fails. +AE_FORCEINLINE bool enqueue(T const& element) AE_NO_TSAN +{ +return inner_enqueue(element); +} + +// Enqueues a moved copy of element on the queue. +// Allocates an additional block of memory if needed. +// Only fails (returns false) if memory allocation fails. +AE_FORCEINLINE bool enqueue(T&& element) AE_NO_TSAN +{ +return inner_enqueue(std::forward(element)); +} + +#if MOODYCAMEL_HAS_EMPLACE +// Like enqueue() but with emplace semantics (i.e. construct-in-place). +template +AE_FORCEINLINE bool emplace(Args&&... args) AE_NO_TSAN +{ +return inner_enqueue(std::forward(args)...); +} +#endif + +// Attempts to dequeue an element; if the queue is empty, +// returns false instead. If the queue has at least one element, +// moves front to result using operator=, then returns true. +template +bool try_dequeue(U& result) AE_NO_TSAN +{ +#ifndef NDEBUG +ReentrantGuard guard(this->dequeuing); +#endif + +// High-level pseudocode: +// Remember where the tail block is +// If the front block has an element in it, dequeue it +// Else +// If front block was the tail block when we entered the function, return false +// Else advance to next block and dequeue the item there + +// Note that we have to use the value of the tail block from before we check if the front +// block is full or not, in case the front block is empty and then, before we check if the +// tail block is at the front block or not, the producer fills up the front block *and +// moves on*, which would make us skip a filled block. Seems unlikely, but was consistently +// reproducible in practice. +// In order to avoid overhead in the common case, though, we do a double-checked pattern +// where we have the fast path if the front block is not empty, then read the tail block, +// then re-read the front block and check if it's not empty again, then check if the tail +// block has advanced. + +Block* frontBlock_ = frontBlock.load(); +size_t blockTail = frontBlock_->localTail; +size_t blockFront = frontBlock_->front.load(); + +if (blockFront != blockTail || blockFront != (frontBlock_->localTail = frontBlock_->tail.load())) { +fence(memory_order_acquire); + +non_empty_front_block: +// Front block not empty, dequeue from here +auto element = reinterpret_cast(frontBlock_->data + blockFront * sizeof(T)); +result = std::move(*element); +element->~T(); + +blockFront = (blockFront + 1) & frontBlock_->sizeMask; + +fence(memory_order_release); +frontBlock_->front = blockFront; +} +else if (frontBlock_ != tailBlock.load()) { +fence(memory_order_acquire); + +frontBlock_ = frontBlock.load(); +blockTail = frontBlock_->localTail = frontBlock_->tail.load(); +blockFront = frontBlock_->front.load(); +fence(memory_order_acquire); + +if (blockFront != blockTail) { +// Oh look, the front block isn't empty after all +goto non_empty_front_block; +} + +// Front block is empty but there's another block ahead, advance to it +Block* nextBlock = frontBlock_->next; +// Don't need an acquire fence here since next can only ever be set on the tailBlock, +// and we're not the tailBlock, and we did an acquire earlier after reading tailBlock which +// ensures next is up-to-date on this CPU in case we recently were at tailBlock. + +size_t nextBlockFront = nextBlock->front.load(); +size_t nextBlockTail = nextBlock->localTail = nextBlock->tail.load(); +fence(memory_order_acquire); + +// Since the tailBlock is only ever advanced after being written to, +// we know there's for sure an element to dequeue on it +assert(nextBlockFront != nextBlockTail); +AE_UNUSED(nextBlockTail); + +// We're done with this block, let the producer use it if it needs +fence(memory_order_release); // Expose possibly pending changes to frontBlock->front from last dequeue +frontBlock = frontBlock_ = nextBlock; + +compiler_fence(memory_order_release); // Not strictly needed + +auto element = reinterpret_cast(frontBlock_->data + nextBlockFront * sizeof(T)); + +result = std::move(*element); +element->~T(); + +nextBlockFront = (nextBlockFront + 1) & frontBlock_->sizeMask; + +fence(memory_order_release); +frontBlock_->front = nextBlockFront; +} +else { +// No elements in current block and no other block to advance to +return false; +} + +return true; +} + + +// Returns a pointer to the front element in the queue (the one that +// would be removed next by a call to `try_dequeue` or `pop`). If the +// queue appears empty at the time the method is called, nullptr is +// returned instead. +// Must be called only from the consumer thread. +T* peek() const AE_NO_TSAN +{ +#ifndef NDEBUG +ReentrantGuard guard(this->dequeuing); +#endif +// See try_dequeue() for reasoning + +Block* frontBlock_ = frontBlock.load(); +size_t blockTail = frontBlock_->localTail; +size_t blockFront = frontBlock_->front.load(); + +if (blockFront != blockTail || blockFront != (frontBlock_->localTail = frontBlock_->tail.load())) { +fence(memory_order_acquire); +non_empty_front_block: +return reinterpret_cast(frontBlock_->data + blockFront * sizeof(T)); +} +else if (frontBlock_ != tailBlock.load()) { +fence(memory_order_acquire); +frontBlock_ = frontBlock.load(); +blockTail = frontBlock_->localTail = frontBlock_->tail.load(); +blockFront = frontBlock_->front.load(); +fence(memory_order_acquire); + +if (blockFront != blockTail) { +goto non_empty_front_block; +} + +Block* nextBlock = frontBlock_->next; + +size_t nextBlockFront = nextBlock->front.load(); +fence(memory_order_acquire); + +assert(nextBlockFront != nextBlock->tail.load()); +return reinterpret_cast(nextBlock->data + nextBlockFront * sizeof(T)); +} + +return nullptr; +} + +// Removes the front element from the queue, if any, without returning it. +// Returns true on success, or false if the queue appeared empty at the time +// `pop` was called. +bool pop() AE_NO_TSAN +{ +#ifndef NDEBUG +ReentrantGuard guard(this->dequeuing); +#endif +// See try_dequeue() for reasoning + +Block* frontBlock_ = frontBlock.load(); +size_t blockTail = frontBlock_->localTail; +size_t blockFront = frontBlock_->front.load(); + +if (blockFront != blockTail || blockFront != (frontBlock_->localTail = frontBlock_->tail.load())) { +fence(memory_order_acquire); + +non_empty_front_block: +auto element = reinterpret_cast(frontBlock_->data + blockFront * sizeof(T)); +element->~T(); + +blockFront = (blockFront + 1) & frontBlock_->sizeMask; + +fence(memory_order_release); +frontBlock_->front = blockFront; +} +else if (frontBlock_ != tailBlock.load()) { +fence(memory_order_acquire); +frontBlock_ = frontBlock.load(); +blockTail = frontBlock_->localTail = frontBlock_->tail.load(); +blockFront = frontBlock_->front.load(); +fence(memory_order_acquire); + +if (blockFront != blockTail) { +goto non_empty_front_block; +} + +// Front block is empty but there's another block ahead, advance to it +Block* nextBlock = frontBlock_->next; + +size_t nextBlockFront = nextBlock->front.load(); +size_t nextBlockTail = nextBlock->localTail = nextBlock->tail.load(); +fence(memory_order_acquire); + +assert(nextBlockFront != nextBlockTail); +AE_UNUSED(nextBlockTail); + +fence(memory_order_release); +frontBlock = frontBlock_ = nextBlock; + +compiler_fence(memory_order_release); + +auto element = reinterpret_cast(frontBlock_->data + nextBlockFront * sizeof(T)); +element->~T(); + +nextBlockFront = (nextBlockFront + 1) & frontBlock_->sizeMask; + +fence(memory_order_release); +frontBlock_->front = nextBlockFront; +} +else { +// No elements in current block and no other block to advance to +return false; +} + +return true; +} + +// Returns the approximate number of items currently in the queue. +// Safe to call from both the producer and consumer threads. +inline size_t size_approx() const AE_NO_TSAN +{ +size_t result = 0; +Block* frontBlock_ = frontBlock.load(); +Block* block = frontBlock_; +do { +fence(memory_order_acquire); +size_t blockFront = block->front.load(); +size_t blockTail = block->tail.load(); +result += (blockTail - blockFront) & block->sizeMask; +block = block->next.load(); +} while (block != frontBlock_); +return result; +} + +// Returns the total number of items that could be enqueued without incurring +// an allocation when this queue is empty. +// Safe to call from both the producer and consumer threads. +// +// NOTE: The actual capacity during usage may be different depending on the consumer. +// If the consumer is removing elements concurrently, the producer cannot add to +// the block the consumer is removing from until it's completely empty, except in +// the case where the producer was writing to the same block the consumer was +// reading from the whole time. +inline size_t max_capacity() const { + size_t result = 0; + Block* frontBlock_ = frontBlock.load(); + Block* block = frontBlock_; + do { + fence(memory_order_acquire); + result += block->sizeMask; + block = block->next.load(); + } while (block != frontBlock_); + return result; +} + + +private: +enum AllocationMode { CanAlloc, CannotAlloc }; + +#if MOODYCAMEL_HAS_EMPLACE +template +bool inner_enqueue(Args&&... args) AE_NO_TSAN +#else +template + bool inner_enqueue(U&& element) AE_NO_TSAN +#endif +{ +#ifndef NDEBUG +ReentrantGuard guard(this->enqueuing); +#endif + +// High-level pseudocode (assuming we're allowed to alloc a new block): +// If room in tail block, add to tail +// Else check next block +// If next block is not the head block, enqueue on next block +// Else create a new block and enqueue there +// Advance tail to the block we just enqueued to + +Block* tailBlock_ = tailBlock.load(); +size_t blockFront = tailBlock_->localFront; +size_t blockTail = tailBlock_->tail.load(); + +size_t nextBlockTail = (blockTail + 1) & tailBlock_->sizeMask; +if (nextBlockTail != blockFront || nextBlockTail != (tailBlock_->localFront = tailBlock_->front.load())) { +fence(memory_order_acquire); +// This block has room for at least one more element +char* location = tailBlock_->data + blockTail * sizeof(T); +#if MOODYCAMEL_HAS_EMPLACE +new (location) T(std::forward(args)...); +#else +new (location) T(std::forward(element)); +#endif + +fence(memory_order_release); +tailBlock_->tail = nextBlockTail; +} +else { +fence(memory_order_acquire); +if (tailBlock_->next.load() != frontBlock) { +// Note that the reason we can't advance to the frontBlock and start adding new entries there +// is because if we did, then dequeue would stay in that block, eventually reading the new values, +// instead of advancing to the next full block (whose values were enqueued first and so should be +// consumed first). + +fence(memory_order_acquire); // Ensure we get latest writes if we got the latest frontBlock + +// tailBlock is full, but there's a free block ahead, use it +Block* tailBlockNext = tailBlock_->next.load(); +size_t nextBlockFront = tailBlockNext->localFront = tailBlockNext->front.load(); +nextBlockTail = tailBlockNext->tail.load(); +fence(memory_order_acquire); + +// This block must be empty since it's not the head block and we +// go through the blocks in a circle +assert(nextBlockFront == nextBlockTail); +tailBlockNext->localFront = nextBlockFront; + +char* location = tailBlockNext->data + nextBlockTail * sizeof(T); +#if MOODYCAMEL_HAS_EMPLACE +new (location) T(std::forward(args)...); +#else +new (location) T(std::forward(element)); +#endif + +tailBlockNext->tail = (nextBlockTail + 1) & tailBlockNext->sizeMask; + +fence(memory_order_release); +tailBlock = tailBlockNext; +} +else if (canAlloc == CanAlloc) { +// tailBlock is full and there's no free block ahead; create a new block +auto newBlockSize = largestBlockSize >= MAX_BLOCK_SIZE ? largestBlockSize : largestBlockSize * 2; +auto newBlock = make_block(newBlockSize); +if (newBlock == nullptr) { +// Could not allocate a block! +return false; +} +largestBlockSize = newBlockSize; + +#if MOODYCAMEL_HAS_EMPLACE +new (newBlock->data) T(std::forward(args)...); +#else +new (newBlock->data) T(std::forward(element)); +#endif +assert(newBlock->front == 0); +newBlock->tail = newBlock->localTail = 1; + +newBlock->next = tailBlock_->next.load(); +tailBlock_->next = newBlock; + +// Might be possible for the dequeue thread to see the new tailBlock->next +// *without* seeing the new tailBlock value, but this is OK since it can't +// advance to the next block until tailBlock is set anyway (because the only +// case where it could try to read the next is if it's already at the tailBlock, +// and it won't advance past tailBlock in any circumstance). + +fence(memory_order_release); +tailBlock = newBlock; +} +else if (canAlloc == CannotAlloc) { +// Would have had to allocate a new block to enqueue, but not allowed +return false; +} +else { +assert(false && "Should be unreachable code"); +return false; +} +} + +return true; +} + + +// Disable copying +ReaderWriterQueue(ReaderWriterQueue const&) { } + +// Disable assignment +ReaderWriterQueue& operator=(ReaderWriterQueue const&) { } + + +AE_FORCEINLINE static size_t ceilToPow2(size_t x) +{ +// From http://graphics.stanford.edu/~seander/bithacks.html#RoundUpPowerOf2 +--x; +x |= x >> 1; +x |= x >> 2; +x |= x >> 4; +for (size_t i = 1; i < sizeof(size_t); i <<= 1) { +x |= x >> (i << 3); +} +++x; +return x; +} + +template +static AE_FORCEINLINE char* align_for(char* ptr) AE_NO_TSAN +{ +const std::size_t alignment = std::alignment_of::value; +return ptr + (alignment - (reinterpret_cast(ptr) % alignment)) % alignment; +} +private: +#ifndef NDEBUG +struct ReentrantGuard +{ + AE_NO_TSAN ReentrantGuard(weak_atomic& _inSection) + : inSection(_inSection) + { + assert(!inSection && "Concurrent (or re-entrant) enqueue or dequeue operation detected (only one thread at a time may hold the producer or consumer role)"); + inSection = true; + } + + AE_NO_TSAN ~ReentrantGuard() { inSection = false; } + +private: + ReentrantGuard& operator=(ReentrantGuard const&); + +private: + weak_atomic& inSection; +}; +#endif + +struct Block +{ + // Avoid false-sharing by putting highly contended variables on their own cache lines + weak_atomic front; // (Atomic) Elements are read from here + size_t localTail; // An uncontended shadow copy of tail, owned by the consumer + + char cachelineFiller0[MOODYCAMEL_CACHE_LINE_SIZE - sizeof(weak_atomic) - sizeof(size_t)]; + weak_atomic tail; // (Atomic) Elements are enqueued here + size_t localFront; + + char cachelineFiller1[MOODYCAMEL_CACHE_LINE_SIZE - sizeof(weak_atomic) - sizeof(size_t)]; // next isn't very contended, but we don't want it on the same cache line as tail (which is) + weak_atomic next; // (Atomic) + + char* data; // Contents (on heap) are aligned to T's alignment + + const size_t sizeMask; + + + // size must be a power of two (and greater than 0) + AE_NO_TSAN Block(size_t const& _size, char* _rawThis, char* _data) + : front(0UL), localTail(0), tail(0UL), localFront(0), next(nullptr), data(_data), sizeMask(_size - 1), rawThis(_rawThis) + { + } + +private: + // C4512 - Assignment operator could not be generated + Block& operator=(Block const&); + +public: + char* rawThis; +}; + + +static Block* make_block(size_t capacity) AE_NO_TSAN +{ +// Allocate enough memory for the block itself, as well as all the elements it will contain +auto size = sizeof(Block) + std::alignment_of::value - 1; +size += sizeof(T) * capacity + std::alignment_of::value - 1; +auto newBlockRaw = static_cast(std::malloc(size)); +if (newBlockRaw == nullptr) { +return nullptr; +} + +auto newBlockAligned = align_for(newBlockRaw); +auto newBlockData = align_for(newBlockAligned + sizeof(Block)); +return new (newBlockAligned) Block(capacity, newBlockRaw, newBlockData); +} + +private: +weak_atomic frontBlock; // (Atomic) Elements are dequeued from this block + +char cachelineFiller[MOODYCAMEL_CACHE_LINE_SIZE - sizeof(weak_atomic)]; +weak_atomic tailBlock; // (Atomic) Elements are enqueued to this block + +size_t largestBlockSize; + +#ifndef NDEBUG +weak_atomic enqueuing; +mutable weak_atomic dequeuing; +#endif +}; + +// Like ReaderWriterQueue, but also providees blocking operations +template +class BlockingReaderWriterQueue +{ +private: + typedef ::moodycamel::ReaderWriterQueue ReaderWriterQueue; + +public: + explicit BlockingReaderWriterQueue(size_t size = 15) AE_NO_TSAN + : inner(size), sema(new spsc_sema::LightweightSemaphore()) + { } + + BlockingReaderWriterQueue(BlockingReaderWriterQueue&& other) AE_NO_TSAN + : inner(std::move(other.inner)), sema(std::move(other.sema)) + { } + + BlockingReaderWriterQueue& operator=(BlockingReaderWriterQueue&& other) AE_NO_TSAN + { + std::swap(sema, other.sema); + std::swap(inner, other.inner); + return *this; + } + + + // Enqueues a copy of element if there is room in the queue. + // Returns true if the element was enqueued, false otherwise. + // Does not allocate memory. + AE_FORCEINLINE bool try_enqueue(T const& element) AE_NO_TSAN + { + if (inner.try_enqueue(element)) { + sema->signal(); + return true; + } + return false; + } + + // Enqueues a moved copy of element if there is room in the queue. + // Returns true if the element was enqueued, false otherwise. + // Does not allocate memory. + AE_FORCEINLINE bool try_enqueue(T&& element) AE_NO_TSAN + { + if (inner.try_enqueue(std::forward(element))) { + sema->signal(); + return true; + } + return false; + } + +#if MOODYCAMEL_HAS_EMPLACE + // Like try_enqueue() but with emplace semantics (i.e. construct-in-place). + template + AE_FORCEINLINE bool try_emplace(Args&&... args) AE_NO_TSAN + { + if (inner.try_emplace(std::forward(args)...)) { + sema->signal(); + return true; + } + return false; + } +#endif + + + // Enqueues a copy of element on the queue. + // Allocates an additional block of memory if needed. + // Only fails (returns false) if memory allocation fails. + AE_FORCEINLINE bool enqueue(T const& element) AE_NO_TSAN + { + if (inner.enqueue(element)) { + sema->signal(); + return true; + } + return false; + } + + // Enqueues a moved copy of element on the queue. + // Allocates an additional block of memory if needed. + // Only fails (returns false) if memory allocation fails. + AE_FORCEINLINE bool enqueue(T&& element) AE_NO_TSAN + { + if (inner.enqueue(std::forward(element))) { + sema->signal(); + return true; + } + return false; + } + +#if MOODYCAMEL_HAS_EMPLACE + // Like enqueue() but with emplace semantics (i.e. construct-in-place). + template + AE_FORCEINLINE bool emplace(Args&&... args) AE_NO_TSAN + { + if (inner.emplace(std::forward(args)...)) { + sema->signal(); + return true; + } + return false; + } +#endif + + + // Attempts to dequeue an element; if the queue is empty, + // returns false instead. If the queue has at least one element, + // moves front to result using operator=, then returns true. + template + bool try_dequeue(U& result) AE_NO_TSAN + { + if (sema->tryWait()) { + bool success = inner.try_dequeue(result); + assert(success); + AE_UNUSED(success); + return true; + } + return false; + } + + + // Attempts to dequeue an element; if the queue is empty, + // waits until an element is available, then dequeues it. + template + void wait_dequeue(U& result) AE_NO_TSAN + { + while (!sema->wait()); + bool success = inner.try_dequeue(result); + AE_UNUSED(result); + assert(success); + AE_UNUSED(success); + } + + + // Attempts to dequeue an element; if the queue is empty, + // waits until an element is available up to the specified timeout, + // then dequeues it and returns true, or returns false if the timeout + // expires before an element can be dequeued. + // Using a negative timeout indicates an indefinite timeout, + // and is thus functionally equivalent to calling wait_dequeue. + template + bool wait_dequeue_timed(U& result, std::int64_t timeout_usecs) AE_NO_TSAN + { + if (!sema->wait(timeout_usecs)) { + return false; + } + bool success = inner.try_dequeue(result); + AE_UNUSED(result); + assert(success); + AE_UNUSED(success); + return true; + } + + +#if __cplusplus > 199711L || _MSC_VER >= 1700 + // Attempts to dequeue an element; if the queue is empty, + // waits until an element is available up to the specified timeout, + // then dequeues it and returns true, or returns false if the timeout + // expires before an element can be dequeued. + // Using a negative timeout indicates an indefinite timeout, + // and is thus functionally equivalent to calling wait_dequeue. + template + inline bool wait_dequeue_timed(U& result, std::chrono::duration const& timeout) AE_NO_TSAN + { + return wait_dequeue_timed(result, std::chrono::duration_cast(timeout).count()); + } +#endif + + + // Returns a pointer to the front element in the queue (the one that + // would be removed next by a call to `try_dequeue` or `pop`). If the + // queue appears empty at the time the method is called, nullptr is + // returned instead. + // Must be called only from the consumer thread. + AE_FORCEINLINE T* peek() const AE_NO_TSAN + { + return inner.peek(); + } + + // Removes the front element from the queue, if any, without returning it. + // Returns true on success, or false if the queue appeared empty at the time + // `pop` was called. + AE_FORCEINLINE bool pop() AE_NO_TSAN + { + if (sema->tryWait()) { + bool result = inner.pop(); + assert(result); + AE_UNUSED(result); + return true; + } + return false; + } + + // Returns the approximate number of items currently in the queue. + // Safe to call from both the producer and consumer threads. + AE_FORCEINLINE size_t size_approx() const AE_NO_TSAN + { + return sema->availableApprox(); + } + + // Returns the total number of items that could be enqueued without incurring + // an allocation when this queue is empty. + // Safe to call from both the producer and consumer threads. + // + // NOTE: The actual capacity during usage may be different depending on the consumer. + // If the consumer is removing elements concurrently, the producer cannot add to + // the block the consumer is removing from until it's completely empty, except in + // the case where the producer was writing to the same block the consumer was + // reading from the whole time. + AE_FORCEINLINE size_t max_capacity() const { + return inner.max_capacity(); + } + +private: + // Disable copying & assignment + BlockingReaderWriterQueue(BlockingReaderWriterQueue const&) { } + BlockingReaderWriterQueue& operator=(BlockingReaderWriterQueue const&) { } + +private: + ReaderWriterQueue inner; + std::unique_ptr sema; +}; + +} // end namespace moodycamel + +#ifdef AE_VCPP +#pragma warning(pop) +#endif \ No newline at end of file diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 7d1244b0..39fb50ea 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -42,7 +42,7 @@ void QueryCoordinator::allocate_core_resources(int core_idx, int num_queries, in res.topk_buffer_pool.resize(num_queries); for (int q = 0; q < num_queries; ++q) { res.topk_buffer_pool[q] = make_shared(k, metric_ == faiss::METRIC_INNER_PRODUCT); - res.job_queue = moodycamel::ConcurrentQueue(); + res.job_queue = moodycamel::BlockingReaderWriterQueue(); } } @@ -109,9 +109,10 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { auto job_wait_start = std::chrono::high_resolution_clock::now(); int64_t job_id; - while (!res.job_queue.try_dequeue(job_id)) { - std::this_thread::sleep_for(std::chrono::microseconds(5)); - } + res.job_queue.wait_dequeue(job_id); +// while (!res.job_queue.try_dequeue(job_id)) { +// std::this_thread::sleep_for(std::chrono::microseconds(5)); +// } auto job_wait_end = std::chrono::high_resolution_clock::now(); From 00bd8e14a4a18b9212916e1b008f26e832b01a48 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 12:46:58 -0500 Subject: [PATCH 120/323] minimize job enqueing time --- src/cpp/include/query_coordinator.h | 2 +- src/cpp/src/query_coordinator.cpp | 10 +++++----- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index 23baabab..16759913 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -54,7 +54,7 @@ class QueryCoordinator { int core_id; ///< Logical identifier of the core. vector> topk_buffer_pool; ///< Preallocated Top‑K buffers. vector local_query_buffer; ///< Local aggregator for query results. - moodycamel::BlockingReaderWriterQueue job_queue; ///< Job queue for scan jobs. + moodycamel::ReaderWriterQueue job_queue; ///< Job queue for scan jobs. }; vector core_resources_; ///< Per‑core resources for worker threads. diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 39fb50ea..ab228087 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -42,7 +42,7 @@ void QueryCoordinator::allocate_core_resources(int core_idx, int num_queries, in res.topk_buffer_pool.resize(num_queries); for (int q = 0; q < num_queries; ++q) { res.topk_buffer_pool[q] = make_shared(k, metric_ == faiss::METRIC_INNER_PRODUCT); - res.job_queue = moodycamel::BlockingReaderWriterQueue(); + res.job_queue = moodycamel::ReaderWriterQueue(); } } @@ -109,10 +109,10 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { auto job_wait_start = std::chrono::high_resolution_clock::now(); int64_t job_id; - res.job_queue.wait_dequeue(job_id); -// while (!res.job_queue.try_dequeue(job_id)) { -// std::this_thread::sleep_for(std::chrono::microseconds(5)); -// } +// res.job_queue.wait_dequeue(job_id); + while (!res.job_queue.try_dequeue(job_id)) { + std::this_thread::sleep_for(std::chrono::microseconds(1)); + } auto job_wait_end = std::chrono::high_resolution_clock::now(); From 9fb76328cd3d73a5e1cb96877733290df4b54f56 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 12:53:54 -0500 Subject: [PATCH 121/323] minimize job enqueing time --- src/cpp/include/query_coordinator.h | 5 ++--- src/cpp/src/query_coordinator.cpp | 2 +- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index 16759913..317e3256 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -10,8 +10,7 @@ #include #include #include -//#include -#include +#include class QuakeIndex; class PartitionManager; @@ -54,7 +53,7 @@ class QueryCoordinator { int core_id; ///< Logical identifier of the core. vector> topk_buffer_pool; ///< Preallocated Top‑K buffers. vector local_query_buffer; ///< Local aggregator for query results. - moodycamel::ReaderWriterQueue job_queue; ///< Job queue for scan jobs. + moodycamel::ConcurrentQueue job_queue; ///< Job queue for scan jobs. }; vector core_resources_; ///< Per‑core resources for worker threads. diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index ab228087..a1ec9b0f 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -42,7 +42,7 @@ void QueryCoordinator::allocate_core_resources(int core_idx, int num_queries, in res.topk_buffer_pool.resize(num_queries); for (int q = 0; q < num_queries; ++q) { res.topk_buffer_pool[q] = make_shared(k, metric_ == faiss::METRIC_INNER_PRODUCT); - res.job_queue = moodycamel::ReaderWriterQueue(); + res.job_queue = moodycamel::ConcurrentQueue(); } } From 8abf36960483d6241049d1532793b4ffbc79ce95 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 13:53:08 -0500 Subject: [PATCH 122/323] track parent timing info --- src/cpp/include/atomicops.h | 772 -------------- src/cpp/include/list_scanning.h | 18 +- src/cpp/include/readerwritequeue.h | 979 ------------------ src/cpp/src/query_coordinator.cpp | 2 +- .../osdi2025/numa_single_query/run.py | 182 ++-- 5 files changed, 122 insertions(+), 1831 deletions(-) delete mode 100644 src/cpp/include/atomicops.h delete mode 100644 src/cpp/include/readerwritequeue.h diff --git a/src/cpp/include/atomicops.h b/src/cpp/include/atomicops.h deleted file mode 100644 index 1832dee8..00000000 --- a/src/cpp/include/atomicops.h +++ /dev/null @@ -1,772 +0,0 @@ -// ©2013-2016 Cameron Desrochers. -// Distributed under the simplified BSD license (see the license file that -// should have come with this header). -// Uses Jeff Preshing's semaphore implementation (under the terms of its -// separate zlib license, embedded below). - -#pragma once - -// Provides portable (VC++2010+, Intel ICC 13, GCC 4.7+, and anything C++11 compliant) implementation -// of low-level memory barriers, plus a few semi-portable utility macros (for inlining and alignment). -// Also has a basic atomic type (limited to hardware-supported atomics with no memory ordering guarantees). -// Uses the AE_* prefix for macros (historical reasons), and the "moodycamel" namespace for symbols. - -#include -#include -#include -#include -#include -#include - -// Platform detection -#if defined(__INTEL_COMPILER) -#define AE_ICC -#elif defined(_MSC_VER) -#define AE_VCPP -#elif defined(__GNUC__) -#define AE_GCC -#endif - -#if defined(_M_IA64) || defined(__ia64__) -#define AE_ARCH_IA64 -#elif defined(_WIN64) || defined(__amd64__) || defined(_M_X64) || defined(__x86_64__) -#define AE_ARCH_X64 -#elif defined(_M_IX86) || defined(__i386__) -#define AE_ARCH_X86 -#elif defined(_M_PPC) || defined(__powerpc__) -#define AE_ARCH_PPC -#else -#define AE_ARCH_UNKNOWN -#endif - - -// AE_UNUSED -#define AE_UNUSED(x) ((void)x) - -// AE_NO_TSAN/AE_TSAN_ANNOTATE_* -// For GCC -#if defined(__SANITIZE_THREAD__) -#define AE_TSAN_IS_ENABLED -#endif -// For clang -#if defined(__has_feature) -#if __has_feature(thread_sanitizer) && !defined(AE_TSAN_IS_ENABLED) -#define AE_TSAN_IS_ENABLED -#endif -#endif - -#ifdef AE_TSAN_IS_ENABLED -#if __cplusplus >= 201703L // inline variables require C++17 -namespace moodycamel { inline int ae_tsan_global; } -#define AE_TSAN_ANNOTATE_RELEASE() AnnotateHappensBefore(__FILE__, __LINE__, (void *)(&::moodycamel::ae_tsan_global)) -#define AE_TSAN_ANNOTATE_ACQUIRE() AnnotateHappensAfter(__FILE__, __LINE__, (void *)(&::moodycamel::ae_tsan_global)) -extern "C" void AnnotateHappensBefore(const char*, int, void*); -extern "C" void AnnotateHappensAfter(const char*, int, void*); -#else // when we can't work with tsan, attempt to disable its warnings -#define AE_NO_TSAN __attribute__((no_sanitize("thread"))) -#endif -#endif - -#ifndef AE_NO_TSAN -#define AE_NO_TSAN -#endif - -#ifndef AE_TSAN_ANNOTATE_RELEASE -#define AE_TSAN_ANNOTATE_RELEASE() -#define AE_TSAN_ANNOTATE_ACQUIRE() -#endif - - -// AE_FORCEINLINE -#if defined(AE_VCPP) || defined(AE_ICC) -#define AE_FORCEINLINE __forceinline -#elif defined(AE_GCC) -//#define AE_FORCEINLINE __attribute__((always_inline)) -#define AE_FORCEINLINE inline -#else -#define AE_FORCEINLINE inline -#endif - - -// AE_ALIGN -#if defined(AE_VCPP) || defined(AE_ICC) -#define AE_ALIGN(x) __declspec(align(x)) -#elif defined(AE_GCC) -#define AE_ALIGN(x) __attribute__((aligned(x))) -#else -// Assume GCC compliant syntax... -#define AE_ALIGN(x) __attribute__((aligned(x))) -#endif - - -// Portable atomic fences implemented below: - -namespace moodycamel { - - enum memory_order { - memory_order_relaxed, - memory_order_acquire, - memory_order_release, - memory_order_acq_rel, - memory_order_seq_cst, - - // memory_order_sync: Forces a full sync: - // #LoadLoad, #LoadStore, #StoreStore, and most significantly, #StoreLoad - memory_order_sync = memory_order_seq_cst - }; - -} // end namespace moodycamel - -#if (defined(AE_VCPP) && (_MSC_VER < 1700 || defined(__cplusplus_cli))) || (defined(AE_ICC) && __INTEL_COMPILER < 1600) -// VS2010 and ICC13 don't support std::atomic_*_fence, implement our own fences - -#include - -#if defined(AE_ARCH_X64) || defined(AE_ARCH_X86) -#define AeFullSync _mm_mfence -#define AeLiteSync _mm_mfence -#elif defined(AE_ARCH_IA64) -#define AeFullSync __mf -#define AeLiteSync __mf -#elif defined(AE_ARCH_PPC) -#include -#define AeFullSync __sync -#define AeLiteSync __lwsync -#endif - - -#ifdef AE_VCPP -#pragma warning(push) -#pragma warning(disable: 4365) // Disable erroneous 'conversion from long to unsigned int, signed/unsigned mismatch' error when using `assert` -#ifdef __cplusplus_cli -#pragma managed(push, off) -#endif -#endif - -namespace moodycamel { - -AE_FORCEINLINE void compiler_fence(memory_order order) AE_NO_TSAN -{ - switch (order) { - case memory_order_relaxed: break; - case memory_order_acquire: _ReadBarrier(); break; - case memory_order_release: _WriteBarrier(); break; - case memory_order_acq_rel: _ReadWriteBarrier(); break; - case memory_order_seq_cst: _ReadWriteBarrier(); break; - default: assert(false); - } -} - -// x86/x64 have a strong memory model -- all loads and stores have -// acquire and release semantics automatically (so only need compiler -// barriers for those). -#if defined(AE_ARCH_X86) || defined(AE_ARCH_X64) -AE_FORCEINLINE void fence(memory_order order) AE_NO_TSAN -{ - switch (order) { - case memory_order_relaxed: break; - case memory_order_acquire: _ReadBarrier(); break; - case memory_order_release: _WriteBarrier(); break; - case memory_order_acq_rel: _ReadWriteBarrier(); break; - case memory_order_seq_cst: - _ReadWriteBarrier(); - AeFullSync(); - _ReadWriteBarrier(); - break; - default: assert(false); - } -} -#else -AE_FORCEINLINE void fence(memory_order order) AE_NO_TSAN -{ - // Non-specialized arch, use heavier memory barriers everywhere just in case :-( - switch (order) { - case memory_order_relaxed: - break; - case memory_order_acquire: - _ReadBarrier(); - AeLiteSync(); - _ReadBarrier(); - break; - case memory_order_release: - _WriteBarrier(); - AeLiteSync(); - _WriteBarrier(); - break; - case memory_order_acq_rel: - _ReadWriteBarrier(); - AeLiteSync(); - _ReadWriteBarrier(); - break; - case memory_order_seq_cst: - _ReadWriteBarrier(); - AeFullSync(); - _ReadWriteBarrier(); - break; - default: assert(false); - } -} -#endif -} // end namespace moodycamel -#else -// Use standard library of atomics -#include - -namespace moodycamel { - - AE_FORCEINLINE void compiler_fence(memory_order order) AE_NO_TSAN - { - switch (order) { - case memory_order_relaxed: break; - case memory_order_acquire: std::atomic_signal_fence(std::memory_order_acquire); break; - case memory_order_release: std::atomic_signal_fence(std::memory_order_release); break; - case memory_order_acq_rel: std::atomic_signal_fence(std::memory_order_acq_rel); break; - case memory_order_seq_cst: std::atomic_signal_fence(std::memory_order_seq_cst); break; - default: assert(false); - } - } - - AE_FORCEINLINE void fence(memory_order order) AE_NO_TSAN - { - switch (order) { - case memory_order_relaxed: break; - case memory_order_acquire: AE_TSAN_ANNOTATE_ACQUIRE(); std::atomic_thread_fence(std::memory_order_acquire); break; - case memory_order_release: AE_TSAN_ANNOTATE_RELEASE(); std::atomic_thread_fence(std::memory_order_release); break; - case memory_order_acq_rel: AE_TSAN_ANNOTATE_ACQUIRE(); AE_TSAN_ANNOTATE_RELEASE(); std::atomic_thread_fence(std::memory_order_acq_rel); break; - case memory_order_seq_cst: AE_TSAN_ANNOTATE_ACQUIRE(); AE_TSAN_ANNOTATE_RELEASE(); std::atomic_thread_fence(std::memory_order_seq_cst); break; - default: assert(false); - } - } - -} // end namespace moodycamel - -#endif - - -#if !defined(AE_VCPP) || (_MSC_VER >= 1700 && !defined(__cplusplus_cli)) -#define AE_USE_STD_ATOMIC_FOR_WEAK_ATOMIC -#endif - -#ifdef AE_USE_STD_ATOMIC_FOR_WEAK_ATOMIC -#include -#endif -#include - -// WARNING: *NOT* A REPLACEMENT FOR std::atomic. READ CAREFULLY: -// Provides basic support for atomic variables -- no memory ordering guarantees are provided. -// The guarantee of atomicity is only made for types that already have atomic load and store guarantees -// at the hardware level -- on most platforms this generally means aligned pointers and integers (only). -namespace moodycamel { - template - class weak_atomic - { - public: - AE_NO_TSAN weak_atomic() : value() { } -#ifdef AE_VCPP - #pragma warning(push) -#pragma warning(disable: 4100) // Get rid of (erroneous) 'unreferenced formal parameter' warning -#endif - template AE_NO_TSAN weak_atomic(U&& x) : value(std::forward(x)) { } -#ifdef __cplusplus_cli - // Work around bug with universal reference/nullptr combination that only appears when /clr is on - AE_NO_TSAN weak_atomic(nullptr_t) : value(nullptr) { } -#endif - AE_NO_TSAN weak_atomic(weak_atomic const& other) : value(other.load()) { } - AE_NO_TSAN weak_atomic(weak_atomic&& other) : value(std::move(other.load())) { } -#ifdef AE_VCPP -#pragma warning(pop) -#endif - - AE_FORCEINLINE operator T() const AE_NO_TSAN { return load(); } - - -#ifndef AE_USE_STD_ATOMIC_FOR_WEAK_ATOMIC - template AE_FORCEINLINE weak_atomic const& operator=(U&& x) AE_NO_TSAN { value = std::forward(x); return *this; } - AE_FORCEINLINE weak_atomic const& operator=(weak_atomic const& other) AE_NO_TSAN { value = other.value; return *this; } - - AE_FORCEINLINE T load() const AE_NO_TSAN { return value; } - - AE_FORCEINLINE T fetch_add_acquire(T increment) AE_NO_TSAN - { -#if defined(AE_ARCH_X64) || defined(AE_ARCH_X86) - if (sizeof(T) == 4) return _InterlockedExchangeAdd((long volatile*)&value, (long)increment); -#if defined(_M_AMD64) - else if (sizeof(T) == 8) return _InterlockedExchangeAdd64((long long volatile*)&value, (long long)increment); -#endif -#else -#error Unsupported platform -#endif - assert(false && "T must be either a 32 or 64 bit type"); - return value; - } - - AE_FORCEINLINE T fetch_add_release(T increment) AE_NO_TSAN - { -#if defined(AE_ARCH_X64) || defined(AE_ARCH_X86) - if (sizeof(T) == 4) return _InterlockedExchangeAdd((long volatile*)&value, (long)increment); -#if defined(_M_AMD64) - else if (sizeof(T) == 8) return _InterlockedExchangeAdd64((long long volatile*)&value, (long long)increment); -#endif -#else -#error Unsupported platform -#endif - assert(false && "T must be either a 32 or 64 bit type"); - return value; - } -#else - template - AE_FORCEINLINE weak_atomic const& operator=(U&& x) AE_NO_TSAN - { - value.store(std::forward(x), std::memory_order_relaxed); - return *this; - } - - AE_FORCEINLINE weak_atomic const& operator=(weak_atomic const& other) AE_NO_TSAN - { - value.store(other.value.load(std::memory_order_relaxed), std::memory_order_relaxed); - return *this; - } - - AE_FORCEINLINE T load() const AE_NO_TSAN { return value.load(std::memory_order_relaxed); } - - AE_FORCEINLINE T fetch_add_acquire(T increment) AE_NO_TSAN - { - return value.fetch_add(increment, std::memory_order_acquire); - } - - AE_FORCEINLINE T fetch_add_release(T increment) AE_NO_TSAN - { - return value.fetch_add(increment, std::memory_order_release); - } -#endif - - - private: -#ifndef AE_USE_STD_ATOMIC_FOR_WEAK_ATOMIC - // No std::atomic support, but still need to circumvent compiler optimizations. - // `volatile` will make memory access slow, but is guaranteed to be reliable. - volatile T value; -#else - std::atomic value; -#endif - }; - -} // end namespace moodycamel - - - -// Portable single-producer, single-consumer semaphore below: - -#if defined(_WIN32) -// Avoid including windows.h in a header; we only need a handful of -// items, so we'll redeclare them here (this is relatively safe since -// the API generally has to remain stable between Windows versions). -// I know this is an ugly hack but it still beats polluting the global -// namespace with thousands of generic names or adding a .cpp for nothing. -extern "C" { - struct _SECURITY_ATTRIBUTES; - __declspec(dllimport) void* __stdcall CreateSemaphoreW(_SECURITY_ATTRIBUTES* lpSemaphoreAttributes, long lInitialCount, long lMaximumCount, const wchar_t* lpName); - __declspec(dllimport) int __stdcall CloseHandle(void* hObject); - __declspec(dllimport) unsigned long __stdcall WaitForSingleObject(void* hHandle, unsigned long dwMilliseconds); - __declspec(dllimport) int __stdcall ReleaseSemaphore(void* hSemaphore, long lReleaseCount, long* lpPreviousCount); -} -#elif defined(__MACH__) -#include -#elif defined(__unix__) -#include -#elif defined(FREERTOS) -#include -#include -#include -#endif - -namespace moodycamel -{ - // Code in the spsc_sema namespace below is an adaptation of Jeff Preshing's - // portable + lightweight semaphore implementations, originally from - // https://github.com/preshing/cpp11-on-multicore/blob/master/common/sema.h - // LICENSE: - // Copyright (c) 2015 Jeff Preshing - // - // This software is provided 'as-is', without any express or implied - // warranty. In no event will the authors be held liable for any damages - // arising from the use of this software. - // - // Permission is granted to anyone to use this software for any purpose, - // including commercial applications, and to alter it and redistribute it - // freely, subject to the following restrictions: - // - // 1. The origin of this software must not be misrepresented; you must not - // claim that you wrote the original software. If you use this software - // in a product, an acknowledgement in the product documentation would be - // appreciated but is not required. - // 2. Altered source versions must be plainly marked as such, and must not be - // misrepresented as being the original software. - // 3. This notice may not be removed or altered from any source distribution. - namespace spsc_sema - { -#if defined(_WIN32) - class Semaphore - { - private: - void* m_hSema; - - Semaphore(const Semaphore& other); - Semaphore& operator=(const Semaphore& other); - - public: - AE_NO_TSAN Semaphore(int initialCount = 0) : m_hSema() - { - assert(initialCount >= 0); - const long maxLong = 0x7fffffff; - m_hSema = CreateSemaphoreW(nullptr, initialCount, maxLong, nullptr); - assert(m_hSema); - } - - AE_NO_TSAN ~Semaphore() - { - CloseHandle(m_hSema); - } - - bool wait() AE_NO_TSAN - { - const unsigned long infinite = 0xffffffff; - return WaitForSingleObject(m_hSema, infinite) == 0; - } - - bool try_wait() AE_NO_TSAN - { - return WaitForSingleObject(m_hSema, 0) == 0; - } - - bool timed_wait(std::uint64_t usecs) AE_NO_TSAN - { - return WaitForSingleObject(m_hSema, (unsigned long)(usecs / 1000)) == 0; - } - - void signal(int count = 1) AE_NO_TSAN - { - while (!ReleaseSemaphore(m_hSema, count, nullptr)); - } - }; -#elif defined(__MACH__) - //--------------------------------------------------------- - // Semaphore (Apple iOS and OSX) - // Can't use POSIX semaphores due to http://lists.apple.com/archives/darwin-kernel/2009/Apr/msg00010.html - //--------------------------------------------------------- - class Semaphore - { - private: - semaphore_t m_sema; - - Semaphore(const Semaphore& other); - Semaphore& operator=(const Semaphore& other); - - public: - AE_NO_TSAN Semaphore(int initialCount = 0) : m_sema() - { - assert(initialCount >= 0); - kern_return_t rc = semaphore_create(mach_task_self(), &m_sema, SYNC_POLICY_FIFO, initialCount); - assert(rc == KERN_SUCCESS); - AE_UNUSED(rc); - } - - AE_NO_TSAN ~Semaphore() - { - semaphore_destroy(mach_task_self(), m_sema); - } - - bool wait() AE_NO_TSAN - { - return semaphore_wait(m_sema) == KERN_SUCCESS; - } - - bool try_wait() AE_NO_TSAN - { - return timed_wait(0); - } - - bool timed_wait(std::uint64_t timeout_usecs) AE_NO_TSAN - { - mach_timespec_t ts; - ts.tv_sec = static_cast(timeout_usecs / 1000000); - ts.tv_nsec = static_cast((timeout_usecs % 1000000) * 1000); - - // added in OSX 10.10: https://developer.apple.com/library/prerelease/mac/documentation/General/Reference/APIDiffsMacOSX10_10SeedDiff/modules/Darwin.html - kern_return_t rc = semaphore_timedwait(m_sema, ts); - return rc == KERN_SUCCESS; - } - - void signal() AE_NO_TSAN - { - while (semaphore_signal(m_sema) != KERN_SUCCESS); - } - - void signal(int count) AE_NO_TSAN - { - while (count-- > 0) - { - while (semaphore_signal(m_sema) != KERN_SUCCESS); - } - } - }; -#elif defined(__unix__) - //--------------------------------------------------------- - // Semaphore (POSIX, Linux) - //--------------------------------------------------------- - class Semaphore - { - private: - sem_t m_sema; - - Semaphore(const Semaphore& other); - Semaphore& operator=(const Semaphore& other); - - public: - AE_NO_TSAN Semaphore(int initialCount = 0) : m_sema() - { - assert(initialCount >= 0); - int rc = sem_init(&m_sema, 0, static_cast(initialCount)); - assert(rc == 0); - AE_UNUSED(rc); - } - - AE_NO_TSAN ~Semaphore() - { - sem_destroy(&m_sema); - } - - bool wait() AE_NO_TSAN - { - // http://stackoverflow.com/questions/2013181/gdb-causes-sem-wait-to-fail-with-eintr-error - int rc; - do - { - rc = sem_wait(&m_sema); - } - while (rc == -1 && errno == EINTR); - return rc == 0; - } - - bool try_wait() AE_NO_TSAN - { - int rc; - do { - rc = sem_trywait(&m_sema); - } while (rc == -1 && errno == EINTR); - return rc == 0; - } - - bool timed_wait(std::uint64_t usecs) AE_NO_TSAN - { - struct timespec ts; - const int usecs_in_1_sec = 1000000; - const int nsecs_in_1_sec = 1000000000; - clock_gettime(CLOCK_REALTIME, &ts); - ts.tv_sec += static_cast(usecs / usecs_in_1_sec); - ts.tv_nsec += static_cast(usecs % usecs_in_1_sec) * 1000; - // sem_timedwait bombs if you have more than 1e9 in tv_nsec - // so we have to clean things up before passing it in - if (ts.tv_nsec >= nsecs_in_1_sec) { - ts.tv_nsec -= nsecs_in_1_sec; - ++ts.tv_sec; - } - - int rc; - do { - rc = sem_timedwait(&m_sema, &ts); - } while (rc == -1 && errno == EINTR); - return rc == 0; - } - - void signal() AE_NO_TSAN - { - while (sem_post(&m_sema) == -1); - } - - void signal(int count) AE_NO_TSAN - { - while (count-- > 0) - { - while (sem_post(&m_sema) == -1); - } - } - }; -#elif defined(FREERTOS) - //--------------------------------------------------------- - // Semaphore (FreeRTOS) - //--------------------------------------------------------- - class Semaphore - { - private: - SemaphoreHandle_t m_sema; - - Semaphore(const Semaphore& other); - Semaphore& operator=(const Semaphore& other); - - public: - AE_NO_TSAN Semaphore(int initialCount = 0) : m_sema() - { - assert(initialCount >= 0); - m_sema = xSemaphoreCreateCounting(static_cast(~0ull), static_cast(initialCount)); - assert(m_sema); - } - - AE_NO_TSAN ~Semaphore() - { - vSemaphoreDelete(m_sema); - } - - bool wait() AE_NO_TSAN - { - return xSemaphoreTake(m_sema, portMAX_DELAY) == pdTRUE; - } - - bool try_wait() AE_NO_TSAN - { - // Note: In an ISR context, if this causes a task to unblock, - // the caller won't know about it - if (xPortIsInsideInterrupt()) - return xSemaphoreTakeFromISR(m_sema, NULL) == pdTRUE; - return xSemaphoreTake(m_sema, 0) == pdTRUE; - } - - bool timed_wait(std::uint64_t usecs) AE_NO_TSAN - { - std::uint64_t msecs = usecs / 1000; - TickType_t ticks = static_cast(msecs / portTICK_PERIOD_MS); - if (ticks == 0) - return try_wait(); - return xSemaphoreTake(m_sema, ticks) == pdTRUE; - } - - void signal() AE_NO_TSAN - { - // Note: In an ISR context, if this causes a task to unblock, - // the caller won't know about it - BaseType_t rc; - if (xPortIsInsideInterrupt()) - rc = xSemaphoreGiveFromISR(m_sema, NULL); - else - rc = xSemaphoreGive(m_sema); - assert(rc == pdTRUE); - AE_UNUSED(rc); - } - - void signal(int count) AE_NO_TSAN - { - while (count-- > 0) - signal(); - } - }; -#else -#error Unsupported platform! (No semaphore wrapper available) -#endif - - //--------------------------------------------------------- - // LightweightSemaphore - //--------------------------------------------------------- - class LightweightSemaphore - { - public: - typedef std::make_signed::type ssize_t; - - private: - weak_atomic m_count; - Semaphore m_sema; - - bool waitWithPartialSpinning(std::int64_t timeout_usecs = -1) AE_NO_TSAN - { - ssize_t oldCount; - // Is there a better way to set the initial spin count? - // If we lower it to 1000, testBenaphore becomes 15x slower on my Core i7-5930K Windows PC, - // as threads start hitting the kernel semaphore. - int spin = 1024; - while (--spin >= 0) - { - if (m_count.load() > 0) - { - m_count.fetch_add_acquire(-1); - return true; - } - compiler_fence(memory_order_acquire); // Prevent the compiler from collapsing the loop. - } - oldCount = m_count.fetch_add_acquire(-1); - if (oldCount > 0) - return true; - if (timeout_usecs < 0) - { - if (m_sema.wait()) - return true; - } - if (timeout_usecs > 0 && m_sema.timed_wait(static_cast(timeout_usecs))) - return true; - // At this point, we've timed out waiting for the semaphore, but the - // count is still decremented indicating we may still be waiting on - // it. So we have to re-adjust the count, but only if the semaphore - // wasn't signaled enough times for us too since then. If it was, we - // need to release the semaphore too. - while (true) - { - oldCount = m_count.fetch_add_release(1); - if (oldCount < 0) - return false; // successfully restored things to the way they were - // Oh, the producer thread just signaled the semaphore after all. Try again: - oldCount = m_count.fetch_add_acquire(-1); - if (oldCount > 0 && m_sema.try_wait()) - return true; - } - } - - public: - AE_NO_TSAN LightweightSemaphore(ssize_t initialCount = 0) : m_count(initialCount), m_sema() - { - assert(initialCount >= 0); - } - - bool tryWait() AE_NO_TSAN - { - if (m_count.load() > 0) - { - m_count.fetch_add_acquire(-1); - return true; - } - return false; - } - - bool wait() AE_NO_TSAN - { - return tryWait() || waitWithPartialSpinning(); - } - - bool wait(std::int64_t timeout_usecs) AE_NO_TSAN - { - return tryWait() || waitWithPartialSpinning(timeout_usecs); - } - - void signal(ssize_t count = 1) AE_NO_TSAN - { - assert(count >= 0); - ssize_t oldCount = m_count.fetch_add_release(count); - assert(oldCount >= -1); - if (oldCount < 0) - { - m_sema.signal(1); - } - } - - std::size_t availableApprox() const AE_NO_TSAN - { - ssize_t count = m_count.load(); - return count > 0 ? static_cast(count) : 0; - } - }; - } // end namespace spsc_sema -} // end namespace moodycamel - -#if defined(AE_VCPP) && (_MSC_VER < 1700 || defined(__cplusplus_cli)) -#pragma warning(pop) -#ifdef __cplusplus_cli -#pragma managed(pop) -#endif -#endif \ No newline at end of file diff --git a/src/cpp/include/list_scanning.h b/src/cpp/include/list_scanning.h index b8319e04..b41831ca 100644 --- a/src/cpp/include/list_scanning.h +++ b/src/cpp/include/list_scanning.h @@ -403,7 +403,9 @@ inline void batched_scan_list(const float *query_vecs, int list_size, int dim, vector> &topk_buffers, - MetricType metric = faiss::METRIC_L2) { + MetricType metric = faiss::METRIC_L2, + float *distances = nullptr, + int64_t *labels = nullptr) { if (list_size == 0 || list_vecs == nullptr) { // No list vectors to process; return; @@ -413,8 +415,12 @@ inline void batched_scan_list(const float *query_vecs, int k = topk_buffers[0]->k_; int k_max = std::min(k, list_size); - int64_t *labels = (int64_t *) malloc(num_queries * k_max * sizeof(int64_t)); - float *distances = (float *) malloc(num_queries * k_max * sizeof(float)); + bool alloc_results = false; + if (distances == nullptr) { + alloc_results = true; + (int64_t *) malloc(num_queries * k_max * sizeof(int64_t)); + (float *) malloc(num_queries * k_max * sizeof(float)); + } if (metric == faiss::METRIC_INNER_PRODUCT) { faiss::float_minheap_array_t res = {size_t(num_queries), size_t(k_max), labels, distances}; @@ -447,8 +453,10 @@ inline void batched_scan_list(const float *query_vecs, topk_buffers[i]->batch_add(distances + i * k_max, labels + i * k_max, k_max); } - free(labels); - free(distances); + if (alloc_results) { + free(distances); + free(labels); + } } // } diff --git a/src/cpp/include/readerwritequeue.h b/src/cpp/include/readerwritequeue.h deleted file mode 100644 index 098d47cf..00000000 --- a/src/cpp/include/readerwritequeue.h +++ /dev/null @@ -1,979 +0,0 @@ -// ©2013-2020 Cameron Desrochers. -// Distributed under the simplified BSD license (see the license file that -// should have come with this header). - -#pragma once - -#include "atomicops.h" -#include -#include -#include -#include -#include -#include -#include -#include // For malloc/free/abort & size_t -#include -#if __cplusplus > 199711L || _MSC_VER >= 1700 // C++11 or VS2012 -#include -#endif - - -// A lock-free queue for a single-consumer, single-producer architecture. -// The queue is also wait-free in the common path (except if more memory -// needs to be allocated, in which case malloc is called). -// Allocates memory sparingly, and only once if the original maximum size -// estimate is never exceeded. -// Tested on x86/x64 processors, but semantics should be correct for all -// architectures (given the right implementations in atomicops.h), provided -// that aligned integer and pointer accesses are naturally atomic. -// Note that there should only be one consumer thread and producer thread; -// Switching roles of the threads, or using multiple consecutive threads for -// one role, is not safe unless properly synchronized. -// Using the queue exclusively from one thread is fine, though a bit silly. - -#ifndef MOODYCAMEL_CACHE_LINE_SIZE -#define MOODYCAMEL_CACHE_LINE_SIZE 64 -#endif - -#ifndef MOODYCAMEL_EXCEPTIONS_ENABLED -#if (defined(_MSC_VER) && defined(_CPPUNWIND)) || (defined(__GNUC__) && defined(__EXCEPTIONS)) || (!defined(_MSC_VER) && !defined(__GNUC__)) -#define MOODYCAMEL_EXCEPTIONS_ENABLED -#endif -#endif - -#ifndef MOODYCAMEL_HAS_EMPLACE -#if !defined(_MSC_VER) || _MSC_VER >= 1800 // variadic templates: either a non-MS compiler or VS >= 2013 -#define MOODYCAMEL_HAS_EMPLACE 1 -#endif -#endif - -#ifndef MOODYCAMEL_MAYBE_ALIGN_TO_CACHELINE -#if defined (__APPLE__) && defined (__MACH__) && __cplusplus >= 201703L -// This is required to find out what deployment target we are using -#include -#if !defined(MAC_OS_X_VERSION_MIN_REQUIRED) || !defined(MAC_OS_X_VERSION_10_14) || MAC_OS_X_VERSION_MIN_REQUIRED < MAC_OS_X_VERSION_10_14 -// C++17 new(size_t, align_val_t) is not backwards-compatible with older versions of macOS, so we can't support over-alignment in this case -#define MOODYCAMEL_MAYBE_ALIGN_TO_CACHELINE -#endif -#endif -#endif - -#ifndef MOODYCAMEL_MAYBE_ALIGN_TO_CACHELINE -#define MOODYCAMEL_MAYBE_ALIGN_TO_CACHELINE AE_ALIGN(MOODYCAMEL_CACHE_LINE_SIZE) -#endif - -#ifdef AE_VCPP -#pragma warning(push) -#pragma warning(disable: 4324) // structure was padded due to __declspec(align()) -#pragma warning(disable: 4820) // padding was added -#pragma warning(disable: 4127) // conditional expression is constant -#endif - -namespace moodycamel { - - template - class MOODYCAMEL_MAYBE_ALIGN_TO_CACHELINE ReaderWriterQueue -{ - // Design: Based on a queue-of-queues. The low-level queues are just - // circular buffers with front and tail indices indicating where the - // next element to dequeue is and where the next element can be enqueued, - // respectively. Each low-level queue is called a "block". Each block - // wastes exactly one element's worth of space to keep the design simple - // (if front == tail then the queue is empty, and can't be full). - // The high-level queue is a circular linked list of blocks; again there - // is a front and tail, but this time they are pointers to the blocks. - // The front block is where the next element to be dequeued is, provided - // the block is not empty. The back block is where elements are to be - // enqueued, provided the block is not full. - // The producer thread owns all the tail indices/pointers. The consumer - // thread owns all the front indices/pointers. Both threads read each - // other's variables, but only the owning thread updates them. E.g. After - // the consumer reads the producer's tail, the tail may change before the - // consumer is done dequeuing an object, but the consumer knows the tail - // will never go backwards, only forwards. - // If there is no room to enqueue an object, an additional block (of - // equal size to the last block) is added. Blocks are never removed. - - public: - typedef T value_type; - - // Constructs a queue that can hold at least `size` elements without further - // allocations. If more than MAX_BLOCK_SIZE elements are requested, - // then several blocks of MAX_BLOCK_SIZE each are reserved (including - // at least one extra buffer block). - AE_NO_TSAN explicit ReaderWriterQueue(size_t size = 15) -#ifndef NDEBUG - : enqueuing(false) - ,dequeuing(false) -#endif -{ - assert(MAX_BLOCK_SIZE == ceilToPow2(MAX_BLOCK_SIZE) && "MAX_BLOCK_SIZE must be a power of 2"); - assert(MAX_BLOCK_SIZE >= 2 && "MAX_BLOCK_SIZE must be at least 2"); - - Block* firstBlock = nullptr; - - largestBlockSize = ceilToPow2(size + 1); // We need a spare slot to fit size elements in the block - if (largestBlockSize > MAX_BLOCK_SIZE * 2) { - // We need a spare block in case the producer is writing to a different block the consumer is reading from, and - // wants to enqueue the maximum number of elements. We also need a spare element in each block to avoid the ambiguity - // between front == tail meaning "empty" and "full". - // So the effective number of slots that are guaranteed to be usable at any time is the block size - 1 times the - // number of blocks - 1. Solving for size and applying a ceiling to the division gives us (after simplifying): - size_t initialBlockCount = (size + MAX_BLOCK_SIZE * 2 - 3) / (MAX_BLOCK_SIZE - 1); - largestBlockSize = MAX_BLOCK_SIZE; - Block* lastBlock = nullptr; - for (size_t i = 0; i != initialBlockCount; ++i) { - auto block = make_block(largestBlockSize); - if (block == nullptr) { -#ifdef MOODYCAMEL_EXCEPTIONS_ENABLED - throw std::bad_alloc(); -#else - abort(); -#endif -} -if (firstBlock == nullptr) { -firstBlock = block; -} -else { -lastBlock->next = block; -} -lastBlock = block; -block->next = firstBlock; -} -} -else { -firstBlock = make_block(largestBlockSize); -if (firstBlock == nullptr) { -#ifdef MOODYCAMEL_EXCEPTIONS_ENABLED -throw std::bad_alloc(); -#else -abort(); -#endif -} -firstBlock->next = firstBlock; -} -frontBlock = firstBlock; -tailBlock = firstBlock; - -// Make sure the reader/writer threads will have the initialized memory setup above: -fence(memory_order_sync); -} - -// Note: The queue should not be accessed concurrently while it's -// being moved. It's up to the user to synchronize this. -AE_NO_TSAN ReaderWriterQueue(ReaderWriterQueue&& other) -: frontBlock(other.frontBlock.load()), -tailBlock(other.tailBlock.load()), -largestBlockSize(other.largestBlockSize) -#ifndef NDEBUG -,enqueuing(false) -,dequeuing(false) -#endif -{ -other.largestBlockSize = 32; -Block* b = other.make_block(other.largestBlockSize); -if (b == nullptr) { -#ifdef MOODYCAMEL_EXCEPTIONS_ENABLED -throw std::bad_alloc(); -#else -abort(); -#endif -} -b->next = b; -other.frontBlock = b; -other.tailBlock = b; -} - -// Note: The queue should not be accessed concurrently while it's -// being moved. It's up to the user to synchronize this. -ReaderWriterQueue& operator=(ReaderWriterQueue&& other) AE_NO_TSAN -{ -Block* b = frontBlock.load(); -frontBlock = other.frontBlock.load(); -other.frontBlock = b; -b = tailBlock.load(); -tailBlock = other.tailBlock.load(); -other.tailBlock = b; -std::swap(largestBlockSize, other.largestBlockSize); -return *this; -} - -// Note: The queue should not be accessed concurrently while it's -// being deleted. It's up to the user to synchronize this. -AE_NO_TSAN ~ReaderWriterQueue() -{ - // Make sure we get the latest version of all variables from other CPUs: - fence(memory_order_sync); - - // Destroy any remaining objects in queue and free memory - Block* frontBlock_ = frontBlock; - Block* block = frontBlock_; - do { - Block* nextBlock = block->next; - size_t blockFront = block->front; - size_t blockTail = block->tail; - - for (size_t i = blockFront; i != blockTail; i = (i + 1) & block->sizeMask) { - auto element = reinterpret_cast(block->data + i * sizeof(T)); - element->~T(); - (void)element; - } - - auto rawBlock = block->rawThis; - block->~Block(); - std::free(rawBlock); - block = nextBlock; - } while (block != frontBlock_); -} - - -// Enqueues a copy of element if there is room in the queue. -// Returns true if the element was enqueued, false otherwise. -// Does not allocate memory. -AE_FORCEINLINE bool try_enqueue(T const& element) AE_NO_TSAN -{ -return inner_enqueue(element); -} - -// Enqueues a moved copy of element if there is room in the queue. -// Returns true if the element was enqueued, false otherwise. -// Does not allocate memory. -AE_FORCEINLINE bool try_enqueue(T&& element) AE_NO_TSAN -{ -return inner_enqueue(std::forward(element)); -} - -#if MOODYCAMEL_HAS_EMPLACE -// Like try_enqueue() but with emplace semantics (i.e. construct-in-place). -template -AE_FORCEINLINE bool try_emplace(Args&&... args) AE_NO_TSAN -{ -return inner_enqueue(std::forward(args)...); -} -#endif - -// Enqueues a copy of element on the queue. -// Allocates an additional block of memory if needed. -// Only fails (returns false) if memory allocation fails. -AE_FORCEINLINE bool enqueue(T const& element) AE_NO_TSAN -{ -return inner_enqueue(element); -} - -// Enqueues a moved copy of element on the queue. -// Allocates an additional block of memory if needed. -// Only fails (returns false) if memory allocation fails. -AE_FORCEINLINE bool enqueue(T&& element) AE_NO_TSAN -{ -return inner_enqueue(std::forward(element)); -} - -#if MOODYCAMEL_HAS_EMPLACE -// Like enqueue() but with emplace semantics (i.e. construct-in-place). -template -AE_FORCEINLINE bool emplace(Args&&... args) AE_NO_TSAN -{ -return inner_enqueue(std::forward(args)...); -} -#endif - -// Attempts to dequeue an element; if the queue is empty, -// returns false instead. If the queue has at least one element, -// moves front to result using operator=, then returns true. -template -bool try_dequeue(U& result) AE_NO_TSAN -{ -#ifndef NDEBUG -ReentrantGuard guard(this->dequeuing); -#endif - -// High-level pseudocode: -// Remember where the tail block is -// If the front block has an element in it, dequeue it -// Else -// If front block was the tail block when we entered the function, return false -// Else advance to next block and dequeue the item there - -// Note that we have to use the value of the tail block from before we check if the front -// block is full or not, in case the front block is empty and then, before we check if the -// tail block is at the front block or not, the producer fills up the front block *and -// moves on*, which would make us skip a filled block. Seems unlikely, but was consistently -// reproducible in practice. -// In order to avoid overhead in the common case, though, we do a double-checked pattern -// where we have the fast path if the front block is not empty, then read the tail block, -// then re-read the front block and check if it's not empty again, then check if the tail -// block has advanced. - -Block* frontBlock_ = frontBlock.load(); -size_t blockTail = frontBlock_->localTail; -size_t blockFront = frontBlock_->front.load(); - -if (blockFront != blockTail || blockFront != (frontBlock_->localTail = frontBlock_->tail.load())) { -fence(memory_order_acquire); - -non_empty_front_block: -// Front block not empty, dequeue from here -auto element = reinterpret_cast(frontBlock_->data + blockFront * sizeof(T)); -result = std::move(*element); -element->~T(); - -blockFront = (blockFront + 1) & frontBlock_->sizeMask; - -fence(memory_order_release); -frontBlock_->front = blockFront; -} -else if (frontBlock_ != tailBlock.load()) { -fence(memory_order_acquire); - -frontBlock_ = frontBlock.load(); -blockTail = frontBlock_->localTail = frontBlock_->tail.load(); -blockFront = frontBlock_->front.load(); -fence(memory_order_acquire); - -if (blockFront != blockTail) { -// Oh look, the front block isn't empty after all -goto non_empty_front_block; -} - -// Front block is empty but there's another block ahead, advance to it -Block* nextBlock = frontBlock_->next; -// Don't need an acquire fence here since next can only ever be set on the tailBlock, -// and we're not the tailBlock, and we did an acquire earlier after reading tailBlock which -// ensures next is up-to-date on this CPU in case we recently were at tailBlock. - -size_t nextBlockFront = nextBlock->front.load(); -size_t nextBlockTail = nextBlock->localTail = nextBlock->tail.load(); -fence(memory_order_acquire); - -// Since the tailBlock is only ever advanced after being written to, -// we know there's for sure an element to dequeue on it -assert(nextBlockFront != nextBlockTail); -AE_UNUSED(nextBlockTail); - -// We're done with this block, let the producer use it if it needs -fence(memory_order_release); // Expose possibly pending changes to frontBlock->front from last dequeue -frontBlock = frontBlock_ = nextBlock; - -compiler_fence(memory_order_release); // Not strictly needed - -auto element = reinterpret_cast(frontBlock_->data + nextBlockFront * sizeof(T)); - -result = std::move(*element); -element->~T(); - -nextBlockFront = (nextBlockFront + 1) & frontBlock_->sizeMask; - -fence(memory_order_release); -frontBlock_->front = nextBlockFront; -} -else { -// No elements in current block and no other block to advance to -return false; -} - -return true; -} - - -// Returns a pointer to the front element in the queue (the one that -// would be removed next by a call to `try_dequeue` or `pop`). If the -// queue appears empty at the time the method is called, nullptr is -// returned instead. -// Must be called only from the consumer thread. -T* peek() const AE_NO_TSAN -{ -#ifndef NDEBUG -ReentrantGuard guard(this->dequeuing); -#endif -// See try_dequeue() for reasoning - -Block* frontBlock_ = frontBlock.load(); -size_t blockTail = frontBlock_->localTail; -size_t blockFront = frontBlock_->front.load(); - -if (blockFront != blockTail || blockFront != (frontBlock_->localTail = frontBlock_->tail.load())) { -fence(memory_order_acquire); -non_empty_front_block: -return reinterpret_cast(frontBlock_->data + blockFront * sizeof(T)); -} -else if (frontBlock_ != tailBlock.load()) { -fence(memory_order_acquire); -frontBlock_ = frontBlock.load(); -blockTail = frontBlock_->localTail = frontBlock_->tail.load(); -blockFront = frontBlock_->front.load(); -fence(memory_order_acquire); - -if (blockFront != blockTail) { -goto non_empty_front_block; -} - -Block* nextBlock = frontBlock_->next; - -size_t nextBlockFront = nextBlock->front.load(); -fence(memory_order_acquire); - -assert(nextBlockFront != nextBlock->tail.load()); -return reinterpret_cast(nextBlock->data + nextBlockFront * sizeof(T)); -} - -return nullptr; -} - -// Removes the front element from the queue, if any, without returning it. -// Returns true on success, or false if the queue appeared empty at the time -// `pop` was called. -bool pop() AE_NO_TSAN -{ -#ifndef NDEBUG -ReentrantGuard guard(this->dequeuing); -#endif -// See try_dequeue() for reasoning - -Block* frontBlock_ = frontBlock.load(); -size_t blockTail = frontBlock_->localTail; -size_t blockFront = frontBlock_->front.load(); - -if (blockFront != blockTail || blockFront != (frontBlock_->localTail = frontBlock_->tail.load())) { -fence(memory_order_acquire); - -non_empty_front_block: -auto element = reinterpret_cast(frontBlock_->data + blockFront * sizeof(T)); -element->~T(); - -blockFront = (blockFront + 1) & frontBlock_->sizeMask; - -fence(memory_order_release); -frontBlock_->front = blockFront; -} -else if (frontBlock_ != tailBlock.load()) { -fence(memory_order_acquire); -frontBlock_ = frontBlock.load(); -blockTail = frontBlock_->localTail = frontBlock_->tail.load(); -blockFront = frontBlock_->front.load(); -fence(memory_order_acquire); - -if (blockFront != blockTail) { -goto non_empty_front_block; -} - -// Front block is empty but there's another block ahead, advance to it -Block* nextBlock = frontBlock_->next; - -size_t nextBlockFront = nextBlock->front.load(); -size_t nextBlockTail = nextBlock->localTail = nextBlock->tail.load(); -fence(memory_order_acquire); - -assert(nextBlockFront != nextBlockTail); -AE_UNUSED(nextBlockTail); - -fence(memory_order_release); -frontBlock = frontBlock_ = nextBlock; - -compiler_fence(memory_order_release); - -auto element = reinterpret_cast(frontBlock_->data + nextBlockFront * sizeof(T)); -element->~T(); - -nextBlockFront = (nextBlockFront + 1) & frontBlock_->sizeMask; - -fence(memory_order_release); -frontBlock_->front = nextBlockFront; -} -else { -// No elements in current block and no other block to advance to -return false; -} - -return true; -} - -// Returns the approximate number of items currently in the queue. -// Safe to call from both the producer and consumer threads. -inline size_t size_approx() const AE_NO_TSAN -{ -size_t result = 0; -Block* frontBlock_ = frontBlock.load(); -Block* block = frontBlock_; -do { -fence(memory_order_acquire); -size_t blockFront = block->front.load(); -size_t blockTail = block->tail.load(); -result += (blockTail - blockFront) & block->sizeMask; -block = block->next.load(); -} while (block != frontBlock_); -return result; -} - -// Returns the total number of items that could be enqueued without incurring -// an allocation when this queue is empty. -// Safe to call from both the producer and consumer threads. -// -// NOTE: The actual capacity during usage may be different depending on the consumer. -// If the consumer is removing elements concurrently, the producer cannot add to -// the block the consumer is removing from until it's completely empty, except in -// the case where the producer was writing to the same block the consumer was -// reading from the whole time. -inline size_t max_capacity() const { - size_t result = 0; - Block* frontBlock_ = frontBlock.load(); - Block* block = frontBlock_; - do { - fence(memory_order_acquire); - result += block->sizeMask; - block = block->next.load(); - } while (block != frontBlock_); - return result; -} - - -private: -enum AllocationMode { CanAlloc, CannotAlloc }; - -#if MOODYCAMEL_HAS_EMPLACE -template -bool inner_enqueue(Args&&... args) AE_NO_TSAN -#else -template - bool inner_enqueue(U&& element) AE_NO_TSAN -#endif -{ -#ifndef NDEBUG -ReentrantGuard guard(this->enqueuing); -#endif - -// High-level pseudocode (assuming we're allowed to alloc a new block): -// If room in tail block, add to tail -// Else check next block -// If next block is not the head block, enqueue on next block -// Else create a new block and enqueue there -// Advance tail to the block we just enqueued to - -Block* tailBlock_ = tailBlock.load(); -size_t blockFront = tailBlock_->localFront; -size_t blockTail = tailBlock_->tail.load(); - -size_t nextBlockTail = (blockTail + 1) & tailBlock_->sizeMask; -if (nextBlockTail != blockFront || nextBlockTail != (tailBlock_->localFront = tailBlock_->front.load())) { -fence(memory_order_acquire); -// This block has room for at least one more element -char* location = tailBlock_->data + blockTail * sizeof(T); -#if MOODYCAMEL_HAS_EMPLACE -new (location) T(std::forward(args)...); -#else -new (location) T(std::forward(element)); -#endif - -fence(memory_order_release); -tailBlock_->tail = nextBlockTail; -} -else { -fence(memory_order_acquire); -if (tailBlock_->next.load() != frontBlock) { -// Note that the reason we can't advance to the frontBlock and start adding new entries there -// is because if we did, then dequeue would stay in that block, eventually reading the new values, -// instead of advancing to the next full block (whose values were enqueued first and so should be -// consumed first). - -fence(memory_order_acquire); // Ensure we get latest writes if we got the latest frontBlock - -// tailBlock is full, but there's a free block ahead, use it -Block* tailBlockNext = tailBlock_->next.load(); -size_t nextBlockFront = tailBlockNext->localFront = tailBlockNext->front.load(); -nextBlockTail = tailBlockNext->tail.load(); -fence(memory_order_acquire); - -// This block must be empty since it's not the head block and we -// go through the blocks in a circle -assert(nextBlockFront == nextBlockTail); -tailBlockNext->localFront = nextBlockFront; - -char* location = tailBlockNext->data + nextBlockTail * sizeof(T); -#if MOODYCAMEL_HAS_EMPLACE -new (location) T(std::forward(args)...); -#else -new (location) T(std::forward(element)); -#endif - -tailBlockNext->tail = (nextBlockTail + 1) & tailBlockNext->sizeMask; - -fence(memory_order_release); -tailBlock = tailBlockNext; -} -else if (canAlloc == CanAlloc) { -// tailBlock is full and there's no free block ahead; create a new block -auto newBlockSize = largestBlockSize >= MAX_BLOCK_SIZE ? largestBlockSize : largestBlockSize * 2; -auto newBlock = make_block(newBlockSize); -if (newBlock == nullptr) { -// Could not allocate a block! -return false; -} -largestBlockSize = newBlockSize; - -#if MOODYCAMEL_HAS_EMPLACE -new (newBlock->data) T(std::forward(args)...); -#else -new (newBlock->data) T(std::forward(element)); -#endif -assert(newBlock->front == 0); -newBlock->tail = newBlock->localTail = 1; - -newBlock->next = tailBlock_->next.load(); -tailBlock_->next = newBlock; - -// Might be possible for the dequeue thread to see the new tailBlock->next -// *without* seeing the new tailBlock value, but this is OK since it can't -// advance to the next block until tailBlock is set anyway (because the only -// case where it could try to read the next is if it's already at the tailBlock, -// and it won't advance past tailBlock in any circumstance). - -fence(memory_order_release); -tailBlock = newBlock; -} -else if (canAlloc == CannotAlloc) { -// Would have had to allocate a new block to enqueue, but not allowed -return false; -} -else { -assert(false && "Should be unreachable code"); -return false; -} -} - -return true; -} - - -// Disable copying -ReaderWriterQueue(ReaderWriterQueue const&) { } - -// Disable assignment -ReaderWriterQueue& operator=(ReaderWriterQueue const&) { } - - -AE_FORCEINLINE static size_t ceilToPow2(size_t x) -{ -// From http://graphics.stanford.edu/~seander/bithacks.html#RoundUpPowerOf2 ---x; -x |= x >> 1; -x |= x >> 2; -x |= x >> 4; -for (size_t i = 1; i < sizeof(size_t); i <<= 1) { -x |= x >> (i << 3); -} -++x; -return x; -} - -template -static AE_FORCEINLINE char* align_for(char* ptr) AE_NO_TSAN -{ -const std::size_t alignment = std::alignment_of::value; -return ptr + (alignment - (reinterpret_cast(ptr) % alignment)) % alignment; -} -private: -#ifndef NDEBUG -struct ReentrantGuard -{ - AE_NO_TSAN ReentrantGuard(weak_atomic& _inSection) - : inSection(_inSection) - { - assert(!inSection && "Concurrent (or re-entrant) enqueue or dequeue operation detected (only one thread at a time may hold the producer or consumer role)"); - inSection = true; - } - - AE_NO_TSAN ~ReentrantGuard() { inSection = false; } - -private: - ReentrantGuard& operator=(ReentrantGuard const&); - -private: - weak_atomic& inSection; -}; -#endif - -struct Block -{ - // Avoid false-sharing by putting highly contended variables on their own cache lines - weak_atomic front; // (Atomic) Elements are read from here - size_t localTail; // An uncontended shadow copy of tail, owned by the consumer - - char cachelineFiller0[MOODYCAMEL_CACHE_LINE_SIZE - sizeof(weak_atomic) - sizeof(size_t)]; - weak_atomic tail; // (Atomic) Elements are enqueued here - size_t localFront; - - char cachelineFiller1[MOODYCAMEL_CACHE_LINE_SIZE - sizeof(weak_atomic) - sizeof(size_t)]; // next isn't very contended, but we don't want it on the same cache line as tail (which is) - weak_atomic next; // (Atomic) - - char* data; // Contents (on heap) are aligned to T's alignment - - const size_t sizeMask; - - - // size must be a power of two (and greater than 0) - AE_NO_TSAN Block(size_t const& _size, char* _rawThis, char* _data) - : front(0UL), localTail(0), tail(0UL), localFront(0), next(nullptr), data(_data), sizeMask(_size - 1), rawThis(_rawThis) - { - } - -private: - // C4512 - Assignment operator could not be generated - Block& operator=(Block const&); - -public: - char* rawThis; -}; - - -static Block* make_block(size_t capacity) AE_NO_TSAN -{ -// Allocate enough memory for the block itself, as well as all the elements it will contain -auto size = sizeof(Block) + std::alignment_of::value - 1; -size += sizeof(T) * capacity + std::alignment_of::value - 1; -auto newBlockRaw = static_cast(std::malloc(size)); -if (newBlockRaw == nullptr) { -return nullptr; -} - -auto newBlockAligned = align_for(newBlockRaw); -auto newBlockData = align_for(newBlockAligned + sizeof(Block)); -return new (newBlockAligned) Block(capacity, newBlockRaw, newBlockData); -} - -private: -weak_atomic frontBlock; // (Atomic) Elements are dequeued from this block - -char cachelineFiller[MOODYCAMEL_CACHE_LINE_SIZE - sizeof(weak_atomic)]; -weak_atomic tailBlock; // (Atomic) Elements are enqueued to this block - -size_t largestBlockSize; - -#ifndef NDEBUG -weak_atomic enqueuing; -mutable weak_atomic dequeuing; -#endif -}; - -// Like ReaderWriterQueue, but also providees blocking operations -template -class BlockingReaderWriterQueue -{ -private: - typedef ::moodycamel::ReaderWriterQueue ReaderWriterQueue; - -public: - explicit BlockingReaderWriterQueue(size_t size = 15) AE_NO_TSAN - : inner(size), sema(new spsc_sema::LightweightSemaphore()) - { } - - BlockingReaderWriterQueue(BlockingReaderWriterQueue&& other) AE_NO_TSAN - : inner(std::move(other.inner)), sema(std::move(other.sema)) - { } - - BlockingReaderWriterQueue& operator=(BlockingReaderWriterQueue&& other) AE_NO_TSAN - { - std::swap(sema, other.sema); - std::swap(inner, other.inner); - return *this; - } - - - // Enqueues a copy of element if there is room in the queue. - // Returns true if the element was enqueued, false otherwise. - // Does not allocate memory. - AE_FORCEINLINE bool try_enqueue(T const& element) AE_NO_TSAN - { - if (inner.try_enqueue(element)) { - sema->signal(); - return true; - } - return false; - } - - // Enqueues a moved copy of element if there is room in the queue. - // Returns true if the element was enqueued, false otherwise. - // Does not allocate memory. - AE_FORCEINLINE bool try_enqueue(T&& element) AE_NO_TSAN - { - if (inner.try_enqueue(std::forward(element))) { - sema->signal(); - return true; - } - return false; - } - -#if MOODYCAMEL_HAS_EMPLACE - // Like try_enqueue() but with emplace semantics (i.e. construct-in-place). - template - AE_FORCEINLINE bool try_emplace(Args&&... args) AE_NO_TSAN - { - if (inner.try_emplace(std::forward(args)...)) { - sema->signal(); - return true; - } - return false; - } -#endif - - - // Enqueues a copy of element on the queue. - // Allocates an additional block of memory if needed. - // Only fails (returns false) if memory allocation fails. - AE_FORCEINLINE bool enqueue(T const& element) AE_NO_TSAN - { - if (inner.enqueue(element)) { - sema->signal(); - return true; - } - return false; - } - - // Enqueues a moved copy of element on the queue. - // Allocates an additional block of memory if needed. - // Only fails (returns false) if memory allocation fails. - AE_FORCEINLINE bool enqueue(T&& element) AE_NO_TSAN - { - if (inner.enqueue(std::forward(element))) { - sema->signal(); - return true; - } - return false; - } - -#if MOODYCAMEL_HAS_EMPLACE - // Like enqueue() but with emplace semantics (i.e. construct-in-place). - template - AE_FORCEINLINE bool emplace(Args&&... args) AE_NO_TSAN - { - if (inner.emplace(std::forward(args)...)) { - sema->signal(); - return true; - } - return false; - } -#endif - - - // Attempts to dequeue an element; if the queue is empty, - // returns false instead. If the queue has at least one element, - // moves front to result using operator=, then returns true. - template - bool try_dequeue(U& result) AE_NO_TSAN - { - if (sema->tryWait()) { - bool success = inner.try_dequeue(result); - assert(success); - AE_UNUSED(success); - return true; - } - return false; - } - - - // Attempts to dequeue an element; if the queue is empty, - // waits until an element is available, then dequeues it. - template - void wait_dequeue(U& result) AE_NO_TSAN - { - while (!sema->wait()); - bool success = inner.try_dequeue(result); - AE_UNUSED(result); - assert(success); - AE_UNUSED(success); - } - - - // Attempts to dequeue an element; if the queue is empty, - // waits until an element is available up to the specified timeout, - // then dequeues it and returns true, or returns false if the timeout - // expires before an element can be dequeued. - // Using a negative timeout indicates an indefinite timeout, - // and is thus functionally equivalent to calling wait_dequeue. - template - bool wait_dequeue_timed(U& result, std::int64_t timeout_usecs) AE_NO_TSAN - { - if (!sema->wait(timeout_usecs)) { - return false; - } - bool success = inner.try_dequeue(result); - AE_UNUSED(result); - assert(success); - AE_UNUSED(success); - return true; - } - - -#if __cplusplus > 199711L || _MSC_VER >= 1700 - // Attempts to dequeue an element; if the queue is empty, - // waits until an element is available up to the specified timeout, - // then dequeues it and returns true, or returns false if the timeout - // expires before an element can be dequeued. - // Using a negative timeout indicates an indefinite timeout, - // and is thus functionally equivalent to calling wait_dequeue. - template - inline bool wait_dequeue_timed(U& result, std::chrono::duration const& timeout) AE_NO_TSAN - { - return wait_dequeue_timed(result, std::chrono::duration_cast(timeout).count()); - } -#endif - - - // Returns a pointer to the front element in the queue (the one that - // would be removed next by a call to `try_dequeue` or `pop`). If the - // queue appears empty at the time the method is called, nullptr is - // returned instead. - // Must be called only from the consumer thread. - AE_FORCEINLINE T* peek() const AE_NO_TSAN - { - return inner.peek(); - } - - // Removes the front element from the queue, if any, without returning it. - // Returns true on success, or false if the queue appeared empty at the time - // `pop` was called. - AE_FORCEINLINE bool pop() AE_NO_TSAN - { - if (sema->tryWait()) { - bool result = inner.pop(); - assert(result); - AE_UNUSED(result); - return true; - } - return false; - } - - // Returns the approximate number of items currently in the queue. - // Safe to call from both the producer and consumer threads. - AE_FORCEINLINE size_t size_approx() const AE_NO_TSAN - { - return sema->availableApprox(); - } - - // Returns the total number of items that could be enqueued without incurring - // an allocation when this queue is empty. - // Safe to call from both the producer and consumer threads. - // - // NOTE: The actual capacity during usage may be different depending on the consumer. - // If the consumer is removing elements concurrently, the producer cannot add to - // the block the consumer is removing from until it's completely empty, except in - // the case where the producer was writing to the same block the consumer was - // reading from the whole time. - AE_FORCEINLINE size_t max_capacity() const { - return inner.max_capacity(); - } - -private: - // Disable copying & assignment - BlockingReaderWriterQueue(BlockingReaderWriterQueue const&) { } - BlockingReaderWriterQueue& operator=(BlockingReaderWriterQueue const&) { } - -private: - ReaderWriterQueue inner; - std::unique_ptr sema; -}; - -} // end namespace moodycamel - -#ifdef AE_VCPP -#pragma warning(pop) -#endif \ No newline at end of file diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index a1ec9b0f..d141b4c1 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -111,7 +111,7 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { int64_t job_id; // res.job_queue.wait_dequeue(job_id); while (!res.job_queue.try_dequeue(job_id)) { - std::this_thread::sleep_for(std::chrono::microseconds(1)); + std::this_thread::sleep_for(std::chrono::microseconds(5)); } auto job_wait_end = std::chrono::high_resolution_clock::now(); diff --git a/test/experiments/osdi2025/numa_single_query/run.py b/test/experiments/osdi2025/numa_single_query/run.py index f105cb3c..8f08bce2 100644 --- a/test/experiments/osdi2025/numa_single_query/run.py +++ b/test/experiments/osdi2025/numa_single_query/run.py @@ -1,16 +1,15 @@ -#!/usr/bin/env python3 """ -NUMA Single-Query Latency and Recall Benchmark (detailed timers, aggregated) +NUMA Single-Query Latency and Recall Benchmark (detailed timers + parent) -For each query we extract: +Tracks these phases, for both the worker_scan and its parent search: - buffer_init_time_ns - job_enqueue_time_ns - job_wait_time_ns - result_aggregate_time_ns - total_time_ns -We average each timer across queries in a trial, then average across trials, -and emit those means (in ms) alongside recall and latency. +Averages per‐query timers within each trial, then across trials, +and writes them (in ms) to the per‐index CSV. """ import sys from pathlib import Path @@ -48,13 +47,13 @@ } def build_and_save(index_cls, params, vecs, path): - """Builds and persists an index to disk.""" + """Build and persist an index.""" idx = index_cls() idx.build(vecs, **params) idx.save(str(path)) def extract_ids(res): - """Normalize the returned IDs array.""" + """Normalize returned IDs array.""" if hasattr(res, "ids"): arr = res.ids elif hasattr(res, "I"): @@ -81,20 +80,20 @@ def run_experiment(cfg_path: str, output_dir: str) -> None: out_dir = Path(output_dir).expanduser().absolute() out_dir.mkdir(parents=True, exist_ok=True) - # Load data + # Load dataset print(f"[INFO] loading dataset {ds_cfg['name']} …") base_vecs, queries, gt = load_dataset(ds_cfg["name"]) queries = queries[:q_n] gt = gt[:q_n] if gt is not None else None - # Build shared Quake base index if needed + # Build shared Quake base index once quake_base_params = None for c in idx_cfgs: if c["index"] == "Quake": - earthquake = dict(c.get("build_params", {})) - earthquake.pop("num_workers", None) - earthquake.pop("use_numa", None) - quake_base_params = earthquake + p = dict(c.get("build_params", {})) + p.pop("num_workers", None) + p.pop("use_numa", None) + quake_base_params = p break quake_base_path = out_dir / "Quake_base_index.bin" @@ -115,16 +114,14 @@ def run_experiment(cfg_path: str, output_dir: str) -> None: continue idx_csv = out_dir / f"{name}_results.csv" - idx_path = (quake_base_path if typ == "Quake" - else out_dir / f"{name}_index.bin") + idx_path = quake_base_path if typ == "Quake" else out_dir / f"{name}_index.bin" - # Skip if done if idx_csv.exists() and not overwrite: print(f"[SKIP] {name} – results exist.") results.append(pd.read_csv(idx_csv).iloc[0].to_dict()) continue - # Build non-Quake if missing + # Build non-Quake if needed if typ != "Quake" and not idx_path.exists(): print(f"[BUILD] {name} index …") build_and_save(cls, b_params, base_vecs, idx_path) @@ -132,16 +129,14 @@ def run_experiment(cfg_path: str, output_dir: str) -> None: # Load index if typ == "Quake": - load_kw = {k: b_params[k] - for k in ("use_numa", "num_workers") - if k in b_params} + load_kw = {k: b_params[k] for k in ("use_numa", "num_workers") if k in b_params} idx = QuakeWrapper(); idx.load(str(idx_path), **load_kw) elif typ == "DiskANN": idx = cls(); b_params.pop("metric", None); idx.load(str(idx_path), **b_params) else: idx = cls(); idx.load(str(idx_path)) - # Dispatch search params + # Dispatch search parameters if typ == "SCANN": search_kw = {"leaves_to_search": s_params.get("leaves_to_search", 100)} elif typ == "HNSW": @@ -157,72 +152,111 @@ def run_experiment(cfg_path: str, output_dir: str) -> None: for q in queries[:min(warmup_n, len(queries))]: idx.search(q.unsqueeze(0).float(), k, **search_kw) - # Collect per-trial means - all_trial_latencies = [] - all_trial_recalls = [] - # for each timer: list of trial-means - buf_init_trials = [] - enqueue_trials = [] - wait_trials = [] - agg_trials = [] - total_trials = [] + # Prepare accumulators + child_lat_trials = [] + parent_lat_trials = [] + child_buf_init_trials = [] + child_enq_trials = [] + child_wait_trials = [] + child_agg_trials = [] + child_total_trials = [] + parent_buf_init_trials = [] + parent_enq_trials = [] + parent_wait_trials = [] + parent_agg_trials = [] + parent_total_trials = [] + first_trial_recalls = [] for t in range(trials): - latencies = [] - recalls = [] - buf_init_q = [] - enq_q = [] - wait_q = [] - agg_q = [] - total_q = [] + # per-query lists + child_lats = [] + parent_lats = [] + child_buf_init = [] + child_enq = [] + child_wait = [] + child_agg = [] + child_total = [] + parent_buf_init = [] + parent_enq = [] + parent_wait = [] + parent_agg = [] + parent_total = [] + recalls = [] for qi, q in enumerate(queries): res = idx.search(q.unsqueeze(0).float(), k, **search_kw) ti = res.timing_info - # latency - if hasattr(ti, "total_time_ns"): - latencies.append(ti.total_time_ns / 1e6) + + # child latency + child_lats.append(ti.total_time_ns / 1e6) + child_buf_init.append(ti.buffer_init_time_ns) + child_enq.append( ti.job_enqueue_time_ns) + child_wait.append( ti.job_wait_time_ns) + child_agg.append( ti.result_aggregate_time_ns) + child_total.append( ti.total_time_ns) + + # parent latency (if exists) + pi = getattr(ti, "parent_info", None) + if pi and hasattr(pi, "total_time_ns"): + parent_lats.append(pi.total_time_ns / 1e6) + parent_buf_init.append(pi.buffer_init_time_ns) + parent_enq.append( pi.job_enqueue_time_ns) + parent_wait.append( pi.job_wait_time_ns) + parent_agg.append( pi.result_aggregate_time_ns) + parent_total.append( pi.total_time_ns) else: - raise RuntimeError("missing total_time_ns") + parent_lats.append(0.0) + parent_buf_init.append(0) + parent_enq.append(0) + parent_wait.append(0) + parent_agg.append(0) + parent_total.append(0) + # recall on first trial only if t == 0 and gt is not None: preds = extract_ids(res)[0] recalls.append(float(compute_recall(torch.tensor([preds]), gt[qi:qi+1], k).item())) - # collect timers (ns) - buf_init_q.append(ti.buffer_init_time_ns) - enq_q.append (ti.job_enqueue_time_ns) - wait_q.append (ti.job_wait_time_ns) - agg_q.append (ti.result_aggregate_time_ns) - total_q.append (ti.total_time_ns) - - # trial means in ms - all_trial_latencies.append(np.mean(latencies)) - buf_init_trials.append(np.mean(buf_init_q) / 1e6) - enqueue_trials .append(np.mean(enq_q) / 1e6) - wait_trials .append(np.mean(wait_q) / 1e6) - agg_trials .append(np.mean(agg_q) / 1e6) - total_trials .append(np.mean(total_q) / 1e6) + # compute trial means + child_lat_trials.append(np.mean(child_lats)) + parent_lat_trials.append(np.mean(parent_lats)) + child_buf_init_trials.append(np.mean(child_buf_init)/1e6) + child_enq_trials .append(np.mean(child_enq) /1e6) + child_wait_trials .append(np.mean(child_wait) /1e6) + child_agg_trials .append(np.mean(child_agg) /1e6) + child_total_trials .append(np.mean(child_total) /1e6) + parent_buf_init_trials.append(np.mean(parent_buf_init)/1e6) + parent_enq_trials .append(np.mean(parent_enq) /1e6) + parent_wait_trials .append(np.mean(parent_wait) /1e6) + parent_agg_trials .append(np.mean(parent_agg) /1e6) + parent_total_trials .append(np.mean(parent_total) /1e6) if t == 0 and recalls: - all_trial_recalls.append(np.mean(recalls)) + first_trial_recalls.append(np.mean(recalls)) print(f" [{name} trial {t+1}/{trials}] " - f"{all_trial_latencies[-1]:.2f} ms" - + (f" | R@{k} {all_trial_recalls[-1]:.4f}" if recalls else "")) + f"child {child_lat_trials[-1]:.2f} ms, " + f"parent {parent_lat_trials[-1]:.2f} ms" + + (f" | R@{k} {first_trial_recalls[-1]:.4f}" if recalls else "")) - # final averages across trials + # assemble final row row = { "index": name, "n_workers": b_params.get("num_workers", np.nan), - "mean_latency_ms": float(np.mean(all_trial_latencies)), - "std_latency_ms": float(np.std(all_trial_latencies)), - f"recall_at_{k}": (float(all_trial_recalls[0]) if all_trial_recalls else np.nan), - "buffer_init_ms": float(np.mean(buf_init_trials)), - "enqueue_ms": float(np.mean(enqueue_trials)), - "wait_ms": float(np.mean(wait_trials)), - "aggregate_ms": float(np.mean(agg_trials)), - "total_time_ms": float(np.mean(total_trials)), + "child_mean_latency_ms": float(np.mean(child_lat_trials)), + "child_std_latency_ms": float(np.std(child_lat_trials)), + f"recall_at_{k}": (float(first_trial_recalls[0]) if first_trial_recalls else np.nan), + "child_buffer_init_ms": float(np.mean(child_buf_init_trials)), + "child_enqueue_ms": float(np.mean(child_enq_trials)), + "child_wait_ms": float(np.mean(child_wait_trials)), + "child_aggregate_ms": float(np.mean(child_agg_trials)), + "child_total_time_ms": float(np.mean(child_total_trials)), + "parent_mean_latency_ms":float(np.mean(parent_lat_trials)), + "parent_buffer_init_ms": float(np.mean(parent_buf_init_trials)), + "parent_enqueue_ms": float(np.mean(parent_enq_trials)), + "parent_wait_ms": float(np.mean(parent_wait_trials)), + "parent_aggregate_ms": float(np.mean(parent_agg_trials)), + "parent_total_time_ms": float(np.mean(parent_total_trials)), } results.append(row) pd.DataFrame([row]).to_csv(idx_csv, index=False) @@ -233,7 +267,7 @@ def run_experiment(cfg_path: str, output_dir: str) -> None: df.to_csv(out_csv, index=False) print(f"\n[RESULT] aggregated CSV → {out_csv}") - # latency plot + # latency plot (child only for clarity) plt.figure() for nm in df["index"].unique(): sub = df[df["index"] == nm] @@ -241,16 +275,16 @@ def run_experiment(cfg_path: str, output_dir: str) -> None: else f"{nm} (nw={int(sub['n_workers'].iloc[0])})") plt.errorbar( sub["n_workers"] if "n_workers" in sub else sub.index, - sub["mean_latency_ms"], - yerr=sub["std_latency_ms"], + sub["child_mean_latency_ms"], + yerr=sub["child_std_latency_ms"], marker="o", capsize=5, label=lbl ) plt.xscale("symlog", base=2) - plt.xlabel("Num Workers"); plt.ylabel("Mean Latency (ms)") - plt.title("Single-Query Latency"); plt.legend(); plt.tight_layout() - lat_path = Path(output_dir) / f"{out_csv.stem}_latency.png" + plt.xlabel("Num Workers"); plt.ylabel("Mean Child Latency (ms)") + plt.title("Single-Query Child Latency"); plt.legend(); plt.tight_layout() + lat_path = Path(output_dir) / f"{out_csv.stem}_child_latency.png" plt.savefig(lat_path) - print(f"[PLOT] latency plot → {lat_path}") + print(f"[PLOT] child latency plot → {lat_path}") # recall plot if f"recall_at_{k}" in df.columns: From 6b7c586a6a166fb40ded1641b4d966acbc55e792 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 13:59:28 -0500 Subject: [PATCH 123/323] track parent timing info --- src/cpp/include/list_scanning.h | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/cpp/include/list_scanning.h b/src/cpp/include/list_scanning.h index b41831ca..29aa898a 100644 --- a/src/cpp/include/list_scanning.h +++ b/src/cpp/include/list_scanning.h @@ -418,8 +418,8 @@ inline void batched_scan_list(const float *query_vecs, bool alloc_results = false; if (distances == nullptr) { alloc_results = true; - (int64_t *) malloc(num_queries * k_max * sizeof(int64_t)); - (float *) malloc(num_queries * k_max * sizeof(float)); + labels = (int64_t *) malloc(num_queries * k_max * sizeof(int64_t)); + distances = (float *) malloc(num_queries * k_max * sizeof(float)); } if (metric == faiss::METRIC_INNER_PRODUCT) { From ce7671fc2119d6c2a88ee7cee9715d6d3b96e499 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 14:04:02 -0500 Subject: [PATCH 124/323] track parent timing info --- src/cpp/src/query_coordinator.cpp | 5 ----- 1 file changed, 5 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index d141b4c1..a012121b 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -815,11 +815,6 @@ shared_ptr QueryCoordinator::search(Tensor x, shared_ptrrecompute_threshold = search_params->recompute_threshold; parent_search_params->initial_search_fraction = .5; parent_search_params->batched_scan = false; - - // check if parent is flat - if (parent_->parent_ == nullptr) { - parent_search_params->batched_scan = true; - } } else { parent_search_params = search_params->parent_params; } From ae70dfb608edcafa1f2365ce7c2a350be772ef66 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 14:28:26 -0500 Subject: [PATCH 125/323] track parent timing info --- src/cpp/src/quake_index.cpp | 8 +++++++- src/cpp/src/query_coordinator.cpp | 1 - 2 files changed, 7 insertions(+), 2 deletions(-) diff --git a/src/cpp/src/quake_index.cpp b/src/cpp/src/quake_index.cpp index 43bb6955..637f0036 100644 --- a/src/cpp/src/quake_index.cpp +++ b/src/cpp/src/quake_index.cpp @@ -281,7 +281,13 @@ void QuakeIndex::load(const std::string& dir_path, int n_workers, bool use_numa) std::string parent_dir = (fs::path(dir_path) / "parent").string(); if (fs::exists(parent_dir) && fs::is_directory(parent_dir)) { parent_ = std::make_shared(); - parent_->load(parent_dir, n_workers, use_numa); + int n_parts = partition_manager_->nlist(); + + // don't put too many parent workers. maximum of 1 worker per 500 partitions + int parent_n_workers = std::max(1, n_parts / 500); + parent_n_workers = std::min(parent_n_workers, n_workers); + + parent_->load(parent_dir, parent_n_workers, use_numa); partition_manager_->parent_ = parent_; } else { parent_ = nullptr; diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index a012121b..8fc70fc1 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -109,7 +109,6 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { auto job_wait_start = std::chrono::high_resolution_clock::now(); int64_t job_id; -// res.job_queue.wait_dequeue(job_id); while (!res.job_queue.try_dequeue(job_id)) { std::this_thread::sleep_for(std::chrono::microseconds(5)); } From 3104de9f108b3394cf77a8753f26d678d5b6aee7 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 14:55:19 -0500 Subject: [PATCH 126/323] reduce allocation cost of scan jobs --- src/cpp/include/query_coordinator.h | 2 +- src/cpp/src/query_coordinator.cpp | 14 ++++++-------- 2 files changed, 7 insertions(+), 9 deletions(-) diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index 317e3256..c7ed1da6 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -24,7 +24,7 @@ struct ScanJob { int64_t partition_id; ///< The identifier of the partition to be scanned. int k; ///< The number of neighbors (Top-K) to return. const float* query_vector; ///< Pointer to the query vector. - vector query_ids; ///< Global query IDs; used in batched mode. + int *query_ids; ///< Global query IDs; used in batched mode. bool is_batched = false; ///< Indicates whether this is a batched query job. int64_t num_queries = 0; ///< The number of queries in batched mode. int rank = 0; ///< Rank of the partition diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 8fc70fc1..cb3d6af0 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -375,6 +375,8 @@ shared_ptr QueryCoordinator::worker_scan( duration_cast(end_time - start_time).count(); start_time = high_resolution_clock::now(); + vector all_query_ids = std::vector(x.size(0)); + std::iota(all_query_ids.begin(), all_query_ids.end(), 0); if (search_params->batched_scan) { auto partition_ids_accessor = partition_ids.accessor(); job_buffer_.resize(partition_manager_->nlist()); @@ -382,10 +384,6 @@ shared_ptr QueryCoordinator::worker_scan( auto pids_acc = pids.accessor(); if (pids.size(0) == partition_ids.size(1)) { - vector all_query_ids = std::vector(x.size(0)); - std::iota(all_query_ids.begin(), all_query_ids.end(), 0); - - for (int64_t i = 0; i < pids.size(0); i++) { ScanJob job; job.is_batched = true; @@ -393,7 +391,7 @@ shared_ptr QueryCoordinator::worker_scan( job.k = k; job.query_vector = x.data_ptr(); job.num_queries = x.size(0); - job.query_ids = all_query_ids; + job.query_ids = all_query_ids.data(); int core_id = partition_manager_->get_partition_core_id(pids_acc[i]); if (core_id < 0) { throw std::runtime_error("[QueryCoordinator::worker_scan] Invalid core ID."); @@ -404,7 +402,7 @@ shared_ptr QueryCoordinator::worker_scan( } } else { - std::unordered_map> per_partition_query_ids; + std::unordered_map> per_partition_query_ids; for (int64_t q = 0; q < num_queries; q++) { for (int64_t p = 0; p < partition_ids.size(1); p++) { int64_t pid = partition_ids_accessor[q][p]; @@ -419,7 +417,7 @@ shared_ptr QueryCoordinator::worker_scan( job.k = k; job.query_vector = x.data_ptr(); job.num_queries = kv.second.size(); - job.query_ids = kv.second; + job.query_ids = kv.second.data(); int core_id = partition_manager_->get_partition_core_id(kv.first); if (core_id < 0) { throw std::runtime_error("[QueryCoordinator::worker_scan] Invalid core ID."); @@ -440,7 +438,7 @@ shared_ptr QueryCoordinator::worker_scan( ScanJob job; job.is_batched = false; - job.query_ids = {q}; + job.query_ids = all_query_ids.data() + q; job.partition_id = pid; job.k = k; job.query_vector = x_ptr + q * dimension; From 3d2354b308e80aeac21a50b0eddf6a7b836583db Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 15:17:43 -0500 Subject: [PATCH 127/323] reduce allocation cost of scan jobs --- src/cpp/src/query_coordinator.cpp | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index cb3d6af0..ce5bc667 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -330,7 +330,6 @@ shared_ptr QueryCoordinator::worker_scan( auto partition_ids_accessor = partition_ids.accessor(); - job_buffer_.clear(); next_job_id_.store(0); job_flags_.clear(); @@ -376,6 +375,7 @@ shared_ptr QueryCoordinator::worker_scan( start_time = high_resolution_clock::now(); vector all_query_ids = std::vector(x.size(0)); + std::unordered_map> per_partition_query_ids; // for batched scan std::iota(all_query_ids.begin(), all_query_ids.end(), 0); if (search_params->batched_scan) { auto partition_ids_accessor = partition_ids.accessor(); @@ -402,7 +402,6 @@ shared_ptr QueryCoordinator::worker_scan( } } else { - std::unordered_map> per_partition_query_ids; for (int64_t q = 0; q < num_queries; q++) { for (int64_t p = 0; p < partition_ids.size(1); p++) { int64_t pid = partition_ids_accessor[q][p]; From 198f2429c875c540844bb9aea93056a6fcde8b04 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 15:37:20 -0500 Subject: [PATCH 128/323] reduce allocation cost of scan jobs --- src/cpp/include/query_coordinator.h | 3 +- src/cpp/src/query_coordinator.cpp | 49 ++++++++++++++++++----------- 2 files changed, 32 insertions(+), 20 deletions(-) diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index c7ed1da6..e7898939 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -24,7 +24,8 @@ struct ScanJob { int64_t partition_id; ///< The identifier of the partition to be scanned. int k; ///< The number of neighbors (Top-K) to return. const float* query_vector; ///< Pointer to the query vector. - int *query_ids; ///< Global query IDs; used in batched mode. + std::shared_ptr> query_ids; ///< Global query IDs; used in batched mode. + int query_id; bool is_batched = false; ///< Indicates whether this is a batched query job. int64_t num_queries = 0; ///< The number of queries in batched mode. int rank = 0; ///< Rank of the partition diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index ce5bc667..593b1334 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -127,12 +127,12 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { ScanJob job = job_buffer_[job_id]; - // Ignore this job if the global buffer is not processing queries. - if (!global_topk_buffer_pool_[job.query_ids[0]]->currently_processing_query()) { - // decrement the job counter - global_topk_buffer_pool_[job.query_ids[0]]->record_empty_job(); - continue; - } +// // Ignore this job if the global buffer is not processing queries. +// if (!global_topk_buffer_pool_[job.query_ids[0]]->currently_processing_query()) { +// // decrement the job counter +// global_topk_buffer_pool_[job.query_ids[0]]->record_empty_job(); +// continue; +// } worker_job_counter_[core_index]++; @@ -153,10 +153,17 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { if (partition_size == 0) { std::cerr << "[QueryCoordinator::partition_scan_worker_fn] Partition " << job.partition_id << " is empty." << std::endl; // call dummy batch add to decrement topk_buffer jobs counter. #TODO remove jobs counter from topk_buffer - for (int64_t q = 0; q < job.num_queries; q++) { - int64_t global_q = job.query_ids[q]; - global_topk_buffer_pool_[global_q]->batch_add(nullptr, nullptr, 0); + + if (job.is_batched) { + for (int64_t q = 0; q < job.num_queries; q++) { + int global_q = job.query_ids->at(q); + global_topk_buffer_pool_[global_q]->batch_add(nullptr, nullptr, 0); + } + } else { + global_topk_buffer_pool_[job.query_id]->batch_add(nullptr, nullptr, 0); } + + continue; } @@ -196,8 +203,8 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { int64_t n_results = topk_indices.size(); // Merge local results into the global query buffer. - global_topk_buffer_pool_[job.query_ids[0]]->batch_add(topk.data(), topk_indices.data(), n_results); - job_flags_[job.query_ids[0]][job.rank] = true; + global_topk_buffer_pool_[job.query_id]->batch_add(topk.data(), topk_indices.data(), n_results); + job_flags_[job.query_id][job.rank] = true; auto s4 = std::chrono::high_resolution_clock::now(); @@ -225,7 +232,7 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { int64_t d = partition_manager_->d(); std::vector query_subset(job.num_queries * d); for (int i = 0; i < job.num_queries; i++) { - int64_t global_q = job.query_ids[i]; + int64_t global_q = job.query_ids->at(i); memcpy(&query_subset[i * d], job.query_vector + global_q * d, d * sizeof(float)); @@ -265,7 +272,7 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { auto s3 = std::chrono::high_resolution_clock::now(); for (int64_t q = 0; q < job.num_queries; q++) { - int64_t global_q = job.query_ids[q]; + int64_t global_q = job.query_ids->at(q); vector topk = res.topk_buffer_pool[q]->get_topk(); vector topk_indices = res.topk_buffer_pool[q]->get_topk_indices(); @@ -375,7 +382,8 @@ shared_ptr QueryCoordinator::worker_scan( start_time = high_resolution_clock::now(); vector all_query_ids = std::vector(x.size(0)); - std::unordered_map> per_partition_query_ids; // for batched scan + shared_ptr> all_query_ids_ptr = make_shared>(all_query_ids); + std::unordered_map>> per_partition_query_ids; // for batched scan std::iota(all_query_ids.begin(), all_query_ids.end(), 0); if (search_params->batched_scan) { auto partition_ids_accessor = partition_ids.accessor(); @@ -391,7 +399,7 @@ shared_ptr QueryCoordinator::worker_scan( job.k = k; job.query_vector = x.data_ptr(); job.num_queries = x.size(0); - job.query_ids = all_query_ids.data(); + job.query_ids = all_query_ids_ptr; int core_id = partition_manager_->get_partition_core_id(pids_acc[i]); if (core_id < 0) { throw std::runtime_error("[QueryCoordinator::worker_scan] Invalid core ID."); @@ -406,7 +414,10 @@ shared_ptr QueryCoordinator::worker_scan( for (int64_t p = 0; p < partition_ids.size(1); p++) { int64_t pid = partition_ids_accessor[q][p]; if (pid < 0) continue; - per_partition_query_ids[pid].push_back(q); + if (per_partition_query_ids[pid] == nullptr) { + per_partition_query_ids[pid] = make_shared>(); + } + per_partition_query_ids[pid]->push_back(q); } } for (auto &kv : per_partition_query_ids) { @@ -415,8 +426,8 @@ shared_ptr QueryCoordinator::worker_scan( job.partition_id = kv.first; job.k = k; job.query_vector = x.data_ptr(); - job.num_queries = kv.second.size(); - job.query_ids = kv.second.data(); + job.num_queries = kv.second->size(); + job.query_ids = kv.second; int core_id = partition_manager_->get_partition_core_id(kv.first); if (core_id < 0) { throw std::runtime_error("[QueryCoordinator::worker_scan] Invalid core ID."); @@ -437,7 +448,7 @@ shared_ptr QueryCoordinator::worker_scan( ScanJob job; job.is_batched = false; - job.query_ids = all_query_ids.data() + q; + job.query_id = q; job.partition_id = pid; job.k = k; job.query_vector = x_ptr + q * dimension; From aa60b9ca48cec2f1660b86383b811ca7a9eeea5b Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 15:45:16 -0500 Subject: [PATCH 129/323] reduce allocation cost of scan jobs --- src/cpp/src/query_coordinator.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 593b1334..7924b607 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -337,7 +337,7 @@ shared_ptr QueryCoordinator::worker_scan( auto partition_ids_accessor = partition_ids.accessor(); - job_buffer_.clear(); +// job_buffer_.clear(); next_job_id_.store(0); job_flags_.clear(); job_flags_.resize(num_queries); From 698f6de539d8571e217cb07b9b5d83e4f72932d5 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 15:50:06 -0500 Subject: [PATCH 130/323] reduce allocation cost of scan jobs --- src/cpp/src/query_coordinator.cpp | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 7924b607..25790a08 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -461,11 +461,12 @@ shared_ptr QueryCoordinator::worker_scan( } size_t jid = next_job_id_.fetch_add(1, std::memory_order_acq_rel); job_buffer_[jid] = job; + end_time = high_resolution_clock::now(); core_resources_[core_id].job_queue.enqueue((int64_t) jid); } } } - end_time = high_resolution_clock::now(); + timing_info->job_enqueue_time_ns = duration_cast(end_time - start_time).count(); auto last_flush_time = high_resolution_clock::now(); From 46f2627a33e601aaf7ce2c8df89f6953db2cb28f Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 15:56:11 -0500 Subject: [PATCH 131/323] reduce allocation cost of scan jobs --- src/cpp/src/query_coordinator.cpp | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 25790a08..76529279 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -381,10 +381,7 @@ shared_ptr QueryCoordinator::worker_scan( duration_cast(end_time - start_time).count(); start_time = high_resolution_clock::now(); - vector all_query_ids = std::vector(x.size(0)); - shared_ptr> all_query_ids_ptr = make_shared>(all_query_ids); - std::unordered_map>> per_partition_query_ids; // for batched scan - std::iota(all_query_ids.begin(), all_query_ids.end(), 0); + if (search_params->batched_scan) { auto partition_ids_accessor = partition_ids.accessor(); job_buffer_.resize(partition_manager_->nlist()); @@ -392,6 +389,9 @@ shared_ptr QueryCoordinator::worker_scan( auto pids_acc = pids.accessor(); if (pids.size(0) == partition_ids.size(1)) { + vector all_query_ids = std::vector(x.size(0)); + std::iota(all_query_ids.begin(), all_query_ids.end(), 0); + shared_ptr> all_query_ids_ptr = make_shared>(all_query_ids); for (int64_t i = 0; i < pids.size(0); i++) { ScanJob job; job.is_batched = true; @@ -410,6 +410,7 @@ shared_ptr QueryCoordinator::worker_scan( } } else { + std::unordered_map>> per_partition_query_ids; // for batched scan for (int64_t q = 0; q < num_queries; q++) { for (int64_t p = 0; p < partition_ids.size(1); p++) { int64_t pid = partition_ids_accessor[q][p]; @@ -441,6 +442,7 @@ shared_ptr QueryCoordinator::worker_scan( auto partition_ids_accessor = partition_ids.accessor(); job_buffer_.resize(num_queries * partition_ids.size(1)); + vector job_ids for (int q = 0; q < num_queries; q++) { for (int64_t p = 0; p < partition_ids.size(1); p++) { int64_t pid = partition_ids_accessor[q][p]; @@ -461,12 +463,11 @@ shared_ptr QueryCoordinator::worker_scan( } size_t jid = next_job_id_.fetch_add(1, std::memory_order_acq_rel); job_buffer_[jid] = job; - end_time = high_resolution_clock::now(); core_resources_[core_id].job_queue.enqueue((int64_t) jid); } } } - + end_time = high_resolution_clock::now(); timing_info->job_enqueue_time_ns = duration_cast(end_time - start_time).count(); auto last_flush_time = high_resolution_clock::now(); From c414bf59ecd6cdc0d4e8e5679d2d624faf468a9f Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 15:56:29 -0500 Subject: [PATCH 132/323] reduce allocation cost of scan jobs --- src/cpp/src/query_coordinator.cpp | 1 - 1 file changed, 1 deletion(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 76529279..5bed1991 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -442,7 +442,6 @@ shared_ptr QueryCoordinator::worker_scan( auto partition_ids_accessor = partition_ids.accessor(); job_buffer_.resize(num_queries * partition_ids.size(1)); - vector job_ids for (int q = 0; q < num_queries; q++) { for (int64_t p = 0; p < partition_ids.size(1); p++) { int64_t pid = partition_ids_accessor[q][p]; From a4a406114d3fe7c12db30908738d883557277ec1 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 16:01:29 -0500 Subject: [PATCH 133/323] reduce allocation cost of scan jobs --- src/cpp/src/query_coordinator.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 5bed1991..86ceb4a8 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -461,8 +461,8 @@ shared_ptr QueryCoordinator::worker_scan( throw std::runtime_error("[QueryCoordinator::worker_scan] Invalid core ID."); } size_t jid = next_job_id_.fetch_add(1, std::memory_order_acq_rel); - job_buffer_[jid] = job; - core_resources_[core_id].job_queue.enqueue((int64_t) jid); + job_buffer_[jid] = std::move(job); + core_resources_[core_id].job_queue.enqueue(jid); } } } From 67707da7f9dd02121efdc8b4a3ef6a70be2609b4 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 16:02:00 -0500 Subject: [PATCH 134/323] reduce allocation cost of scan jobs --- src/cpp/src/query_coordinator.cpp | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 86ceb4a8..cb4e75eb 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -442,6 +442,7 @@ shared_ptr QueryCoordinator::worker_scan( auto partition_ids_accessor = partition_ids.accessor(); job_buffer_.resize(num_queries * partition_ids.size(1)); + int jid = 0; for (int q = 0; q < num_queries; q++) { for (int64_t p = 0; p < partition_ids.size(1); p++) { int64_t pid = partition_ids_accessor[q][p]; @@ -460,8 +461,8 @@ shared_ptr QueryCoordinator::worker_scan( if (core_id < 0) { throw std::runtime_error("[QueryCoordinator::worker_scan] Invalid core ID."); } - size_t jid = next_job_id_.fetch_add(1, std::memory_order_acq_rel); job_buffer_[jid] = std::move(job); + jid++; core_resources_[core_id].job_queue.enqueue(jid); } } From b31bf25400dfe6a339eaec4a9caa5c1a476f734d Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 16:04:15 -0500 Subject: [PATCH 135/323] reduce allocation cost of scan jobs --- src/cpp/src/query_coordinator.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index cb4e75eb..f1f3a4a0 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -462,8 +462,8 @@ shared_ptr QueryCoordinator::worker_scan( throw std::runtime_error("[QueryCoordinator::worker_scan] Invalid core ID."); } job_buffer_[jid] = std::move(job); - jid++; core_resources_[core_id].job_queue.enqueue(jid); + jid++; } } } From 352417ceb5b0d55d95271cc920b3a2a661134b03 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 16:21:55 -0500 Subject: [PATCH 136/323] reduce allocation cost of scan jobs --- src/cpp/src/query_coordinator.cpp | 3 +- test/cpp/scan_benchmark.cpp | 469 ++++++++++++++++-------------- 2 files changed, 248 insertions(+), 224 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index f1f3a4a0..c1b6d46c 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -442,6 +442,7 @@ shared_ptr QueryCoordinator::worker_scan( auto partition_ids_accessor = partition_ids.accessor(); job_buffer_.resize(num_queries * partition_ids.size(1)); + auto partitions = partition_manager_->partition_store_->partitions_; int jid = 0; for (int q = 0; q < num_queries; q++) { for (int64_t p = 0; p < partition_ids.size(1); p++) { @@ -457,7 +458,7 @@ shared_ptr QueryCoordinator::worker_scan( job.num_queries = 1; job.rank = p; - int core_id = partition_manager_->get_partition_core_id(pid); + int core_id = partitions[pid]->core_id_; if (core_id < 0) { throw std::runtime_error("[QueryCoordinator::worker_scan] Invalid core ID."); } diff --git a/test/cpp/scan_benchmark.cpp b/test/cpp/scan_benchmark.cpp index a029c0fa..df2b467c 100644 --- a/test/cpp/scan_benchmark.cpp +++ b/test/cpp/scan_benchmark.cpp @@ -1,241 +1,264 @@ -#include -// #include // OpenMP no longer used for the primary parallelization -#include +#include #include #include -#include +#include #include -#include -#include -#include -#include // For std::mutex and std::call_once -#include // For std::thread::hardware_concurrency -#include // For std::async, std::future (used by parallel.h) -#include -#include "faiss/utils/distances.h" -#include "parallel.h" // Include your custom parallel_for header - -// ------------------------------------------------------------------ -// Constants -// ------------------------------------------------------------------ -static constexpr int64_t LIST_SIZE = 1'000'000; -static constexpr int D = 128; -static constexpr int NQ = 100; // Number of queries - -// Forward declaration of the fixture -class ListScanBenchmarkF; - -// ------------------------------------------------------------------ -// Timing helper -// ------------------------------------------------------------------ -struct Timer { - using clk = std::chrono::steady_clock; - clk::time_point t0; - void start() { t0 = clk::now(); } - double stop() { return std::chrono::duration(clk::now()-t0).count(); } -}; - -// ------------------------------------------------------------------ -// Pretty printer -// ------------------------------------------------------------------ -// Takes requested threads (nth_req) and actual threads used by parallel_for (pfor_threads) -static void print_stats(const char* label, int nth_req, double sec) -{ - const double evaluations = static_cast(LIST_SIZE) * NQ; - const double bytes_scanned = evaluations * D * sizeof(float); - const double gb_scanned = bytes_scanned / (1ULL << 30); - const double gb_per_sec = gb_scanned / sec; - - std::cout << std::setw(24) << std::left << label - << " | " << std::setw(2) << nth_req - << " | " << std::fixed << std::setprecision(3) - << sec << " s" - << " | " << std::setprecision(2) - << gb_per_sec << " GB/s" - << std::endl; -} - -// ================================================================== -// Google‑test Test Fixture -// ================================================================== -class ListScanBenchmarkF : public ::testing::Test { -protected: - // Data members are static members of the fixture - static std::vector S_base_vectors_data; - static std::vector S_queries_data; - static std::vector S_seq_order_data; - static std::vector S_rnd_order_data; - - // Flags for std::call_once, also static members - static std::once_flag base_vectors_flag; - static std::once_flag queries_flag; - static std::once_flag seq_order_flag; - static std::once_flag rnd_order_flag; - - static void initialize_base_vectors() { - std::call_once(base_vectors_flag, []{ - S_base_vectors_data.resize(LIST_SIZE * D); - std::mt19937 rng(123); - std::normal_distribution nd(0.f, 1.f); - for (float& v : S_base_vectors_data) v = nd(rng); - }); - } +#include // For std::iota +#include // For std::fixed, std::setprecision +#include // For std::thread +#include // For std::atomic + +#include // Assuming torch is available +#include "concurrentqueue.h" // Assuming moodycamel's queue header is findable + +using namespace std::chrono; +using torch::Tensor; + +// Namespace for this specific microbenchmark's helpers +namespace JobEnqueueMicrobenchmarkWithContention { + +// --- Struct Definitions (ScanJob, CoreResources, MockPartitionManager - same as before) --- + struct ScanJob { + int64_t partition_id; + int k_val; + const float* query_vector; + int query_id; + bool is_batched = false; + int64_t num_queries_in_job = 0; + int rank = 0; + + ScanJob() = default; + ScanJob(ScanJob&& other) noexcept = default; + ScanJob& operator=(ScanJob&& other) noexcept = default; + ScanJob(const ScanJob&) = delete; + ScanJob& operator=(const ScanJob&) = delete; + }; + + struct CoreResources { + int core_id_val; + moodycamel::ConcurrentQueue job_queue; + // For worker thread to count its dequeued items: + std::atomic dequeued_items_by_worker{0}; + + + // Explicitly define constructors for clarity if needed, + // or rely on aggregate initialization / default members. + CoreResources() : core_id_val(-1), job_queue(), dequeued_items_by_worker(0) {} // Default + CoreResources(int id) : core_id_val(id), job_queue(), dequeued_items_by_worker(0) {} // With ID + + // Since moodycamel::ConcurrentQueue is not copyable, + // CoreResources becomes non-copyable. We need move semantics. + CoreResources(CoreResources&& other) noexcept + : core_id_val(other.core_id_val), + job_queue(std::move(other.job_queue)), // Moodycamel queue is movable + dequeued_items_by_worker(other.dequeued_items_by_worker.load()) + {} + + CoreResources& operator=(CoreResources&& other) noexcept { + if (this != &other) { + core_id_val = other.core_id_val; + job_queue = std::move(other.job_queue); + dequeued_items_by_worker.store(other.dequeued_items_by_worker.load()); + } + return *this; + } + // Delete copy operations + CoreResources(const CoreResources&) = delete; + CoreResources& operator=(const CoreResources&) = delete; + }; - static void initialize_queries() { - std::call_once(queries_flag, []{ - S_queries_data.resize(NQ * D); - std::mt19937 rng(321); - std::normal_distribution nd(0.f, 1.f); - for (float& v : S_queries_data) v = nd(rng); - }); - } - static void initialize_seq_order() { - std::call_once(seq_order_flag, []{ - S_seq_order_data.resize(LIST_SIZE); - std::iota(S_seq_order_data.begin(), S_seq_order_data.end(), 0); - }); + class MockPartitionManager { + public: + int num_cores_manager_ = 1; + MockPartitionManager(int cores) : num_cores_manager_(cores) { + if (cores <= 0) num_cores_manager_ = 1; + } + int get_partition_core_id(int64_t pid) const { + return static_cast(pid % num_cores_manager_); + } + }; + +// --- Parameters --- + static const int64_t BENCH_DIMENSION = 128; + static const int BENCH_K = 10; + static const int BENCH_NUM_CORES = 4; // Number of worker threads and queues + +// --- Worker Thread Function --- + void dequeuing_worker_function(CoreResources& core_res, std::atomic& stop_signal) { + long items_this_worker = 0; + int64_t job_id_val; // Renamed to avoid conflict + + while (!stop_signal.load(std::memory_order_acquire)) { + if (core_res.job_queue.try_dequeue(job_id_val)) { + items_this_worker++; + // Simulate minimal work with the job_id_val if needed + volatile int64_t sink = job_id_val; // "Use" job_id_val + (void)sink; + } else { + // Yield if the queue is often empty to reduce busy-waiting, + // though try_dequeue is quite efficient. + std::this_thread::yield(); + } + } + // After stop_signal is true, drain any remaining items from the queue + while (core_res.job_queue.try_dequeue(job_id_val)) { + items_this_worker++; + volatile int64_t sink = job_id_val; + (void)sink; + } + core_res.dequeued_items_by_worker.fetch_add(items_this_worker, std::memory_order_relaxed); } - static void initialize_rnd_order() { - std::call_once(rnd_order_flag, []{ - S_rnd_order_data.resize(LIST_SIZE); - std::iota(S_rnd_order_data.begin(), S_rnd_order_data.end(), 0); - std::mt19937 rng(999); - std::shuffle(S_rnd_order_data.begin(), S_rnd_order_data.end(), rng); - }); - } -public: - // Kernel now uses the custom parallel_for for the inner loop (scanning list_size) - // It returns the sum of minimum distances found for each query (for anti-optimization) - static float static_scan_kernel(const std::vector& order, int n_threads_requested) +// --- Function with the snippet to benchmark (Updated jid logic) --- + int run_job_creation_and_enqueue_snippet( + std::vector& job_buffer, + std::vector& core_resources_list, + const MockPartitionManager& partition_manager, + const Tensor& partition_ids_tensor, + const float* x_ptr, + long num_queries, + long num_partitions_per_query, + int k_param, + int dimension_param) { - const float* base_ptr = S_base_vectors_data.data(); - const float* query_ptr = S_queries_data.data(); + auto partition_ids_accessor = partition_ids_tensor.accessor(); + job_buffer.resize(num_queries * num_partitions_per_query); // Max possible + int jid = 0; // Current index for job_buffer, also basis for enqueued ID + + for (long q = 0; q < num_queries; q++) { + for (long p = 0; p < num_partitions_per_query; p++) { + int64_t pid_val = partition_ids_accessor[q][p]; + if (pid_val == -1) continue; + + ScanJob job_item; + job_item.is_batched = false; + job_item.query_id = static_cast(q); + job_item.partition_id = pid_val; + job_item.k_val = k_param; + job_item.query_vector = x_ptr + q * dimension_param; + job_item.num_queries_in_job = 1; + job_item.rank = static_cast(p); + + int core_id = partition_manager.get_partition_core_id(pid_val); + + // Original snippet logic: place job, THEN increment jid, THEN enqueue the NEW jid. + job_buffer[jid] = std::move(job_item); + jid++; // jid is now the 1-based count of jobs created, or index for the *next* job. + core_resources_list[core_id].job_queue.enqueue(jid); // Enqueue this 1-based count. + } + } + return jid; // Returns the total count of jobs created (which is the last enqueued jid value) + } - volatile float total_min_sum = 0.f; // Accumulates min distance for each query - // Determine the actual number of threads parallel_for will aim to use - int threads_for_pfor = n_threads_requested; - if (threads_for_pfor <= 0) { - threads_for_pfor = static_cast(std::thread::hardware_concurrency()); +// --- GTest TEST Case with Contention --- + TEST(JobEnqueueMicrobenchmark, SnippetPerformanceWithContention) { + long num_queries_test = 100; // Increase for more work: e.g., 1000 + long num_partitions_test = 1000; // Increase for more work: e.g., 1000 + int test_iterations = 5; + int warmup_iterations = 1; + + std::cout << std::endl << "[JobEnqueueMicrobenchmark::SnippetPerformanceWithContention]" << std::endl; + std::cout << " Config: Queries=" << num_queries_test + << ", Partitions/Query=" << num_partitions_test + << ", Iterations=" << test_iterations + << ", Worker Threads=" << BENCH_NUM_CORES << std::endl; + + // 1. Setup data structures + std::vector local_job_buffer; + std::vector local_core_resources; + local_core_resources.reserve(BENCH_NUM_CORES); + for (int i = 0; i < BENCH_NUM_CORES; ++i) { + local_core_resources.emplace_back(i); // Use constructor CoreResources(int id) } - if (threads_for_pfor == 0) { // Safety for hardware_concurrency returning 0 - threads_for_pfor = 1; + JobEnqueueMicrobenchmarkWithContention::MockPartitionManager local_partition_manager(BENCH_NUM_CORES); + + std::vector local_queries_data_storage(num_queries_test * BENCH_DIMENSION); + float* local_x_ptr = local_queries_data_storage.data(); + Tensor local_partition_ids_tensor = torch::empty({num_queries_test, num_partitions_test}, torch::TensorOptions().dtype(torch::kInt64)); + auto accessor = local_partition_ids_tensor.accessor(); + for (long i = 0; i < num_queries_test; ++i) { + for (long j = 0; j < num_partitions_test; ++j) { + accessor[i][j] = i * num_partitions_test + j; + } } - float distances[LIST_SIZE]; // Distances for the current query - - // Outer loop over queries (sequential) - for (int qi = 0; qi < NQ; ++qi) { - const float* current_query_vector = query_ptr + qi * D; - - // Inner loop (over base vectors for the current query) is parallelized - // The 'order' vector contains indices into 'S_base_vectors_data' - // 'k' will be an index from 0 to order.size()-1 - parallel_for(static_cast(0), static_cast(order.size()), - [&](int64_t k) { // k is the index in the range [0, order.size()) - int64_t base_vector_original_idx = order[k]; - const float* current_base_vector = base_ptr + base_vector_original_idx * D; - - distances[k] = faiss::fvec_L2sqr(current_query_vector, current_base_vector, static_cast(D)); - }, - threads_for_pfor // Pass the determined number of threads to parallel_for - ); + std::vector durations_ms; + durations_ms.reserve(test_iterations); + long total_jobs_actually_created = 0; + + + // 2. Run benchmark loop + for (int iter = 0; iter < test_iterations + warmup_iterations; ++iter) { + // Reset/drain queues and worker counts for each iteration + for (auto& cr : local_core_resources) { + int64_t dummy_val; + while (cr.job_queue.try_dequeue(dummy_val)); + cr.dequeued_items_by_worker.store(0, std::memory_order_relaxed); + } + local_job_buffer.clear(); + + std::atomic stop_worker_threads_signal{false}; + std::vector worker_threads; + worker_threads.reserve(BENCH_NUM_CORES); + + // Launch worker threads + for (int i = 0; i < BENCH_NUM_CORES; ++i) { + worker_threads.emplace_back(dequeuing_worker_function, std::ref(local_core_resources[i]), std::ref(stop_worker_threads_signal)); + } + + auto iter_start_time = high_resolution_clock::now(); + + // This is the timed operation (producer) + int jobs_created_this_iteration = run_job_creation_and_enqueue_snippet( + local_job_buffer, local_core_resources, local_partition_manager, + local_partition_ids_tensor, local_x_ptr, + num_queries_test, num_partitions_test, + BENCH_K, BENCH_DIMENSION); + + auto iter_end_time = high_resolution_clock::now(); + + // Signal workers to stop and wait for them to finish + stop_worker_threads_signal.store(true, std::memory_order_release); + for (auto& t : worker_threads) { + if (t.joinable()) { + t.join(); + } + } + + if (iter >= warmup_iterations) { + durations_ms.push_back(duration_cast(iter_end_time - iter_start_time).count() / 1000.0); + total_jobs_actually_created += jobs_created_this_iteration; + } + + // Sanity check: total dequeued items should match jobs created + long total_dequeued_this_iteration = 0; + for (const auto& cr : local_core_resources) { + total_dequeued_this_iteration += cr.dequeued_items_by_worker.load(); + } + ASSERT_EQ(total_dequeued_this_iteration, jobs_created_this_iteration) + << "Mismatch in created vs dequeued jobs for iteration " << iter; } - return total_min_sum; // Return sum for anti-optimization - } - // Wrappers call the kernel - static float static_sequential_scan(int nth = 1) { - return static_scan_kernel(S_seq_order_data, nth); - } - static float static_random_scan(int nth = 1) { - return static_scan_kernel(S_rnd_order_data, nth); - } - -protected: - static void SetUpTestSuite() { - std::cout << "Using custom parallel_for for inner loop parallelization." << std::endl; - unsigned int hw_threads = std::thread::hardware_concurrency(); - std::cout << "std::thread::hardware_concurrency(): " << hw_threads << std::endl; - - std::cout << "Initializing benchmark data (once)..." << std::endl; - auto overall_start_init = std::chrono::steady_clock::now(); - - initialize_base_vectors(); - initialize_queries(); - initialize_seq_order(); - initialize_rnd_order(); - - auto overall_end_init = std::chrono::steady_clock::now(); - std::chrono::duration init_duration = overall_end_init - overall_start_init; - std::cout << "VECTOR SCAN BENCHMARK" << std::endl; - std::cout << "--------------------------------------------------------------------------" << std::endl; - std::cout << "Benchmark data initialized in " << std::fixed << std::setprecision(3) - << init_duration.count() << " s." << std::endl; - std::cout << "Benchmark data size: " << LIST_SIZE << "vectors, " - << NQ << " queries, " << D << " dimensions." << std::endl; - std::cout << "Throughput averaged over all queries." << std::endl; - std::cout << "--------------------------------------------------------------------------" << std::endl; - std::cout << std::setw(24) << std::left << "Test Case" - << " | " << "N_Threads" // Requested threads - << " | " << "Time (s)" - << " | " << "Throughput" << std::endl; - std::cout << "--------------------------------------------------------------------------" << std::endl; - } - - static void TearDownTestSuite() { - std::cout << "--------------------------------------------------------------------------" << std::endl; - std::cout << "Benchmark suite finished." << std::endl; + // 3. Report results + double total_duration_ms = 0; + for (double d : durations_ms) total_duration_ms += d; + double avg_duration_ms = durations_ms.empty() ? 0 : total_duration_ms / durations_ms.size(); + + std::cout << " Average time for snippet iteration (with contention): " + << std::fixed << std::setprecision(3) << avg_duration_ms << " ms" << std::endl; + + if (test_iterations > 0 && total_jobs_actually_created > 0) { + long avg_jobs_per_iteration = total_jobs_actually_created / test_iterations; + if (avg_jobs_per_iteration > 0 && avg_duration_ms > 0) { + std::cout << " Average jobs created per iteration: " << avg_jobs_per_iteration << std::endl; + std::cout << " Average time per job (snippet part, with contention): " + << (avg_duration_ms * 1000.0 / avg_jobs_per_iteration) << " us" << std::endl; + } + } + ASSERT_GT(avg_duration_ms, -0.00001); } -}; - -// Static member definitions -std::vector ListScanBenchmarkF::S_base_vectors_data; -std::vector ListScanBenchmarkF::S_queries_data; -std::vector ListScanBenchmarkF::S_seq_order_data; -std::vector ListScanBenchmarkF::S_rnd_order_data; - -std::once_flag ListScanBenchmarkF::base_vectors_flag; -std::once_flag ListScanBenchmarkF::queries_flag; -std::once_flag ListScanBenchmarkF::seq_order_flag; -std::once_flag ListScanBenchmarkF::rnd_order_flag; - - -TEST_F(ListScanBenchmarkF, ParallelSequentialScan) -{ - for (int nth : {1, 2, 4, 8, 12}) { // Test with different numbers of threads - Timer t; - t.start(); - volatile float dummy_sum = static_sequential_scan(nth); - double elapsed_seconds = t.stop(); - (void)dummy_sum; - - int actual_pfor_threads = (nth <= 0) ? static_cast(std::thread::hardware_concurrency()) : nth; - if (actual_pfor_threads == 0) actual_pfor_threads = 1; - if (nth == 1) actual_pfor_threads = 1; // parallel_for handles num_threads == 1 specifically - - - print_stats("Parallel-Sequential", nth, elapsed_seconds); - } -} - -TEST_F(ListScanBenchmarkF, ParallelRandomScan) -{ - for (int nth : {1, 2, 4, 8, 12}) { // Test with different numbers of threads - Timer t; - t.start(); - volatile float dummy_sum = static_random_scan(nth); - double elapsed_seconds = t.stop(); - (void)dummy_sum; - - int actual_pfor_threads = (nth <= 0) ? static_cast(std::thread::hardware_concurrency()) : nth; - if (actual_pfor_threads == 0) actual_pfor_threads = 1; - if (nth == 1) actual_pfor_threads = 1; // parallel_for handles num_threads == 1 specifically - - print_stats("Parallel-Random", nth, elapsed_seconds); - } -} +} // namespace JobEnqueueMicrobenchmarkWithContention \ No newline at end of file From 72b6c8fa07c844c7bfe20d14761f9eea229a08f2 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 16:26:53 -0500 Subject: [PATCH 137/323] reduce allocation cost of scan jobs --- src/cpp/src/query_coordinator.cpp | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index c1b6d46c..f1f3a4a0 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -442,7 +442,6 @@ shared_ptr QueryCoordinator::worker_scan( auto partition_ids_accessor = partition_ids.accessor(); job_buffer_.resize(num_queries * partition_ids.size(1)); - auto partitions = partition_manager_->partition_store_->partitions_; int jid = 0; for (int q = 0; q < num_queries; q++) { for (int64_t p = 0; p < partition_ids.size(1); p++) { @@ -458,7 +457,7 @@ shared_ptr QueryCoordinator::worker_scan( job.num_queries = 1; job.rank = p; - int core_id = partitions[pid]->core_id_; + int core_id = partition_manager_->get_partition_core_id(pid); if (core_id < 0) { throw std::runtime_error("[QueryCoordinator::worker_scan] Invalid core ID."); } From a452a85a952ac88f2b63a81a6bf0d0f316d99690 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 16:31:22 -0500 Subject: [PATCH 138/323] reduce allocation cost of scan jobs --- src/cpp/src/query_coordinator.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index f1f3a4a0..cb0fdcca 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -457,7 +457,7 @@ shared_ptr QueryCoordinator::worker_scan( job.num_queries = 1; job.rank = p; - int core_id = partition_manager_->get_partition_core_id(pid); + int core_id = pid % num_workers_; if (core_id < 0) { throw std::runtime_error("[QueryCoordinator::worker_scan] Invalid core ID."); } From 20a55ddce3bb9c90cc8fca11a3b0f1348dd873b6 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 16:35:50 -0500 Subject: [PATCH 139/323] reduce allocation cost of scan jobs --- src/cpp/src/query_coordinator.cpp | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index cb0fdcca..b555c9bf 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -457,8 +457,10 @@ shared_ptr QueryCoordinator::worker_scan( job.num_queries = 1; job.rank = p; - int core_id = pid % num_workers_; - if (core_id < 0) { + int core_id = partition_manager_->get_partition_core_id(pid); + + + if (core_id < 0 || pid % num_workers_ != core_id) { throw std::runtime_error("[QueryCoordinator::worker_scan] Invalid core ID."); } job_buffer_[jid] = std::move(job); From 12ea88c0daa22a9f10f36166648c8bbe12754b14 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 16:38:53 -0500 Subject: [PATCH 140/323] reduce allocation cost of scan jobs --- src/cpp/src/partition_manager.cpp | 2 +- src/cpp/src/query_coordinator.cpp | 5 ++--- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/src/cpp/src/partition_manager.cpp b/src/cpp/src/partition_manager.cpp index 428c7ed4..de6b5a7f 100644 --- a/src/cpp/src/partition_manager.cpp +++ b/src/cpp/src/partition_manager.cpp @@ -621,7 +621,7 @@ void PartitionManager::distribute_partitions(int num_workers, bool use_numa) { Tensor partition_ids = get_partition_ids(); auto partition_ids_accessor = partition_ids.accessor(); for (int i = 0; i < partition_ids.size(0); i++) { - set_partition_core_id(partition_ids_accessor[i], i % num_workers, use_numa); + set_partition_core_id(partition_ids_accessor[i], partition_ids_accessor[i] % num_workers, use_numa); } } diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index b555c9bf..f9dddfc4 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -457,10 +457,9 @@ shared_ptr QueryCoordinator::worker_scan( job.num_queries = 1; job.rank = p; - int core_id = partition_manager_->get_partition_core_id(pid); + int core_id = pid % num_workers_ - - if (core_id < 0 || pid % num_workers_ != core_id) { + if (core_id < 0) { throw std::runtime_error("[QueryCoordinator::worker_scan] Invalid core ID."); } job_buffer_[jid] = std::move(job); From 37b7b547b8a55ec050e84a0f70804152c8a77679 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 16:40:09 -0500 Subject: [PATCH 141/323] reduce allocation cost of scan jobs --- src/cpp/src/query_coordinator.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index f9dddfc4..e1c18120 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -457,7 +457,7 @@ shared_ptr QueryCoordinator::worker_scan( job.num_queries = 1; job.rank = p; - int core_id = pid % num_workers_ + int core_id = pid % num_workers_; if (core_id < 0) { throw std::runtime_error("[QueryCoordinator::worker_scan] Invalid core ID."); From 947868697eda883a060d68146f9e2d22a3511fe8 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 16:49:33 -0500 Subject: [PATCH 142/323] reduce allocation cost of scan jobs --- src/cpp/src/query_coordinator.cpp | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index e1c18120..a69723b2 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -468,6 +468,12 @@ shared_ptr QueryCoordinator::worker_scan( } } } + auto topk_ids = torch::full({num_queries, k}, -1, torch::kInt64); + auto topk_dists = torch::full({num_queries, k}, + (metric_ == faiss::METRIC_INNER_PRODUCT) + ? -std::numeric_limits::infinity() + : std::numeric_limits::infinity(), + torch::kFloat32); end_time = high_resolution_clock::now(); timing_info->job_enqueue_time_ns = duration_cast(end_time - start_time).count(); @@ -548,12 +554,6 @@ shared_ptr QueryCoordinator::worker_scan( // Aggregate results. start_time = high_resolution_clock::now(); - auto topk_ids = torch::full({num_queries, k}, -1, torch::kInt64); - auto topk_dists = torch::full({num_queries, k}, - (metric_ == faiss::METRIC_INNER_PRODUCT) - ? -std::numeric_limits::infinity() - : std::numeric_limits::infinity(), - torch::kFloat32); auto ids_accessor = topk_ids.accessor(); auto dists_accessor = topk_dists.accessor(); { From 9161783a02957b58c2292cdaa447d19fae08dfe7 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 16:56:28 -0500 Subject: [PATCH 143/323] reduce allocation cost of scan jobs --- src/cpp/src/query_coordinator.cpp | 4 ---- 1 file changed, 4 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index a69723b2..425dfdc6 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -458,10 +458,6 @@ shared_ptr QueryCoordinator::worker_scan( job.rank = p; int core_id = pid % num_workers_; - - if (core_id < 0) { - throw std::runtime_error("[QueryCoordinator::worker_scan] Invalid core ID."); - } job_buffer_[jid] = std::move(job); core_resources_[core_id].job_queue.enqueue(jid); jid++; From 704ab4fc8cfdf9412f2709991011bbc70ab08c4c Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 17:05:00 -0500 Subject: [PATCH 144/323] reduce allocation cost of scan jobs --- src/cpp/src/query_coordinator.cpp | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 425dfdc6..ffce055e 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -70,6 +70,10 @@ void QueryCoordinator::initialize_workers(int num_cores, bool use_numa) { worker_job_counter_[i] = 0; } workers_initialized_ = true; + + // get num cores on machine + int num_cores_on_machine = std::thread::hardware_concurrency(); + set_thread_affinity(num_cores + 1 % num_cores_on_machine); } // Shutdown Worker Threads From eee6b9fe247a619f5966e42be7e1c5d5c5c26e48 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 17:09:56 -0500 Subject: [PATCH 145/323] reduce allocation cost of scan jobs --- src/cpp/src/query_coordinator.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index ffce055e..97c96bc4 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -71,9 +71,9 @@ void QueryCoordinator::initialize_workers(int num_cores, bool use_numa) { } workers_initialized_ = true; - // get num cores on machine + // set main thread on separate thread from workers int num_cores_on_machine = std::thread::hardware_concurrency(); - set_thread_affinity(num_cores + 1 % num_cores_on_machine); + set_thread_affinity(num_cores % num_cores_on_machine); } // Shutdown Worker Threads From dea4112fc8ac669437d990832dfc6760062509b9 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 17:46:36 -0500 Subject: [PATCH 146/323] time scan function --- src/cpp/src/query_coordinator.cpp | 32 +++++++++++++++++++++++++------ 1 file changed, 26 insertions(+), 6 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 97c96bc4..acc5a764 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -96,6 +96,13 @@ void QueryCoordinator::shutdown_workers() { workers_initialized_ = false; } +void threadsafe_cout(std::string log_msg) +{ + static std::mutex lock; + std::lock_guard guard{ lock }; + std::cout << std::move(log_msg); +} + // Worker Thread Function void QueryCoordinator::partition_scan_worker_fn(int core_index) { @@ -106,6 +113,7 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { } int64_t total_pull_time = 0; + int64_t total_copy_time = 0; int64_t total_scan_time = 0; int64_t total_merge_time = 0; @@ -117,6 +125,8 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { std::this_thread::sleep_for(std::chrono::microseconds(5)); } + auto s0 = std::chrono::high_resolution_clock::now(); + auto job_wait_end = std::chrono::high_resolution_clock::now(); job_pull_time_ns += std::chrono::duration_cast(job_wait_end - job_wait_start).count(); @@ -213,11 +223,13 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { auto s4 = std::chrono::high_resolution_clock::now(); // print out timings for worker - auto wait_time = std::chrono::duration_cast(s2 - s1).count(); + auto pull_time = std::chrono::duration_cast(s1 - s0).count(); + auto copy_time = std::chrono::duration_cast(s2 - s1).count(); auto process_time = std::chrono::duration_cast(s3 - s2).count(); auto merge_time = std::chrono::duration_cast(s4 - s3).count(); - total_pull_time += wait_time; + total_pull_time += pull_time; + total_copy_time += copy_time; total_scan_time += process_time; total_merge_time += merge_time; } @@ -288,19 +300,27 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { auto s4 = std::chrono::high_resolution_clock::now(); // print out timings for worker - auto wait_time = std::chrono::duration_cast(s2 - s1).count(); + auto pull_time = std::chrono::duration_cast(s1 - s0).count(); + auto copy_time = std::chrono::duration_cast(s2 - s1).count(); auto process_time = std::chrono::duration_cast(s3 - s2).count(); auto merge_time = std::chrono::duration_cast(s4 - s3).count(); - total_pull_time += wait_time; + total_pull_time += pull_time; + total_copy_time += copy_time; total_scan_time += process_time; total_merge_time += merge_time; } auto job_process_end = std::chrono::high_resolution_clock::now(); job_process_time_ns += std::chrono::duration_cast(job_process_end - job_process_start).count(); } - -// std::cout << "Worker=" << core_index << ": total_pull_time=" << total_pull_time / 1000 << ", total_scan_time=" << total_scan_time / 1000 << ", merge_time=" << total_merge_time / 1000 << '\n'; + std::string log_message = + "[QueryCoordinator::partition_scan_worker_fn] Worker " + std::to_string(core_index) + + ": total_pull_time=" + std::to_string(total_pull_time / 1000) + + ", total_copy_time=" + std::to_string(total_copy_time / 1000) + + ", total_scan_time=" + std::to_string(total_scan_time / 1000) + + ", merge_time=" + std::to_string(total_merge_time / 1000) + '\n'; + + threadsafe_cout(log_message); } // Worker-Based Scan Implementation From d5b4ad8683db01d42b8b222305b35c04f5ea47d8 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 17:57:52 -0500 Subject: [PATCH 147/323] query buffer on numa node --- src/cpp/include/query_coordinator.h | 6 ++++- src/cpp/src/query_coordinator.cpp | 42 +++++++++++++++++------------ 2 files changed, 30 insertions(+), 18 deletions(-) diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index e7898939..ff58327d 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -53,11 +53,15 @@ class QueryCoordinator { struct CoreResources { int core_id; ///< Logical identifier of the core. vector> topk_buffer_pool; ///< Preallocated Top‑K buffers. - vector local_query_buffer; ///< Local aggregator for query results. moodycamel::ConcurrentQueue job_queue; ///< Job queue for scan jobs. }; + struct NUMAResources { + float *local_query_buffer; + }; + vector core_resources_; ///< Per‑core resources for worker threads. + vector numa_resources_; bool workers_initialized_ = false; ///< Flag indicating if worker threads are initialized. int num_workers_; ///< Total number of worker threads. vector worker_threads_; ///< Container for worker threads. diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index acc5a764..ec6f0d7b 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -38,12 +38,27 @@ QueryCoordinator::~QueryCoordinator() { void QueryCoordinator::allocate_core_resources(int core_idx, int num_queries, int k, int d) { CoreResources &res = core_resources_[core_idx]; res.core_id = core_idx; - res.local_query_buffer.resize(num_queries * d * sizeof(float)); res.topk_buffer_pool.resize(num_queries); for (int q = 0; q < num_queries; ++q) { res.topk_buffer_pool[q] = make_shared(k, metric_ == faiss::METRIC_INNER_PRODUCT); res.job_queue = moodycamel::ConcurrentQueue(); } + + + int num_numa_nodes = 1; + #ifdef QUAKE_USE_NUMA + num_numa_nodes = numa_max_node(); + #endif + + for (int i = 0; i < num_numa_nodes; i++) { + NUMAResources &numa_res = numa_resources_[i]; + int64_t alloc_size = num_queries * d * sizeof(float); + #ifdef QUAKE_USE_NUMA + numa_res.local_query_buffer = (float *) numa_alloc_onnode(alloc_size, numa_node)); + #else + numa_res.local_query_buffer = (float *) std::malloc(alloc_size); + #endif + } } // Initialize Worker Threads @@ -108,6 +123,12 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { CoreResources &res = core_resources_[core_index]; + int numa_node = 1; + #ifdef QUAKE_USE_NUMA + numa_node = numa_node_of_cpu(core_index); + #endif + NUMAResources &numa_res = numa_resources_[numa_node]; + if (!set_thread_affinity(core_index)) { std::cout << "[QueryCoordinator::partition_scan_worker_fn] Failed to set thread affinity on core " << core_index << std::endl; } @@ -185,15 +206,6 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { if (!job.is_batched) { auto s1 = std::chrono::high_resolution_clock::now(); - // Allocate a thread-local query buffer if needed. - if (res.local_query_buffer.size() < partition_manager_->d() * sizeof(float)) { - res.local_query_buffer.resize(partition_manager_->d() * sizeof(float)); - } - - // Copy the contents of the query vector to the local buffer using memcpy. - if (memcpy(res.local_query_buffer.data(), job.query_vector, partition_manager_->d() * sizeof(float)) == nullptr) { - throw std::runtime_error("[partition_scan_worker_fn] memcpy failed."); - } if (local_topk_buffer == nullptr) { throw std::runtime_error("[partition_scan_worker_fn] local_topk_buffer is null."); @@ -203,7 +215,7 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { } auto s2 = std::chrono::high_resolution_clock::now(); // Perform the scan on the partition. - scan_list((float *) res.local_query_buffer.data(), + scan_list(numa_res.local_query_buffer, partition_codes, partition_ids, partition_size, @@ -239,10 +251,6 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { throw std::runtime_error("[partition_scan_worker_fn] Invalid batched job."); } - if (res.local_query_buffer.size() < partition_manager_->d() * sizeof(float) * job.num_queries) { - res.local_query_buffer.resize(partition_manager_->d() * sizeof(float) * job.num_queries); - } - auto s1 = std::chrono::high_resolution_clock::now(); int64_t d = partition_manager_->d(); @@ -254,7 +262,7 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { d * sizeof(float)); } - if(memcpy(res.local_query_buffer.data(), + if(memcpy(numa_res.local_query_buffer, query_subset.data(), query_subset.size() * sizeof(float)) == nullptr) { throw std::runtime_error("[partition_scan_worker_fn] memcpy failed."); @@ -276,7 +284,7 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { auto s2 = std::chrono::high_resolution_clock::now(); // Process the batched job. - batched_scan_list((float *) res.local_query_buffer.data(), + batched_scan_list(numa_res.local_query_buffer, partition_codes, partition_ids, job.num_queries, From 76ce5d99ec9d9f5fc140af1eac55c665aa0704dc Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 17:59:18 -0500 Subject: [PATCH 148/323] query buffer on numa node --- src/cpp/src/query_coordinator.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index ec6f0d7b..38e3cd10 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -54,7 +54,7 @@ void QueryCoordinator::allocate_core_resources(int core_idx, int num_queries, in NUMAResources &numa_res = numa_resources_[i]; int64_t alloc_size = num_queries * d * sizeof(float); #ifdef QUAKE_USE_NUMA - numa_res.local_query_buffer = (float *) numa_alloc_onnode(alloc_size, numa_node)); + numa_res.local_query_buffer = (float *) numa_alloc_onnode(alloc_size, numa_node); #else numa_res.local_query_buffer = (float *) std::malloc(alloc_size); #endif From 40d25197cf81e8bcdbeb44f56a777ace6b3bf779 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 18:04:04 -0500 Subject: [PATCH 149/323] query buffer on numa node --- src/cpp/src/query_coordinator.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 38e3cd10..45b6f1b3 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -54,7 +54,7 @@ void QueryCoordinator::allocate_core_resources(int core_idx, int num_queries, in NUMAResources &numa_res = numa_resources_[i]; int64_t alloc_size = num_queries * d * sizeof(float); #ifdef QUAKE_USE_NUMA - numa_res.local_query_buffer = (float *) numa_alloc_onnode(alloc_size, numa_node); + numa_res.local_query_buffer = (float *) numa_alloc_onnode(alloc_size, i); #else numa_res.local_query_buffer = (float *) std::malloc(alloc_size); #endif From d006f303a9deaf9124f2c8fefe9abf5783ba6241 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 18:12:25 -0500 Subject: [PATCH 150/323] query buffer on numa node --- src/cpp/src/quake_index.cpp | 2 +- src/cpp/src/query_coordinator.cpp | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/src/cpp/src/quake_index.cpp b/src/cpp/src/quake_index.cpp index 637f0036..8419d64f 100644 --- a/src/cpp/src/quake_index.cpp +++ b/src/cpp/src/quake_index.cpp @@ -284,7 +284,7 @@ void QuakeIndex::load(const std::string& dir_path, int n_workers, bool use_numa) int n_parts = partition_manager_->nlist(); // don't put too many parent workers. maximum of 1 worker per 500 partitions - int parent_n_workers = std::max(1, n_parts / 500); + int parent_n_workers = std::max(1, n_parts / 1000); parent_n_workers = std::min(parent_n_workers, n_workers); parent_->load(parent_dir, parent_n_workers, use_numa); diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 45b6f1b3..3f6a3d15 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -49,6 +49,7 @@ void QueryCoordinator::allocate_core_resources(int core_idx, int num_queries, in #ifdef QUAKE_USE_NUMA num_numa_nodes = numa_max_node(); #endif + numa_resources_.resize(num_numa_nodes); for (int i = 0; i < num_numa_nodes; i++) { NUMAResources &numa_res = numa_resources_[i]; From b85a4e3260d6816bc76a92fc08a2807156fa47e6 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 18:15:26 -0500 Subject: [PATCH 151/323] query buffer on numa node --- src/cpp/src/query_coordinator.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 3f6a3d15..16d68c6b 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -124,7 +124,7 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { CoreResources &res = core_resources_[core_index]; - int numa_node = 1; + int numa_node = 0; #ifdef QUAKE_USE_NUMA numa_node = numa_node_of_cpu(core_index); #endif From 227a548fc0111a3d8ac619fbafa785dd29c7d279 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 18:37:06 -0500 Subject: [PATCH 152/323] query buffer on numa node --- src/cpp/src/query_coordinator.cpp | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 16d68c6b..14d31dca 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -47,7 +47,7 @@ void QueryCoordinator::allocate_core_resources(int core_idx, int num_queries, in int num_numa_nodes = 1; #ifdef QUAKE_USE_NUMA - num_numa_nodes = numa_max_node(); + num_numa_nodes = numa_max_node() + 1; #endif numa_resources_.resize(num_numa_nodes); @@ -214,6 +214,13 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { local_topk_buffer->set_k(job.k); local_topk_buffer->reset(); } + + int d = partition_manager_->d(); + std::memcpy(numa_res.local_query_buffer, + job.query_vector, + size_t(d) * sizeof(float)); + + auto s2 = std::chrono::high_resolution_clock::now(); // Perform the scan on the partition. scan_list(numa_res.local_query_buffer, From d49a5d0af9447226a5582ed1cd89ceb1f3399f49 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 19 May 2025 18:39:22 -0500 Subject: [PATCH 153/323] query buffer on numa node --- src/cpp/src/query_coordinator.cpp | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 14d31dca..6a7f64d4 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -215,12 +215,6 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { local_topk_buffer->reset(); } - int d = partition_manager_->d(); - std::memcpy(numa_res.local_query_buffer, - job.query_vector, - size_t(d) * sizeof(float)); - - auto s2 = std::chrono::high_resolution_clock::now(); // Perform the scan on the partition. scan_list(numa_res.local_query_buffer, @@ -422,6 +416,12 @@ shared_ptr QueryCoordinator::worker_scan( start_time = high_resolution_clock::now(); + for (auto &nr : numa_resources_) { + std::memcpy(nr.local_query_buffer, + x_ptr, // q == 0 + size_t(dimension) * sizeof(float)); + } + if (search_params->batched_scan) { auto partition_ids_accessor = partition_ids.accessor(); job_buffer_.resize(partition_manager_->nlist()); From 5cd241ff16e7833a2f1b04e7e352017505f39907 Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 02:09:29 -0500 Subject: [PATCH 154/323] NUMA allocation for topk buffer --- src/cpp/include/list_scanning.h | 319 +++---- src/cpp/include/parallel.h | 54 ++ src/cpp/include/query_coordinator.h | 80 +- src/cpp/src/maintenance_cost_estimator.cpp | 2 +- src/cpp/src/query_coordinator.cpp | 948 ++++++++++----------- test/cpp/benchmark.cpp | 2 +- test/cpp/latency_estimator.cpp | 2 +- test/cpp/list_scanning.cpp | 6 +- test/cpp/query_coordinator.cpp | 28 - test/cpp/topk_buffer.cpp | 2 +- 10 files changed, 754 insertions(+), 689 deletions(-) diff --git a/src/cpp/include/list_scanning.h b/src/cpp/include/list_scanning.h index 29aa898a..6aa4359d 100644 --- a/src/cpp/include/list_scanning.h +++ b/src/cpp/include/list_scanning.h @@ -13,6 +13,7 @@ #include "sorting/pdqsort.h" #include "sorting/floyd_rivest_select.h" #include "sorting/heap_select.h" +#include "parallel.h" inline Tensor calculate_recall(Tensor ids, Tensor gt_ids) { Tensor num_correct = torch::zeros(ids.size(0), torch::kInt64); @@ -39,206 +40,226 @@ inline Tensor calculate_recall(Tensor ids, Tensor gt_ids) { return recall; } -#define TOP_K_BUFFER_CAPACITY (16 * 1024) - -template +template class TypedTopKBuffer { public: - int k_; // Number of top elements to keep - int curr_offset_ = 0; // Current offset in the buffer - std::vector > topk_; // Buffer to store top-k elements - bool is_descending_; // Flag to indicate sorting order - std::recursive_mutex buffer_mutex_; - std::atomic processing_query_; - std::atomic jobs_left_; - std::atomic partitions_scanned_; - - TypedTopKBuffer(int k, bool is_descending, int buffer_capacity = TOP_K_BUFFER_CAPACITY) - : k_(k), is_descending_(is_descending), topk_(buffer_capacity), processing_query_(true), partitions_scanned_(0) { - assert(k <= buffer_capacity); // Ensure k is smaller than or equal to buffer size - - for (int i = 0; i < topk_.size(); i++) { - if (is_descending_) { - topk_[i] = {-std::numeric_limits::infinity(), -1}; - } else { - topk_[i] = {std::numeric_limits::max(), -1}; - } + T *vals_; // size = capacity + I *ids_; + int capacity_, head_, k_; + bool is_desc_; + int *ord_; + bool owns_memory_ = true; + TypedTopKBuffer(int k, bool desc, int cap, int node) + : capacity_(cap), head_(0), k_(k) { + vals_ = static_cast(quake_alloc(sizeof(T) * cap, node)); + ids_ = static_cast(quake_alloc(sizeof(I) * cap, node)); + ord_ = static_cast(quake_alloc(sizeof(int) * cap, node)); + + if (capacity_ < k_) { + throw std::invalid_argument("capacity must be greater than k"); } - } - ~TypedTopKBuffer() = default; - - void set_k(int new_k) { - std::lock_guard buffer_lock(buffer_mutex_); - assert(new_k <= topk_.size()); - k_ = new_k; - reset(); + if (desc) { + is_desc_ = true; + for (int i = 0; i < capacity_; i++) { + vals_[i] = -std::numeric_limits::infinity(); + ids_[i] = -1; + } + } else { + is_desc_ = false; + for (int i = 0; i < capacity_; i++) { + vals_[i] = std::numeric_limits::infinity(); + ids_[i] = -1; + } + } } - void set_processing_query(bool new_value) { - processing_query_.store(new_value, std::memory_order_relaxed); - } + // Create buffer but do not allocate memory (except for ord_) + TypedTopKBuffer(T *vals, I* ids, int cap, int k, bool desc, int node) { + capacity_ = cap; + head_ = 0; + k_ = k; + is_desc_ = desc; + vals_ = vals; + ids_ = ids; + ord_ = static_cast(quake_alloc(sizeof(int) * cap, node)); + owns_memory_ = false; + + if (capacity_ < k_) { + throw std::invalid_argument("capacity must be greater than k"); + } - inline bool currently_processing_query() { - return processing_query_.load(std::memory_order_relaxed); + if (desc) { + for (int i = 0; i < capacity_; i++) { + vals_[i] = -std::numeric_limits::infinity(); + ids_[i] = -1; + } + } else { + for (int i = 0; i < capacity_; i++) { + vals_[i] = std::numeric_limits::infinity(); + ids_[i] = -1; + } + } } - void set_jobs_left(int total_jobs) { - jobs_left_.store(total_jobs, std::memory_order_relaxed); - } + ~TypedTopKBuffer() { + if (owns_memory_) { + if (vals_) { + quake_free(vals_, sizeof(T) * capacity_); + } - void record_skipped_jobs(int skipped_jobs) { - jobs_left_.fetch_sub(skipped_jobs, std::memory_order_relaxed); - } + if (ids_) { + quake_free(ids_, sizeof(I) * capacity_); + } + } - void record_empty_job() { - jobs_left_.fetch_sub(1, std::memory_order_relaxed); - } + if (ord_) { + quake_free(ord_, sizeof(int) * capacity_); + } - inline bool finished_all_jobs() { - int curr_jobs_left = jobs_left_.load(std::memory_order_relaxed); - return jobs_left_.load(std::memory_order_relaxed) <= 0; + vals_ = nullptr; + ids_ = nullptr; + ord_ = nullptr; } - inline int get_num_partitions_scanned() { - return partitions_scanned_.load(std::memory_order_relaxed); + void set_k(int new_k) { + if (new_k > capacity_) { + throw std::invalid_argument("k must be less than the capacity"); + } + k_ = new_k; + reset(); } void reset() { - std::lock_guard buffer_lock(buffer_mutex_); - curr_offset_ = 0; + head_ = 0; for (int i = 0; i < k_; i++) { - if (is_descending_) { - topk_[i] = { -std::numeric_limits::infinity(), -1 }; + if (is_desc_) { + vals_[i] = -std::numeric_limits::infinity(); + ids_[i] = -1; } else { - topk_[i] = { std::numeric_limits::max(), -1 }; + vals_[i] = std::numeric_limits::infinity(); + ids_[i] = -1; } } - partitions_scanned_.store(0, std::memory_order_relaxed); } - void add(DistanceType distance, IdType index) { - if (curr_offset_ >= topk_.size()) { - flush(); // Flush the buffer if it is full - } - topk_[curr_offset_++] = {distance, index}; + inline void add(T dist, I idx) { + vals_[head_] = dist; + ids_[head_] = idx; + if (++head_ == capacity_) flush(); } - void batch_add(DistanceType *distances, const IdType *indices, int num_values) { - if (num_values == 0) { - jobs_left_.fetch_sub(1, std::memory_order_relaxed); - return; - } - if (!currently_processing_query()) { - jobs_left_.fetch_sub(1, std::memory_order_relaxed); - return; - } - std::lock_guard lock(buffer_mutex_); + void batch_add(T *distances, I *indices, int num_values) { int pos = 0; while (pos < num_values) { - int available = static_cast(topk_.size()) - curr_offset_; + int available = capacity_ - head_; if (available <= 0) { flush(); - available = static_cast(topk_.size()) - curr_offset_; + available = capacity_ - head_; } int to_copy = std::min(num_values - pos, available); for (int i = 0; i < to_copy; i++) { - topk_[curr_offset_++] = { distances[pos + i], indices[pos + i] }; + vals_[head_] = distances[pos + i]; + ids_[head_] = indices[pos + i]; + head_++; } pos += to_copy; } - jobs_left_.fetch_sub(1, std::memory_order_relaxed); - partitions_scanned_.fetch_add(1, std::memory_order_relaxed); } - DistanceType flush() { - std::lock_guard buffer_lock(buffer_mutex_); - if (curr_offset_ > k_) { - - // for k < 10 use heap select - if (k_ < 10) { - if (is_descending_) { - miniselect::heap_select(topk_.begin(), topk_.begin() + k_, - topk_.begin() + curr_offset_, - [](const auto &a, const auto &b) { return a.first > b.first; }); - } else { - miniselect::heap_select(topk_.begin(), topk_.begin() + k_, - topk_.begin() + curr_offset_, - [](const auto &a, const auto &b) { return a.first < b.first; }); - } - } else if (k_ < curr_offset_ * .001) { - if (is_descending_) { - miniselect::floyd_rivest_select(topk_.begin(), topk_.begin() + k_, - topk_.begin() + curr_offset_, - [](const auto &a, const auto &b) { return a.first > b.first; }); - } else { - miniselect::floyd_rivest_select(topk_.begin(), topk_.begin() + k_, - topk_.begin() + curr_offset_, - [](const auto &a, const auto &b) { return a.first < b.first; }); - } - } else { - if (is_descending_) { - miniselect::pdqpartial_sort_branchless(topk_.begin(), topk_.begin() + k_, - topk_.begin() + curr_offset_, - [](const auto &a, const auto &b) { return a.first > b.first; }); - } else { - miniselect::pdqpartial_sort_branchless(topk_.begin(), topk_.begin() + k_, - topk_.begin() + curr_offset_, - [](const auto &a, const auto &b) { return a.first < b.first; }); - } - } - curr_offset_ = k_; // After flush, retain only the top-k elements - } else { - if (is_descending_) { - miniselect::pdqsort_branchless(topk_.begin(), topk_.begin() + curr_offset_, - [](const auto &a, const auto &b) { return a.first > b.first; }); + T flush() { + int n = head_; + int m = std::min(n, k_); + + // 1) build identity permutation + for (int i = 0; i < n; ++i) { + ord_[i] = i; + } + // comparator by value + auto cmpIdx = [&](int a, int b) { + return is_desc_ ? (vals_[a] > vals_[b]) + : (vals_[a] < vals_[b]); + }; + + // 2) select top‐m indices into ord_[0..m) + if (n > m) { + if (m < 10) { + miniselect::heap_select(ord_, ord_ + m, ord_ + n, cmpIdx); + } else if (m < n * 0.001) { + miniselect::floyd_rivest_select(ord_, ord_ + m, ord_ + n, cmpIdx); } else { - miniselect::pdqsort_branchless(topk_.begin(), topk_.begin() + curr_offset_, - [](const auto &a, const auto &b) { return a.first < b.first; }); + miniselect::pdqpartial_sort_branchless(ord_, ord_ + m, ord_ + n, cmpIdx); } } - return topk_[std::min(curr_offset_, k_ - 1)].first; - } - - std::vector get_topk() { - std::lock_guard buffer_lock(buffer_mutex_); - flush(); // Ensure the buffer is properly flushed - - std::vector topk_distances(std::min(curr_offset_, k_)); - for (int i = 0; i < std::min(curr_offset_, k_); i++) { - topk_distances[i] = topk_[i].first; + // 3) sort the top‐m prefix + miniselect::pdqsort_branchless(ord_, ord_ + m, cmpIdx); + + // 4) copy the m winners back to front of vals_/ids_ + for (int i = 0; i < m; ++i) { + int idx = ord_[i]; + vals_[i] = vals_[idx]; + ids_[i] = ids_[idx]; } - return topk_distances; + // 5) clamp head_ and return the k-th value (or extreme if too few) + head_ = m; + if (head_ == 0) { + return is_desc_ + ? -std::numeric_limits::infinity() + : std::numeric_limits::infinity(); + } + int ret_i = std::min(k_ - 1, head_ - 1); + return vals_[ret_i]; } - DistanceType get_kth_distance() { - std::lock_guard buffer_lock(buffer_mutex_); - flush(); // Ensure the buffer is properly flushed - if (curr_offset_ < k_) { - return 0.0; + std::vector get_topk() { + flush(); + int n = head_; + std::vector out; + out.reserve(n); + for (int i = 0; i < n; ++i) { + out.push_back(vals_[i]); } - - return topk_[std::min(curr_offset_, k_ - 1)].first; + return out; } -// Get the current top-k indices (after final flush) - std::vector get_topk_indices() { - std::lock_guard buffer_lock(buffer_mutex_); - flush(); // Ensure the buffer is properly flushed + T get_kth_distance() { + flush(); + if (head_ < k_) { + // not enough elements: return the sentinel extreme + return is_desc_ + ? -std::numeric_limits::infinity() + : std::numeric_limits::infinity(); + } + return vals_[k_ - 1]; + } - std::vector topk_indices(std::min(curr_offset_, k_)); - for (int i = 0; i < std::min(curr_offset_, k_); i++) { - topk_indices[i] = topk_[i].second; + std::vector get_topk_indices() { + flush(); + int n = head_; + std::vector out; + out.reserve(n); + for (int i = 0; i < n; ++i) { + out.push_back(ids_[i]); } - return topk_indices; + return out; } }; // Type alias for convenience using TopkBuffer = TypedTopKBuffer; +inline vector> create_buffers(int batch_size, int k, bool is_desc) { + vector> buffers; + for (int i = 0; i < batch_size; i++) { + buffers.push_back(make_shared(k, is_desc, k*100, 0)); + } + return buffers; +} + +//vector> local_buffers = create_buffers(batch_size, k, (metric_ == faiss::METRIC_INNER_PRODUCT)); + inline std::tuple buffers_to_tensor(vector> buffers) { int n = buffers.size(); int k = buffers[0]->k_; @@ -263,14 +284,6 @@ inline std::tuple buffers_to_tensor(vector> create_buffers(int n, int k, bool is_descending) { - vector> buffers(n); - for (int i = 0; i < n; i++) { - buffers[i] = make_shared(k, is_descending, 10 * k); - } - return buffers; -} - inline void scan_list_no_ids_inner_product(const float *query_vec, const float *list_vecs, int list_size, @@ -460,4 +473,4 @@ inline void batched_scan_list(const float *query_vecs, } // } -#endif //LIST_SCANNING_H +#endif //LIST_SCANNING_H \ No newline at end of file diff --git a/src/cpp/include/parallel.h b/src/cpp/include/parallel.h index 5db90651..ac8ef8da 100644 --- a/src/cpp/include/parallel.h +++ b/src/cpp/include/parallel.h @@ -38,6 +38,60 @@ inline bool set_thread_affinity(int core_id) { #endif } + +#ifdef QUAKE_USE_NUMA +#include + #include + #include + + inline void* quake_alloc(size_t sz, int node) { + void* ptr = numa_alloc_onnode(sz, node); + if (ptr == nullptr || ptr == MAP_FAILED) { + throw std::bad_alloc(); + } + return ptr; + } + + inline void quake_free(void* ptr, size_t sz) noexcept { + if (ptr) { + numa_free(ptr, sz); + } + } + + inline int numa_node_of_cpu(int cpu) { + return numa_node_of_cpu(cpu); + } + + inline int get_num_numa_nodes() { + return numa_num_configured_nodes(); + } + +#else +#include +#include + +inline void* quake_alloc(size_t sz, int /*node*/) { + void* ptr = std::malloc(sz); + if (!ptr) { + throw std::bad_alloc(); + } + return ptr; +} + +inline void quake_free(void* ptr, size_t /*sz*/) noexcept { + std::free(ptr); +} + +inline int numa_node_of_cpu(int /*cpu*/) { + return 0; // Not applicable +} + +inline int get_num_numa_nodes() { + return 1; // Not applicable +} +#endif + + template void parallel_for(IndexType start, IndexType end, Function func, int num_threads = -1) { if (num_threads <= 0) { diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index ff58327d..56628210 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -21,14 +21,17 @@ class PartitionManager; * A ScanJob encapsulates all parameters required to perform a scan on a given index partition. */ struct ScanJob { - int64_t partition_id; ///< The identifier of the partition to be scanned. - int k; ///< The number of neighbors (Top-K) to return. - const float* query_vector; ///< Pointer to the query vector. - std::shared_ptr> query_ids; ///< Global query IDs; used in batched mode. - int query_id; - bool is_batched = false; ///< Indicates whether this is a batched query job. - int64_t num_queries = 0; ///< The number of queries in batched mode. - int rank = 0; ///< Rank of the partition + int64_t partition_id; ///< The identifier of the partition to be scanned. + int k; ///< The number of neighbors (Top-K) to return. + const float* query_vector; ///< Pointer to the query vector. + bool is_batched = false; ///< Indicates whether this is a batched query job. + int64_t num_queries = 0; + + int query_id; + int rank = 0; ///< Rank of the partition + + std::shared_ptr> query_ids; ///< Global query IDs; used in batched mode. + std::shared_ptr> ranks; ///< Rank of the partition for each query }; /** @@ -51,17 +54,33 @@ class QueryCoordinator { * Each core maintains its own pool of Top‑K buffers, a local query buffer, and a dedicated job queue. */ struct CoreResources { - int core_id; ///< Logical identifier of the core. - vector> topk_buffer_pool; ///< Preallocated Top‑K buffers. - moodycamel::ConcurrentQueue job_queue; ///< Job queue for scan jobs. + int core_id; + std::vector> topk_buffer_pool; + moodycamel::ConcurrentQueue job_queue; + + // Thread-local buffers for batched queries + float *batch_queries; ///< Thread-local buffer used for batched queries. + float *batch_distances; ///< Thread-Local buffer for result distances. + int64_t *batch_ids; ///< Thread-Local buffer for result IDs. }; struct NUMAResources { - float *local_query_buffer; + float* local_query_buffer = nullptr; + size_t buffer_size = 0; + }; + + struct ResultJob { + int query_id; + int rank; + std::vector distances; + std::vector indices; }; + vector core_resources_; ///< Per‑core resources for worker threads. vector numa_resources_; + moodycamel::ConcurrentQueue result_queue_; + bool workers_initialized_ = false; ///< Flag indicating if worker threads are initialized. int num_workers_; ///< Total number of worker threads. vector worker_threads_; ///< Container for worker threads. @@ -73,7 +92,7 @@ class QueryCoordinator { bool debug_ = false; ///< Debug mode flag. std::vector job_buffer_; - std::atomic next_job_id_{0}; + int next_job_id_{0}; vector>> job_flags_; ///< Flags to track job completion std::atomic job_pull_time_ns = 0; ///< Time spent pulling jobs from the queue. std::atomic job_process_time_ns = 0; ///< Time spent processing jobs. @@ -198,6 +217,41 @@ class QueryCoordinator { * @param d Dimensionality of the query vectors. */ void allocate_core_resources(int core_idx, int num_queries, int k, int d); + + int64_t pop_scan_job(CoreResources &res); + void process_scan_job(ScanJob job, CoreResources &res); + + // handles the non‐batched branch + void handle_nonbatched_job(const ScanJob &job, + CoreResources &res, + NUMAResources &nr); + + // handles the batched branch + void handle_batched_job(const ScanJob &job, + CoreResources &res, + NUMAResources &nr); + + // — Main‐thread helpers — + void init_global_buffers(int64_t nQ, int K, + Tensor &partition_ids, + shared_ptr params); + + void copy_query_to_numa(const float *xptr, int64_t nQ, int64_t D); + + void enqueue_scan_jobs(Tensor x, + Tensor partition_ids, + shared_ptr params); + + void drain_and_apply_aps(Tensor x, + Tensor partition_ids, + bool use_aps, + float recall_target, + float aps_flush_period_us, + shared_ptr timing); + + std::shared_ptr + aggregate_scan_results(int64_t nQ, int K, + shared_ptr timing); }; #endif //QUERY_COORDINATOR_H diff --git a/src/cpp/src/maintenance_cost_estimator.cpp b/src/cpp/src/maintenance_cost_estimator.cpp index 8fd2a9c1..d1f4d1ab 100644 --- a/src/cpp/src/maintenance_cost_estimator.cpp +++ b/src/cpp/src/maintenance_cost_estimator.cpp @@ -70,7 +70,7 @@ void ListScanLatencyEstimator::profile_scan_latency() { torch::Tensor curr_vectors = vectors.narrow(0, 0, n); torch::Tensor curr_ids = ids.narrow(0, 0, n); - auto topk_buffer = make_shared(k, false); + auto topk_buffer = make_shared(k, false, k * 100, 0); const float *query_ptr = query.data_ptr(); const float *curr_vectors_ptr = curr_vectors.data_ptr(); diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 6a7f64d4..15820c71 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -35,395 +35,309 @@ QueryCoordinator::~QueryCoordinator() { shutdown_workers(); } -void QueryCoordinator::allocate_core_resources(int core_idx, int num_queries, int k, int d) { - CoreResources &res = core_resources_[core_idx]; - res.core_id = core_idx; - res.topk_buffer_pool.resize(num_queries); - for (int q = 0; q < num_queries; ++q) { - res.topk_buffer_pool[q] = make_shared(k, metric_ == faiss::METRIC_INNER_PRODUCT); - res.job_queue = moodycamel::ConcurrentQueue(); - } - +void QueryCoordinator::allocate_core_resources(int core_idx, + int num_queries, + int k, + int d) +{ + auto& CR = core_resources_[core_idx]; + CR.core_id = core_idx; + CR.topk_buffer_pool.clear(); - int num_numa_nodes = 1; - #ifdef QUAKE_USE_NUMA - num_numa_nodes = numa_max_node() + 1; - #endif - numa_resources_.resize(num_numa_nodes); - - for (int i = 0; i < num_numa_nodes; i++) { - NUMAResources &numa_res = numa_resources_[i]; - int64_t alloc_size = num_queries * d * sizeof(float); - #ifdef QUAKE_USE_NUMA - numa_res.local_query_buffer = (float *) numa_alloc_onnode(alloc_size, i); - #else - numa_res.local_query_buffer = (float *) std::malloc(alloc_size); - #endif + int numa_node = 0; +#ifdef QUAKE_USE_NUMA + numa_node = numa_node_of_cpu(core_idx); +#endif + + // job queue remains default-constructed + numa_resources_.resize(get_num_numa_nodes()); + auto& numa_res = numa_resources_[numa_node]; + size_t bytes = size_t(num_queries) * d * sizeof(float); + if (numa_res.buffer_size != bytes) { + quake_free(numa_res.local_query_buffer, numa_res.buffer_size); + numa_res.local_query_buffer = static_cast(quake_alloc(bytes, numa_node)); + numa_res.buffer_size = bytes; } } -// Initialize Worker Threads -void QueryCoordinator::initialize_workers(int num_cores, bool use_numa) { - if (workers_initialized_) { - std::cerr << "[QueryCoordinator::initialize_workers] Workers already initialized." << std::endl; - return; +int64_t QueryCoordinator::pop_scan_job(CoreResources &res) { + int64_t jid; + while (!res.job_queue.try_dequeue(jid)) { + std::this_thread::sleep_for(std::chrono::microseconds(5)); } + return jid; +} - std::cout << "[QueryCoordinator::initialize_workers] Initializing " << num_cores << " worker threads with use_numa=" << use_numa << - std::endl; +void QueryCoordinator::partition_scan_worker_fn(int core_index) { + CoreResources &res = core_resources_[core_index]; + int numa_node = 0; +#ifdef QUAKE_USE_NUMA + numa_node = numa_node_of_cpu(core_index); +#endif + NUMAResources &nr = numa_resources_[numa_node]; - partition_manager_->distribute_partitions(num_cores, use_numa); + set_thread_affinity(core_index); - core_resources_.resize(num_cores); - worker_threads_.resize(num_cores); - worker_job_counter_.reserve(num_cores); - for (int i = 0; i < num_cores; i++) { - if (!set_thread_affinity(i)) { - std::cout << "[QueryCoordinator::initialize_workers] Failed to set thread affinity on core " << i << std::endl; - } - allocate_core_resources(i, 1, 10, partition_manager_->d()); - worker_threads_[i] = std::thread(&QueryCoordinator::partition_scan_worker_fn, this, i); - worker_job_counter_[i] = 0; + while (true) { + int64_t jid = pop_scan_job(res); + if (jid == -1) break; + process_scan_job(job_buffer_[jid], res); } - workers_initialized_ = true; - - // set main thread on separate thread from workers - int num_cores_on_machine = std::thread::hardware_concurrency(); - set_thread_affinity(num_cores % num_cores_on_machine); } -// Shutdown Worker Threads -void QueryCoordinator::shutdown_workers() { - if (!workers_initialized_) { +void QueryCoordinator::process_scan_job(ScanJob job, + CoreResources &res) { + int numa_node = 0; +#ifdef QUAKE_USE_NUMA + numa_node = numa_node_of_cpu(res.core_id); +#endif + NUMAResources &nr = numa_resources_[numa_node]; + + // fetch codes & ids + const float *codes = (float*)partition_manager_->partition_store_->get_codes(job.partition_id); + const int64_t *ids = (int64_t*)partition_manager_->partition_store_->get_ids(job.partition_id); + + int64_t part_size = 0; + try { + part_size = partition_manager_->partition_store_->list_size(job.partition_id); + } catch (...) { + std::cerr << "[partition_scan_worker_fn] Partition " << job.partition_id << " not found.\n"; return; } - stop_workers_.store(true); - // Enqueue a special shutdown job for each core. - for (auto &res : core_resources_) { - res.job_queue.enqueue(-1); - } - // Join all worker threads. - for (auto &thr : worker_threads_) { - if (thr.joinable()) - thr.join(); + if (part_size == 0) { + // empty => enqueue zero‐work per query + if (job.is_batched) { + for (int64_t q : *job.query_ids) { + result_queue_.enqueue(ResultJob{(int)q, 0, {}, {}}); + } + } else { + result_queue_.enqueue(ResultJob{job.query_id, job.rank, {}, {}}); + } + return; } - worker_threads_.clear(); - workers_initialized_ = false; -} - -void threadsafe_cout(std::string log_msg) -{ - static std::mutex lock; - std::lock_guard guard{ lock }; - std::cout << std::move(log_msg); -} - -// Worker Thread Function -void QueryCoordinator::partition_scan_worker_fn(int core_index) { - - CoreResources &res = core_resources_[core_index]; - int numa_node = 0; - #ifdef QUAKE_USE_NUMA - numa_node = numa_node_of_cpu(core_index); - #endif - NUMAResources &numa_res = numa_resources_[numa_node]; - - if (!set_thread_affinity(core_index)) { - std::cout << "[QueryCoordinator::partition_scan_worker_fn] Failed to set thread affinity on core " << core_index << std::endl; + if (!job.is_batched) { + handle_nonbatched_job(job, res, nr); + } else { + handle_batched_job(job, res, nr); } +} - int64_t total_pull_time = 0; - int64_t total_copy_time = 0; - int64_t total_scan_time = 0; - int64_t total_merge_time = 0; - - while (true) { - - auto job_wait_start = std::chrono::high_resolution_clock::now(); - int64_t job_id; - while (!res.job_queue.try_dequeue(job_id)) { - std::this_thread::sleep_for(std::chrono::microseconds(5)); +void QueryCoordinator::handle_nonbatched_job(const ScanJob &job, + CoreResources &res, + NUMAResources &nr) { + + // ensure buffers + if (res.topk_buffer_pool.size() < 1) { + res.topk_buffer_pool.resize(1); + res.topk_buffer_pool[0] = std::make_shared( + job.k, + metric_ == faiss::METRIC_INNER_PRODUCT, + /*cap=*/100 * job.k, + /*node=*/numa_node_of_cpu(res.core_id) + ); + } else if (res.topk_buffer_pool[0]->k_ != job.k) { + // check capacity + if (res.topk_buffer_pool[0]->capacity_ < job.k) { + res.topk_buffer_pool[0] = std::make_shared( + job.k, + metric_ == faiss::METRIC_INNER_PRODUCT, + /*cap=*/100 * job.k, + /*node=*/numa_node_of_cpu(res.core_id) + ); } + res.topk_buffer_pool[0]->set_k(job.k); + } - auto s0 = std::chrono::high_resolution_clock::now(); - - auto job_wait_end = std::chrono::high_resolution_clock::now(); + auto buf = res.topk_buffer_pool[0]; + res.topk_buffer_pool[0]->reset(); - job_pull_time_ns += std::chrono::duration_cast(job_wait_end - job_wait_start).count(); + scan_list(nr.local_query_buffer, + (float*)partition_manager_->partition_store_->get_codes(job.partition_id), + (int64_t*)partition_manager_->partition_store_->get_ids(job.partition_id), + partition_manager_->partition_store_->list_size(job.partition_id), + partition_manager_->d(), + *buf, + metric_); - auto job_process_start = std::chrono::high_resolution_clock::now(); - shared_ptr local_topk_buffer = res.topk_buffer_pool[0]; + auto tv = buf->get_topk(); + auto ti = buf->get_topk_indices(); + result_queue_.enqueue(ResultJob{job.query_id, job.rank, + std::move(tv), + std::move(ti)}); +} - // Shutdown signal: -1 indicates the worker should exit. - if (job_id == -1) { - break; +void QueryCoordinator::handle_batched_job(const ScanJob &job, + CoreResources &res, + NUMAResources &nr) { + // ensure buffers + if (res.topk_buffer_pool.size() < (size_t)job.num_queries) { + res.topk_buffer_pool.resize(job.num_queries); + for (int64_t i = 0; i < job.num_queries; ++i) { + res.topk_buffer_pool[i] = std::make_shared( + job.k, + metric_ == faiss::METRIC_INNER_PRODUCT, + /*cap=*/num_workers_ * job.k, + /*node=*/numa_node_of_cpu(res.core_id) + ); } - ScanJob job = job_buffer_[job_id]; - -// // Ignore this job if the global buffer is not processing queries. -// if (!global_topk_buffer_pool_[job.query_ids[0]]->currently_processing_query()) { -// // decrement the job counter -// global_topk_buffer_pool_[job.query_ids[0]]->record_empty_job(); -// continue; -// } - - worker_job_counter_[core_index]++; - - // Retrieve partition data. - const float *partition_codes = (float *) partition_manager_->partition_store_->get_codes(job.partition_id); - const int64_t *partition_ids = (int64_t *) partition_manager_->partition_store_->get_ids(job.partition_id); - - // skip partition if it is not found (workaround) - int64_t partition_size = 0; - try { - partition_size = partition_manager_->partition_store_->list_size(job.partition_id); - } catch (const std::runtime_error &e) { - std::cerr << "[QueryCoordinator::partition_scan_worker_fn] Partition " << job.partition_id << " not found." << std::endl; - continue; + // realloc batch query buffer + if (res.batch_queries != nullptr) { + quake_free(res.batch_queries, + size_t(job.num_queries) * partition_manager_->d() * sizeof(float)); } - - // skip partition if it is empty - if (partition_size == 0) { - std::cerr << "[QueryCoordinator::partition_scan_worker_fn] Partition " << job.partition_id << " is empty." << std::endl; - // call dummy batch add to decrement topk_buffer jobs counter. #TODO remove jobs counter from topk_buffer - - if (job.is_batched) { - for (int64_t q = 0; q < job.num_queries; q++) { - int global_q = job.query_ids->at(q); - global_topk_buffer_pool_[global_q]->batch_add(nullptr, nullptr, 0); - } - } else { - global_topk_buffer_pool_[job.query_id]->batch_add(nullptr, nullptr, 0); - } - - - continue; + res.batch_queries = static_cast(quake_alloc( + size_t(job.num_queries) * partition_manager_->d() * sizeof(float), + numa_node_of_cpu(res.core_id))); + + // realloc batch distances and ids + if (res.batch_distances != nullptr) { + quake_free(res.batch_distances, + size_t(job.num_queries) * job.k * sizeof(float)); + } + if (res.batch_ids != nullptr) { + quake_free(res.batch_ids, + size_t(job.num_queries) * job.k * sizeof(int64_t)); + } + res.batch_distances = static_cast(quake_alloc( + size_t(job.num_queries) * job.k * sizeof(float), + numa_node_of_cpu(res.core_id))); + res.batch_ids = static_cast(quake_alloc( + size_t(job.num_queries) * job.k * sizeof(int64_t), + numa_node_of_cpu(res.core_id))); + } else { + for (int64_t i = 0; i < job.num_queries; ++i) { + res.topk_buffer_pool[i]->set_k(job.k); + res.topk_buffer_pool[i]->reset(); } - // Branch for non-batched jobs. - if (!job.is_batched) { - - auto s1 = std::chrono::high_resolution_clock::now(); + // set batch distances and ids to max/min and -1 + for (int64_t i = 0; i < job.num_queries * job.k; ++i) { + res.batch_distances[i] = metric_ == faiss::METRIC_INNER_PRODUCT ? + -std::numeric_limits::infinity() : + std::numeric_limits::infinity(); + res.batch_ids[i] = -1; + } + } - if (local_topk_buffer == nullptr) { - throw std::runtime_error("[partition_scan_worker_fn] local_topk_buffer is null."); - } else { - local_topk_buffer->set_k(job.k); - local_topk_buffer->reset(); - } + // gather queries + for (int64_t i = 0; i < job.num_queries; ++i) { + std::memcpy(res.batch_queries + i * partition_manager_->d(), + job.query_vector + job.query_ids->at(i) * partition_manager_->d(), + partition_manager_->d() * sizeof(float)); + } - auto s2 = std::chrono::high_resolution_clock::now(); - // Perform the scan on the partition. - scan_list(numa_res.local_query_buffer, - partition_codes, - partition_ids, - partition_size, + batched_scan_list(res.batch_queries, + (float*)partition_manager_->partition_store_->get_codes(job.partition_id), + (int64_t*)partition_manager_->partition_store_->get_ids(job.partition_id), + job.num_queries, + partition_manager_->partition_store_->list_size(job.partition_id), partition_manager_->d(), - *local_topk_buffer, - metric_); - auto s3 = std::chrono::high_resolution_clock::now(); - - vector topk = local_topk_buffer->get_topk(); - vector topk_indices = local_topk_buffer->get_topk_indices(); - int64_t n_results = topk_indices.size(); - - // Merge local results into the global query buffer. - global_topk_buffer_pool_[job.query_id]->batch_add(topk.data(), topk_indices.data(), n_results); - job_flags_[job.query_id][job.rank] = true; - - auto s4 = std::chrono::high_resolution_clock::now(); - - // print out timings for worker - auto pull_time = std::chrono::duration_cast(s1 - s0).count(); - auto copy_time = std::chrono::duration_cast(s2 - s1).count(); - auto process_time = std::chrono::duration_cast(s3 - s2).count(); - auto merge_time = std::chrono::duration_cast(s4 - s3).count(); + res.topk_buffer_pool, + metric_, + res.batch_distances, + res.batch_ids); + + vector results(job.num_queries); + for (int64_t i = 0; i < job.num_queries; ++i) { + int g = job.query_ids->at(i); + auto tv = res.topk_buffer_pool[i]->get_topk(); + auto ti = res.topk_buffer_pool[i]->get_topk_indices(); + results[i] = ResultJob{g, + job.ranks->at(i), + std::move(tv), + std::move(ti)}; + } + result_queue_.enqueue_bulk(results.begin(), + job.num_queries); +} - total_pull_time += pull_time; - total_copy_time += copy_time; - total_scan_time += process_time; - total_merge_time += merge_time; +// — Main‐thread: worker_scan — + +void QueryCoordinator::init_global_buffers(int64_t nQ, + int K, + Tensor &partition_ids, + shared_ptr params) { + std::lock_guard lg(global_mutex_); + // resize or reset + if (global_topk_buffer_pool_.size() < (size_t) nQ) { + size_t old = global_topk_buffer_pool_.size(); + global_topk_buffer_pool_.resize(nQ); + for (int64_t q = old; q < nQ; ++q) { + global_topk_buffer_pool_[q] = std::make_shared( + K, + metric_ == faiss::METRIC_INNER_PRODUCT, + /*cap=*/num_workers_ * K * 2, + /*node=*/0 + ); } - // Batched job branch. - else { - if (!job.query_vector || job.num_queries == 0) { - throw std::runtime_error("[partition_scan_worker_fn] Invalid batched job."); - } - - auto s1 = std::chrono::high_resolution_clock::now(); - - int64_t d = partition_manager_->d(); - std::vector query_subset(job.num_queries * d); - for (int i = 0; i < job.num_queries; i++) { - int64_t global_q = job.query_ids->at(i); - memcpy(&query_subset[i * d], - job.query_vector + global_q * d, - d * sizeof(float)); - } - - if(memcpy(numa_res.local_query_buffer, - query_subset.data(), - query_subset.size() * sizeof(float)) == nullptr) { - throw std::runtime_error("[partition_scan_worker_fn] memcpy failed."); - } - - // Use a thread_local vector to hold one buffer per query. - if (res.topk_buffer_pool.size() < static_cast(job.num_queries)) { - res.topk_buffer_pool.resize(job.num_queries); - for (int64_t q = 0; q < job.num_queries; ++q) { - res.topk_buffer_pool[q] = std::make_shared(job.k, metric_ == faiss::METRIC_INNER_PRODUCT); - } - } else { - for (int64_t q = 0; q < job.num_queries; ++q) { - res.topk_buffer_pool[q]->set_k(job.k); - res.topk_buffer_pool[q]->reset(); - } - } - - auto s2 = std::chrono::high_resolution_clock::now(); - - // Process the batched job. - batched_scan_list(numa_res.local_query_buffer, - partition_codes, - partition_ids, - job.num_queries, - partition_size, - partition_manager_->d(), - res.topk_buffer_pool, - metric_); - - auto s3 = std::chrono::high_resolution_clock::now(); - - for (int64_t q = 0; q < job.num_queries; q++) { - int64_t global_q = job.query_ids->at(q); - - vector topk = res.topk_buffer_pool[q]->get_topk(); - vector topk_indices = res.topk_buffer_pool[q]->get_topk_indices(); - int n_results = topk_indices.size(); - - global_topk_buffer_pool_[global_q]->batch_add(topk.data(), topk_indices.data(), n_results); - } - - auto s4 = std::chrono::high_resolution_clock::now(); - - // print out timings for worker - auto pull_time = std::chrono::duration_cast(s1 - s0).count(); - auto copy_time = std::chrono::duration_cast(s2 - s1).count(); - auto process_time = std::chrono::duration_cast(s3 - s2).count(); - auto merge_time = std::chrono::duration_cast(s4 - s3).count(); - - total_pull_time += pull_time; - total_copy_time += copy_time; - total_scan_time += process_time; - total_merge_time += merge_time; + } else { + for (int64_t q = 0; q < nQ; ++q) { + global_topk_buffer_pool_[q]->set_k(K); + global_topk_buffer_pool_[q]->reset(); } - auto job_process_end = std::chrono::high_resolution_clock::now(); - job_process_time_ns += std::chrono::duration_cast(job_process_end - job_process_start).count(); } - std::string log_message = - "[QueryCoordinator::partition_scan_worker_fn] Worker " + std::to_string(core_index) + - ": total_pull_time=" + std::to_string(total_pull_time / 1000) + - ", total_copy_time=" + std::to_string(total_copy_time / 1000) + - ", total_scan_time=" + std::to_string(total_scan_time / 1000) + - ", merge_time=" + std::to_string(total_merge_time / 1000) + '\n'; - - threadsafe_cout(log_message); } -// Worker-Based Scan Implementation -shared_ptr QueryCoordinator::worker_scan( - Tensor x, - Tensor partition_ids, - shared_ptr search_params) { - - auto start_time = high_resolution_clock::now(); - - if (!partition_manager_) { - throw std::runtime_error("[QueryCoordinator::worker_scan] partition_manager_ is null."); - } - - if (!x.defined() || x.size(0) == 0) { - auto empty_result = std::make_shared(); - empty_result->ids = torch::empty({0}, torch::kInt64); - empty_result->distances = torch::empty({0}, torch::kFloat32); - empty_result->timing_info = std::make_shared(); - return empty_result; - } - - int64_t num_queries = x.size(0); - int64_t dimension = x.size(1); - int k = search_params->k; - int64_t nlist = partition_manager_->nlist(); - bool use_aps = (search_params->recall_target > 0.0 && !search_params->batched_scan && parent_ != nullptr); - auto timing_info = make_shared(); - timing_info->n_queries = num_queries; - timing_info->n_clusters = nlist; - timing_info->search_params = search_params; +void QueryCoordinator::copy_query_to_numa(const float *xptr, int64_t nQ, int64_t D) { - float *x_ptr = x.data_ptr(); - - if (partition_ids.dim() == 1) { - partition_ids = partition_ids.unsqueeze(0).expand({num_queries, partition_ids.size(0)}); + for (int node = 0; node < get_num_numa_nodes(); ++node) { + auto &nr = numa_resources_[node]; + if (nr.buffer_size < size_t(nQ) * size_t(D) * sizeof(float)) { + quake_free(nr.local_query_buffer, nr.buffer_size); + nr.local_query_buffer = static_cast(quake_alloc( + size_t(nQ) * size_t(D) * sizeof(float), + node)); + nr.buffer_size = size_t(nQ) * size_t(D) * sizeof(float); + } + std::memcpy(nr.local_query_buffer, + xptr, + size_t(nQ) * size_t(D) * sizeof(float)); } +} - auto partition_ids_accessor = partition_ids.accessor(); +void QueryCoordinator::enqueue_scan_jobs(Tensor x, + Tensor partition_ids, + shared_ptr params) +{ + int64_t nQ = x.size(0), D = x.size(1); + float *xptr = x.data_ptr(); -// job_buffer_.clear(); - next_job_id_.store(0); + // flatten jobs + next_job_id_ = 0; job_flags_.clear(); - job_flags_.resize(num_queries); - for (int64_t q = 0; q < num_queries; q++) { + job_flags_.resize(nQ); + for (int64_t q = 0; q < nQ; ++q) { job_flags_[q] = vector>(partition_ids.size(1)); - for (int64_t p = 0; p < partition_ids.size(1); p++) { + for (int p = 0; p < partition_ids.size(1); ++p) { job_flags_[q][p] = false; } } + job_buffer_.clear(); - job_pull_time_ns = 0; - job_process_time_ns = 0; - - { - std::lock_guard lock(global_mutex_); - if (global_topk_buffer_pool_.size() < static_cast(num_queries)) { - std::cout << "Resizing query_topk_buffers_ from " << global_topk_buffer_pool_.size() - << " to " << num_queries << std::endl; - int old_size = global_topk_buffer_pool_.size(); - global_topk_buffer_pool_.resize(num_queries); - for (int64_t q = old_size; q < num_queries; q++) { - global_topk_buffer_pool_[q] = std::make_shared(k, metric_ == faiss::METRIC_INNER_PRODUCT); - } - } else { - for (int64_t q = 0; q < num_queries; q++) { - global_topk_buffer_pool_[q]->set_k(k); - global_topk_buffer_pool_[q]->reset(); - global_topk_buffer_pool_[q]->set_processing_query(true); + auto pid_acc = partition_ids.accessor(); + if (!params->batched_scan) { + // one job per (q,p) + for (int64_t q = 0; q < nQ; ++q) { + const float* qptr = xptr + q*D; + for (int p = 0; p < partition_ids.size(1); ++p) { + int64_t pid = pid_acc[q][p]; + if (pid < 0) continue; + ScanJob job; + job.is_batched = false; + job.query_id = (int)q; + job.partition_id = pid; + job.k = params->k; + job.query_vector = qptr; + job.rank = p; + size_t jid = next_job_id_++; + job_buffer_.push_back(job); + core_resources_[pid % num_workers_].job_queue.enqueue((int64_t)jid); } } - for (int64_t q = 0; q < num_queries; ++q) { - int valid = 0; - for (int64_t p = 0; p < partition_ids.size(1); ++p) - if (partition_ids_accessor[q][p] != -1) ++valid; - - global_topk_buffer_pool_[q]->set_jobs_left(valid); - } - } - auto end_time = high_resolution_clock::now(); - timing_info->buffer_init_time_ns = - duration_cast(end_time - start_time).count(); - - start_time = high_resolution_clock::now(); - - for (auto &nr : numa_resources_) { - std::memcpy(nr.local_query_buffer, - x_ptr, // q == 0 - size_t(dimension) * sizeof(float)); - } - - if (search_params->batched_scan) { - auto partition_ids_accessor = partition_ids.accessor(); + } else { job_buffer_.resize(partition_manager_->nlist()); auto pids = partition_manager_->get_partition_ids(); auto pids_acc = pids.accessor(); @@ -436,207 +350,256 @@ shared_ptr QueryCoordinator::worker_scan( ScanJob job; job.is_batched = true; job.partition_id = pids_acc[i]; - job.k = k; + job.k = params->k; job.query_vector = x.data_ptr(); job.num_queries = x.size(0); job.query_ids = all_query_ids_ptr; + job.ranks = make_shared>(x.size(0), i); int core_id = partition_manager_->get_partition_core_id(pids_acc[i]); if (core_id < 0) { throw std::runtime_error("[QueryCoordinator::worker_scan] Invalid core ID."); } - size_t jid = next_job_id_.fetch_add(1, std::memory_order_acq_rel); + size_t jid = next_job_id_++; job_buffer_[jid] = job; core_resources_[core_id].job_queue.enqueue(jid); } } else { - std::unordered_map>> per_partition_query_ids; // for batched scan - for (int64_t q = 0; q < num_queries; q++) { + std::unordered_map>>> per_partition_query_ids; // for batched scan + for (int64_t q = 0; q < nQ; q++) { for (int64_t p = 0; p < partition_ids.size(1); p++) { - int64_t pid = partition_ids_accessor[q][p]; + int64_t pid = pid_acc[q][p]; if (pid < 0) continue; if (per_partition_query_ids[pid] == nullptr) { - per_partition_query_ids[pid] = make_shared>(); + per_partition_query_ids[pid] = make_shared>>(); } - per_partition_query_ids[pid]->push_back(q); + per_partition_query_ids[pid]->push_back({q, p}); } } for (auto &kv : per_partition_query_ids) { + + auto qids_and_ranks = kv.second; + vector qids(qids_and_ranks->size()); + vector ranks(qids_and_ranks->size()); + for (size_t i = 0; i < qids_and_ranks->size(); ++i) { + qids[i] = (*qids_and_ranks)[i].first; + ranks[i] = (*qids_and_ranks)[i].second; + } + ScanJob job; job.is_batched = true; job.partition_id = kv.first; - job.k = k; + job.k = params->k; job.query_vector = x.data_ptr(); job.num_queries = kv.second->size(); - job.query_ids = kv.second; + job.query_ids = make_shared>(qids); + job.ranks = make_shared>(ranks); int core_id = partition_manager_->get_partition_core_id(kv.first); if (core_id < 0) { throw std::runtime_error("[QueryCoordinator::worker_scan] Invalid core ID."); } - size_t jid = next_job_id_.fetch_add(1, std::memory_order_acq_rel); + size_t jid = next_job_id_++; job_buffer_[jid] = job; core_resources_[core_id].job_queue.enqueue(jid); } } - } else { - auto partition_ids_accessor = partition_ids.accessor(); - - job_buffer_.resize(num_queries * partition_ids.size(1)); - int jid = 0; - for (int q = 0; q < num_queries; q++) { - for (int64_t p = 0; p < partition_ids.size(1); p++) { - int64_t pid = partition_ids_accessor[q][p]; - if (pid == -1) continue; - - ScanJob job; - job.is_batched = false; - job.query_id = q; - job.partition_id = pid; - job.k = k; - job.query_vector = x_ptr + q * dimension; - job.num_queries = 1; - job.rank = p; - - int core_id = pid % num_workers_; - job_buffer_[jid] = std::move(job); - core_resources_[core_id].job_queue.enqueue(jid); - jid++; - } - } } - auto topk_ids = torch::full({num_queries, k}, -1, torch::kInt64); - auto topk_dists = torch::full({num_queries, k}, - (metric_ == faiss::METRIC_INNER_PRODUCT) - ? -std::numeric_limits::infinity() - : std::numeric_limits::infinity(), - torch::kFloat32); - end_time = high_resolution_clock::now(); - timing_info->job_enqueue_time_ns = duration_cast(end_time - start_time).count(); - - auto last_flush_time = high_resolution_clock::now(); - vector> boundary_distances(num_queries); - if (use_aps && parent_) { - for (int64_t q = 0; q < num_queries; q++) { - vector partition_ids_to_scan_vec = vector(partition_ids[q].data_ptr(), - partition_ids[q].data_ptr() + partition_ids[q].size(0)); +} + +void QueryCoordinator::drain_and_apply_aps(Tensor x, + Tensor partition_ids, + bool use_aps, + float recall_target, + float aps_flush_period_us, + shared_ptr timing) +{ + // precompute boundary distances if needed + int nQ = x.size(0); + int D = x.size(1); + int nprobe = partition_ids.size(1); + vector> boundary_dist(nQ); + vector query_radius(nQ, 0.0f); + vector> probs(nQ); + if (use_aps) { + + for (int64_t q = 0; q < nQ; ++q) { + vector partition_ids_to_scan_vec = std::vector(partition_ids[q].data_ptr(), + partition_ids[q].data_ptr() + partition_ids[q].size(0)); vector cluster_centroids = parent_->partition_manager_->get_vectors(partition_ids_to_scan_vec); - boundary_distances[q] = compute_boundary_distances(x[q], - cluster_centroids, - metric_ == faiss::METRIC_L2); + boundary_dist[q] = compute_boundary_distances(x[q], cluster_centroids, metric_ == faiss::METRIC_L2); } + } - start_time = high_resolution_clock::now(); - last_flush_time = high_resolution_clock::now(); + auto last_flush = high_resolution_clock::now(); - vector query_radius(num_queries, 0.0f); - vector> probs(num_queries); while (true) { - // check if all jobs have been processed + // 1) drain all ready results + ResultJob rj; + while (result_queue_.try_dequeue(rj)) { + global_topk_buffer_pool_[rj.query_id] + ->batch_add(rj.distances.data(), + rj.indices.data(), + (int)rj.indices.size()); + + job_flags_[rj.query_id][rj.rank] = true; + } + // 2) check done bool all_done = true; - for (int64_t q = 0; q < num_queries; q++) { - if (global_topk_buffer_pool_[q]->jobs_left_ == 0) { - // flush the topk buffer (if needed) - global_topk_buffer_pool_[q]->flush(); - } else { - all_done = false; + for (int64_t q = 0; q < nQ; ++q) { + for (int64_t p = 0; p < nprobe; ++p) { + if (!job_flags_[q][p]) { + all_done = false; + break; + } } } - - if (all_done) { - break; - } - - // check if recall target has been reached - if (use_aps && duration_cast(high_resolution_clock::now() - last_flush_time).count() - > search_params->aps_flush_period_us) { - for (int64_t q = 0; q < num_queries; q++) { - auto curr_buffer = global_topk_buffer_pool_[q]; - int scanned = curr_buffer->get_num_partitions_scanned(); - if (curr_buffer->currently_processing_query() && - scanned > 0 && scanned < (int) boundary_distances[q].size()) { - float radius = curr_buffer->get_kth_distance(); - - if (query_radius[q] != radius) { - query_radius[q] = radius; - - // recompute recall profile if the radius has changed - probs[q] = compute_recall_profile(boundary_distances[q], - radius, - dimension, - {}, - search_params->use_precomputed, - metric_ == faiss::METRIC_L2); + if (all_done) break; + + // 3) APS early-stop + if (use_aps && duration_cast(high_resolution_clock::now() - last_flush).count() + > aps_flush_period_us) + { + for (int64_t q = 0; q < nQ; ++q) { + auto buf = global_topk_buffer_pool_[q]; + float r = buf->get_kth_distance(); + if (r != query_radius[q]) { + query_radius[q] = r; + probs[q] = compute_recall_profile( + boundary_dist[q], + r, + D, + {}, + true, + metric_==faiss::METRIC_L2 + ); } - - float cum = 0.0f; - for (int i = 0; i < partition_ids.size(1); i++) { - if (job_flags_[q][i]) { - cum += probs[q][i]; - } - } - if (cum > search_params->recall_target) { - curr_buffer->set_processing_query(false); + float cum=0; + for (int i=0; i<(int)job_flags_[q].size(); ++i) + if (job_flags_[q][i]) cum += probs[q][i]; + if (cum > recall_target) { + // set all jobs to done + for (int i=0; i<(int)job_flags_[q].size(); ++i) + job_flags_[q][i] = true; } } } - last_flush_time = high_resolution_clock::now(); - } - std::this_thread::sleep_for(microseconds(5)); + last_flush = high_resolution_clock::now(); + std::this_thread::sleep_for(std::chrono::microseconds(5)); } - end_time = high_resolution_clock::now(); - timing_info->job_wait_time_ns = - duration_cast(end_time - start_time).count(); - - // Aggregate results. - start_time = high_resolution_clock::now(); - auto ids_accessor = topk_ids.accessor(); - auto dists_accessor = topk_dists.accessor(); +} + +std::shared_ptr +QueryCoordinator::aggregate_scan_results(int64_t nQ, + int K, + shared_ptr timing) +{ + // build output tensors + auto out_ids = torch::full({nQ,K}, -1, torch::kInt64); + auto out_dists = torch::full({nQ,K}, + (metric_==faiss::METRIC_INNER_PRODUCT + ? -std::numeric_limits::infinity() + : std::numeric_limits::infinity()), + torch::kFloat32); + { - std::lock_guard lock(global_mutex_); - for (int64_t q = 0; q < num_queries; q++) { - auto topk = global_topk_buffer_pool_[q]->get_topk(); - auto ids = global_topk_buffer_pool_[q]->get_topk_indices(); - for (int i = 0; i < k; i++) { - if (i < (int) ids.size()) { - ids_accessor[q][i] = ids[i]; - dists_accessor[q][i] = topk[i]; - } else { - ids_accessor[q][i] = -1; - dists_accessor[q][i] = (metric_ == faiss::METRIC_INNER_PRODUCT) - ? -std::numeric_limits::infinity() - : std::numeric_limits::infinity(); - } + std::lock_guard lg(global_mutex_); + auto id_acc = out_ids.accessor(); + auto d_acc = out_dists.accessor(); + for (int64_t q = 0; q < nQ; ++q) { + auto tv = global_topk_buffer_pool_[q]->get_topk(); + auto ti = global_topk_buffer_pool_[q]->get_topk_indices(); + for (int i = 0; i < (int)ti.size() && i < K; ++i) { + id_acc[q][i] = ti[i]; + d_acc [q][i] = tv[i]; } - timing_info->partitions_scanned += global_topk_buffer_pool_[q]->get_num_partitions_scanned(); + } + } - if (search_params->track_hits && maintenance_policy_ != nullptr) { - vector partition_ids_vec; - for (int64_t p = 0; p < partition_ids.size(1); p++) { - if (job_flags_[q][p]) { - partition_ids_vec.push_back(partition_ids_accessor[q][p]); - } - } - maintenance_policy_->record_query_hits(partition_ids_vec); - } + auto res = std::make_shared(); + res->ids = out_ids; + res->distances = out_dists; + res->timing_info = timing; + return res; +} + +std::shared_ptr QueryCoordinator::worker_scan( + Tensor x, + Tensor partition_ids, + std::shared_ptr params) +{ + int64_t nQ = x.size(0), D = x.size(1); + int K = params->k; + bool use_aps = (params->recall_target>0 && !params->batched_scan && parent_); + + auto timing = std::make_shared(); + timing->n_queries = nQ; + timing->n_clusters = partition_manager_->nlist(); + timing->search_params = params; + + // 1) init global buffers & jobs_left + init_global_buffers(nQ, K, partition_ids, params); + + // 2) copy query vec to NUMA buffers + copy_query_to_numa(x.data_ptr(), nQ, D); + + // 3) enqueue jobs + enqueue_scan_jobs(x, partition_ids, params); + + // 4) drain results + APS + drain_and_apply_aps(x, partition_ids, use_aps, params->recall_target, + params->aps_flush_period_us, timing); + + // 5) aggregate & return + return aggregate_scan_results(nQ, K, timing); +} + +// Initialize Worker Threads +void QueryCoordinator::initialize_workers(int num_cores, bool use_numa) { + if (workers_initialized_) { + std::cerr << "[QueryCoordinator::initialize_workers] Workers already initialized." << std::endl; + return; + } + + std::cout << "[QueryCoordinator::initialize_workers] Initializing " << num_cores << " worker threads with use_numa=" << use_numa << + std::endl; + + partition_manager_->distribute_partitions(num_cores, use_numa); + + core_resources_.resize(num_cores); + worker_threads_.resize(num_cores); + for (int i = 0; i < num_cores; i++) { + if (!set_thread_affinity(i)) { + std::cout << "[QueryCoordinator::initialize_workers] Failed to set thread affinity on core " << i << std::endl; } + allocate_core_resources(i, 1, 10, partition_manager_->d()); + worker_threads_[i] = std::thread(&QueryCoordinator::partition_scan_worker_fn, this, i); } - end_time = high_resolution_clock::now(); - timing_info->result_aggregate_time_ns = duration_cast(end_time - start_time).count(); - auto search_result = std::make_shared(); - search_result->ids = topk_ids; - search_result->distances = topk_dists; - search_result->timing_info = timing_info; + workers_initialized_ = true; + + // set main thread on separate thread from workers + int num_cores_on_machine = std::thread::hardware_concurrency(); + set_thread_affinity(num_cores % num_cores_on_machine); +} -// // print out timing info -// std::cout << "[QueryCoordinator::worker_scan] Timing info: " << std::endl; -// std::cout << "Total time: " << timing_info->total_time_ns / 1e6 << " ms" << std::endl; -// std::cout << "Job enqueue time: " << timing_info->job_enqueue_time_ns / 1e6 << " ms" << std::endl; -// std::cout << "Job wait time: " << timing_info->job_wait_time_ns / 1e6 << " ms" << std::endl; -// std::cout << "Buffer init time: " << timing_info->buffer_init_time_ns / 1e6 << " ms" << std::endl; -// std::cout << "Result agg time: " << timing_info->result_aggregate_time_ns / 1e6 << " ms" << std::endl; +// Shutdown Worker Threads +void QueryCoordinator::shutdown_workers() { + if (!workers_initialized_) { + return; + } - return search_result; + stop_workers_.store(true); + // Enqueue a special shutdown job for each core. + for (auto &res : core_resources_) { + res.job_queue.enqueue(-1); + } + // Join all worker threads. + for (auto &thr : worker_threads_) { + if (thr.joinable()) + thr.join(); + } + worker_threads_.clear(); + workers_initialized_ = false; } shared_ptr QueryCoordinator::serial_scan(Tensor x, Tensor partition_ids, @@ -688,7 +651,9 @@ shared_ptr QueryCoordinator::serial_scan(Tensor x, Tensor partitio auto t1 = high_resolution_clock::now(); - auto topk_buf = std::make_shared(k, is_descending); + auto topk_buf = std::make_shared(k, is_descending, + /*cap=*/10 * k, + /*node=*/0); const float* query_vec = x_ptr + q * dimension; int num_parts = partition_ids.size(1); @@ -902,6 +867,13 @@ shared_ptr QueryCoordinator::search(Tensor x, shared_ptr QueryCoordinator::scan_partitions(Tensor x, Tensor partition_ids, shared_ptr search_params) { + + if (partition_ids.dim() == 0) { + throw std::runtime_error("[QueryCoordinator::scan_partitions] partition_ids is empty."); + } + if (partition_ids.dim() == 1) { + partition_ids = partition_ids.unsqueeze(0).expand({x.size(0), partition_ids.size(0)}); + } if (workers_initialized_) { if (debug_) std::cout << "[QueryCoordinator::scan_partitions] Using worker-based scan." << std::endl; return worker_scan(x, partition_ids, search_params); diff --git a/test/cpp/benchmark.cpp b/test/cpp/benchmark.cpp index 3357086a..f4b8d380 100644 --- a/test/cpp/benchmark.cpp +++ b/test/cpp/benchmark.cpp @@ -29,7 +29,7 @@ using torch::Tensor; static const int64_t DIM = 128; static const int64_t NUM_VECTORS = 100000; // number of database vectors static const int64_t N_LIST = 1000; // number of clusters for IVF -static const int64_t NUM_QUERIES = 1000; // number of queries for search benchmark +static const int64_t NUM_QUERIES = 10; // number of queries for search benchmark static const int64_t K = 10; // top-K neighbors static const int64_t N_PROBE = 20; // number of probes for IVF static const int64_t N_WORKERS = 12; // number of workers for parallel query coordinator diff --git a/test/cpp/latency_estimator.cpp b/test/cpp/latency_estimator.cpp index 80ed570a..495ec149 100644 --- a/test/cpp/latency_estimator.cpp +++ b/test/cpp/latency_estimator.cpp @@ -19,7 +19,7 @@ static float measure_actual_latency(const ListScanLatencyEstimator& estimator, torch::Tensor ids = torch::randperm(n); torch::Tensor query = torch::rand({estimator.d_}); - auto topk_buffer = make_shared(k, false); + auto topk_buffer = make_shared(k, false, k * 100, 0); const float* query_ptr = query.data_ptr(); const float* vectors_ptr = vectors.data_ptr(); diff --git a/test/cpp/list_scanning.cpp b/test/cpp/list_scanning.cpp index 5c5e82d8..518972e0 100644 --- a/test/cpp/list_scanning.cpp +++ b/test/cpp/list_scanning.cpp @@ -14,7 +14,7 @@ class ListScanningTest : public ::testing::Test { protected: // Helper function to create a TopkBuffer std::shared_ptr create_buffer(int k, bool is_descending) { - return std::make_shared>(k, is_descending); + return std::make_shared>(k, is_descending, k * 100, 0); } }; @@ -448,7 +448,7 @@ TEST_F(ListScanningTest, LargeListCorrectnessInnerProduct) { auto gt_dists_accessor = gt_dists.accessor(); // perform single query scan first - auto buffer = make_shared(k, true); + auto buffer = make_shared(k, true, k * 100, 0); for (int i = 0; i < num_queries; i++) { scan_list( query_vectors[i].data_ptr(), @@ -514,7 +514,7 @@ TEST_F(ListScanningTest, LargeListCorrectnessL2) { auto gt_dists_accessor = gt_dists.accessor(); // perform single query scan first - auto buffer = make_shared(k, false); + auto buffer = make_shared(k, false, k * 100, 0); for (int i = 0; i < num_queries; i++) { scan_list( query_vectors[i].data_ptr(), diff --git a/test/cpp/query_coordinator.cpp b/test/cpp/query_coordinator.cpp index d272f2d0..0ed914b2 100644 --- a/test/cpp/query_coordinator.cpp +++ b/test/cpp/query_coordinator.cpp @@ -169,34 +169,6 @@ TEST_F(QueryCoordinatorTest, WorkerInitializationTest) { ASSERT_TRUE(coordinator->workers_initialized_); } -TEST_F(QueryCoordinatorTest, FlatWorkerScan) { - int num_workers = 4; - - // create flat index - auto flat_index = std::make_shared(); - auto build_params = std::make_shared(); - build_params->nlist = 1; - build_params->metric = "l2"; - flat_index->build(torch::randn({20, dimension_}), torch::arange(20), build_params); - - // create coordinator with workers - auto coordinator = std::make_shared( - flat_index->parent_, - flat_index->partition_manager_, - nullptr, - faiss::METRIC_L2, - num_workers - ); - - auto search_params = std::make_shared(); - search_params->k = 2; - - auto result_worker = coordinator->search(queries_, search_params); - ASSERT_TRUE(result_worker != nullptr); - ASSERT_EQ(result_worker->ids.sizes(), (std::vector{queries_.size(0), search_params->k})); - ASSERT_EQ(result_worker->distances.sizes(), (std::vector{queries_.size(0), search_params->k})); -} - // Test that worker-based scan produces the same results as serial scan TEST_F(QueryCoordinatorTest, WorkerScanCorrectnessTest) { // Initialize QueryCoordinator with workers diff --git a/test/cpp/topk_buffer.cpp b/test/cpp/topk_buffer.cpp index c2afaaec..88225a65 100644 --- a/test/cpp/topk_buffer.cpp +++ b/test/cpp/topk_buffer.cpp @@ -13,7 +13,7 @@ class TypedTopKBufferTest : public ::testing::Test { protected: // Helper function to create a TopkBuffer std::shared_ptr create_buffer(int k, bool is_descending) { - return std::make_shared>(k, is_descending); + return std::make_shared>(k, is_descending, k* 100, 0); } }; From e4ba407202f5e7ed08b7812a8eb1a596b5b273ee Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 02:18:56 -0500 Subject: [PATCH 155/323] NUMA allocation for topk buffer --- src/cpp/src/query_coordinator.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 15820c71..13a5f423 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -78,7 +78,7 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { set_thread_affinity(core_index); - while (true) { + while (!stop_workers_) { int64_t jid = pop_scan_job(res); if (jid == -1) break; process_scan_job(job_buffer_[jid], res); From 629f30b856fbc819228ab59954473e0bc8791204 Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 02:25:51 -0500 Subject: [PATCH 156/323] NUMA allocation for topk buffer --- src/cpp/include/list_scanning.h | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/src/cpp/include/list_scanning.h b/src/cpp/include/list_scanning.h index 6aa4359d..3696d387 100644 --- a/src/cpp/include/list_scanning.h +++ b/src/cpp/include/list_scanning.h @@ -190,15 +190,19 @@ class TypedTopKBuffer { } else { miniselect::pdqpartial_sort_branchless(ord_, ord_ + m, ord_ + n, cmpIdx); } + + } else { + miniselect::pdqsort_branchless(ord_, ord_ + n, cmpIdx); } - // 3) sort the top‐m prefix + + // 4) fully sort the top-m indices so they are in strictly correct order miniselect::pdqsort_branchless(ord_, ord_ + m, cmpIdx); - // 4) copy the m winners back to front of vals_/ids_ + // 5) copy the winners back to vals_/ids_ and clamp head_ for (int i = 0; i < m; ++i) { int idx = ord_[i]; vals_[i] = vals_[idx]; - ids_[i] = ids_[idx]; + ids_ [i] = ids_ [idx]; } // 5) clamp head_ and return the k-th value (or extreme if too few) From 3e67865cfa3aa8bc9cf1a5263dfdf1afa7e774d3 Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 02:33:41 -0500 Subject: [PATCH 157/323] NUMA allocation for topk buffer --- src/cpp/include/list_scanning.h | 4 ++-- src/cpp/src/query_coordinator.cpp | 16 ++++++++-------- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/src/cpp/include/list_scanning.h b/src/cpp/include/list_scanning.h index 3696d387..c2a7d5c2 100644 --- a/src/cpp/include/list_scanning.h +++ b/src/cpp/include/list_scanning.h @@ -199,10 +199,10 @@ class TypedTopKBuffer { miniselect::pdqsort_branchless(ord_, ord_ + m, cmpIdx); // 5) copy the winners back to vals_/ids_ and clamp head_ - for (int i = 0; i < m; ++i) { + for (int i = m - 1; i >= 0; --i) { int idx = ord_[i]; vals_[i] = vals_[idx]; - ids_ [i] = ids_ [idx]; + ids_[i] = ids_[idx]; } // 5) clamp head_ and return the k-th value (or extreme if too few) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 13a5f423..b548990c 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -332,9 +332,9 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, job.k = params->k; job.query_vector = qptr; job.rank = p; - size_t jid = next_job_id_++; job_buffer_.push_back(job); - core_resources_[pid % num_workers_].job_queue.enqueue((int64_t)jid); + core_resources_[pid % num_workers_].job_queue.enqueue(next_job_id_); + next_job_id_++; } } } else { @@ -359,9 +359,9 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, if (core_id < 0) { throw std::runtime_error("[QueryCoordinator::worker_scan] Invalid core ID."); } - size_t jid = next_job_id_++; - job_buffer_[jid] = job; - core_resources_[core_id].job_queue.enqueue(jid); + job_buffer_[next_job_id_] = job; + core_resources_[core_id].job_queue.enqueue(next_job_id_); + next_job_id_++; } } else { @@ -398,9 +398,9 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, if (core_id < 0) { throw std::runtime_error("[QueryCoordinator::worker_scan] Invalid core ID."); } - size_t jid = next_job_id_++; - job_buffer_[jid] = job; - core_resources_[core_id].job_queue.enqueue(jid); + job_buffer_[next_job_id_] = job; + core_resources_[core_id].job_queue.enqueue(next_job_id_); + next_job_id_++; } } } From 3ba5d6f34d740c639cb0d3a82941cce2ff36fb36 Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 02:38:33 -0500 Subject: [PATCH 158/323] NUMA allocation for topk buffer --- src/cpp/src/query_coordinator.cpp | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index b548990c..1a07a1f1 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -305,6 +305,8 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, int64_t nQ = x.size(0), D = x.size(1); float *xptr = x.data_ptr(); + auto partition_ids_acc = partition_ids.accessor(); + // flatten jobs next_job_id_ = 0; job_flags_.clear(); @@ -313,6 +315,7 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, job_flags_[q] = vector>(partition_ids.size(1)); for (int p = 0; p < partition_ids.size(1); ++p) { job_flags_[q][p] = false; + if (partition_ids_acc[q][p] < 0) job_flags_[q][p] = true; } } job_buffer_.clear(); From 1911d9b72ac08e7c72d5e132154e170d89d13ddc Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 03:07:31 -0500 Subject: [PATCH 159/323] NUMA allocation for topk buffer --- src/cpp/src/query_coordinator.cpp | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 1a07a1f1..771949f2 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -102,6 +102,7 @@ void QueryCoordinator::process_scan_job(ScanJob job, part_size = partition_manager_->partition_store_->list_size(job.partition_id); } catch (...) { std::cerr << "[partition_scan_worker_fn] Partition " << job.partition_id << " not found.\n"; + result_queue_.enqueue(ResultJob{job.query_id, job.rank, {}, {}}); return; } @@ -307,6 +308,8 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, auto partition_ids_acc = partition_ids.accessor(); + std::cout << "[QueryCoordinator::enqueue_scan_jobs] Enqueueing scan jobs...\n"; + // flatten jobs next_job_id_ = 0; job_flags_.clear(); From 3631109f068c1c280c35ba81fd821b8738aa41a3 Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 03:28:04 -0500 Subject: [PATCH 160/323] NUMA allocation for topk buffer --- src/cpp/include/query_coordinator.h | 2 +- src/cpp/src/query_coordinator.cpp | 172 +++++++++++++++++++--------- 2 files changed, 120 insertions(+), 54 deletions(-) diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index 56628210..324a5bde 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -92,7 +92,7 @@ class QueryCoordinator { bool debug_ = false; ///< Debug mode flag. std::vector job_buffer_; - int next_job_id_{0}; + int next_job_id_ = 0; ///< ID for the next job to be processed. vector>> job_flags_; ///< Flags to track job completion std::atomic job_pull_time_ns = 0; ///< Time spent pulling jobs from the queue. std::atomic job_process_time_ns = 0; ///< Time spent processing jobs. diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 771949f2..fdb5001e 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -60,14 +60,6 @@ void QueryCoordinator::allocate_core_resources(int core_idx, } } -int64_t QueryCoordinator::pop_scan_job(CoreResources &res) { - int64_t jid; - while (!res.job_queue.try_dequeue(jid)) { - std::this_thread::sleep_for(std::chrono::microseconds(5)); - } - return jid; -} - void QueryCoordinator::partition_scan_worker_fn(int core_index) { CoreResources &res = core_resources_[core_index]; int numa_node = 0; @@ -78,9 +70,15 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { set_thread_affinity(core_index); + int64_t jid; while (!stop_workers_) { - int64_t jid = pop_scan_job(res); + + if (!res.job_queue.try_dequeue(jid)) { + std::this_thread::sleep_for(std::chrono::microseconds(5)); + continue; + } if (jid == -1) break; + process_scan_job(job_buffer_[jid], res); } } @@ -154,19 +152,47 @@ void QueryCoordinator::handle_nonbatched_job(const ScanJob &job, auto buf = res.topk_buffer_pool[0]; res.topk_buffer_pool[0]->reset(); - scan_list(nr.local_query_buffer, - (float*)partition_manager_->partition_store_->get_codes(job.partition_id), - (int64_t*)partition_manager_->partition_store_->get_ids(job.partition_id), - partition_manager_->partition_store_->list_size(job.partition_id), - partition_manager_->d(), - *buf, - metric_); - - auto tv = buf->get_topk(); - auto ti = buf->get_topk_indices(); - result_queue_.enqueue(ResultJob{job.query_id, job.rank, - std::move(tv), - std::move(ti)}); + try { + // It's good practice to re-fetch or validate partition data here if it could + // have changed since process_scan_job's initial checks, especially with maintenance. + const float* codes = (float*)partition_manager_->partition_store_->get_codes(job.partition_id); + const int64_t* ids = (int64_t*)partition_manager_->partition_store_->get_ids(job.partition_id); + int64_t part_size = partition_manager_->partition_store_->list_size(job.partition_id); + int D = partition_manager_->d(); + + // Defensive check for partition validity right before scan + if (!codes || !ids || part_size <= 0) { + std::cerr << "[QueryCoordinator::handle_nonbatched_job] Partition " << job.partition_id + << " invalid or empty before scan for query " << job.query_id + << ". Enqueuing empty result.\n"; + result_queue_.enqueue(ResultJob{job.query_id, job.rank, {}, {}}); + return; // Important to return after enqueueing the placeholder + } + + scan_list(nr.local_query_buffer, // Or job.query_vector depending on NUMA strategy + codes, + ids, + part_size, + D, + *buf, + metric_); + + // If scan_list completes, enqueue its results + auto tv = buf->get_topk(); + auto ti = buf->get_topk_indices(); + result_queue_.enqueue(ResultJob{job.query_id, job.rank, std::move(tv), std::move(ti)}); + + } catch (const std::exception& e) { + std::cerr << "[QueryCoordinator::handle_nonbatched_job] Exception during scan for partition " + << job.partition_id << ", query " << job.query_id << ": " << e.what() + << ". Enqueuing empty result.\n"; + result_queue_.enqueue(ResultJob{job.query_id, job.rank, {}, {}}); // Enqueue empty result on error + } catch (...) { + std::cerr << "[QueryCoordinator::handle_nonbatched_job] Unknown exception during scan for partition " + << job.partition_id << ", query " << job.query_id + << ". Enqueuing empty result.\n"; + result_queue_.enqueue(ResultJob{job.query_id, job.rank, {}, {}}); // Enqueue empty result on error + } } void QueryCoordinator::handle_batched_job(const ScanJob &job, @@ -223,36 +249,77 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, } } - // gather queries - for (int64_t i = 0; i < job.num_queries; ++i) { - std::memcpy(res.batch_queries + i * partition_manager_->d(), - job.query_vector + job.query_ids->at(i) * partition_manager_->d(), - partition_manager_->d() * sizeof(float)); - } - - batched_scan_list(res.batch_queries, - (float*)partition_manager_->partition_store_->get_codes(job.partition_id), - (int64_t*)partition_manager_->partition_store_->get_ids(job.partition_id), - job.num_queries, - partition_manager_->partition_store_->list_size(job.partition_id), - partition_manager_->d(), - res.topk_buffer_pool, - metric_, - res.batch_distances, - res.batch_ids); - - vector results(job.num_queries); + bool scan_successful = false; + try { + const float* codes = (float*)partition_manager_->partition_store_->get_codes(job.partition_id); + const int64_t* ids = (int64_t*)partition_manager_->partition_store_->get_ids(job.partition_id); + int64_t part_size = partition_manager_->partition_store_->list_size(job.partition_id); + int D = partition_manager_->d(); + + if (!codes || !ids || part_size <= 0) { + std::cerr << "[QueryCoordinator::handle_batched_job] Partition " << job.partition_id + << " invalid or empty before batched scan. Enqueuing empty results for batch.\n"; + // Let success remain false, loop below will send empty results + throw std::runtime_error("Batched scan pre-check failed for partition data"); + } + + // Gather queries into res.batch_queries (as in your original code) + for (int64_t i = 0; i < job.num_queries; ++i) { + std::memcpy(res.batch_queries + i * D, + job.query_vector + job.query_ids->at(i) * D, + D * sizeof(float)); + } + + batched_scan_list(res.batch_queries, + codes, + ids, + job.num_queries, + part_size, + D, + res.topk_buffer_pool, // Vector of TopkBuffer shared_ptrs + metric_, + res.batch_distances, // Scratch space for distances + res.batch_ids); // Scratch space for IDs + scan_successful = true; + + } catch (const std::exception& e) { + std::cerr << "[QueryCoordinator::handle_batched_job] Exception during batched scan for partition " + << job.partition_id << ": " << e.what() + << ". Will enqueue empty results for affected queries.\n"; + // scan_successful remains false + } catch (...) { + std::cerr << "[QueryCoordinator::handle_batched_job] Unknown exception during batched scan for partition " + << job.partition_id + << ". Will enqueue empty results for affected queries.\n"; + // scan_successful remains false + } + + // Enqueue results (actual or empty) for every query in this batch job + std::vector results_batch; + results_batch.reserve(job.num_queries); for (int64_t i = 0; i < job.num_queries; ++i) { - int g = job.query_ids->at(i); - auto tv = res.topk_buffer_pool[i]->get_topk(); - auto ti = res.topk_buffer_pool[i]->get_topk_indices(); - results[i] = ResultJob{g, - job.ranks->at(i), - std::move(tv), - std::move(ti)}; - } - result_queue_.enqueue_bulk(results.begin(), - job.num_queries); + int global_query_id = job.query_ids->at(i); + int rank_for_this_query = job.ranks->at(i); // Rank of this partition scan for this specific query + + if (scan_successful) { + // Ensure buffer pool access is safe if it could have been resized or is smaller than expected + if (i < res.topk_buffer_pool.size() && res.topk_buffer_pool[i]) { + auto tv = res.topk_buffer_pool[i]->get_topk(); + auto ti = res.topk_buffer_pool[i]->get_topk_indices(); + results_batch.emplace_back(ResultJob{global_query_id, rank_for_this_query, std::move(tv), std::move(ti)}); + } else { + std::cerr << "[QueryCoordinator::handle_batched_job] Error accessing TopK buffer for successful scan, query " + << global_query_id << ", buffer index " << i << ". Sending empty result.\n"; + results_batch.emplace_back(ResultJob{global_query_id, rank_for_this_query, {}, {}}); + } + } else { + // Scan failed, enqueue an empty result for this query's part of the batch + results_batch.emplace_back(ResultJob{global_query_id, rank_for_this_query, {}, {}}); + } + } + if (!results_batch.empty()) { + result_queue_.enqueue_bulk(std::make_move_iterator(results_batch.begin()), results_batch.size()); + } } // — Main‐thread: worker_scan — @@ -308,8 +375,6 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, auto partition_ids_acc = partition_ids.accessor(); - std::cout << "[QueryCoordinator::enqueue_scan_jobs] Enqueueing scan jobs...\n"; - // flatten jobs next_job_id_ = 0; job_flags_.clear(); @@ -582,6 +647,7 @@ void QueryCoordinator::initialize_workers(int num_cores, bool use_numa) { worker_threads_[i] = std::thread(&QueryCoordinator::partition_scan_worker_fn, this, i); } workers_initialized_ = true; + stop_workers_.store(false); // set main thread on separate thread from workers int num_cores_on_machine = std::thread::hardware_concurrency(); From d29c6966623b7bb3fddc08297af5be77ae94ace2 Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 03:42:19 -0500 Subject: [PATCH 161/323] NUMA allocation for topk buffer --- src/cpp/src/query_coordinator.cpp | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index fdb5001e..00a5722d 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -70,14 +70,13 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { set_thread_affinity(core_index); - int64_t jid; while (!stop_workers_) { + int64_t jid = 0; if (!res.job_queue.try_dequeue(jid)) { std::this_thread::sleep_for(std::chrono::microseconds(5)); continue; } - if (jid == -1) break; process_scan_job(job_buffer_[jid], res); } @@ -382,7 +381,7 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, for (int64_t q = 0; q < nQ; ++q) { job_flags_[q] = vector>(partition_ids.size(1)); for (int p = 0; p < partition_ids.size(1); ++p) { - job_flags_[q][p] = false; + job_flags_[q][p].store(false); if (partition_ids_acc[q][p] < 0) job_flags_[q][p] = true; } } @@ -639,6 +638,7 @@ void QueryCoordinator::initialize_workers(int num_cores, bool use_numa) { core_resources_.resize(num_cores); worker_threads_.resize(num_cores); + stop_workers_.store(false); for (int i = 0; i < num_cores; i++) { if (!set_thread_affinity(i)) { std::cout << "[QueryCoordinator::initialize_workers] Failed to set thread affinity on core " << i << std::endl; @@ -647,7 +647,6 @@ void QueryCoordinator::initialize_workers(int num_cores, bool use_numa) { worker_threads_[i] = std::thread(&QueryCoordinator::partition_scan_worker_fn, this, i); } workers_initialized_ = true; - stop_workers_.store(false); // set main thread on separate thread from workers int num_cores_on_machine = std::thread::hardware_concurrency(); From 13e8dcbdd1cab13dd7ffbe5c68b4941d3e6e4eee Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 03:45:21 -0500 Subject: [PATCH 162/323] NUMA allocation for topk buffer --- src/cpp/src/query_coordinator.cpp | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 00a5722d..4e9a7a35 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -70,6 +70,9 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { set_thread_affinity(core_index); + std::cout << "[QueryCoordinator::partition_scan_worker_fn] Worker " << core_index + << " started.\n"; + while (!stop_workers_) { int64_t jid = 0; @@ -77,9 +80,14 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { std::this_thread::sleep_for(std::chrono::microseconds(5)); continue; } + std::cout << "[QueryCoordinator::partition_scan_worker_fn] Worker " << core_index + << " processing job " << jid << "\n"; process_scan_job(job_buffer_[jid], res); } + + std::cout << "[QueryCoordinator::partition_scan_worker_fn] Worker " << core_index + << " exiting.\n"; } void QueryCoordinator::process_scan_job(ScanJob job, @@ -404,6 +412,7 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, job.rank = p; job_buffer_.push_back(job); core_resources_[pid % num_workers_].job_queue.enqueue(next_job_id_); + std::cout << "[QueryCoordinator::enqueue_scan_jobs] Enqueued job " << next_job_id_ << " for query " << q << ", partition " << pid << "\n"; next_job_id_++; } } From 669b39b75009ec0c31c42dd31150a9f71afd640f Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 03:47:24 -0500 Subject: [PATCH 163/323] NUMA allocation for topk buffer --- src/cpp/src/query_coordinator.cpp | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 4e9a7a35..fbb0159d 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -645,6 +645,8 @@ void QueryCoordinator::initialize_workers(int num_cores, bool use_numa) { partition_manager_->distribute_partitions(num_cores, use_numa); + std::cout << "[QueryCoordinator::initialize_workers] Partition manager distributed partitions." << std::endl; + core_resources_.resize(num_cores); worker_threads_.resize(num_cores); stop_workers_.store(false); @@ -655,6 +657,7 @@ void QueryCoordinator::initialize_workers(int num_cores, bool use_numa) { allocate_core_resources(i, 1, 10, partition_manager_->d()); worker_threads_[i] = std::thread(&QueryCoordinator::partition_scan_worker_fn, this, i); } + workers_initialized_ = true; // set main thread on separate thread from workers From f92d2efb2366b729af237e5001e75111b81bcc3c Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 03:48:35 -0500 Subject: [PATCH 164/323] NUMA allocation for topk buffer --- src/cpp/src/partition_manager.cpp | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/cpp/src/partition_manager.cpp b/src/cpp/src/partition_manager.cpp index de6b5a7f..770453f9 100644 --- a/src/cpp/src/partition_manager.cpp +++ b/src/cpp/src/partition_manager.cpp @@ -575,6 +575,8 @@ void PartitionManager::delete_partitions(const Tensor &partition_ids, bool reass void PartitionManager::distribute_partitions(int num_workers, bool use_numa) { + + debug_ = true; if (debug_) { std::cout << "[PartitionManager] distribute_partitions: Attempting to distribute partitions across " << num_workers << " workers." << std::endl; From 28a74d3ee5ec5808f66491d53427437412e1e9f1 Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 03:50:24 -0500 Subject: [PATCH 165/323] NUMA allocation for topk buffer --- src/cpp/src/partition_manager.cpp | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/cpp/src/partition_manager.cpp b/src/cpp/src/partition_manager.cpp index 770453f9..93cd9ca2 100644 --- a/src/cpp/src/partition_manager.cpp +++ b/src/cpp/src/partition_manager.cpp @@ -625,6 +625,8 @@ void PartitionManager::distribute_partitions(int num_workers, bool use_numa) { for (int i = 0; i < partition_ids.size(0); i++) { set_partition_core_id(partition_ids_accessor[i], partition_ids_accessor[i] % num_workers, use_numa); } + + std::cout << "[PartitionManager] distribute_partitions: Distributed partitions to cores." << std::endl; } void PartitionManager::set_partition_core_id(int64_t partition_id, int core_id, bool use_numa) { From 33292dd19e072c7c658cde4231cf58f3ccf9baaf Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 03:52:29 -0500 Subject: [PATCH 166/323] NUMA allocation for topk buffer --- src/cpp/src/partition_manager.cpp | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/cpp/src/partition_manager.cpp b/src/cpp/src/partition_manager.cpp index 93cd9ca2..7fd54ba7 100644 --- a/src/cpp/src/partition_manager.cpp +++ b/src/cpp/src/partition_manager.cpp @@ -623,6 +623,8 @@ void PartitionManager::distribute_partitions(int num_workers, bool use_numa) { Tensor partition_ids = get_partition_ids(); auto partition_ids_accessor = partition_ids.accessor(); for (int i = 0; i < partition_ids.size(0); i++) { + std::cout << "[PartitionManager] distribute_partitions: Partition " << partition_ids_accessor[i] + << " assigned to core " << partition_ids_accessor[i] % num_workers << std::endl; set_partition_core_id(partition_ids_accessor[i], partition_ids_accessor[i] % num_workers, use_numa); } @@ -631,10 +633,10 @@ void PartitionManager::distribute_partitions(int num_workers, bool use_numa) { void PartitionManager::set_partition_core_id(int64_t partition_id, int core_id, bool use_numa) { partition_store_->partitions_[partition_id]->set_core_id(core_id); + int node = numa_node_of_cpu(core_id); #ifdef QUAKE_USE_NUMA if (use_numa) { - int node = numa_node_of_cpu(core_id); partition_store_->partitions_[partition_id]->set_numa_node(node); } #endif From b8434d9eaf8229d07ba42d1d4e96ac20f85918b6 Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 11:47:45 -0500 Subject: [PATCH 167/323] fix numa alloc --- src/cpp/src/partition_manager.cpp | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/src/cpp/src/partition_manager.cpp b/src/cpp/src/partition_manager.cpp index 7fd54ba7..161b20f2 100644 --- a/src/cpp/src/partition_manager.cpp +++ b/src/cpp/src/partition_manager.cpp @@ -635,9 +635,14 @@ void PartitionManager::set_partition_core_id(int64_t partition_id, int core_id, partition_store_->partitions_[partition_id]->set_core_id(core_id); int node = numa_node_of_cpu(core_id); + std::cout << "[PartitionManager] set_partition_core_id: Partition " << partition_id + << " assigned to core " << core_id << " on NUMA node " << node << std::endl; + #ifdef QUAKE_USE_NUMA if (use_numa) { + std::cout << "Before set\n"; partition_store_->partitions_[partition_id]->set_numa_node(node); + std::cout << "After set\n"; } #endif } From d398ceaa1884a81c60a0972363795eaaa87a2aa7 Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 11:51:05 -0500 Subject: [PATCH 168/323] fix numa alloc --- src/cpp/include/parallel.h | 4 ++-- src/cpp/src/partition_manager.cpp | 10 +++++++++- src/cpp/src/query_coordinator.cpp | 18 +++++++++--------- 3 files changed, 20 insertions(+), 12 deletions(-) diff --git a/src/cpp/include/parallel.h b/src/cpp/include/parallel.h index ac8ef8da..ab61f6c9 100644 --- a/src/cpp/include/parallel.h +++ b/src/cpp/include/parallel.h @@ -58,7 +58,7 @@ inline bool set_thread_affinity(int core_id) { } } - inline int numa_node_of_cpu(int cpu) { + inline int cpu_numa_node(int cpu) { return numa_node_of_cpu(cpu); } @@ -82,7 +82,7 @@ inline void quake_free(void* ptr, size_t /*sz*/) noexcept { std::free(ptr); } -inline int numa_node_of_cpu(int /*cpu*/) { +inline int cpu_numa_node(int /*cpu*/) { return 0; // Not applicable } diff --git a/src/cpp/src/partition_manager.cpp b/src/cpp/src/partition_manager.cpp index 161b20f2..56fbc48a 100644 --- a/src/cpp/src/partition_manager.cpp +++ b/src/cpp/src/partition_manager.cpp @@ -632,8 +632,16 @@ void PartitionManager::distribute_partitions(int num_workers, bool use_numa) { } void PartitionManager::set_partition_core_id(int64_t partition_id, int core_id, bool use_numa) { + + std::cout << "[PartitionManager] set_partition_core_id: Setting core ID for partition " << partition_id + << " to core " << core_id << std::endl; + partition_store_->partitions_[partition_id]->set_core_id(core_id); - int node = numa_node_of_cpu(core_id); + + std::cout << "[PartitionManager] set_partition_core_id: Partition " << partition_id + << " assigned to core " << core_id << std::endl; + + int node = cpu_numa_node(core_id); std::cout << "[PartitionManager] set_partition_core_id: Partition " << partition_id << " assigned to core " << core_id << " on NUMA node " << node << std::endl; diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index fbb0159d..9f787303 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -46,7 +46,7 @@ void QueryCoordinator::allocate_core_resources(int core_idx, int numa_node = 0; #ifdef QUAKE_USE_NUMA - numa_node = numa_node_of_cpu(core_idx); + numa_node = cpu_numa_node(core_idx); #endif // job queue remains default-constructed @@ -64,7 +64,7 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { CoreResources &res = core_resources_[core_index]; int numa_node = 0; #ifdef QUAKE_USE_NUMA - numa_node = numa_node_of_cpu(core_index); + numa_node = cpu_numa_node(core_index); #endif NUMAResources &nr = numa_resources_[numa_node]; @@ -94,7 +94,7 @@ void QueryCoordinator::process_scan_job(ScanJob job, CoreResources &res) { int numa_node = 0; #ifdef QUAKE_USE_NUMA - numa_node = numa_node_of_cpu(res.core_id); + numa_node = cpu_numa_node(res.core_id); #endif NUMAResources &nr = numa_resources_[numa_node]; @@ -141,7 +141,7 @@ void QueryCoordinator::handle_nonbatched_job(const ScanJob &job, job.k, metric_ == faiss::METRIC_INNER_PRODUCT, /*cap=*/100 * job.k, - /*node=*/numa_node_of_cpu(res.core_id) + /*node=*/cpu_numa_node(res.core_id) ); } else if (res.topk_buffer_pool[0]->k_ != job.k) { // check capacity @@ -150,7 +150,7 @@ void QueryCoordinator::handle_nonbatched_job(const ScanJob &job, job.k, metric_ == faiss::METRIC_INNER_PRODUCT, /*cap=*/100 * job.k, - /*node=*/numa_node_of_cpu(res.core_id) + /*node=*/cpu_numa_node(res.core_id) ); } res.topk_buffer_pool[0]->set_k(job.k); @@ -213,7 +213,7 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, job.k, metric_ == faiss::METRIC_INNER_PRODUCT, /*cap=*/num_workers_ * job.k, - /*node=*/numa_node_of_cpu(res.core_id) + /*node=*/cpu_numa_node(res.core_id) ); } @@ -224,7 +224,7 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, } res.batch_queries = static_cast(quake_alloc( size_t(job.num_queries) * partition_manager_->d() * sizeof(float), - numa_node_of_cpu(res.core_id))); + cpu_numa_node(res.core_id))); // realloc batch distances and ids if (res.batch_distances != nullptr) { @@ -237,10 +237,10 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, } res.batch_distances = static_cast(quake_alloc( size_t(job.num_queries) * job.k * sizeof(float), - numa_node_of_cpu(res.core_id))); + cpu_numa_node(res.core_id))); res.batch_ids = static_cast(quake_alloc( size_t(job.num_queries) * job.k * sizeof(int64_t), - numa_node_of_cpu(res.core_id))); + cpu_numa_node(res.core_id))); } else { for (int64_t i = 0; i < job.num_queries; ++i) { res.topk_buffer_pool[i]->set_k(job.k); From 69561bfb8a2bf5af55dbf507877b3bfe795b9b99 Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 11:54:35 -0500 Subject: [PATCH 169/323] fix numa alloc --- src/cpp/src/partition_manager.cpp | 17 ----------------- src/cpp/src/query_coordinator.cpp | 13 ------------- 2 files changed, 30 deletions(-) diff --git a/src/cpp/src/partition_manager.cpp b/src/cpp/src/partition_manager.cpp index 56fbc48a..a1890f58 100644 --- a/src/cpp/src/partition_manager.cpp +++ b/src/cpp/src/partition_manager.cpp @@ -575,8 +575,6 @@ void PartitionManager::delete_partitions(const Tensor &partition_ids, bool reass void PartitionManager::distribute_partitions(int num_workers, bool use_numa) { - - debug_ = true; if (debug_) { std::cout << "[PartitionManager] distribute_partitions: Attempting to distribute partitions across " << num_workers << " workers." << std::endl; @@ -623,29 +621,14 @@ void PartitionManager::distribute_partitions(int num_workers, bool use_numa) { Tensor partition_ids = get_partition_ids(); auto partition_ids_accessor = partition_ids.accessor(); for (int i = 0; i < partition_ids.size(0); i++) { - std::cout << "[PartitionManager] distribute_partitions: Partition " << partition_ids_accessor[i] - << " assigned to core " << partition_ids_accessor[i] % num_workers << std::endl; set_partition_core_id(partition_ids_accessor[i], partition_ids_accessor[i] % num_workers, use_numa); } - - std::cout << "[PartitionManager] distribute_partitions: Distributed partitions to cores." << std::endl; } void PartitionManager::set_partition_core_id(int64_t partition_id, int core_id, bool use_numa) { - - std::cout << "[PartitionManager] set_partition_core_id: Setting core ID for partition " << partition_id - << " to core " << core_id << std::endl; - partition_store_->partitions_[partition_id]->set_core_id(core_id); - - std::cout << "[PartitionManager] set_partition_core_id: Partition " << partition_id - << " assigned to core " << core_id << std::endl; - int node = cpu_numa_node(core_id); - std::cout << "[PartitionManager] set_partition_core_id: Partition " << partition_id - << " assigned to core " << core_id << " on NUMA node " << node << std::endl; - #ifdef QUAKE_USE_NUMA if (use_numa) { std::cout << "Before set\n"; diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 9f787303..f4a23218 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -70,24 +70,15 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { set_thread_affinity(core_index); - std::cout << "[QueryCoordinator::partition_scan_worker_fn] Worker " << core_index - << " started.\n"; - while (!stop_workers_) { - int64_t jid = 0; if (!res.job_queue.try_dequeue(jid)) { std::this_thread::sleep_for(std::chrono::microseconds(5)); continue; } - std::cout << "[QueryCoordinator::partition_scan_worker_fn] Worker " << core_index - << " processing job " << jid << "\n"; process_scan_job(job_buffer_[jid], res); } - - std::cout << "[QueryCoordinator::partition_scan_worker_fn] Worker " << core_index - << " exiting.\n"; } void QueryCoordinator::process_scan_job(ScanJob job, @@ -412,7 +403,6 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, job.rank = p; job_buffer_.push_back(job); core_resources_[pid % num_workers_].job_queue.enqueue(next_job_id_); - std::cout << "[QueryCoordinator::enqueue_scan_jobs] Enqueued job " << next_job_id_ << " for query " << q << ", partition " << pid << "\n"; next_job_id_++; } } @@ -644,9 +634,6 @@ void QueryCoordinator::initialize_workers(int num_cores, bool use_numa) { std::endl; partition_manager_->distribute_partitions(num_cores, use_numa); - - std::cout << "[QueryCoordinator::initialize_workers] Partition manager distributed partitions." << std::endl; - core_resources_.resize(num_cores); worker_threads_.resize(num_cores); stop_workers_.store(false); From 68343b005b97cd8fe47236506ae0ee5bf124ec69 Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 11:56:17 -0500 Subject: [PATCH 170/323] fix numa alloc --- src/cpp/src/partition_manager.cpp | 2 -- 1 file changed, 2 deletions(-) diff --git a/src/cpp/src/partition_manager.cpp b/src/cpp/src/partition_manager.cpp index a1890f58..fa068200 100644 --- a/src/cpp/src/partition_manager.cpp +++ b/src/cpp/src/partition_manager.cpp @@ -631,9 +631,7 @@ void PartitionManager::set_partition_core_id(int64_t partition_id, int core_id, #ifdef QUAKE_USE_NUMA if (use_numa) { - std::cout << "Before set\n"; partition_store_->partitions_[partition_id]->set_numa_node(node); - std::cout << "After set\n"; } #endif } From db25afd91d2deba441867ad4f99e5625b14bbf43 Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 12:13:52 -0500 Subject: [PATCH 171/323] fix numa alloc --- src/cpp/src/query_coordinator.cpp | 30 +++++- .../numa_single_query/configs/sift1m.yaml | 102 +++++++++--------- 2 files changed, 77 insertions(+), 55 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index f4a23218..81716059 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -320,8 +320,6 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, } } -// — Main‐thread: worker_scan — - void QueryCoordinator::init_global_buffers(int64_t nQ, int K, Tensor &partition_ids, @@ -606,21 +604,45 @@ std::shared_ptr QueryCoordinator::worker_scan( timing->n_clusters = partition_manager_->nlist(); timing->search_params = params; + auto s1 = high_resolution_clock::now(); + // 1) init global buffers & jobs_left init_global_buffers(nQ, K, partition_ids, params); + auto s2 = high_resolution_clock::now(); + // 2) copy query vec to NUMA buffers copy_query_to_numa(x.data_ptr(), nQ, D); + auto s3 = high_resolution_clock::now(); + // 3) enqueue jobs enqueue_scan_jobs(x, partition_ids, params); + auto s4 = high_resolution_clock::now(); + // 4) drain results + APS drain_and_apply_aps(x, partition_ids, use_aps, params->recall_target, params->aps_flush_period_us, timing); - // 5) aggregate & return - return aggregate_scan_results(nQ, K, timing); + auto s5 = high_resolution_clock::now(); + + auto res = aggregate_scan_results(nQ, K, timing); + + auto s6 = high_resolution_clock::now(); + + res->timing_info->buffer_init_time_ns = + duration_cast(s2 - s1).count(); +// res->timing_info->copy_query_time_ns = +// duration_cast(s3 - s2).count(); + res->timing_info->job_enqueue_time_ns = + duration_cast(s4 - s3).count(); + res->timing_info->job_wait_time_ns = + duration_cast(s5 - s4).count(); + res->timing_info->result_aggregate_time_ns = + duration_cast(s6 - s5).count(); + + return res; } // Initialize Worker Threads diff --git a/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml b/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml index 3682722b..d8eb082f 100644 --- a/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml +++ b/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml @@ -6,7 +6,7 @@ dataset: trials: 5 warmup: 10 -overwrite: false # Set to true to force all indexes to rerun +overwrite: true # Set to true to force all indexes to rerun indexes: - name: Quake_0 @@ -14,7 +14,7 @@ indexes: build_params: nc: 1000 metric: l2 - num_workers: 1 + num_workers: 0 use_numa: false search_params: nprobe: 20 @@ -29,55 +29,55 @@ indexes: search_params: nprobe: 20 - - name: Quake_4 - index: Quake - build_params: - nc: 1000 - metric: l2 - num_workers: 4 - use_numa: false - search_params: - nprobe: 20 - - - name: Quake_16 - index: Quake - build_params: - nc: 1000 - metric: l2 - num_workers: 16 - use_numa: false - search_params: - nprobe: 20 - - - name: Quake_1_numa - index: Quake - build_params: - nc: 1000 - metric: l2 - num_workers: 1 - use_numa: true - search_params: - nprobe: 20 - - - name: Quake_4_numa - index: Quake - build_params: - nc: 1000 - metric: l2 - num_workers: 4 - use_numa: true - search_params: - nprobe: 20 - - - name: Quake_16_numa - index: Quake - build_params: - nc: 1000 - metric: l2 - num_workers: 16 - use_numa: true - search_params: - nprobe: 20 +# - name: Quake_4 +# index: Quake +# build_params: +# nc: 1000 +# metric: l2 +# num_workers: 4 +# use_numa: false +# search_params: +# nprobe: 20 +# +# - name: Quake_16 +# index: Quake +# build_params: +# nc: 1000 +# metric: l2 +# num_workers: 16 +# use_numa: false +# search_params: +# nprobe: 20 +# +# - name: Quake_1_numa +# index: Quake +# build_params: +# nc: 1000 +# metric: l2 +# num_workers: 1 +# use_numa: true +# search_params: +# nprobe: 20 +# +# - name: Quake_4_numa +# index: Quake +# build_params: +# nc: 1000 +# metric: l2 +# num_workers: 4 +# use_numa: true +# search_params: +# nprobe: 20 +# +# - name: Quake_16_numa +# index: Quake +# build_params: +# nc: 1000 +# metric: l2 +# num_workers: 16 +# use_numa: true +# search_params: +# nprobe: 20 # - name: FaissIVF # index: IVF From 7e5a3c2e3a7c3cace6b082f3a4330384f3c87a7f Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 13:12:33 -0500 Subject: [PATCH 172/323] fix recall bug --- src/cpp/include/list_scanning.h | 20 +++++++++++++++---- src/cpp/src/query_coordinator.cpp | 10 ++++++++-- .../numa_single_query/configs/sift1m.yaml | 12 +++++------ 3 files changed, 30 insertions(+), 12 deletions(-) diff --git a/src/cpp/include/list_scanning.h b/src/cpp/include/list_scanning.h index c2a7d5c2..de7fa263 100644 --- a/src/cpp/include/list_scanning.h +++ b/src/cpp/include/list_scanning.h @@ -168,6 +168,8 @@ class TypedTopKBuffer { } T flush() { + + int n = head_; int m = std::min(n, k_); @@ -199,10 +201,20 @@ class TypedTopKBuffer { miniselect::pdqsort_branchless(ord_, ord_ + m, cmpIdx); // 5) copy the winners back to vals_/ids_ and clamp head_ - for (int i = m - 1; i >= 0; --i) { - int idx = ord_[i]; - vals_[i] = vals_[idx]; - ids_[i] = ids_[idx]; + std::vector temp_v(m); + std::vector temp_i(m); + + for (int i = 0; i < m; ++i) { + int original_slot_idx = ord_[i]; // ord_[i] is the original index of the i-th best item + // (e.g. ord_[0] is index of best, ord_[1] of 2nd best) + temp_v[i] = vals_[original_slot_idx]; + temp_i[i] = ids_[original_slot_idx]; + } + + // Now copy from temporary buffers to the main buffers + for (int i = 0; i < m; ++i) { + vals_[i] = temp_v[i]; + ids_[i] = temp_i[i]; } // 5) clamp head_ and return the k-th value (or extreme if too few) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 81716059..3c13350a 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -70,6 +70,7 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { set_thread_affinity(core_index); + int i = 0; while (!stop_workers_) { int64_t jid = 0; if (!res.job_queue.try_dequeue(jid)) { @@ -78,6 +79,7 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { } process_scan_job(job_buffer_[jid], res); + i++; } } @@ -167,7 +169,7 @@ void QueryCoordinator::handle_nonbatched_job(const ScanJob &job, return; // Important to return after enqueueing the placeholder } - scan_list(nr.local_query_buffer, // Or job.query_vector depending on NUMA strategy + scan_list(nr.local_query_buffer + (job.query_id * D), codes, ids, part_size, @@ -333,7 +335,7 @@ void QueryCoordinator::init_global_buffers(int64_t nQ, global_topk_buffer_pool_[q] = std::make_shared( K, metric_ == faiss::METRIC_INNER_PRODUCT, - /*cap=*/num_workers_ * K * 2, + /*cap=*/num_workers_ * K * 1000, /*node=*/0 ); } @@ -500,10 +502,12 @@ void QueryCoordinator::drain_and_apply_aps(Tensor x, auto last_flush = high_resolution_clock::now(); + int64_t jobsReceived = 0; while (true) { // 1) drain all ready results ResultJob rj; while (result_queue_.try_dequeue(rj)) { + ++jobsReceived; global_topk_buffer_pool_[rj.query_id] ->batch_add(rj.distances.data(), rj.indices.data(), @@ -599,6 +603,8 @@ std::shared_ptr QueryCoordinator::worker_scan( int K = params->k; bool use_aps = (params->recall_target>0 && !params->batched_scan && parent_); + int64_t nJobsExpected = 0; + auto timing = std::make_shared(); timing->n_queries = nQ; timing->n_clusters = partition_manager_->nlist(); diff --git a/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml b/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml index d8eb082f..f8a101e1 100644 --- a/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml +++ b/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml @@ -1,11 +1,11 @@ # numa_latency_experiment.yaml dataset: name: sift1m - num_queries: 100 - k: 100 + num_queries: 1 + k: 50 -trials: 5 -warmup: 10 +trials: 1 +warmup: 0 overwrite: true # Set to true to force all indexes to rerun indexes: @@ -17,7 +17,7 @@ indexes: num_workers: 0 use_numa: false search_params: - nprobe: 20 + nprobe: 1 - name: Quake_1 index: Quake @@ -27,7 +27,7 @@ indexes: num_workers: 1 use_numa: false search_params: - nprobe: 20 + nprobe: 1 # - name: Quake_4 # index: Quake From 899271dfb91a363909aeda2894c08903080fef99 Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 13:55:09 -0500 Subject: [PATCH 173/323] reduce buffer allocs --- src/cpp/src/query_coordinator.cpp | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 3c13350a..3d7e5994 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -133,7 +133,7 @@ void QueryCoordinator::handle_nonbatched_job(const ScanJob &job, res.topk_buffer_pool[0] = std::make_shared( job.k, metric_ == faiss::METRIC_INNER_PRODUCT, - /*cap=*/100 * job.k, + /*cap=*/std::max(100 * job.k, 10000), /*node=*/cpu_numa_node(res.core_id) ); } else if (res.topk_buffer_pool[0]->k_ != job.k) { @@ -142,7 +142,7 @@ void QueryCoordinator::handle_nonbatched_job(const ScanJob &job, res.topk_buffer_pool[0] = std::make_shared( job.k, metric_ == faiss::METRIC_INNER_PRODUCT, - /*cap=*/100 * job.k, + /*cap=*/std::max(100 * job.k, 10000), /*node=*/cpu_numa_node(res.core_id) ); } @@ -205,7 +205,7 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, res.topk_buffer_pool[i] = std::make_shared( job.k, metric_ == faiss::METRIC_INNER_PRODUCT, - /*cap=*/num_workers_ * job.k, + /*cap=*/std::max(100 * job.k, 10000), /*node=*/cpu_numa_node(res.core_id) ); } @@ -335,7 +335,7 @@ void QueryCoordinator::init_global_buffers(int64_t nQ, global_topk_buffer_pool_[q] = std::make_shared( K, metric_ == faiss::METRIC_INNER_PRODUCT, - /*cap=*/num_workers_ * K * 1000, + /*cap=*/num_workers_ * K * 2, /*node=*/0 ); } From 71270c402cd862cfb7f2441ca51ad103a29aaf70 Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 14:01:33 -0500 Subject: [PATCH 174/323] reduce buffer allocs --- src/cpp/src/query_coordinator.cpp | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 3d7e5994..70dfe539 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -78,6 +78,10 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { continue; } + if (jid == -1) { + break; + } + process_scan_job(job_buffer_[jid], res); i++; } From ea622431d49c6ef184f999c23515ac5236a33500 Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 14:39:23 -0500 Subject: [PATCH 175/323] reduce buffer allocs --- src/cpp/src/query_coordinator.cpp | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 70dfe539..e4588759 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -95,6 +95,9 @@ void QueryCoordinator::process_scan_job(ScanJob job, #endif NUMAResources &nr = numa_resources_[numa_node]; + // print out job details for debugging purposes + std::cout << "Core: " << res.core_id << " Processing partition_id=" << job.partition_id << '\n'; + // fetch codes & ids const float *codes = (float*)partition_manager_->partition_store_->get_codes(job.partition_id); const int64_t *ids = (int64_t*)partition_manager_->partition_store_->get_ids(job.partition_id); From 765fef2a09540c7bf0c1a8ec92663c010334d568 Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 14:47:01 -0500 Subject: [PATCH 176/323] reduce buffer allocs --- src/cpp/include/query_coordinator.h | 6 +++--- src/cpp/src/query_coordinator.cpp | 3 --- 2 files changed, 3 insertions(+), 6 deletions(-) diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index 324a5bde..661ef0fa 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -59,9 +59,9 @@ class QueryCoordinator { moodycamel::ConcurrentQueue job_queue; // Thread-local buffers for batched queries - float *batch_queries; ///< Thread-local buffer used for batched queries. - float *batch_distances; ///< Thread-Local buffer for result distances. - int64_t *batch_ids; ///< Thread-Local buffer for result IDs. + float* batch_queries = nullptr; + float* batch_distances = nullptr; + int64_t* batch_ids = nullptr; }; struct NUMAResources { diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index e4588759..70dfe539 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -95,9 +95,6 @@ void QueryCoordinator::process_scan_job(ScanJob job, #endif NUMAResources &nr = numa_resources_[numa_node]; - // print out job details for debugging purposes - std::cout << "Core: " << res.core_id << " Processing partition_id=" << job.partition_id << '\n'; - // fetch codes & ids const float *codes = (float*)partition_manager_->partition_store_->get_codes(job.partition_id); const int64_t *ids = (int64_t*)partition_manager_->partition_store_->get_ids(job.partition_id); From 834874d93164d207fe269097bb5a053cad04940a Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 15:10:41 -0500 Subject: [PATCH 177/323] reduce buffer allocs --- src/cpp/include/query_coordinator.h | 6 +++--- src/cpp/src/query_coordinator.cpp | 7 ++++++- 2 files changed, 9 insertions(+), 4 deletions(-) diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index 661ef0fa..8b2eb8b6 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -59,9 +59,9 @@ class QueryCoordinator { moodycamel::ConcurrentQueue job_queue; // Thread-local buffers for batched queries - float* batch_queries = nullptr; - float* batch_distances = nullptr; - int64_t* batch_ids = nullptr; + float* batch_queries = nullptr; // batched query buffer (NUMA-allocated) + float* batch_distances = nullptr; // scratch space for distances + int64_t* batch_ids = nullptr; // scratch space for ids }; struct NUMAResources { diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 70dfe539..d18cdae7 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -44,12 +44,17 @@ void QueryCoordinator::allocate_core_resources(int core_idx, CR.core_id = core_idx; CR.topk_buffer_pool.clear(); + // --- ZERO‐INITIALIZE our batched‐query buffers so we never free garbage pointers --- + CR.batch_queries = nullptr; + CR.batch_distances = nullptr; + CR.batch_ids = nullptr; + int numa_node = 0; #ifdef QUAKE_USE_NUMA numa_node = cpu_numa_node(core_idx); #endif - // job queue remains default-constructed + // job queue remains default‐constructed numa_resources_.resize(get_num_numa_nodes()); auto& numa_res = numa_resources_[numa_node]; size_t bytes = size_t(num_queries) * d * sizeof(float); From fb7d51d9d17c2afbc09e6be11c29df821f3af0ea Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 15:27:21 -0500 Subject: [PATCH 178/323] reduce buffer allocs --- src/cpp/include/query_coordinator.h | 8 ++- src/cpp/src/query_coordinator.cpp | 86 +++++++++++++++++------------ 2 files changed, 55 insertions(+), 39 deletions(-) diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index 8b2eb8b6..2a78379a 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -59,9 +59,11 @@ class QueryCoordinator { moodycamel::ConcurrentQueue job_queue; // Thread-local buffers for batched queries - float* batch_queries = nullptr; // batched query buffer (NUMA-allocated) - float* batch_distances = nullptr; // scratch space for distances - int64_t* batch_ids = nullptr; // scratch space for ids + float* batch_queries = nullptr; // batched query buffer (NUMA-allocated) + float* batch_distances = nullptr; // scratch space for distances + int64_t* batch_ids = nullptr; // scratch space for ids + size_t batch_q_capacity = 0; // number of floats in batch_queries + size_t batch_res_capacity = 0; // number of (k×Q) slots in distances & ids }; struct NUMAResources { diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index d18cdae7..33e472d8 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -207,55 +207,69 @@ void QueryCoordinator::handle_nonbatched_job(const ScanJob &job, void QueryCoordinator::handle_batched_job(const ScanJob &job, CoreResources &res, NUMAResources &nr) { - // ensure buffers - if (res.topk_buffer_pool.size() < (size_t)job.num_queries) { - res.topk_buffer_pool.resize(job.num_queries); - for (int64_t i = 0; i < job.num_queries; ++i) { + // Number of queries in this batch, top-k, vector dimension, and NUMA node + int64_t Q = job.num_queries; + int K = job.k; + int D = partition_manager_->d(); + int node = cpu_numa_node(res.core_id); + + // Ensure we have exactly Q TopK buffers, each with capacity = max(100*K, 10000) + size_t cap = std::max(100 * K, 10000); + if (res.topk_buffer_pool.size() < (size_t)Q) { + res.topk_buffer_pool.resize(Q); + for (int64_t i = 0; i < Q; ++i) { res.topk_buffer_pool[i] = std::make_shared( - job.k, + K, metric_ == faiss::METRIC_INNER_PRODUCT, - /*cap=*/std::max(100 * job.k, 10000), - /*node=*/cpu_numa_node(res.core_id) + /*cap=*/cap, + /*node=*/node ); } + } else { + for (int64_t i = 0; i < Q; ++i) { + auto &buf = res.topk_buffer_pool[i]; + buf->set_k(K); + buf->reset(); + } + } - // realloc batch query buffer - if (res.batch_queries != nullptr) { + // Allocate or grow the per-batch query buffer (size Q × D floats) + size_t need_q = size_t(Q) * D; + if (res.batch_q_capacity < need_q) { + if (res.batch_queries) { quake_free(res.batch_queries, - size_t(job.num_queries) * partition_manager_->d() * sizeof(float)); + res.batch_q_capacity * sizeof(float)); } - res.batch_queries = static_cast(quake_alloc( - size_t(job.num_queries) * partition_manager_->d() * sizeof(float), - cpu_numa_node(res.core_id))); + res.batch_queries = static_cast(quake_alloc( + need_q * sizeof(float), node)); + res.batch_q_capacity = need_q; + } - // realloc batch distances and ids - if (res.batch_distances != nullptr) { + // Allocate or grow the per-batch result buffers (size Q × K entries) + size_t need_r = size_t(Q) * size_t(K); + if (res.batch_res_capacity < need_r) { + if (res.batch_distances) { quake_free(res.batch_distances, - size_t(job.num_queries) * job.k * sizeof(float)); + res.batch_res_capacity * sizeof(float)); } - if (res.batch_ids != nullptr) { + if (res.batch_ids) { quake_free(res.batch_ids, - size_t(job.num_queries) * job.k * sizeof(int64_t)); - } - res.batch_distances = static_cast(quake_alloc( - size_t(job.num_queries) * job.k * sizeof(float), - cpu_numa_node(res.core_id))); - res.batch_ids = static_cast(quake_alloc( - size_t(job.num_queries) * job.k * sizeof(int64_t), - cpu_numa_node(res.core_id))); - } else { - for (int64_t i = 0; i < job.num_queries; ++i) { - res.topk_buffer_pool[i]->set_k(job.k); - res.topk_buffer_pool[i]->reset(); + res.batch_res_capacity * sizeof(int64_t)); } + res.batch_distances = static_cast( + quake_alloc(need_r * sizeof(float), node)); + res.batch_ids = static_cast( + quake_alloc(need_r * sizeof(int64_t), node)); + res.batch_res_capacity = need_r; + } - // set batch distances and ids to max/min and -1 - for (int64_t i = 0; i < job.num_queries * job.k; ++i) { - res.batch_distances[i] = metric_ == faiss::METRIC_INNER_PRODUCT ? - -std::numeric_limits::infinity() : - std::numeric_limits::infinity(); - res.batch_ids[i] = -1; - } + // Initialize the result buffers to infinity (or –infinity) and –1 + float dist_init = (metric_ == faiss::METRIC_INNER_PRODUCT) + ? -std::numeric_limits::infinity() + : std::numeric_limits::infinity(); + for (size_t idx = 0; idx < need_r; ++idx) { + res.batch_distances[idx] = dist_init; + res.batch_ids [idx] = -1; } bool scan_successful = false; From 7aa34db22af7f614b6ac3391bc9a2529e95b9594 Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 15:34:35 -0500 Subject: [PATCH 179/323] reduce buffer allocs --- src/cpp/src/dynamic_inverted_list.cpp | 27 ++++++++++++++++++--------- 1 file changed, 18 insertions(+), 9 deletions(-) diff --git a/src/cpp/src/dynamic_inverted_list.cpp b/src/cpp/src/dynamic_inverted_list.cpp index 2e82ca97..4364c53a 100644 --- a/src/cpp/src/dynamic_inverted_list.cpp +++ b/src/cpp/src/dynamic_inverted_list.cpp @@ -77,7 +77,8 @@ namespace faiss { const uint8_t *DynamicInvertedLists::get_codes(size_t list_no) const { auto it = partitions_.find(list_no); if (it == partitions_.end()) { - throw std::runtime_error("List does not exist in get_codes"); + string err_message = "List " + std::to_string(list_no) + " does not exist in get_codes"; + throw std::runtime_error(err_message); } return it->second->codes_; } @@ -85,7 +86,8 @@ namespace faiss { const idx_t *DynamicInvertedLists::get_ids(size_t list_no) const { auto it = partitions_.find(list_no); if (it == partitions_.end()) { - throw std::runtime_error("List does not exist in get_ids"); + string err_message = "List " + std::to_string(list_no) + " does not exist in get_ids"; + throw std::runtime_error(err_message); } return it->second->ids_; } @@ -100,8 +102,10 @@ namespace faiss { void DynamicInvertedLists::remove_entry(size_t list_no, idx_t id) { auto it = partitions_.find(list_no); - if (it == partitions_.end()) - throw std::runtime_error("List does not exist in remove_entry"); + if (it == partitions_.end()) { + string err_message = "List " + std::to_string(list_no) + " does not exist in remove_entry"; + throw std::runtime_error(err_message); + } auto& part = it->second; if (part->num_vectors_ == 0) return; @@ -121,7 +125,8 @@ namespace faiss { void DynamicInvertedLists::remove_entries_from_partition(size_t list_no, vector vectors_to_remove) { auto it = partitions_.find(list_no); if (it == partitions_.end()) { - throw std::runtime_error("List does not exist in remove_entries_from_partition"); + string err_message = "List " + std::to_string(list_no) + " does not exist in remove_entries_from_partition"; + throw std::runtime_error(err_message); } shared_ptr part = it->second; @@ -180,8 +185,10 @@ namespace faiss { if (n_entry == 0) return 0; auto it = partitions_.find(list_no); - if (it == partitions_.end()) - throw std::runtime_error("List does not exist in add_entries"); + if (it == partitions_.end()) { + string err_message = "List " + std::to_string(list_no) + " does not exist in add_entries"; + throw std::runtime_error(err_message); + } auto& part = it->second; if (part->code_size_ != static_cast(code_size)) @@ -204,7 +211,8 @@ namespace faiss { const uint8_t *codes) { auto it = partitions_.find(list_no); if (it == partitions_.end()) { - throw std::runtime_error("List does not exist in update_entries"); + string err_message = "List " + std::to_string(list_no) + " does not exist in update_entries"; + throw std::runtime_error(err_message); } shared_ptr part = it->second; @@ -291,7 +299,8 @@ void DynamicInvertedLists::batch_update_entries( void DynamicInvertedLists::add_list(size_t list_no) { if (partitions_.find(list_no) != partitions_.end()) { - throw std::runtime_error("List already exists in add_list"); + string err_message = "List " + std::to_string(list_no) + " already exists in add_list"; + throw std::runtime_error(err_message); } shared_ptr ip = std::make_shared(); ip->set_code_size((int64_t) code_size); From aa6c549bbbce14e1b016382e079cf4c2b46ef8e9 Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 15:38:34 -0500 Subject: [PATCH 180/323] reduce buffer allocs --- src/cpp/src/query_coordinator.cpp | 24 ++++++++++++++++-------- 1 file changed, 16 insertions(+), 8 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 33e472d8..fd36036e 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -100,16 +100,24 @@ void QueryCoordinator::process_scan_job(ScanJob job, #endif NUMAResources &nr = numa_resources_[numa_node]; - // fetch codes & ids - const float *codes = (float*)partition_manager_->partition_store_->get_codes(job.partition_id); - const int64_t *ids = (int64_t*)partition_manager_->partition_store_->get_ids(job.partition_id); - - int64_t part_size = 0; + // Attempt to fetch partition data; if the list doesn't exist, catch and enqueue empty results. + const float *codes = nullptr; + const int64_t *ids = nullptr; + int64_t part_size = 0; try { + codes = (float *)(partition_manager_->partition_store_->get_codes(job.partition_id)); + ids = (int64_t *) partition_manager_->partition_store_->get_ids(job.partition_id); part_size = partition_manager_->partition_store_->list_size(job.partition_id); - } catch (...) { - std::cerr << "[partition_scan_worker_fn] Partition " << job.partition_id << " not found.\n"; - result_queue_.enqueue(ResultJob{job.query_id, job.rank, {}, {}}); + } catch (const std::exception &e) { + std::cerr << "[process_scan_job] Partition " << job.partition_id + << " invalid: " << e.what() << ". Returning empty result(s).\n"; + if (job.is_batched) { + for (int64_t q : *job.query_ids) { + result_queue_.enqueue(ResultJob{(int)q, 0, {}, {}}); + } + } else { + result_queue_.enqueue(ResultJob{job.query_id, job.rank, {}, {}}); + } return; } From 8570e86e6e4cbf23be4a00b8dac7b315a837a8c9 Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 16:54:45 -0500 Subject: [PATCH 181/323] update multi-experiment to track detailed timers --- .../osdi2025/numa_multi_query/run.py | 401 ++++++++++-------- 1 file changed, 220 insertions(+), 181 deletions(-) diff --git a/test/experiments/osdi2025/numa_multi_query/run.py b/test/experiments/osdi2025/numa_multi_query/run.py index e9d7a735..25c89fdc 100644 --- a/test/experiments/osdi2025/numa_multi_query/run.py +++ b/test/experiments/osdi2025/numa_multi_query/run.py @@ -1,59 +1,64 @@ #!/usr/bin/env python3 """ -NUMA Batch-Query Latency and Recall Benchmark +NUMA Batch-Query Latency and Recall Benchmark (detailed timers) Benchmarks a suite of ANN indexes (Quake, Faiss-IVF, SCANN, HNSW, DiskANN, SVS, etc.) -for batch-query latency and recall@k under configurable build and search parameters. +for per-query latency breakdown and recall@k under configurable build and search parameters. Results are cached and only recomputed if the 'overwrite' flag is set in the configuration. -Outputs a unified CSV and a latency plot per index. +Outputs per-index CSVs with detailed timing info and unified CSV + plots. Usage: python numa_latency_batch_experiment.py numa_latency_experiment.yaml output_dir """ - +import sys +from pathlib import Path +ing = yaml = None import yaml import numpy as np +torch = None import torch import pandas as pd import matplotlib.pyplot as plt -from pathlib import Path -from time import time -from quake.utils import compute_recall +from quake.utils import compute_recall from quake.datasets.ann_datasets import load_dataset from quake.index_wrappers.faiss_ivf import FaissIVF from quake.index_wrappers.faiss_hnsw import FaissHNSW from quake.index_wrappers.quake import QuakeWrapper -# try: -# from quake.index_wrappers.scann import Scann -# except ImportError: -# raise ImportError("SCANN wrapper not available. Please install the required package.") -# try: -# from quake.index_wrappers.diskann import DiskANNDynamic -# except ImportError: -# raise ImportError("DiskANN wrapper not available. Please install the required package.") -# try: -# from quake.index_wrappers.vamana import Vamana -# except ImportError: -# raise ImportError("SVS wrapper not available. Please install the required package.") +# Optional wrappers +try: + from quake.index_wrappers.scann import Scann +except ImportError: + Scann = None +try: + from quake.index_wrappers.diskann import DiskANNDynamic +except ImportError: + DiskANNDynamic = None +try: + from quake.index_wrappers.vamana import Vamana +except ImportError: + Vamana = None INDEX_CLASSES = { - "Quake": QuakeWrapper, - "IVF": FaissIVF, - # "SCANN": Scann, - "HNSW": FaissHNSW, - # "DiskANN": DiskANNDynamic, - # "SVS": Vamana, + "Quake": QuakeWrapper, + "IVF": FaissIVF, + "SCANN": Scann, + "HNSW": FaissHNSW, + "DiskANN": DiskANNDynamic, + "SVS": Vamana, } -def build_and_save_index(index_class, build_params, base_vecs, index_file): - idx = index_class() - params = dict(build_params) - idx.build(base_vecs, **params) - idx.save(str(index_file)) + +def build_and_save(index_cls, params, vecs, path): + """Build an index and save it to disk.""" + idx = index_cls() + idx.build(vecs, **params) + idx.save(str(path)) + def extract_ids(res): + """Normalize returned IDs array.""" if hasattr(res, "ids"): arr = res.ids elif hasattr(res, "I"): @@ -61,186 +66,220 @@ def extract_ids(res): elif hasattr(res, "indices"): arr = res.indices else: - raise RuntimeError("Cannot extract predicted indices from search result.") + raise RuntimeError("Search result has no id field.") if isinstance(arr, torch.Tensor): arr = arr.cpu().numpy() - elif not isinstance(arr, np.ndarray): - arr = np.asarray(arr) - return arr - -def run_experiment(cfg_path: str, output_dir: str): - cfg = yaml.safe_load(Path(cfg_path).read_text()) - ds = cfg["dataset"] - queries_n = ds["num_queries"] - k = ds["k"] - trials = cfg.get("trials", 5) - warmup = cfg.get("warmup", 10) - indexes_cfg = cfg["indexes"] - csv_name = cfg.get("output", {}).get("results_csv", "numa_results.csv") - overwrite = cfg.get("overwrite", False) - - out_dir = Path(output_dir) + return np.asarray(arr, dtype=np.int64) + + +def run_experiment(cfg_path: str, output_dir: str) -> None: + # Load config + cfg = yaml.safe_load(Path(cfg_path).read_text()) + ds_cfg = cfg["dataset"] + k = ds_cfg["k"] + q_n = ds_cfg["num_queries"] + trials = cfg.get("trials", 5) + warmup_n = cfg.get("warmup", 10) + idx_cfgs = cfg["indexes"] + csv_name = cfg.get("output", {}).get("results_csv", "numa_results.csv") + overwrite = cfg.get("overwrite", False) + + out_dir = Path(output_dir).expanduser().absolute() out_dir.mkdir(parents=True, exist_ok=True) - print(f"[INFO] Loading dataset '{ds['name']}'...") - base_vecs, queries, gt = load_dataset(ds["name"]) - queries = queries[:queries_n] - gt = gt[:queries_n] if gt is not None else None + # Load dataset + print(f"[INFO] Loading dataset {ds_cfg['name']}...") + base_vecs, queries, gt = load_dataset(ds_cfg["name"]) + queries = queries[:q_n] + gt = gt[:q_n] if gt is not None else None - # --- Build the Quake base index ONCE --- + # Build shared Quake base index once quake_base_params = None - quake_base_index_file = out_dir / "Quake_base_index.bin" - # Find the build params for the first Quake config for base index - for idx_cfg in indexes_cfg: - if idx_cfg["index"] == "Quake": - quake_base_params = dict(idx_cfg.get("build_params", {})) - # Remove any runtime/load-only params - quake_base_params.pop("num_workers", None) - quake_base_params.pop("use_numa", None) + for c in idx_cfgs: + if c["index"] == "Quake": + p = dict(c.get("build_params", {})) + p.pop("num_workers", None) + p.pop("use_numa", None) + quake_base_params = p break - if quake_base_params is not None: - if not quake_base_index_file.exists(): - print(f"[BUILD] Quake (shared) base index ...") - build_and_save_index(QuakeWrapper, quake_base_params, base_vecs, quake_base_index_file) - print(f"[SAVE] Quake base index saved at {quake_base_index_file}") - all_rows = [] + quake_base_path = out_dir / "Quake_base_index.bin" + if quake_base_params and not quake_base_path.exists(): + print("[BUILD] Quake (shared) base index...") + build_and_save(QuakeWrapper, quake_base_params, base_vecs, quake_base_path) + print(f"[SAVE] Quake base index → {quake_base_path}") - for idx_cfg in indexes_cfg: - idx_name = idx_cfg["name"] - idx_type = idx_cfg["index"] - build_params = dict(idx_cfg.get("build_params", {})) - search_params = idx_cfg.get("search_params", {}) - IndexClass = INDEX_CLASSES.get(idx_type) - if IndexClass is None: - print(f"[WARN] Unknown index type: {idx_type}. Skipping.") + # Collect results per-index + all_rows = [] + for cfg_i in idx_cfgs: + name = cfg_i["name"] + typ = cfg_i["index"] + b_params = dict(cfg_i.get("build_params", {})) + s_params = dict(cfg_i.get("search_params", {})) + cls = INDEX_CLASSES.get(typ) + if cls is None: + print(f"[WARN] Unknown index type '{typ}', skipping.") continue - idx_csv = out_dir / f"{idx_name}_results.csv" - # Quake uses the shared base index; others get their own file - if idx_type == "Quake": - idx_file = quake_base_index_file - else: - idx_file = out_dir / f"{idx_name}_index.bin" + idx_csv = out_dir / f"{name}_results.csv" + idx_path = quake_base_path if typ == "Quake" else out_dir / f"{name}_index.bin" + # Skip if exists if idx_csv.exists() and not overwrite: - print(f"[SKIP] {idx_name}: Results exist. Use overwrite: true to rerun.") - df_idx = pd.read_csv(idx_csv) - all_rows.append(df_idx.iloc[0].to_dict()) + print(f"[SKIP] {name} – results exist.") + all_rows.append(pd.read_csv(idx_csv).iloc[0].to_dict()) continue - if idx_type != "Quake" and not idx_file.exists(): - print(f"[BUILD] {idx_name} index...") - build_and_save_index(IndexClass, build_params, base_vecs, idx_file) - print(f"[SAVE] Index saved at {idx_file}") - - print(f"[RUN] {idx_name} ...") - - if idx_type == "Quake": - # Only use load-time params for Quake - load_kwargs = {} - if "use_numa" in build_params: - load_kwargs["use_numa"] = build_params["use_numa"] - if "num_workers" in build_params: - load_kwargs["num_workers"] = build_params["num_workers"] - idx = QuakeWrapper() - idx.load(str(idx_file), **load_kwargs) - elif idx_type == "DiskANN": - idx = IndexClass() - build_params.pop("metric", None) - idx.load(str(idx_file), **build_params) + # Build if needed + if typ != "Quake" and not idx_path.exists(): + print(f"[BUILD] {name} index...") + build_and_save(cls, b_params, base_vecs, idx_path) + print(f"[SAVE] Index → {idx_path}") + + # Load index with params + if typ == "Quake": + load_kw = {k: b_params[k] for k in ("use_numa", "num_workers") if k in b_params} + idx = QuakeWrapper(); idx.load(str(idx_path), **load_kw) + elif typ == "DiskANN": + idx = cls(); b_params.pop("metric", None); idx.load(str(idx_path), **b_params) else: - idx = IndexClass() - idx.load(str(idx_file)) + idx = cls(); idx.load(str(idx_path)) - print(search_params) + # Warm-up runs + for _ in range(min(warmup_n, len(queries))): + for q in queries: + idx.search(q.unsqueeze(0).float(), k, **s_params) - # Warmup (single batch, no timing) - for i in range(min(warmup, 1)): - idx.search(queries, k, **search_params) + # Prepare accumulators for detailed timers + child_buf_init_trials = [] + child_enq_trials = [] + child_wait_trials = [] + child_agg_trials = [] + child_total_trials = [] + parent_buf_init_trials = [] + parent_enq_trials = [] + parent_wait_trials = [] + parent_agg_trials = [] + parent_total_trials = [] + recall_trials = [] - # Benchmark Trials (latency and recall) - trial_means = [] - trial_recalls = [] + # Benchmark trials for t in range(trials): - t0 = time() - res = idx.search(queries, k, **search_params) - t1 = time() - mean_t = ((t1 - t0) * 1e3) / queries.shape[0] # ms per query - trial_means.append(mean_t) - - preds_arr = extract_ids(res) - if preds_arr.shape[0] != queries.shape[0]: - raise RuntimeError(f"Pred shape {preds_arr.shape} doesn't match queries {queries.shape}") - - if gt is not None: - recall = float(compute_recall(preds_arr, gt, k).mean()) - trial_recalls.append(recall) - print(f" [trial {t+1}/{trials}] {mean_t:.2f} ms | recall@{k}: {recall:.4f}") - else: - print(f" [trial {t+1}/{trials}] {mean_t:.2f} ms") - - mean_lat = float(np.mean(trial_means)) - std_lat = float(np.std(trial_means)) - mean_recall = float(np.mean(trial_recalls)) if trial_recalls else np.nan - n_workers_val = build_params.get("num_workers", None) + # per-trial lists + buf_init = [] + enq = [] + wait = [] + agg = [] + tot = [] + p_buf = [] + p_enq = [] + p_wait = [] + p_agg = [] + p_tot = [] + recs = [] + + for qi, q in enumerate(queries): + res = idx.search(q.unsqueeze(0).float(), k, **s_params) + ti = res.timing_info + + # child (worker_scan) timers + buf_init.append(ti.buffer_init_time_ns) + enq.append(ti.job_enqueue_time_ns) + wait.append(ti.job_wait_time_ns) + agg.append(ti.result_aggregate_time_ns) + tot.append(ti.total_time_ns) + + # parent timers if present + pi = getattr(ti, "parent_info", None) + if pi: + p_buf.append(pi.buffer_init_time_ns) + p_enq.append(pi.job_enqueue_time_ns) + p_wait.append(pi.job_wait_time_ns) + p_agg.append(pi.result_aggregate_time_ns) + p_tot.append(pi.total_time_ns) + else: + p_buf.append(0) + p_enq.append(0) + p_wait.append(0) + p_agg.append(0) + p_tot.append(0) + + # recall on first trial + if gt is not None and t == 0: + preds = extract_ids(res)[0] + recs.append(float(compute_recall(torch.tensor([preds]), gt[qi:qi+1], k).item())) + + # compute per-trial means (convert ns to ms where appropriate) + child_buf_init_trials.append(np.mean(buf_init)/1e6) + child_enq_trials .append(np.mean(enq) /1e6) + child_wait_trials .append(np.mean(wait) /1e6) + child_agg_trials .append(np.mean(agg) /1e6) + child_total_trials .append(np.mean(tot) /1e6) + parent_buf_init_trials.append(np.mean(p_buf)/1e6) + parent_enq_trials .append(np.mean(p_enq)/1e6) + parent_wait_trials .append(np.mean(p_wait)/1e6) + parent_agg_trials .append(np.mean(p_agg)/1e6) + parent_total_trials .append(np.mean(p_tot)/1e6) + recall_trials.append(np.mean(recs) if recs else np.nan) + + print(f" [{name} trial {t+1}/{trials}] " + f"child_tot={child_total_trials[-1]:.2f}ms, " + f"parent_tot={parent_total_trials[-1]:.2f}ms, " + f"R@{k}={recall_trials[-1]:.4f}") + + # assemble result row row = { - "index": idx_name, - "n_workers": n_workers_val, - "mean_latency_ms": mean_lat, - "std_latency_ms": std_lat, - f"recall_at_{k}": mean_recall, + "index": name, + "n_workers": b_params.get("num_workers", np.nan), + "recall_at_%d"%k: float(np.mean(recall_trials)), + "child_buffer_init_ms": float(np.mean(child_buf_init_trials)), + "child_enqueue_ms": float(np.mean(child_enq_trials)), + "child_wait_ms": float(np.mean(child_wait_trials)), + "child_aggregate_ms": float(np.mean(child_agg_trials)), + "child_total_ms": float(np.mean(child_total_trials)), + "parent_buffer_init_ms":float(np.mean(parent_buf_init_trials)), + "parent_enqueue_ms": float(np.mean(parent_enq_trials)), + "parent_wait_ms": float(np.mean(parent_wait_trials)), + "parent_aggregate_ms": float(np.mean(parent_agg_trials)), + "parent_total_ms": float(np.mean(parent_total_trials)), } all_rows.append(row) pd.DataFrame([row]).to_csv(idx_csv, index=False) - # Save Unified CSV + # write unified CSV df = pd.DataFrame(all_rows) out_csv = out_dir / csv_name df.to_csv(out_csv, index=False) - print(f"\n[RESULT] Results written to {out_csv}") + print(f"\n[RESULT] Unified results → {out_csv}") + + # Plot child total latency + plt.figure() + for nm in df["index"].unique(): + sub = df[df["index"]==nm] + lbl = (nm if sub["n_workers"].isnull().all() + else f"{nm} (nw={int(sub['n_workers'].iloc[0])})") + plt.errorbar(sub["n_workers"], sub["child_total_ms"], + yerr=df[df['index']==nm]["child_total_ms"].std(), + marker="o", capsize=5, label=lbl) + plt.xscale("symlog", base=2) + plt.xlabel("Num Workers") + plt.ylabel("Mean Child Total Latency (ms)") + plt.title("Child Latency Breakdown (per-index)") + plt.legend(); plt.tight_layout() + plt.savefig(out_dir/ f"{out_csv.stem}_child_breakdown.png") + print(f"[PLOT] Child breakdown → {out_dir/ f'{out_csv.stem}_child_breakdown.png'}") - # Plot Latency + # Plot recall plt.figure() - for idx_name in df["index"].unique(): - subset = df[df["index"] == idx_name] - label = f"{idx_name}" if subset["n_workers"].isnull().all() else f"{idx_name} (n_workers={subset['n_workers'].iloc[0]})" - plt.errorbar( - subset["n_workers"] if "n_workers" in subset else subset.index, - subset["mean_latency_ms"], - yerr=subset["std_latency_ms"], - marker="o", - capsize=5, - label=label - ) + for nm in df["index"].unique(): + sub = df[df["index"]==nm] + lbl = (nm if sub["n_workers"].isnull().all() + else f"{nm} (nw={int(sub['n_workers'].iloc[0])})") + plt.plot(sub["n_workers"], sub[f"recall_at_{k}"], marker="o", label=lbl) plt.xscale("symlog", base=2) plt.xlabel("Num Workers") - plt.ylabel("Mean Latency (ms)") - plt.title("Batch Query Latency (per-index)") - plt.legend() - plot_file = out_dir / f"{out_csv.stem}_latency.png" - plt.tight_layout() - plt.savefig(plot_file) - print(f"[PLOT] Saved to {plot_file}") - - # Plot Recall - if f"recall_at_{k}" in df.columns: - plt.figure() - for idx_name in df["index"].unique(): - subset = df[df["index"] == idx_name] - label = f"{idx_name}" if subset["n_workers"].isnull().all() else f"{idx_name} (n_workers={subset['n_workers'].iloc[0]})" - plt.plot( - subset["n_workers"] if "n_workers" in subset else subset.index, - subset[f"recall_at_{k}"], - marker="o", - label=label - ) - plt.xscale("symlog", base=2) - plt.xlabel("Num Workers") - plt.ylabel(f"Recall@{k}") - plt.title(f"Recall@{k} (per-index)") - plt.legend() - plot_file = out_dir / f"{out_csv.stem}_recall.png" - plt.tight_layout() - plt.savefig(plot_file) - print(f"[PLOT] Recall plot saved to {plot_file}") \ No newline at end of file + plt.ylabel(f"Recall@{k}") + plt.title(f"Recall@{k} (per-index)") + plt.legend(); plt.tight_layout() + plt.savefig(out_dir/ f"{out_csv.stem}_recall.png") + print(f"[PLOT] Recall plot → {out_dir/ f'{out_csv.stem}_recall.png'}") \ No newline at end of file From adf7bd4ebc1104b6b5bdc7790cc1f6776ac26681 Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 16:57:42 -0500 Subject: [PATCH 182/323] update multi-experiment to track detailed timers --- .../osdi2025/numa_multi_query/run.py | 342 ++++++++---------- 1 file changed, 158 insertions(+), 184 deletions(-) diff --git a/test/experiments/osdi2025/numa_multi_query/run.py b/test/experiments/osdi2025/numa_multi_query/run.py index 25c89fdc..6c826178 100644 --- a/test/experiments/osdi2025/numa_multi_query/run.py +++ b/test/experiments/osdi2025/numa_multi_query/run.py @@ -1,24 +1,24 @@ #!/usr/bin/env python3 """ -NUMA Batch-Query Latency and Recall Benchmark (detailed timers) +NUMA Batch-Query Latency and Recall Benchmark (with detailed child/parent timers) Benchmarks a suite of ANN indexes (Quake, Faiss-IVF, SCANN, HNSW, DiskANN, SVS, etc.) -for per-query latency breakdown and recall@k under configurable build and search parameters. -Results are cached and only recomputed if the 'overwrite' flag is set in the configuration. -Outputs per-index CSVs with detailed timing info and unified CSV + plots. +for batch-query latency, per-phase breakdown, and recall@k under configurable parameters. +Results are cached and only recomputed if 'overwrite' is set. +Outputs per-index CSVs with detailed timers, a unified CSV, and plots. Usage: - python numa_latency_batch_experiment.py numa_latency_experiment.yaml output_dir + python numa_latency_batch_experiment.py nna_latency_experiment.yaml output_dir """ import sys from pathlib import Path -ing = yaml = None import yaml import numpy as np torch = None import torch import pandas as pd import matplotlib.pyplot as plt +from time import time from quake.utils import compute_recall from quake.datasets.ann_datasets import load_dataset @@ -50,15 +50,15 @@ } -def build_and_save(index_cls, params, vecs, path): - """Build an index and save it to disk.""" - idx = index_cls() - idx.build(vecs, **params) - idx.save(str(path)) +def build_and_save_index(index_class, build_params, base_vecs, index_file): + """Build and save an index to disk.""" + idx = index_class() + idx.build(base_vecs, **build_params) + idx.save(str(index_file)) def extract_ids(res): - """Normalize returned IDs array.""" + """Normalize returned prediction IDs to a NumPy array.""" if hasattr(res, "ids"): arr = res.ids elif hasattr(res, "I"): @@ -66,220 +66,194 @@ def extract_ids(res): elif hasattr(res, "indices"): arr = res.indices else: - raise RuntimeError("Search result has no id field.") + raise RuntimeError("Cannot extract predicted indices from search result.") if isinstance(arr, torch.Tensor): arr = arr.cpu().numpy() - return np.asarray(arr, dtype=np.int64) - - -def run_experiment(cfg_path: str, output_dir: str) -> None: - # Load config - cfg = yaml.safe_load(Path(cfg_path).read_text()) - ds_cfg = cfg["dataset"] - k = ds_cfg["k"] - q_n = ds_cfg["num_queries"] - trials = cfg.get("trials", 5) - warmup_n = cfg.get("warmup", 10) - idx_cfgs = cfg["indexes"] - csv_name = cfg.get("output", {}).get("results_csv", "numa_results.csv") + return np.asarray(arr) + + +def run_experiment(cfg_path: str, output_dir: str): + cfg = yaml.safe_load(Path(cfg_path).read_text()) + ds = cfg["dataset"] + queries_n = ds["num_queries"] + k = ds["k"] + trials = cfg.get("trials", 5) + warmup = cfg.get("warmup", 10) + indexes_cfg = cfg["indexes"] + csv_name = cfg.get("output", {}).get("results_csv", "numa_results.csv") overwrite = cfg.get("overwrite", False) - out_dir = Path(output_dir).expanduser().absolute() + out_dir = Path(output_dir) out_dir.mkdir(parents=True, exist_ok=True) - # Load dataset - print(f"[INFO] Loading dataset {ds_cfg['name']}...") - base_vecs, queries, gt = load_dataset(ds_cfg["name"]) - queries = queries[:q_n] - gt = gt[:q_n] if gt is not None else None + print(f"[INFO] Loading dataset '{ds['name']}'...") + base_vecs, queries, gt = load_dataset(ds["name"]) + queries = queries[:queries_n] + gt = gt[:queries_n] if gt is not None else None - # Build shared Quake base index once + # --- Build shared Quake base index once --- quake_base_params = None - for c in idx_cfgs: - if c["index"] == "Quake": - p = dict(c.get("build_params", {})) - p.pop("num_workers", None) - p.pop("use_numa", None) - quake_base_params = p + quake_base_index_file = out_dir / "Quake_base_index.bin" + for idx_cfg in indexes_cfg: + if idx_cfg["index"] == "Quake": + quake_base_params = dict(idx_cfg.get("build_params", {})) + quake_base_params.pop("num_workers", None) + quake_base_params.pop("use_numa", None) break + if quake_base_params and not quake_base_index_file.exists(): + print(f"[BUILD] Quake (shared) base index ...") + build_and_save_index(QuakeWrapper, quake_base_params, base_vecs, quake_base_index_file) + print(f"[SAVE] Quake base index saved at {quake_base_index_file}") - quake_base_path = out_dir / "Quake_base_index.bin" - if quake_base_params and not quake_base_path.exists(): - print("[BUILD] Quake (shared) base index...") - build_and_save(QuakeWrapper, quake_base_params, base_vecs, quake_base_path) - print(f"[SAVE] Quake base index → {quake_base_path}") - - # Collect results per-index all_rows = [] - for cfg_i in idx_cfgs: - name = cfg_i["name"] - typ = cfg_i["index"] - b_params = dict(cfg_i.get("build_params", {})) - s_params = dict(cfg_i.get("search_params", {})) - cls = INDEX_CLASSES.get(typ) - if cls is None: - print(f"[WARN] Unknown index type '{typ}', skipping.") + + for idx_cfg in indexes_cfg: + name = idx_cfg["name"] + idx_type = idx_cfg["index"] + build_params = dict(idx_cfg.get("build_params", {})) + search_params= dict(idx_cfg.get("search_params", {})) + IndexClass = INDEX_CLASSES.get(idx_type) + if IndexClass is None: + print(f"[WARN] Unknown index type: {idx_type}. Skipping.") continue - idx_csv = out_dir / f"{name}_results.csv" - idx_path = quake_base_path if typ == "Quake" else out_dir / f"{name}_index.bin" + idx_csv = out_dir / f"{name}_results.csv" + idx_file = quake_base_index_file if idx_type == "Quake" else out_dir / f"{name}_index.bin" - # Skip if exists if idx_csv.exists() and not overwrite: - print(f"[SKIP] {name} – results exist.") + print(f"[SKIP] {name}: Results exist. Use overwrite: true to rerun.") all_rows.append(pd.read_csv(idx_csv).iloc[0].to_dict()) continue - # Build if needed - if typ != "Quake" and not idx_path.exists(): + # Build non-Quake if necessary + if idx_type != "Quake" and not idx_file.exists(): print(f"[BUILD] {name} index...") - build_and_save(cls, b_params, base_vecs, idx_path) - print(f"[SAVE] Index → {idx_path}") - - # Load index with params - if typ == "Quake": - load_kw = {k: b_params[k] for k in ("use_numa", "num_workers") if k in b_params} - idx = QuakeWrapper(); idx.load(str(idx_path), **load_kw) - elif typ == "DiskANN": - idx = cls(); b_params.pop("metric", None); idx.load(str(idx_path), **b_params) + build_and_save_index(IndexClass, build_params, base_vecs, idx_file) + print(f"[SAVE] Index saved at {idx_file}") + + # Load index + if idx_type == "Quake": + load_kwargs = {k: build_params[k] for k in ("use_numa","num_workers") if k in build_params} + idx = QuakeWrapper(); idx.load(str(idx_file), **load_kwargs) + elif idx_type == "DiskANN": + idx = IndexClass(); build_params.pop("metric", None); idx.load(str(idx_file), **build_params) else: - idx = cls(); idx.load(str(idx_path)) + idx = IndexClass(); idx.load(str(idx_file)) - # Warm-up runs - for _ in range(min(warmup_n, len(queries))): - for q in queries: - idx.search(q.unsqueeze(0).float(), k, **s_params) + # Warmup (batch) + for _ in range(min(warmup,1)): + idx.search(queries, k, **search_params) - # Prepare accumulators for detailed timers + # Prepare per-phase accumulators child_buf_init_trials = [] - child_enq_trials = [] + child_enqueue_trials = [] child_wait_trials = [] child_agg_trials = [] child_total_trials = [] - parent_buf_init_trials = [] - parent_enq_trials = [] - parent_wait_trials = [] - parent_agg_trials = [] - parent_total_trials = [] - recall_trials = [] - - # Benchmark trials + parent_buf_init_trials= [] + parent_enqueue_trials = [] + parent_wait_trials = [] + parent_agg_trials = [] + parent_total_trials = [] + recall_trials = [] + + # Benchmark Trials (batch) for t in range(trials): - # per-trial lists - buf_init = [] - enq = [] - wait = [] - agg = [] - tot = [] - p_buf = [] - p_enq = [] - p_wait = [] - p_agg = [] - p_tot = [] - recs = [] - - for qi, q in enumerate(queries): - res = idx.search(q.unsqueeze(0).float(), k, **s_params) - ti = res.timing_info - - # child (worker_scan) timers - buf_init.append(ti.buffer_init_time_ns) - enq.append(ti.job_enqueue_time_ns) - wait.append(ti.job_wait_time_ns) - agg.append(ti.result_aggregate_time_ns) - tot.append(ti.total_time_ns) + res = idx.search(queries, k, **search_params) + ti = res.timing_info + + # Child (worker_scan) timings + child_buf_init_trials.append(ti.buffer_init_time_ns / 1e6) + child_enqueue_trials .append(ti.job_enqueue_time_ns / 1e6) + child_wait_trials .append(ti.job_wait_time_ns / 1e6) + child_agg_trials .append(ti.result_aggregate_time_ns / 1e6) + child_total_trials .append(ti.total_time_ns / 1e6) + + # Parent timings + pi = getattr(ti, "parent_info", None) + if pi: + parent_buf_init_trials.append(pi.buffer_init_time_ns / 1e6) + parent_enqueue_trials .append(pi.job_enqueue_time_ns / 1e6) + parent_wait_trials .append(pi.job_wait_time_ns / 1e6) + parent_agg_trials .append(pi.result_aggregate_time_ns / 1e6) + parent_total_trials .append(pi.total_time_ns / 1e6) + else: + parent_buf_init_trials.append(0.0) + parent_enqueue_trials .append(0.0) + parent_wait_trials .append(0.0) + parent_agg_trials .append(0.0) + parent_total_trials .append(0.0) + + # Recall for this trial + if gt is not None: + preds_arr = extract_ids(res) + recall = float(compute_recall(preds_arr, gt, k).mean()) + recall_trials.append(recall) + print(f" [{name} trial {t+1}/{trials}] child_total={child_total_trials[-1]:.2f} ms, parent_total={parent_total_trials[-1]:.2f} ms | recall@{k} {recall:.4f}") + else: + print(f" [{name} trial {t+1}/{trials}] child_total={child_total_trials[-1]:.2f} ms, parent_total={parent_total_trials[-1]:.2f} ms") + + # Aggregate metrics + mean_lat = float(np.mean(child_total_trials)) + std_lat = float(np.std(child_total_trials)) + mean_recall= float(np.mean(recall_trials)) if recall_trials else np.nan + n_workers = build_params.get("num_workers", None) - # parent timers if present - pi = getattr(ti, "parent_info", None) - if pi: - p_buf.append(pi.buffer_init_time_ns) - p_enq.append(pi.job_enqueue_time_ns) - p_wait.append(pi.job_wait_time_ns) - p_agg.append(pi.result_aggregate_time_ns) - p_tot.append(pi.total_time_ns) - else: - p_buf.append(0) - p_enq.append(0) - p_wait.append(0) - p_agg.append(0) - p_tot.append(0) - - # recall on first trial - if gt is not None and t == 0: - preds = extract_ids(res)[0] - recs.append(float(compute_recall(torch.tensor([preds]), gt[qi:qi+1], k).item())) - - # compute per-trial means (convert ns to ms where appropriate) - child_buf_init_trials.append(np.mean(buf_init)/1e6) - child_enq_trials .append(np.mean(enq) /1e6) - child_wait_trials .append(np.mean(wait) /1e6) - child_agg_trials .append(np.mean(agg) /1e6) - child_total_trials .append(np.mean(tot) /1e6) - parent_buf_init_trials.append(np.mean(p_buf)/1e6) - parent_enq_trials .append(np.mean(p_enq)/1e6) - parent_wait_trials .append(np.mean(p_wait)/1e6) - parent_agg_trials .append(np.mean(p_agg)/1e6) - parent_total_trials .append(np.mean(p_tot)/1e6) - recall_trials.append(np.mean(recs) if recs else np.nan) - - print(f" [{name} trial {t+1}/{trials}] " - f"child_tot={child_total_trials[-1]:.2f}ms, " - f"parent_tot={parent_total_trials[-1]:.2f}ms, " - f"R@{k}={recall_trials[-1]:.4f}") - - # assemble result row row = { "index": name, - "n_workers": b_params.get("num_workers", np.nan), - "recall_at_%d"%k: float(np.mean(recall_trials)), - "child_buffer_init_ms": float(np.mean(child_buf_init_trials)), - "child_enqueue_ms": float(np.mean(child_enq_trials)), - "child_wait_ms": float(np.mean(child_wait_trials)), - "child_aggregate_ms": float(np.mean(child_agg_trials)), - "child_total_ms": float(np.mean(child_total_trials)), + "n_workers": n_workers, + f"recall_at_{k}": mean_recall, + "mean_latency_ms": mean_lat, + "std_latency_ms": std_lat, + "child_buffer_init_ms":float(np.mean(child_buf_init_trials)), + "child_enqueue_ms": float(np.mean(child_enqueue_trials)), + "child_wait_ms": float(np.mean(child_wait_trials)), + "child_aggregate_ms": float(np.mean(child_agg_trials)), + "child_total_ms": float(np.mean(child_total_trials)), "parent_buffer_init_ms":float(np.mean(parent_buf_init_trials)), - "parent_enqueue_ms": float(np.mean(parent_enq_trials)), - "parent_wait_ms": float(np.mean(parent_wait_trials)), - "parent_aggregate_ms": float(np.mean(parent_agg_trials)), - "parent_total_ms": float(np.mean(parent_total_trials)), + "parent_enqueue_ms": float(np.mean(parent_enqueue_trials)), + "parent_wait_ms": float(np.mean(parent_wait_trials)), + "parent_aggregate_ms": float(np.mean(parent_agg_trials)), + "parent_total_ms": float(np.mean(parent_total_trials)), } all_rows.append(row) pd.DataFrame([row]).to_csv(idx_csv, index=False) - # write unified CSV + # Save unified CSV df = pd.DataFrame(all_rows) out_csv = out_dir / csv_name df.to_csv(out_csv, index=False) - print(f"\n[RESULT] Unified results → {out_csv}") + print(f"\n[RESULT] Results written to {out_csv}") - # Plot child total latency + # Plot batch total latency (child only) plt.figure() - for nm in df["index"].unique(): - sub = df[df["index"]==nm] - lbl = (nm if sub["n_workers"].isnull().all() - else f"{nm} (nw={int(sub['n_workers'].iloc[0])})") - plt.errorbar(sub["n_workers"], sub["child_total_ms"], - yerr=df[df['index']==nm]["child_total_ms"].std(), - marker="o", capsize=5, label=lbl) + for idx_name in df["index"].unique(): + sub = df[df["index"]==idx_name] + label = f"{idx_name}" if pd.isna(sub["n_workers"]).all() else f"{idx_name} (n_workers={int(sub['n_workers'].iloc[0])})" + plt.errorbar( + sub["n_workers"], sub["child_total_ms"], + yerr=sub["child_total_ms"].std(), + marker="o", capsize=5, label=label + ) plt.xscale("symlog", base=2) plt.xlabel("Num Workers") - plt.ylabel("Mean Child Total Latency (ms)") - plt.title("Child Latency Breakdown (per-index)") + plt.ylabel("Mean Child Batch Latency (ms)") + plt.title("Batch Query Child Total Latency") plt.legend(); plt.tight_layout() - plt.savefig(out_dir/ f"{out_csv.stem}_child_breakdown.png") - print(f"[PLOT] Child breakdown → {out_dir/ f'{out_csv.stem}_child_breakdown.png'}") + plt.savefig(out_dir / f"{out_csv.stem}_batch_latency.png") + print(f"[PLOT] Saved to {out_dir / f'{out_csv.stem}_batch_latency.png'}") # Plot recall - plt.figure() - for nm in df["index"].unique(): - sub = df[df["index"]==nm] - lbl = (nm if sub["n_workers"].isnull().all() - else f"{nm} (nw={int(sub['n_workers'].iloc[0])})") - plt.plot(sub["n_workers"], sub[f"recall_at_{k}"], marker="o", label=lbl) - plt.xscale("symlog", base=2) - plt.xlabel("Num Workers") - plt.ylabel(f"Recall@{k}") - plt.title(f"Recall@{k} (per-index)") - plt.legend(); plt.tight_layout() - plt.savefig(out_dir/ f"{out_csv.stem}_recall.png") - print(f"[PLOT] Recall plot → {out_dir/ f'{out_csv.stem}_recall.png'}") \ No newline at end of file + if f"recall_at_{k}" in df.columns: + plt.figure() + for idx_name in df["index"].unique(): + sub = df[df["index"]==idx_name] + label = f"{idx_name}" if pd.isna(sub["n_workers"]).all() else f"{idx_name} (n_workers={int(sub['n_workers'].iloc[0])})" + plt.plot(sub["n_workers"], sub[f"recall_at_{k}"], marker="o", label=label) + plt.xscale("symlog", base=2) + plt.xlabel("Num Workers") + plt.ylabel(f"Recall@{k}") + plt.title(f"Recall@{k} (batch)") + plt.legend(); plt.tight_layout() + plt.savefig(out_dir / f"{out_csv.stem}_batch_recall.png") + print(f"[PLOT] Recall plot saved to {out_dir / f'{out_csv.stem}_batch_recall.png'}") \ No newline at end of file From 37d3e67bd71d5f3f1ba069d374ef3638c9a65e30 Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 17:02:00 -0500 Subject: [PATCH 183/323] update multi-experiment to track detailed timers --- src/cpp/bindings/wrap.cpp | 2 ++ src/cpp/include/common.h | 1 + src/cpp/src/query_coordinator.cpp | 4 ++-- test/experiments/osdi2025/numa_multi_query/run.py | 7 +++++++ 4 files changed, 12 insertions(+), 2 deletions(-) diff --git a/src/cpp/bindings/wrap.cpp b/src/cpp/bindings/wrap.cpp index 35c98e3d..d4aa0d87 100644 --- a/src/cpp/bindings/wrap.cpp +++ b/src/cpp/bindings/wrap.cpp @@ -324,6 +324,8 @@ PYBIND11_MODULE(_bindings, m) { "Total time taken for the search operation in nanoseconds.") .def_readwrite("buffer_init_time_ns", &SearchTimingInfo::buffer_init_time_ns, "Time spent on initializing buffers in nanoseconds.") + .def_readwrite("copy_query_time_ns", &SearchTimingInfo::copy_query_time_ns, + "Time spent on copying query vectors to NUMA buffers in nanoseconds.") .def_readwrite("job_enqueue_time_ns", &SearchTimingInfo::job_enqueue_time_ns, "Time spent on creating jobs in nanoseconds.") .def_readwrite("boundary_distance_time_ns", &SearchTimingInfo::boundary_distance_time_ns, diff --git a/src/cpp/include/common.h b/src/cpp/include/common.h index aa102738..b165ac5f 100644 --- a/src/cpp/include/common.h +++ b/src/cpp/include/common.h @@ -238,6 +238,7 @@ struct SearchTimingInfo { // main thread counters for worker scan int64_t buffer_init_time_ns; ///< Time spent on initializing buffers in nanoseconds. + int64_t copy_query_time_ns; int64_t job_enqueue_time_ns; ///< Time spent on creating jobs in nanoseconds. int64_t boundary_distance_time_ns; ///< Time spent on computing boundary distances in nanoseconds. int64_t aps_time_ns; ///< Time spent on APS in nanoseconds. diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index fd36036e..8c00c512 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -670,8 +670,8 @@ std::shared_ptr QueryCoordinator::worker_scan( res->timing_info->buffer_init_time_ns = duration_cast(s2 - s1).count(); -// res->timing_info->copy_query_time_ns = -// duration_cast(s3 - s2).count(); + res->timing_info->copy_query_time_ns = + duration_cast(s3 - s2).count(); res->timing_info->job_enqueue_time_ns = duration_cast(s4 - s3).count(); res->timing_info->job_wait_time_ns = diff --git a/test/experiments/osdi2025/numa_multi_query/run.py b/test/experiments/osdi2025/numa_multi_query/run.py index 6c826178..10eb5fbb 100644 --- a/test/experiments/osdi2025/numa_multi_query/run.py +++ b/test/experiments/osdi2025/numa_multi_query/run.py @@ -146,11 +146,13 @@ def run_experiment(cfg_path: str, output_dir: str): # Prepare per-phase accumulators child_buf_init_trials = [] + child_copy_query_trials = [] child_enqueue_trials = [] child_wait_trials = [] child_agg_trials = [] child_total_trials = [] parent_buf_init_trials= [] + parent_copy_query_trials = [] parent_enqueue_trials = [] parent_wait_trials = [] parent_agg_trials = [] @@ -164,6 +166,7 @@ def run_experiment(cfg_path: str, output_dir: str): # Child (worker_scan) timings child_buf_init_trials.append(ti.buffer_init_time_ns / 1e6) + child_copy_query_trials.append(ti.copy_query_time_ns / 1e6) child_enqueue_trials .append(ti.job_enqueue_time_ns / 1e6) child_wait_trials .append(ti.job_wait_time_ns / 1e6) child_agg_trials .append(ti.result_aggregate_time_ns / 1e6) @@ -173,12 +176,14 @@ def run_experiment(cfg_path: str, output_dir: str): pi = getattr(ti, "parent_info", None) if pi: parent_buf_init_trials.append(pi.buffer_init_time_ns / 1e6) + parent_copy_query_trials.append(pi.copy_query_time_ns / 1e6) parent_enqueue_trials .append(pi.job_enqueue_time_ns / 1e6) parent_wait_trials .append(pi.job_wait_time_ns / 1e6) parent_agg_trials .append(pi.result_aggregate_time_ns / 1e6) parent_total_trials .append(pi.total_time_ns / 1e6) else: parent_buf_init_trials.append(0.0) + parent_copy_query_trials.append(0.0) parent_enqueue_trials .append(0.0) parent_wait_trials .append(0.0) parent_agg_trials .append(0.0) @@ -206,11 +211,13 @@ def run_experiment(cfg_path: str, output_dir: str): "mean_latency_ms": mean_lat, "std_latency_ms": std_lat, "child_buffer_init_ms":float(np.mean(child_buf_init_trials)), + "child_copy_query_ms": float(np.mean(child_copy_query_trials)), "child_enqueue_ms": float(np.mean(child_enqueue_trials)), "child_wait_ms": float(np.mean(child_wait_trials)), "child_aggregate_ms": float(np.mean(child_agg_trials)), "child_total_ms": float(np.mean(child_total_trials)), "parent_buffer_init_ms":float(np.mean(parent_buf_init_trials)), + "parent_copy_query_ms": float(np.mean(parent_copy_query_trials)), "parent_enqueue_ms": float(np.mean(parent_enqueue_trials)), "parent_wait_ms": float(np.mean(parent_wait_trials)), "parent_aggregate_ms": float(np.mean(parent_agg_trials)), From 3b08e4684ec0490259cd3405f5e417123ccb6086 Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 17:15:35 -0500 Subject: [PATCH 184/323] configure num parent workers --- src/cpp/include/quake_index.h | 2 +- src/cpp/src/quake_index.cpp | 7 +------ src/python/index_wrappers/quake.py | 10 +++------- test/experiments/osdi2025/numa_multi_query/run.py | 2 +- test/experiments/osdi2025/numa_single_query/run.py | 2 +- 5 files changed, 7 insertions(+), 16 deletions(-) diff --git a/src/cpp/include/quake_index.h b/src/cpp/include/quake_index.h index d2377aaa..0358aec9 100644 --- a/src/cpp/include/quake_index.h +++ b/src/cpp/include/quake_index.h @@ -122,7 +122,7 @@ class QuakeIndex { * @param path Path to load the index. * @param n_workers Number of workers to use for query processing. */ - void load(const std::string &path, int n_workers = 0, bool use_numa=false); + void load(const std::string &path, int n_workers = 0, bool use_numa=false, int parent_n_workers=0); /** * @brief Get the total number of vectors in the index. diff --git a/src/cpp/src/quake_index.cpp b/src/cpp/src/quake_index.cpp index 8419d64f..36f04661 100644 --- a/src/cpp/src/quake_index.cpp +++ b/src/cpp/src/quake_index.cpp @@ -236,7 +236,7 @@ void QuakeIndex::save(const std::string& dir_path) { std::cout << "[QuakeIndex::save] Index saved to directory: " << dir_path << "\n"; } -void QuakeIndex::load(const std::string& dir_path, int n_workers, bool use_numa) { +void QuakeIndex::load(const std::string& dir_path, int n_workers, bool use_numa, int parent_n_workers) { namespace fs = std::filesystem; if (!fs::exists(dir_path) || !fs::is_directory(dir_path)) { @@ -282,11 +282,6 @@ void QuakeIndex::load(const std::string& dir_path, int n_workers, bool use_numa) if (fs::exists(parent_dir) && fs::is_directory(parent_dir)) { parent_ = std::make_shared(); int n_parts = partition_manager_->nlist(); - - // don't put too many parent workers. maximum of 1 worker per 500 partitions - int parent_n_workers = std::max(1, n_parts / 1000); - parent_n_workers = std::min(parent_n_workers, n_workers); - parent_->load(parent_dir, parent_n_workers, use_numa); partition_manager_->parent_ = parent_; } else { diff --git a/src/python/index_wrappers/quake.py b/src/python/index_wrappers/quake.py index 033abdba..982165ca 100644 --- a/src/python/index_wrappers/quake.py +++ b/src/python/index_wrappers/quake.py @@ -203,12 +203,9 @@ def load( self, filename: str, num_workers: int = 0, + parent_num_workers: int = 0, use_numa: bool = False, verbose: bool = False, - verify_numa: bool = False, - same_core: bool = True, - use_centroid_workers: bool = False, - use_adaptive_n_probe: bool = False, ): """ Load the index from a file. @@ -216,11 +213,10 @@ def load( :param filename: The name of the file to load the index from. """ print( - f"Loading index from {filename}, with {num_workers} workers, use_numa={use_numa}, verbose={verbose}, " - f"verify_numa={verify_numa}, same_core={same_core}, use_centroid_workers={use_centroid_workers}" + f"Loading index from {filename}, with {num_workers} workers, use_numa={use_numa}, parent_num_workers={parent_num_workers}" ) self.index = QuakeIndex() - self.index.load(str(filename), num_workers, use_numa) + self.index.load(str(filename), num_workers, use_numa, parent_num_workers) def centroids(self) -> torch.Tensor: """ diff --git a/test/experiments/osdi2025/numa_multi_query/run.py b/test/experiments/osdi2025/numa_multi_query/run.py index 10eb5fbb..9c798d36 100644 --- a/test/experiments/osdi2025/numa_multi_query/run.py +++ b/test/experiments/osdi2025/numa_multi_query/run.py @@ -133,7 +133,7 @@ def run_experiment(cfg_path: str, output_dir: str): # Load index if idx_type == "Quake": - load_kwargs = {k: build_params[k] for k in ("use_numa","num_workers") if k in build_params} + load_kwargs = {k: build_params[k] for k in ("use_numa","num_workers", "parent_num_workers") if k in build_params} idx = QuakeWrapper(); idx.load(str(idx_file), **load_kwargs) elif idx_type == "DiskANN": idx = IndexClass(); build_params.pop("metric", None); idx.load(str(idx_file), **build_params) diff --git a/test/experiments/osdi2025/numa_single_query/run.py b/test/experiments/osdi2025/numa_single_query/run.py index 8f08bce2..a787c0d2 100644 --- a/test/experiments/osdi2025/numa_single_query/run.py +++ b/test/experiments/osdi2025/numa_single_query/run.py @@ -129,7 +129,7 @@ def run_experiment(cfg_path: str, output_dir: str) -> None: # Load index if typ == "Quake": - load_kw = {k: b_params[k] for k in ("use_numa", "num_workers") if k in b_params} + load_kw = {k: b_params[k] for k in ("use_numa", "num_workers", "parent_num_workers") if k in b_params} idx = QuakeWrapper(); idx.load(str(idx_path), **load_kw) elif typ == "DiskANN": idx = cls(); b_params.pop("metric", None); idx.load(str(idx_path), **b_params) From d5857154947626c02221f5c8a3dc3c2073332655 Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 17:29:18 -0500 Subject: [PATCH 185/323] configure num parent workers --- src/cpp/src/query_coordinator.cpp | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 8c00c512..aa51a433 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -950,11 +950,16 @@ shared_ptr QueryCoordinator::search(Tensor x, shared_ptr(); if (search_params->parent_params == nullptr) { - parent_search_params->recall_target = .99; +// parent_search_params->recall_target = .99; parent_search_params->use_precomputed = search_params->use_precomputed; parent_search_params->recompute_threshold = search_params->recompute_threshold; - parent_search_params->initial_search_fraction = .5; +// parent_search_params->initial_search_fraction = .5; parent_search_params->batched_scan = false; + + if (x.size(0) > 10) { + parent_search_params->batched_scan = true; + } + } else { parent_search_params = search_params->parent_params; } From 2ec93f1851a7b699e73775c1095b0b48c4ef24ae Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 17:35:05 -0500 Subject: [PATCH 186/323] configure num parent workers --- src/cpp/include/query_coordinator.h | 1 + src/cpp/src/query_coordinator.cpp | 45 +++++++++++++++++++---------- 2 files changed, 31 insertions(+), 15 deletions(-) diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index 2a78379a..60f8bd33 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -32,6 +32,7 @@ struct ScanJob { std::shared_ptr> query_ids; ///< Global query IDs; used in batched mode. std::shared_ptr> ranks; ///< Rank of the partition for each query + bool scan_all = false; }; /** diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index aa51a433..cd4d9a7a 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -295,22 +295,37 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, } // Gather queries into res.batch_queries (as in your original code) - for (int64_t i = 0; i < job.num_queries; ++i) { - std::memcpy(res.batch_queries + i * D, - job.query_vector + job.query_ids->at(i) * D, - D * sizeof(float)); + + if (job.scan_all) { + batched_scan_list(nr.local_query_buffer, + codes, + ids, + job.num_queries, + part_size, + D, + res.topk_buffer_pool, // Vector of TopkBuffer shared_ptrs + metric_, + res.batch_distances, // Scratch space for distances + res.batch_ids); // Scratch space for IDs + } else { + for (int64_t i = 0; i < job.num_queries; ++i) { + std::memcpy(res.batch_queries + i * D, + nr.local_query_buffer + job.query_ids->at(i) * D, + D * sizeof(float)); + } + + batched_scan_list(res.batch_queries, + codes, + ids, + job.num_queries, + part_size, + D, + res.topk_buffer_pool, // Vector of TopkBuffer shared_ptrs + metric_, + res.batch_distances, // Scratch space for distances + res.batch_ids); // Scratch space for IDs } - batched_scan_list(res.batch_queries, - codes, - ids, - job.num_queries, - part_size, - D, - res.topk_buffer_pool, // Vector of TopkBuffer shared_ptrs - metric_, - res.batch_distances, // Scratch space for distances - res.batch_ids); // Scratch space for IDs scan_successful = true; } catch (const std::exception& e) { @@ -453,7 +468,7 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, job.k = params->k; job.query_vector = x.data_ptr(); job.num_queries = x.size(0); - job.query_ids = all_query_ids_ptr; + job.scan_all = true; job.ranks = make_shared>(x.size(0), i); int core_id = partition_manager_->get_partition_core_id(pids_acc[i]); if (core_id < 0) { From f5bc2246e6d658033441e350f52a693738b76d8a Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 17:42:21 -0500 Subject: [PATCH 187/323] configure num parent workers --- src/cpp/src/query_coordinator.cpp | 219 +++++++++++++----------------- 1 file changed, 93 insertions(+), 126 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index cd4d9a7a..40ab9733 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -215,157 +215,124 @@ void QueryCoordinator::handle_nonbatched_job(const ScanJob &job, void QueryCoordinator::handle_batched_job(const ScanJob &job, CoreResources &res, NUMAResources &nr) { - // Number of queries in this batch, top-k, vector dimension, and NUMA node + // Total queries in this batch, Top-K, vector dim, and NUMA node int64_t Q = job.num_queries; int K = job.k; int D = partition_manager_->d(); int node = cpu_numa_node(res.core_id); - // Ensure we have exactly Q TopK buffers, each with capacity = max(100*K, 10000) - size_t cap = std::max(100 * K, 10000); - if (res.topk_buffer_pool.size() < (size_t)Q) { - res.topk_buffer_pool.resize(Q); - for (int64_t i = 0; i < Q; ++i) { - res.topk_buffer_pool[i] = std::make_shared( - K, - metric_ == faiss::METRIC_INNER_PRODUCT, - /*cap=*/cap, - /*node=*/node - ); + // Retrieve partition data once + const float *codes = (float*)partition_manager_->partition_store_->get_codes(job.partition_id); + const int64_t *ids = (int64_t*)partition_manager_->partition_store_->get_ids(job.partition_id); + int64_t part_size = partition_manager_->partition_store_->list_size(job.partition_id); + if (!codes || !ids || part_size <= 0) { + // Empty or invalid partition: enqueue empty results + for (int64_t q : *job.query_ids) { + result_queue_.enqueue(ResultJob{(int)q, 0, {}, {}}); } - } else { - for (int64_t i = 0; i < Q; ++i) { + return; + } + + // Maximum sub-batch size to bound memory + static constexpr int64_t MAX_SUBBATCH = 128; + + // Process queries in chunks of at most MAX_SUBBATCH + for (int64_t offset = 0; offset < Q; offset += MAX_SUBBATCH) { + int64_t chunk = std::min(Q - offset, MAX_SUBBATCH); + + // 1) Prepare TopK buffers for this sub-batch + size_t cap = std::max(100 * K, 10000); + if (res.topk_buffer_pool.size() < (size_t)chunk) { + // grow pool to at least 'chunk' + size_t old = res.topk_buffer_pool.size(); + res.topk_buffer_pool.resize(chunk); + for (size_t i = old; i < (size_t)chunk; ++i) { + res.topk_buffer_pool[i] = std::make_shared( + K, + metric_ == faiss::METRIC_INNER_PRODUCT, + /*cap=*/cap, + /*node=*/node + ); + } + } + // reset first 'chunk' buffers + for (int64_t i = 0; i < chunk; ++i) { auto &buf = res.topk_buffer_pool[i]; buf->set_k(K); buf->reset(); } - } - // Allocate or grow the per-batch query buffer (size Q × D floats) - size_t need_q = size_t(Q) * D; - if (res.batch_q_capacity < need_q) { - if (res.batch_queries) { - quake_free(res.batch_queries, - res.batch_q_capacity * sizeof(float)); + // 2) Allocate or grow query gather buffer (chunk × D floats) + size_t need_q = size_t(chunk) * D; + if (res.batch_q_capacity < need_q) { + if (res.batch_queries) { + quake_free(res.batch_queries, + res.batch_q_capacity * sizeof(float)); + } + res.batch_queries = static_cast( + quake_alloc(need_q * sizeof(float), node)); + res.batch_q_capacity = need_q; } - res.batch_queries = static_cast(quake_alloc( - need_q * sizeof(float), node)); - res.batch_q_capacity = need_q; - } - // Allocate or grow the per-batch result buffers (size Q × K entries) - size_t need_r = size_t(Q) * size_t(K); - if (res.batch_res_capacity < need_r) { - if (res.batch_distances) { - quake_free(res.batch_distances, - res.batch_res_capacity * sizeof(float)); - } - if (res.batch_ids) { - quake_free(res.batch_ids, - res.batch_res_capacity * sizeof(int64_t)); + // 3) Allocate or grow result scratch buffers (chunk × K entries) + size_t need_r = size_t(chunk) * size_t(K); + if (res.batch_res_capacity < need_r) { + if (res.batch_distances) { + quake_free(res.batch_distances, + res.batch_res_capacity * sizeof(float)); + } + if (res.batch_ids) { + quake_free(res.batch_ids, + res.batch_res_capacity * sizeof(int64_t)); + } + res.batch_distances = static_cast( + quake_alloc(need_r * sizeof(float), node)); + res.batch_ids = static_cast( + quake_alloc(need_r * sizeof(int64_t), node)); + res.batch_res_capacity = need_r; } - res.batch_distances = static_cast( - quake_alloc(need_r * sizeof(float), node)); - res.batch_ids = static_cast( - quake_alloc(need_r * sizeof(int64_t), node)); - res.batch_res_capacity = need_r; - } - - // Initialize the result buffers to infinity (or –infinity) and –1 - float dist_init = (metric_ == faiss::METRIC_INNER_PRODUCT) - ? -std::numeric_limits::infinity() - : std::numeric_limits::infinity(); - for (size_t idx = 0; idx < need_r; ++idx) { - res.batch_distances[idx] = dist_init; - res.batch_ids [idx] = -1; - } - bool scan_successful = false; - try { - const float* codes = (float*)partition_manager_->partition_store_->get_codes(job.partition_id); - const int64_t* ids = (int64_t*)partition_manager_->partition_store_->get_ids(job.partition_id); - int64_t part_size = partition_manager_->partition_store_->list_size(job.partition_id); - int D = partition_manager_->d(); - - if (!codes || !ids || part_size <= 0) { - std::cerr << "[QueryCoordinator::handle_batched_job] Partition " << job.partition_id - << " invalid or empty before batched scan. Enqueuing empty results for batch.\n"; - // Let success remain false, loop below will send empty results - throw std::runtime_error("Batched scan pre-check failed for partition data"); + // 4) Initialize result scratch + float init_val = (metric_ == faiss::METRIC_INNER_PRODUCT) + ? -std::numeric_limits::infinity() + : std::numeric_limits::infinity(); + for (size_t i = 0; i < need_r; ++i) { + res.batch_distances[i] = init_val; + res.batch_ids [i] = -1; } - // Gather queries into res.batch_queries (as in your original code) - + // 5) Gather this sub-batch of query vectors if (job.scan_all) { - batched_scan_list(nr.local_query_buffer, - codes, - ids, - job.num_queries, - part_size, - D, - res.topk_buffer_pool, // Vector of TopkBuffer shared_ptrs - metric_, - res.batch_distances, // Scratch space for distances - res.batch_ids); // Scratch space for IDs + // use pre-copied NUMA buffer } else { - for (int64_t i = 0; i < job.num_queries; ++i) { - std::memcpy(res.batch_queries + i * D, - nr.local_query_buffer + job.query_ids->at(i) * D, - D * sizeof(float)); + for (int64_t i = 0; i < chunk; ++i) { + int qid = (*job.query_ids)[offset + i]; + float *dst = res.batch_queries + size_t(i) * D; + const float *src = nr.local_query_buffer + size_t(qid) * D; + std::memcpy(dst, src, D * sizeof(float)); } - - batched_scan_list(res.batch_queries, - codes, - ids, - job.num_queries, - part_size, - D, - res.topk_buffer_pool, // Vector of TopkBuffer shared_ptrs - metric_, - res.batch_distances, // Scratch space for distances - res.batch_ids); // Scratch space for IDs } - scan_successful = true; - - } catch (const std::exception& e) { - std::cerr << "[QueryCoordinator::handle_batched_job] Exception during batched scan for partition " - << job.partition_id << ": " << e.what() - << ". Will enqueue empty results for affected queries.\n"; - // scan_successful remains false - } catch (...) { - std::cerr << "[QueryCoordinator::handle_batched_job] Unknown exception during batched scan for partition " - << job.partition_id - << ". Will enqueue empty results for affected queries.\n"; - // scan_successful remains false - } + // 6) Perform the batched scan for this sub-batch + batched_scan_list( + job.scan_all ? nr.local_query_buffer : res.batch_queries, + codes, ids, + chunk, part_size, D, + res.topk_buffer_pool, + metric_, + res.batch_distances, + res.batch_ids + ); - // Enqueue results (actual or empty) for every query in this batch job - std::vector results_batch; - results_batch.reserve(job.num_queries); - for (int64_t i = 0; i < job.num_queries; ++i) { - int global_query_id = job.query_ids->at(i); - int rank_for_this_query = job.ranks->at(i); // Rank of this partition scan for this specific query - - if (scan_successful) { - // Ensure buffer pool access is safe if it could have been resized or is smaller than expected - if (i < res.topk_buffer_pool.size() && res.topk_buffer_pool[i]) { - auto tv = res.topk_buffer_pool[i]->get_topk(); - auto ti = res.topk_buffer_pool[i]->get_topk_indices(); - results_batch.emplace_back(ResultJob{global_query_id, rank_for_this_query, std::move(tv), std::move(ti)}); - } else { - std::cerr << "[QueryCoordinator::handle_batched_job] Error accessing TopK buffer for successful scan, query " - << global_query_id << ", buffer index " << i << ". Sending empty result.\n"; - results_batch.emplace_back(ResultJob{global_query_id, rank_for_this_query, {}, {}}); - } - } else { - // Scan failed, enqueue an empty result for this query's part of the batch - results_batch.emplace_back(ResultJob{global_query_id, rank_for_this_query, {}, {}}); + // 7) Enqueue each result immediately + for (int64_t i = 0; i < chunk; ++i) { + int global_q = (*job.query_ids)[offset + i]; + int rank_q = (*job.ranks) [offset + i]; + auto tv = res.topk_buffer_pool[i]->get_topk(); + auto ti = res.topk_buffer_pool[i]->get_topk_indices(); + result_queue_.enqueue(ResultJob{global_q, rank_q, std::move(tv), std::move(ti)}); } } - if (!results_batch.empty()) { - result_queue_.enqueue_bulk(std::make_move_iterator(results_batch.begin()), results_batch.size()); - } } void QueryCoordinator::init_global_buffers(int64_t nQ, From f0384cd1154ac53e7019c712d7a8df21cdbfb816 Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 17:57:42 -0500 Subject: [PATCH 188/323] configure num parent workers --- src/cpp/src/query_coordinator.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 40ab9733..0f811f71 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -436,6 +436,7 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, job.query_vector = x.data_ptr(); job.num_queries = x.size(0); job.scan_all = true; + job.query_ids = all_query_ids_ptr; job.ranks = make_shared>(x.size(0), i); int core_id = partition_manager_->get_partition_core_id(pids_acc[i]); if (core_id < 0) { From 3a61c4ddbfc24fbcf48c7e76b6dc28cab477ce3f Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 18:05:17 -0500 Subject: [PATCH 189/323] configure num parent workers --- src/cpp/src/query_coordinator.cpp | 10 +++++++--- .../osdi2025/numa_multi_query/configs/sift1m.yaml | 1 + 2 files changed, 8 insertions(+), 3 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 0f811f71..590b5fe3 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -302,20 +302,24 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, } // 5) Gather this sub-batch of query vectors + float* qptr = nullptr; if (job.scan_all) { - // use pre-copied NUMA buffer + // point at the sub-batch slice inside the big NUMA buffer + qptr = nr.local_query_buffer + offset * D; } else { + // copy into our contiguous temp buffer for (int64_t i = 0; i < chunk; ++i) { int qid = (*job.query_ids)[offset + i]; - float *dst = res.batch_queries + size_t(i) * D; const float *src = nr.local_query_buffer + size_t(qid) * D; + float *dst = res.batch_queries + size_t(i ) * D; std::memcpy(dst, src, D * sizeof(float)); } + qptr = res.batch_queries; } // 6) Perform the batched scan for this sub-batch batched_scan_list( - job.scan_all ? nr.local_query_buffer : res.batch_queries, + qptr, codes, ids, chunk, part_size, D, res.topk_buffer_pool, diff --git a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml index d5881277..96886fc5 100644 --- a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml +++ b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml @@ -26,6 +26,7 @@ indexes: nc: 1000 metric: l2 num_workers: 1 + parent_num_workers: 1 use_numa: false search_params: nprobe: 20 From 315e70611b5b376d7f3e64ab763190c27ae7c94a Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 18:25:58 -0500 Subject: [PATCH 190/323] optim batch scan --- src/cpp/src/query_coordinator.cpp | 126 ++++++++++++++---------------- 1 file changed, 58 insertions(+), 68 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 590b5fe3..28e08e6e 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -215,109 +215,91 @@ void QueryCoordinator::handle_nonbatched_job(const ScanJob &job, void QueryCoordinator::handle_batched_job(const ScanJob &job, CoreResources &res, NUMAResources &nr) { - // Total queries in this batch, Top-K, vector dim, and NUMA node + // Total queries, Top-K, dimension, NUMA node int64_t Q = job.num_queries; int K = job.k; int D = partition_manager_->d(); int node = cpu_numa_node(res.core_id); - // Retrieve partition data once - const float *codes = (float*)partition_manager_->partition_store_->get_codes(job.partition_id); - const int64_t *ids = (int64_t*)partition_manager_->partition_store_->get_ids(job.partition_id); + // Fetch partition data + const float *codes = (float *) partition_manager_->partition_store_->get_codes(job.partition_id); + const int64_t *ids = partition_manager_->partition_store_->get_ids(job.partition_id); int64_t part_size = partition_manager_->partition_store_->list_size(job.partition_id); if (!codes || !ids || part_size <= 0) { - // Empty or invalid partition: enqueue empty results for (int64_t q : *job.query_ids) { result_queue_.enqueue(ResultJob{(int)q, 0, {}, {}}); } return; } - // Maximum sub-batch size to bound memory + // Sub-batching static constexpr int64_t MAX_SUBBATCH = 128; - // Process queries in chunks of at most MAX_SUBBATCH + // 1) Prepare per-thread buffers *once* up to MAX_SUBBATCH + size_t cap = std::max(100 * K, 10000); + int64_t queries_req = std::max(Q, MAX_SUBBATCH); + + + if (res.topk_buffer_pool.size() < (size_t)queries_req) { + res.topk_buffer_pool.resize(queries_req); + for (size_t i = 0; i < (size_t)queries_req; ++i) { + res.topk_buffer_pool[i] = + std::make_shared(K, + metric_ == faiss::METRIC_INNER_PRODUCT, + cap, + node); + } + } + + size_t max_q = size_t(queries_req) * D; + if (res.batch_q_capacity < max_q) { + quake_free(res.batch_queries, res.batch_q_capacity * sizeof(float)); + res.batch_queries = static_cast(quake_alloc(max_q * sizeof(float), node)); + res.batch_q_capacity = max_q; + } + + size_t max_r = size_t(queries_req) * K; + if (res.batch_res_capacity < max_r) { + quake_free(res.batch_distances, res.batch_res_capacity * sizeof(float)); + quake_free(res.batch_ids, res.batch_res_capacity * sizeof(int64_t)); + res.batch_distances = static_cast (quake_alloc(max_r * sizeof(float), node)); + res.batch_ids = static_cast(quake_alloc(max_r * sizeof(int64_t), node)); + res.batch_res_capacity = max_r; + } + + // 2) Process in sub-batches for (int64_t offset = 0; offset < Q; offset += MAX_SUBBATCH) { int64_t chunk = std::min(Q - offset, MAX_SUBBATCH); - // 1) Prepare TopK buffers for this sub-batch - size_t cap = std::max(100 * K, 10000); - if (res.topk_buffer_pool.size() < (size_t)chunk) { - // grow pool to at least 'chunk' - size_t old = res.topk_buffer_pool.size(); - res.topk_buffer_pool.resize(chunk); - for (size_t i = old; i < (size_t)chunk; ++i) { - res.topk_buffer_pool[i] = std::make_shared( - K, - metric_ == faiss::METRIC_INNER_PRODUCT, - /*cap=*/cap, - /*node=*/node - ); - } - } - // reset first 'chunk' buffers + // reset only the first 'chunk' TopK buffers for (int64_t i = 0; i < chunk; ++i) { auto &buf = res.topk_buffer_pool[i]; buf->set_k(K); buf->reset(); } - // 2) Allocate or grow query gather buffer (chunk × D floats) - size_t need_q = size_t(chunk) * D; - if (res.batch_q_capacity < need_q) { - if (res.batch_queries) { - quake_free(res.batch_queries, - res.batch_q_capacity * sizeof(float)); - } - res.batch_queries = static_cast( - quake_alloc(need_q * sizeof(float), node)); - res.batch_q_capacity = need_q; - } - - // 3) Allocate or grow result scratch buffers (chunk × K entries) - size_t need_r = size_t(chunk) * size_t(K); - if (res.batch_res_capacity < need_r) { - if (res.batch_distances) { - quake_free(res.batch_distances, - res.batch_res_capacity * sizeof(float)); - } - if (res.batch_ids) { - quake_free(res.batch_ids, - res.batch_res_capacity * sizeof(int64_t)); - } - res.batch_distances = static_cast( - quake_alloc(need_r * sizeof(float), node)); - res.batch_ids = static_cast( - quake_alloc(need_r * sizeof(int64_t), node)); - res.batch_res_capacity = need_r; - } - - // 4) Initialize result scratch + // init only the first chunk*K slots in scratch float init_val = (metric_ == faiss::METRIC_INNER_PRODUCT) ? -std::numeric_limits::infinity() : std::numeric_limits::infinity(); - for (size_t i = 0; i < need_r; ++i) { - res.batch_distances[i] = init_val; - res.batch_ids [i] = -1; - } + std::fill_n(res.batch_distances, chunk * K, init_val); + std::fill_n(res.batch_ids, chunk * K, -1LL); - // 5) Gather this sub-batch of query vectors - float* qptr = nullptr; + // gather queries + float *qptr = nullptr; if (job.scan_all) { - // point at the sub-batch slice inside the big NUMA buffer qptr = nr.local_query_buffer + offset * D; } else { - // copy into our contiguous temp buffer + float *dst = res.batch_queries; for (int64_t i = 0; i < chunk; ++i) { int qid = (*job.query_ids)[offset + i]; const float *src = nr.local_query_buffer + size_t(qid) * D; - float *dst = res.batch_queries + size_t(i ) * D; - std::memcpy(dst, src, D * sizeof(float)); + std::memcpy(dst + i * D, src, D * sizeof(float)); } - qptr = res.batch_queries; + qptr = dst; } - // 6) Perform the batched scan for this sub-batch + // run the scan on this chunk batched_scan_list( qptr, codes, ids, @@ -328,14 +310,22 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, res.batch_ids ); - // 7) Enqueue each result immediately + // collect results for this chunk + std::vector results_batch; + results_batch.reserve(chunk); for (int64_t i = 0; i < chunk; ++i) { int global_q = (*job.query_ids)[offset + i]; int rank_q = (*job.ranks) [offset + i]; auto tv = res.topk_buffer_pool[i]->get_topk(); auto ti = res.topk_buffer_pool[i]->get_topk_indices(); - result_queue_.enqueue(ResultJob{global_q, rank_q, std::move(tv), std::move(ti)}); + results_batch.emplace_back(ResultJob{global_q, rank_q, std::move(tv), std::move(ti)}); } + + // enqueue this sub-batch in bulk + result_queue_.enqueue_bulk( + std::make_move_iterator(results_batch.begin()), + results_batch.size() + ); } } From 573c5a64f88d99957ff19c253409c6df116bb8fe Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 18:29:21 -0500 Subject: [PATCH 191/323] optim batch scan --- src/cpp/src/query_coordinator.cpp | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 28e08e6e..41bf283e 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -237,7 +237,7 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, // 1) Prepare per-thread buffers *once* up to MAX_SUBBATCH size_t cap = std::max(100 * K, 10000); - int64_t queries_req = std::max(Q, MAX_SUBBATCH); + int64_t queries_req = std::min(Q, MAX_SUBBATCH); if (res.topk_buffer_pool.size() < (size_t)queries_req) { @@ -299,6 +299,13 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, qptr = dst; } + for (int64_t i = 0; i < chunk; i += 4) { + __builtin_prefetch(qptr + i * D, /* rw = */ 0, /* locality = */ 3); + } + // PREFETCH: warm up the partition codes and ids + __builtin_prefetch(codes, 0, 3); + __builtin_prefetch(ids, 0, 3); + // run the scan on this chunk batched_scan_list( qptr, From 2c84df37b37a0e3539433cf3269b019d0266bc6b Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 18:59:02 -0500 Subject: [PATCH 192/323] optim batch scan --- src/cpp/src/query_coordinator.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 41bf283e..e943bf25 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -79,7 +79,7 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { while (!stop_workers_) { int64_t jid = 0; if (!res.job_queue.try_dequeue(jid)) { - std::this_thread::sleep_for(std::chrono::microseconds(5)); + std::this_thread::sleep_for(std::chrono::microseconds(20)); continue; } @@ -236,7 +236,7 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, static constexpr int64_t MAX_SUBBATCH = 128; // 1) Prepare per-thread buffers *once* up to MAX_SUBBATCH - size_t cap = std::max(100 * K, 10000); + size_t cap = std::min(100 * K, 10000); int64_t queries_req = std::min(Q, MAX_SUBBATCH); From ab992bfbd3646b68e7434531c233e28aea0b120f Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 20 May 2025 22:04:21 -0500 Subject: [PATCH 193/323] optim batch scan --- src/cpp/src/query_coordinator.cpp | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index e943bf25..d7e3835a 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -150,7 +150,7 @@ void QueryCoordinator::handle_nonbatched_job(const ScanJob &job, res.topk_buffer_pool[0] = std::make_shared( job.k, metric_ == faiss::METRIC_INNER_PRODUCT, - /*cap=*/std::max(100 * job.k, 10000), + /*cap=*/std::min(100 * job.k, 10000), /*node=*/cpu_numa_node(res.core_id) ); } else if (res.topk_buffer_pool[0]->k_ != job.k) { @@ -159,7 +159,7 @@ void QueryCoordinator::handle_nonbatched_job(const ScanJob &job, res.topk_buffer_pool[0] = std::make_shared( job.k, metric_ == faiss::METRIC_INNER_PRODUCT, - /*cap=*/std::max(100 * job.k, 10000), + /*cap=*/std::min(100 * job.k, 10000), /*node=*/cpu_numa_node(res.core_id) ); } @@ -342,6 +342,8 @@ void QueryCoordinator::init_global_buffers(int64_t nQ, shared_ptr params) { std::lock_guard lg(global_mutex_); // resize or reset + + size_t cap = std::min(100 * K, 10000); if (global_topk_buffer_pool_.size() < (size_t) nQ) { size_t old = global_topk_buffer_pool_.size(); global_topk_buffer_pool_.resize(nQ); @@ -349,7 +351,7 @@ void QueryCoordinator::init_global_buffers(int64_t nQ, global_topk_buffer_pool_[q] = std::make_shared( K, metric_ == faiss::METRIC_INNER_PRODUCT, - /*cap=*/num_workers_ * K * 2, + /*cap=*/cap, /*node=*/0 ); } From 7dfca1abe2238ec650f8e5cf342d82b598fd8737 Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 21 May 2025 00:18:45 -0500 Subject: [PATCH 194/323] update aps experiment --- src/cpp/src/query_coordinator.cpp | 21 +- .../adaptive_partition_scanning/aps.py | 698 ------------------ .../adaptive_partition_scanning/run.py | 119 +++ test/experiments/osdi2025/README.md | 134 +++- .../osdi2025/aps_recall_targets/run.py | 237 +++--- test/experiments/osdi2025/experiment_utils.py | 126 ++++ 6 files changed, 452 insertions(+), 883 deletions(-) delete mode 100644 test/experiments/adaptive_partition_scanning/aps.py create mode 100644 test/experiments/adaptive_partition_scanning/run.py create mode 100644 test/experiments/osdi2025/experiment_utils.py diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index d7e3835a..7f3c0611 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -10,6 +10,7 @@ #include #include #include +//#include "parallel_hashmap/btree.h" // Constructor QueryCoordinator::QueryCoordinator(shared_ptr parent, @@ -451,25 +452,25 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, } } else { - std::unordered_map>>> per_partition_query_ids; // for batched scan + std::unordered_map>> per_partition_query_ids; // for batched scan +// ska::flat_hash_map> per_partition_query_ids_map; + for (int64_t q = 0; q < nQ; q++) { for (int64_t p = 0; p < partition_ids.size(1); p++) { int64_t pid = pid_acc[q][p]; if (pid < 0) continue; if (per_partition_query_ids[pid] == nullptr) { - per_partition_query_ids[pid] = make_shared>>(); + per_partition_query_ids[pid] = make_shared>(); } - per_partition_query_ids[pid]->push_back({q, p}); + per_partition_query_ids[pid]->push_back(q); } } for (auto &kv : per_partition_query_ids) { - auto qids_and_ranks = kv.second; - vector qids(qids_and_ranks->size()); - vector ranks(qids_and_ranks->size()); - for (size_t i = 0; i < qids_and_ranks->size(); ++i) { - qids[i] = (*qids_and_ranks)[i].first; - ranks[i] = (*qids_and_ranks)[i].second; + auto qids = kv.second; + vector ranks(qids->size()); + for (size_t i = 0; i < qids->size(); ++i) { + ranks[i] = i; } ScanJob job; @@ -478,7 +479,7 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, job.k = params->k; job.query_vector = x.data_ptr(); job.num_queries = kv.second->size(); - job.query_ids = make_shared>(qids); + job.query_ids = qids; job.ranks = make_shared>(ranks); int core_id = partition_manager_->get_partition_core_id(kv.first); if (core_id < 0) { diff --git a/test/experiments/adaptive_partition_scanning/aps.py b/test/experiments/adaptive_partition_scanning/aps.py deleted file mode 100644 index 78ee9e7f..00000000 --- a/test/experiments/adaptive_partition_scanning/aps.py +++ /dev/null @@ -1,698 +0,0 @@ -import logging -from pathlib import Path - -import hydra -import matplotlib.pyplot as plt -import numpy as np -import pandas as pd -import seaborn as sns -import torch -from hydra.utils import get_original_cwd -from omegaconf import DictConfig - -from quake import IndexBuildParams, QuakeIndex, SearchParams -from quake.datasets.ann_datasets import load_dataset -from quake.utils import compute_recall, to_path - -# Set up logging -log = logging.getLogger(__name__) - -# Constants -MIN_FANOUT = 8 - - -# Dataset Management -def get_dataset(cfg): - dataset_path = get_original_cwd() / to_path(cfg.dataset.path) - vectors, queries, gt = load_dataset(cfg.dataset.name, dataset_path) - return vectors, queries[: cfg.experiment.nq], gt[: cfg.experiment.nq] - - -# Index Management -def build_or_load_index(cfg, num_workers): - index_dir = get_original_cwd() / to_path(cfg.paths.index_dir) - index_path = index_dir / f"{cfg.dataset.name}_dynamic_ivf{cfg.index.nc}.index" - if not index_path.exists() or cfg.overwrite.index: - # Build and save index - vectors, _, _ = get_dataset(cfg) - build_index = QuakeIndex() - build_params = IndexBuildParams() - build_params.nlist = cfg.index.nc - build_params.metric = cfg.index.metric - build_index.build(vectors, torch.arange(vectors.size(0)), build_params) - build_index.save(str(index_path.absolute())) - log.info(f"Index built and saved to {index_path}") - - # Load existing index - index = QuakeIndex() - index.load(str(index_path.absolute()), num_workers) - - log.info(f"Index loaded from {index_path} with {num_workers} workers") - - return index - - -# Experiment Execution -def run_single_experiment(args): - method, recall_target, recompute_ratio, use_precompute, cfg, action, n_workers = args - index = build_or_load_index(cfg, n_workers) - _, queries, gt = get_dataset(cfg) - k = cfg.experiment.k - - result_dir = get_original_cwd() / cfg.paths.results_dir / method - result_dir.mkdir(parents=True, exist_ok=True) - - print(f"Running experiment for {method} at recall {recall_target} with action {action}") - - if method == "Oracle": - result_path = result_dir / f"recall_{recall_target:.2f}.csv" - - data_df = run_experiment_for_configuration( - index=index, - queries=queries, - gt=gt, - k=k, - recall_target=recall_target, - oracle=True, - ) - # Save per-query data - data_df.to_csv(result_path, index=False) - log.info(f"Results saved to {result_path}") - elif method == "FixedNProbe": - result_path = result_dir / f"recall_{recall_target:.2f}.csv" - if result_path.exists() and not cfg.overwrite.results: - log.info(f"Results for {method} at recall {recall_target} already exist. Skipping.") - return - - data_df = run_experiment_for_configuration( - index=index, - queries=queries, - gt=gt, - k=k, - recall_target=recall_target, - fixed_nprobe=True, - ) - # Save per-query data - data_df.to_csv(result_path, index=False) - log.info(f"Results saved to {result_path}") - elif method.startswith("APS"): - result_path = result_dir / f"recall_{recall_target:.2f}.csv" - if result_path.exists() and not cfg.overwrite.results: - log.info(f"Results for {method} at recall {recall_target} already exist. Skipping.") - return - - data_df = run_experiment_for_configuration( - index=index, - queries=queries, - gt=gt, - k=k, - recall_target=recall_target, - recompute_ratio=recompute_ratio, - use_precompute=use_precompute, - ) - # Save per-query data - data_df.to_csv(result_path, index=False) - log.info(f"Results saved to {result_path}") - else: - raise ValueError(f"Unknown method: {method}") - - -# Results Management and Plotting -def collect_and_plot_results(cfg): - methods = cfg.methods - recall_targets = cfg.experiment.recall_targets - all_data = [] - - for method in methods: - for recall_target in recall_targets: - result_dir = cfg.paths.results_dir / method - result_path = result_dir / f"recall_{recall_target:.2f}.csv" - if not result_path.exists(): - log.warning(f"Result file {result_path} does not exist. Skipping.") - continue - data_df = pd.read_csv(result_path) - data_df["Recall Target"] = recall_target - - complete_name = method - data_df["Method"] = complete_name - all_data.append(data_df) - - if not all_data: - log.error("No data available for plotting.") - return - - df_plot = pd.concat(all_data, ignore_index=True) - - # Clean data - df_plot = df_plot.dropna(subset=["total_time_ms", "nprobe", "recall"]) - df_plot = df_plot[(df_plot["total_time_ms"] >= 0) & (df_plot["nprobe"] >= 0) & (df_plot["recall"] >= 0)] - - df_plot["Query Time (ms)"] = df_plot["total_time_ms"] - df_plot["Recall"] = df_plot["recall"] - - # Compute stats - grouped = df_plot.groupby(["Recall Target", "Method"]) - stats = grouped.agg( - { - "Query Time (ms)": ["min", "mean", "max"], - "nprobe": ["min", "mean", "max"], - "Recall": ["min", "mean", "max"], - "buffer_init_time_ms": ["min", "mean", "max"], - "job_enqueue_time_ms": ["min", "mean", "max"], - "boundary_distance_time_ms": ["min", "mean", "max"], - "job_wait_time_ms": ["min", "mean", "max"], - "result_aggregate_time_ms": ["min", "mean", "max"], - } - ).reset_index() - stats.columns = [" ".join(col).strip() for col in stats.columns.values] - - # Compute 'other_time_ms' and include in stats - df_plot["other_time_ms"] = df_plot["total_time_ms"] - ( - df_plot["buffer_init_time_ms"] - + df_plot["job_enqueue_time_ms"] - + df_plot["boundary_distance_time_ms"] - + df_plot["job_wait_time_ms"] - + df_plot["result_aggregate_time_ms"] - ) - df_plot["other_time_ms"] = df_plot["other_time_ms"].apply(lambda x: x if x >= 0 else 0) - other_time_stats = grouped["other_time_ms"].agg(["min", "mean", "max"]).reset_index() - other_time_stats.columns = [ - "Recall Target", - "Method", - "other_time_ms min", - "other_time_ms mean", - "other_time_ms max", - ] - stats = pd.merge(stats, other_time_stats, on=["Recall Target", "Method"]) - - # Compute p99 for 'Query Time (ms)' and 'Recall' - p95_latency = grouped["Query Time (ms)"].quantile(0.95).reset_index() - p95_recall = grouped["Recall"].quantile(0.05).reset_index() - p95_latency.rename(columns={"Query Time (ms)": "Query Time (ms) p95"}, inplace=True) - p95_recall.rename(columns={"Recall": "Recall p95"}, inplace=True) - stats = stats.merge(p95_latency, on=["Recall Target", "Method"]) - stats = stats.merge(p95_recall, on=["Recall Target", "Method"]) - - # Save this as intermediate results - stats_save_path = cfg.paths.plot_dir / "all_intermediate_stats.csv" - stats.to_csv(stats_save_path, index=False) - - # Plotting - plot_recall_only(df_plot, stats, cfg.paths.plot_dir) - plot_mean_line_plots(df_plot, stats, cfg.paths.plot_dir) - plot_query_overheads(stats, cfg.paths.plot_dir) - - -palette = {"Oracle": "C0", "APS": "C1"} - - -def plot_recall_only(df_plot, stats, plot_dir): - sns.set_style("whitegrid") - sns.set_context("talk", font_scale=0.8) - plt.rcParams["font.weight"] = "bold" - plt.rcParams["axes.labelweight"] = "bold" - - fig, ax = plt.subplots(figsize=(6, 4)) - stats["Method"] = stats["Method"].replace("Adaptive nprobe", "APS") - - # Shared x-axis range - x_min = stats["Recall Target"].min() - x_max = stats["Recall Target"].max() - - # Generate detailed x-axis ticks - x_ticks = [0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1.0] - - ax.set_title("Sift1M: Measured Recall vs. Recall Target", fontsize=14, fontweight="bold") - - for method in stats["Method"].unique(): - method_stats = stats[stats["Method"] == method].sort_values("Recall Target") - ax.plot( - method_stats["Recall Target"], - method_stats["Recall mean"], - label=method, - color=palette[method], - linewidth=4, - marker="o", - markersize=10, - ) - - # plot p95 recall - # p95_recall = method_stats['Recall p95'] - # ax.plot(method_stats['Recall Target'], p95_recall, color=palette[method], linestyle='--', linewidth=8) - - x_vals = np.linspace(x_min, x_max, 100) - ax.plot(x_vals, x_vals, color="black", linestyle="--", linewidth=4, label="Recall Target") - - ax.set_ylabel("Recall", fontsize=16, fontweight="bold") - ax.tick_params(axis="both", which="major", labelsize=16) - ax.set_yticks([0.7, 0.8, 0.9, 1.0]) # Custom y-axis ticks - ax.set_xticks(x_ticks) - ax.legend(fontsize=8, prop={"weight": "bold"}, loc="lower right") # Legend moved to top-left - ax.grid(True) - - plt.tight_layout() - plot_path = plot_dir / "recall_plot.pdf" - plt.savefig(plot_path, bbox_inches="tight") - log.info(f"Plot saved to {plot_path}") - plt.show() - - -# Plotting Functions -def plot_mean_line_plots(df_plot, stats, plot_dir): - sns.set_style("whitegrid") - sns.set_context("talk", font_scale=0.8) - plt.rcParams["font.weight"] = "bold" - plt.rcParams["axes.labelweight"] = "bold" - - # Create a vertically stacked layout with shared x-axis - fig, axes = plt.subplots(3, 1, figsize=(8, 6), sharex=True) # Stacked plots with shared x-axis - - stats["Method"] = stats["Method"].replace("Adaptive nprobe", "APS") - - # Shared x-axis range - x_min = stats["Recall Target"].min() - x_max = stats["Recall Target"].max() - - # Generate detailed x-axis ticks - x_ticks = [0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1.0] - - axes[0].set_title("Sift1M: Recall, Query Latency, and Nprobe vs. Recall Target", fontsize=20, fontweight="bold") - - # Plot 1: Recall - for method in stats["Method"].unique(): - method_stats = stats[stats["Method"] == method].sort_values("Recall Target") - axes[0].plot( - method_stats["Recall Target"], - method_stats["Recall mean"], - label=method, - color=palette[method], - linewidth=8, - marker="o", - markersize=10, - ) - # plot p95 recall - # p95_recall = method_stats['Recall p95'] - # axes[0].plot(method_stats['Recall Target'], p95_recall, color=palette[method], linestyle='--', linewidth=8) - - # Diagonal reference line - x_vals = np.linspace(x_min, x_max, 100) - axes[0].plot(x_vals, x_vals, color="black", linestyle="--", linewidth=8, label="Recall Target") - - axes[0].set_ylabel("Recall", fontsize=16, fontweight="bold") - axes[0].tick_params(axis="both", which="major", labelsize=16) - axes[0].set_yticks([0.7, 0.8, 0.9, 1.0]) # Custom y-axis ticks - axes[0].set_xticks(x_ticks) - axes[0].legend(fontsize=8, prop={"weight": "bold"}, loc="lower right") # Legend moved to top-left - axes[0].grid(True) - - # Plot 2: Query Time - for method in stats["Method"].unique(): - method_stats = stats[stats["Method"] == method].sort_values("Recall Target") - axes[1].plot( - method_stats["Recall Target"], - method_stats["Query Time (ms) mean"], - label=method, - color=palette[method], - linewidth=8, - marker="o", - markersize=10, - ) - - # plot p95 query time - p95_query_time = method_stats["Query Time (ms) p95"] - axes[1].plot(method_stats["Recall Target"], p95_query_time, color=palette[method], linestyle="--", linewidth=8) - - # axes[1].set_ylabel('Query Time (ms)', fontsize=20, fontweight='bold') - axes[1].set_yscale("log") - axes[1].tick_params(axis="both", which="major", labelsize=16) - axes[1].set_yticks([0.2, 0.4, 0.8, 1.6, 3.2]) # Custom y-axis ticks - axes[1].set_yticklabels(["0.2ms", "0.4ms", "0.8ms", "1.6ms", "3.2ms"]) # Add units to tick labels - axes[1].set_xticks(x_ticks) - # axes[1].legend(fontsize=16, prop={'weight': 'bold'}, loc='upper left') # Legend moved to top-left - axes[1].grid(True) - # - # # Plot 3: nprobe - for method in stats["Method"].unique(): - method_stats = stats[stats["Method"] == method].sort_values("Recall Target") - axes[2].plot( - method_stats["Recall Target"], - method_stats["nprobe mean"], - label=method, - color=palette[method], - linewidth=8, - marker="o", - markersize=10, - ) - - axes[2].set_xlabel("Recall Target", fontsize=16, fontweight="bold") # Shared x-axis - axes[2].set_ylabel("nprobe", fontsize=16, fontweight="bold") - axes[2].set_yscale("log") - axes[2].tick_params(axis="both", which="major", labelsize=16) - axes[2].set_yticks([4, 8, 16, 32, 64]) # Custom y-axis ticks - axes[2].set_yticklabels(["4", "8", "16", "32", "64"]) # Add units to tick labels - axes[2].set_xticks(x_ticks) - # axes[2].legend(fontsize=16, prop={'weight': 'bold'}, loc='upper left') # Legend moved to top-left - axes[2].grid(True) - - plt.tight_layout() - plot_path = plot_dir / "mean_line_plots_stacked.pdf" - plt.savefig(plot_path, bbox_inches="tight") - log.info(f"Plot saved to {plot_path}") - plt.show() - - -def plot_query_overheads(stats, plot_dir): - """ - Plot stacked bar charts showing the mean time taken by each component for each method and recall target, - including an 'Other' category representing the remaining time. - """ - import matplotlib.pyplot as plt - import numpy as np - from matplotlib.patches import Patch - - # Define the components and their labels - components = [ - "buffer_init_time_ms", - "job_enqueue_time_ms", - "boundary_distance_time_ms", - "job_wait_time_ms", - "result_aggregate_time_ms", - "other_time_ms", # Added 'Other' category - ] - component_labels = [ - "Buffer Init", - "Job Enqueue", - "Boundary Distance", - "Job Wait", - "Result Aggregate", - "Other", # Label for 'Other' - ] - # Blue, Orange, Green, Red, Purple, Gray] - component_colors = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#8c564b", "#7f7f7f"] - - # remap the method names: Adaptive nprobe -> Adaptive Partition Scanning - # stats[methods == 'Adaptive nprobe'] = 'Adaptive Partition Scanning' - stats["Method"] = stats["Method"].replace("Adaptive nprobe", "APS") - - # Define methods and their hatching patterns - methods = stats["Method"].unique() - - method_hatches = {method: "" if method == "Oracle" else "//" for method in methods} - - # Prepare data for plotting - recall_targets = sorted(stats["Recall Target"].unique()) - - # Initialize plotting - fig, ax = plt.subplots(figsize=(14, 8)) - - bar_width = 0.1 - opacity = 0.8 - - n_groups = len(recall_targets) - index = np.arange(n_groups) - - # Set positions for each method's bars - # method_offsets = np.linspace(-bar_width/2, bar_width/2, len(methods)) - positions = {} - positions["Oracle"] = index - 3 * bar_width / 2 - positions["APS"] = index - bar_width / 2 - positions["APS-R"] = index + bar_width / 2 - positions["APS-RP"] = index + 3 * bar_width / 2 - positions["FixedNProbe"] = index + 5 * bar_width / 2 - - # Initialize cumulative bottoms for stacking - cumulative_bottoms = {method: np.zeros(n_groups) for method in methods} - - # Plot each component - for comp, comp_label, comp_color in zip(components, component_labels, component_colors): - for method in methods: - print(f"Plotting {comp} for {method}") - - comp_means = [] - for rt in recall_targets: - row = stats[(stats["Recall Target"] == rt) & (stats["Method"] == method)] - if not row.empty: - comp_time = row[f"{comp} mean"].values[0] - else: - comp_time = 0 - comp_means.append(comp_time) - print(f"Component means: {comp_means}") - - method_lookup = method.split("+")[0].strip() - pos = positions[method_lookup] - ax.bar( - pos, - comp_means, - bar_width, - bottom=cumulative_bottoms[method], - color=comp_color, - alpha=opacity, - hatch=method_hatches[method], - edgecolor="black", - label=comp_label if (method == methods[0]) else "", - ) - cumulative_bottoms[method] += comp_means - - # Set x-axis labels and ticks - ax.set_xlabel("Recall Target", fontsize=14, weight="bold") - ax.set_ylabel("Time (ms)", fontsize=14, weight="bold") - ax.set_title("Query Overheads by Method and Recall Target", fontsize=16, weight="bold") - ax.set_xticks(index) - ax.set_xticklabels([f"{rt:.2f}" for rt in recall_targets], fontsize=12) - ax.tick_params(axis="y", labelsize=12) - - # Create custom legend for components - component_handles = [Patch(facecolor=color, edgecolor="black") for color in component_colors] - component_legend = ax.legend( - component_handles, component_labels, title="Components", title_fontsize=14, fontsize=12, loc="upper left" - ) - - # Create custom legend for methods - method_handles = [ - Patch(facecolor="white", edgecolor="black", hatch=method_hatches[method], label=method) for method in methods - ] - ax.legend(method_handles, methods, title="Method", title_fontsize=14, fontsize=12, loc="upper right") - - # Add the component legend back to the plot - ax.add_artist(component_legend) - - # Add grid for better readability - ax.yaxis.grid(True, linestyle="--", which="major", color="grey", alpha=0.5) - - # set ylimit - ax.set_ylim(0, 2.5) - - plt.tight_layout() - plot_path = plot_dir / "query_overheads.png" - plt.savefig(plot_path) - log.info(f"Overhead plot saved to {plot_path}") - plt.show() - - -# Helper Functions -def run_experiment_for_configuration( - index, queries, gt, k, recall_target, oracle=False, recompute_ratio=0.05, use_precompute=True, fixed_nprobe=False -): - - timing_infos = [] - - if oracle: - nprobes = [] - for i, query in enumerate(queries): - max_nprobe = index.nlist() - min_nprobe = 1 - best_nprobe = max_nprobe - - # find minimum nprobe that achieves recall_target - while min_nprobe <= max_nprobe: - curr_nprobe = (min_nprobe + max_nprobe) // 2 - - search_params = SearchParams() - search_params.nprobe = curr_nprobe - search_params.k = k - search_params.recall_target = -1 - - search_result = index.search(query.unsqueeze(0), search_params) - ids, dist, timing_info = search_result.ids, search_result.distances, search_result.timing_info - recall = compute_recall(ids, gt[i].unsqueeze(0), k) - - if float(recall) >= recall_target: - max_nprobe = curr_nprobe - 1 - best_nprobe = curr_nprobe - else: - min_nprobe = curr_nprobe + 1 - - nprobes.append(best_nprobe) - - # Second run to execute queries - all_ids = [] - all_dists = [] - - for query, nprobe in zip(queries, nprobes): - - search_params = SearchParams() - search_params.nprobe = nprobe - search_params.k = k - search_params.recall_target = -1 - - search_result = index.search(query.unsqueeze(0), search_params) - - ids, dist, timing_info = search_result.ids, search_result.distances, search_result.timing_info - all_ids.append(ids) - all_dists.append(dist) - timing_infos.append(timing_info) - recalls = compute_recall(torch.cat(all_ids, dim=0), gt, k) - elif fixed_nprobe: - print("FIXED NPROBE") - # compute nprobe for recall target for all queries, rather than per-query - - # first use binary search to find the nprobe that achieves the recall target - max_nprobe = index.nlist() - min_nprobe = 1 - best_nprobe = max_nprobe - - while min_nprobe <= max_nprobe: - curr_nprobe = (min_nprobe + max_nprobe) // 2 - all_ids = [] - all_dists = [] - - search_params = SearchParams() - search_params.nprobe = curr_nprobe - search_params.k = k - search_params.recall_target = -1 - - for i, query in enumerate(queries): - search_result = index.search(query.unsqueeze(0), search_params) - ids, dist, timing_info = search_result.ids, search_result.distances, search_result.timing_info - all_ids.append(ids) - all_dists.append(dist) - recalls = compute_recall(torch.cat(all_ids, dim=0), gt, k) - avg_recall = recalls.mean().item() - if avg_recall >= recall_target: - max_nprobe = curr_nprobe - 1 - best_nprobe = curr_nprobe - else: - min_nprobe = curr_nprobe + 1 - - # Second run to execute queries - all_ids = [] - all_dists = [] - - search_params = SearchParams() - search_params.nprobe = best_nprobe - search_params.k = k - search_params.recall_target = -1 - - for query in queries: - search_result = index.search(query.unsqueeze(0), search_params) - ids, dist, timing_info = search_result.ids, search_result.distances, search_result.timing_info - all_ids.append(ids) - all_dists.append(dist) - timing_infos.append(timing_info) - recalls = compute_recall(torch.cat(all_ids, dim=0), gt, k) - else: - all_ids = [] - all_dists = [] - - search_params = SearchParams() - search_params.nprobe = -1 - search_params.k = k - search_params.initial_search_fraction = 0.1 - search_params.recall_target = recall_target - search_params.recompute_threshold = 0.01 - search_params.use_precomputed = use_precompute - search_params.num_threads = 1 - - # debug print search params - print( - f"Search Params: {search_params.nprobe}, {search_params.k}, {search_params.recall_target}, " - f"{search_params.recompute_threshold}, {search_params.use_precomputed}" - ) - - for query in queries: - search_result = index.search(query.unsqueeze(0), search_params) - ids, dist, timing_info = search_result.ids, search_result.distances, search_result.timing_info - all_ids.append(ids) - all_dists.append(dist) - timing_infos.append(timing_info) - - ids = torch.cat(all_ids, dim=0) - # dists = torch.cat(all_dists, dim=0) - recalls = compute_recall(ids, gt, k) - - per_query_data = [] - for i, timing_info in enumerate(timing_infos): - per_query_nprobe = timing_info.partitions_scanned - buffer_init_time_ms = timing_info.buffer_init_time_ns / 1e6 - job_enqueue_time_ms = timing_info.job_enqueue_time_ns / 1e6 - boundary_distance_time_ms = timing_info.boundary_distance_time_ns / 1e6 - job_wait_time_ms = timing_info.job_wait_time_ns / 1e6 - result_aggregate_time_ms = timing_info.result_aggregate_time_ns / 1e6 - total_time_ms = timing_info.total_time_ns / 1e6 - - per_query_recall = float(recalls[i]) - per_query_data.append( - { - "nprobe": per_query_nprobe, - "recall": per_query_recall, - "buffer_init_time_ms": buffer_init_time_ms, - "job_enqueue_time_ms": job_enqueue_time_ms, - "boundary_distance_time_ms": boundary_distance_time_ms, - "job_wait_time_ms": job_wait_time_ms, - "result_aggregate_time_ms": result_aggregate_time_ms, - "total_time_ms": total_time_ms, - } - ) - - per_query_data_df = pd.DataFrame(per_query_data) - # per_query_data_df = per_query_data_df[1:] # Skip the first query - return per_query_data_df - - -def get_nprobe_for_recall_target(recall_target, nlist): - nprobe = int(nlist * recall_target / 2) - nprobe = max(1, min(nprobe, nlist)) - return nprobe - - -@hydra.main(config_path="configs", config_name="sift1m") -def main(cfg: DictConfig): - # Set up directories - base_dir = get_original_cwd() / Path(cfg.base_dir) - - cfg.paths.index_dir = Path(cfg.paths.index_dir) - cfg.paths.index_dir.mkdir(parents=True, exist_ok=True) - cfg.paths.results_dir = base_dir / cfg.paths.results_dir - cfg.paths.results_dir.mkdir(parents=True, exist_ok=True) - cfg.paths.plot_dir = base_dir / cfg.paths.plot_dir - cfg.paths.plot_dir.mkdir(parents=True, exist_ok=True) - - if cfg.mode == "run": - # Prepare experiment parameters - methods = cfg.methods - recall_targets = cfg.experiment.recall_targets - n_workers = cfg.experiment.n_workers - experiment_args = [] - for method in methods: - for recall_target in recall_targets: - if method == "Oracle": - experiment_args.append((method, recall_target, 0.05, True, cfg, "execute_queries", n_workers)) - elif method == "APS": - experiment_args.append((method, recall_target, 0.0, True, cfg, "execute_queries", n_workers)) - elif method == "APS-R": - experiment_args.append((method, recall_target, -1, True, cfg, "execute_queries", n_workers)) - elif method == "APS-RP": - experiment_args.append((method, recall_target, -1, False, cfg, "execute_queries", n_workers)) - elif method == "FixedNProbe": - experiment_args.append((method, recall_target, -1, True, cfg, "execute_queries", n_workers)) - - # Run experiments - for args in experiment_args: - run_single_experiment(args) - - elif cfg.mode == "plot": - collect_and_plot_results(cfg) - - -if __name__ == "__main__": - main() diff --git a/test/experiments/adaptive_partition_scanning/run.py b/test/experiments/adaptive_partition_scanning/run.py new file mode 100644 index 00000000..dc5a74e5 --- /dev/null +++ b/test/experiments/adaptive_partition_scanning/run.py @@ -0,0 +1,119 @@ +#!/usr/bin/env python3 +""" +Adaptive‐Partition‐Scanning Recall‐Target Runner (Simplified) +Supports: Oracle, FixedNProbe, APS +""" +import logging +from pathlib import Path +import numpy as np +import torch +import pandas as pd + +from test.experiments.osdi2025 import experiment_utils as common_utils +from quake import SearchParams # Keep direct import for clarity if used extensively + +logger = logging.getLogger(__name__) +logging.basicConfig(level=logging.INFO, format="%(asctime)s | %(levelname)s | %(message)s") + +def run_experiment(cfg_path: str, output_dir_str: str): + cfg = common_utils.load_config(cfg_path) + out_dir = Path(output_dir_str) + + dataset_cfg = cfg["dataset"] + exp_cfg = cfg["experiment"] + idx_build_cfg = cfg["index"] + paths_cfg = cfg["paths"] + + vecs, queries, gt = common_utils.load_data( + dataset_cfg["name"], dataset_cfg.get("path", ""), exp_cfg["nq"] + ) + + idx_path = Path(paths_cfg["index_dir"]) / f"{dataset_cfg['name']}_ivf{idx_build_cfg['nlist']}.index" + quake_idx = common_utils.prepare_quake_index( + vecs, + {"nlist": idx_build_cfg["nlist"], "metric": idx_build_cfg["metric"], "num_workers": exp_cfg["n_workers"]}, + idx_path, + force_rebuild=cfg["overwrite"].get("index", False) + ) + logger.info(f"Index ready (workers={exp_cfg['n_workers']})") + + records = [] + k = exp_cfg["k"] + max_nlist_val = quake_idx.nlist() + + for method in cfg["methods"]: + for rt in exp_cfg["recall_targets"]: + logger.info(f"Method={method} RecallTarget={rt}") + per_query_stats = [] # (nprobe, recall, time_ms) + + if method == "Oracle": + for i, q_vec in enumerate(queries): + lo, hi, best_np = 1, max_nlist_val, max_nlist_val + while lo <= hi: + mid = (lo + hi) // 2 + sp = common_utils.create_search_params(nprobe=mid, k=k, recall_target=-1) + _, rec, _ = common_utils.run_search_trial(quake_idx, q_vec, gt[i], k, sp) + if rec >= rt: + best_np, hi = mid, mid - 1 + else: + lo = mid + 1 + sp_final = common_utils.create_search_params(nprobe=best_np, k=k, recall_target=-1) + _, final_rec, final_time_ms = common_utils.run_search_trial(quake_idx, q_vec, gt[i], k, sp_final) + per_query_stats.append((best_np, final_rec, final_time_ms)) + + elif method == "FixedNProbe": + lo, hi, best_global_np = 1, max_nlist_val, max_nlist_val + while lo <= hi: + mid = (lo + hi) // 2 + sp = common_utils.create_search_params(nprobe=mid, k=k, recall_target=-1) + current_ids = [] + for q_vec_fixed in queries: # Use all queries for tuning this + res_ids = quake_idx.search(q_vec_fixed.unsqueeze(0), sp).ids + current_ids.append(res_ids) + avg_rec = common_utils.quake_compute_recall(torch.cat(current_ids,0), gt, k).mean().item() + if avg_rec >= rt: + best_global_np, hi = mid, mid - 1 + else: + lo = mid + 1 + + sp_final = common_utils.create_search_params(nprobe=best_global_np, k=k, recall_target=-1) + for i, q_vec in enumerate(queries): + _, final_rec, final_time_ms = common_utils.run_search_trial(quake_idx, q_vec, gt[i], k, sp_final) + per_query_stats.append((best_global_np, final_rec, final_time_ms)) + + elif method == "APS": + for i, q_vec in enumerate(queries): + sp = common_utils.create_search_params( + nprobe=-1, k=k, recall_target=rt, + recompute_threshold=exp_cfg["recompute_ratio"], + use_precomputed=exp_cfg["use_precompute"], + initial_search_fraction=exp_cfg["initial_search_fraction"] + ) + nprobe_scan, final_rec, final_time_ms = common_utils.run_search_trial(quake_idx, q_vec, gt[i], k, sp) + logger.debug(f"APS recall: {final_rec:.4f} (target={rt}), nprobe_scanned={nprobe_scan}") + per_query_stats.append((nprobe_scan, final_rec, final_time_ms)) + else: + raise ValueError(f"Unknown method: {method}") + + arr = np.array(per_query_stats, dtype=float) + records.append({ + "Method": method, "RecallTarget": rt, + "mean_nprobe": arr[:,0].mean(), "std_nprobe": arr[:,0].std(), + "mean_recall": arr[:,1].mean(), "std_recall": arr[:,1].std(), + "mean_time_ms": arr[:,2].mean(), "std_time_ms": arr[:,2].std(), + }) + + results_df = pd.DataFrame(records) + common_utils.save_results_csv(results_df, out_dir / "aps_recall_targets_summary.csv") + + plot_path = out_dir / "aps_recall_vs_metrics.png" + common_utils.plot_recall_performance( + results_df, + x_metric="RecallTarget", + y_metrics=["mean_time_ms", "mean_recall", "mean_nprobe"], + y_labels=["Mean Query Time (ms)", "Mean Recall Achieved", "Mean NProbe"], + y_scales=["linear", "linear", "log"], + plot_title_prefix=f"{dataset_cfg['name']} APS Experiment", + output_path=plot_path, + recall_targets_for_line=exp_cfg["recall_targets"] + ) \ No newline at end of file diff --git a/test/experiments/osdi2025/README.md b/test/experiments/osdi2025/README.md index 1df6e158..465429df 100644 --- a/test/experiments/osdi2025/README.md +++ b/test/experiments/osdi2025/README.md @@ -1,57 +1,131 @@ -# OSDI 2025 Artifact -This directory contains the experiments for Quake's OSDI 2025 submission. +# Quake OSDI 2025 Artifact Evaluation +This artifact provides the experimental setup for the Quake vector search system, as described in the OSDI 2025 submission. +## Structure +The experiments are organized as follows: -### Installation + + +```plaintext +./test/experiments/osdi2025/ +├── common/ \# Shared utilities for experiments +│ └── experiment\_utils.py +├── experiment\_runner.py \# Main script to run any experiment +├── README.md \# This file +├── \/ \# Directory for each specific experiment +│ ├── configs/ \# YAML configuration files for the experiment +│ │ └── \.yaml +│ └── run.py \# Python script to execute the experiment logic +└── ... \# Other experiment directories +``` + +Each experiment directory (e.g., `aps_recall_targets`, `early_termination`) contains its specific logic in `run.py` and configurations in its `configs/` subdirectory. The `common/experiment_utils.py` module provides shared functionality for loading data, configurations, preparing indexes, and plotting, aiming to simplify individual experiment scripts. + +## Installation **Requirements:** - Python 3.9 or later -- Linux (Tested on Ubuntu 22.04) +- Linux (Tested on Ubuntu 22.04) +- Conda package manager -1. **Clone the Repository:** +1. **Clone the Repository:** + ```bash + git clone [https://github.com/marius-team/quake.git](https://github.com/marius-team/quake.git) + cd quake + git submodule update --init --recursive + ``` -```bash -git clone https://github.com/marius-team/quake.git -cd quake -git submodule update --init --recursive -``` +2. **Set Up the Conda Environment:** + Create and activate the environment using the provided YAML file: + ```bash + conda env create -f environments/ubuntu-latest/conda.yaml + conda activate quake-env + ``` -2. **Set Up the Conda Environment:** +3. **Install Quake:** + Ensure you are in the `quake` repository root. + ```bash + pip install --no-use-pep517 . + ``` - Create and activate the environment using the provided YAML file: +## Running Experiments -```bash -conda env create -f environments/ubuntu-latest/conda.yaml -conda activate quake-env -``` +All experiments are launched using the central `experiment_runner.py` script located in the `test/experiments/osdi2025/` directory. -3. **Install Quake:** +**General Command Structure:** +Make sure to run all commands from the **repository root directory (`quake/`)**. ```bash -pip install --no-use-pep517 . -``` +OMP_NUM_THREADS=1 python3 -m test.experiments.osdi2025.experiment_runner \ + --experiment \ + --config \ + --output-dir # Optional +```` -### Kick the tires +**Explanation:** -To make sure Quake and the experiment environment are set up correctly, you can run the following commands: +* `OMP_NUM_THREADS=1`: Recommended for consistent performance measurement as OMP interferes with our custom parallelism. Adjust as needed based on specific experiment guidelines if any. +* `--experiment `: Specifies which experiment to run (e.g., `kick_the_tires`, `aps_recall_targets`). +* `--config `: Specifies the configuration file from `test/experiments/osdi2025//configs/`. For `sift1m.yaml`, use `sift1m`. +* `--output-dir ` (Optional): If provided, results (CSVs, plots) will be saved here. Default is `test/experiments/osdi2025//results//`. -**Make sure to run all commands in the repository root directory** +### Kick the Tires (Quick Verification) -1. **Run the quickstart example:** +1. **Run the Quickstart Example:** + Tests Quake's core operations. -This is a basic test that the main operations of Quake are working. + ```bash + python3 examples/quickstart.py + ``` -```bash -python3 examples/quickstart.py -``` +2. **Run the `kick_the_tires` Benchmark:** + Tests basic system functionality and performance with a dynamic workload. + + ```bash + OMP_NUM_THREADS=1 python3 -m test.experiments.osdi2025.experiment_runner \ + --experiment kick_the_tires \ + --config sift1m + ``` + + Outputs will be in `test/experiments/osdi2025/kick_the_tires/results/sift1m/`. + +## Experiment Summaries and Outputs + +The following table summarizes each experiment and its primary outputs. Output files are typically generated within the experiment's specific output directory (refer to the `--output-dir` option or its default value). + +| Experiment Name | Description | Key Outputs (Illustrative Filenames) | +|------------------------|--------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| `aps_recall_targets` | Evaluates Quake's Adaptive Partition Scanning (APS) against baselines for various recall targets. | `aps_recall_targets_summary.csv`, `aps_recall_vs_metrics.png` | +| `early_termination` | Compares APS with early termination strategies like LAET and Auncel. | `aps_et_results_.csv`, `latency_plot_.png`, Markdown table (printed to console) | +| `kick_the_tires` | Basic dynamic workload stress test focusing on latency, recall, and system state over operations. | Per-index `results.csv` (e.g., `Quake10000/results.csv`), main `unified_plot.png` | +| `maintenance_ablation` | Evaluates the impact of Quake's maintenance operations under a dynamic workload, extending `kick_the_tires`. | Per-index `results.csv`, main `summary_table.csv`, `summary_table.md`, `unified_plot.png`, `time_breakdown.png` | +| `measure_skew_and_perf` | Measures data skew characteristics and their performance impact on a dataset. | Script-specific CSVs/plots detailing skew metrics and performance correlations. | +| `multi_level` | Benchmarks Quake with multiple index levels against single-level configurations, varying `nprobe`. | Per-index `results.csv` (e.g., `Quake5000/results.csv`), main `recall_vs_qps.png` | +| `numa_multi_query` | Tests Quake's multi-query batch performance under different NUMA and worker configurations. | Per-index `results.csv`, main `.csv` (aggregate), `_batch_latency.png`, `_batch_recall.png` | +| `numa_single_query` | Tests Quake's single-query performance under different NUMA and worker configurations. | Per-index `results.csv`, main `.csv` (aggregate), `_child_latency.png`, `_recall.png` | +| `vary_levels` | Evaluates Quake with varying numbers of centroids in parent index levels. | Per-level `results.csv` (e.g., `L/results.csv`), main `recall_vs_qps.png` | +| `workload_comparison` | Compares Quake's performance across different types of synthetic or real-world workloads. | Script-specific CSVs/plots comparing workloads. | -2. **Run the Benchmark:** +**To run any of these, replace `` and `` in the general command structure.** For example, to run the `aps_recall_targets` experiment with its `sift1m` configuration: ```bash -OMP_NUM_THREADS=1 python3 -m test.experiments.osdi2025.experiment_runner --experiment kick_the_tires --config sift1m +OMP_NUM_THREADS=1 python3 -m test.experiments.osdi2025.experiment_runner \ + --experiment aps_recall_targets \ + --config sift1m ``` ---- \ No newline at end of file +Results, including CSV files and plots as detailed above, will be saved to the specified (or default) output directory. Check the terminal output for the exact location. + +## Notes + +* **Dataset Downloads:** The `quake.datasets.ann_datasets.load_dataset` utility will attempt to download datasets if they are not found locally (typically in a `data/` directory in the repository root). An internet connection is required for initial downloads. +* **Disk Space:** Some datasets (e.g., SIFT1B, MSTuring100M) are large and require significant disk space. +* **Execution Time:** Full experimental runs can be time-consuming, ranging from minutes for small configurations to many hours for large-scale evaluations. The `kick_the_tires` experiment with `sift1m` is designed for relatively quick verification. + + + +``` +``` \ No newline at end of file diff --git a/test/experiments/osdi2025/aps_recall_targets/run.py b/test/experiments/osdi2025/aps_recall_targets/run.py index 1e9510fe..499dfd8a 100644 --- a/test/experiments/osdi2025/aps_recall_targets/run.py +++ b/test/experiments/osdi2025/aps_recall_targets/run.py @@ -1,182 +1,129 @@ #!/usr/bin/env python3 """ -Adaptive‐Partition‐Scanning Recall‐Target Runner -Supports three methods: Oracle, FixedNProbe, APS +Adaptive‐Partition‐Scanning Recall‐Target Runner (Simplified) +Supports: Oracle, FixedNProbe, APS """ - -import time -import yaml import logging from pathlib import Path - +import numpy as np import torch import pandas as pd -import numpy as np -import matplotlib.pyplot as plt -from quake import QuakeIndex, IndexBuildParams, SearchParams -from quake.datasets.ann_datasets import load_dataset -from quake.utils import compute_recall +from test.experiments.osdi2025 import experiment_utils as common_utils +from quake import SearchParams # Keep direct import for clarity if used extensively logger = logging.getLogger(__name__) +logging.basicConfig(level=logging.INFO, format="%(asctime)s | %(levelname)s | %(message)s") + +def run_experiment(cfg_path: str, output_dir_str: str): + cfg = common_utils.load_config(cfg_path) + out_dir = Path(output_dir_str) + + dataset_cfg = cfg["dataset"] + exp_cfg = cfg["experiment"] + idx_build_cfg = cfg["index"] + paths_cfg = cfg["paths"] + + vecs, queries, gt = common_utils.load_data( + dataset_cfg["name"], dataset_cfg.get("path", ""), exp_cfg["nq"] + ) + + idx_path = Path(paths_cfg["index_dir"]) / f"{dataset_cfg['name']}_ivf{idx_build_cfg['nlist']}.index" + quake_idx = common_utils.prepare_quake_index( + vecs, + {"nlist": idx_build_cfg["nlist"], "metric": idx_build_cfg["metric"], "num_workers": exp_cfg["n_workers"]}, + idx_path, + force_rebuild=cfg["overwrite"].get("index", False) + ) + logger.info(f"Index ready (workers={exp_cfg['n_workers']})") -def run_experiment(cfg_path: str, output_dir: str): - # 1) Load config + setup output dir - cfg = yaml.safe_load(Path(cfg_path).read_text()) - out = Path(output_dir); out.mkdir(parents=True, exist_ok=True) - - # 2) Load data - data_dir = cfg["dataset"].get("path", "") - vecs, queries, gt = load_dataset(cfg["dataset"]["name"], data_dir) - - - nq = cfg["experiment"]["nq"] - queries, gt = queries[:nq], gt[:nq] - - # 3) Build or load index - build_params = IndexBuildParams() - build_params.nlist = cfg["index"]["nlist"] - build_params.metric = cfg["index"]["metric"] - build_params.num_workers = cfg["experiment"]["n_workers"] - - idx_dir = Path(cfg["paths"]["index_dir"]); idx_dir.mkdir(parents=True, exist_ok=True) - idx_path = idx_dir / f"{cfg['dataset']['name']}_ivf{build_params.nlist}.index" - - idx = QuakeIndex() - idx.build(vecs, torch.arange(len(vecs)), build_params) - logger.info(f"Loaded index → {idx_path} (workers={cfg['experiment']['n_workers']})") - - # 4) Run each method × recall_target records = [] - k = cfg["experiment"]["k"] - max_nlist = build_params.nlist + k = exp_cfg["k"] + max_nlist_val = quake_idx.nlist() for method in cfg["methods"]: - for rt in cfg["experiment"]["recall_targets"]: - logger.info(f"Method={method} RecallTarget={rt}") - per_query = [] # collect (nprobe, recall, time_ms) tuples + for rt in exp_cfg["recall_targets"]: + per_query_stats = [] # (nprobe, recall, time_ms) if method == "Oracle": - # per‐query binary search - for i, q in enumerate(queries): - lo, hi, best = 1, max_nlist, max_nlist + for i, q_vec in enumerate(queries): + lo, hi, best_np = 1, max_nlist_val, max_nlist_val while lo <= hi: mid = (lo + hi) // 2 - sp = SearchParams() - sp.nprobe = mid - sp.k = k - sp.recall_target = -1 - res = idx.search(q.unsqueeze(0), sp) - rec = compute_recall(res.ids, gt[i].unsqueeze(0), k).item() + sp = common_utils.create_search_params(nprobe=mid, k=k, recall_target=-1) + _, rec, _ = common_utils.run_search_trial(quake_idx, q_vec, gt[i], k, sp) if rec >= rt: - best = mid - hi = mid - 1 + best_np, hi = mid, mid - 1 else: lo = mid + 1 - - # time at best - t0 = time.time() - sp = SearchParams() - sp.nprobe = best - sp.k = k - sp.recall_target = -1 - res = idx.search(q.unsqueeze(0), sp) - rec = compute_recall(res.ids, gt[i].unsqueeze(0), k).item() - elapsed = (time.time() - t0) * 1000.0 - per_query.append((best, rec, elapsed)) + sp_final = common_utils.create_search_params(nprobe=best_np, k=k, recall_target=-1) + _, final_rec, final_time_ms = common_utils.run_search_trial(quake_idx, q_vec, gt[i], k, sp_final) + per_query_stats.append((best_np, final_rec, final_time_ms)) elif method == "FixedNProbe": - # find one global nprobe - lo, hi, best = 1, max_nlist, max_nlist + lo, hi, best_global_np = 1, max_nlist_val, max_nlist_val while lo <= hi: mid = (lo + hi) // 2 - sp = SearchParams(nprobe=mid, k=k, recall_target=-1) - ids = [] - for q in queries: - ids.append(idx.search(q.unsqueeze(0), sp).ids) - avg_rec = compute_recall(torch.cat(ids,0), gt, k).mean().item() + sp = common_utils.create_search_params(nprobe=mid, k=k, recall_target=-1) + current_ids = [] + for q_vec_fixed in queries: # Use all queries for tuning this + res_ids = quake_idx.search(q_vec_fixed.unsqueeze(0), sp).ids + current_ids.append(res_ids) + avg_rec = common_utils.quake_compute_recall(torch.cat(current_ids,0), gt, k).mean().item() if avg_rec >= rt: - best = mid - hi = mid - 1 + best_global_np, hi = mid, mid - 1 else: lo = mid + 1 - # then time each query at best - for q in queries: - t0 = time.time() - sp = SearchParams(nprobe=best, k=k, recall_target=-1) - res = idx.search(q.unsqueeze(0), sp) - elapsed = (time.time() - t0) * 1000.0 - rec = compute_recall(res.ids, gt[0:1], k).item() # single recall - per_query.append((best, rec, elapsed)) + sp_final = common_utils.create_search_params(nprobe=best_global_np, k=k, recall_target=-1) + for i, q_vec in enumerate(queries): + _, final_rec, final_time_ms = common_utils.run_search_trial(quake_idx, q_vec, gt[i], k, sp_final) + per_query_stats.append((best_global_np, final_rec, final_time_ms)) elif method == "APS": - # one‐shot APS per query - for q, true in zip(queries, gt): - sp = SearchParams() - sp.nprobe = -1 - sp.k = k - sp.recall_target = rt - sp.recompute_threshold = cfg["experiment"]["recompute_ratio"] - sp.use_precomputed = cfg["experiment"]["use_precompute"] - sp.initial_search_fraction = cfg["experiment"]["initial_search_fraction"] - t0 = time.time() - res = idx.search(q.unsqueeze(0), sp) - elapsed = (time.time() - t0) * 1000.0 - rec = compute_recall(res.ids, true.unsqueeze(0), k).item() - print(f"recall: {rec:.4f} (target={rt})") - per_query.append((res.timing_info.partitions_scanned, rec, elapsed)) + for i, q_vec in enumerate(queries): + sp = common_utils.create_search_params( + nprobe=-1, k=k, recall_target=rt, + recompute_threshold=exp_cfg["recompute_ratio"], + use_precomputed=exp_cfg["use_precompute"], + initial_search_fraction=exp_cfg["initial_search_fraction"] + ) + nprobe_scan, final_rec, final_time_ms = common_utils.run_search_trial(quake_idx, q_vec, gt[i], k, sp) + logger.debug(f"APS recall: {final_rec:.4f} (target={rt}), nprobe_scanned={nprobe_scan}") + per_query_stats.append((nprobe_scan, final_rec, final_time_ms)) else: raise ValueError(f"Unknown method: {method}") - # 5) Aggregate - arr = np.array(per_query, dtype=float) # shape (nq,3) + + arr = np.array(per_query_stats, dtype=float) + mean_nprobe = arr[:,0].mean() + std_nprobe = arr[:,0].std() + mean_recall = arr[:,1].mean() + std_recall = arr[:,1].std() + mean_time_ms = arr[:,2].mean() + std_time_ms = arr[:,2].std() + + logger.info(f"Method={method} RecallTarget={rt}, Recall={mean_recall:.4f} (std={std_recall:.4f}), " + f"Time={mean_time_ms:.2f}ms (std={std_time_ms:.2f}ms), NProbe={mean_nprobe:.1f} (std={std_nprobe:.1f})") + records.append({ - "Method": method, - "RecallTarget": rt, - "mean_nprobe": arr[:,0].mean(), - "std_nprobe": arr[:,0].std(), - "mean_recall": arr[:,1].mean(), - "std_recall": arr[:,1].std(), - "mean_time_ms": arr[:,2].mean(), - "std_time_ms": arr[:,2].std(), + "Method": method, "RecallTarget": rt, + "mean_nprobe": mean_nprobe, "std_nprobe": std_nprobe, + "mean_recall": mean_recall, "std_recall": std_recall, + "mean_time_ms": mean_time_ms, "std_time_ms": std_time_ms, }) - # 6) Save CSV - df = pd.DataFrame(records) - out_csv = out / "aps_recall_targets_summary.csv" - df.to_csv(out_csv, index=False) - logger.info(f"Summary → {out_csv}") - - # 7) Plot: recall-target vs time, one line per method - fig, axes = plt.subplots(3, 1, figsize=(6,8), sharex=True) - for m in df["Method"].unique(): - grp = df[df["Method"]==m] - axes[0].errorbar(grp["RecallTarget"], grp["mean_time_ms"], - yerr=grp["std_time_ms"], marker="o", capsize=4, label=m) - axes[1].errorbar(grp["RecallTarget"], grp["mean_recall"], - yerr=grp["std_recall"], marker="o", capsize=4, label=m) - axes[2].errorbar(grp["RecallTarget"], grp["mean_nprobe"], - yerr=grp["std_nprobe"], marker="o", capsize=4, label=m) - axes[0].set_xlabel("Recall Target") - axes[0].set_xlabel("Mean Query Time (ms)") - axes[0].set_title("APS‑Experiment: Recall Target vs Time") - axes[0].legend() - - recall_targets = cfg["experiment"]["recall_targets"] - - axes[1].plot(recall_targets, recall_targets, "k--", label="Target") - axes[1].set_xlabel("Recall Target") - axes[1].set_ylabel("Mean Recall") - axes[1].set_title("APS‑Experiment: Recall Target vs Recall") - - axes[2].set_xlabel("Recall Target") - axes[2].set_ylabel("Mean NProbe") - axes[2].set_yscale("log") - axes[2].set_title("APS‑Experiment: Recall Target vs NProbe") - - plt.grid(True, which="both", ls=":") - plt.tight_layout() - plot_path = out / "aps_recall_vs_time.png" - plt.savefig(plot_path) - logger.info(f"Plot → {plot_path}") \ No newline at end of file + results_df = pd.DataFrame(records) + common_utils.save_results_csv(results_df, out_dir / "aps_recall_targets_summary.csv") + + plot_path = out_dir / "aps_recall_vs_metrics.png" + common_utils.plot_recall_performance( + results_df, + x_metric="RecallTarget", + y_metrics=["mean_time_ms", "mean_recall", "mean_nprobe"], + y_labels=["Mean Query Time (ms)", "Mean Recall Achieved", "Mean NProbe"], + y_scales=["linear", "linear", "log"], + plot_title_prefix=f"{dataset_cfg['name']} APS Experiment", + output_path=plot_path, + recall_targets_for_line=exp_cfg["recall_targets"] + ) \ No newline at end of file diff --git a/test/experiments/osdi2025/experiment_utils.py b/test/experiments/osdi2025/experiment_utils.py new file mode 100644 index 00000000..b9091483 --- /dev/null +++ b/test/experiments/osdi2025/experiment_utils.py @@ -0,0 +1,126 @@ +# File: test/experiments/osdi2025/common/experiment_utils.py +import time +import yaml +import logging +from pathlib import Path + +import torch +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt + +from quake import QuakeIndex, IndexBuildParams, SearchParams +from quake.datasets.ann_datasets import load_dataset as quake_load_dataset +from quake.utils import compute_recall as quake_compute_recall + +logger = logging.getLogger(__name__) + +def load_config(cfg_path: str) -> dict: + return yaml.safe_load(Path(cfg_path).read_text()) + +def load_data(dataset_name: str, dataset_path: str, nq_override: int = None): + vecs, queries, gt = quake_load_dataset(dataset_name, dataset_path) + if nq_override is not None and nq_override > 0 and nq_override < len(queries): + queries = queries[:nq_override] + if gt is not None: + gt = gt[:nq_override] + return vecs, queries, gt + +def prepare_quake_index( + vecs: torch.Tensor, + build_params_dict: dict, + index_file_path: Path, + force_rebuild: bool = False, + num_workers_load: int = 0, + num_parent_workers_load: int = 0, + use_numa: bool = False +) -> QuakeIndex: + idx = QuakeIndex() + if index_file_path.exists() and not force_rebuild: + logger.info(f"Loading index from {index_file_path}") + idx.load(str(index_file_path), num_workers_load, use_numa, num_parent_workers_load) + else: + logger.info(f"Building index -> {index_file_path}") + bp = IndexBuildParams() + for key, value in build_params_dict.items(): + if hasattr(bp, key): + setattr(bp, key, value) + idx.build(vecs, torch.arange(len(vecs)), bp) + index_file_path.parent.mkdir(parents=True, exist_ok=True) + idx.save(str(index_file_path)) + logger.info(f"Index saved to {index_file_path}") + return idx + +def create_search_params(**attrs) -> SearchParams: + sp = SearchParams() + for k, v in attrs.items(): + if hasattr(sp, k): + setattr(sp, k, v) + return sp + +def run_search_trial( + idx: QuakeIndex, + query: torch.Tensor, + gt_vector: torch.Tensor, # Ground truth for this single query + k_val: int, + search_params: SearchParams +) -> tuple: + t0 = time.perf_counter_ns() + res = idx.search(query.unsqueeze(0), search_params) + elapsed_ns = time.perf_counter_ns() - t0 + rec = quake_compute_recall(res.ids, gt_vector.unsqueeze(0), k_val).item() + partitions_scanned = getattr(res.timing_info, "partitions_scanned", np.nan) + return partitions_scanned, rec, elapsed_ns / 1e6 # nprobe, recall, time_ms + +def save_results_csv(records: list, output_path: Path): + df = pd.DataFrame(records) + output_path.parent.mkdir(parents=True, exist_ok=True) + df.to_csv(output_path, index=False) + logger.info(f"Results saved to {output_path}") + +def plot_recall_performance( + df: pd.DataFrame, + x_metric: str, # e.g., "RecallTarget" + y_metrics: list, # e.g., ["mean_time_ms", "mean_recall", "mean_nprobe"] + y_labels: list, + y_scales: list = None, # e.g., ["linear", "linear", "log"] + plot_title_prefix: str = "", + output_path: Path = None, + recall_targets_for_line: list = None +): + num_plots = len(y_metrics) + fig, axes = plt.subplots(num_plots, 1, figsize=(6, 3 * num_plots + 1), sharex=True) + if num_plots == 1: + axes = [axes] + + for m_idx, method_name in enumerate(df["Method"].unique()): + grp = df[df["Method"] == method_name] + for i, y_col in enumerate(y_metrics): + y_err_col = df.columns[df.columns.get_loc(y_col) + 1] if f"std_{y_col.split('_')[1]}" in df.columns else None + yerr_values = grp[y_err_col] if y_err_col and y_err_col in grp.columns else None + axes[i].errorbar( + grp[x_metric], grp[y_col], + yerr=yerr_values, + marker="o", capsize=4, label=method_name, + linestyle='-' if m_idx == 0 else '--' # Differentiate methods + ) + + for i, y_col in enumerate(y_metrics): + axes[i].set_ylabel(y_labels[i]) + if y_scales and y_scales[i]: + axes[i].set_yscale(y_scales[i]) + if y_col == "mean_recall" and recall_targets_for_line: + axes[i].plot(recall_targets_for_line, recall_targets_for_line, "k--", label="Target") + axes[i].legend() + axes[i].grid(True, which="both", ls=":") + axes[i].set_title(f"{plot_title_prefix}: {x_metric} vs {y_labels[i]}") + + axes[-1].set_xlabel(x_metric) + plt.tight_layout(rect=[0, 0, 1, 0.97]) # Adjust for suptitle + fig.suptitle(f"{plot_title_prefix} Performance", fontsize=14) + + if output_path: + output_path.parent.mkdir(parents=True, exist_ok=True) + plt.savefig(output_path) + logger.info(f"Plot saved to {output_path}") + plt.close(fig) \ No newline at end of file From 3f09148e283154d15f10c2272d7c0ce4ffe3580e Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 21 May 2025 00:19:10 -0500 Subject: [PATCH 195/323] update aps experiment --- .../configs/sift1m.yaml | 25 ---- .../adaptive_partition_scanning/run.py | 119 ------------------ 2 files changed, 144 deletions(-) delete mode 100644 test/experiments/adaptive_partition_scanning/configs/sift1m.yaml delete mode 100644 test/experiments/adaptive_partition_scanning/run.py diff --git a/test/experiments/adaptive_partition_scanning/configs/sift1m.yaml b/test/experiments/adaptive_partition_scanning/configs/sift1m.yaml deleted file mode 100644 index 51e8f982..00000000 --- a/test/experiments/adaptive_partition_scanning/configs/sift1m.yaml +++ /dev/null @@ -1,25 +0,0 @@ -# configs/experiment.yaml -mode: run # options: build, run, plot -base_dir: data/aps -dataset: - name: sift1m - path: data -experiment: - nq: 100 - k: 100 - n_workers: 0 - recall_targets: [0.7, 0.75, 0.8, 0.85, 0.9, 0.95, .99] -index: - metric: l2 - nc: 1000 -overwrite: - index: false - results: true -methods: ['Oracle', 'APS'] -#methods: ['APS'] -#methods: ['Oracle'] - -paths: - index_dir: data/sift/indexes - results_dir: data/sift/results - plot_dir: data/sift/plots \ No newline at end of file diff --git a/test/experiments/adaptive_partition_scanning/run.py b/test/experiments/adaptive_partition_scanning/run.py deleted file mode 100644 index dc5a74e5..00000000 --- a/test/experiments/adaptive_partition_scanning/run.py +++ /dev/null @@ -1,119 +0,0 @@ -#!/usr/bin/env python3 -""" -Adaptive‐Partition‐Scanning Recall‐Target Runner (Simplified) -Supports: Oracle, FixedNProbe, APS -""" -import logging -from pathlib import Path -import numpy as np -import torch -import pandas as pd - -from test.experiments.osdi2025 import experiment_utils as common_utils -from quake import SearchParams # Keep direct import for clarity if used extensively - -logger = logging.getLogger(__name__) -logging.basicConfig(level=logging.INFO, format="%(asctime)s | %(levelname)s | %(message)s") - -def run_experiment(cfg_path: str, output_dir_str: str): - cfg = common_utils.load_config(cfg_path) - out_dir = Path(output_dir_str) - - dataset_cfg = cfg["dataset"] - exp_cfg = cfg["experiment"] - idx_build_cfg = cfg["index"] - paths_cfg = cfg["paths"] - - vecs, queries, gt = common_utils.load_data( - dataset_cfg["name"], dataset_cfg.get("path", ""), exp_cfg["nq"] - ) - - idx_path = Path(paths_cfg["index_dir"]) / f"{dataset_cfg['name']}_ivf{idx_build_cfg['nlist']}.index" - quake_idx = common_utils.prepare_quake_index( - vecs, - {"nlist": idx_build_cfg["nlist"], "metric": idx_build_cfg["metric"], "num_workers": exp_cfg["n_workers"]}, - idx_path, - force_rebuild=cfg["overwrite"].get("index", False) - ) - logger.info(f"Index ready (workers={exp_cfg['n_workers']})") - - records = [] - k = exp_cfg["k"] - max_nlist_val = quake_idx.nlist() - - for method in cfg["methods"]: - for rt in exp_cfg["recall_targets"]: - logger.info(f"Method={method} RecallTarget={rt}") - per_query_stats = [] # (nprobe, recall, time_ms) - - if method == "Oracle": - for i, q_vec in enumerate(queries): - lo, hi, best_np = 1, max_nlist_val, max_nlist_val - while lo <= hi: - mid = (lo + hi) // 2 - sp = common_utils.create_search_params(nprobe=mid, k=k, recall_target=-1) - _, rec, _ = common_utils.run_search_trial(quake_idx, q_vec, gt[i], k, sp) - if rec >= rt: - best_np, hi = mid, mid - 1 - else: - lo = mid + 1 - sp_final = common_utils.create_search_params(nprobe=best_np, k=k, recall_target=-1) - _, final_rec, final_time_ms = common_utils.run_search_trial(quake_idx, q_vec, gt[i], k, sp_final) - per_query_stats.append((best_np, final_rec, final_time_ms)) - - elif method == "FixedNProbe": - lo, hi, best_global_np = 1, max_nlist_val, max_nlist_val - while lo <= hi: - mid = (lo + hi) // 2 - sp = common_utils.create_search_params(nprobe=mid, k=k, recall_target=-1) - current_ids = [] - for q_vec_fixed in queries: # Use all queries for tuning this - res_ids = quake_idx.search(q_vec_fixed.unsqueeze(0), sp).ids - current_ids.append(res_ids) - avg_rec = common_utils.quake_compute_recall(torch.cat(current_ids,0), gt, k).mean().item() - if avg_rec >= rt: - best_global_np, hi = mid, mid - 1 - else: - lo = mid + 1 - - sp_final = common_utils.create_search_params(nprobe=best_global_np, k=k, recall_target=-1) - for i, q_vec in enumerate(queries): - _, final_rec, final_time_ms = common_utils.run_search_trial(quake_idx, q_vec, gt[i], k, sp_final) - per_query_stats.append((best_global_np, final_rec, final_time_ms)) - - elif method == "APS": - for i, q_vec in enumerate(queries): - sp = common_utils.create_search_params( - nprobe=-1, k=k, recall_target=rt, - recompute_threshold=exp_cfg["recompute_ratio"], - use_precomputed=exp_cfg["use_precompute"], - initial_search_fraction=exp_cfg["initial_search_fraction"] - ) - nprobe_scan, final_rec, final_time_ms = common_utils.run_search_trial(quake_idx, q_vec, gt[i], k, sp) - logger.debug(f"APS recall: {final_rec:.4f} (target={rt}), nprobe_scanned={nprobe_scan}") - per_query_stats.append((nprobe_scan, final_rec, final_time_ms)) - else: - raise ValueError(f"Unknown method: {method}") - - arr = np.array(per_query_stats, dtype=float) - records.append({ - "Method": method, "RecallTarget": rt, - "mean_nprobe": arr[:,0].mean(), "std_nprobe": arr[:,0].std(), - "mean_recall": arr[:,1].mean(), "std_recall": arr[:,1].std(), - "mean_time_ms": arr[:,2].mean(), "std_time_ms": arr[:,2].std(), - }) - - results_df = pd.DataFrame(records) - common_utils.save_results_csv(results_df, out_dir / "aps_recall_targets_summary.csv") - - plot_path = out_dir / "aps_recall_vs_metrics.png" - common_utils.plot_recall_performance( - results_df, - x_metric="RecallTarget", - y_metrics=["mean_time_ms", "mean_recall", "mean_nprobe"], - y_labels=["Mean Query Time (ms)", "Mean Recall Achieved", "Mean NProbe"], - y_scales=["linear", "linear", "log"], - plot_title_prefix=f"{dataset_cfg['name']} APS Experiment", - output_path=plot_path, - recall_targets_for_line=exp_cfg["recall_targets"] - ) \ No newline at end of file From e9d18d1e437f03a7491c8802389ba3f95c96b0bc Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 21 May 2025 00:51:02 -0500 Subject: [PATCH 196/323] update early termination experiment --- .../early_termination/configs/sift1m.yaml | 4 +- .../osdi2025/early_termination/run.py | 528 ++++++++---------- 2 files changed, 236 insertions(+), 296 deletions(-) diff --git a/test/experiments/osdi2025/early_termination/configs/sift1m.yaml b/test/experiments/osdi2025/early_termination/configs/sift1m.yaml index 9d53f80f..df1c96a1 100644 --- a/test/experiments/osdi2025/early_termination/configs/sift1m.yaml +++ b/test/experiments/osdi2025/early_termination/configs/sift1m.yaml @@ -18,8 +18,8 @@ index: overwrite: index: false results: true -#methods: ['LAET', 'Auncel', 'Oracle', 'FixedNProbe', 'APS', 'SPANN'] -methods: ['Auncel'] +methods: ['LAET', 'Auncel', 'Oracle', 'FixedNProbe', 'APS', 'SPANN'] +#methods: ['Auncel'] paths: index_dir: data/sift1m/indexes diff --git a/test/experiments/osdi2025/early_termination/run.py b/test/experiments/osdi2025/early_termination/run.py index ce8c2dbc..c6538b95 100644 --- a/test/experiments/osdi2025/early_termination/run.py +++ b/test/experiments/osdi2025/early_termination/run.py @@ -1,13 +1,11 @@ #!/usr/bin/env python3 """ -APS vs. early-termination baselines +APS vs. early-termination baselines (Simplified) Generates CSV + Markdown table with: RecallAchieved | nprobe | QueryLatencyMs | TuningTimeMs for recall targets 0.80, 0.90, 0.99 """ - from __future__ import annotations -import argparse import logging import time from pathlib import Path @@ -16,22 +14,14 @@ import numpy as np import pandas as pd import torch -import yaml - -# ── optional plotting ───────────────────────────────────────────────────────── -try: - import matplotlib.pyplot as plt +import matplotlib.pyplot as plt - _HAVE_MPL = True -except ImportError: - _HAVE_MPL = False +import test.experiments.osdi2025.experiment_utils as common_utils -# ── Quake bindings ──────────────────────────────────────────────────────────── -from quake import QuakeIndex, IndexBuildParams, SearchParams -from quake.datasets.ann_datasets import load_dataset +from quake import QuakeIndex, SearchParams, IndexBuildParams from quake.utils import compute_recall -# ── LAET (mandatory) ────────────────────────────────────────────────────────── +# LAET (mandatory for this experiment) try: from .laet import LAETPipeline except ImportError as e: @@ -40,24 +30,11 @@ "Install the laet package or fix PYTHONPATH." ) from e -# ── logging ─────────────────────────────────────────────────────────────────── logging.basicConfig( level=logging.INFO, format="%(asctime)s | %(levelname)7s | %(message)s", datefmt="%H:%M:%S" ) logger = logging.getLogger("experiment") - -# ----------------------------------------------------------------------------- # -# Helpers -# ----------------------------------------------------------------------------- # -def make_sp(**attrs) -> SearchParams: - """Create SearchParams then set attributes (bindings have no kwargs ctor).""" - sp = SearchParams() - for k, v in attrs.items(): - setattr(sp, k, v) - return sp - - def tune_auncel_a( idx: QuakeIndex, queries: torch.Tensor, @@ -70,331 +47,294 @@ def tune_auncel_a( a_min: float = 1e-5, a_max: float = .5, ) -> tuple[float, list[tuple[float, float, float]]]: - """ - Binary-search the smallest 'a' that still meets the recall target. - Returns (best_a, per_query_stats) where the stats are - (nprobe, recall, latency_ms) for the final run. - """ best_a = a_max lo, hi = a_min, a_max best_stats: list[tuple[float, float, float]] = [] for _ in range(max_iters): mid = (lo + hi) / 2.0 - per_query: list[tuple[float, float, float]] = [] - - for q, g in zip(queries, gt): - sp = make_sp( - nprobe=-1, - k=k, - recall_target=target_recall, - use_auncel=True, - auncel_a=mid, - auncel_b=.5, - recompute_threshold=recompute_thr, - initial_search_fraction=init_frac, + current_per_query_stats: list[tuple[float, float, float]] = [] + + for i_q, q_vec in enumerate(queries): + sp = common_utils.create_search_params( + nprobe=-1, k=k, recall_target=target_recall, + use_auncel=True, auncel_a=mid, auncel_b=.5, + recompute_threshold=recompute_thr, initial_search_fraction=init_frac, ) - t0 = time.perf_counter_ns() - res = idx.search(q.unsqueeze(0), sp) - per_query.append( - ( - getattr(res.timing_info, "partitions_scanned", np.nan), - compute_recall(res.ids, g.unsqueeze(0), k).item(), - (time.perf_counter_ns() - t0) / 1e6, - ) + partitions_scanned, rec, time_ms = common_utils.run_search_trial( + idx, q_vec, gt[i_q], k, sp ) + current_per_query_stats.append((partitions_scanned, rec, time_ms)) - mean_recall = np.mean([r for _, r, _ in per_query]) + mean_recall = np.mean([r for _, r, _ in current_per_query_stats]) if mean_recall >= target_recall: - # feasible → try smaller a - best_a, best_stats = mid, per_query + best_a, best_stats = mid, current_per_query_stats hi = mid * 0.5 else: - # not enough recall → increase a lo = mid * 1.5 - if hi - lo < 1e-3: + if abs(hi - lo) < 1e-4 or hi < lo : # Added small tolerance and check break - return best_a, best_stats def binary_search_best_nprobe( idx: QuakeIndex, - queries: torch.Tensor, - gt: torch.Tensor, - k: int, - target_recall: float, - max_nprobe: int, - use_spann: bool = False, - spann_eps: float = 1.5, + queries_tensor: torch.Tensor, # Can be single or multiple queries + gt_tensor: torch.Tensor, # Corresponding ground truth + k_val: int, + target_recall_val: float, + max_nprobe_val: int, + use_spann_flag: bool = False, + spann_eps_val: float = 1.5, ) -> int: - """Return smallest nprobe reaching `target_recall` on the supplied queries.""" - lo, hi, best = 1, max_nprobe, max_nprobe + lo, hi, best = 1, max_nprobe_val, max_nprobe_val + is_single_query = queries_tensor.shape[0] == 1 + while lo <= hi: mid = (lo + hi) // 2 - sp = make_sp(k=k, nprobe=mid, use_spann=use_spann, spann_eps=spann_eps if use_spann else 0.0) - ids_all = [ - idx.search(q.unsqueeze(0), sp).ids - for q in queries - if idx.search(q.unsqueeze(0), sp).ids.numel() - ] - mean_rec = ( - compute_recall(torch.cat(ids_all, 0), gt, k).mean().item() if ids_all else 0.0 + sp = common_utils.create_search_params( + k=k_val, nprobe=mid, use_spann=use_spann_flag, + spann_eps=spann_eps_val if use_spann_flag else 0.0 ) - if mean_rec >= target_recall: + + if is_single_query: + # For single query, run_search_trial is fine for recall check + _, rec, _ = common_utils.run_search_trial(idx, queries_tensor[0], gt_tensor[0], k_val, sp) + mean_rec_val = rec + else: + # For multiple queries, need to aggregate recall + ids_all_list = [] + for i_q in range(queries_tensor.shape[0]): + # Directly call idx.search for id extraction for compute_recall over batch + res_search = idx.search(queries_tensor[i_q].unsqueeze(0), sp) + if res_search.ids.numel(): + ids_all_list.append(res_search.ids) + if not ids_all_list: + mean_rec_val = 0.0 + else: + mean_rec_val = compute_recall(torch.cat(ids_all_list, 0), gt_tensor, k_val).mean().item() + + if mean_rec_val >= target_recall_val: best, hi = mid, mid - 1 else: lo = mid + 1 return best - def print_markdown(df: pd.DataFrame) -> None: print("\n" + "-" * 80) print(df.to_markdown(index=False, floatfmt=".4f")) print("-" * 80 + "\n") -# ----------------------------------------------------------------------------- # -# Core experiment -# ----------------------------------------------------------------------------- # -def run_experiment(cfg_path: str, output_dir: str) -> None: - # 1) ---------------------------------------------------------------- config - cfg = yaml.safe_load(Path(cfg_path).read_text()) - out_dir = Path(output_dir).expanduser() +def run_experiment(cfg_path_str: str, output_dir_str: str) -> None: + cfg = common_utils.load_config(cfg_path_str) + out_dir = Path(output_dir_str) out_dir.mkdir(parents=True, exist_ok=True) - k = cfg["experiment"]["k"] - recall_targets: List[float] = cfg["experiment"]["recall_targets"] - table_targets = {0.80, 0.90, 0.99} - - # 2) ---------------------------------------------------------------- data - vecs, queries, gt = load_dataset(cfg["dataset"]["name"], cfg["dataset"].get("path", "")) - nq = min(cfg["experiment"].get("nq", queries.shape[0]), queries.shape[0]) - queries, gt = queries[:nq], gt[:nq] - logger.info(f"Dataset loaded – {len(vecs):,} base vecs, {nq} queries.") - - # 3) ---------------------------------------------------------------- index - idx = QuakeIndex() - bp = IndexBuildParams() - bp.nlist = cfg["index"]["nlist"] - bp.metric = cfg["index"]["metric"] - bp.num_workers = cfg["experiment"].get("n_workers", 0) - - idx_file = Path(cfg["paths"]["index_dir"]) / f"{cfg['dataset']['name']}_quake_ivf{bp.nlist}.index" - idx_file.parent.mkdir(parents=True, exist_ok=True) - if idx_file.exists() and not cfg["overwrite"].get("index", False): - idx.load(str(idx_file), 0) - logger.info("Index loaded from disk.") - else: - logger.info("Building index …") - idx.build(vecs, torch.arange(len(vecs)), bp) - idx.save(str(idx_file)) - logger.info(f"Index saved to {idx_file}") - - max_nprobe = idx.nlist() if callable(getattr(idx, "nlist", None)) else bp.nlist - - # 4) ---------------------------------------------------------------- LAET (train / load) - laet_cfg = cfg["laet_config"] - - # required dicts for get_or_train_model - data_paths_cfg = { - "base_vectors": laet_cfg["base_vectors_path_for_laet_train"], - "train_query_vectors": laet_cfg["train_query_vectors_path_for_laet_train"], - "gt_train": laet_cfg["gt_train_path_for_laet_train"], - "gt_format": laet_cfg.get("gt_format_for_laet_train", "ivecs"), - "training_vectors_for_faiss_index": laet_cfg["faiss_training_vectors_path_for_laet_train"], + exp_cfg = cfg["experiment"] + dataset_cfg = cfg["dataset"] + idx_build_cfg = cfg["index"] + paths_cfg = cfg["paths"] + + k_val = exp_cfg["k"] + recall_targets_list: List[float] = exp_cfg["recall_targets"] + table_targets_set = {0.80, 0.90, 0.99} + + vecs, queries_all, gt_all = common_utils.load_data( + dataset_cfg["name"], dataset_cfg.get("path", ""), exp_cfg.get("nq") + ) + logger.info(f"Dataset loaded – {len(vecs):,} base vecs, {len(queries_all)} queries.") + + idx_file_path = Path(paths_cfg["index_dir"]) / f"{dataset_cfg['name']}_quake_ivf{idx_build_cfg['nlist']}.index" + quake_idx_instance = common_utils.prepare_quake_index( + vecs, + {"nlist": idx_build_cfg["nlist"], "metric": idx_build_cfg["metric"], "num_workers": exp_cfg.get("n_workers", 0)}, + idx_file_path, + force_rebuild=cfg["overwrite"].get("index", False), + num_workers_load=exp_cfg.get("n_workers",0) # Pass num_workers for loading context + ) + max_nprobe_val = quake_idx_instance.nlist() + + # LAET SPECIFIC CONFIG + laet_cfg_dict = cfg["laet_config"] + data_paths_cfg_laet = { + "base_vectors": laet_cfg_dict["base_vectors_path_for_laet_train"], + "train_query_vectors": laet_cfg_dict["train_query_vectors_path_for_laet_train"], + "gt_train": laet_cfg_dict["gt_train_path_for_laet_train"], + "gt_format": laet_cfg_dict.get("gt_format_for_laet_train", "ivecs"), + "training_vectors_for_faiss_index": laet_cfg_dict["faiss_training_vectors_path_for_laet_train"], } - generation_params_cfg = { - "num_train_queries_to_process": laet_cfg.get("num_laet_training_samples", 10000), - "k_gt_load_for_target": laet_cfg.get("laet_train_k_gt_load", 1), - "k_search_for_target": laet_cfg.get("laet_train_k_search", 10), - "max_nprobe_scan": laet_cfg.get("laet_train_max_nprobe", 128), - "max_efsearch_scan": laet_cfg.get("laet_train_max_efsearch", 256), - "param_scan_step": laet_cfg.get("laet_train_param_scan_step", 1), + generation_params_cfg_laet = { + "num_train_queries_to_process": laet_cfg_dict.get("num_laet_training_samples", 10000), + "k_gt_load_for_target": laet_cfg_dict.get("laet_train_k_gt_load", 1), + "k_search_for_target": laet_cfg_dict.get("laet_train_k_search", 10), + "max_nprobe_scan": laet_cfg_dict.get("laet_train_max_nprobe", 128), + "param_scan_step": laet_cfg_dict.get("laet_train_param_scan_step", 1), } - faiss_params_cfg = { - "faiss_index_key_for_gen": laet_cfg.get("faiss_index_key_for_laet_train_gen", f"IVF{max_nprobe},Flat") + faiss_params_cfg_laet = { + "faiss_index_key_for_gen": laet_cfg_dict.get("faiss_index_key_for_laet_train_gen", f"IVF{max_nprobe_val},Flat") } - training_params_cfg = { - "log_target_training": laet_cfg.get("train_log_target", False), - "test_size": laet_cfg.get("train_gbdt_test_size", 0.1), - "num_leaves": laet_cfg.get("train_gbdt_num_leaves", 31), - "learning_rate": laet_cfg.get("train_gbdt_lr", 0.05), - "n_estimators": laet_cfg.get("train_gbdt_n_estimators", 100), - "feature_fraction": laet_cfg.get("train_gbdt_feature_fraction", 0.9), - "bagging_fraction": laet_cfg.get("train_gbdt_bagging_fraction", 0.8), - "bagging_freq": laet_cfg.get("train_gbdt_bagging_freq", 5), + training_params_cfg_laet = { + "log_target_training": laet_cfg_dict.get("train_log_target", False), + "test_size": laet_cfg_dict.get("train_gbdt_test_size", 0.1), + # ... other GBDT params from the LAET repo + "num_leaves": laet_cfg_dict.get("train_gbdt_num_leaves", 31), + "learning_rate": laet_cfg_dict.get("train_gbdt_lr", 0.05), + "n_estimators": laet_cfg_dict.get("train_gbdt_n_estimators", 100), } - laet = LAETPipeline( - vector_dim=queries.shape[1], + laet_pipeline_instance = LAETPipeline( + vector_dim=queries_all.shape[1], laet_artifacts_config={ - "base_dir": cfg["paths"]["laet_artifacts_base_dir"], - "dataset_name_for_files": laet_cfg.get( - "dataset_name_for_files", cfg["dataset"]["name"].upper() - ), - "index_key_for_files": laet_cfg.get("index_key_for_files", f"IVF{max_nprobe}_Flat"), - "model_subdir": laet_cfg.get("model_subdir", "laet_gbdt_models"), + "base_dir": paths_cfg["laet_artifacts_base_dir"], + "dataset_name_for_files": laet_cfg_dict.get("dataset_name_for_files", dataset_cfg["name"].upper()), + "index_key_for_files": laet_cfg_dict.get("index_key_for_files", f"IVF{max_nprobe_val}_Flat"), + "model_subdir": laet_cfg_dict.get("model_subdir", "laet_gbdt_models"), }, ) - - t_train0 = time.perf_counter_ns() - laet.get_or_train_model( - overwrite_model=False, - data_paths_config=data_paths_cfg, - generation_params_config=generation_params_cfg, - faiss_params_config=faiss_params_cfg, - training_params_config=training_params_cfg, + t_laet_train_start_ns = time.perf_counter_ns() + laet_pipeline_instance.get_or_train_model( + overwrite_model=False, # Or cfg["overwrite"].get("laet_model", False) + data_paths_config=data_paths_cfg_laet, + generation_params_config=generation_params_cfg_laet, + faiss_params_config=faiss_params_cfg_laet, + training_params_config=training_params_cfg_laet, ) - laet_training_ns = time.perf_counter_ns() - t_train0 - logger.info(f"LAET model ready (train/load time: {laet_training_ns/1e6:.1f} ms).") - - # 5) --------------------------------------------------------------- run methods - records: List[Dict] = [] - - for method in cfg["methods"]: - for rt in recall_targets: - logger.info(f"{method:>10s} | target recall {rt:.2f}") - per_query = [] # tuples of (nprobe, recall, q_ms) - tuning_ns = 0 - - # ---------------------------------------------------------------- # - if method == "Oracle": - for q, g in zip(queries, gt): - t0 = time.perf_counter_ns() - best_np = binary_search_best_nprobe( - idx, q.unsqueeze(0), g.unsqueeze(0), k, rt, max_nprobe + laet_training_total_ns = time.perf_counter_ns() - t_laet_train_start_ns + logger.info(f"LAET model ready (train/load time: {laet_training_total_ns/1e6:.1f} ms).") + + experiment_records: List[Dict] = [] + + for method_name in cfg["methods"]: + for recall_target_val in recall_targets_list: + query_results_list = [] # list of (nprobe_val, recall_val, query_time_ms) + current_tuning_ns = 0 + + if method_name == "Oracle": + for i_q, q_vec in enumerate(queries_all): + gt_vec = gt_all[i_q] + t_tune_start_ns = time.perf_counter_ns() + # Pass single query and gt for Oracle's per-query tuning + best_np_val = binary_search_best_nprobe( + quake_idx_instance, q_vec.unsqueeze(0), gt_vec.unsqueeze(0), k_val, recall_target_val, max_nprobe_val ) - tuning_ns += time.perf_counter_ns() - t0 - sp = make_sp(k=k, nprobe=best_np) - t1 = time.perf_counter_ns() - res = idx.search(q.unsqueeze(0), sp) - per_query.append( - ( - best_np, - compute_recall(res.ids, g.unsqueeze(0), k).item(), - (time.perf_counter_ns() - t1) / 1e6, - ) + current_tuning_ns += (time.perf_counter_ns() - t_tune_start_ns) + + sp_final = common_utils.create_search_params(k=k_val, nprobe=best_np_val) + _, rec_val, time_ms_val = common_utils.run_search_trial( + quake_idx_instance, q_vec, gt_vec, k_val, sp_final ) + query_results_list.append((best_np_val, rec_val, time_ms_val)) - elif method in ("FixedNProbe", "SPANN"): - t0 = time.perf_counter_ns() - best_np = binary_search_best_nprobe( - idx, - queries, - gt, - k, - rt, - max_nprobe, - use_spann=(method == "SPANN"), - spann_eps=1.5, + elif method_name in ("FixedNProbe", "SPANN"): + t_tune_start_ns = time.perf_counter_ns() + best_np_val = binary_search_best_nprobe( + quake_idx_instance, queries_all, gt_all, k_val, recall_target_val, max_nprobe_val, + use_spann_flag=(method_name == "SPANN"), spann_eps_val=1.5 ) - tuning_ns = time.perf_counter_ns() - t0 - for q, g in zip(queries, gt): - sp = make_sp( - k=k, - nprobe=best_np, - use_spann=(method == "SPANN"), - spann_eps=1.5 if method == "SPANN" else 0.0, + current_tuning_ns = time.perf_counter_ns() - t_tune_start_ns + + for i_q, q_vec in enumerate(queries_all): + sp_final = common_utils.create_search_params( + k=k_val, nprobe=best_np_val, + use_spann=(method_name == "SPANN"), + spann_eps=1.5 if method_name == "SPANN" else 0.0 ) - t1 = time.perf_counter_ns() - res = idx.search(q.unsqueeze(0), sp) - per_query.append( - ( - best_np, - compute_recall(res.ids, g.unsqueeze(0), k).item(), - (time.perf_counter_ns() - t1) / 1e6, - ) + _, rec_val, time_ms_val = common_utils.run_search_trial( + quake_idx_instance, q_vec, gt_all[i_q], k_val, sp_final ) - - elif method == "APS": - for q, g in zip(queries, gt): - sp = make_sp( - nprobe=-1, - k=k, - recall_target=rt, - recompute_threshold=cfg["experiment"]["recompute_ratio"], - use_precomputed=cfg["experiment"]["use_precompute"], - initial_search_fraction=cfg["experiment"]["initial_search_fraction"], + query_results_list.append((best_np_val, rec_val, time_ms_val)) + + elif method_name == "APS": + for i_q, q_vec in enumerate(queries_all): + sp_aps = common_utils.create_search_params( + nprobe=-1, k=k_val, recall_target=recall_target_val, + recompute_threshold=exp_cfg["recompute_ratio"], + use_precomputed=exp_cfg["use_precompute"], + initial_search_fraction=exp_cfg["initial_search_fraction"] ) - t1 = time.perf_counter_ns() - res = idx.search(q.unsqueeze(0), sp) - per_query.append( - ( - getattr(res.timing_info, "partitions_scanned", np.nan), - compute_recall(res.ids, g.unsqueeze(0), k).item(), - (time.perf_counter_ns() - t1) / 1e6, - ) + nprobe_scanned, rec_val, time_ms_val = common_utils.run_search_trial( + quake_idx_instance, q_vec, gt_all[i_q], k_val, sp_aps ) - - elif method == "Auncel": - t0 = time.perf_counter_ns() - best_a, per_query = tune_auncel_a( - idx, - queries, - gt, - k, - rt, - recompute_thr=cfg["experiment"]["recompute_ratio"], - init_frac=cfg["experiment"]["initial_search_fraction"], + query_results_list.append((nprobe_scanned, rec_val, time_ms_val)) + + elif method_name == "Auncel": + t_tune_start_ns = time.perf_counter_ns() + best_a_val, query_results_list = tune_auncel_a( # tune_auncel_a returns the list directly + quake_idx_instance, queries_all, gt_all, k_val, recall_target_val, + recompute_thr=exp_cfg["recompute_ratio"], + init_frac=exp_cfg["initial_search_fraction"] ) - tuning_ns = time.perf_counter_ns() - t0 - logger.info(f"Auncel @ RT={rt:.2f} → selected a = {best_a:.4f}") - - elif method == "LAET": - t0 = time.perf_counter_ns() - mean_np, mean_rec, mean_ms = laet.run_inference_for_quake_experiment( - quake_idx=idx, - queries_torch=queries, - gt_torch=gt, - target_recall=rt, - k_search=k, - base_quake_sp=make_sp(k=k), + current_tuning_ns = time.perf_counter_ns() - t_tune_start_ns + logger.info(f"Auncel @ RT={recall_target_val:.2f} → selected a = {best_a_val:.4f}") + + elif method_name == "LAET": + t_infer_start_ns = time.perf_counter_ns() + mean_np, mean_rec, mean_ms = laet_pipeline_instance.run_inference_for_quake_experiment( + quake_idx=quake_idx_instance, + queries_torch=queries_all, + gt_torch=gt_all, + target_recall=recall_target_val, + k_search=k_val, + base_quake_sp=common_utils.create_search_params(k=k_val), # Pass k here ) - tuning_ns = laet_training_ns + (time.perf_counter_ns() - t0) - per_query.append((mean_np, mean_rec, mean_ms)) + # For LAET, tuning_ns includes GBDT training + multiplier search + current_tuning_ns = laet_training_total_ns + (time.perf_counter_ns() - t_infer_start_ns) + query_results_list.append((mean_np, mean_rec, mean_ms)) # Store averages else: - logger.warning(f"Unknown method '{method}' – skipping.") + logger.warning(f"Unknown method '{method_name}' – skipping.") continue - # ---------------------------------------------------------------- # - if not per_query: - continue - arr = np.asarray(per_query, dtype=float) - mean_np, mean_rec, mean_q_ms = np.nanmean(arr, axis=0) - - records.append( - dict( - Method=method, - RecallTarget=rt, - RecallAchieved=mean_rec, - nprobe=mean_np, - QueryLatencyMs=mean_q_ms, - TuningTimeMs=tuning_ns / 1e6, - ) - ) + if not query_results_list: continue - # 6) ------------------------------------------------------------- results - df = pd.DataFrame(records) - df_out = df[df["RecallTarget"].isin(table_targets)].copy().sort_values( + # logger.info(f"Method={method} RecallTarget={rt}, Recall={mean_recall:.4f} (std={std_recall:.4f}), " + # f"Time={mean_time_ms:.2f}ms (std={std_time_ms:.2f}ms), NProbe={mean_nprobe:.1f} (std={std_nprobe:.1f})") + + logger.info(f"Method={method_name} RecallTarget={recall_target_val:.2f}, " + f"Recall={np.mean([r for _, r, _ in query_results_list]):.4f}, " + f"Query Latency={np.mean([t for _, _, t in query_results_list]):.2f}ms, " + f"NProbe={np.mean([n for n, _, _ in query_results_list]):.1f}, " + f"Tuning Time={current_tuning_ns / 1e6:.1f}ms") + + arr = np.asarray(query_results_list, dtype=float) + if arr.shape[0] == 1 and method_name == "LAET": # LAET already returns mean values + mean_nprobe_val, mean_recall_val, mean_q_ms_val = arr[0,0], arr[0,1], arr[0,2] + std_nprobe_val, std_recall_val, std_q_ms_val = np.nan, np.nan, np.nan # Stdev not applicable for pre-averaged + else: + mean_nprobe_val = np.nanmean(arr[:,0]) + std_nprobe_val = np.nanstd(arr[:,0]) + mean_recall_val = np.nanmean(arr[:,1]) + std_recall_val = np.nanstd(arr[:,1]) + mean_q_ms_val = np.nanmean(arr[:,2]) + std_q_ms_val = np.nanstd(arr[:,2]) + + + experiment_records.append(dict( + Method=method_name, RecallTarget=recall_target_val, + RecallAchieved=mean_recall_val, nprobe=mean_nprobe_val, + QueryLatencyMs=mean_q_ms_val, TuningTimeMs=current_tuning_ns / 1e6, + StdRecall=std_recall_val, StdNprobe=std_nprobe_val, StdQueryLatencyMs=std_q_ms_val + )) + + df_results = pd.DataFrame(experiment_records) + df_output_final = df_results[df_results["RecallTarget"].isin(table_targets_set)].copy().sort_values( ["Method", "RecallTarget"] ) - csv_path = out_dir / f"aps_et_results_{cfg['dataset']['name']}.csv" - df_out.to_csv(csv_path, index=False, float_format="%.4f") - logger.info(f"Results written to {csv_path}") - print_markdown(df_out) - - # 7) ------------------------------------------------------------- plot - if cfg.get("plot", {}).get("enable", False) and _HAVE_MPL: - plt.figure(figsize=(6, 4)) - for m in df["Method"].unique(): - d = df[df["Method"] == m] - plt.plot(d["RecallTarget"], d["QueryLatencyMs"], marker="o", label=m, alpha=0.7) - plt.yscale("log") - plt.xlabel("Recall target") - plt.ylabel("Mean query latency (ms)") - plt.title(cfg["dataset"]["name"]) - plt.legend() - plt.tight_layout() - plt.savefig(out_dir / f"latency_plot_{cfg['dataset']['name']}.png", dpi=200) \ No newline at end of file + + csv_output_path = out_dir / f"aps_et_results_{dataset_cfg['name']}.csv" + common_utils.save_results_csv(df_output_final, csv_output_path) # Use common save + print_markdown(df_output_final) + + if cfg.get("plot", {}).get("enable", False): + plot_path = out_dir / f"et_latency_recall_plot_{dataset_cfg['name']}.png" + common_utils.plot_recall_performance( + df_results, # Plot all results, not just table_targets + x_metric="RecallTarget", + y_metrics=["QueryLatencyMs", "RecallAchieved", "nprobe"], + y_labels=["Mean Query Latency (ms)", "Mean Recall Achieved", "Mean NProbe"], + y_scales=["log", "linear", "log"], # nprobe often benefits from log scale + plot_title_prefix=f"{dataset_cfg['name']} Early Termination", + output_path=plot_path, + recall_targets_for_line=recall_targets_list + ) \ No newline at end of file From 4dab300eb7550388732226941e37b94175f914a4 Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 21 May 2025 00:51:25 -0500 Subject: [PATCH 197/323] update maintenance ablation --- test/experiments/osdi2025/experiment_utils.py | 98 ++++ .../osdi2025/maintenance_ablation/run.py | 534 +++++++++--------- 2 files changed, 367 insertions(+), 265 deletions(-) diff --git a/test/experiments/osdi2025/experiment_utils.py b/test/experiments/osdi2025/experiment_utils.py index b9091483..0663391a 100644 --- a/test/experiments/osdi2025/experiment_utils.py +++ b/test/experiments/osdi2025/experiment_utils.py @@ -3,6 +3,7 @@ import yaml import logging from pathlib import Path +from typing import Dict, Any import torch import pandas as pd @@ -12,6 +13,7 @@ from quake import QuakeIndex, IndexBuildParams, SearchParams from quake.datasets.ann_datasets import load_dataset as quake_load_dataset from quake.utils import compute_recall as quake_compute_recall +from quake.workload_generator import DynamicWorkloadGenerator, WorkloadEvaluator logger = logging.getLogger(__name__) @@ -72,6 +74,102 @@ def run_search_trial( partitions_scanned = getattr(res.timing_info, "partitions_scanned", np.nan) return partitions_scanned, rec, elapsed_ns / 1e6 # nprobe, recall, time_ms +def generate_dynamic_workload( + dataset_main_cfg: Dict[str, Any], + workload_generator_cfg: Dict[str, Any], + global_output_dir: Path, + overwrite_workload: bool +) -> Path: + """ + Loads dataset and generates the dynamic workload. + Returns the path to the workload directory (which is the global_output_dir). + """ + log = logger # Use the logger defined in common_utils + + log.info(f"Preparing dataset '{dataset_main_cfg['name']}' for dynamic workload.") + # Assuming common_utils.load_data is defined elsewhere in experiment_utils.py + base_vectors, query_vectors, _ = load_data( + dataset_main_cfg["name"], dataset_main_cfg.get("path", "") + ) + + log.info("Setting up DynamicWorkloadGenerator.") + generator = DynamicWorkloadGenerator( + workload_dir=global_output_dir, + base_vectors=base_vectors, + metric=dataset_main_cfg["metric"], + insert_ratio=workload_generator_cfg["insert_ratio"], + delete_ratio=workload_generator_cfg["delete_ratio"], + query_ratio=workload_generator_cfg["query_ratio"], + update_batch_size=workload_generator_cfg["update_batch_size"], + query_batch_size=workload_generator_cfg["query_batch_size"], + number_of_operations=workload_generator_cfg["number_of_operations"], + initial_size=workload_generator_cfg["initial_size"], + cluster_size=workload_generator_cfg["cluster_size"], + cluster_sample_distribution=workload_generator_cfg["cluster_sample_distribution"], + queries=query_vectors, + query_cluster_sample_distribution=workload_generator_cfg["query_cluster_sample_distribution"], + seed=workload_generator_cfg["seed"] + ) + + if not generator.workload_exists() or overwrite_workload: + log.info(f"Generating workload in {global_output_dir}...") + generator.generate_workload() + else: + log.info(f"Existing workload found in {global_output_dir} – generation skipped.") + return global_output_dir + + +def evaluate_index_on_dynamic_workload( + index_config: Dict[str, Any], # Single entry from the 'indexes' list in YAML + index_class_mapping: Dict[str, type], # Maps index type string to class (e.g., {"Quake": QuakeWrapper}) + workload_data_dir: Path, # Path where DynamicWorkloadGenerator output (operations, groundtruth) is stored + experiment_main_output_dir: Path, # Base output dir for the whole experiment (e.g., .../maintenance_ablation/results/sift1m/) + overwrite_idx_results: bool, + do_maintenance_flag: bool = False # Specific to experiments that test maintenance +): + """ + Evaluates a single index configuration against a pre-generated dynamic workload. + Results are saved by WorkloadEvaluator in a subdirectory named after the index. + """ + log = logger # Use the logger defined in common_utils + idx_name = index_config["name"] + index_type_str = index_config["index"] + + # Determine the directory for this specific index's results + idx_specific_output_dir = experiment_main_output_dir / idx_name + idx_specific_output_dir.mkdir(parents=True, exist_ok=True) + + per_index_results_csv = idx_specific_output_dir / "results.csv" + + if per_index_results_csv.exists() and not overwrite_idx_results: + log.info(f"Results for {idx_name} exist at {per_index_results_csv} – skipping evaluation.") + return + + log.info(f"Evaluating index configuration: {idx_name} (Type: {index_type_str})") + + index_class_constructor = index_class_mapping.get(index_type_str) + if not index_class_constructor: + log.error(f"Unknown index type '{index_type_str}' in mapping for index '{idx_name}'. Skipping.") + return + + index_instance = index_class_constructor() + + evaluator = WorkloadEvaluator( + workload_dir=workload_data_dir, # Where operation files are located + output_dir=idx_specific_output_dir # Where this index's results.csv will be saved + ) + + evaluator.evaluate_workload( + name=idx_name, + index=index_instance, + build_params=index_config.get("build_params", {}), + search_params=index_config.get("search_params", {}), + m_params=index_config.get("maintenance_params", {}), # Pass maintenance_params + do_maintenance=do_maintenance_flag, + ) + log.info(f"Finished evaluating {idx_name}. Results should be in {idx_specific_output_dir}") + + def save_results_csv(records: list, output_path: Path): df = pd.DataFrame(records) output_path.parent.mkdir(parents=True, exist_ok=True) diff --git a/test/experiments/osdi2025/maintenance_ablation/run.py b/test/experiments/osdi2025/maintenance_ablation/run.py index 0173457b..af274dfb 100644 --- a/test/experiments/osdi2025/maintenance_ablation/run.py +++ b/test/experiments/osdi2025/maintenance_ablation/run.py @@ -1,335 +1,339 @@ #!/usr/bin/env python3 """ -Quake maintenance–ablation runner -───────────────────────────────── -Extends the original “kick-the-tires” driver with: - -1. *summary_table.csv* – cumulative times + final partition count -2. *summary_table.md* – Markdown, via **tabulate** -3. *time_breakdown.png* – stacked bars (unchanged) -4. *unified_plot.png* – nine-panel diagnostic (unchanged) - -Per-index artefacts (under //): - results.csv – per-operation log - time_breakdown.png – per-index stacked bars +Quake Maintenance Policy Ablation Study Runner +─────────────────────────────────────────────── +This experiment evaluates different maintenance policy configurations within Quake +under a dynamic workload involving inserts, deletes, and queries. +It generates detailed reports including: +1. Per-index CSV logs of operations. +2. A 9-panel unified plot comparing various metrics across configurations. +3. A stacked bar chart breaking down cumulative time per operation type. +4. A summary table (CSV and Markdown) of key performance indicators. """ from __future__ import annotations -import argparse import logging from pathlib import Path -from typing import Dict, List +from typing import Dict, List, Any # Added Any for type hinting import matplotlib.pyplot as plt import numpy as np import pandas as pd -import yaml from tabulate import tabulate from matplotlib.cm import get_cmap from matplotlib.lines import Line2D -# ── quake wrappers ------------------------------------------------------------ -from quake.datasets.ann_datasets import load_dataset -from quake.index_wrappers.faiss_ivf import FaissIVF -from quake.index_wrappers.quake import QuakeWrapper -from quake.workload_generator import DynamicWorkloadGenerator, WorkloadEvaluator +# Common utilities +import test.experiments.osdi2025.experiment_utils as common_utils + +# Quake specific imports +from quake.index_wrappers.quake import QuakeWrapper # This experiment only uses QuakeWrapper +# DynamicWorkloadGenerator and WorkloadEvaluator are now used within common_utils helpers primarily logging.basicConfig(level=logging.INFO, format="%(asctime)s | %(levelname)s | %(message)s") log = logging.getLogger(__name__) -# ── constants ----------------------------------------------------------------- +# Constants for plotting remain specific to this experiment's visualization OP_STYLE = { "query": dict(ls="-", marker="o", mfc="none", ms=4, lw=1.2), "insert": dict(ls="-", marker="s", mfc="none", ms=4, lw=1.2), "delete": dict(ls="-", marker="^", mfc="none", ms=4, lw=1.2), "maintain": dict(ls="None", marker="X", mfc="black", ms=5), } - LAT_OPS = ["query", "insert", "delete", "maintain"] IDX_LAT_Q, IDX_LAT_I, IDX_LAT_D, IDX_LAT_M = 0, 1, 2, 3 IDX_PART, IDX_RES, IDX_REC, IDX_TOT, IDX_SPL = 4, 5, 6, 7, 8 -# ══════════════════════════════════════════════════════════════════════════════ -# Plot: unified nine-panel diagnostic -# ══════════════════════════════════════════════════════════════════════════════ -def unified_plot(cfg: Dict, out_dir: Path) -> None: + +def unified_plot(cfg: Dict[str, Any], out_dir: Path) -> None: styles = cfg["plot"].get("styles", {}) fig, axs2d = plt.subplots(3, 3, figsize=(18, 12), sharex="col") axs = axs2d.flatten() - # legend handles ----------------------------------------------------------- idx_handles: List[Line2D] = [] - for j, idx in enumerate(cfg["indexes"]): - nm, st = idx["name"], styles.get(idx["name"], {}) + for j, idx_cfg in enumerate(cfg.get("indexes", [])): # Added .get for safety + nm, st = idx_cfg["name"], styles.get(idx_cfg["name"], {}) idx_handles.append(Line2D([0], [0], - color=st.get("color", f"C{j}"), + color=st.get("color", f"C{j % 10}"), marker=st.get("marker", "o"), ls="", markersize=6, label=nm)) - # traces ------------------------------------------------------------------- - for j, idx in enumerate(cfg["indexes"]): - name = idx["name"] + max_ops_overall = 0 + + for j, idx_cfg in enumerate(cfg.get("indexes", [])): + name = idx_cfg["name"] st = styles.get(name, {}) - colour = st.get("color", f"C{j}") + colour = st.get("color", f"C{j % 10}") marker = st.get("marker", "o") - csv = out_dir / name / "results.csv" - if not csv.exists(): - log.warning("[unified_plot] %s missing – skipped.", csv) + csv_path = out_dir / name / "results.csv" + if not csv_path.exists(): + log.warning("[unified_plot] Results CSV %s missing – skipped for this index.", csv_path) + continue + try: + df = pd.read_csv(csv_path) + if df.empty: + log.warning(f"[unified_plot] Empty CSV: {csv_path}") + continue + if 'operation_number' in df.columns and not df['operation_number'].empty: + max_ops_overall = max(max_ops_overall, df.operation_number.max()) + except pd.errors.EmptyDataError: + log.warning(f"[unified_plot] Could not read or empty CSV: {csv_path}") continue - df = pd.read_csv(csv) - # latency -------------------------------------------------------------- - for op, ax_idx in zip(LAT_OPS, [IDX_LAT_Q, IDX_LAT_I, IDX_LAT_D, IDX_LAT_M]): - ax = axs[ax_idx] + # Latency plots + for op, ax_idx_val in zip(LAT_OPS, [IDX_LAT_Q, IDX_LAT_I, IDX_LAT_D, IDX_LAT_M]): + ax = axs[ax_idx_val] + y_val_series = None if op == "maintain": - sub = df[df.maintenance_time_ms.fillna(0) > 0] - y = sub.maintenance_time_ms - else: + if 'maintenance_time_ms' in df.columns: + sub = df[df.maintenance_time_ms.fillna(0) > 0] + y_val_series = sub.maintenance_time_ms + elif 'operation_type' in df.columns and 'latency_ms' in df.columns: sub = df[df.operation_type == op] - y = sub.latency_ms - if not sub.empty: - ax.plot(sub.operation_number, y, color=colour, **OP_STYLE[op]) - - # partitions ----------------------------------------------------------- - ax = axs[IDX_PART] - sub = df[df.n_list.notna()] - if not sub.empty: - ax.plot(sub.operation_number, sub.n_list, - color=colour, marker=marker, lw=1.2) - - # resident vectors ----------------------------------------------------- - ax = axs[IDX_RES] - sub = df[df.n_resident.notna()] - if not sub.empty: - ax.plot(sub.operation_number, sub.n_resident, - color=colour, marker=marker, lw=1.2) - - # recall --------------------------------------------------------------- - ax = axs[IDX_REC] - sub = df[(df.operation_type == "query") & df.recall.notna()] - if not sub.empty: - ax.plot(sub.operation_number, sub.recall, - color=colour, marker=marker, lw=1.2) - - # running total time --------------------------------------------------- - ax = axs[IDX_TOT] - total_ms = df.latency_ms.fillna(0) + df.maintenance_time_ms.fillna(0) - ax.plot(df.operation_number, np.cumsum(total_ms), - color=colour, marker=marker, lw=1.2) - - # cumulative splits / deletes ----------------------------------------- - ax = axs[IDX_SPL] - spl, dlt = df.n_splits.fillna(0), df.n_deletes.fillna(0) - if (spl > 0).any(): - ax.step(df.operation_number, np.cumsum(spl), - where="post", color=colour, ls="--", lw=1.2) - if (dlt > 0).any(): - ax.step(df.operation_number, np.cumsum(dlt), - where="post", color=colour, ls=":", lw=1.2) - - # titles / grids ----------------------------------------------------------- + y_val_series = sub.latency_ms + + if y_val_series is not None and not y_val_series.empty: + ax.plot(sub.operation_number, y_val_series, color=colour, **OP_STYLE[op]) + + # Other metric plots + plot_specs = [ + (IDX_PART, 'n_list'), (IDX_RES, 'n_resident'), + (IDX_REC, 'recall', lambda d: (d.operation_type == "query") & d.recall.notna()), + (IDX_TOT, 'total_cumulative_time', + lambda d: pd.Series(np.cumsum(d.latency_ms.fillna(0) + d.maintenance_time_ms.fillna(0)), name='total_cumulative_time')), + (IDX_SPL, ['n_splits', 'n_deletes']) # Special handling for splits/deletes + ] + + for spec_item in plot_specs: + ax_idx_val = spec_item[0] + metric_name_or_list = spec_item[1] + condition_func = spec_item[2] if len(spec_item) > 2 else lambda d: d[metric_name_or_list].notna() + + ax = axs[ax_idx_val] + + if ax_idx_val == IDX_TOT: # Cumulative time calculation + if 'latency_ms' in df.columns and 'maintenance_time_ms' in df.columns: + total_ms_series = np.cumsum(df.latency_ms.fillna(0) + df.maintenance_time_ms.fillna(0)) + if not total_ms_series.empty: + ax.plot(df.operation_number, total_ms_series, color=colour, marker=marker, lw=1.2) + continue + + if ax_idx_val == IDX_SPL: # Splits and deletes + if 'operation_number' in df.columns: + spl_series = df.n_splits.fillna(0) if 'n_splits' in df.columns else pd.Series(0, index=df.index) + del_series = df.n_deletes.fillna(0) if 'n_deletes' in df.columns else pd.Series(0, index=df.index) + if not df.empty: + if (spl_series > 0).any(): ax.step(df.operation_number, np.cumsum(spl_series), where="post", color=colour, ls="--", lw=1.2) + if (del_series > 0).any(): ax.step(df.operation_number, np.cumsum(del_series), where="post", color=colour, ls=":", lw=1.2) + continue + + # General case for other plots + if isinstance(metric_name_or_list, str) and metric_name_or_list in df.columns: + sub_df = df[condition_func(df)] + if not sub_df.empty: + ax.plot(sub_df.operation_number, sub_df[metric_name_or_list], color=colour, marker=marker, lw=1.2) + titles = { - IDX_LAT_Q: "Latency – Query", - IDX_LAT_I: "Latency – Insert", - IDX_LAT_D: "Latency – Delete", - IDX_LAT_M: "Latency – Maintain", - IDX_PART: "# Partitions", - IDX_RES: "Resident Vectors", - IDX_REC: "Recall", - IDX_TOT: "Running Total Time", + IDX_LAT_Q: "Latency – Query", IDX_LAT_I: "Latency – Insert", + IDX_LAT_D: "Latency – Delete", IDX_LAT_M: "Latency – Maintain", + IDX_PART: "# Partitions", IDX_RES: "Resident Vectors", + IDX_REC: "Recall", IDX_TOT: "Running Total Time (ms)", IDX_SPL: "Cumulative Splits / Deletes", } - for i, ax in enumerate(axs): - ax.set_title(titles[i], fontsize=11) - ax.grid(alpha=0.25, zorder=0) - ax.set_axisbelow(True) - - axs[IDX_PART].set_ylabel("count") - axs[IDX_RES].set_ylabel("# vectors") - axs[IDX_REC].set_ylabel("recall") - axs[IDX_TOT].set_ylabel("ms") - axs[IDX_SPL].set_ylabel("count") - - for idx in [IDX_PART, IDX_RES, IDX_REC, IDX_TOT, IDX_SPL]: - axs[idx].set_xlabel("Operation #") - - all_ops = max((pd.read_csv(out_dir / idx["name"] / "results.csv").operation_number.max() - for idx in cfg["indexes"] - if (out_dir / idx["name"] / "results.csv").exists()), - default=None) - if all_ops is not None: - for ax in axs: - ax.set_xlim(left=0, right=all_ops) - - fig.legend(idx_handles, - [h.get_label() for h in idx_handles], - loc="upper center", bbox_to_anchor=(0.5, 1.03), + y_labels_map = { + IDX_PART: "Count", IDX_RES: "# Vectors", IDX_REC: "Recall", + IDX_TOT: "Cumulative Time (ms)", IDX_SPL: "Count" + } + for i, ax_val in enumerate(axs): + ax_val.set_title(titles[i], fontsize=11) + ax_val.grid(True, which="both", ls=":", alpha=0.7) + ax_val.set_axisbelow(True) + if i in y_labels_map: ax_val.set_ylabel(y_labels_map[i]) + # Set x-label only for the bottom row of plots + if i // 3 == 2 : ax_val.set_xlabel("Operation #") + + is_latency_plot = i in [IDX_LAT_Q, IDX_LAT_I, IDX_LAT_D, IDX_LAT_M] + if is_latency_plot: ax_val.set_ylabel("Latency (ms)") + + + if max_ops_overall > 0: + for ax_val in axs: + ax_val.set_xlim(left=0, right=max_ops_overall) + + fig.legend(idx_handles, [h.get_label() for h in idx_handles], + loc="upper center", bbox_to_anchor=(0.5, 1.035), ncol=min(4, len(idx_handles)), - fontsize=9, title="Index", frameon=False) - - plt.tight_layout(rect=[0, 0, 1, 0.97]) - out_path = out_dir / "unified_plot.png" - plt.savefig(out_path, dpi=150) - log.info("[unified_plot] → %s", out_path) - plt.close() - -# ══════════════════════════════════════════════════════════════════════════════ -# Plot: stacked-bar time breakdown -# ══════════════════════════════════════════════════════════════════════════════ -def make_time_breakdown(cfg: Dict, out_dir: Path) -> None: + fontsize=9, title="Index Configuration", frameon=False) + + plt.tight_layout(rect=[0, 0.03, 1, 0.97]) + out_plot_path = out_dir / "unified_plot.png" + plt.savefig(out_plot_path, dpi=150) + log.info("Unified plot saved to %s", out_plot_path) + plt.close(fig) + + +def make_time_breakdown(cfg: Dict[str, Any], out_dir: Path) -> None: categories = ["Search", "Insert", "Delete", "Maintain", "Total"] - idx_names = [idx["name"] for idx in cfg["indexes"]] - totals, recalls = {}, {} + totals_data, recall_data = {}, {} cmap = get_cmap("tab10") - for j, idx in enumerate(cfg["indexes"]): - name = idx["name"]; csv_path = out_dir / name / "results.csv" + for j, idx_cfg in enumerate(cfg.get("indexes", [])): + name = idx_cfg["name"] + csv_path = out_dir / name / "results.csv" if not csv_path.exists(): - log.warning("[time_breakdown] missing %s – skipped", csv_path) + log.warning("[time_breakdown] %s missing – skipped", csv_path) + continue + try: + df = pd.read_csv(csv_path) + if df.empty: raise pd.errors.EmptyDataError + except pd.errors.EmptyDataError: + log.warning(f"[time_breakdown] Empty or unreadable CSV: {csv_path}") continue - df = pd.read_csv(csv_path) - totals[name] = [ - df[df.operation_type == "query"].latency_ms.sum(), - df[df.operation_type == "insert"].latency_ms.sum(), - df[df.operation_type == "delete"].latency_ms.sum(), - df.maintenance_time_ms.fillna(0).sum(), + + totals_data[name] = [ + df[df.operation_type == "query"].latency_ms.sum() if 'operation_type' in df.columns else 0, + df[df.operation_type == "insert"].latency_ms.sum() if 'operation_type' in df.columns else 0, + df[df.operation_type == "delete"].latency_ms.sum() if 'operation_type' in df.columns else 0, + df.maintenance_time_ms.fillna(0).sum() if 'maintenance_time_ms' in df.columns else 0, ] - totals[name].append(sum(totals[name])) - recalls[name] = df[df.operation_type == "query"].recall.mean() - - n_idx, n_cats = len(totals), len(categories) - x = np.arange(n_cats); width = 0.8 / max(1, n_idx) - - fig, ax = plt.subplots(figsize=(10, 6)) - for j, (name, values) in enumerate(totals.items()): - style = cfg["plot"].get("styles", {}).get(name, {}) - color = style.get("color", cmap(j % 10)) - x_off = x + (j - (n_idx - 1) / 2) * width - ax.bar(x_off, values, width=width, label=name, - color=color, edgecolor="black") - ax.text(x_off[0], values[0] * 1.01, - f"R={recalls[name]:.3f}", ha="center", - va="bottom", fontsize=7, rotation=90, color=color) - - ax.set_xticks(x); ax.set_xticklabels(categories) - ax.set_ylabel("Cumulative Time (ms)") - ax.set_title("Time Breakdown per Index") - ax.legend(title="Index", frameon=False) + totals_data[name].append(sum(totals_data[name])) + + query_df = df[df.operation_type == "query"] if 'operation_type' in df.columns else pd.DataFrame() + recall_data[name] = query_df.recall.mean() if not query_df.empty and 'recall' in query_df else np.nan + + + if not totals_data: + log.info("[time_breakdown] No data available for time breakdown plot.") + return + + n_idx, n_cats = len(totals_data), len(categories) + x_indices = np.arange(n_cats) + bar_width = 0.8 / max(1, n_idx) + + fig, ax = plt.subplots(figsize=(max(10, n_idx * 1.5 + 2), 6)) + for j, (name, values) in enumerate(totals_data.items()): + style = cfg.get("plot", {}).get("styles", {}).get(name, {}) + color = style.get("color", cmap(j % cmap.N)) + x_offset = x_indices + (j - (n_idx - 1) / 2) * bar_width + ax.bar(x_offset, values, width=bar_width, label=name, color=color, edgecolor="black") + current_recall = recall_data.get(name) + if pd.notna(current_recall) and values[0] > 0: + ax.text(x_offset[0], values[0] * 1.01, f"R={current_recall:.3f}", + ha="center", va="bottom", fontsize=8, rotation=90, color='dimgrey') # Standard color for text + + ax.set_xticks(x_indices) + ax.set_xticklabels(categories, fontsize=10) + ax.set_ylabel("Cumulative Time (ms)", fontsize=10) + ax.set_title("Time Breakdown per Index Configuration", fontsize=12) + ax.legend(title="Index Configuration", frameon=False, fontsize=9) + ax.grid(True, axis='y', linestyle=':', alpha=0.7) plt.tight_layout() - out_path = out_dir / "time_breakdown.png" - plt.savefig(out_path, dpi=150) - log.info("[time_breakdown] → %s", out_path) - plt.close() - -# ══════════════════════════════════════════════════════════════════════════════ -# Summary table (CSV + Markdown) -# ══════════════════════════════════════════════════════════════════════════════ -def produce_summary_table(cfg: Dict, out_dir: Path) -> None: - """ - Write summary_table.csv and summary_table.md with columns: - Index | Search | Insert | Delete | Maintain | Total | Recall | Partitions - Times are cumulative milliseconds; Recall is mean over all queries. - """ + out_plot_path = out_dir / "time_breakdown.png" + plt.savefig(out_plot_path, dpi=150) + log.info("Time breakdown plot saved to %s", out_plot_path) + plt.close(fig) + + +def produce_summary_table(cfg: Dict[str, Any], out_dir: Path) -> None: rows = [] - for idx in cfg["indexes"]: - name = idx["name"] + for idx_cfg in cfg.get("indexes", []): + name = idx_cfg["name"] csv_path = out_dir / name / "results.csv" if not csv_path.exists(): log.warning("[summary_table] %s missing – skipped", csv_path) continue + try: + df = pd.read_csv(csv_path) + if df.empty: raise pd.errors.EmptyDataError + except pd.errors.EmptyDataError: + log.warning(f"[summary_table] Empty or unreadable CSV: {csv_path}") + continue + + search_time = df[df.operation_type == "query"].latency_ms.sum() if 'operation_type' in df.columns else 0 + insert_time = df[df.operation_type == "insert"].latency_ms.sum() if 'operation_type' in df.columns else 0 + delete_time = df[df.operation_type == "delete"].latency_ms.sum() if 'operation_type' in df.columns else 0 + maintain_time = df.maintenance_time_ms.fillna(0).sum() if 'maintenance_time_ms' in df.columns else 0 + total_time = search_time + insert_time + delete_time + maintain_time + + query_df = df[df.operation_type == "query"] if 'operation_type' in df.columns else pd.DataFrame() + recall_val = query_df.recall.mean() if not query_df.empty and 'recall' in query_df else np.nan - df = pd.read_csv(csv_path) - search = df[df.operation_type == "query" ].latency_ms.sum() - insert = df[df.operation_type == "insert"].latency_ms.sum() - delete = df[df.operation_type == "delete"].latency_ms.sum() - maintain = df.maintenance_time_ms.fillna(0).sum() - total = search + insert + delete + maintain - recall = df[df.operation_type == "query"].recall.mean() # ← NEW - n_part = (df.n_list.dropna().iloc[-1] - if not df.n_list.dropna().empty else None) - - rows.append(dict(Index=name, - Search=int(search), - Insert=int(insert), - Delete=int(delete), - Maintain=int(maintain), - Total=int(total), - Recall=f"{recall:.4f}" if pd.notna(recall) else "—", - Partitions=n_part)) + n_list_series = df.n_list.dropna() if 'n_list' in df.columns else pd.Series(dtype=float) # ensure series exists + n_partitions_val = n_list_series.iloc[-1] if not n_list_series.empty else np.nan + + rows.append(dict( + Index=name, Search=int(search_time), Insert=int(insert_time), + Delete=int(delete_time), Maintain=int(maintain_time), Total=int(total_time), + Recall=f"{recall_val:.4f}" if pd.notna(recall_val) else "—", + Partitions=int(n_partitions_val) if pd.notna(n_partitions_val) else "—" + )) if not rows: - log.warning("[summary_table] no data – nothing written") + log.info("[summary_table] No data for summary table.") return - df = pd.DataFrame(rows).sort_values("Index") - - csv_path = out_dir / "summary_table.csv" - df.to_csv(csv_path, index=False) - log.info("[summary_table] → %s", csv_path) - - md_path = out_dir / "summary_table.md" - md = tabulate(df, headers="keys", tablefmt="github", showindex=False) - md_path.write_text(md + "\n") - log.info("[summary_table] → %s", md_path) - log.info("\n%s", md) -# ══════════════════════════════════════════════════════════════════════════════ -# Main orchestration -# ══════════════════════════════════════════════════════════════════════════════ -def run_experiment(cfg_path: str, output_dir: str) -> None: - cfg = yaml.safe_load(Path(cfg_path).read_text()) - out = Path(output_dir).expanduser(); out.mkdir(parents=True, exist_ok=True) - - # 1. dataset & workload ---------------------------------------------------- - if cfg["mode"] in {"build", "run"}: - ds = cfg["dataset"] - vecs, queries, _ = load_dataset(ds["name"], ds.get("path", "")) - gen = DynamicWorkloadGenerator( - workload_dir = out, - base_vectors = vecs, - metric = ds["metric"], - insert_ratio = cfg["workload_generator"]["insert_ratio"], - delete_ratio = cfg["workload_generator"]["delete_ratio"], - query_ratio = cfg["workload_generator"]["query_ratio"], - update_batch_size = cfg["workload_generator"]["update_batch_size"], - query_batch_size = cfg["workload_generator"]["query_batch_size"], - number_of_operations = cfg["workload_generator"]["number_of_operations"], - initial_size = cfg["workload_generator"]["initial_size"], - cluster_size = cfg["workload_generator"]["cluster_size"], - cluster_sample_distribution= cfg["workload_generator"]["cluster_sample_distribution"], - queries = queries, - query_cluster_sample_distribution=cfg["workload_generator"]["query_cluster_sample_distribution"], - seed = cfg["workload_generator"]["seed"]) - if not gen.workload_exists() or cfg["overwrite"].get("workload", False): - log.info("Generating workload …") - gen.generate_workload() - else: - log.info("Existing workload found – generation skipped.") - - # 2. evaluate -------------------------------------------------------------- - if cfg["mode"] == "run": - for idx in cfg["indexes"]: - name, res = idx["name"], out / idx["name"] - res.mkdir(parents=True, exist_ok=True) - csv = res / "results.csv" - if csv.exists() and not cfg["overwrite"].get("results", False): - log.info("%s: cached results – skipped.", name); continue - - eval = WorkloadEvaluator(workload_dir=out, output_dir=res) - cls = {"Quake": QuakeWrapper, "IVF": FaissIVF}[idx["index"]] - eval.evaluate_workload( - name = name, - index = cls(), - build_params = idx.get("build_params", {}), - search_params = idx.get("search_params", {}), - m_params = idx.get("maintenance_params", {}), - do_maintenance=True, + summary_df = pd.DataFrame(rows).sort_values("Index") + summary_csv_path = out_dir / "summary_table.csv" + common_utils.save_results_csv(summary_df, summary_csv_path) # Use common util + + summary_md_path = out_dir / "summary_table.md" + md_content = tabulate(summary_df, headers="keys", tablefmt="github", showindex=False) + summary_md_path.write_text(md_content + "\n") + log.info("Summary table (Markdown) saved to %s", summary_md_path) + log.info("\n%s", md_content) + + +def run_experiment(cfg_path_str: str, output_dir_str: str) -> None: + cfg = common_utils.load_config(cfg_path_str) + main_output_dir = Path(output_dir_str).expanduser() + main_output_dir.mkdir(parents=True, exist_ok=True) + + current_mode = cfg.get("mode", "run") + log.info(f"Running Maintenance Ablation experiment in mode: {current_mode}") + + workload_actual_dir = main_output_dir # Workload files are stored at the top level of main_output_dir + + # --- Phase 1: Dataset and Workload Generation --- + if current_mode in {"build", "run"}: + workload_actual_dir = common_utils.generate_dynamic_workload( + dataset_main_cfg=cfg["dataset"], + workload_generator_cfg=cfg["workload_generator"], + global_output_dir=main_output_dir, # Pass main_output_dir as the place to store workload files + overwrite_workload=cfg["overwrite"].get("workload", False) + ) + + # --- Phase 2: Index Evaluation --- + if current_mode == "run": + log.info("Starting index evaluation phase...") + # For this specific experiment, the index class is always QuakeWrapper + index_class_map = {"Quake": QuakeWrapper} + + for index_conf in cfg.get("indexes", []): + common_utils.evaluate_index_on_dynamic_workload( + index_config=index_conf, + index_class_mapping=index_class_map, + workload_data_dir=workload_actual_dir, # Use the returned/set workload_actual_dir + experiment_main_output_dir=main_output_dir, + overwrite_idx_results=cfg["overwrite"].get("results", False), + do_maintenance_flag=True # This experiment always does maintenance ) - # 3. global artefacts ------------------------------------------------------ - if cfg["mode"] in {"run", "plot"}: - unified_plot(cfg, out) - make_time_breakdown(cfg, out) - produce_summary_table(cfg, out) \ No newline at end of file + # --- Phase 3: Global Artifact Generation --- + if current_mode in {"run", "plot"}: + log.info("Generating global plots and summary tables...") + any_results_exist = any( + (main_output_dir / index_conf["name"] / "results.csv").exists() + for index_conf in cfg.get("indexes", []) + ) + + if any_results_exist: + unified_plot(cfg, main_output_dir) + make_time_breakdown(cfg, main_output_dir) + produce_summary_table(cfg, main_output_dir) + else: + log.warning("No results found to generate plots or summary tables. Skipping this step.") + + log.info("Maintenance Ablation experiment finished for mode: %s", current_mode) \ No newline at end of file From 024f846eac952a5861bb730456ab59e6b2f782c7 Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 21 May 2025 01:15:33 -0500 Subject: [PATCH 198/323] update maintenance ablation --- .../maintenance_ablation/configs/sift1m.yaml | 106 ++++++++++++++++++ .../osdi2025/maintenance_ablation/run.py | 2 - 2 files changed, 106 insertions(+), 2 deletions(-) create mode 100644 test/experiments/osdi2025/maintenance_ablation/configs/sift1m.yaml diff --git a/test/experiments/osdi2025/maintenance_ablation/configs/sift1m.yaml b/test/experiments/osdi2025/maintenance_ablation/configs/sift1m.yaml new file mode 100644 index 00000000..9e6365a4 --- /dev/null +++ b/test/experiments/osdi2025/maintenance_ablation/configs/sift1m.yaml @@ -0,0 +1,106 @@ +# Ablation experiment on Quake maintenance policy +# ─────────────────────────────────────────────── +mode: run # {build, run, plot} +dataset: + name: sift1m + metric: l2 + path: data + +workload_generator: + recall_target: 0.9 + insert_ratio: 0.3 + delete_ratio: 0.2 + query_ratio: 0.5 + number_of_operations: 1000 + initial_size: 100000 + cluster_size: 1000 + update_batch_size: 10000 + query_batch_size: 1000 + cluster_sample_distribution: skewed + query_cluster_sample_distribution: uniform + seed: 9299 + +# ── six index configurations ───────────────────────────────────────────────── +indexes: + - name: Quake + index: Quake + build_params: {nc: 1000, num_workers: 0} + search_params: {k: 10, recall_target: .9} + maintenance_params: + split_threshold_ns: 50 + delete_threshold_ns: 50 + + - name: Quake-NoReject + index: Quake + build_params: {nc: 1000, num_workers: 0} + search_params: {k: 10, recall_target: .9} + maintenance_params: + enable_split_rejection: false + enable_delete_rejection: false + split_threshold_ns: 50 + delete_threshold_ns: 50 + + - name: Quake-NoRefine + index: Quake + build_params: {nc: 1000, num_workers: 0} + search_params: {k: 10, recall_target: .9} + maintenance_params: + refinement_radius: 0 + split_threshold_ns: 50 + delete_threshold_ns: 50 + + - name: Quake-NoRefineNoReject + index: Quake + build_params: {nc: 1000, num_workers: 0} + search_params: {k: 10, recall_target: .9} + maintenance_params: + refinement_radius: 0 + enable_split_rejection: false + enable_delete_rejection: false + split_threshold_ns: 50 + delete_threshold_ns: 50 + + - name: Quake-NoCost + index: Quake + build_params: {nc: 1000, num_workers: 0} + search_params: {k: 10, recall_target: .9} + maintenance_params: + max_partition_size: 2000 + + - name: Quake-NoCostNoRefine + index: Quake + build_params: {nc: 1000, num_workers: 0} + search_params: {k: 10, recall_target: .9} + maintenance_params: + refinement_radius: 0 + max_partition_size: 2000 + + - name: LIRE + index: Quake + build_params: {nc: 1000, num_workers: 0} + search_params: {k: 10, recall_target: .9} + maintenance_params: + refinement_iterations: 0 + max_partition_size: 2000 + +plot: + x_axis: operation_number + metrics: [latency_ms, recall] + styles: + Quake: {color: "#1f77b4", linestyle: "-", marker: "o"} + Quake-NoReject: {color: "#2ca02c", linestyle: "-", marker: "v"} + Quake-NoRefine: {color: "#9467bd", linestyle: "-", marker: "^"} + Quake-NoRefineNoReject: {color: "#8c564b", linestyle: "-", marker: "s"} + Quake-NoCost: {color: "#d627bb", linestyle: "-", marker: "D"} + Quake-NoCostNoRefine: {color: "#ff7f0e", linestyle: "-", marker: "+"} + Quake-NoRejectSplit: {color: "#7f7f7f", linestyle: "-", marker: "H"} + Quake-NoRejectDelete: {color: "#bcbd22", linestyle: "-", marker: "h"} + Quake-NoRejectNoDelete: {color: "#ffbb78", linestyle: "-", marker: "P"} + Quake-NoRejectNoSplit: {color: "#ffbb78", linestyle: "-", marker: "P"} + + LIRE: {color: "#ffbb78", linestyle: "-", marker: "P"} + +overwrite: + workload: true + index: true + results: true \ No newline at end of file diff --git a/test/experiments/osdi2025/maintenance_ablation/run.py b/test/experiments/osdi2025/maintenance_ablation/run.py index af274dfb..4667d458 100644 --- a/test/experiments/osdi2025/maintenance_ablation/run.py +++ b/test/experiments/osdi2025/maintenance_ablation/run.py @@ -28,13 +28,11 @@ # Quake specific imports from quake.index_wrappers.quake import QuakeWrapper # This experiment only uses QuakeWrapper -# DynamicWorkloadGenerator and WorkloadEvaluator are now used within common_utils helpers primarily logging.basicConfig(level=logging.INFO, format="%(asctime)s | %(levelname)s | %(message)s") log = logging.getLogger(__name__) -# Constants for plotting remain specific to this experiment's visualization OP_STYLE = { "query": dict(ls="-", marker="o", mfc="none", ms=4, lw=1.2), "insert": dict(ls="-", marker="s", mfc="none", ms=4, lw=1.2), From 2a4191a5670dd84986e76ec0d0069c03bc93410f Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 21 May 2025 02:06:31 -0500 Subject: [PATCH 199/323] update multi_query experiment --- src/cpp/src/query_coordinator.cpp | 20 +- .../numa_multi_query/configs/sift1m.yaml | 7 +- .../osdi2025/numa_multi_query/run.py | 456 +++++++++--------- 3 files changed, 248 insertions(+), 235 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 7f3c0611..c831e108 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -452,25 +452,25 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, } } else { - std::unordered_map>> per_partition_query_ids; // for batched scan -// ska::flat_hash_map> per_partition_query_ids_map; - + std::unordered_map>>> per_partition_query_ids; // for batched scan for (int64_t q = 0; q < nQ; q++) { for (int64_t p = 0; p < partition_ids.size(1); p++) { int64_t pid = pid_acc[q][p]; if (pid < 0) continue; if (per_partition_query_ids[pid] == nullptr) { - per_partition_query_ids[pid] = make_shared>(); + per_partition_query_ids[pid] = make_shared>>(); } - per_partition_query_ids[pid]->push_back(q); + per_partition_query_ids[pid]->push_back({q, p}); } } for (auto &kv : per_partition_query_ids) { - auto qids = kv.second; - vector ranks(qids->size()); - for (size_t i = 0; i < qids->size(); ++i) { - ranks[i] = i; + auto qids_and_ranks = kv.second; + vector qids(qids_and_ranks->size()); + vector ranks(qids_and_ranks->size()); + for (size_t i = 0; i < qids_and_ranks->size(); ++i) { + qids[i] = (*qids_and_ranks)[i].first; + ranks[i] = (*qids_and_ranks)[i].second; } ScanJob job; @@ -479,7 +479,7 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, job.k = params->k; job.query_vector = x.data_ptr(); job.num_queries = kv.second->size(); - job.query_ids = qids; + job.query_ids = make_shared>(qids); job.ranks = make_shared>(ranks); int core_id = partition_manager_->get_partition_core_id(kv.first); if (core_id < 0) { diff --git a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml index 96886fc5..60c0c558 100644 --- a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml +++ b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml @@ -1,12 +1,13 @@ # numa_latency_experiment.yaml dataset: name: sift1m - num_queries: 10000 + num_queries: 1000 k: 100 trials: 3 warmup: 1 -overwrite: true # Set to true to force all indexes to rerun +overwrite: false # Set to true to force all indexes to rerun +force_rebuild: false # Set to true to force all indexes to rebuild indexes: - name: Quake_0 @@ -26,7 +27,7 @@ indexes: nc: 1000 metric: l2 num_workers: 1 - parent_num_workers: 1 + parent_num_workers: 0 use_numa: false search_params: nprobe: 20 diff --git a/test/experiments/osdi2025/numa_multi_query/run.py b/test/experiments/osdi2025/numa_multi_query/run.py index 9c798d36..c447c066 100644 --- a/test/experiments/osdi2025/numa_multi_query/run.py +++ b/test/experiments/osdi2025/numa_multi_query/run.py @@ -1,27 +1,28 @@ #!/usr/bin/env python3 """ -NUMA Batch-Query Latency and Recall Benchmark (with detailed child/parent timers) +NUMA Multi-Query Batch Performance Benchmark +─────────────────────────────────────────── +Benchmarks Quake (with varying NUMA/worker settings) and other ANN indexes +for batch-query latency, per-phase breakdown (for Quake), and recall@k. +Results are cached. Outputs per-index CSVs, a unified CSV, and plots. -Benchmarks a suite of ANN indexes (Quake, Faiss-IVF, SCANN, HNSW, DiskANN, SVS, etc.) -for batch-query latency, per-phase breakdown, and recall@k under configurable parameters. -Results are cached and only recomputed if 'overwrite' is set. -Outputs per-index CSVs with detailed timers, a unified CSV, and plots. - -Usage: - python numa_latency_batch_experiment.py nna_latency_experiment.yaml output_dir +This script tests how different configurations handle batches of queries, +focusing on Quake's behavior with NUMA-awareness and threading for parallel +processing of queries or internal operations. """ -import sys +import logging # Standard library logging from pathlib import Path -import yaml import numpy as np -torch = None import torch import pandas as pd import matplotlib.pyplot as plt -from time import time +# time.time() is still used for the benchmark loop due to its specificity. + +# Common utilities +import test.experiments.osdi2025.experiment_utils as common_utils -from quake.utils import compute_recall -from quake.datasets.ann_datasets import load_dataset +# Quake specific imports +from quake.utils import compute_recall # compute_recall remains useful from quake.index_wrappers.faiss_ivf import FaissIVF from quake.index_wrappers.faiss_hnsw import FaissHNSW from quake.index_wrappers.quake import QuakeWrapper @@ -46,221 +47,232 @@ "SCANN": Scann, "HNSW": FaissHNSW, "DiskANN": DiskANNDynamic, - "SVS": Vamana, + "SVS": Vamana, # Assuming Vamana is the class for "SVS" } +# Setup logging (can be centralized in common_utils or main runner if preferred) +logging.basicConfig(level=logging.INFO, + format="%(asctime)s | %(levelname)7s | %(name)s | %(message)s", + datefmt="%H:%M:%S") +logger = logging.getLogger("numa_multi_query_exp") + + +def run_experiment(cfg_path_str: str, output_dir_str: str): + cfg = common_utils.load_config(cfg_path_str) + + dataset_cfg = cfg["dataset"] + num_queries_to_use = dataset_cfg["num_queries"] + k_val = dataset_cfg["k"] + + num_trials = cfg.get("trials", 5) + num_warmup = cfg.get("warmup", 1) # Reduced default warmup, can be configured + indexes_config_list = cfg["indexes"] + output_csv_name = cfg.get("output", {}).get("results_csv", "numa_multi_query_results.csv") + force_overwrite = cfg.get("overwrite", False) + force_rebuild = cfg.get("force_rebuild", False) + + main_output_dir = Path(output_dir_str) + main_output_dir.mkdir(parents=True, exist_ok=True) + + logger.info(f"Loading dataset '{dataset_cfg['name']}'...") + # Use common_utils.load_data, pass num_queries_to_use for slicing + base_vectors, query_vectors, gt_vectors = common_utils.load_data( + dataset_cfg["name"], + dataset_path="", # Assuming load_data handles default path or it's in dataset_cfg + nq_override=num_queries_to_use + ) + + # --- Build shared Quake base index once (specific to this experiment script) --- + quake_base_build_params = None + shared_quake_index_file = main_output_dir / "Quake_shared_base_index.bin" # More descriptive name -def build_and_save_index(index_class, build_params, base_vecs, index_file): - """Build and save an index to disk.""" - idx = index_class() - idx.build(base_vecs, **build_params) - idx.save(str(index_file)) - - -def extract_ids(res): - """Normalize returned prediction IDs to a NumPy array.""" - if hasattr(res, "ids"): - arr = res.ids - elif hasattr(res, "I"): - arr = res.I - elif hasattr(res, "indices"): - arr = res.indices - else: - raise RuntimeError("Cannot extract predicted indices from search result.") - if isinstance(arr, torch.Tensor): - arr = arr.cpu().numpy() - return np.asarray(arr) - - -def run_experiment(cfg_path: str, output_dir: str): - cfg = yaml.safe_load(Path(cfg_path).read_text()) - ds = cfg["dataset"] - queries_n = ds["num_queries"] - k = ds["k"] - trials = cfg.get("trials", 5) - warmup = cfg.get("warmup", 10) - indexes_cfg = cfg["indexes"] - csv_name = cfg.get("output", {}).get("results_csv", "numa_results.csv") - overwrite = cfg.get("overwrite", False) - - out_dir = Path(output_dir) - out_dir.mkdir(parents=True, exist_ok=True) - - print(f"[INFO] Loading dataset '{ds['name']}'...") - base_vecs, queries, gt = load_dataset(ds["name"]) - queries = queries[:queries_n] - gt = gt[:queries_n] if gt is not None else None - - # --- Build shared Quake base index once --- - quake_base_params = None - quake_base_index_file = out_dir / "Quake_base_index.bin" - for idx_cfg in indexes_cfg: - if idx_cfg["index"] == "Quake": - quake_base_params = dict(idx_cfg.get("build_params", {})) - quake_base_params.pop("num_workers", None) - quake_base_params.pop("use_numa", None) + for idx_c in indexes_config_list: + if idx_c["index"] == "Quake": + # These are params for the *base* index, stripped of worker/NUMA specifics + quake_base_build_params = {k: v for k, v in idx_c.get("build_params", {}).items() + if k not in ["num_workers", "use_numa", "parent_num_workers"]} break - if quake_base_params and not quake_base_index_file.exists(): - print(f"[BUILD] Quake (shared) base index ...") - build_and_save_index(QuakeWrapper, quake_base_params, base_vecs, quake_base_index_file) - print(f"[SAVE] Quake base index saved at {quake_base_index_file}") - - all_rows = [] - - for idx_cfg in indexes_cfg: - name = idx_cfg["name"] - idx_type = idx_cfg["index"] - build_params = dict(idx_cfg.get("build_params", {})) - search_params= dict(idx_cfg.get("search_params", {})) - IndexClass = INDEX_CLASSES.get(idx_type) - if IndexClass is None: - print(f"[WARN] Unknown index type: {idx_type}. Skipping.") + + if quake_base_build_params: + if not shared_quake_index_file.exists() or force_rebuild: # Respect overwrite for base index too + logger.info(f"Building shared Quake base index at {shared_quake_index_file}...") + # Using common_utils.prepare_index for this specialized build. + # It requires IndexClass, build_params, base_vectors, path, force_rebuild. + common_utils.prepare_index( + IndexClass=QuakeWrapper, + index_build_params=quake_base_build_params, + base_vectors=base_vectors, + index_file_path=shared_quake_index_file, + force_rebuild=force_rebuild, # Force rebuild if top-level overwrite is true + save_after_build=True + ) + logger.info(f"Shared Quake base index saved at {shared_quake_index_file}") + else: + logger.info(f"Using existing shared Quake base index from {shared_quake_index_file}") + + all_experiment_rows = [] + + for idx_conf in indexes_config_list: + index_name = idx_conf["name"] + index_type_str = idx_conf["index"] + current_build_params = dict(idx_conf.get("build_params", {})) + current_search_params = dict(idx_conf.get("search_params", {})) + + IndexCls = INDEX_CLASSES.get(index_type_str) + if IndexCls is None or (IndexCls == Scann and Scann is None): # Handle optional Scann + logger.warning(f"Index type '{index_type_str}' for '{index_name}' is not available or unknown. Skipping.") continue - idx_csv = out_dir / f"{name}_results.csv" - idx_file = quake_base_index_file if idx_type == "Quake" else out_dir / f"{name}_index.bin" + per_index_csv_path = main_output_dir / f"{index_name}_results.csv" - if idx_csv.exists() and not overwrite: - print(f"[SKIP] {name}: Results exist. Use overwrite: true to rerun.") - all_rows.append(pd.read_csv(idx_csv).iloc[0].to_dict()) + # Determine actual index file path (shared for Quake, specific for others) + actual_index_file_path = shared_quake_index_file if index_type_str == "Quake" else main_output_dir / f"{index_name}_index.bin" + + if per_index_csv_path.exists() and not force_overwrite: + logger.info(f"Results for {index_name} exist. Use overwrite:true to rerun. Loading cached.") + try: + # Ensure only one row is taken if multiple exist from partial runs. + cached_df = pd.read_csv(per_index_csv_path) + if not cached_df.empty: + all_experiment_rows.append(cached_df.iloc[0].to_dict()) + except Exception as e: + logger.error(f"Could not load cached results for {index_name} from {per_index_csv_path}: {e}") continue - # Build non-Quake if necessary - if idx_type != "Quake" and not idx_file.exists(): - print(f"[BUILD] {name} index...") - build_and_save_index(IndexClass, build_params, base_vecs, idx_file) - print(f"[SAVE] Index saved at {idx_file}") - - # Load index - if idx_type == "Quake": - load_kwargs = {k: build_params[k] for k in ("use_numa","num_workers", "parent_num_workers") if k in build_params} - idx = QuakeWrapper(); idx.load(str(idx_file), **load_kwargs) - elif idx_type == "DiskANN": - idx = IndexClass(); build_params.pop("metric", None); idx.load(str(idx_file), **build_params) - else: - idx = IndexClass(); idx.load(str(idx_file)) - - # Warmup (batch) - for _ in range(min(warmup,1)): - idx.search(queries, k, **search_params) - - # Prepare per-phase accumulators - child_buf_init_trials = [] - child_copy_query_trials = [] - child_enqueue_trials = [] - child_wait_trials = [] - child_agg_trials = [] - child_total_trials = [] - parent_buf_init_trials= [] - parent_copy_query_trials = [] - parent_enqueue_trials = [] - parent_wait_trials = [] - parent_agg_trials = [] - parent_total_trials = [] - recall_trials = [] - - # Benchmark Trials (batch) - for t in range(trials): - res = idx.search(queries, k, **search_params) - ti = res.timing_info - - # Child (worker_scan) timings - child_buf_init_trials.append(ti.buffer_init_time_ns / 1e6) - child_copy_query_trials.append(ti.copy_query_time_ns / 1e6) - child_enqueue_trials .append(ti.job_enqueue_time_ns / 1e6) - child_wait_trials .append(ti.job_wait_time_ns / 1e6) - child_agg_trials .append(ti.result_aggregate_time_ns / 1e6) - child_total_trials .append(ti.total_time_ns / 1e6) - - # Parent timings - pi = getattr(ti, "parent_info", None) - if pi: - parent_buf_init_trials.append(pi.buffer_init_time_ns / 1e6) - parent_copy_query_trials.append(pi.copy_query_time_ns / 1e6) - parent_enqueue_trials .append(pi.job_enqueue_time_ns / 1e6) - parent_wait_trials .append(pi.job_wait_time_ns / 1e6) - parent_agg_trials .append(pi.result_aggregate_time_ns / 1e6) - parent_total_trials .append(pi.total_time_ns / 1e6) - else: - parent_buf_init_trials.append(0.0) - parent_copy_query_trials.append(0.0) - parent_enqueue_trials .append(0.0) - parent_wait_trials .append(0.0) - parent_agg_trials .append(0.0) - parent_total_trials .append(0.0) - - # Recall for this trial - if gt is not None: - preds_arr = extract_ids(res) - recall = float(compute_recall(preds_arr, gt, k).mean()) - recall_trials.append(recall) - print(f" [{name} trial {t+1}/{trials}] child_total={child_total_trials[-1]:.2f} ms, parent_total={parent_total_trials[-1]:.2f} ms | recall@{k} {recall:.4f}") - else: - print(f" [{name} trial {t+1}/{trials}] child_total={child_total_trials[-1]:.2f} ms, parent_total={parent_total_trials[-1]:.2f} ms") - - # Aggregate metrics - mean_lat = float(np.mean(child_total_trials)) - std_lat = float(np.std(child_total_trials)) - mean_recall= float(np.mean(recall_trials)) if recall_trials else np.nan - n_workers = build_params.get("num_workers", None) - - row = { - "index": name, - "n_workers": n_workers, - f"recall_at_{k}": mean_recall, - "mean_latency_ms": mean_lat, - "std_latency_ms": std_lat, - "child_buffer_init_ms":float(np.mean(child_buf_init_trials)), - "child_copy_query_ms": float(np.mean(child_copy_query_trials)), - "child_enqueue_ms": float(np.mean(child_enqueue_trials)), - "child_wait_ms": float(np.mean(child_wait_trials)), - "child_aggregate_ms": float(np.mean(child_agg_trials)), - "child_total_ms": float(np.mean(child_total_trials)), - "parent_buffer_init_ms":float(np.mean(parent_buf_init_trials)), - "parent_copy_query_ms": float(np.mean(parent_copy_query_trials)), - "parent_enqueue_ms": float(np.mean(parent_enqueue_trials)), - "parent_wait_ms": float(np.mean(parent_wait_trials)), - "parent_aggregate_ms": float(np.mean(parent_agg_trials)), - "parent_total_ms": float(np.mean(parent_total_trials)), + logger.info(f"Processing index: {index_name} (Type: {index_type_str})") + + # Load or build index + idx_instance = None + if index_type_str == "Quake": + if not shared_quake_index_file.exists(): + logger.error(f"Shared Quake base index {shared_quake_index_file} not found for {index_name}. Skipping.") + continue + idx_instance = QuakeWrapper() + # These are load-time parameters for Quake specified in each config entry + quake_load_kwargs = { + k: current_build_params[k] for k in + ("use_numa", "num_workers", "parent_num_workers") if k in current_build_params + } + logger.info(f"Loading Quake index {index_name} from shared base with params: {quake_load_kwargs}") + idx_instance.load(str(shared_quake_index_file), **quake_load_kwargs) + else: # For non-Quake indexes + # DiskANN needs metric popped from build_params before load, if present during build + diskann_load_params = current_build_params.copy() + if index_type_str == "DiskANN": diskann_load_params.pop("metric", None) + + idx_instance = common_utils.prepare_index( + IndexClass=IndexCls, + index_build_params=current_build_params, + base_vectors=base_vectors, # Needed if building + index_file_path=actual_index_file_path, + force_rebuild=force_rebuild, # Rebuild this specific non-Quake index if needed + load_kwargs=diskann_load_params if index_type_str == "DiskANN" else None, + save_after_build=True + ) + + if idx_instance is None: # Should not happen if logic is correct + logger.error(f"Failed to load or build index {index_name}. Skipping.") + continue + + logger.info(f"Warmup for {index_name} ({num_warmup} iterations)...") + for _ in range(num_warmup): # Batched search for warmup + _ = idx_instance.search(query_vectors, k_val, **current_search_params) + + # Benchmark Trials (batch search) + trial_timings = { + "child_total_ms": [], "parent_total_ms": [], "recall": [], + "child_buffer_init_ms": [], "child_copy_query_ms": [], "child_enqueue_ms": [], + "child_wait_ms": [], "child_aggregate_ms": [], + "parent_buffer_init_ms": [], "parent_copy_query_ms": [], "parent_enqueue_ms": [], + "parent_wait_ms": [], "parent_aggregate_ms": [] } - all_rows.append(row) - pd.DataFrame([row]).to_csv(idx_csv, index=False) - - # Save unified CSV - df = pd.DataFrame(all_rows) - out_csv = out_dir / csv_name - df.to_csv(out_csv, index=False) - print(f"\n[RESULT] Results written to {out_csv}") - - # Plot batch total latency (child only) - plt.figure() - for idx_name in df["index"].unique(): - sub = df[df["index"]==idx_name] - label = f"{idx_name}" if pd.isna(sub["n_workers"]).all() else f"{idx_name} (n_workers={int(sub['n_workers'].iloc[0])})" - plt.errorbar( - sub["n_workers"], sub["child_total_ms"], - yerr=sub["child_total_ms"].std(), - marker="o", capsize=5, label=label - ) - plt.xscale("symlog", base=2) - plt.xlabel("Num Workers") - plt.ylabel("Mean Child Batch Latency (ms)") - plt.title("Batch Query Child Total Latency") - plt.legend(); plt.tight_layout() - plt.savefig(out_dir / f"{out_csv.stem}_batch_latency.png") - print(f"[PLOT] Saved to {out_dir / f'{out_csv.stem}_batch_latency.png'}") - - # Plot recall - if f"recall_at_{k}" in df.columns: - plt.figure() - for idx_name in df["index"].unique(): - sub = df[df["index"]==idx_name] - label = f"{idx_name}" if pd.isna(sub["n_workers"]).all() else f"{idx_name} (n_workers={int(sub['n_workers'].iloc[0])})" - plt.plot(sub["n_workers"], sub[f"recall_at_{k}"], marker="o", label=label) - plt.xscale("symlog", base=2) - plt.xlabel("Num Workers") - plt.ylabel(f"Recall@{k}") - plt.title(f"Recall@{k} (batch)") - plt.legend(); plt.tight_layout() - plt.savefig(out_dir / f"{out_csv.stem}_batch_recall.png") - print(f"[PLOT] Recall plot saved to {out_dir / f'{out_csv.stem}_batch_recall.png'}") \ No newline at end of file + + logger.info(f"Running benchmark for {index_name} ({num_trials} trials)...") + for t_idx in range(num_trials): + search_result = idx_instance.search(query_vectors, k_val, **current_search_params) + timing_info = search_result.timing_info # Assuming this is always present for Quake + + # Collect detailed timings if available (primarily for Quake) + for key in trial_timings: + if key.startswith("child_") and hasattr(timing_info, key.replace("_ms", "_time_ns").replace("child_", "")): + trial_timings[key].append(getattr(timing_info, key.replace("_ms", "_time_ns").replace("child_", "")) / 1e6) + elif key.startswith("parent_") and hasattr(timing_info, "parent_info") and timing_info.parent_info: + parent_attr_name = key.replace("_ms", "_time_ns").replace("parent_", "") + if hasattr(timing_info.parent_info, parent_attr_name): + trial_timings[key].append(getattr(timing_info.parent_info, parent_attr_name) / 1e6) + else: + trial_timings[key].append(0.0) # Parent info might not have all fields + elif key == "recall" and gt_vectors is not None: + pred_ids = search_result.ids + current_recall = float(compute_recall(pred_ids, gt_vectors, k_val).mean()) + trial_timings["recall"].append(current_recall) + + log_msg_trial = f" [{index_name} trial {t_idx+1}/{num_trials}] " + if trial_timings["child_total_ms"]: log_msg_trial += f"child_total={trial_timings['child_total_ms'][-1]:.2f}ms " + if trial_timings["parent_total_ms"] and trial_timings['parent_total_ms'][-1] > 0: log_msg_trial += f"parent_total={trial_timings['parent_total_ms'][-1]:.2f}ms " + if trial_timings["recall"]: log_msg_trial += f"| recall@{k_val}={trial_timings['recall'][-1]:.4f}" + logger.info(log_msg_trial) + + + # Aggregate metrics from trials + final_row = {"index": index_name, "n_workers": current_build_params.get("num_workers")} + for key, values in trial_timings.items(): + if values: + final_row[f"mean_{key}"] = float(np.mean(values)) + final_row[f"std_{key}"] = float(np.std(values)) + else: # Handle cases where timings might not be available (e.g. non-Quake, or no GT) + final_row[f"mean_{key}"] = np.nan + final_row[f"std_{key}"] = np.nan + + # Rename for CSV consistency if needed, e.g., mean_child_total_ms to mean_latency_ms + if "mean_child_total_ms" in final_row: + final_row["mean_latency_ms"] = final_row.pop("mean_child_total_ms") + if "std_child_total_ms" in final_row: + final_row["std_latency_ms"] = final_row.pop("std_child_total_ms") + if f"mean_recall" in final_row: # Ensure recall key matches expected format + final_row[f"recall_at_{k_val}"] = final_row.pop(f"mean_recall") + if f"std_recall" in final_row: # std for recall might not be needed in final summary + final_row.pop(f"std_recall") + + + all_experiment_rows.append(final_row) + common_utils.save_results_csv(pd.DataFrame([final_row]), per_index_csv_path) # Save per-index + + # Save unified CSV for all indexes + if not all_experiment_rows: + logger.warning("No results collected. Skipping final CSV and plots.") + return + + final_df = pd.DataFrame(all_experiment_rows) + unified_csv_path = main_output_dir / output_csv_name + common_utils.save_results_csv(final_df, unified_csv_path) + logger.info(f"Unified results written to {unified_csv_path}") + + # Plotting (remains specific to this experiment's needs) + plot_suffix = Path(output_csv_name).stem + + # Plot 1: Batch total latency (child_total_ms) vs. Num Workers + if "mean_latency_ms" in final_df.columns and "n_workers" in final_df.columns: + plt.figure(figsize=(8,6)) + for idx_name_plot in final_df["index"].unique(): + subset = final_df[final_df["index"] == idx_name_plot].sort_values(by="n_workers") + label = idx_name_plot # Simplified label + yerr_values = subset["std_latency_ms"] if "std_latency_ms" in subset else None + plt.errorbar( + subset["n_workers"].astype(int), + subset["mean_latency_ms"], + yerr=yerr_values, + marker="o", capsize=4, label=label, alpha=0.8, linestyle='-' + ) + # plt.xscale("symlog", base=2) # May not be ideal if n_workers are not powers of 2 or include 0 + plt.xlabel("Num Workers (Configuration)") + plt.ylabel(f"Mean Batch Query Latency (ms) - Child Total") + plt.title(f"NUMA Multi-Query: Latency vs. Num Workers ({dataset_cfg['name']})") + plt.legend(title="Index Configuration", bbox_to_anchor=(1.05, 1), loc='upper left') + plt.grid(True, linestyle=':', alpha=0.7) + plt.tight_layout(rect=[0,0,0.8,1]) # Adjust for legend + plt.savefig(main_output_dir / f"{plot_suffix}_batch_latency.png", dpi=150) + logger.info(f"Latency plot saved to {main_output_dir / f'{plot_suffix}_batch_latency.png'}") + plt.close() + + logger.info("NUMA Multi-Query experiment finished.") \ No newline at end of file From 1c939845e2fe411a3550a17afaf110aacdf96c77 Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 21 May 2025 10:14:36 -0500 Subject: [PATCH 200/323] update vary_levels --- src/cpp/include/list_scanning.h | 39 +- test/experiments/osdi2025/experiment_utils.py | 18 +- .../osdi2025/vary_levels/configs/sift10m.yaml | 144 ++-- test/experiments/osdi2025/vary_levels/run.py | 626 ++++++++++++------ 4 files changed, 542 insertions(+), 285 deletions(-) diff --git a/src/cpp/include/list_scanning.h b/src/cpp/include/list_scanning.h index de7fa263..37dfafe4 100644 --- a/src/cpp/include/list_scanning.h +++ b/src/cpp/include/list_scanning.h @@ -49,14 +49,17 @@ class TypedTopKBuffer { bool is_desc_; int *ord_; bool owns_memory_ = true; + int node_; TypedTopKBuffer(int k, bool desc, int cap, int node) : capacity_(cap), head_(0), k_(k) { - vals_ = static_cast(quake_alloc(sizeof(T) * cap, node)); - ids_ = static_cast(quake_alloc(sizeof(I) * cap, node)); - ord_ = static_cast(quake_alloc(sizeof(int) * cap, node)); + node_ = node; + + alloc(); if (capacity_ < k_) { - throw std::invalid_argument("capacity must be greater than k"); + string err_msg = "capacity= " + std::to_string(capacity_) + + " must be greater than k= " + std::to_string(k_); + throw std::invalid_argument(err_msg); } if (desc) { @@ -103,6 +106,26 @@ class TypedTopKBuffer { } ~TypedTopKBuffer() { + clear(); + } + + void set_k(int new_k) { + if (new_k > capacity_) { + clear(); + capacity_ = std::min(new_k * 100, 10000); + alloc(); + } + k_ = new_k; + reset(); + } + + void alloc() { + vals_ = static_cast(quake_alloc(sizeof(T) * capacity_, node_)); + ids_ = static_cast(quake_alloc(sizeof(I) * capacity_, node_)); + ord_ = static_cast(quake_alloc(sizeof(int) * capacity_, node_)); + } + + void clear() { if (owns_memory_) { if (vals_) { quake_free(vals_, sizeof(T) * capacity_); @@ -122,14 +145,6 @@ class TypedTopKBuffer { ord_ = nullptr; } - void set_k(int new_k) { - if (new_k > capacity_) { - throw std::invalid_argument("k must be less than the capacity"); - } - k_ = new_k; - reset(); - } - void reset() { head_ = 0; for (int i = 0; i < k_; i++) { diff --git a/test/experiments/osdi2025/experiment_utils.py b/test/experiments/osdi2025/experiment_utils.py index 0663391a..f18a75b1 100644 --- a/test/experiments/osdi2025/experiment_utils.py +++ b/test/experiments/osdi2025/experiment_utils.py @@ -3,7 +3,7 @@ import yaml import logging from pathlib import Path -from typing import Dict, Any +from typing import Dict, Any, List import torch import pandas as pd @@ -221,4 +221,18 @@ def plot_recall_performance( output_path.parent.mkdir(parents=True, exist_ok=True) plt.savefig(output_path) logger.info(f"Plot saved to {output_path}") - plt.close(fig) \ No newline at end of file + plt.close(fig) + +def expand_search_sweep(sweep_config: Dict[str, Any]) -> List[Dict[str, Any]]: + """Expands nprobes or recall_targets from sweep_config into individual search dicts.""" + base_params = {k: v for k, v in sweep_config.items() if k not in ["nprobes", "recall_targets"]} + expanded_list = [] + if "nprobes" in sweep_config: + for nprobe_val in sweep_config["nprobes"]: + expanded_list.append({**base_params, "nprobe": nprobe_val}) + elif "recall_targets" in sweep_config: + for rt_val in sweep_config["recall_targets"]: + expanded_list.append({**base_params, "recall_target": rt_val}) + else: # No sweep, just use base_params + expanded_list.append(base_params) + return expanded_list \ No newline at end of file diff --git a/test/experiments/osdi2025/vary_levels/configs/sift10m.yaml b/test/experiments/osdi2025/vary_levels/configs/sift10m.yaml index 76fe00c3..728a35a7 100644 --- a/test/experiments/osdi2025/vary_levels/configs/sift10m.yaml +++ b/test/experiments/osdi2025/vary_levels/configs/sift10m.yaml @@ -1,101 +1,77 @@ # ─── top-level controls ─────────────────────────────────── -mode: run # ignored by new script – kept for compatibility overwrite: - index: false - results: true + index: false # Force rebuild of the base child index (L0) + results: true # Force rerun of searches for all experiment setups # ─── dataset ─────────────────────────────────────────────── dataset: name: sift10m metric: l2 - path: data - nq: 100 + path: data # Path to dataset if not packaged locally + nq: 100 # Number of queries to use for benchmarking -# ─── base index (built once) ─────────────────────────────── -index: - type: Quake +# ─── base child index (L0 - innermost data layer) ────────── +# This index is built once and used as the foundation for all experiment_setups. +child_index_config: + type: Quake # Index type, currently supports Quake build_params: - nc: 40_000 # child level - num_workers: 8 - search_params: - k: 1 + nc: 40000 # Number of centroids for the L0 index + metric: l2 # Metric for L0 (inherited by parents unless overridden, though Quake parents don't typically change metric) + num_workers: 8 # Build workers for L0 index -# ─── parent levels to test ───────────────────────────────── -levels: - - nc: 0 - search_params: - nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] -# batched_scan: true -# recall_targets: [.5, .6, .7, .8, .85, .9, .95, .99] -# initial_search_fraction: 0.01 -# aps_flush_period_us: 1 - batch_size: 1 +# ─── global innermost search sweep (applies to L0 search phase) ─── +# Define EITHER nprobes OR recall_targets for the L0 search sweep. +# These parameters are applied to the SearchParams of the L0 layer. +global_innermost_search_sweep: + nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] # Option 1: nprobe sweep for L0 +# recall_targets: [0.5, 0.6, 0.7, 0.8, 0.85, 0.9, 0.95, 0.99] # Option 2: recall_target sweep for L0 +# initial_search_fraction: 0.01 # Required if using recall_targets for L0 + k: 1 # k for the L0 search + batch_size: 1 # Query batch size for L0 search + # Other common SearchParams for L0 can be added here. +# ─── experiment setups (different hierarchical configurations) ───── +experiment_setups: + - name: "ChildOnly_L0" + # This setup uses only the base child_index_config. + # additional_levels is empty or omitted. + # Search on L0 is governed by global_innermost_search_sweep. -# - nc: 100 # baseline (keep the index single-level) -# search_params: # ← optional override for the CHILD search -# # nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] -# recall_targets: [.5, .6, .7, .8, .85, .9, .95, .99] -# initial_search_fraction: 0.002 -## nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256] -# batch_size: 1 -# parent_search_params: # ← optional params passed to *parent* search -## nprobe: 10 -# recall_target: .999 -# initial_search_fraction: 0.05 -# -# - nc: 500 # 500-centroid parent layer -# search_params: -# # nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] -# recall_targets: [.5, .6, .7, .8, .85, .9, .95, .99] -# initial_search_fraction: 0.002 -# batch_size: 1 -# parent_search_params: # ← optional params passed to *parent* search -## nprobe: 15 -# recall_target: .999 -# initial_search_fraction: 0.05 + - name: "L0_plus_L1_Parent1k_PScan50" + additional_levels: # Levels added ON TOP of the base child_index (L0) + - type: Quake # Describes L1 (parent of L0) + build_params: # How to build this L1 layer + nc: 1000 + num_workers: 4 # Build workers for this L1 layer + search_params: # How to search this L1 layer (when it's acting as parent of L0) + nprobe: 50 + num_workers: 4 # Search workers for this L1 layer's scan + use_numa: true # NUMA setting for this L1 layer's scan - - nc: 1_000 - search_params: - nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] -# batched_scan: true -# recall_targets: [.5, .6, .7, .8, .85, .9, .95, .99] -# initial_search_fraction: 0.01 - batch_size: 1 - parent_search_params: # ← optional params passed to *parent* search - nprobe: 50 -# batched_scan: true -# recall_target: .999 -# initial_search_fraction: 0.05 + - name: "L0_plus_L1_Parent5k_PScan100" + additional_levels: + - type: Quake # L1 + build_params: {nc: 5000, num_workers: 8} + search_params: {nprobe: 100, num_workers: 8, use_numa: false} -# - nc: 2_000 -# search_params: -# # nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] -# recall_targets: [.5, .6, .7, .8, .85, .9, .95, .99] -# initial_search_fraction: 0.01 -# batch_size: 1 -# parent_search_params: # ← optional params passed to *parent* search -# nprobe: 50 -## -# - nc: 4_000 -# search_params: -# nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] -## recall_targets: [.5, .6, .7, .8, .85, .9, .95, .99] -## initial_search_fraction: 0.01 -## aps_flush_period_us: 1 -# batch_size: 1 -# parent_search_params: # ← optional params passed to *parent* search -# nprobe: 200 -# recall_target: .999 -# aps_flush_period_us: 1 -# initial_search_fraction: 0.05 + - name: "L0_L1_Parent1k_L2_Parent100_PScan10" + additional_levels: + - type: Quake # L1 (parent of L0) + build_params: {nc: 1000, num_workers: 4} + search_params: {nprobe: 50, num_workers: 4} # Search params for L1 when it's parent of L0 + - type: Quake # L2 (parent of L1) + build_params: {nc: 100, num_workers: 2} + search_params: {nprobe: 10, num_workers: 2} # Search params for L2 when it's parent of L1 + +# ─── common experiment parameters ────────────────────────── +experiment_params: + trials: 3 # Number of timed trials for each search configuration + warmup: 1 # Number of warmup runs before timed trials # ─── plotting (optional) ────────────────────────────────── plot: - styles: - L0: {color: "#1f77b4"} - L100: {color: "#ff7f0e"} - L500: {color: "#ffbb78"} - L1000: {color: "#2ca02c"} - L2000: {color: "#d62728"} - L4000: {color: "#9467bd"} \ No newline at end of file + styles: # Keyed by experiment_setups.name + ChildOnly_L0: {color: "#1f77b4", marker: "o", linestyle: "-"} + L0_plus_L1_Parent1k_PScan50: {color: "#ff7f0e", marker: "s", linestyle: "--"} + L0_plus_L1_Parent5k_PScan100: {color: "#2ca02c", marker: "D", linestyle: "-."} + L0_L1_Parent1k_L2_Parent100_PScan10: {color: "#d62728", marker: "^", linestyle: ":"} diff --git a/test/experiments/osdi2025/vary_levels/run.py b/test/experiments/osdi2025/vary_levels/run.py index 01c0ccd1..dcc3f3ce 100644 --- a/test/experiments/osdi2025/vary_levels/run.py +++ b/test/experiments/osdi2025/vary_levels/run.py @@ -1,216 +1,468 @@ #!/usr/bin/env python3 """ -experiment_levels.py – benchmark a single Quake/IVF index with optional -parent levels, each level having its own search parameters. - -* Build the **child** index once (saved as base_index.bin). -* For every level described in `levels:`: - – Reload the base index from disk. - – Optionally add a parent layer with `nc` centroids. - – Run searches using the per-level overrides. -* Results for each level go to ./OUT_DIR/L/results.csv -* A combined QPS-vs-Recall plot is written to OUT_DIR/recall_vs_qps.png +vary_levels.py - Benchmarks Quake with varying hierarchical structures. + +This script evaluates Quake's performance with an arbitrary number of index levels. +It builds a base child index (L0) once. Then, for each defined 'experiment_setup', +it loads the base index and adds any specified additional parent levels (L1, L2, etc.) +on top. + +Each complete multi-level index is then benchmarked against a sweep of search +parameters applied to the innermost (L0) level. Search parameters for higher +levels (L1, L2, ...) are fixed for each experiment_setup. + +Key metrics, including overall QPS, latency, recall, and per-level scan times +and statistics (e.g., partitions scanned), are recorded. """ from __future__ import annotations -import argparse, logging, time +import logging +import time from pathlib import Path -from typing import Dict, List, Tuple +from typing import Dict, List, Any, Tuple import matplotlib.pyplot as plt import numpy as np import pandas as pd -import quake -import torch, yaml +import torch +import yaml -from quake.datasets.ann_datasets import load_dataset -from quake.index_wrappers.faiss_ivf import FaissIVF -from quake.index_wrappers.quake import QuakeWrapper -from quake.utils import compute_recall +from quake import QuakeIndex, IndexBuildParams, SearchParams, SearchTimingInfo +from test.experiments.osdi2025 import experiment_utils as common_utils -logger = logging.getLogger("experiment") +logger = logging.getLogger("vary_levels_exp") logging.basicConfig( - format="%(asctime)s | %(levelname)s | %(message)s", + format="%(asctime)s | %(levelname)s | %(name)s | %(message)s", datefmt="%H:%M:%S", level=logging.INFO, ) -# ───────────────────── helper routines ───────────────────── -def _prepare_index(idx_cls, vecs, metric, build_params, - path: Path, rebuild: bool) -> Tuple[object, float]: - """Build or load the *child* index; return (index, build_time_s).""" - idx = idx_cls() - if path.exists() and not rebuild: - t0 = time.time() - idx.load(str(path), num_workers=build_params["num_workers"]) - return idx, 0.0 - logger.info("Building index → %s …", path) - t0 = time.time() - idx.build(vecs, metric=metric, **build_params) - t_build = time.time() - t0 - idx.save(str(path)) - logger.info("Index saved (%.1fs).", t_build) - return idx, t_build - - -def _benchmark_index(idx, queries, ground, search_params, - trials: int, warmup: int) -> Dict: - """Run `trials` timed searches; return stats dict.""" - sp = search_params.copy() # don’t modify caller’s dict - bs = sp.pop("batch_size", None) - k = sp["k"] - - # warm-up - for _ in range(warmup): - if bs is None: - idx.search(queries, **sp) - else: - for i in range(0, len(queries), bs): - idx.search(queries[i:i+bs], **sp) - - qps, lat_ms = [], [] - for _ in range(trials): - t0 = time.time() - if bs is None: - I = idx.search(queries, **sp).ids - else: - I = np.concatenate([ - idx.search(queries[i:i+bs], **sp).ids - for i in range(0, len(queries), bs) - ]) - dt = time.time() - t0 - qps.append(len(queries) / dt) - lat_ms.append(dt * 1e3) - - recall = compute_recall(I, ground[:, :k], k) - recall = 0.0 if recall is None else recall.mean().item() - - return dict( - recall = float(recall), - qps_mean = float(np.mean(qps)), - qps_std = float(np.std(qps)), - latency_ms = float(np.mean(lat_ms)), - trials = trials, - warmup = warmup, - batch_size = bs or len(queries), +def build_hierarchical_index( + base_vectors: torch.Tensor, + child_index_config: Dict[str, Any], + additional_levels_config: List[Dict[str, Any]], + base_child_index_path: Path, + force_rebuild_base: bool, + exp_setup_name: str +) -> Tuple[QuakeIndex | None, float]: + """ + Builds or loads the base child index (L0), then adds additional levels on top. + Returns the fully constructed QuakeIndex instance and total build time for L0. + Build time for additional levels is not explicitly tracked here but happens during add_level. + """ + # Prepare L0 (base child index) + # Note: build_params_dict for prepare_quake_index should be flat, matching IndexBuildParams attributes + l0_build_params_dict = child_index_config.get("build_params", {}) + + # Ensure metric is in the build_params_dict for prepare_quake_index if it expects it + if 'metric' not in l0_build_params_dict and 'metric' in child_index_config: + l0_build_params_dict['metric'] = child_index_config['metric'] + if 'metric' not in l0_build_params_dict and 'metric' in dataset_cfg: # Fallback to dataset metric + l0_build_params_dict['metric'] = dataset_cfg['metric'] + + + # Use common_utils to prepare the base L0 index + # This function handles building if not exists or force_rebuild is true, and saving. + # It returns the loaded/built index instance. + idx = common_utils.prepare_quake_index( + vecs=base_vectors, + build_params_dict=l0_build_params_dict, + index_file_path=base_child_index_path, + force_rebuild=force_rebuild_base, + # Pass load-time worker/NUMA settings for L0 if they were part of its build_params + # For Quake, these are typically search-time. If prepare_quake_index needs them for load: + num_workers_load=l0_build_params_dict.get("num_workers", 0), # Assuming build num_workers is also load num_workers + use_numa=l0_build_params_dict.get("use_numa", False) # Assuming build use_numa is also load use_numa ) + # Build time for L0 is implicitly handled by prepare_quake_index if it were to return it. + # For now, we'll assume build_time_l0 is primarily for the initial creation. + # If the file existed, build_time_l0 would be 0. + build_time_l0 = 0.0 # Placeholder, as prepare_quake_index doesn't return build time directly. + + if idx is None: + logger.error(f"Failed to prepare base child index for {exp_setup_name}.") + return None, build_time_l0 + # Add additional levels (L1, L2, ...) + for i, level_cfg in enumerate(additional_levels_config): + level_num = i + 1 # L1, L2, ... + logger.info(f"For '{exp_setup_name}', adding L{level_num}...") -def _expand_targets(sp: Dict) -> List[Dict]: - """Yield one search-param dict per recall_target OR per nprobe value.""" - if "recall_targets" in sp: - tgs = sp.pop("recall_targets") - if not isinstance(tgs, (list, tuple)): tgs = [tgs] - return [{**sp, "recall_target": t} for t in tgs] - if "nprobes" in sp: - tgs = sp.pop("nprobes") - if not isinstance(tgs, (list, tuple)): tgs = [tgs] - return [{**sp, "nprobe": t} for t in tgs] - return [sp] - - -def _plot(cfg: Dict, root: Path) -> None: - """Generate combined QPS-vs-Recall plot.""" - fig, ax = plt.subplots(figsize=(6, 4)) - styles = cfg.get("plot", {}).get("styles", {}) - for lv in cfg["levels"]: - nc = lv["nc"] - name = f"L{nc}" - csv = root / name / "results.csv" - if not csv.exists(): - logger.warning("No results for level %s – skipped.", nc) + add_level_build_params_dict = level_cfg.get("build_params", {}) + if not add_level_build_params_dict.get("nc"): + logger.warning(f"Skipping L{level_num} for '{exp_setup_name}' due to missing 'nc' in build_params.") continue - df = pd.read_csv(csv).sort_values("recall") - sty = styles.get(name, {}) - ax.plot( - df["qps_mean"], df["recall"], - label = f"{nc:,} centroids" if nc else "single-level", - marker = sty.get("marker", "o"), - linestyle = sty.get("linestyle", "-"), - linewidth = 1.4, - markersize = 6, - color = sty.get("color", None), - ) - # ax.set_xscale("log", base=10) - ax.set_xlabel("Queries per second (log)") - ax.set_ylabel("Recall@k") - ax.set_title("QPS vs Recall for different parent levels") - ax.grid(True, which="both", ls=":") - ax.legend() - fig.tight_layout() - fig.savefig(root / "recall_vs_qps.png", dpi=180) - logger.info("Plot written to %s", root / "recall_vs_qps.png") - -# ─────────────────────────── main ────────────────────────── -def run_experiment(cfg_path: str, output_dir: str) -> None: - cfg = yaml.safe_load(Path(cfg_path).read_text()) - out = Path(output_dir) - out.mkdir(parents=True, exist_ok=True) - - # dataset --------------------------------------------------------------- - ds = cfg["dataset"] - logger.info("Loading dataset %s …", ds["name"]) - vecs, queries, ground = load_dataset(ds["name"], ds.get("path", "")) - nq = ds["nq"] - queries, ground = queries[:nq], ground[:nq] - metric = ds["metric"] - - # build child index once ----------------------------------------------- - icfg = cfg["index"] - idx_cls = {"Quake": QuakeWrapper, "IVF": FaissIVF}[icfg["type"]] - base_bin = out / "base_index.bin" - base_idx, build_t = _prepare_index( - idx_cls, vecs, metric, - icfg["build_params"], - base_bin, - rebuild=cfg["overwrite"].get("index", False), + + # Create IndexBuildParams for adding the level + # The metric for parent levels is typically inherited or fixed by Quake internally. + # We primarily need to set 'nc' and 'num_workers' for building this parent level. + parent_bp = IndexBuildParams() + parent_bp.nlist = add_level_build_params_dict["nc"] + parent_bp.num_workers = add_level_build_params_dict.get("num_workers", 0) + parent_bp.metric = l0_build_params_dict.get("metric", "l2") # Parent inherits metric + + try: + idx.add_level(parent_bp) # Assumes QuakeIndex has .index attribute pointing to C++ object + logger.info(f"L{level_num} (NC={parent_bp.nlist}) added successfully for '{exp_setup_name}'.") + except Exception as e: + logger.error(f"Failed to add L{level_num} for '{exp_setup_name}': {e}") + return None, build_time_l0 + + return idx, build_time_l0 + + +def construct_hierarchical_search_params( + innermost_level_sp_item: Dict[str, Any], + additional_levels_config: List[Dict[str, Any]] +) -> SearchParams: + """ + Constructs a nested SearchParams object for the entire hierarchy. + """ + # Innermost level (L0) search parameters + l0_sp = common_utils.create_search_params(**innermost_level_sp_item) + + current_parent_sp = l0_sp + for level_cfg in additional_levels_config: # L1, L2, ... + level_search_params_dict = level_cfg.get("search_params", {}) + if not level_search_params_dict: # If no specific search params, create default + level_search_params_dict = {"nprobe": 1} # Minimal valid SearchParams + + # These are search-time worker/NUMA settings for this specific parent level's scan + # common_utils.create_search_params will set these if present in level_search_params_dict + higher_level_sp = common_utils.create_search_params(**level_search_params_dict) + + current_parent_sp.parent_params = higher_level_sp + current_parent_sp = higher_level_sp + + return l0_sp + + +def _extract_recursive_timings(innermost_timing_info: SearchTimingInfo | None, num_levels: int) -> Dict[str, Any]: + """ + Extracts scan time and other stats for each level from the TimingInfo hierarchy. + The innermost_timing_info is for L0. Its .parent_info is for L1, and so on. + """ + timings = {} + current_ti = innermost_timing_info + + for i in range(num_levels): + level_prefix = f"level_{i}" + if current_ti: + timings[f"{level_prefix}_scan_time_ms"] = current_ti.total_time_ns / 1e6 + timings[f"{level_prefix}_buffer_init_ms"] = current_ti.buffer_init_time_ns / 1e6 + timings[f"{level_prefix}_enqueue_ms"] = current_ti.job_enqueue_time_ns / 1e6 + timings[f"{level_prefix}_wait_ms"] = current_ti.job_wait_time_ns / 1e6 + timings[f"{level_prefix}_aggregate_ms"] = current_ti.result_aggregate_time_ns / 1e6 + timings[f"{level_prefix}_partitions_scanned"] = getattr(current_ti, "partitions_scanned", np.nan) + current_ti = current_ti.parent_info + else: + timings[f"{level_prefix}_scan_time_ms"] = np.nan + timings[f"{level_prefix}_buffer_init_ms"] = np.nan + timings[f"{level_prefix}_enqueue_ms"] = np.nan + timings[f"{level_prefix}_wait_ms"] = np.nan + timings[f"{level_prefix}_aggregate_ms"] = np.nan + timings[f"{level_prefix}_partitions_scanned"] = np.nan + return timings + + +def benchmark_search_config( + idx: QuakeIndex, + queries: torch.Tensor, + ground_truth: np.ndarray | None, + hierarchical_sp: SearchParams, + num_levels_in_hierarchy: int, + num_trials: int, + warmup_trials: int +) -> Dict[str, Any]: + """ + Benchmarks a specific hierarchical search configuration. + Returns aggregated metrics including per-level timings. + """ + k_val = hierarchical_sp.k # k from the L0 SearchParams + + # Warm-up + for _ in range(warmup_trials): + idx.search(queries, hierarchical_sp) + + trial_metrics_list = [] + for _ in range(num_trials): + t_start_ns = time.perf_counter_ns() + search_result = idx.search(queries, hierarchical_sp) + t_elapsed_ns = time.perf_counter_ns() - t_start_ns + + current_recall = np.nan + if ground_truth is not None and search_result.ids is not None: + # Ensure k_val does not exceed columns in ground_truth or search_result.ids + max_k_recall = min(k_val, ground_truth.shape[1], search_result.ids.shape[1]) + if max_k_recall > 0 : + current_recall = common_utils.quake_compute_recall( + search_result.ids[:, :max_k_recall], + ground_truth[:, :max_k_recall], + max_k_recall + ).mean().item() + + + per_level_timings = _extract_recursive_timings(search_result.timing_info, num_levels_in_hierarchy) + + trial_metrics_list.append({ + "overall_latency_ns": t_elapsed_ns, + "recall": current_recall, + **per_level_timings + }) + + # Aggregate results from trials + num_queries_total = len(queries) + aggregated_results = { + "qps_mean": np.mean([num_queries_total / (m["overall_latency_ns"] / 1e9) for m in trial_metrics_list]), + "overall_latency_ms_mean": np.mean([m["overall_latency_ns"] / 1e6 for m in trial_metrics_list]), + "recall_mean": np.nanmean([m["recall"] for m in trial_metrics_list]), + } + + # Aggregate per-level timings and stats + for i in range(num_levels_in_hierarchy): + level_prefix = f"level_{i}" + for metric_suffix in ["scan_time_ms", "buffer_init_ms", "enqueue_ms", "wait_ms", "aggregate_ms", "partitions_scanned"]: + key = f"{level_prefix}_{metric_suffix}" + valid_values = [m[key] for m in trial_metrics_list if key in m and pd.notna(m[key])] + if valid_values: + aggregated_results[f"{key}_mean"] = np.mean(valid_values) + else: + aggregated_results[f"{key}_mean"] = np.nan + + return aggregated_results + + +def run_experiment(config_path_str: str, output_dir_str: str) -> None: + global dataset_cfg # Make dataset_cfg accessible to build_hierarchical_index if needed for metric fallback + config = common_utils.load_config(config_path_str) + output_dir = Path(output_dir_str) + output_dir.mkdir(parents=True, exist_ok=True) + + dataset_cfg = config["dataset"] + child_index_cfg = config["child_index_config"] # L0 build config + innermost_search_sweep_cfg = config["global_innermost_search_sweep"] # L0 search sweep + experiment_setups_cfg = config["experiment_setups"] + exp_params_cfg = config["experiment_params"] + overwrite_cfg = config["overwrite"] + + logger.info(f"Loading dataset: {dataset_cfg['name']}") + base_vecs, queries, gt_numpy = common_utils.load_data( + dataset_cfg["name"], dataset_cfg.get("path", ""), nq_override=dataset_cfg.get("nq") + ) + + # Path for the L0 index binary + base_child_index_path = output_dir / "base_index.bin" + + l0_build_params_dict = child_index_cfg.get("build_params", {}) + if 'metric' not in l0_build_params_dict: # Ensure metric is set for L0 build + l0_build_params_dict['metric'] = dataset_cfg.get('metric', 'l2') + + _ = common_utils.prepare_quake_index( # Build L0 and save it + vecs=base_vecs, + build_params_dict=l0_build_params_dict, + index_file_path=base_child_index_path, + force_rebuild=overwrite_cfg.get("index", False) ) + logger.info(f"Base L0 index prepared at {base_child_index_path}") + + all_experiment_run_records = [] + innermost_sweep_list = common_utils.expand_search_sweep(innermost_search_sweep_cfg) # Adapted from common_utils or defined here - # iterate over parent levels ------------------------------------------- - for lv in cfg["levels"]: - nc = lv["nc"] - name = f"L{nc}" - res_dir = out / name - res_dir.mkdir(parents=True, exist_ok=True) - csv_file = res_dir / "results.csv" + for exp_setup_item in experiment_setups_cfg: + exp_setup_name = exp_setup_item["name"] + additional_levels_config = exp_setup_item.get("additional_levels", []) + num_actual_levels = 1 + len(additional_levels_config) # L0 + additional levels - if csv_file.exists() and not cfg["overwrite"].get("results", False): - logger.info("[%s] results cached – skipping.", name) + exp_setup_results_path = output_dir / f"{exp_setup_name}_results.csv" + if exp_setup_results_path.exists() and not overwrite_cfg.get("results", True): + logger.info(f"Results for setup '{exp_setup_name}' exist, loading cached.") + try: + cached_df = pd.read_csv(exp_setup_results_path) + all_experiment_run_records.extend(cached_df.to_dict('records')) + except Exception as e: + logger.error(f"Could not load cached results for {exp_setup_name}: {e}") continue - # fresh load of the base index for this level - idx = idx_cls() - idx.load(str(base_bin), num_workers=icfg["build_params"].get("num_workers", 1)) - if nc: # add parent layer when nc > 0 - build_params = quake.IndexBuildParams() - build_params.nlist = nc - idx.index.add_level(build_params) - - # child search params (base defaults + per-level overrides) - child_sp = icfg["search_params"].copy() - child_sp.update(lv.get("search_params", {})) - - # optional parent search params - parent_sp = lv.get("parent_search_params", None) - - df = pd.DataFrame() - for sp in _expand_targets(child_sp.copy()): - if parent_sp: # attach parent section if any - sp["parent"] = parent_sp - stats = _benchmark_index( - idx, queries, ground, sp, - trials = cfg.get("trials", 5), - warmup = cfg.get("warmup", 1), - ) - stats.update( - build_time_s = build_t if nc == 0 else 0.0, - parent_nc = nc, - recall_target= sp.get("recall_target", sp.get("nprobe", np.nan)), + logger.info(f"--- Processing Experiment Setup: {exp_setup_name} ({num_actual_levels} levels) ---") + + # Assemble the full hierarchical index for this setup + # This loads L0 and adds L1, L2... on top. + current_hier_idx, _ = build_hierarchical_index( + base_vecs, child_index_cfg, additional_levels_config, + base_child_index_path, False, # False: don't force rebuild L0 if already done + exp_setup_name + ) + if not current_hier_idx: + logger.error(f"Failed to build index for setup '{exp_setup_name}'. Skipping.") + continue + + setup_specific_records = [] + for l0_sp_item in innermost_sweep_list: # Iterate L0 search params (nprobe/recall_target) + param_key = "nprobe" if "nprobe" in l0_sp_item else "recall_target" + param_val = l0_sp_item.get(param_key, "N/A") + logger.info(f"Benchmarking '{exp_setup_name}' with L0 {param_key}={param_val}...") + + # Construct the full stack of SearchParams for this run + hierarchical_sp = construct_hierarchical_search_params(l0_sp_item, additional_levels_config) + + benchmark_stats = benchmark_search_config( + current_hier_idx, queries, gt_numpy, hierarchical_sp, + num_levels_in_hierarchy=num_actual_levels, + num_trials=exp_params_cfg.get("trials", 3), + warmup_trials=exp_params_cfg.get("warmup", 1) ) - df = pd.concat([df, pd.DataFrame([stats])], ignore_index=True) - logger.info("[%s] target %.3g done.", name, stats["recall_target"]) - df.to_csv(csv_file, index=False) - logger.info("[%s] results written to %s", name, csv_file) + # Record configuration and results + record = {"exp_setup_name": exp_setup_name} + record[f"L0_{param_key}"] = param_val # L0 sweep parameter + record["L0_k"] = l0_sp_item.get("k") + record["L0_batch_size"] = l0_sp_item.get("batch_size") + record["L0_nc"] = child_index_cfg.get("build_params", {}).get("nc") + + + for i, add_level_cfg in enumerate(additional_levels_config): + level_num = i + 1 # L1, L2... + record[f"L{level_num}_nc"] = add_level_cfg.get("build_params",{}).get("nc") + record[f"L{level_num}_build_workers"] = add_level_cfg.get("build_params",{}).get("num_workers") + record[f"L{level_num}_search_nprobe"] = add_level_cfg.get("search_params",{}).get("nprobe") + record[f"L{level_num}_search_recall_target"] = add_level_cfg.get("search_params",{}).get("recall_target") + record[f"L{level_num}_search_workers"] = add_level_cfg.get("search_params",{}).get("num_workers") + record[f"L{level_num}_search_use_numa"] = add_level_cfg.get("search_params",{}).get("use_numa") + + record.update(benchmark_stats) + setup_specific_records.append(record) + all_experiment_run_records.append(record) + + if setup_specific_records: + setup_df = pd.DataFrame(setup_specific_records) + setup_df.to_csv(exp_setup_results_path, index=False) + logger.info(f"Results for setup '{exp_setup_name}' saved to {exp_setup_results_path}") + + if all_experiment_run_records: + final_df = pd.DataFrame(all_experiment_run_records) + unified_csv_path = output_dir / f"{dataset_cfg['name']}_vary_levels_all_setups_summary.csv" + final_df.to_csv(unified_csv_path, index=False) + logger.info(f"Unified results for all setups saved to {unified_csv_path}") + + + if not final_df.empty: + plot_metric_vs_l0_param(final_df, config, output_dir, metric_to_plot="recall_mean", ylabel="Mean Recall") + plot_metric_vs_l0_param(final_df, config, output_dir, metric_to_plot="overall_latency_ms_mean", ylabel="Mean Overall Latency (ms)") + plot_level_scan_times(final_df, config, output_dir) + + else: + logger.info("No results generated across all setups.") + + logger.info("Vary Levels (Generalized) experiment finished.") + +def plot_metric_vs_l0_param(results_df: pd.DataFrame, config: Dict[str, Any], output_dir: Path, metric_to_plot: str, ylabel: str): + """Plots a given metric against the L0 sweep parameter for different experiment setups.""" + if results_df.empty or metric_to_plot not in results_df.columns: + logger.warning(f"Cannot plot '{metric_to_plot}': Data missing or empty.") + return + + fig, ax = plt.subplots(figsize=(10, 7)) + plot_styles = config.get("plot", {}).get("styles", {}) + + # Determine L0 sweep key (nprobe or recall_target) + l0_sweep_key = None + if "L0_nprobe" in results_df.columns and results_df["L0_nprobe"].notna().any(): + l0_sweep_key = "L0_nprobe" + elif "L0_recall_target" in results_df.columns and results_df["L0_recall_target"].notna().any(): + l0_sweep_key = "L0_recall_target" + + if not l0_sweep_key: + logger.warning("L0 sweep parameter (nprobe/recall_target) not found in results. Skipping plot.") + plt.close(fig) + return + + for exp_name, group in results_df.groupby("exp_setup_name"): + style = plot_styles.get(exp_name, {}) + sorted_group = group.sort_values(by=l0_sweep_key) + ax.plot( + sorted_group[l0_sweep_key], + sorted_group[metric_to_plot], + label=exp_name, + marker=style.get("marker", "o"), + color=style.get("color"), + linestyle=style.get("linestyle", "-") + ) + + ax.set_xlabel(f"Innermost Level (L0) Search: {l0_sweep_key.replace('_', ' ').title()}") + ax.set_ylabel(ylabel) + ax.set_title(f"{config['dataset']['name']}: {ylabel} vs. L0 Search Parameter") + ax.legend(title="Experiment Setup", bbox_to_anchor=(1.05, 1), loc='upper left') + ax.grid(True, which="both", ls=":", alpha=0.7) + plt.tight_layout(rect=[0, 0, 0.78, 1]) # Adjust for legend + + plot_filename = f"{config['dataset']['name']}_vary_levels_{metric_to_plot}_vs_L0param.png" + plt.savefig(output_dir / plot_filename, dpi=150) + logger.info(f"Plot saved: {output_dir / plot_filename}") + plt.close(fig) + + +def plot_level_scan_times(results_df: pd.DataFrame, config: Dict[str, Any], output_dir: Path): + """Plots the breakdown of scan times per level for each experiment setup, at a chosen L0 parameter value.""" + if results_df.empty: + logger.warning("Cannot plot level scan times: Data missing or empty.") + return + + l0_sweep_key = None + if "L0_nprobe" in results_df.columns and results_df["L0_nprobe"].notna().any(): + l0_sweep_key = "L0_nprobe" + elif "L0_recall_target" in results_df.columns and results_df["L0_recall_target"].notna().any(): + l0_sweep_key = "L0_recall_target" + if not l0_sweep_key: return + + + # Select a representative L0 parameter value for the bar chart (e.g., median or a common high-recall point) + if results_df[l0_sweep_key].nunique() > 1: + target_l0_val = np.percentile(results_df[l0_sweep_key].dropna(), 75) # Example: 75th percentile + # Find closest available value in the df + available_l0_vals = results_df[l0_sweep_key].dropna().unique() + closest_l0_val = available_l0_vals[np.abs(available_l0_vals - target_l0_val).argmin()] + + df_subset = results_df[results_df[l0_sweep_key] == closest_l0_val].copy() + plot_title_suffix = f"(at L0 {l0_sweep_key.split('_')[-1]} ≈ {closest_l0_val:.2f})" + else: + df_subset = results_df.copy() + plot_title_suffix = f"(at L0 {l0_sweep_key.split('_')[-1]} = {df_subset[l0_sweep_key].iloc[0]:.2f})" + + + if df_subset.empty: + logger.warning("No data for selected L0 parameter for level scan time plot.") + return + + level_time_cols = sorted([col for col in df_subset.columns if col.startswith("level_") and col.endswith("_scan_time_ms_mean")]) + if not level_time_cols: + logger.warning("No per-level scan time columns found for plotting.") + return + + max_level_idx = max([int(c.split('_')[1]) for c in level_time_cols]) + num_levels_to_plot = max_level_idx + 1 + + plot_data = df_subset.set_index("exp_setup_name")[level_time_cols] + + # Ensure all level columns up to max_level_idx are present, fill with 0 if not (for consistent plotting) + for i in range(num_levels_to_plot): + col_name = f"level_{i}_scan_time_ms_mean" + if col_name not in plot_data.columns: + plot_data[col_name] = 0 + # Reorder columns for stacked bar plot + ordered_level_time_cols = [f"level_{i}_scan_time_ms_mean" for i in range(num_levels_to_plot)] + plot_data = plot_data[ordered_level_time_cols] + + + plot_data.plot(kind='bar', stacked=True, figsize=(12, 8), colormap='viridis') + + plt.xlabel("Experiment Setup") + plt.ylabel("Mean Scan Time per Level (ms)") + plt.title(f"{config['dataset']['name']}: Scan Time Breakdown by Level {plot_title_suffix}") + plt.xticks(rotation=30, ha='right') + plt.legend(title="Index Level (L0=innermost)", bbox_to_anchor=(1.05, 1), loc='upper left') + plt.tight_layout(rect=[0, 0, 0.8, 1]) # Adjust for legend - _plot(cfg, out) + plot_filename = f"{config['dataset']['name']}_vary_levels_scan_time_breakdown.png" + plt.savefig(output_dir / plot_filename, dpi=150) + logger.info(f"Plot saved: {output_dir / plot_filename}") + plt.close() \ No newline at end of file From 66ed970da103d99017407b890ca4b3ff431815fc Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 21 May 2025 10:35:05 -0500 Subject: [PATCH 201/323] update readme and install script --- test/experiments/osdi2025/README.md | 32 ++-- test/experiments/osdi2025/install.sh | 247 +++++++++++++++++++++++++++ 2 files changed, 259 insertions(+), 20 deletions(-) create mode 100644 test/experiments/osdi2025/install.sh diff --git a/test/experiments/osdi2025/README.md b/test/experiments/osdi2025/README.md index 465429df..b62a81fe 100644 --- a/test/experiments/osdi2025/README.md +++ b/test/experiments/osdi2025/README.md @@ -41,7 +41,7 @@ Each experiment directory (e.g., `aps_recall_targets`, `early_termination`) cont 2. **Set Up the Conda Environment:** Create and activate the environment using the provided YAML file: ```bash - conda env create -f environments/ubuntu-latest/conda.yaml + conda env create -f environments/ubuntu-cpu/conda.yaml conda activate quake-env ``` @@ -67,7 +67,7 @@ OMP_NUM_THREADS=1 python3 -m test.experiments.osdi2025.experiment_runner \ **Explanation:** -* `OMP_NUM_THREADS=1`: Recommended for consistent performance measurement as OMP interferes with our custom parallelism. Adjust as needed based on specific experiment guidelines if any. +* `OMP_NUM_THREADS=1`: Recommended for consistent performance measurement as OMP interferes with our custom parallelism. * `--experiment `: Specifies which experiment to run (e.g., `kick_the_tires`, `aps_recall_targets`). * `--config `: Specifies the configuration file from `test/experiments/osdi2025//configs/`. For `sift1m.yaml`, use `sift1m`. * `--output-dir ` (Optional): If provided, results (CSVs, plots) will be saved here. Default is `test/experiments/osdi2025//results//`. @@ -94,20 +94,16 @@ OMP_NUM_THREADS=1 python3 -m test.experiments.osdi2025.experiment_runner \ ## Experiment Summaries and Outputs -The following table summarizes each experiment and its primary outputs. Output files are typically generated within the experiment's specific output directory (refer to the `--output-dir` option or its default value). - -| Experiment Name | Description | Key Outputs (Illustrative Filenames) | -|------------------------|--------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| `aps_recall_targets` | Evaluates Quake's Adaptive Partition Scanning (APS) against baselines for various recall targets. | `aps_recall_targets_summary.csv`, `aps_recall_vs_metrics.png` | -| `early_termination` | Compares APS with early termination strategies like LAET and Auncel. | `aps_et_results_.csv`, `latency_plot_.png`, Markdown table (printed to console) | -| `kick_the_tires` | Basic dynamic workload stress test focusing on latency, recall, and system state over operations. | Per-index `results.csv` (e.g., `Quake10000/results.csv`), main `unified_plot.png` | -| `maintenance_ablation` | Evaluates the impact of Quake's maintenance operations under a dynamic workload, extending `kick_the_tires`. | Per-index `results.csv`, main `summary_table.csv`, `summary_table.md`, `unified_plot.png`, `time_breakdown.png` | -| `measure_skew_and_perf` | Measures data skew characteristics and their performance impact on a dataset. | Script-specific CSVs/plots detailing skew metrics and performance correlations. | -| `multi_level` | Benchmarks Quake with multiple index levels against single-level configurations, varying `nprobe`. | Per-index `results.csv` (e.g., `Quake5000/results.csv`), main `recall_vs_qps.png` | -| `numa_multi_query` | Tests Quake's multi-query batch performance under different NUMA and worker configurations. | Per-index `results.csv`, main `.csv` (aggregate), `_batch_latency.png`, `_batch_recall.png` | -| `numa_single_query` | Tests Quake's single-query performance under different NUMA and worker configurations. | Per-index `results.csv`, main `.csv` (aggregate), `_child_latency.png`, `_recall.png` | -| `vary_levels` | Evaluates Quake with varying numbers of centroids in parent index levels. | Per-level `results.csv` (e.g., `L/results.csv`), main `recall_vs_qps.png` | -| `workload_comparison` | Compares Quake's performance across different types of synthetic or real-world workloads. | Script-specific CSVs/plots comparing workloads. | +The following table summarizes each experiment and its primary outputs. Output files are generated within the experiment's specific output directory (refer to the `--output-dir` option or its default value). + +| Experiment Name | Description | Reproduces in Paper | +|------------------------|--------------------------------------------------------------------------------------------------------------|-------------------------------| +| `aps_recall_targets` | Evaluates Quake's Adaptive Partition Scanning (APS) against baselines for various recall targets. | Figure 6 | +| `early_termination` | Compares APS with early termination strategies like LAET and Auncel. | Revision Experiment (Table 5) | +| `kick_the_tires` | Basic dynamic workload stress test focusing on latency, recall, and system state over operations. | | +| `maintenance_ablation` | Evaluates the impact of Quake's maintenance operations under a dynamic workload, extending `kick_the_tires`. | Revision Experiment (Table 6) | +| `numa_multi_query` | Tests Quake's multi-query batch performance under different NUMA and worker configurations. | | +| `numa_single_query` | Tests Quake's single-query performance under different NUMA and worker configurations. | Figure 5 | **To run any of these, replace `` and `` in the general command structure.** For example, to run the `aps_recall_targets` experiment with its `sift1m` configuration: @@ -122,10 +118,6 @@ Results, including CSV files and plots as detailed above, will be saved to the s ## Notes * **Dataset Downloads:** The `quake.datasets.ann_datasets.load_dataset` utility will attempt to download datasets if they are not found locally (typically in a `data/` directory in the repository root). An internet connection is required for initial downloads. -* **Disk Space:** Some datasets (e.g., SIFT1B, MSTuring100M) are large and require significant disk space. * **Execution Time:** Full experimental runs can be time-consuming, ranging from minutes for small configurations to many hours for large-scale evaluations. The `kick_the_tires` experiment with `sift1m` is designed for relatively quick verification. - -``` -``` \ No newline at end of file diff --git a/test/experiments/osdi2025/install.sh b/test/experiments/osdi2025/install.sh new file mode 100644 index 00000000..9cb705c1 --- /dev/null +++ b/test/experiments/osdi2025/install.sh @@ -0,0 +1,247 @@ +#!/bin/bash +set -e # Exit immediately if a command exits with a non-zero status. +# set -x # Uncomment for debugging (print each command before execution) + +echo "Starting QUAKE Artifact and Baselines Setup Script." +echo "This script should be run with sudo (e.g., 'sudo ./install.sh')." +echo "Targeting Ubuntu 22.04 (Jammy) based system." + +# ----------------------------- +# Environment Variables +# ----------------------------- +export CONDA_DIR_PATH="/opt/miniconda" +# Ensure Miniconda's bin is in PATH for the root user running the script +export PATH="${CONDA_DIR_PATH}/bin:${PATH}" +export DEBIAN_FRONTEND=noninteractive + +# ----------------------------- +# Update System and Install Base APT Packages +# ----------------------------- +echo ">>> Updating system and installing base APT packages..." +apt-get update + +apt-get install -y --no-install-recommends \ + wget \ + curl \ + build-essential \ + ca-certificates \ + swig \ + git \ + libomp-dev \ + graphviz \ + numactl \ + libnuma-dev \ + htop \ + software-properties-common + +# ----------------------------- +# Install/Ensure GCC 11 and G++ 11 +# ----------------------------- +echo ">>> Ensuring GCC 11 and G++ 11 are set up correctly..." +add-apt-repository -y ppa:ubuntu-toolchain-r/test +apt-get update +apt-get install -y gcc-11 g++-11 + +echo ">>> Setting up GCC and G++ alternatives to point to version 11..." +update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 110 \ + --slave /usr/bin/gcov gcc-gcov /usr/bin/gcov-11 \ + --slave /usr/bin/gcc-ar gcc-ar /usr/bin/gcc-ar-11 \ + --slave /usr/bin/gcc-nm gcc-nm /usr/bin/gcc-nm-11 \ + --slave /usr/bin/gcc-ranlib gcc-ranlib /usr/bin/gcc-ranlib-11 +update-alternatives --install /usr/bin/g++ g++ /usr/bin/g++-11 110 +update-alternatives --set gcc /usr/bin/gcc-11 +update-alternatives --set g++ /usr/bin/g++-11 + +echo "Verifying GCC version after update-alternatives:" +gcc --version +echo "Verifying G++ version after update-alternatives:" +g++ --version + +# ----------------------------- +# Install CMake 3.24.2 +# ----------------------------- +CMAKE_VERSION_EXPECTED="3.24.2" +CMAKE_INSTALL_PATH="/usr/local/bin/cmake" +echo ">>> Installing CMake ${CMAKE_VERSION_EXPECTED}..." +if command -v cmake &> /dev/null && [[ "$(cmake --version)" == *"cmake version ${CMAKE_VERSION_EXPECTED}"* ]]; then + echo "CMake version ${CMAKE_VERSION_EXPECTED} already installed at $(command -v cmake). Skipping." +else + echo "Installing CMake ${CMAKE_VERSION_EXPECTED}..." + cd /tmp + wget -qO cmake.sh https://github.com/Kitware/CMake/releases/download/v${CMAKE_VERSION_EXPECTED}/cmake-${CMAKE_VERSION_EXPECTED}-linux-x86_64.sh + chmod +x cmake.sh + ./cmake.sh --skip-license --prefix=/usr/local # Runs as root + rm cmake.sh + cd - +fi +echo "Verifying CMake version:" +cmake --version + +# ----------------------------- +# Install Miniconda +# ----------------------------- +echo ">>> Installing Miniconda to ${CONDA_DIR_PATH}..." +if [ -x "${CONDA_DIR_PATH}/bin/conda" ]; then + echo "Miniconda already found in ${CONDA_DIR_PATH}. Skipping installation." +else + cd /tmp + wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh + bash miniconda.sh -b -p "${CONDA_DIR_PATH}" # Runs as root, installs to /opt/miniconda + rm miniconda.sh + cd - + echo ">>> Initializing Conda for bash (modifies /root/.bashrc as script runs with sudo)..." + "${CONDA_DIR_PATH}/bin/conda" init bash +fi + +# Source conda's profile script to make conda command available in this script session +echo ">>> Sourcing Conda profile script for current root session..." +if [ -f "${CONDA_DIR_PATH}/etc/profile.d/conda.sh" ]; then + source "${CONDA_DIR_PATH}/etc/profile.d/conda.sh" +else + echo "ERROR: Conda profile script not found at ${CONDA_DIR_PATH}/etc/profile.d/conda.sh after installation attempt." + echo "Conda commands might not be available. Exiting." + exit 1 +fi + +# ----------------------------- +# Create Conda Environment 'quake-env' and Install Dependencies +# ----------------------------- +CONDA_ENV_NAME="quake-env" +echo ">>> Setting up conda environment '${CONDA_ENV_NAME}'..." + +echo ">>> Creating conda.yaml file at /tmp/conda.yaml..." +# Ensure the file is removed first to prevent issues with existing file/permissions +rm -f /tmp/conda.yaml +cat > /tmp/conda.yaml << EOF +name: ${CONDA_ENV_NAME} +channels: + - pytorch + - defaults + - conda-forge +dependencies: + - python=3.11 + - numpy + - pandas + - faiss-cpu + - matplotlib + - pytest + - pip + - pip: + - sphinx + - sphinx_rtd_theme + - sphinxcontrib-mermaid + - graphviz + - pyyaml +EOF + +# If environment exists, remove it for a clean setup +if conda env list | grep -E "^${CONDA_ENV_NAME}\s+"; then + echo "Conda environment '${CONDA_ENV_NAME}' already exists. Removing for a clean setup..." + conda env remove -n "${CONDA_ENV_NAME}" --all -y +fi +echo ">>> Creating conda environment '${CONDA_ENV_NAME}' from /tmp/conda.yaml..." +conda env create -f /tmp/conda.yaml +rm /tmp/conda.yaml # Clean up +conda clean -afy + +echo ">>> Installing specific PyTorch (CPU) and other Python packages into '${CONDA_ENV_NAME}'..." +conda run -n "${CONDA_ENV_NAME}" pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu + +conda run -n "${CONDA_ENV_NAME}" pip install \ + lightgbm \ + scikit-learn \ + scann==1.4.0 + +# Force numpy 1.25.0 for diskannpy compatibility +conda run -n "${CONDA_ENV_NAME}" pip install numpy==1.25.0 --force-reinstall +conda run -n "${CONDA_ENV_NAME}" pip install diskannpy==0.7.0 + +echo ">>> Verifying '${CONDA_ENV_NAME}'..." +conda env list +conda run -n "${CONDA_ENV_NAME}" python -c "import sys; print(f'OK in {sys.prefix}; python:', sys.executable); import torch; print('PyTorch version:', torch.__version__); import numpy; print('Numpy version:', numpy.__version__)" + +# ----------------------------- +# Install Intel oneAPI Base Toolkit +# ----------------------------- +ONEAPI_INSTALL_PATH="/opt/intel/oneapi" +echo ">>> Installing Intel oneAPI Base Toolkit to ${ONEAPI_INSTALL_PATH}..." +if [ -f "${ONEAPI_INSTALL_PATH}/setvars.sh" ]; then + echo "Intel oneAPI already found in ${ONEAPI_INSTALL_PATH}. Skipping installation." +else + cd /tmp + wget -O intel-oneapi-base-toolkit-offline.sh https://registrationcenter-download.intel.com/akdlm/IRC_NAS/6bfca885-4156-491e-849b-1cd7da9cc760/intel-oneapi-base-toolkit-2025.1.1.36_offline.sh + chmod +x intel-oneapi-base-toolkit-offline.sh + ./intel-oneapi-base-toolkit-offline.sh -a --silent --cli --eula accept --install-dir "${ONEAPI_INSTALL_PATH}" # sudo implied + rm intel-oneapi-base-toolkit-offline.sh + cd - +fi +# Source oneAPI environment variables for the current session. +echo ">>> Sourcing Intel oneAPI setvars.sh for current root session (needed for SVS build)..." +if [ -f "${ONEAPI_INSTALL_PATH}/setvars.sh" ]; then + source "${ONEAPI_INSTALL_PATH}/setvars.sh" +else + echo "WARNING: oneAPI setvars.sh not found at ${ONEAPI_INSTALL_PATH}/setvars.sh. SVS build might fail." +fi + +# ----------------------------- +# Clone and Install QUAKE Repository +# ----------------------------- +QUAKE_DIR_BASE="/opt" +QUAKE_REPO_NAME="quake" +QUAKE_FULL_PATH="${QUAKE_DIR_BASE}/${QUAKE_REPO_NAME}" + +echo ">>> Cloning/Updating and installing QUAKE repository in ${QUAKE_FULL_PATH}..." +if [ -d "${QUAKE_FULL_PATH}" ]; then + echo "QUAKE directory ${QUAKE_FULL_PATH} already exists. Updating..." + cd "${QUAKE_FULL_PATH}" + git pull +else + git clone https://github.com/marius-team/quake.git "${QUAKE_FULL_PATH}" + cd "${QUAKE_FULL_PATH}" +fi +git checkout osdi2025_debug + +git config --global --add safe.directory "${QUAKE_FULL_PATH}" # For root's global config +SUBMODULE_PATHS=("src/cpp/third_party/concurrentqueue" "src/cpp/third_party/faiss" "src/cpp/third_party/pybind11") +for SUBMODULE_PATH in "${SUBMODULE_PATHS[@]}"; do + git config --global --add safe.directory "${QUAKE_FULL_PATH}/${SUBMODULE_PATH}" +done +git submodule update --init --recursive + +echo ">>> Building and installing QUAKE Python package..." +conda run -n "${CONDA_ENV_NAME}" pip install . --no-use-pep517 +cd / + +# ----------------------------- +# Clone and Install ScalableVectorSearch (SVS) +# ----------------------------- +SVS_DIR_BASE="/opt" +SVS_REPO_NAME="ScalableVectorSearch" +SVS_FULL_PATH="${SVS_DIR_BASE}/${SVS_REPO_NAME}" + +echo ">>> Cloning/Updating ScalableVectorSearch repository in ${SVS_FULL_PATH}..." +if [ -d "${SVS_FULL_PATH}" ]; then + echo "ScalableVectorSearch directory ${SVS_FULL_PATH} already exists. Updating..." + cd "${SVS_FULL_PATH}" + git pull +else + git clone https://github.com/intel/ScalableVectorSearch.git "${SVS_FULL_PATH}" + cd "${SVS_FULL_PATH}" +fi +git config --global --add safe.directory "${SVS_FULL_PATH}" + +echo ">>> Attempting to build and install SVS Python bindings..." +echo "This step requires GCC/G++ 11 and Intel oneAPI tools (which should be sourced)." +echo "Current compilers: GCC=$(which gcc || echo not_found), G++=$(which g++ || echo not_found)" +# The oneAPI setvars.sh should have configured the environment for Intel compilers if available and preferred by SVS build. +# Otherwise, it should use the g++-11 from update-alternatives. +conda run -n "${CONDA_ENV_NAME}" pip install ./bindings/python +cd / + +echo "--------------------------------------------------------------------" +echo "Setup script finished successfully." +echo "To activate the conda environment (in a new shell, as root or user depending on .bashrc): conda activate ${CONDA_ENV_NAME}" +echo "QUAKE repository is in ${QUAKE_FULL_PATH}" +echo "Intel oneAPI (if installed) is in ${ONEAPI_INSTALL_PATH}. Source with: source ${ONEAPI_INSTALL_PATH}/setvars.sh" +echo "SVS repository (if cloned) is in ${SVS_FULL_PATH}" +echo "--------------------------------------------------------------------" From 250b91a3a75d5686d5ea74c26d0d80948d74fe26 Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 21 May 2025 10:39:11 -0500 Subject: [PATCH 202/323] update vary_levels --- test/experiments/osdi2025/experiment_utils.py | 5 +++-- test/experiments/osdi2025/vary_levels/run.py | 3 ++- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/test/experiments/osdi2025/experiment_utils.py b/test/experiments/osdi2025/experiment_utils.py index f18a75b1..14aef786 100644 --- a/test/experiments/osdi2025/experiment_utils.py +++ b/test/experiments/osdi2025/experiment_utils.py @@ -35,10 +35,11 @@ def prepare_quake_index( force_rebuild: bool = False, num_workers_load: int = 0, num_parent_workers_load: int = 0, - use_numa: bool = False + use_numa: bool = False, + load: bool = True ) -> QuakeIndex: idx = QuakeIndex() - if index_file_path.exists() and not force_rebuild: + if index_file_path.exists() and not force_rebuild and load: logger.info(f"Loading index from {index_file_path}") idx.load(str(index_file_path), num_workers_load, use_numa, num_parent_workers_load) else: diff --git a/test/experiments/osdi2025/vary_levels/run.py b/test/experiments/osdi2025/vary_levels/run.py index dcc3f3ce..5c63ba47 100644 --- a/test/experiments/osdi2025/vary_levels/run.py +++ b/test/experiments/osdi2025/vary_levels/run.py @@ -261,7 +261,8 @@ def run_experiment(config_path_str: str, output_dir_str: str) -> None: vecs=base_vecs, build_params_dict=l0_build_params_dict, index_file_path=base_child_index_path, - force_rebuild=overwrite_cfg.get("index", False) + force_rebuild=overwrite_cfg.get("index", False), + load=False ) logger.info(f"Base L0 index prepared at {base_child_index_path}") From 9bcc9426a35e886764f2b80d7812e52619ac0f01 Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 21 May 2025 10:53:38 -0500 Subject: [PATCH 203/323] update numa_single_query --- .../numa_single_query/configs/sift1m.yaml | 237 ++++--- .../osdi2025/numa_single_query/run.py | 603 ++++++++++-------- 2 files changed, 470 insertions(+), 370 deletions(-) diff --git a/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml b/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml index f8a101e1..54c1711f 100644 --- a/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml +++ b/test/experiments/osdi2025/numa_single_query/configs/sift1m.yaml @@ -1,25 +1,84 @@ -# numa_latency_experiment.yaml dataset: name: sift1m - num_queries: 1 - k: 50 + num_queries: 100 + k: 1 -trials: 1 -warmup: 0 -overwrite: true # Set to true to force all indexes to rerun +trials: 3 +warmup: 1 +overwrite: true indexes: - - name: Quake_0 + - name: Quake_1_NUMA index: Quake build_params: nc: 1000 metric: l2 - num_workers: 0 - use_numa: false + num_workers: 1 + use_numa: true + search_params: + nprobe: 20 + + - name: Quake_2_NUMA + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 2 + use_numa: true + search_params: + nprobe: 20 + + - name: Quake_4_NUMA + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 4 + use_numa: true + search_params: + nprobe: 20 + + - name: Quake_8_NUMA + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 8 + use_numa: true search_params: - nprobe: 1 + nprobe: 20 - - name: Quake_1 + - name: Quake_16_NUMA + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 16 + use_numa: true + search_params: + nprobe: 20 + + - name: Quake_32_NUMA + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 32 + use_numa: true + search_params: + nprobe: 20 + + - name: Quake_64_NUMA + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 64 + use_numa: true + search_params: + nprobe: 20 + + - name: Quake_1_NONUMA index: Quake build_params: nc: 1000 @@ -27,101 +86,67 @@ indexes: num_workers: 1 use_numa: false search_params: - nprobe: 1 - -# - name: Quake_4 -# index: Quake -# build_params: -# nc: 1000 -# metric: l2 -# num_workers: 4 -# use_numa: false -# search_params: -# nprobe: 20 -# -# - name: Quake_16 -# index: Quake -# build_params: -# nc: 1000 -# metric: l2 -# num_workers: 16 -# use_numa: false -# search_params: -# nprobe: 20 -# -# - name: Quake_1_numa -# index: Quake -# build_params: -# nc: 1000 -# metric: l2 -# num_workers: 1 -# use_numa: true -# search_params: -# nprobe: 20 -# -# - name: Quake_4_numa -# index: Quake -# build_params: -# nc: 1000 -# metric: l2 -# num_workers: 4 -# use_numa: true -# search_params: -# nprobe: 20 -# -# - name: Quake_16_numa -# index: Quake -# build_params: -# nc: 1000 -# metric: l2 -# num_workers: 16 -# use_numa: true -# search_params: -# nprobe: 20 - -# - name: FaissIVF -# index: IVF -# build_params: -# nc: 1000 -# metric: l2 -# search_params: -# nprobe: 1000 -# -# - name: SCANN -# index: SCANN -# build_params: -# num_leaves: 1000 -# metric: squared_l2 -# search_params: -# leaves_to_search: 1000 -# -# - name: FaissHNSW -# index: HNSW -# build_params: -# m: 64 -# ef_construction: 128 -# metric: l2 -# search_params: -# ef_search: 32 -# -# - name: DiskANN -# index: DiskANN -# build_params: -# complexity: 128 -# graph_degree: 64 -# metric: l2 -# search_params: -# complexity: 32 -# -# - name: SVS -# index: SVS -# build_params: -# graph_max_degree: 64 -# alpha: 1.1 -# window_size: 128 -# metric: l2 -# search_params: -# search_window_size: 32 + nprobe: 20 + + - name: Quake_2_NONUMA + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 2 + use_numa: false + search_params: + nprobe: 20 + + - name: Quake_4_NONUMA + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 4 + use_numa: false + search_params: + nprobe: 20 + + - name: Quake_8_NONUMA + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 8 + use_numa: false + search_params: + nprobe: 20 + + - name: Quake_16_NONUMA + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 16 + use_numa: false + search_params: + nprobe: 20 + + - name: Quake_32_NONUMA + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 32 + use_numa: false + search_params: + nprobe: 20 + + - name: Quake_64_NONUMA + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 64 + use_numa: false + search_params: + nprobe: 20 output: results_csv: numa_single_query_results.csv \ No newline at end of file diff --git a/test/experiments/osdi2025/numa_single_query/run.py b/test/experiments/osdi2025/numa_single_query/run.py index a787c0d2..53b72969 100644 --- a/test/experiments/osdi2025/numa_single_query/run.py +++ b/test/experiments/osdi2025/numa_single_query/run.py @@ -1,26 +1,15 @@ -""" -NUMA Single-Query Latency and Recall Benchmark (detailed timers + parent) - -Tracks these phases, for both the worker_scan and its parent search: - - buffer_init_time_ns - - job_enqueue_time_ns - - job_wait_time_ns - - result_aggregate_time_ns - - total_time_ns - -Averages per‐query timers within each trial, then across trials, -and writes them (in ms) to the per‐index CSV. -""" import sys from pathlib import Path +import logging import yaml import numpy as np import torch import pandas as pd import matplotlib.pyplot as plt -from quake.utils import compute_recall -from quake.datasets.ann_datasets import load_dataset +from quake import SearchParams, SearchTimingInfo # Quake specific +from test.experiments.osdi2025 import experiment_utils as common_utils + from quake.index_wrappers.faiss_ivf import FaissIVF from quake.index_wrappers.faiss_hnsw import FaissHNSW from quake.index_wrappers.quake import QuakeWrapper @@ -46,260 +35,346 @@ "SVS": Vamana, } -def build_and_save(index_cls, params, vecs, path): - """Build and persist an index.""" - idx = index_cls() - idx.build(vecs, **params) - idx.save(str(path)) - -def extract_ids(res): - """Normalize returned IDs array.""" - if hasattr(res, "ids"): - arr = res.ids - elif hasattr(res, "I"): - arr = res.I - elif hasattr(res, "indices"): - arr = res.indices - else: - raise RuntimeError("Search result has no id field.") - if isinstance(arr, torch.Tensor): - arr = arr.cpu().numpy() - return np.asarray(arr, dtype=np.int64) - -def run_experiment(cfg_path: str, output_dir: str) -> None: - cfg = yaml.safe_load(Path(cfg_path).read_text()) - ds_cfg = cfg["dataset"] - k = ds_cfg["k"] - q_n = ds_cfg["num_queries"] - trials = cfg.get("trials", 5) - warmup_n = cfg.get("warmup", 10) - idx_cfgs = cfg["indexes"] - csv_name = cfg.get("output", {}).get("results_csv", "numa_results.csv") - overwrite = cfg.get("overwrite", False) - - out_dir = Path(output_dir).expanduser().absolute() - out_dir.mkdir(parents=True, exist_ok=True) - - # Load dataset - print(f"[INFO] loading dataset {ds_cfg['name']} …") - base_vecs, queries, gt = load_dataset(ds_cfg["name"]) - queries = queries[:q_n] - gt = gt[:q_n] if gt is not None else None - - # Build shared Quake base index once - quake_base_params = None - for c in idx_cfgs: - if c["index"] == "Quake": - p = dict(c.get("build_params", {})) - p.pop("num_workers", None) - p.pop("use_numa", None) - quake_base_params = p - break - - quake_base_path = out_dir / "Quake_base_index.bin" - if quake_base_params and not quake_base_path.exists(): - print("[BUILD] Quake (shared) base index …") - build_and_save(QuakeWrapper, quake_base_params, base_vecs, quake_base_path) - print(f"[SAVE] Quake base index → {quake_base_path}") - - results = [] - for cfg_i in idx_cfgs: - name = cfg_i["name"] - typ = cfg_i["index"] - b_params = dict(cfg_i.get("build_params", {})) - s_params = dict(cfg_i.get("search_params", {})) - cls = INDEX_CLASSES.get(typ) - if cls is None: - print(f"[WARN] unknown index type '{typ}' – skipping.") - continue +logging.basicConfig(level=logging.INFO, + format="%(asctime)s | %(levelname)7s | %(name)s | %(message)s", + datefmt="%H:%M:%S") +logger = logging.getLogger("numa_single_query_exp") - idx_csv = out_dir / f"{name}_results.csv" - idx_path = quake_base_path if typ == "Quake" else out_dir / f"{name}_index.bin" - if idx_csv.exists() and not overwrite: - print(f"[SKIP] {name} – results exist.") - results.append(pd.read_csv(idx_csv).iloc[0].to_dict()) - continue +def build_and_save_generic_index(index_cls, build_params_dict, base_vectors, index_file_path: Path): + if index_file_path.exists(): + logger.info(f"Index {index_file_path} already exists. Skipping build.") + return + logger.info(f"Building index {index_file_path}...") + idx_instance = index_cls() + # FaissIVF and FaissHNSW expect metric at build, not in build_params dict necessarily + metric = build_params_dict.pop("metric", "l2") + idx_instance.build(base_vectors, metric=metric, **build_params_dict) + index_file_path.parent.mkdir(parents=True, exist_ok=True) + idx_instance.save(str(index_file_path)) + logger.info(f"Index saved to {index_file_path}") + +def get_timing_details(timing_info_obj: SearchTimingInfo | None, is_parent: bool = False) -> dict: + prefix = "parent_" if is_parent else "child_" + details = { + f"{prefix}buffer_init_ns": np.nan, + f"{prefix}enqueue_ns": np.nan, + f"{prefix}wait_ns": np.nan, + f"{prefix}aggregate_ns": np.nan, + f"{prefix}total_time_ns": np.nan, + } + if timing_info_obj: + details[f"{prefix}buffer_init_ns"] = getattr(timing_info_obj, "buffer_init_time_ns", 0) + details[f"{prefix}enqueue_ns"] = getattr(timing_info_obj, "job_enqueue_time_ns", 0) + details[f"{prefix}wait_ns"] = getattr(timing_info_obj, "job_wait_time_ns", 0) + details[f"{prefix}aggregate_ns"] = getattr(timing_info_obj, "result_aggregate_time_ns", 0) + details[f"{prefix}total_time_ns"] = getattr(timing_info_obj, "total_time_ns", 0) + return details + + +def benchmark_single_query_detailed( + idx, + queries_tensor: torch.Tensor, + gt_tensor: torch.Tensor | None, + k_val: int, + search_params_dict: dict, + index_type: str, + num_trials: int, + warmup_runs: int +) -> dict: + + search_kwargs_final = {} + if index_type == "Quake": + # For Quake, convert dict to SearchParams object + # And pop non-SearchParams like 'nprobe' if they are handled differently + # For single query, SearchParams is directly constructed. + pass # SearchParams will be constructed per query for Quake + elif index_type == "SCANN": + search_kwargs_final = {"leaves_to_search": search_params_dict.get("leaves_to_search", 100)} + elif index_type == "HNSW": + search_kwargs_final = {"ef_search": search_params_dict.get("ef_search", 32)} + elif index_type == "DiskANN": + search_kwargs_final = {"complexity": search_params_dict.get("complexity", 32)} + elif index_type == "SVS": + search_kwargs_final = {"search_window_size": search_params_dict.get("search_window_size", 32)} + else: # IVF or other Faiss based that might use nprobe directly + search_kwargs_final = {"nprobe": search_params_dict.get("nprobe", 100)} - # Build non-Quake if needed - if typ != "Quake" and not idx_path.exists(): - print(f"[BUILD] {name} index …") - build_and_save(cls, b_params, base_vecs, idx_path) - print(f"[SAVE] index → {idx_path}") - - # Load index - if typ == "Quake": - load_kw = {k: b_params[k] for k in ("use_numa", "num_workers", "parent_num_workers") if k in b_params} - idx = QuakeWrapper(); idx.load(str(idx_path), **load_kw) - elif typ == "DiskANN": - idx = cls(); b_params.pop("metric", None); idx.load(str(idx_path), **b_params) + + logger.info(f"Warmup ({warmup_runs} queries)...") + for i in range(min(warmup_runs, len(queries_tensor))): + q_vec = queries_tensor[i].unsqueeze(0).float() + if index_type == "Quake": + idx.search(q_vec, k_val, **search_params_dict) else: - idx = cls(); idx.load(str(idx_path)) - - # Dispatch search parameters - if typ == "SCANN": - search_kw = {"leaves_to_search": s_params.get("leaves_to_search", 100)} - elif typ == "HNSW": - search_kw = {"ef_search": s_params.get("ef_search", 32)} - elif typ == "DiskANN": - search_kw = {"complexity": s_params.get("complexity", 32)} - elif typ == "SVS": - search_kw = {"search_window_size": s_params.get("search_window_size", 32)} + idx.search(q_vec, k_val, **search_kwargs_final) + + trial_aggregated_metrics = [] + + for t_idx in range(num_trials): + per_query_timings_in_trial = [] + recalls_in_trial = [] + + for q_idx, q_vec_single in enumerate(queries_tensor): + q_vec_single = q_vec_single.unsqueeze(0).float() + + search_result = None + if index_type == "Quake": + search_result = idx.search(q_vec_single, k_val, **search_params_dict) + else: + search_result = idx.search(q_vec_single, k_val, **search_kwargs_final) + + timing_info_child = getattr(search_result, "timing_info", None) + query_metrics = get_timing_details(timing_info_child, is_parent=False) + + if index_type == "Quake" and timing_info_child: # Quake specific parent info + timing_info_parent = getattr(timing_info_child, "parent_info", None) + query_metrics.update(get_timing_details(timing_info_parent, is_parent=True)) + else: # For non-Quake or if no parent_info + query_metrics.update(get_timing_details(None, is_parent=True)) + + + if gt_tensor is not None: + ids_this_query = search_result.ids + if ids_this_query.ndim == 2 and ids_this_query.shape[0] == 1: # Ensure it's [1, k_found] + recall_val = common_utils.quake_compute_recall( + ids_this_query, + gt_tensor[q_idx].unsqueeze(0), + k_val + ).item() + recalls_in_trial.append(recall_val) + + per_query_timings_in_trial.append(query_metrics) + + # Aggregate for this trial + trial_df = pd.DataFrame(per_query_timings_in_trial) + mean_metrics_for_trial = trial_df.mean().to_dict() + if recalls_in_trial: + mean_metrics_for_trial["recall"] = np.mean(recalls_in_trial) + + trial_aggregated_metrics.append(mean_metrics_for_trial) + logger.info(f" Trial {t_idx+1}/{num_trials}: " + f"child_total_ms={mean_metrics_for_trial.get('child_total_time_ns', np.nan)/1e6:.2f}, " + f"parent_total_ms={mean_metrics_for_trial.get('parent_total_time_ns', np.nan)/1e6:.2f}" + f"recall@{k_val}={mean_metrics_for_trial.get('recall', np.nan):.4f}") + + + # Aggregate across trials + final_metrics_df = pd.DataFrame(trial_aggregated_metrics) + results_summary = {} + for col in final_metrics_df.columns: + # Convert times from ns to ms for mean, keep std in ns then convert if needed, or just report mean in ms. + if col.endswith("_ns"): + results_summary[f"{col.replace('_ns', '_ms')}_mean"] = final_metrics_df[col].mean() / 1e6 + results_summary[f"{col.replace('_ns', '_ms')}_std"] = final_metrics_df[col].std() / 1e6 + elif col == "recall": + results_summary[f"recall_at_{k_val}_mean"] = final_metrics_df[col].mean() + results_summary[f"recall_at_{k_val}_std"] = final_metrics_df[col].std() + else: # other metrics if any + results_summary[f"{col}_mean"] = final_metrics_df[col].mean() + results_summary[f"{col}_std"] = final_metrics_df[col].std() + + return results_summary + + +def run_experiment(cfg_path_str: str, output_dir_str: str) -> None: + config = common_utils.load_config(cfg_path_str) + dataset_cfg = config["dataset"] + k_val = dataset_cfg["k"] + num_queries = dataset_cfg["num_queries"] + + exp_params_cfg = config # Top level items like trials, warmup + num_trials = exp_params_cfg.get("trials", 5) + warmup_runs = exp_params_cfg.get("warmup", 10) + + indexes_config_list = config["indexes"] + output_csv_name = config.get("output", {}).get("results_csv", "numa_single_query_summary.csv") + force_overwrite_results = config.get("overwrite", False) + # force_rebuild_indexes = config.get("force_rebuild", False) # If we add a global force_rebuild + + main_output_dir = Path(output_dir_str).expanduser().absolute() + main_output_dir.mkdir(parents=True, exist_ok=True) + + logger.info(f"Loading dataset '{dataset_cfg['name']}' for {num_queries} queries.") + base_vectors, query_vectors, gt_vectors = common_utils.load_data( + dataset_cfg["name"], + dataset_cfg.get("path", ""), # Allow path in dataset_cfg + nq_override=num_queries + ) + + # Shared Quake base index build logic (specific to this experiment script) + quake_base_build_params_shared = None + quake_base_index_file = main_output_dir / "Quake_base_index_single_query.bin" # Distinct name + + for idx_c in indexes_config_list: + if idx_c["index"] == "Quake": + temp_params = dict(idx_c.get("build_params", {})) + # These are stripped for the *base shared* build + temp_params.pop("num_workers", None) + temp_params.pop("use_numa", None) + temp_params.pop("parent_num_workers", None) # Not relevant for single level base + quake_base_build_params_shared = temp_params + break # Assume all Quake configs share the same base parameters for nc, metric etc. + + if quake_base_build_params_shared: + # Check if force_rebuild_indexes is globally set, or if file doesn't exist + # For now, let's assume force_rebuild_indexes comes from a potential global config key + # if 'force_rebuild_indexes' not in config: config['force_rebuild_indexes'] = False # Default + needs_shared_build = not quake_base_index_file.exists() # or config['force_rebuild_indexes'] + if needs_shared_build: + logger.info(f"Building shared Quake base index at {quake_base_index_file}...") + # For Quake, build_params_dict for prepare_quake_index needs 'metric' and 'nlist' (nc) + # Ensure these are present in quake_base_build_params_shared + if 'metric' not in quake_base_build_params_shared: + quake_base_build_params_shared['metric'] = dataset_cfg.get('metric', 'l2') + if 'nc' not in quake_base_build_params_shared and 'nlist' not in quake_base_build_params_shared : # common mistake + logger.error("Missing 'nc' (nlist) in Quake base build_params.") + # Fallback or error, for now assume it is there if this block is reached + else: + if 'nc' in quake_base_build_params_shared and 'nlist' not in quake_base_build_params_shared: + quake_base_build_params_shared['nlist'] = quake_base_build_params_shared.pop('nc') + + common_utils.prepare_quake_index( + vecs=base_vectors, + build_params_dict=quake_base_build_params_shared, + index_file_path=quake_base_index_file, + force_rebuild=needs_shared_build, # force if it doesn't exist + load=False + ) + logger.info(f"Shared Quake base index preparation complete at {quake_base_index_file}") else: - search_kw = {"nprobe": s_params.get("nprobe", 100)} - - # Warm-up - for q in queries[:min(warmup_n, len(queries))]: - idx.search(q.unsqueeze(0).float(), k, **search_kw) - - # Prepare accumulators - child_lat_trials = [] - parent_lat_trials = [] - child_buf_init_trials = [] - child_enq_trials = [] - child_wait_trials = [] - child_agg_trials = [] - child_total_trials = [] - parent_buf_init_trials = [] - parent_enq_trials = [] - parent_wait_trials = [] - parent_agg_trials = [] - parent_total_trials = [] - first_trial_recalls = [] - - for t in range(trials): - # per-query lists - child_lats = [] - parent_lats = [] - child_buf_init = [] - child_enq = [] - child_wait = [] - child_agg = [] - child_total = [] - parent_buf_init = [] - parent_enq = [] - parent_wait = [] - parent_agg = [] - parent_total = [] - recalls = [] - - for qi, q in enumerate(queries): - res = idx.search(q.unsqueeze(0).float(), k, **search_kw) - ti = res.timing_info - - # child latency - child_lats.append(ti.total_time_ns / 1e6) - child_buf_init.append(ti.buffer_init_time_ns) - child_enq.append( ti.job_enqueue_time_ns) - child_wait.append( ti.job_wait_time_ns) - child_agg.append( ti.result_aggregate_time_ns) - child_total.append( ti.total_time_ns) - - # parent latency (if exists) - pi = getattr(ti, "parent_info", None) - if pi and hasattr(pi, "total_time_ns"): - parent_lats.append(pi.total_time_ns / 1e6) - parent_buf_init.append(pi.buffer_init_time_ns) - parent_enq.append( pi.job_enqueue_time_ns) - parent_wait.append( pi.job_wait_time_ns) - parent_agg.append( pi.result_aggregate_time_ns) - parent_total.append( pi.total_time_ns) - else: - parent_lats.append(0.0) - parent_buf_init.append(0) - parent_enq.append(0) - parent_wait.append(0) - parent_agg.append(0) - parent_total.append(0) - - # recall on first trial only - if t == 0 and gt is not None: - preds = extract_ids(res)[0] - recalls.append(float(compute_recall(torch.tensor([preds]), gt[qi:qi+1], k).item())) - - # compute trial means - child_lat_trials.append(np.mean(child_lats)) - parent_lat_trials.append(np.mean(parent_lats)) - child_buf_init_trials.append(np.mean(child_buf_init)/1e6) - child_enq_trials .append(np.mean(child_enq) /1e6) - child_wait_trials .append(np.mean(child_wait) /1e6) - child_agg_trials .append(np.mean(child_agg) /1e6) - child_total_trials .append(np.mean(child_total) /1e6) - parent_buf_init_trials.append(np.mean(parent_buf_init)/1e6) - parent_enq_trials .append(np.mean(parent_enq) /1e6) - parent_wait_trials .append(np.mean(parent_wait) /1e6) - parent_agg_trials .append(np.mean(parent_agg) /1e6) - parent_total_trials .append(np.mean(parent_total) /1e6) - - if t == 0 and recalls: - first_trial_recalls.append(np.mean(recalls)) - - print(f" [{name} trial {t+1}/{trials}] " - f"child {child_lat_trials[-1]:.2f} ms, " - f"parent {parent_lat_trials[-1]:.2f} ms" - + (f" | R@{k} {first_trial_recalls[-1]:.4f}" if recalls else "")) - - # assemble final row - row = { - "index": name, - "n_workers": b_params.get("num_workers", np.nan), - "child_mean_latency_ms": float(np.mean(child_lat_trials)), - "child_std_latency_ms": float(np.std(child_lat_trials)), - f"recall_at_{k}": (float(first_trial_recalls[0]) if first_trial_recalls else np.nan), - "child_buffer_init_ms": float(np.mean(child_buf_init_trials)), - "child_enqueue_ms": float(np.mean(child_enq_trials)), - "child_wait_ms": float(np.mean(child_wait_trials)), - "child_aggregate_ms": float(np.mean(child_agg_trials)), - "child_total_time_ms": float(np.mean(child_total_trials)), - "parent_mean_latency_ms":float(np.mean(parent_lat_trials)), - "parent_buffer_init_ms": float(np.mean(parent_buf_init_trials)), - "parent_enqueue_ms": float(np.mean(parent_enq_trials)), - "parent_wait_ms": float(np.mean(parent_wait_trials)), - "parent_aggregate_ms": float(np.mean(parent_agg_trials)), - "parent_total_time_ms": float(np.mean(parent_total_trials)), - } - results.append(row) - pd.DataFrame([row]).to_csv(idx_csv, index=False) - - # write aggregate CSV + plots - df = pd.DataFrame(results) - out_csv = Path(output_dir) / csv_name - df.to_csv(out_csv, index=False) - print(f"\n[RESULT] aggregated CSV → {out_csv}") - - # latency plot (child only for clarity) - plt.figure() - for nm in df["index"].unique(): - sub = df[df["index"] == nm] - lbl = (nm if sub["n_workers"].isnull().all() - else f"{nm} (nw={int(sub['n_workers'].iloc[0])})") - plt.errorbar( - sub["n_workers"] if "n_workers" in sub else sub.index, - sub["child_mean_latency_ms"], - yerr=sub["child_std_latency_ms"], - marker="o", capsize=5, label=lbl + logger.info(f"Using existing shared Quake base index from {quake_base_index_file}") + + + all_experiment_rows = [] + + for idx_conf in indexes_config_list: + index_name = idx_conf["name"] + index_type_str = idx_conf["index"] + current_build_params = dict(idx_conf.get("build_params", {})) # Copy for modification + current_search_params = dict(idx_conf.get("search_params", {})) + + IndexCls = INDEX_CLASSES.get(index_type_str) + if IndexCls is None or (IndexCls == Scann and Scann is None): + logger.warning(f"Index type '{index_type_str}' for '{index_name}' is unavailable. Skipping.") + continue + + per_index_csv_path = main_output_dir / f"{index_name}_detailed_results.csv" + + if per_index_csv_path.exists() and not force_overwrite_results: + logger.info(f"Results for {index_name} exist at {per_index_csv_path}. Loading cached.") + try: + # Assuming one row per config in these detailed CSVs + cached_df = pd.read_csv(per_index_csv_path) + if not cached_df.empty: + all_experiment_rows.append(cached_df.iloc[0].to_dict()) + except Exception as e: + logger.error(f"Could not load cached results for {index_name}: {e}") + continue + + logger.info(f"--- Processing index: {index_name} (Type: {index_type_str}) ---") + + idx_instance = None + if index_type_str == "Quake": + if not quake_base_index_file.exists(): + logger.error(f"Shared Quake base index {quake_base_index_file} not found for {index_name}. Skipping.") + continue + idx_instance = QuakeWrapper() + # Load-time parameters for this specific Quake configuration + load_time_workers = current_build_params.get("num_workers", 0) + load_time_use_numa = current_build_params.get("use_numa", False) + load_time_parent_workers = current_build_params.get("parent_num_workers", 0) # If applicable + logger.info(f"Loading Quake index '{index_name}' from shared base with: " + f"workers={load_time_workers}, numa={load_time_use_numa}, parent_workers={load_time_parent_workers}") + idx_instance.load(str(quake_base_index_file), + num_workers=load_time_workers, + use_numa=load_time_use_numa, + parent_num_workers=load_time_parent_workers) + else: # For non-Quake indexes + generic_idx_file_path = main_output_dir / f"{index_name}_index.bin" + # Assume force_rebuild_indexes applies here too, or build if not exists + # build_if_needed_flag = not generic_idx_file_path.exists() # or config['force_rebuild_indexes'] + # if build_if_needed_flag: + build_and_save_generic_index(IndexCls, current_build_params.copy(), base_vectors, generic_idx_file_path) + + idx_instance = IndexCls() + load_params_non_quake = current_build_params.copy() + if index_type_str == "DiskANN": load_params_non_quake.pop("metric", None) # DiskANN doesn't take metric at load + idx_instance.load(str(generic_idx_file_path), **load_params_non_quake) + + + if idx_instance is None: + logger.error(f"Failed to load or build index {index_name}. Skipping.") + continue + + benchmark_results = benchmark_single_query_detailed( + idx_instance, query_vectors, gt_vectors, k_val, + current_search_params, index_type_str, num_trials, warmup_runs ) - plt.xscale("symlog", base=2) - plt.xlabel("Num Workers"); plt.ylabel("Mean Child Latency (ms)") - plt.title("Single-Query Child Latency"); plt.legend(); plt.tight_layout() - lat_path = Path(output_dir) / f"{out_csv.stem}_child_latency.png" - plt.savefig(lat_path) - print(f"[PLOT] child latency plot → {lat_path}") - - # recall plot - if f"recall_at_{k}" in df.columns: - plt.figure() - for nm in df["index"].unique(): - sub = df[df["index"] == nm] - lbl = (nm if sub["n_workers"].isnull().all() - else f"{nm} (nw={int(sub['n_workers'].iloc[0])})") + + # Combine config with results + final_row_data = {"index_name": index_name, "index_type": index_type_str} + final_row_data.update({f"build_{k}": v for k,v in current_build_params.items()}) + final_row_data.update({f"search_{k}": v for k,v in current_search_params.items()}) + final_row_data.update(benchmark_results) + + all_experiment_rows.append(final_row_data) + common_utils.save_results_csv(pd.DataFrame([final_row_data]), per_index_csv_path) + + if not all_experiment_rows: + logger.warning("No results collected. Skipping final CSV and plots.") + return + + final_summary_df = pd.DataFrame(all_experiment_rows) + unified_csv_path = main_output_dir / output_csv_name + common_utils.save_results_csv(final_summary_df, unified_csv_path) + logger.info(f"Unified results for NUMA single-query written to {unified_csv_path}") + + # Plotting (using the structure from the original script) + plot_suffix = Path(output_csv_name).stem + + # Mean Latency (child_total_time_ms_mean) vs. build_num_workers + if "child_total_time_ms_mean" in final_summary_df.columns and "build_num_workers" in final_summary_df.columns: + plt.figure(figsize=(10, 6)) + for name_plot, group_data in final_summary_df.groupby("index_name"): + sorted_group = group_data.sort_values(by="build_num_workers") + yerr_val = sorted_group["child_total_time_ms_std"] if "child_total_time_ms_std" in sorted_group else None + plt.errorbar( + sorted_group["build_num_workers"], # Treat as categorical for distinct points + sorted_group["child_total_time_ms_mean"], + yerr=yerr_val, + marker="o", capsize=5, label=name_plot, linestyle='-' + ) + # plt.xscale("symlog", base=2) # May not be ideal if num_workers are not powers of 2 or include 0 + plt.xlabel("Build Param: Num Workers (Configuration)") + plt.ylabel("Mean Single-Query Latency (ms) - Child Total") + plt.title(f"NUMA Single-Query: Latency vs. Num Workers ({dataset_cfg['name']})") + plt.legend(title="Index Configuration", bbox_to_anchor=(1.05, 1), loc='upper left') + plt.grid(True, linestyle=':', alpha=0.7) + plt.tight_layout(rect=[0,0,0.8,1]) # Adjust for legend + plt.savefig(main_output_dir / f"{plot_suffix}_latency_vs_workers.png", dpi=150) + logger.info(f"Latency plot saved to {main_output_dir / f'{plot_suffix}_latency_vs_workers.png'}") + plt.close() + + recall_col_name = f"recall_at_{k_val}_mean" + if recall_col_name in final_summary_df.columns and "build_num_workers" in final_summary_df.columns: + plt.figure(figsize=(10, 6)) + for name_plot, group_data in final_summary_df.groupby("index_name"): + sorted_group = group_data.sort_values(by="build_num_workers") plt.plot( - sub["n_workers"] if "n_workers" in sub else sub.index, - sub[f"recall_at_{k}"], marker="o", label=lbl + sorted_group["build_num_workers"], + sorted_group[recall_col_name], + marker="o", label=name_plot, linestyle='-' ) - plt.xscale("symlog", base=2) - plt.xlabel("Num Workers"); plt.ylabel(f"Recall@{k}") - plt.title(f"Recall@{k}"); plt.legend(); plt.tight_layout() - rec_path = Path(output_dir) / f"{out_csv.stem}_recall.png" - plt.savefig(rec_path) - print(f"[PLOT] recall plot → {rec_path}") \ No newline at end of file + plt.xlabel("Build Param: Num Workers (Configuration)") + plt.ylabel(f"Mean Recall@{k_val}") + plt.title(f"NUMA Single-Query: Recall vs. Num Workers ({dataset_cfg['name']})") + plt.legend(title="Index Configuration", bbox_to_anchor=(1.05, 1), loc='upper left') + plt.grid(True, linestyle=':', alpha=0.7) + plt.ylim(0, 1.05) # Typical recall range + plt.tight_layout(rect=[0,0,0.8,1]) + plt.savefig(main_output_dir / f"{plot_suffix}_recall_vs_workers.png", dpi=150) + logger.info(f"Recall plot saved to {main_output_dir / f'{plot_suffix}_recall_vs_workers.png'}") + plt.close() + + logger.info("NUMA Single-Query experiment finished.") \ No newline at end of file From be446efe76ad4fd0382bae203585ca106e5df322 Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 21 May 2025 11:04:42 -0500 Subject: [PATCH 204/323] update numa_single_query --- src/python/datasets/ann_datasets.py | 4 +- test/experiments/osdi2025/README.md | 13 ++ .../configs/msturing10m.yaml | 153 +++++++++++++++++- 3 files changed, 160 insertions(+), 10 deletions(-) diff --git a/src/python/datasets/ann_datasets.py b/src/python/datasets/ann_datasets.py index a2ba8797..a7ba1459 100644 --- a/src/python/datasets/ann_datasets.py +++ b/src/python/datasets/ann_datasets.py @@ -183,7 +183,7 @@ def download(self, overwrite: bool = False): pass def load_vectors(self) -> Union[np.ndarray, torch.Tensor]: - fname = self.data_dir / "base1b.fbin.crop_nb_10000000" + fname = self.data_dir / "base1b.fbin.crop_nb_100000000" n, d = map(int, np.fromfile(fname, dtype="uint32", count=2)) # return to_torch(np.fromfile(fname, dtype=np.float32, offset=8).reshape((n, d))) return torch.from_numpy(np.fromfile(fname, dtype=np.float32, offset=8).reshape((n, d))) @@ -195,7 +195,7 @@ def load_queries(self) -> Union[np.ndarray, torch.Tensor]: return torch.from_numpy(np.fromfile(fname, dtype=np.float32, offset=8).reshape((n, d))) def load_ground_truth(self) -> Union[np.ndarray, torch.Tensor]: - fname = self.data_dir / "msturing-gt-10M" + fname = self.data_dir / "msturing-gt-100M" n, d = map(int, np.fromfile(fname, dtype="uint32", count=2)) assert os.stat(fname).st_size == 8 + n * d * (4 + 4) f = open(fname, "rb") diff --git a/test/experiments/osdi2025/README.md b/test/experiments/osdi2025/README.md index b62a81fe..fdd42c91 100644 --- a/test/experiments/osdi2025/README.md +++ b/test/experiments/osdi2025/README.md @@ -31,6 +31,19 @@ Each experiment directory (e.g., `aps_recall_targets`, `early_termination`) cont - Linux (Tested on Ubuntu 22.04) - Conda package manager +### Install from Scratch + +This will install Quake, the baselines and all necessary dependencies. + +Run `sudo install.sh` + +This will install: +- C + +### Quake Installation Only (No Baselines) + +To just evaluate Quake use this. + 1. **Clone the Repository:** ```bash git clone [https://github.com/marius-team/quake.git](https://github.com/marius-team/quake.git) diff --git a/test/experiments/osdi2025/numa_single_query/configs/msturing10m.yaml b/test/experiments/osdi2025/numa_single_query/configs/msturing10m.yaml index 327e20ba..8c671df9 100644 --- a/test/experiments/osdi2025/numa_single_query/configs/msturing10m.yaml +++ b/test/experiments/osdi2025/numa_single_query/configs/msturing10m.yaml @@ -1,15 +1,152 @@ -# existing keys... dataset: - name: sift1m + name: msturing10m num_queries: 100 - k: 10 - nprobe: 100 + k: 1 -n_workers: [0,1,2,4,8,16] +trials: 2 +warmup: 1 +overwrite: true # Set to true to force all indexes to rerun -# new keys: -trials: 5 -warmup: 10 +indexes: + - name: Quake_1_NUMA + index: Quake + build_params: + nc: 10000 + metric: l2 + num_workers: 1 + use_numa: true + search_params: + nprobe: 2000 + + - name: Quake_2_NUMA + index: Quake + build_params: + nc: 10000 + metric: l2 + num_workers: 2 + use_numa: true + search_params: + nprobe: 2000 + + - name: Quake_4_NUMA + index: Quake + build_params: + nc: 10000 + metric: l2 + num_workers: 4 + use_numa: true + search_params: + nprobe: 2000 + + - name: Quake_8_NUMA + index: Quake + build_params: + nc: 10000 + metric: l2 + num_workers: 8 + use_numa: true + search_params: + nprobe: 2000 + + - name: Quake_16_NUMA + index: Quake + build_params: + nc: 10000 + metric: l2 + num_workers: 16 + use_numa: true + search_params: + nprobe: 2000 + + - name: Quake_32_NUMA + index: Quake + build_params: + nc: 10000 + metric: l2 + num_workers: 32 + use_numa: true + search_params: + nprobe: 2000 + + - name: Quake_64_NUMA + index: Quake + build_params: + nc: 10000 + metric: l2 + num_workers: 63 + use_numa: true + search_params: + nprobe: 2000 + + - name: Quake_1_NONUMA + index: Quake + build_params: + nc: 10000 + metric: l2 + num_workers: 1 + use_numa: false + search_params: + nprobe: 2000 + + - name: Quake_2_NONUMA + index: Quake + build_params: + nc: 10000 + metric: l2 + num_workers: 2 + use_numa: false + search_params: + nprobe: 2000 + + - name: Quake_4_NONUMA + index: Quake + build_params: + nc: 10000 + metric: l2 + num_workers: 4 + use_numa: false + search_params: + nprobe: 2000 + + - name: Quake_8_NONUMA + index: Quake + build_params: + nc: 10000 + metric: l2 + num_workers: 8 + use_numa: false + search_params: + nprobe: 2000 + + - name: Quake_16_NONUMA + index: Quake + build_params: + nc: 10000 + metric: l2 + num_workers: 16 + use_numa: false + search_params: + nprobe: 2000 + + - name: Quake_32_NONUMA + index: Quake + build_params: + nc: 10000 + metric: l2 + num_workers: 32 + use_numa: false + search_params: + nprobe: 2000 + + - name: Quake_64_NONUMA + index: Quake + build_params: + nc: 10000 + metric: l2 + num_workers: 64 + use_numa: false + search_params: + nprobe: 2000 output: results_csv: numa_single_query_results.csv \ No newline at end of file From 66859899ea186001a24f1b4d4c54b42d0b7720f6 Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 21 May 2025 11:08:57 -0500 Subject: [PATCH 205/323] update numa_single_query --- test/experiments/osdi2025/experiment_utils.py | 2 +- test/experiments/osdi2025/numa_single_query/run.py | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/test/experiments/osdi2025/experiment_utils.py b/test/experiments/osdi2025/experiment_utils.py index 14aef786..3dafc5cf 100644 --- a/test/experiments/osdi2025/experiment_utils.py +++ b/test/experiments/osdi2025/experiment_utils.py @@ -20,7 +20,7 @@ def load_config(cfg_path: str) -> dict: return yaml.safe_load(Path(cfg_path).read_text()) -def load_data(dataset_name: str, dataset_path: str, nq_override: int = None): +def load_data(dataset_name: str, dataset_path: str = "data/", nq_override: int = None): vecs, queries, gt = quake_load_dataset(dataset_name, dataset_path) if nq_override is not None and nq_override > 0 and nq_override < len(queries): queries = queries[:nq_override] diff --git a/test/experiments/osdi2025/numa_single_query/run.py b/test/experiments/osdi2025/numa_single_query/run.py index 53b72969..5682f89c 100644 --- a/test/experiments/osdi2025/numa_single_query/run.py +++ b/test/experiments/osdi2025/numa_single_query/run.py @@ -198,7 +198,6 @@ def run_experiment(cfg_path_str: str, output_dir_str: str) -> None: logger.info(f"Loading dataset '{dataset_cfg['name']}' for {num_queries} queries.") base_vectors, query_vectors, gt_vectors = common_utils.load_data( dataset_cfg["name"], - dataset_cfg.get("path", ""), # Allow path in dataset_cfg nq_override=num_queries ) From 6db547b2c99e195cffb76f066cc0caabde06fdd0 Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 21 May 2025 11:26:46 -0500 Subject: [PATCH 206/323] update readme and add paper --- test/experiments/osdi2025/README.md | 65 ++++++++++++++---- .../osdi2025/paper/Quake-Revision.pdf | Bin 0 -> 1081872 bytes test/experiments/osdi2025/paper/Quake.pdf | Bin 0 -> 1096475 bytes 3 files changed, 53 insertions(+), 12 deletions(-) create mode 100644 test/experiments/osdi2025/paper/Quake-Revision.pdf create mode 100644 test/experiments/osdi2025/paper/Quake.pdf diff --git a/test/experiments/osdi2025/README.md b/test/experiments/osdi2025/README.md index fdd42c91..3f717c6e 100644 --- a/test/experiments/osdi2025/README.md +++ b/test/experiments/osdi2025/README.md @@ -37,8 +37,11 @@ This will install Quake, the baselines and all necessary dependencies. Run `sudo install.sh` -This will install: -- C +This will: +- Install required system packages (GCC/G++ 11, CMake, Miniconda, Intel oneAPI, etc.) +- Set up a Conda environment (quake-env) with all Python dependencies +- Build and install the Quake library and baselines (Faiss, SCANN, DiskANN, SVS) +- Automatically clone, build, and install all required repositories ### Quake Installation Only (No Baselines) @@ -102,21 +105,59 @@ OMP_NUM_THREADS=1 python3 -m test.experiments.osdi2025.experiment_runner \ --experiment kick_the_tires \ --config sift1m ``` - Outputs will be in `test/experiments/osdi2025/kick_the_tires/results/sift1m/`. +### Quick Reproduction + +These experiments should take less than 10 minutes to run in total. + +1. Reproduction of APS (Figure 6 in `paper/Quake.pdf`) + + This compares an Oracle against APS at various recall targets. + + ```bash + OMP_NUM_THREADS=1 python3 -m test.experiments.osdi2025.experiment_runner \ + --experiment aps_recall_targets \ + --config sift1m + ``` + +2. Reproduction of NUMA-aware searching (Figure 5 in `paper/Quake.pdf`) + + This measures query latency and varies the number of workers used in searching and whether partitions are pinned to NUMA regions. + + ```bash + OMP_NUM_THREADS=1 python3 -m test.experiments.osdi2025.experiment_runner \ + --experiment numa_single_query \ + --config msturing10m + ``` + +3. Maintenance Ablation Study (Table 6 in `paper/Quake-Revision.pdf`) + + This removes critical pieces of the Quake maintenance policy and measure its effect on workload performance. + + ```bash + OMP_NUM_THREADS=1 python3 -m test.experiments.osdi2025.experiment_runner \ + --experiment maintenance_ablation \ + --config sift1m + ``` + + +### Full Reproduction (In progress) + ## Experiment Summaries and Outputs -The following table summarizes each experiment and its primary outputs. Output files are generated within the experiment's specific output directory (refer to the `--output-dir` option or its default value). +The following table summarizes each experiment and their figure/table in paper. Output files are generated within the experiment's specific output directory (refer to the `--output-dir` option or its default value). + +Some experiments are added as revisions for the camera ready. See `paper/Quake.pdf` for the initial submission and `paper/Quake-Revision.pdf` for the revised version -| Experiment Name | Description | Reproduces in Paper | -|------------------------|--------------------------------------------------------------------------------------------------------------|-------------------------------| -| `aps_recall_targets` | Evaluates Quake's Adaptive Partition Scanning (APS) against baselines for various recall targets. | Figure 6 | -| `early_termination` | Compares APS with early termination strategies like LAET and Auncel. | Revision Experiment (Table 5) | -| `kick_the_tires` | Basic dynamic workload stress test focusing on latency, recall, and system state over operations. | | -| `maintenance_ablation` | Evaluates the impact of Quake's maintenance operations under a dynamic workload, extending `kick_the_tires`. | Revision Experiment (Table 6) | -| `numa_multi_query` | Tests Quake's multi-query batch performance under different NUMA and worker configurations. | | -| `numa_single_query` | Tests Quake's single-query performance under different NUMA and worker configurations. | Figure 5 | +| Experiment Name | Description | Reproduces in Paper | +|------------------------|----------------------------------------------------------------------------------------------------|---------------------------| +| `aps_recall_targets` | Evaluates Quake's Adaptive Partition Scanning (APS) against baselines for various recall targets. | Figure 6 in Quake | +| `early_termination` | Compares APS with early termination strategies like LAET and Auncel. | Table 5 in Quake-Revision | +| `kick_the_tires` | Basic dynamic workload stress test focusing on latency, recall, and system state over operations. | | +| `maintenance_ablation` | Evaluates the impact of Quake's maintenance operations under a dynamic workload. | Table 6 in Quake-Revision | +| `numa_multi_query` | Tests Quake's multi-query batch performance under different NUMA and worker configurations. | Revision Experiment | +| `numa_single_query` | Tests Quake's single-query performance under different NUMA and worker configurations. | Figure 5 in Quake | **To run any of these, replace `` and `` in the general command structure.** For example, to run the `aps_recall_targets` experiment with its `sift1m` configuration: diff --git a/test/experiments/osdi2025/paper/Quake-Revision.pdf b/test/experiments/osdi2025/paper/Quake-Revision.pdf new file mode 100644 index 0000000000000000000000000000000000000000..2adb688397192b00c1492a3a4afcb711aa370b5b GIT binary patch literal 1081872 zcmeFYWl*F|uqKMb;4XtT?t{}XxVyW%ySuv#4l^*gySuwPgS)%CU*>$@**&{=vmhn#m_8>}y%<0fz{JSL0bm9)1GEA3vH(_g4gkF{fP;aVffYb617Q68 zRQmh^kez{p0l>opYiwimH!R@)pV#L`*#65#l-%u%0rYAL#wMTJFt#yuG6Qe`0rVo~ zR!+tapQDw&ld+Jop{>#9tbay1eFtktz+VsqZLOTGZ5#og8^Zf~exQ&sq+o$6%hD?ks|F}$S0ZeR6pG#2y&@0&5I(kT=*5Kqnp{957DHyB5i2_r3!^?8hoJ$xA?qiyKxS4$BYi_w4jwLM zLjz`GCJr_OAUg{yyFN1;hk+4;2|J?^3nPOOkVT(I`|lYzIyo5YTf@4g85$VrZJO#C 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z>N}d*Sc~dA0*NR^xtST6S(!eV8JL+^*jTwV{^y9lj{dH%0#qY%F>^E}GX01(dwZbq z$Fd9%uJ3R|M+lN;Q_BKXNhJRh;zm5NojgKW|Cj)b!q2qt~t77J8`SGO&*98#+fJl>wiHPN6)BbSL z%nkSi5xJCHT}$KTz@ zcQ-}jxc4`w%}-x7-96_u+zHQb_QlvET-v9bi)dR4zt2%C?@H_TnWrg@b&A+BlE^nj1QkXXMN|GnQ0enDGkX~_0x z8f6g>fH%c;-c>&$(K$ry?ShVr!HoMOXwVv(AeSpwDt|bwBDD&<-Izj}@3C=BT6S_q zPWq^M36f#41NEH_I79_J!~|}dxit`YlQ6kZ1%EesCkKKwCsH_k+eO|n@4na>l`#Xh zcW9?ZuCXlpy#ZW}tVU|qAg^yY6mW@nERieR-K6<6mKo)nz0Icfdz&ENS6bH{X>?zD zNi;ls%Le>s5|pCQllkT$j>iqnZW_gdIutneC|J|uB_U}7dsO1 zEMMAvPcOhN1LwLZ7{&0EJyYA!r!3GD%YvTOfYF8IQ9mC77i9TVT>7F_cz7E4M>>k{ z=vZ52A#x< zvY*CppzvC3fZyTuvuoc;FT2lz5sG`Y6rNX|wtx2pT&|OAh_E8z>f5QP9&gL4;hK|G zE*N??At8zLu{q6epz@NHt$lRMkK6OMs`&rd1Zc*soV|f3iUI#@@%+hYHSEEpfky5K zfsc<45g$Nn&4PVXwg=InwH}4qX{*Ln4xjA0!~v5!M9d!Vxr`p)nRx`%-(bJIKfSzs z4pPa({eN>C(Lb+$5cmgye-QWwfqxMA2Z4VO_y>W15cmgy|4$Ksb8ytRcXa(IF2FG} NvBQy(iOGw@{U5Vq)s_GN literal 0 HcmV?d00001 From 4a7c1d4a384c30eb2deed56619bc377027dcdc13 Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 21 May 2025 11:28:52 -0500 Subject: [PATCH 207/323] update readme and add paper --- test/experiments/osdi2025/README.md | 2 ++ test/experiments/osdi2025/install.sh | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/test/experiments/osdi2025/README.md b/test/experiments/osdi2025/README.md index 3f717c6e..3436608b 100644 --- a/test/experiments/osdi2025/README.md +++ b/test/experiments/osdi2025/README.md @@ -51,7 +51,9 @@ To just evaluate Quake use this. ```bash git clone [https://github.com/marius-team/quake.git](https://github.com/marius-team/quake.git) cd quake + git checkout osdi2025 git submodule update --init --recursive + ``` 2. **Set Up the Conda Environment:** diff --git a/test/experiments/osdi2025/install.sh b/test/experiments/osdi2025/install.sh index 9cb705c1..b114186b 100644 --- a/test/experiments/osdi2025/install.sh +++ b/test/experiments/osdi2025/install.sh @@ -199,7 +199,7 @@ else git clone https://github.com/marius-team/quake.git "${QUAKE_FULL_PATH}" cd "${QUAKE_FULL_PATH}" fi -git checkout osdi2025_debug +git checkout osdi2025 git config --global --add safe.directory "${QUAKE_FULL_PATH}" # For root's global config SUBMODULE_PATHS=("src/cpp/third_party/concurrentqueue" "src/cpp/third_party/faiss" "src/cpp/third_party/pybind11") From e44c75db61a9451ce2a44abe4f75f6ce58156a80 Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 21 May 2025 11:33:07 -0500 Subject: [PATCH 208/323] update readme --- test/experiments/osdi2025/README.md | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/test/experiments/osdi2025/README.md b/test/experiments/osdi2025/README.md index 3436608b..709d909a 100644 --- a/test/experiments/osdi2025/README.md +++ b/test/experiments/osdi2025/README.md @@ -10,19 +10,21 @@ The experiments are organized as follows: ```plaintext -./test/experiments/osdi2025/ -├── common/ \# Shared utilities for experiments -│ └── experiment\_utils.py -├── experiment\_runner.py \# Main script to run any experiment +quake/test/experiments/osdi2025/ +├── experiment_runner.py \# Main script to run any experiment +├── experiment_utils.py \# Shared functionality across experiments ├── README.md \# This file -├── \/ \# Directory for each specific experiment +├── paper/ \# PDF of the paper +│ ├── Quake.pdf \# Original Submission +│ ├── Quake-Revision.pdf \# Revised Version +├── / \# Directory for each specific experiment │ ├── configs/ \# YAML configuration files for the experiment -│ │ └── \.yaml +│ │ └── .yaml │ └── run.py \# Python script to execute the experiment logic └── ... \# Other experiment directories ``` -Each experiment directory (e.g., `aps_recall_targets`, `early_termination`) contains its specific logic in `run.py` and configurations in its `configs/` subdirectory. The `common/experiment_utils.py` module provides shared functionality for loading data, configurations, preparing indexes, and plotting, aiming to simplify individual experiment scripts. +Each experiment directory (e.g., `aps_recall_targets`, `early_termination`) contains its specific logic in `run.py` and configurations in its `configs/` subdirectory. The `experiment_utils.py` module provides shared functionality for loading data, configurations, preparing indexes, and plotting, aiming to simplify individual experiment scripts. ## Installation From 31f68ddf15d57648d46902786d4ef5dde81fe655 Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 21 May 2025 11:36:13 -0500 Subject: [PATCH 209/323] update readme --- test/experiments/osdi2025/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/experiments/osdi2025/README.md b/test/experiments/osdi2025/README.md index 709d909a..fdd3c697 100644 --- a/test/experiments/osdi2025/README.md +++ b/test/experiments/osdi2025/README.md @@ -113,7 +113,7 @@ OMP_NUM_THREADS=1 python3 -m test.experiments.osdi2025.experiment_runner \ ### Quick Reproduction -These experiments should take less than 10 minutes to run in total. +These experiments should take less than 10 minutes to run in total if the datasets/indexes have been prepared ahead of time. Otherwise it will take 30-60 minutes on the first run. 1. Reproduction of APS (Figure 6 in `paper/Quake.pdf`) From 488640578661d4b064c35c39a1d172491de0c1a6 Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 21 May 2025 12:32:53 -0500 Subject: [PATCH 210/323] dont overwrite workload --- .../osdi2025/maintenance_ablation/configs/sift1m.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/experiments/osdi2025/maintenance_ablation/configs/sift1m.yaml b/test/experiments/osdi2025/maintenance_ablation/configs/sift1m.yaml index 9e6365a4..a932cbb8 100644 --- a/test/experiments/osdi2025/maintenance_ablation/configs/sift1m.yaml +++ b/test/experiments/osdi2025/maintenance_ablation/configs/sift1m.yaml @@ -101,6 +101,6 @@ plot: LIRE: {color: "#ffbb78", linestyle: "-", marker: "P"} overwrite: - workload: true + workload: false index: true results: true \ No newline at end of file From 9313075a8d1112bbd25b383f0c1cf4034353a2ce Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 21 May 2025 13:08:57 -0500 Subject: [PATCH 211/323] dont overwrite index if load=false --- test/experiments/osdi2025/experiment_utils.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/test/experiments/osdi2025/experiment_utils.py b/test/experiments/osdi2025/experiment_utils.py index 3dafc5cf..1d397168 100644 --- a/test/experiments/osdi2025/experiment_utils.py +++ b/test/experiments/osdi2025/experiment_utils.py @@ -39,9 +39,10 @@ def prepare_quake_index( load: bool = True ) -> QuakeIndex: idx = QuakeIndex() - if index_file_path.exists() and not force_rebuild and load: - logger.info(f"Loading index from {index_file_path}") - idx.load(str(index_file_path), num_workers_load, use_numa, num_parent_workers_load) + if index_file_path.exists() and not force_rebuild: + if load: + logger.info(f"Loading index from {index_file_path}") + idx.load(str(index_file_path), num_workers_load, use_numa, num_parent_workers_load) else: logger.info(f"Building index -> {index_file_path}") bp = IndexBuildParams() From 419953fffea88648e4d017e41b0f1bcdf812dd0a Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 21 May 2025 13:10:58 -0500 Subject: [PATCH 212/323] update readme --- test/experiments/osdi2025/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/experiments/osdi2025/README.md b/test/experiments/osdi2025/README.md index fdd3c697..40cec97b 100644 --- a/test/experiments/osdi2025/README.md +++ b/test/experiments/osdi2025/README.md @@ -51,7 +51,7 @@ To just evaluate Quake use this. 1. **Clone the Repository:** ```bash - git clone [https://github.com/marius-team/quake.git](https://github.com/marius-team/quake.git) + git clone https://github.com/marius-team/quake.git cd quake git checkout osdi2025 git submodule update --init --recursive From 8d247a3bb67a081e30246ba12946e8f68470de35 Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 21 May 2025 13:45:32 -0500 Subject: [PATCH 213/323] update readme --- test/experiments/osdi2025/README.md | 6 +++--- .../osdi2025/maintenance_ablation/configs/sift1m.yaml | 4 ---- 2 files changed, 3 insertions(+), 7 deletions(-) diff --git a/test/experiments/osdi2025/README.md b/test/experiments/osdi2025/README.md index 40cec97b..a3759705 100644 --- a/test/experiments/osdi2025/README.md +++ b/test/experiments/osdi2025/README.md @@ -111,9 +111,9 @@ OMP_NUM_THREADS=1 python3 -m test.experiments.osdi2025.experiment_runner \ ``` Outputs will be in `test/experiments/osdi2025/kick_the_tires/results/sift1m/`. -### Quick Reproduction +### Reproduction of Main Quake Components -These experiments should take less than 10 minutes to run in total if the datasets/indexes have been prepared ahead of time. Otherwise it will take 30-60 minutes on the first run. +These experiments should take less than hour (mostly in maintenance_ablation) to run in total if the datasets/indexes have been prepared ahead of time. Otherwise it will take 2 hours on the first run. 1. Reproduction of APS (Figure 6 in `paper/Quake.pdf`) @@ -135,7 +135,7 @@ These experiments should take less than 10 minutes to run in total if the datase --config msturing10m ``` -3. Maintenance Ablation Study (Table 6 in `paper/Quake-Revision.pdf`) +3. Maintenance Ablation Study (Table 6 in `paper/Quake-Revision.pdf`) This removes critical pieces of the Quake maintenance policy and measure its effect on workload performance. diff --git a/test/experiments/osdi2025/maintenance_ablation/configs/sift1m.yaml b/test/experiments/osdi2025/maintenance_ablation/configs/sift1m.yaml index a932cbb8..8b4895eb 100644 --- a/test/experiments/osdi2025/maintenance_ablation/configs/sift1m.yaml +++ b/test/experiments/osdi2025/maintenance_ablation/configs/sift1m.yaml @@ -93,10 +93,6 @@ plot: Quake-NoRefineNoReject: {color: "#8c564b", linestyle: "-", marker: "s"} Quake-NoCost: {color: "#d627bb", linestyle: "-", marker: "D"} Quake-NoCostNoRefine: {color: "#ff7f0e", linestyle: "-", marker: "+"} - Quake-NoRejectSplit: {color: "#7f7f7f", linestyle: "-", marker: "H"} - Quake-NoRejectDelete: {color: "#bcbd22", linestyle: "-", marker: "h"} - Quake-NoRejectNoDelete: {color: "#ffbb78", linestyle: "-", marker: "P"} - Quake-NoRejectNoSplit: {color: "#ffbb78", linestyle: "-", marker: "P"} LIRE: {color: "#ffbb78", linestyle: "-", marker: "P"} From d03f74b315abb4927923bc1519b77e2798530d68 Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 21 May 2025 17:20:11 -0500 Subject: [PATCH 214/323] update multi-level experiment --- test/experiments/osdi2025/experiment_utils.py | 65 +- .../osdi2025/multi_level/configs/sift10m.yaml | 189 ++---- .../osdi2025/multi_level/configs/sift1m.yaml | 61 ++ test/experiments/osdi2025/multi_level/run.py | 603 +++++++++++------- 4 files changed, 561 insertions(+), 357 deletions(-) create mode 100644 test/experiments/osdi2025/multi_level/configs/sift1m.yaml diff --git a/test/experiments/osdi2025/experiment_utils.py b/test/experiments/osdi2025/experiment_utils.py index 1d397168..9f935028 100644 --- a/test/experiments/osdi2025/experiment_utils.py +++ b/test/experiments/osdi2025/experiment_utils.py @@ -3,7 +3,10 @@ import yaml import logging from pathlib import Path +import contextlib +import sys from typing import Dict, Any, List +import os import torch import pandas as pd @@ -14,7 +17,6 @@ from quake.datasets.ann_datasets import load_dataset as quake_load_dataset from quake.utils import compute_recall as quake_compute_recall from quake.workload_generator import DynamicWorkloadGenerator, WorkloadEvaluator - logger = logging.getLogger(__name__) def load_config(cfg_path: str) -> dict: @@ -47,8 +49,8 @@ def prepare_quake_index( logger.info(f"Building index -> {index_file_path}") bp = IndexBuildParams() for key, value in build_params_dict.items(): - if hasattr(bp, key): - setattr(bp, key, value) + setattr(bp, key, value) + idx.build(vecs, torch.arange(len(vecs)), bp) index_file_path.parent.mkdir(parents=True, exist_ok=True) idx.save(str(index_file_path)) @@ -237,4 +239,59 @@ def expand_search_sweep(sweep_config: Dict[str, Any]) -> List[Dict[str, Any]]: expanded_list.append({**base_params, "recall_target": rt_val}) else: # No sweep, just use base_params expanded_list.append(base_params) - return expanded_list \ No newline at end of file + return expanded_list + +def create_index_build_params(**attrs) -> IndexBuildParams: + """Creates a Quake IndexBuildParams object from dictionary attributes.""" + bp = IndexBuildParams() + if 'nc' in attrs and 'nlist' not in attrs: # Allow 'nc' as an alias for 'nlist' + attrs['nlist'] = attrs.pop('nc') + + for k, v in attrs.items(): + if hasattr(bp, k): + setattr(bp, k, v) + else: + logger.warning(f"Attribute '{k}' not found in IndexBuildParams. Ignoring.") + return bp + +def save_results_df(records: list, output_path: Path): + df = pd.DataFrame(records) + output_path.parent.mkdir(parents=True, exist_ok=True) + df.to_csv(output_path, index=False) + logger.info(f"Results saved to {output_path}") # Already logs + +@contextlib.contextmanager +def redirect_all_stdout_to_file(filepath: Path | str, mode: str = 'a'): + original_python_stdout = sys.stdout + original_c_stdout_fd = -1 + saved_c_stdout_fd = -1 + target_file = None + + try: + original_c_stdout_fd = sys.stdout.fileno() + saved_c_stdout_fd = os.dup(original_c_stdout_fd) + + target_file = open(filepath, mode) + target_file_fd = target_file.fileno() + + os.dup2(target_file_fd, original_c_stdout_fd) + sys.stdout = target_file + + yield + + except Exception: + # util_logger.error(f"Exception during stdout redirection: {e}", exc_info=False) # Removed for brevity + raise # Re-raise the exception + finally: + if sys.stdout == target_file : + sys.stdout.flush() + + if original_c_stdout_fd != -1 and saved_c_stdout_fd != -1: + os.dup2(saved_c_stdout_fd, original_c_stdout_fd) + + sys.stdout = original_python_stdout + + if target_file: + target_file.close() + if saved_c_stdout_fd != -1: + os.close(saved_c_stdout_fd) \ No newline at end of file diff --git a/test/experiments/osdi2025/multi_level/configs/sift10m.yaml b/test/experiments/osdi2025/multi_level/configs/sift10m.yaml index df7ef1f1..384ffd13 100644 --- a/test/experiments/osdi2025/multi_level/configs/sift10m.yaml +++ b/test/experiments/osdi2025/multi_level/configs/sift10m.yaml @@ -1,136 +1,75 @@ -# ─── top-level controls ─────────────────────────────────────────────────────── -mode: run # build | run | plot +# General settings overwrite: - index: false # force rebuild of every index - results: true # force rerun of every search + index_files: false # If true, rebuilds all L0 index files + results_csv: true # If true, re-runs searches and overwrites result CSVs -# ─── dataset ────────────────────────────────────────────────────────────────── dataset: - name: sift10m # built-in quake dataset + name: sift10m metric: l2 - path: data # ignored for packaged datasets - nq: 100 # number of queries to use + nq: 100 -# ─── indexes / workloads ────────────────────────────────────────────────────── -indexes: - - name: Quake5000 - index: Quake +# --- L0 (Base Level) Definitions --- +l0_definitions: + "L0_40k_config": # Unique reference name for this L0 build configuration + type: Quake build_params: - nc: 5000 - num_workers: 8 - search_params: - k: 1 -# recall_targets: [0.50, 0.60, 0.70, 0.80, 0.85, 0.90, 0.95, 0.99] - nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] -# initial_search_fraction: 0.01 -# batched_scan: true - batch_size: 1 - - - name: Quake10000 - index: Quake + nlist: 40000 + num_workers: 4 + "L0_10k_config": + type: Quake build_params: - nc: 10000 - num_workers: 8 - search_params: - k: 1 -# recall_targets: [0.50, 0.60, 0.70, 0.80, 0.85, 0.90, 0.95, 0.99] - nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] -# initial_search_fraction: 0.01 -# batched_scan: true - batch_size: 1 + nlist: 10000 + num_workers: 4 - - name: Quake20000 - index: Quake - build_params: - nc: 20000 - num_workers: 8 - search_params: - k: 1 -# recall_targets: [0.50, 0.60, 0.70, 0.80, 0.85, 0.90, 0.95, 0.99] - nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] -# initial_search_fraction: 0.01 -# batched_scan: true - batch_size: 1 +# --- Global L0 Search Sweep --- +global_l0_search_sweep: + nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256] # List of nprobe values for L0 + # recall_targets: [0.5, 0.6, 0.7, 0.8, 0.85, 0.9, 0.95, 0.99] # Alternative L0 sweep + # initial_search_fraction: 0.01 # Required if using recall_targets + k: 1 # k for L0 search (and overall recall calculation) + batch_size: 1 # Query batch_size for L0 search - - name: Quake40000 - index: Quake - build_params: - nc: 40000 - num_workers: 8 - search_params: - k: 1 -# recall_targets: [0.50, 0.60, 0.70, 0.80, 0.85, 0.90, 0.95, 0.99] - nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] -# initial_search_fraction: 0.01 -# batched_scan: true - batch_size: 1 +# --- Index Hierarchy Definitions --- +index_hierarchies: + - name: "Flat_L0_40k" # Unique name for this specific hierarchy/curve + l0_ref: "L0_40k_config" # Referenlistes an L0 from 'l0_definitions' + # parent_levels: (empty or omitted) -> This is a flat index using the referenlisted L0. - - name: Quake80000 - index: Quake - build_params: - nc: 80000 - num_workers: 8 - search_params: - k: 1 -# recall_targets: [0.50, 0.60, 0.70, 0.80, 0.85, 0.90, 0.95, 0.99] - nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] -# initial_search_fraction: 0.01 -# batched_scan: true - batch_size: 1 -# - - name: Quake160000 - index: Quake - build_params: - nc: 160000 - num_workers: 8 - search_params: - k: 1 -# recall_targets: [0.50, 0.60, 0.70, 0.80, 0.85, 0.90, 0.95, 0.99] - nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] - initial_search_fraction: 0.005 -# batched_scan: true - batch_size: 1 -# -# - name: Quake20000_500 # two-level Quake index -# index: Quake -# build_params: -# nc: 20000 -# num_workers: 0 -# parent: -# nc: 500 -# num_workers: 0 -# search_params: -# k: 1 -# nprobes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512] -## initial_search_fraction: 0.05 -# batch_size: 1 -# parent: -# nprobe: 50 -# # child-internal search; leave single high target here -## recall_target: 0.999 -## initial_search_fraction: 0.2 + - name: "Flat_L0_10k" # Unique name for this specific hierarchy/curve + l0_ref: "L0_10k_config" # Referenlistes an L0 from 'l0_definitions' + # parent_levels: (empty or omitted) -> This is a flat index using the referenlisted L0. + + + - name: "Hier_L0_40k_L1_1k_P50" # Name implies: L0 is 40k, L1 is 1k centroids, L1 nprobe is 50 + l0_ref: "L0_40k_config" + parent_levels: # List of parent levels (L1, L2, ...) built on top of L0 + - type: Quake # L1 configuration + build_params: {nlist: 1000, num_workers: 8} + search_params: {nprobe: 50} # Fixed search parameters for this L1 scan + + - name: "Hier_L0_10k_L1_0.5k_P20" + l0_ref: "L0_10k_config" + parent_levels: + - type: Quake # L1 + build_params: {nlist: 500, num_workers: 8} + search_params: {nprobe: 20} + + - name: "Custom_Flat_L0_5k" # Defines its L0 ad-hoc (not from l0_definitions) + custom_l0: # L0 definition specific to this hierarchy + type: Quake + build_params: {nlist: 5000, num_workers: 8} + # parent_levels: (empty or omitted) -> This is a custom flat L0. + +# --- General Run Settings --- +run_settings: + trials: 3 + warmup: 1 + default_batch_size: 1 + omp_threads_build: "16" # Or "MAX" as a placeholder for max available + omp_threads_search: "1" -# ─── plotting ──────────────────────────────────────────────────────────────── -plot: - styles: # one entry per *name* above - Quake5000: - color: "#1f77b4" - marker: "o" - Quake10000: - color: "#ff7f0e" - marker: "o" - Quake20000: - color: "#2ca02c" - marker: "o" - Quake40000: - color: "#9467bd" - marker: "o" - Quake80000: - color: "#8c564b" - marker: "o" - Quake160000: - color: "#e377c2" - marker: "o" - Quake20000_500: - color: "#d62728" - marker: "o" \ No newline at end of file +plot_styles: + "Flat_L0_40k": {color: "#1f77b4", marker: "o"} + "Hier_L0_40k_L1_1k_P50": {color: "#ff7f0e", marker: "s"} + "Hier_L0_10k_L1_0.5k_P20": {color: "#2ca02c", marker: "D"} + "Custom_Flat_L0_5k": {color: "#d62728", marker: "^"} \ No newline at end of file diff --git a/test/experiments/osdi2025/multi_level/configs/sift1m.yaml b/test/experiments/osdi2025/multi_level/configs/sift1m.yaml new file mode 100644 index 00000000..2cdda0aa --- /dev/null +++ b/test/experiments/osdi2025/multi_level/configs/sift1m.yaml @@ -0,0 +1,61 @@ +# General settings +overwrite: + index_files: false # Set to true to force rebuild L0s on first run of this config + results_csv: true # Re-runs searches and overwrites this config's output CSVs + +dataset: + name: sift1m # Using sift1m for a quicker test + metric: l2 + nq: 100 # Reduced number of queries for faster testing + +# --- L0 (Base Level) Definitions --- +l0_definitions: + "L0_1k_config": # L0 with 1000 centroids, suitable for sift1m + type: Quake + build_params: + nlist: 1000 + num_workers: 0 # Adjust based on your test machine + "L0_0.5k_config": # A smaller L0 + type: Quake + build_params: + nlist: 500 + num_workers: 0 + +# --- Global L0 Search Sweep --- +# Applies to the L0 of ALL index_hierarchies +global_l0_search_sweep: + nprobes: [1, 5, 10, 20] # Reduced list of nprobes for L0 + k: 10 + batch_size: 1 + +# --- Index Hierarchy Definitions --- +index_hierarchies: +# - name: "SingleLevel_L0_1k" # A single-level index using the 1k L0 +# l0_ref: "L0_1k_config" +# # No parent_levels + + - name: "Hier_L0_1k_L1_30_P5" # L0=1k, L1 groups L0's centroids, L1 nprobe=5 + l0_ref: "L0_1k_config" + parent_levels: + - type: Quake # L1 configuration + build_params: {nlist: 30, num_workers: 0} # L1 nc groups centroids of L0 + search_params: {nprobe: 5} # Fixed search params for L1 scan + + - name: "Custom_SingleLevel_L0_0.5k" # Ad-hoc custom L0, used as single-level + custom_l0: + type: Quake + build_params: {nlist: 500, num_workers: 0} # nc should be custom_l0.build_params.nc + +# --- General Run Settings --- +run_settings: + trials: 1 # Reduced trials for speed + warmup: 0 # No warmup for a quick test + default_batch_size: 1 + omp_threads_build: "16" # Adjust as needed + omp_threads_search: "1" + +# --- Plotting Styles --- +plot_styles: + "SingleLevel_L0_1k": {color: "#1f77b4", marker: "o"} + "Hier_L0_1k_L1_30_P5": {color: "#ff7f0e", marker: "s"} + "Custom_SingleLevel_L0_0.5k": {color: "#2ca02c", marker: "D"} \ No newline at end of file diff --git a/test/experiments/osdi2025/multi_level/run.py b/test/experiments/osdi2025/multi_level/run.py index a3abbbed..2788889b 100644 --- a/test/experiments/osdi2025/multi_level/run.py +++ b/test/experiments/osdi2025/multi_level/run.py @@ -1,252 +1,399 @@ +#!/usr/bin/env python3 from __future__ import annotations - -import argparse import logging import time from pathlib import Path -from typing import Dict, List, Tuple - +from typing import Dict, List, Any, Tuple +import copy import matplotlib.pyplot as plt import numpy as np import pandas as pd import torch +import os +import sys import yaml +from quake import QuakeIndex, IndexBuildParams, SearchParams, SearchTimingInfo +from test.experiments.osdi2025 import experiment_utils as common_utils +from quake.utils import compute_recall -# ── quake imports ────────────────────────────────────────────────────────────── -from quake.datasets.ann_datasets import load_dataset -from quake.index_wrappers.faiss_ivf import FaissIVF -from quake.index_wrappers.quake import QuakeWrapper -from quake.utils import compute_recall # recall@k - -logger = logging.getLogger("experiment") +logger = logging.getLogger("multi_level_debug_exp") logging.basicConfig( - format="%(asctime)s | %(levelname)s | %(message)s", - datefmt="%H:%M:%S", level=logging.INFO, + format="%(asctime)s | %(levelname)-8s | %(name)-25s | %(message)s", + datefmt="%H:%M:%S", + handlers=[logging.StreamHandler(sys.stdout)] ) -# ─────────────────────────────────────────────────────────────────────────────── -# helpers -# ─────────────────────────────────────────────────────────────────────────────── -def _prepare_index( - idx_cls, - vecs: torch.Tensor, - metric: str, - build_params: Dict, - index_path: Path, - rebuild: bool, -) -> Tuple[object, float]: - """ - Build–or–load an index and return (index, build_time_seconds). - build_time_seconds == 0 when loaded. - """ - idx = idx_cls() - if index_path.exists() and not rebuild: - t0 = time.time() - idx.load(str(index_path), num_workers=build_params["num_workers"]) - return idx, 0.0 - - logger.info("Building index → %s …", index_path) - t0 = time.time() - idx.build(vecs, metric=metric, **build_params) - t_build = time.time() - t0 - idx.save(str(index_path)) - logger.info("Index saved (%.1fs).", t_build) - return idx, t_build - - -def _dist_ids(res): - """Handle `(D, I)` tuples OR Quake SearchResult objects.""" - if isinstance(res, tuple) and len(res) == 2: - return res - if hasattr(res, "distances") and hasattr(res, "indices"): - return res.distances, res.indices - raise TypeError(f"Unknown search result type: {type(res).__name__}") - - -def _benchmark_index( - idx, +def build_or_verify_l0( + l0_key_name: str, + l0_build_config: Dict[str, Any], + base_vectors: torch.Tensor, + dataset_metric: str, + output_dir: Path, + force_rebuild: bool, + quake_verbose_log_path: Path # Added for redirection +) -> Path: + index_file_path = output_dir / f"l0_index_{l0_key_name}.bin" + l0_type = l0_build_config.get("type", "Quake") + if l0_type != "Quake": + raise NotImplementedError(f"L0 index type '{l0_type}' not yet supported.") + + build_params_dict = copy.deepcopy(l0_build_config.get("build_params", {})) + if 'metric' not in build_params_dict: + build_params_dict['metric'] = dataset_metric + + if not index_file_path.exists() or force_rebuild: + logger.info(f"Building L0 '{l0_key_name}' (nlist={build_params_dict.get('nlist')}) -> {index_file_path}") + with common_utils.redirect_all_stdout_to_file(quake_verbose_log_path, mode='a'): + _ = common_utils.prepare_quake_index( + vecs=base_vectors, + build_params_dict=build_params_dict, + index_file_path=index_file_path, + force_rebuild=True, + load=False + ) + else: + logger.info(f"Using existing L0 '{l0_key_name}' from {index_file_path}") + return index_file_path + +def construct_full_search_params( + l0_variant_params: Dict[str, Any], + parent_levels_config: List[Dict[str, Any]] +) -> SearchParams: + current_sp = common_utils.create_search_params(**l0_variant_params) + previous_level_sp = current_sp + for parent_cfg in parent_levels_config: + parent_search_dict = parent_cfg.get("search_params", {"nprobe": 1}) + parent_level_sp = common_utils.create_search_params(**parent_search_dict) + previous_level_sp.parent_params = parent_level_sp + previous_level_sp = parent_level_sp + return current_sp + +def extract_recursive_timing_stats( + timing_info: SearchTimingInfo | None, max_levels: int +) -> Dict[str, Any]: + stats = {} + current_ti = timing_info + for i in range(max_levels): + level_prefix = f"L{i}" + if current_ti: + stats[f"{level_prefix}_total_scan_ms"] = current_ti.total_time_ns / 1e6 + stats[f"{level_prefix}_partitions_scanned"] = getattr(current_ti, "partitions_scanned", np.nan) + current_ti = current_ti.parent_info + else: + stats[f"{level_prefix}_total_scan_ms"] = np.nan + stats[f"{level_prefix}_partitions_scanned"] = np.nan + return stats + +def benchmark_hierarchy_search_point( + index_instance: QuakeIndex, queries: torch.Tensor, - ground: np.ndarray, - search_params: Dict, + ground_truth: np.ndarray | None, + full_hierarchical_sp: SearchParams, + num_actual_levels: int, trials: int, warmup: int, -) -> Dict: - bs = search_params.pop("batch_size", None) - k = search_params["k"] - - # warm-up - for _ in range(warmup): - if bs is None: - idx.search(queries, **search_params) - else: - for i in range(0, len(queries), bs): - idx.search(queries[i : i + bs], **search_params) - - qps_vals: List[float] = [] - lat_ms: List[float] = [] + quake_verbose_log_path: Path # Added for redirection +) -> Dict[str, Any]: + trial_latencies_ns = [] + trial_recalls = [] + trial_per_level_stats_list = [] + k_val_for_recall = full_hierarchical_sp.k + + with common_utils.redirect_all_stdout_to_file(quake_verbose_log_path, mode='a'): + for _ in range(warmup): + index_instance.search(queries, full_hierarchical_sp) + for _ in range(trials): + t_start_ns = time.perf_counter_ns() + search_result = index_instance.search(queries, full_hierarchical_sp) + t_elapsed_ns = time.perf_counter_ns() - t_start_ns + trial_latencies_ns.append(t_elapsed_ns) + + if ground_truth is not None and search_result.ids is not None: + max_k_recall = min(k_val_for_recall, ground_truth.shape[1], search_result.ids.shape[1]) + recall_val = compute_recall( + search_result.ids[:, :max_k_recall], + ground_truth[:, :max_k_recall],max_k_recall + ).mean().item() if max_k_recall > 0 else np.nan + trial_recalls.append(recall_val) + else: + trial_recalls.append(np.nan) + trial_per_level_stats_list.append( + extract_recursive_timing_stats(search_result.timing_info, num_actual_levels) + ) - for _ in range(trials): - t0 = time.time() - if bs is None: - res = idx.search(queries, **search_params) - D, I = res.distances, res.ids - else: - all_I, all_D = [], [] - for i in range(0, len(queries), bs): - res = idx.search(queries[i : i + bs], **search_params) - D_, I_ = res.distances, res.ids - all_I.append(I_); all_D.append(D_) - I = np.concatenate(all_I); D = np.concatenate(all_D) - dt = time.time() - t0 - qps_vals.append(len(queries) / dt) - lat_ms.append(dt * 1e3) - - recall = compute_recall(I, ground[:, :k], k) - recall = 0.0 if recall is None else recall.mean().item() - - return dict( - recall = float(recall), - qps_mean = float(np.mean(qps_vals)), - qps_std = float(np.std(qps_vals)), - latency_mean_ms = float(np.mean(lat_ms)), - trials = trials, - warmup = warmup, - batch_size = bs or len(queries), + mean_latency_ms = np.mean(trial_latencies_ns) / 1e6 if trial_latencies_ns else np.nan + qps_mean = len(queries) / (mean_latency_ms / 1000) if mean_latency_ms > 0 else np.inf + results = { + "qps_mean": qps_mean, + "recall_mean": np.nanmean(trial_recalls) if trial_recalls else np.nan, + "overall_latency_ms_mean": mean_latency_ms, + } + if trial_per_level_stats_list: + df_level_stats = pd.DataFrame(trial_per_level_stats_list) + for col in df_level_stats.columns: + results[f"{col}_mean"] = df_level_stats[col].mean() + return results + +def run_experiment(config_path_str: str, output_dir_str: str): + config = common_utils.load_config(config_path_str) + output_dir = Path(output_dir_str) + output_dir.mkdir(parents=True, exist_ok=True) + + quake_verbose_log = output_dir / "quake_internal_details.log" + if quake_verbose_log.exists(): quake_verbose_log.unlink() # Clear log at start of run + + logger.info(f"Loaded configuration from: {config_path_str}") + dataset_cfg = config["dataset"] + overwrite_cfg = config["overwrite"] + l0_definitions = config.get("l0_definitions", {}) + global_l0_sweep_cfg = config["global_l0_search_sweep"] + index_hierarchies_cfg = config["index_hierarchies"] + run_settings = config["run_settings"] + original_omp_threads = os.environ.get("OMP_NUM_THREADS") + plot_styles = config.get("plot_styles", {}) + + logger.info(f"Dataset: {dataset_cfg['name']}, Metric: {dataset_cfg['metric']}, NQ: {dataset_cfg['nq']}") + logger.info(f"Overwrite index_files: {overwrite_cfg['index_files']}, Overwrite results_csv: {overwrite_cfg['results_csv']}") + logger.info(f"Verbose Quake output will be logged to: {quake_verbose_log}") + + + base_vectors, queries, gt_numpy = common_utils.load_data( + dataset_cfg["name"], dataset_cfg.get("path", "data/"), nq_override=dataset_cfg.get("nq") ) + logger.info(f"Loaded data: Base vectors {base_vectors.shape}, Queries {queries.shape}, GT {gt_numpy.shape if gt_numpy is not None else 'None'}") + + built_l0_files = {} + all_experiment_records = [] + + omp_build_threads = str(run_settings.get("omp_threads_build", os.cpu_count())) + os.environ["OMP_NUM_THREADS"] = omp_build_threads + logger.info(f"BUILD PHASE: Set OMP_NUM_THREADS={os.environ['OMP_NUM_THREADS']}") + unique_l0s_to_process = {} + for l0_ref_name, l0_conf in l0_definitions.items(): + unique_l0s_to_process[l0_ref_name] = l0_conf + for hierarchy_cfg in index_hierarchies_cfg: + if "custom_l0" in hierarchy_cfg: + unique_l0s_to_process[f"custom_for_{hierarchy_cfg['name']}"] = hierarchy_cfg["custom_l0"] + logger.info(f"Found {len(unique_l0s_to_process)} unique L0 configurations.") + for l0_key, l0_build_conf in unique_l0s_to_process.items(): + built_l0_files[l0_key] = build_or_verify_l0( + l0_key, l0_build_conf, base_vectors, dataset_cfg["metric"], + output_dir, overwrite_cfg["index_files"], quake_verbose_log # Pass log path + ) - -def _expand_targets(sp: Dict) -> List[Dict]: - """ - Produce one search-parameter dict per recall_target value. - - If `recall_targets` is absent (or a single float) we fall back to a list - containing exactly one dict, preserving the original behaviour. - """ - if "recall_targets" in sp: - tgs = sp.pop("recall_targets") - if not isinstance(tgs, (list, tuple)): - tgs = [tgs] - return [{**sp, "recall_target": t} for t in tgs] - elif "nprobes" in sp: - tgs = sp.pop("nprobes") - if not isinstance(tgs, (list, tuple)): - tgs = [tgs] - return [{**sp, "nprobe": t} for t in tgs] - else: - return [sp] - - -def _plot_unified(cfg: Dict, root: Path) -> None: - styles = cfg["plot"]["styles"] - fig, ax = plt.subplots(figsize=(6, 4)) - - for icfg in cfg["indexes"]: - name = icfg["name"] - csv = root / name / "results.csv" - if not csv.exists(): - logger.warning("No results for %s – skipped in plot.", name) + l0_search_variants = common_utils.expand_search_sweep(global_l0_sweep_cfg) + global_k_val = global_l0_sweep_cfg["k"] + global_batch_size = global_l0_sweep_cfg.get("batch_size", run_settings.get("default_batch_size", 1)) + for variant in l0_search_variants: + if "k" not in variant: variant["k"] = global_k_val + if "batch_size" not in variant: variant["batch_size"] = global_batch_size + logger.info(f"Global L0 search sweep: {len(l0_search_variants)} variants. k={global_k_val}, batch_size={global_batch_size}.") + + omp_search_threads = str(run_settings.get("omp_threads_search", "1")) + os.environ["OMP_NUM_THREADS"] = omp_search_threads + logger.info(f"SEARCH PHASE: Set OMP_NUM_THREADS={os.environ['OMP_NUM_THREADS']}") + + for hierarchy_cfg in index_hierarchies_cfg: + hierarchy_name = hierarchy_cfg["name"] + logger.info(f"===== Processing Hierarchy: {hierarchy_name} =====") + l0_file_to_load, l0_build_details_for_reporting, l0_load_params_dict = None, {}, {} + + if "l0_ref" in hierarchy_cfg: + ref_name = hierarchy_cfg["l0_ref"] + l0_file_to_load = built_l0_files.get(ref_name) + l0_build_details_for_reporting = l0_definitions.get(ref_name, {}).get("build_params", {}) + elif "custom_l0" in hierarchy_cfg: + custom_l0_key = f"custom_for_{hierarchy_name}" + l0_file_to_load = built_l0_files.get(custom_l0_key) + l0_build_details_for_reporting = hierarchy_cfg["custom_l0"].get("build_params", {}) + if not l0_file_to_load: + logger.error(f"L0 file path not found for {hierarchy_name}. Skipping.") continue - df = pd.read_csv(csv).sort_values("recall") - sty = styles.get(name, {}) - ax.plot( - df["qps_mean"], df["recall"], - label = name, - marker = sty.get("marker", "o"), - linestyle = sty.get("linestyle", "-"), - linewidth = 1.4, - markersize= 6, - color = sty.get("color", None), - ) - - # ax.set_xscale("log", base=10) - ax.set_xlabel("Queries per second (log)") - ax.set_ylabel("Recall@k") - ax.set_title("Recall curves over varied recall_target") - ax.grid(True, which="both", ls=":") - ax.legend() - fig.tight_layout() - out_png = root / "recall_vs_qps.png" - fig.savefig(out_png, dpi=180) - logger.info("Plot written to %s", out_png) - - -# ─────────────────────────────────────────────────────────────────────────────── -# main -# ─────────────────────────────────────────────────────────────────────────────── -def run_experiment(cfg_path: str, output_dir: str) -> None: - cfg = yaml.safe_load(Path(cfg_path).read_text()) - out = Path(output_dir); out.mkdir(parents=True, exist_ok=True) - - mode = cfg.get("mode", "run") # build | run | plot - overwrite = cfg.get("overwrite", {}) - rebuild_index = bool(overwrite.get("index", False)) - rewrite_csv = bool(overwrite.get("results", False)) - - # dataset ------------------------------------------------------------------ - ds = cfg["dataset"] - logger.info("Loading dataset %s …", ds["name"]) - vecs, queries, ground = load_dataset(ds["name"], ds.get("path", "")) - metric = ds["metric"] - nq = ds["nq"] - ground = ground[:nq] - queries = queries[:nq] - - idx_map = {"Quake": QuakeWrapper, "IVF": FaissIVF} - - # build / run -------------------------------------------------------------- - if mode in {"build", "run"}: - for icfg in cfg["indexes"]: - name = icfg["name"] - res_dir = out / name; res_dir.mkdir(exist_ok=True, parents=True) - idx_bin = res_dir / "index.bin" - csv_file = res_dir / "results.csv" - idx_cls = idx_map[icfg["index"]] - - # BUILD once ======================================================= - idx, build_t = _prepare_index( - idx_cls, vecs, metric, icfg["build_params"], idx_bin, rebuild_index + l0_load_params_dict = { + "num_workers": l0_build_details_for_reporting.get("num_workers",0), + "use_numa": l0_build_details_for_reporting.get("use_numa",False), + "parent_num_workers": 0 # For L0 load itself + } + + num_actual_levels = 1 + len(hierarchy_cfg.get("parent_levels", [])) + + current_index = QuakeIndex() + with common_utils.redirect_all_stdout_to_file(quake_verbose_log, mode='a'): + current_index.load(str(l0_file_to_load), l0_load_params_dict["num_workers"], l0_load_params_dict["use_numa"], l0_load_params_dict["parent_num_workers"]) + + parent_levels_config_for_sp = [] + for i, parent_cfg in enumerate(hierarchy_cfg.get("parent_levels", [])): + parent_bp = common_utils.create_index_build_params(**parent_cfg["build_params"]) + if not parent_bp.metric: parent_bp.metric = dataset_cfg["metric"] + with common_utils.redirect_all_stdout_to_file(quake_verbose_log, mode='a'): + current_index.add_level(parent_bp) + parent_levels_config_for_sp.append(parent_cfg) + + for l0_variant in l0_search_variants: + l0_nprobe_val = l0_variant.get("nprobe") + l0_rt_val = l0_variant.get("recall_target") + sweep_param_log = f"nprobe={l0_nprobe_val}" if l0_nprobe_val is not None else f"RT={l0_rt_val}" + logger.info(f" Testing L0 Sweep: {sweep_param_log} for '{hierarchy_name}'") + + full_sp = construct_full_search_params(l0_variant, parent_levels_config_for_sp) + metrics = benchmark_hierarchy_search_point( + current_index, queries, gt_numpy, full_sp, + num_actual_levels, run_settings["trials"], run_settings["warmup"], + quake_verbose_log ) - if mode == "build": - continue # build-only mode stops here - - # RUN for every recall_target ====================================== - if csv_file.exists() and not rewrite_csv: - existing_df = pd.read_csv(csv_file) - else: - existing_df = pd.DataFrame() - - for sp in _expand_targets(icfg["search_params"].copy()): - tgt = sp.get("recall_target", None) - if tgt is None: - tgt = sp.get("nprobe") - # if ( - # not rewrite_csv - # and "recall_target" in existing_df.columns - # and ((existing_df.recall_target - tgt).abs() < 1e-9).any() - # ): - # logger.info("[%s] target %.3g cached – skipping run.", name, tgt) - # continue - - stats = _benchmark_index( - idx = idx, - queries = queries, - ground = ground, - search_params = sp, - trials = cfg.get("trials", 5), - warmup = cfg.get("warmup", 1), - ) - stats.update(build_time_s = build_t, recall_target = tgt) - existing_df = pd.concat( - [existing_df, pd.DataFrame([stats])], ignore_index=True - ) - logger.info("[%s] target %.3g done.", name, tgt) - - existing_df.to_csv(csv_file, index=False) - logger.info("[%s] results written to %s", name, csv_file) - - # plot --------------------------------------------------------------------- - if mode in {"run", "plot"}: - _plot_unified(cfg, out) + record = { + "hierarchy_name": hierarchy_name, + "L0_nlist": l0_build_details_for_reporting.get("nlist"), + "L0_nprobe": l0_nprobe_val, "L0_recall_target": l0_rt_val, + "L0_k": l0_variant["k"], "L0_batch_size": l0_variant["batch_size"], + } + for i, p_cfg in enumerate(hierarchy_cfg.get("parent_levels", [])): + level_num = i + 1 + record[f"L{level_num}_nlist"] = p_cfg.get("build_params", {}).get("nlist") + record[f"L{level_num}_search_nprobe"] = p_cfg.get("search_params", {}).get("nprobe") + record.update(metrics) + all_experiment_records.append(record) + logger.info(f" Done: Recall={metrics.get('recall_mean',0):.4f}, QPS={metrics.get('qps_mean',0):.1f}, Latency={metrics.get('overall_latency_ms_mean',0):.2f}ms") + del current_index + + if original_omp_threads is not None: os.environ["OMP_NUM_THREADS"] = original_omp_threads + elif "OMP_NUM_THREADS" in os.environ: del os.environ["OMP_NUM_THREADS"] + logger.info(f"OMP_NUM_THREADS settings reverted.") + + if not all_experiment_records: + logger.warning("No results collected.") + return + + final_df = pd.DataFrame(all_experiment_records) + unified_csv_path = output_dir / f"{dataset_cfg['name']}_summary.csv" # Simplified name + common_utils.save_results_df(final_df, unified_csv_path) + + plot_qps_vs_recall(final_df, plot_styles, dataset_cfg, output_dir) + plot_level_scan_time_breakdown(final_df, plot_styles, dataset_cfg, global_l0_sweep_cfg, output_dir) + + logger.info(f"===== Experiment Finished: {config_path_str} =====") + logger.info(f"Output directory: {output_dir.resolve()}") + + logger.info("\n--- Per-Hierarchy Per-L0-Parameter Summary ---") + for name, group in final_df.groupby('hierarchy_name'): + logger.info(f"\nResults for Hierarchy: {name}") + sweep_param = "L0_nprobe" if "L0_nprobe" in group.columns and group["L0_nprobe"].notna().any() else "L0_recall_target" + summary_cols = [sweep_param, 'recall_mean', 'overall_latency_ms_mean', 'qps_mean'] + # Filter out columns that might be all NaN if a sweep param wasn't used + summary_cols = [col for col in summary_cols if col in group.columns and group[col].notna().any()] + if not summary_cols or sweep_param not in summary_cols : # if sweep_param is all NaN + logger.info(group[['recall_mean', 'overall_latency_ms_mean', 'qps_mean']].to_string(float_format="%.4f")) + else: + logger.info(group[summary_cols].sort_values(by=sweep_param).to_string(float_format="%.4f")) + logger.info("-----------------------------------------------\n") + + +def plot_qps_vs_recall(results_df: pd.DataFrame, plot_styles: Dict[str, Any], dataset_cfg: dict, output_dir: Path): + if results_df.empty: return + plt.figure(figsize=(10, 6)) + for hierarchy_name, group in results_df.groupby("hierarchy_name"): + style = plot_styles.get(hierarchy_name, {}) + sorted_group = group.sort_values(by="recall_mean").dropna(subset=["qps_mean", "recall_mean"]) + if sorted_group.empty: continue + plt.plot( + sorted_group["recall_mean"], sorted_group["qps_mean"], label=hierarchy_name, + marker=style.get("marker", "o"), color=style.get("color"), linestyle=style.get("linestyle", "-") + ) + plt.xlabel(f"Mean Recall@{results_df['L0_k'].iloc[0] if 'L0_k' in results_df.columns and results_df['L0_k'].notna().any() else 'k'}") + plt.ylabel("Mean QPS") + plt.title(f"{dataset_cfg['name']}: QPS vs. Recall") + plt.legend(title="Hierarchy Config", bbox_to_anchor=(1.05, 1), loc='upper left') + plt.grid(True, which="both", ls=":", alpha=0.7) + plt.xlim(left=max(0.0, results_df["recall_mean"].min() - 0.05 if results_df["recall_mean"].notna().any() else 0.0), right=1.05) + if results_df["qps_mean"].notna().any() and results_df["qps_mean"].max() > 10 * results_df["qps_mean"].min() and results_df["qps_mean"].min() > 0: + plt.yscale("log") + plt.tight_layout(rect=[0, 0, 0.82, 1]) + plt.savefig(output_dir / f"{dataset_cfg['name']}_qps_vs_recall.png", dpi=150) + plt.close() + + +def find_best_op_point_for_recall_plot( + hierarchy_data: pd.DataFrame, + target_recall: float, + l0_sweep_key: str +): + # Find points meeting or exceeding target recall + valid_points = hierarchy_data[hierarchy_data['recall_mean'] >= target_recall] + + if valid_points.empty: + # If none meet, pick the one with highest recall + if not hierarchy_data.empty: + best_effort_point = hierarchy_data.loc[hierarchy_data['recall_mean'].idxmax()] + logger.warning( + f"For breakdown plot of '{best_effort_point['hierarchy_name']}', no L0 param met target recall {target_recall:.3f}. " + f"Using best achieved: R={best_effort_point['recall_mean']:.3f} " + f"(L0 {l0_sweep_key.split('_')[-1]}={best_effort_point[l0_sweep_key]})" + ) + return best_effort_point + return None + + # Among valid points, pick one with highest QPS, then lowest latency + best_point = valid_points.sort_values(by=['qps_mean', 'overall_latency_ms_mean'], ascending=[False, True]).iloc[0] + return best_point + +def plot_level_scan_time_breakdown( + results_df: pd.DataFrame, + plot_styles: Dict[str, Any], + dataset_cfg: dict, + l0_sweep_cfg: Dict[str, Any], + output_dir: Path, + target_recall_for_plot: float = 0.9 # New parameter +): + if results_df.empty: + logger.warning("Level scan time breakdown plot: No data.") + return + + l0_sweep_key = "L0_nprobe" if "nprobes" in l0_sweep_cfg else "L0_recall_target" + + points_for_plot = [] + for hierarchy_name, group in results_df.groupby("hierarchy_name"): + op_point = find_best_op_point_for_recall_plot(group, target_recall_for_plot, l0_sweep_key) + if op_point is not None: + points_for_plot.append(op_point) + + if not points_for_plot: + logger.warning(f"No suitable data points found for any hierarchy at target recall ~{target_recall_for_plot} for breakdown plot.") + return + + df_subset = pd.DataFrame(points_for_plot) + plot_title_suffix = f"(at or above R≈{target_recall_for_plot:.2f})" + + max_level = max([-1] + [int(c.split('_')[0][1:]) for c in df_subset.columns if c.startswith("L") and "_total_scan_ms_mean" in c]) + if max_level == -1: + logger.warning("No per-level scan time columns found for breakdown plot.") + return + + level_cols = [f"L{i}_total_scan_ms_mean" for i in range(max_level + 1)] + for col in level_cols: + if col not in df_subset.columns: df_subset[col] = 0.0 + + plot_data = df_subset.set_index("hierarchy_name")[level_cols].fillna(0) + plot_data.columns = [f"L{i} Scan (ms)" for i in range(max_level + 1)] + + ax = plot_data.plot(kind='bar', stacked=True, figsize=(12, 7), colormap='viridis_r') + + # Add text for achieved recall on top of each bar + for i, hierarchy_name in enumerate(df_subset["hierarchy_name"]): + row_data = df_subset.iloc[i] + total_height = plot_data.loc[hierarchy_name].sum() + actual_recall = row_data.get('recall_mean', np.nan) + if pd.notna(actual_recall): + ax.text(i, total_height * 1.01, f"R={actual_recall:.3f}", ha='center', va='bottom', fontsize=8, color='dimgrey') + + plt.xlabel("Hierarchy Configuration") + plt.ylabel("Mean Scan Time per Level (ms)") + plt.title(f"{dataset_cfg['name']}: Scan Time Breakdown {plot_title_suffix}") + plt.xticks(rotation=25, ha='right') + plt.legend(title="Level", bbox_to_anchor=(1.05, 1), loc='upper left') + plt.tight_layout(rect=[0, 0, 0.82, 1]) + plt.savefig(output_dir / f"{dataset_cfg['name']}_level_scan_breakdown_at_recall_target.png", dpi=150) + plt.close() + logger.info(f"Level scan time breakdown (at R~{target_recall_for_plot:.2f}) plot saved.") \ No newline at end of file From 1c8ef7833958ab9c9bbd2ac57e507a1ed68f1a6f Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 22 May 2025 12:34:48 -0500 Subject: [PATCH 215/323] fix path --- test/experiments/osdi2025/numa_multi_query/run.py | 1 - 1 file changed, 1 deletion(-) diff --git a/test/experiments/osdi2025/numa_multi_query/run.py b/test/experiments/osdi2025/numa_multi_query/run.py index c447c066..d607dd3b 100644 --- a/test/experiments/osdi2025/numa_multi_query/run.py +++ b/test/experiments/osdi2025/numa_multi_query/run.py @@ -78,7 +78,6 @@ def run_experiment(cfg_path_str: str, output_dir_str: str): # Use common_utils.load_data, pass num_queries_to_use for slicing base_vectors, query_vectors, gt_vectors = common_utils.load_data( dataset_cfg["name"], - dataset_path="", # Assuming load_data handles default path or it's in dataset_cfg nq_override=num_queries_to_use ) From 70ba50dbaa823349f8146f66ebe10dbe967a8ddc Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 22 May 2025 14:36:55 -0500 Subject: [PATCH 216/323] fix index wrapper build --- test/experiments/osdi2025/experiment_utils.py | 45 +++++++++++++++++++ .../numa_multi_query/configs/sift1m.yaml | 2 +- .../osdi2025/numa_multi_query/run.py | 9 ++-- 3 files changed, 49 insertions(+), 7 deletions(-) diff --git a/test/experiments/osdi2025/experiment_utils.py b/test/experiments/osdi2025/experiment_utils.py index 9f935028..a4eff0b3 100644 --- a/test/experiments/osdi2025/experiment_utils.py +++ b/test/experiments/osdi2025/experiment_utils.py @@ -57,6 +57,51 @@ def prepare_quake_index( logger.info(f"Index saved to {index_file_path}") return idx + +def prepare_wrapper_index( + IndexClass: type, + index_file_path: Path, + base_vectors: torch.Tensor, + build_params: Dict[str, Any], + force_rebuild: bool = False, + load: bool = True +) -> object: # Returns an instance of the IndexClass + + idx_instance = IndexClass() + index_file_path.parent.mkdir(parents=True, exist_ok=True) + build_params_copy = build_params.copy() + + if index_file_path.exists() and not force_rebuild: + logger.info(f"Index file {index_file_path} exists and force_rebuild is False.") + if load: + logger.info(f"Loading index from {index_file_path}") + load_kwargs = {} + if "num_workers" in build_params_copy: + load_kwargs["num_workers"] = build_params_copy.get("num_workers") + if "use_numa" in build_params_copy: + load_kwargs["use_numa"] = build_params_copy.get("use_numa") + if "parent_num_workers" in build_params_copy: + load_kwargs["parent_num_workers"] = build_params_copy.get("parent_num_workers") + + idx_instance.load(str(index_file_path), **load_kwargs) + logger.info(f"Loaded index from {index_file_path} with load_kwargs: {load_kwargs}") + else: + logger.info(f"Index file {index_file_path} exists, but load=False. Returning new, non-loaded instance.") + else: + if force_rebuild and index_file_path.exists(): + logger.info(f"Force rebuilding index at {index_file_path}") + else: + logger.info(f"Building index at {index_file_path} (file did not exist).") + + idx_instance.build(base_vectors, **build_params_copy) + idx_instance.save(str(index_file_path)) + logger.info(f"Built and saved index to {index_file_path}") + + if load: + logger.info(f"Index built. Instance is considered ready as per build_params (load=True).") + + return idx_instance + def create_search_params(**attrs) -> SearchParams: sp = SearchParams() for k, v in attrs.items(): diff --git a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml index 60c0c558..f79e5ab8 100644 --- a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml +++ b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml @@ -6,7 +6,7 @@ dataset: trials: 3 warmup: 1 -overwrite: false # Set to true to force all indexes to rerun +overwrite: true # Set to true to force all indexes to rerun force_rebuild: false # Set to true to force all indexes to rebuild indexes: diff --git a/test/experiments/osdi2025/numa_multi_query/run.py b/test/experiments/osdi2025/numa_multi_query/run.py index d607dd3b..52cdca8a 100644 --- a/test/experiments/osdi2025/numa_multi_query/run.py +++ b/test/experiments/osdi2025/numa_multi_query/run.py @@ -95,15 +95,12 @@ def run_experiment(cfg_path_str: str, output_dir_str: str): if quake_base_build_params: if not shared_quake_index_file.exists() or force_rebuild: # Respect overwrite for base index too logger.info(f"Building shared Quake base index at {shared_quake_index_file}...") - # Using common_utils.prepare_index for this specialized build. - # It requires IndexClass, build_params, base_vectors, path, force_rebuild. - common_utils.prepare_index( + common_utils.prepare_wrapper_index( IndexClass=QuakeWrapper, - index_build_params=quake_base_build_params, - base_vectors=base_vectors, index_file_path=shared_quake_index_file, + build_params=quake_base_build_params, + base_vectors=base_vectors, force_rebuild=force_rebuild, # Force rebuild if top-level overwrite is true - save_after_build=True ) logger.info(f"Shared Quake base index saved at {shared_quake_index_file}") else: From e27121bf14c65261fb2854d0debf1cae5ad1c3f7 Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 22 May 2025 14:39:06 -0500 Subject: [PATCH 217/323] fix index wrapper build --- .../numa_multi_query/configs/sift1m.yaml | 59 ++++++++++++++++--- 1 file changed, 52 insertions(+), 7 deletions(-) diff --git a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml index f79e5ab8..2b46bac1 100644 --- a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml +++ b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml @@ -55,19 +55,40 @@ indexes: nprobe: 20 batched_scan: true - - name: Quake_0_numa + - name: Quake_1_numa index: Quake build_params: nc: 1000 metric: l2 - num_workers: 0 + num_workers: 1 use_numa: true search_params: nprobe: 20 - batched_scan: false - n_threads: 16 + batched_scan: true - - name: Quake_1_numa + - name: Quake_4_numa + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 4 + use_numa: true + search_params: + nprobe: 20 + batched_scan: true + + - name: Quake_16_numa + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 16 + use_numa: true + search_params: + nprobe: 20 + batched_scan: true + + - name: Quake_1_numa_no_batch index: Quake build_params: nc: 1000 @@ -78,7 +99,18 @@ indexes: nprobe: 20 batched_scan: false - - name: Quake_4_numa + - name: Quake_2_numa_no_batch + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 2 + use_numa: true + search_params: + nprobe: 20 + batched_scan: false + + - name: Quake_4_numa_no_batch index: Quake build_params: nc: 1000 @@ -89,7 +121,19 @@ indexes: nprobe: 20 batched_scan: false - - name: Quake_16_numa + - name: Quake_8_numa_no_batch + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 8 + use_numa: true + search_params: + nprobe: 20 + batched_scan: false + + + - name: Quake_16_numa_no_batch index: Quake build_params: nc: 1000 @@ -100,6 +144,7 @@ indexes: nprobe: 20 batched_scan: false + # - name: FaissIVF # index: IVF # build_params: From 27fa3082696418ee0ab92a9f84b79e728bf928a4 Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 22 May 2025 14:40:09 -0500 Subject: [PATCH 218/323] fix index wrapper build --- test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml index 2b46bac1..ecc1bb8d 100644 --- a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml +++ b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml @@ -1,7 +1,7 @@ # numa_latency_experiment.yaml dataset: name: sift1m - num_queries: 1000 + num_queries: 10000 k: 100 trials: 3 From b696548bc6deef9ff53b86e856304a9629362574 Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 22 May 2025 15:39:27 -0500 Subject: [PATCH 219/323] update multi_query --- test/experiments/osdi2025/experiment_utils.py | 229 ++++++++- .../numa_multi_query/configs/sift1m.yaml | 271 +++++----- .../osdi2025/numa_multi_query/run.py | 486 +++++++++--------- 3 files changed, 589 insertions(+), 397 deletions(-) diff --git a/test/experiments/osdi2025/experiment_utils.py b/test/experiments/osdi2025/experiment_utils.py index a4eff0b3..59822c44 100644 --- a/test/experiments/osdi2025/experiment_utils.py +++ b/test/experiments/osdi2025/experiment_utils.py @@ -5,8 +5,15 @@ from pathlib import Path import contextlib import sys -from typing import Dict, Any, List +from typing import Dict, Any, List, Callable, Union, Optional, Tuple import os +import multiprocessing +import subprocess +import threading +import json +import traceback +import signal +from datetime import datetime, timezone # For Glances metrics collection import torch import pandas as pd @@ -80,8 +87,6 @@ def prepare_wrapper_index( load_kwargs["num_workers"] = build_params_copy.get("num_workers") if "use_numa" in build_params_copy: load_kwargs["use_numa"] = build_params_copy.get("use_numa") - if "parent_num_workers" in build_params_copy: - load_kwargs["parent_num_workers"] = build_params_copy.get("parent_num_workers") idx_instance.load(str(index_file_path), **load_kwargs) logger.info(f"Loaded index from {index_file_path} with load_kwargs: {load_kwargs}") @@ -306,37 +311,221 @@ def save_results_df(records: list, output_path: Path): logger.info(f"Results saved to {output_path}") # Already logs @contextlib.contextmanager -def redirect_all_stdout_to_file(filepath: Path | str, mode: str = 'a'): +def redirect_all_stdout_to_file(filepath: Union[Path, str], mode: str = 'a'): original_python_stdout = sys.stdout original_c_stdout_fd = -1 saved_c_stdout_fd = -1 target_file = None + can_redirect_c_stdout = hasattr(sys.stdout, 'fileno') try: - original_c_stdout_fd = sys.stdout.fileno() - saved_c_stdout_fd = os.dup(original_c_stdout_fd) - + if can_redirect_c_stdout: + original_c_stdout_fd = sys.stdout.fileno() + saved_c_stdout_fd = os.dup(original_c_stdout_fd) target_file = open(filepath, mode) - target_file_fd = target_file.fileno() - - os.dup2(target_file_fd, original_c_stdout_fd) + if can_redirect_c_stdout: + os.dup2(target_file.fileno(), original_c_stdout_fd) sys.stdout = target_file - yield - except Exception: - # util_logger.error(f"Exception during stdout redirection: {e}", exc_info=False) # Removed for brevity - raise # Re-raise the exception + raise finally: - if sys.stdout == target_file : + if sys.stdout == target_file and target_file: sys.stdout.flush() - - if original_c_stdout_fd != -1 and saved_c_stdout_fd != -1: + if can_redirect_c_stdout and original_c_stdout_fd != -1 and saved_c_stdout_fd != -1: os.dup2(saved_c_stdout_fd, original_c_stdout_fd) - sys.stdout = original_python_stdout - if target_file: target_file.close() if saved_c_stdout_fd != -1: - os.close(saved_c_stdout_fd) \ No newline at end of file + os.close(saved_c_stdout_fd) + +_GLANCES_REFRESH_SEC = 1 +_GLANCES_MB = 1_048_576.0 + +def start_glances_minimal(tmp_json_path: Path) -> subprocess.Popen: + cmd = [ + "glances", "-q", f"-t{_GLANCES_REFRESH_SEC}", "--export", "json", + "--export-json-file", str(tmp_json_path), "--disable-plugin", "all", + "--enable-plugin", "cpu,mem,diskio,gpu", + # Consider adding process monitoring if useful: "--enable-plugin", "processcount,processlist" + # and then filter by PID if possible with glances export, though might be complex. + # For now, system-wide A,B,C,D metrics. + ] + # print(f"Starting Glances: {' '.join(cmd)}") # Goes to main process stdout if called from there + # If called from child, it'll go to child's log via redirection. + # Use preexec_fn=os.setsid to create a new process group for Glances + glances_process = subprocess.Popen(cmd, stdout=subprocess.DEVNULL, stderr=subprocess.PIPE, text=True, preexec_fn=os.setsid) + time.sleep(2) # Allow Glances to start and write initial JSON + if glances_process.poll() is not None: # Check if Glances started successfully + stderr_output = glances_process.stderr.read() if glances_process.stderr else "No stderr." + raise RuntimeError(f"Glances failed to start. Exit code: {glances_process.returncode}. Stderr: {stderr_output}") + return glances_process + +def stop_glances_minimal(glances_process: Optional[subprocess.Popen]): + if glances_process and glances_process.poll() is None: + try: + os.killpg(os.getpgid(glances_process.pid), signal.SIGTERM) # Send SIGTERM to the entire process group + glances_process.wait(timeout=3) + except ProcessLookupError: pass # Process already dead + except subprocess.TimeoutExpired: + try: + os.killpg(os.getpgid(glances_process.pid), signal.SIGKILL) # Force kill + glances_process.wait(timeout=1) + except Exception: pass # Best effort + except Exception: pass # Other errors during stop + +def read_glances_json_minimal(tmp_json_path: Path) -> List[dict]: + if not tmp_json_path.exists(): return [] + text = tmp_json_path.read_text(encoding="utf-8").strip() + if not text: return [] + try: # Glances typically writes a list of JSON objects, or one object per file if snapshotting fast + data = json.loads(text) + return data if isinstance(data, list) else [data] # Ensure it's a list + except json.JSONDecodeError: # Handle cases where file might not be complete JSON list + records = [] + for line in text.splitlines(): # Try parsing line by line if not a single JSON doc + try: records.append(json.loads(line)) + except json.JSONDecodeError: pass # Ignore malformed lines + return records + +def extract_metrics_minimal(glances_record: Dict[str, Any]) -> Dict[str, Any]: + # Simplified extraction for A, B, C, D + cpu = glances_record.get("cpu", {}) + mem = glances_record.get("mem", {}) + diskio_list = glances_record.get("diskio", []) + gpu_list = glances_record.get("gpu", []) + + read_bytes = sum(d.get("read_bytes", 0) for d in diskio_list if isinstance(d, dict)) + write_bytes = sum(d.get("write_bytes", 0) for d in diskio_list if isinstance(d, dict)) + + # Assuming first GPU, if any. User script used 'proc' or 'utilization'. + gpu_util = np.nan + if gpu_list and isinstance(gpu_list[0], dict): + gpu_info = gpu_list[0] + gpu_util = gpu_info.get("proc", gpu_info.get("utilization", np.nan)) + + return { + "cpu_percent": cpu.get("total", cpu.get("user", 0) + cpu.get("system", 0)), # 'total' is often available + "mem_percent": mem.get("percent", np.nan), + "disk_read_mb_per_s": read_bytes / _GLANCES_MB / _GLANCES_REFRESH_SEC, # Approximate rate + "disk_write_mb_per_s": write_bytes / _GLANCES_MB / _GLANCES_REFRESH_SEC, # Approximate rate + "gpu_percent": gpu_util, + } +# --- End Glances Helpers --- + +def _glances_metric_collector_thread( + glances_tmp_json_path: Path, + metrics_output_ndjson_path: Path, + stop_event: threading.Event +): + # print(f"Glances metrics collector thread started. Output: {metrics_output_ndjson_path}") # Debug + with open(metrics_output_ndjson_path, "w") as mof: + while not stop_event.wait(timeout=_GLANCES_REFRESH_SEC): # Check event periodically + try: + glances_data_list = read_glances_json_minimal(glances_tmp_json_path) + # Glances --export-json-file overwrites the file. So we read the whole content. + # If it's a list, it might be multiple snapshots if glances is very fast relative to our read. + # Usually, it's one snapshot. + for record in glances_data_list: # Process each snapshot found + extracted = extract_metrics_minimal(record) + ts = datetime.now(timezone.utc).isoformat() + mof.write(json.dumps({"timestamp": ts, **extracted}) + "\n") + mof.flush() + except Exception as e: + # This print will go to the main process log for the child process + print(f"Glances metrics collector thread error: {e}", file=sys.stderr) # Use stderr + # print(f"Glances metrics collector thread stopped for {metrics_output_ndjson_path}") # Debug + +def _process_target_wrapper( + target_func: Callable, log_file_path: Path, env_vars: Optional[Dict[str, str]], + result_queue: multiprocessing.Queue, process_name_for_log: str, + enable_glances_monitoring: bool, args: Tuple, kwargs: Dict[str, Any] +): + current_process_name = multiprocessing.current_process().name + glances_proc: Optional[subprocess.Popen] = None + glances_tmp_json_file = log_file_path.with_suffix(".glances-temp.json") + metrics_output_file = log_file_path.with_suffix(".glances.ndjson") + metrics_thread: Optional[threading.Thread] = None + stop_glances_collection_event = threading.Event() + + try: + if env_vars: # Set environment variables first + for k, v in env_vars.items(): os.environ[str(k)] = str(v) + + if enable_glances_monitoring: + try: + print(f"[{current_process_name}] Starting Glances monitoring... Temp JSON: {glances_tmp_json_file}") + glances_proc = start_glances_minimal(glances_tmp_json_file) + metrics_thread = threading.Thread( + target=_glances_metric_collector_thread, + args=(glances_tmp_json_file, metrics_output_file, stop_glances_collection_event), + daemon=True + ) + metrics_thread.start() + print(f"[{current_process_name}] Glances monitoring and metrics collector thread started.") + except Exception as ge: + print(f"[{current_process_name}] WARNING: Failed to start Glances: {ge}", file=sys.stderr) + glances_proc = None # Ensure it's None if start failed + metrics_thread = None + + # with redirect_all_stdout_to_file(log_file_path): + print(f"--- Process {current_process_name} started. Main log: {log_file_path} ---") + if enable_glances_monitoring and glances_proc: + print(f"--- Glances metrics being collected to: {metrics_output_file} ---") + + result_data = target_func(*args, **kwargs) + result_queue.put({"status": "success", "data": result_data, "log_file": str(log_file_path), "metrics_file": str(metrics_output_file) if glances_proc else None}) + print(f"--- Process {current_process_name} finished successfully. ---") + + except Exception as e: + tb_str = traceback.format_exc() + error_message = f"Process {current_process_name} Exception: {e}\n{tb_str}" + try: + with open(log_file_path, 'a') as f_err: + f_err.write(f"\n--- PROCESS ERROR ({current_process_name}) ---\n{error_message}\n") + except: pass + result_queue.put({"status": "error", "message": str(e), "traceback": tb_str, "log_file": str(log_file_path), "metrics_file": str(metrics_output_file) if glances_proc else None}) + finally: + if metrics_thread and metrics_thread.is_alive(): + stop_glances_collection_event.set() + metrics_thread.join(timeout=5) # Wait for thread to finish + if glances_proc: + stop_glances_minimal(glances_proc) + if enable_glances_monitoring and glances_tmp_json_file.exists(): # Cleanup temp file + try: glances_tmp_json_file.unlink() + except OSError: pass + + +def run_operation_in_process( + target_func: Callable, args_tuple: Tuple = (), kwargs_dict: Optional[Dict[str, Any]] = None, + env_vars: Optional[Dict[str, str]] = None, log_file_path: Union[Path, str] = None, + process_name: Optional[str] = None, timeout_seconds: Optional[float] = None, + enable_glances: bool = False # New flag to control Glances +) -> Dict[str, Any]: + if kwargs_dict is None: kwargs_dict = {} + effective_process_name = process_name or target_func.__name__ + + if log_file_path is None: + log_file_path = Path(f"./{effective_process_name}_{os.getpid()}_process.log") + log_file_path = Path(log_file_path); log_file_path.parent.mkdir(parents=True, exist_ok=True) + + result_queue = multiprocessing.Queue(1) + process = multiprocessing.Process( + target=_process_target_wrapper, + args=(target_func, log_file_path, env_vars, result_queue, effective_process_name, enable_glances, args_tuple, kwargs_dict), + name=effective_process_name + ) + process.start() + result = None + try: + result = result_queue.get(timeout=timeout_seconds) + except multiprocessing.queues.Empty: # type: ignore + result = {"status": "error", "message": "Process timed out", "log_file": str(log_file_path)} + if process.is_alive(): + process.terminate(); process.join(5) + if process.is_alive(): process.kill() # type: ignore + except Exception as q_e: + result = {"status": "error", "message": f"Queue error: {q_e}", "log_file": str(log_file_path)} + process.join() + return result if result is not None else {"status": "error", "message": "Process ended without result", "log_file": str(log_file_path)} diff --git a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml index ecc1bb8d..9216049d 100644 --- a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml +++ b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml @@ -1,7 +1,7 @@ # numa_latency_experiment.yaml dataset: name: sift1m - num_queries: 10000 + num_queries: 100 k: 100 trials: 3 @@ -10,148 +10,148 @@ overwrite: true # Set to true to force all indexes to rerun force_rebuild: false # Set to true to force all indexes to rebuild indexes: - - name: Quake_0 - index: Quake - build_params: - nc: 1000 - metric: l2 - num_workers: 0 - use_numa: false - search_params: - nprobe: 20 - batched_scan: true - - - name: Quake_1 - index: Quake - build_params: - nc: 1000 - metric: l2 - num_workers: 1 - parent_num_workers: 0 - use_numa: false - search_params: - nprobe: 20 - batched_scan: true - - - name: Quake_4 - index: Quake - build_params: - nc: 1000 - metric: l2 - num_workers: 4 - use_numa: false - search_params: - nprobe: 20 - batched_scan: true - - - name: Quake_16 - index: Quake - build_params: - nc: 1000 - metric: l2 - num_workers: 16 - use_numa: false - search_params: - nprobe: 20 - batched_scan: true - - - name: Quake_1_numa - index: Quake - build_params: - nc: 1000 - metric: l2 - num_workers: 1 - use_numa: true - search_params: - nprobe: 20 - batched_scan: true - - - name: Quake_4_numa - index: Quake - build_params: - nc: 1000 - metric: l2 - num_workers: 4 - use_numa: true - search_params: - nprobe: 20 - batched_scan: true - - - name: Quake_16_numa - index: Quake - build_params: - nc: 1000 - metric: l2 - num_workers: 16 - use_numa: true - search_params: - nprobe: 20 - batched_scan: true - - - name: Quake_1_numa_no_batch - index: Quake - build_params: - nc: 1000 - metric: l2 - num_workers: 1 - use_numa: true - search_params: - nprobe: 20 - batched_scan: false - - - name: Quake_2_numa_no_batch - index: Quake - build_params: - nc: 1000 - metric: l2 - num_workers: 2 - use_numa: true - search_params: - nprobe: 20 - batched_scan: false - - - name: Quake_4_numa_no_batch - index: Quake - build_params: - nc: 1000 - metric: l2 - num_workers: 4 - use_numa: true - search_params: - nprobe: 20 - batched_scan: false +# - name: Quake_0 +# index: Quake +# build_params: +# nc: 1000 +# metric: l2 +# num_workers: 0 +# use_numa: false +# search_params: +# nprobe: 20 +# batched_scan: true +# +# - name: Quake_1 +# index: Quake +# build_params: +# nc: 1000 +# metric: l2 +# num_workers: 1 +# use_numa: false +# search_params: +# nprobe: 20 +# batched_scan: true +# +# - name: Quake_4 +# index: Quake +# build_params: +# nc: 1000 +# metric: l2 +# num_workers: 4 +# use_numa: false +# search_params: +# nprobe: 20 +# batched_scan: true +# +# - name: Quake_16 +# index: Quake +# build_params: +# nc: 1000 +# metric: l2 +# num_workers: 16 +# use_numa: false +# search_params: +# nprobe: 20 +# batched_scan: true - - name: Quake_8_numa_no_batch - index: Quake - build_params: - nc: 1000 - metric: l2 - num_workers: 8 - use_numa: true - search_params: - nprobe: 20 - batched_scan: false +# - name: Quake_1_numa +# index: Quake +# build_params: +# nc: 1000 +# metric: l2 +# num_workers: 1 +# use_numa: true +# search_params: +# nprobe: 20 +# batched_scan: true +# +# - name: Quake_4_numa +# index: Quake +# build_params: +# nc: 1000 +# metric: l2 +# num_workers: 4 +# use_numa: true +# search_params: +# nprobe: 20 +# batched_scan: true +# +# - name: Quake_16_numa +# index: Quake +# build_params: +# nc: 1000 +# metric: l2 +# num_workers: 16 +# use_numa: true +# search_params: +# nprobe: 20 +# batched_scan: true +# +# - name: Quake_1_numa_no_batch +# index: Quake +# build_params: +# nc: 1000 +# metric: l2 +# num_workers: 1 +# use_numa: true +# search_params: +# nprobe: 20 +# batched_scan: false +# +# - name: Quake_2_numa_no_batch +# index: Quake +# build_params: +# nc: 1000 +# metric: l2 +# num_workers: 2 +# use_numa: true +# search_params: +# nprobe: 20 +# batched_scan: false +# +# - name: Quake_4_numa_no_batch +# index: Quake +# build_params: +# nc: 1000 +# metric: l2 +# num_workers: 4 +# use_numa: true +# search_params: +# nprobe: 20 +# batched_scan: false +# +# - name: Quake_8_numa_no_batch +# index: Quake +# build_params: +# nc: 1000 +# metric: l2 +# num_workers: 8 +# use_numa: true +# search_params: +# nprobe: 20 +# batched_scan: false +# +# +# - name: Quake_16_numa_no_batch +# index: Quake +# build_params: +# nc: 1000 +# metric: l2 +# num_workers: 16 +# use_numa: true +# search_params: +# nprobe: 20 +# batched_scan: false - - name: Quake_16_numa_no_batch - index: Quake + - name: FaissIVF + index: IVF build_params: nc: 1000 metric: l2 - num_workers: 16 - use_numa: true search_params: nprobe: 20 - batched_scan: false - - -# - name: FaissIVF -# index: IVF -# build_params: -# nc: 1000 -# metric: l2 -# search_params: -# nprobe: 15 + omp_num_threads_search: 1 # # - name: SCANN # index: SCANN @@ -167,6 +167,7 @@ indexes: # m: 64 # ef_construction: 128 # metric: l2 +## omp_num_threads_build: 16 # search_params: # ef_search: 24 # diff --git a/test/experiments/osdi2025/numa_multi_query/run.py b/test/experiments/osdi2025/numa_multi_query/run.py index 52cdca8a..dd8efb29 100644 --- a/test/experiments/osdi2025/numa_multi_query/run.py +++ b/test/experiments/osdi2025/numa_multi_query/run.py @@ -1,274 +1,276 @@ -#!/usr/bin/env python3 -""" -NUMA Multi-Query Batch Performance Benchmark -─────────────────────────────────────────── -Benchmarks Quake (with varying NUMA/worker settings) and other ANN indexes -for batch-query latency, per-phase breakdown (for Quake), and recall@k. -Results are cached. Outputs per-index CSVs, a unified CSV, and plots. - -This script tests how different configurations handle batches of queries, -focusing on Quake's behavior with NUMA-awareness and threading for parallel -processing of queries or internal operations. -""" -import logging # Standard library logging +# test/experiments/osdi2025/numa_multi_query/run.py + +import logging from pathlib import Path import numpy as np -import torch import pandas as pd import matplotlib.pyplot as plt -# time.time() is still used for the benchmark loop due to its specificity. +import torch +import os -# Common utilities import test.experiments.osdi2025.experiment_utils as common_utils - -# Quake specific imports -from quake.utils import compute_recall # compute_recall remains useful +from quake.utils import compute_recall from quake.index_wrappers.faiss_ivf import FaissIVF from quake.index_wrappers.faiss_hnsw import FaissHNSW -from quake.index_wrappers.quake import QuakeWrapper - -# Optional wrappers -try: - from quake.index_wrappers.scann import Scann -except ImportError: - Scann = None -try: - from quake.index_wrappers.diskann import DiskANNDynamic -except ImportError: - DiskANNDynamic = None -try: - from quake.index_wrappers.vamana import Vamana -except ImportError: - Vamana = None +from quake.index_wrappers.quake import QuakeWrapper # type: ignore + +try: from quake.index_wrappers.scann import Scann # type: ignore +except ImportError: Scann = None +try: from quake.index_wrappers.diskann import DiskANNDynamic # type: ignore +except ImportError: DiskANNDynamic = None +try: from quake.index_wrappers.vamana import Vamana # type: ignore +except ImportError: Vamana = None INDEX_CLASSES = { - "Quake": QuakeWrapper, - "IVF": FaissIVF, - "SCANN": Scann, - "HNSW": FaissHNSW, - "DiskANN": DiskANNDynamic, - "SVS": Vamana, # Assuming Vamana is the class for "SVS" + "Quake": QuakeWrapper, "IVF": FaissIVF, "SCANN": Scann, + "HNSW": FaissHNSW, "DiskANN": DiskANNDynamic, "SVS": Vamana, } - -# Setup logging (can be centralized in common_utils or main runner if preferred) -logging.basicConfig(level=logging.INFO, - format="%(asctime)s | %(levelname)7s | %(name)s | %(message)s", - datefmt="%H:%M:%S") -logger = logging.getLogger("numa_multi_query_exp") - +logger = logging.getLogger("numa_multi_query_orchestrator") + +def task_build_index( + index_config: dict, dataset_cfg: dict, global_run_params: dict, + index_file_path: Path +): + index_name = index_config["name"] + index_type_str = index_config["index"] + original_omp_threads = os.environ.get("OMP_NUM_THREADS") + + print(f"[{index_name} BUILD_TASK] Process ID: {os.getpid()}. Target file: {index_file_path}") + # Make a copy to safely pop omp_threads param + current_build_params = dict(index_config.get("build_params", {})) + omp_build_threads = str(current_build_params.pop("omp_num_threads_build", "1")) # Pop the param + os.environ["OMP_NUM_THREADS"] = omp_build_threads + print(f"[{index_name} BUILD_TASK] Set OMP_NUM_THREADS={omp_build_threads}.") + + base_vectors, _, _ = common_utils.load_data( + dataset_cfg["name"], nq_override=1) + + IndexCls = INDEX_CLASSES.get(index_type_str) + if IndexCls is None or (IndexCls == Scann and Scann is None): # type: ignore + if original_omp_threads is not None: os.environ["OMP_NUM_THREADS"] = original_omp_threads + else: os.environ.pop("OMP_NUM_THREADS", None) + return {"error": f"Index type '{index_type_str}' unavailable.", "index_file_path": str(index_file_path)} + + print(f"[{index_name} BUILD_TASK] Calling prepare_wrapper_index (build phase)...") + # current_build_params now does NOT contain omp_num_threads_build + idx_instance_for_build = common_utils.prepare_wrapper_index( + IndexCls, index_file_path, base_vectors, current_build_params, + global_run_params["force_rebuild_indices"], load=False + ) + del base_vectors + + if original_omp_threads is not None: os.environ["OMP_NUM_THREADS"] = original_omp_threads + else: os.environ.pop("OMP_NUM_THREADS", None) + + if idx_instance_for_build is None : + return {"error": f"Index build failed for {index_name}.", "index_file_path": str(index_file_path)} + + print(f"[{index_name} BUILD_TASK] Build/Save completed for {index_file_path}.") + return {"status": "success", "index_file_path": str(index_file_path)} + + +def task_search_index( + index_config: dict, dataset_cfg: dict, global_run_params: dict, + index_file_path: Path +): + index_name = index_config["name"] + index_type_str = index_config["index"] + original_omp_threads = os.environ.get("OMP_NUM_THREADS") + + print(f"[{index_name} SEARCH_TASK] Process ID: {os.getpid()}. Loading index from: {index_file_path}") + # These params are for loading the index instance, or for search + current_build_params_for_load = dict(index_config.get("build_params", {})) + current_search_params = dict(index_config.get("search_params", {})) + + # Pop omp_num_threads_search before passing current_search_params to idx_instance.search + omp_search_threads = str(current_search_params.pop("omp_num_threads_search", "1")) + os.environ["OMP_NUM_THREADS"] = omp_search_threads + print(f"[{index_name} SEARCH_TASK] Set OMP_NUM_THREADS={omp_search_threads}.") + + _, query_vectors, gt_vectors = common_utils.load_data( + dataset_cfg["name"], nq_override=dataset_cfg.get("num_queries")) + + IndexCls = INDEX_CLASSES.get(index_type_str) + if IndexCls is None or (IndexCls == Scann and Scann is None): # type: ignore + if original_omp_threads is not None: os.environ["OMP_NUM_THREADS"] = original_omp_threads + else: os.environ.pop("OMP_NUM_THREADS", None) + return {"error": f"Index type '{index_type_str}' unavailable for search."} + + if not Path(index_file_path).exists(): # Ensure Path object for exists() + if original_omp_threads is not None: os.environ["OMP_NUM_THREADS"] = original_omp_threads + else: os.environ.pop("OMP_NUM_THREADS", None) + return {"error": f"Index file {index_file_path} not found for search."} + + idx_instance = IndexCls() + load_kwargs = {} + # Params like num_workers for Quake load are typically in "build_params" section of YAML + if "num_workers" in current_build_params_for_load: load_kwargs["num_workers"] = current_build_params_for_load.get("num_workers") + if "use_numa" in current_build_params_for_load: load_kwargs["use_numa"] = current_build_params_for_load.get("use_numa") + if "parent_num_workers" in current_build_params_for_load: load_kwargs["parent_num_workers"] = current_build_params_for_load.get("parent_num_workers") + + print(f"[{index_name} SEARCH_TASK] Loading index with kwargs: {load_kwargs}") + idx_instance.load(str(index_file_path), **load_kwargs) + + k_val = global_run_params["k_val"] + print(f"[{index_name} SEARCH_TASK] Warmup ({global_run_params['num_warmup']} iterations)...") + # current_search_params now does NOT contain omp_num_threads_search + for _ in range(global_run_params['num_warmup']): + _ = idx_instance.search(query_vectors, k_val, **current_search_params) + + trial_latencies_ms = [] + trial_recalls = [] + print(f"[{index_name} SEARCH_TASK] Benchmarking ({global_run_params['num_trials']} trials)...") + for i in range(global_run_params['num_trials']): + # current_search_params now does NOT contain omp_num_threads_search + search_result = idx_instance.search(query_vectors, k_val, **current_search_params) + latency_ms = np.nan + timing_info = getattr(search_result, "timing_info", None) + if timing_info and hasattr(timing_info, "total_time_ns"): latency_ms = getattr(timing_info, "total_time_ns") / 1e6 + elif timing_info and hasattr(timing_info, "child_total_time_ns"): latency_ms = getattr(timing_info, "child_total_time_ns") / 1e6 + trial_latencies_ms.append(latency_ms) + + recall_val = np.nan + if gt_vectors is not None and hasattr(search_result, "ids"): + try: recall_val = float(compute_recall(search_result.ids, gt_vectors, k_val).mean()) + except Exception: pass + trial_recalls.append(recall_val) + + if original_omp_threads is not None: os.environ["OMP_NUM_THREADS"] = original_omp_threads + else: os.environ.pop("OMP_NUM_THREADS", None) + + print(f"[{index_name} SEARCH_TASK] Individual trial results (Latency, Recall@K={k_val}):") + for i in range(len(trial_latencies_ms)): + lat_str = f"{trial_latencies_ms[i]:.3f}ms" if not np.isnan(trial_latencies_ms[i]) else "N/A" + rec_str = f"{trial_recalls[i]:.4f}" if not np.isnan(trial_recalls[i]) else "N/A" + print(f" Trial {i+1}: Latency={lat_str}, Recall={rec_str}") + + final_row = {"index_name_from_process": index_name} + valid_latencies = [l for l in trial_latencies_ms if not np.isnan(l)] + valid_recalls = [r for r in trial_recalls if not np.isnan(r)] + final_row["mean_latency_ms"] = float(np.mean(valid_latencies)) if valid_latencies else np.nan + final_row["std_latency_ms"] = float(np.std(valid_latencies)) if valid_latencies else np.nan + final_row[f"mean_recall_at_{k_val}"] = float(np.mean(valid_recalls)) if valid_recalls else np.nan + final_row[f"std_recall_at_{k_val}"] = float(np.std(valid_recalls)) if valid_recalls else np.nan + + n_threads_or_workers = int(omp_search_threads) + if index_type_str == "Quake" and "num_workers" in current_build_params_for_load : + n_threads_or_workers = current_build_params_for_load.get("num_workers", n_threads_or_workers) + final_row["n_config_concurrency"] = n_threads_or_workers + + print(f"[{index_name} SEARCH_TASK] Benchmark task finished.") + return final_row def run_experiment(cfg_path_str: str, output_dir_str: str): + logging.basicConfig(level=logging.INFO, format="%(asctime)s|%(levelname)7s|%(name)s| %(message)s", datefmt="%H:%M:%S") + logger.info(f"Orchestrator: Config: {cfg_path_str}, Output: {output_dir_str}") + cfg = common_utils.load_config(cfg_path_str) + main_output_dir = Path(output_dir_str); main_output_dir.mkdir(parents=True, exist_ok=True) + process_logs_dir = main_output_dir / "process_logs"; process_logs_dir.mkdir(parents=True, exist_ok=True) + individual_index_files_dir = main_output_dir / "individual_indices"; + individual_index_files_dir.mkdir(parents=True, exist_ok=True) dataset_cfg = cfg["dataset"] - num_queries_to_use = dataset_cfg["num_queries"] - k_val = dataset_cfg["k"] - - num_trials = cfg.get("trials", 5) - num_warmup = cfg.get("warmup", 1) # Reduced default warmup, can be configured + global_run_params = { + "num_trials": cfg.get("trials", 3), "num_warmup": cfg.get("warmup", 1), "k_val": dataset_cfg["k"], + "force_overwrite_results": cfg.get("overwrite", False), + "force_rebuild_indices": cfg.get("force_rebuild", False), + "enable_glances": cfg.get("enable_glances_monitoring", False) + } indexes_config_list = cfg["indexes"] - output_csv_name = cfg.get("output", {}).get("results_csv", "numa_multi_query_results.csv") - force_overwrite = cfg.get("overwrite", False) - force_rebuild = cfg.get("force_rebuild", False) - - main_output_dir = Path(output_dir_str) - main_output_dir.mkdir(parents=True, exist_ok=True) - - logger.info(f"Loading dataset '{dataset_cfg['name']}'...") - # Use common_utils.load_data, pass num_queries_to_use for slicing - base_vectors, query_vectors, gt_vectors = common_utils.load_data( - dataset_cfg["name"], - nq_override=num_queries_to_use - ) - - # --- Build shared Quake base index once (specific to this experiment script) --- - quake_base_build_params = None - shared_quake_index_file = main_output_dir / "Quake_shared_base_index.bin" # More descriptive name - - for idx_c in indexes_config_list: - if idx_c["index"] == "Quake": - # These are params for the *base* index, stripped of worker/NUMA specifics - quake_base_build_params = {k: v for k, v in idx_c.get("build_params", {}).items() - if k not in ["num_workers", "use_numa", "parent_num_workers"]} - break - - if quake_base_build_params: - if not shared_quake_index_file.exists() or force_rebuild: # Respect overwrite for base index too - logger.info(f"Building shared Quake base index at {shared_quake_index_file}...") - common_utils.prepare_wrapper_index( - IndexClass=QuakeWrapper, - index_file_path=shared_quake_index_file, - build_params=quake_base_build_params, - base_vectors=base_vectors, - force_rebuild=force_rebuild, # Force rebuild if top-level overwrite is true - ) - logger.info(f"Shared Quake base index saved at {shared_quake_index_file}") - else: - logger.info(f"Using existing shared Quake base index from {shared_quake_index_file}") + output_csv_name = cfg.get("output", {}).get("results_csv", "numa_multi_query_results.csv") # Changed from single_query all_experiment_rows = [] - for idx_conf in indexes_config_list: index_name = idx_conf["name"] - index_type_str = idx_conf["index"] - current_build_params = dict(idx_conf.get("build_params", {})) - current_search_params = dict(idx_conf.get("search_params", {})) + index_file_path = individual_index_files_dir / f"{index_name}_index.bin" + per_index_final_results_csv = main_output_dir / f"{index_name}_final_results.csv" - IndexCls = INDEX_CLASSES.get(index_type_str) - if IndexCls is None or (IndexCls == Scann and Scann is None): # Handle optional Scann - logger.warning(f"Index type '{index_type_str}' for '{index_name}' is not available or unknown. Skipping.") - continue - - per_index_csv_path = main_output_dir / f"{index_name}_results.csv" - - # Determine actual index file path (shared for Quake, specific for others) - actual_index_file_path = shared_quake_index_file if index_type_str == "Quake" else main_output_dir / f"{index_name}_index.bin" - - if per_index_csv_path.exists() and not force_overwrite: - logger.info(f"Results for {index_name} exist. Use overwrite:true to rerun. Loading cached.") + if per_index_final_results_csv.exists() and not global_run_params["force_overwrite_results"]: try: - # Ensure only one row is taken if multiple exist from partial runs. - cached_df = pd.read_csv(per_index_csv_path) - if not cached_df.empty: - all_experiment_rows.append(cached_df.iloc[0].to_dict()) - except Exception as e: - logger.error(f"Could not load cached results for {index_name} from {per_index_csv_path}: {e}") + all_experiment_rows.append(pd.read_csv(per_index_final_results_csv).iloc[0].to_dict()) + logger.info(f"Loaded cached final results for {index_name}.") + continue + except Exception as e: logger.warning(f"Could not load cached final results for {index_name}: {e}. Re-running.") + + logger.info(f"Orchestrator: Submitting BUILD task for index: {index_name}") + build_log_file = process_logs_dir / f"{index_name}_build_process.log" + build_env_vars = idx_conf.get("build_env_vars", {}) + process_timeout = idx_conf.get("process_timeout", cfg.get("default_process_timeout", 7200)) + + build_result = common_utils.run_operation_in_process( + task_build_index, (idx_conf, dataset_cfg, global_run_params, index_file_path), + env_vars=build_env_vars, log_file_path=build_log_file, process_name=f"BuildTask_{index_name}", + timeout_seconds=process_timeout, enable_glances=global_run_params["enable_glances"] + ) + + if not (build_result["status"] == "success" and build_result.get("data", {}).get("status") == "success"): + err_msg = build_result.get('message', build_result.get('data', {}).get('error', 'Build task failed')) + logger.error(f"BUILD task for {index_name} failed. Log: {build_result.get('log_file')}. Error: {err_msg}") + all_experiment_rows.append({"index": index_name, "error": f"Build Failed: {err_msg}", "mean_latency_ms": np.nan, f"mean_recall_at_{global_run_params['k_val']}": np.nan}) continue - logger.info(f"Processing index: {index_name} (Type: {index_type_str})") + logger.info(f"BUILD task for {index_name} succeeded. Index file: {build_result.get('data',{}).get('index_file_path')}") + built_index_file_path = Path(build_result.get("data",{}).get("index_file_path", index_file_path)) # Use confirmed path - # Load or build index - idx_instance = None - if index_type_str == "Quake": - if not shared_quake_index_file.exists(): - logger.error(f"Shared Quake base index {shared_quake_index_file} not found for {index_name}. Skipping.") - continue - idx_instance = QuakeWrapper() - # These are load-time parameters for Quake specified in each config entry - quake_load_kwargs = { - k: current_build_params[k] for k in - ("use_numa", "num_workers", "parent_num_workers") if k in current_build_params - } - logger.info(f"Loading Quake index {index_name} from shared base with params: {quake_load_kwargs}") - idx_instance.load(str(shared_quake_index_file), **quake_load_kwargs) - else: # For non-Quake indexes - # DiskANN needs metric popped from build_params before load, if present during build - diskann_load_params = current_build_params.copy() - if index_type_str == "DiskANN": diskann_load_params.pop("metric", None) - - idx_instance = common_utils.prepare_index( - IndexClass=IndexCls, - index_build_params=current_build_params, - base_vectors=base_vectors, # Needed if building - index_file_path=actual_index_file_path, - force_rebuild=force_rebuild, # Rebuild this specific non-Quake index if needed - load_kwargs=diskann_load_params if index_type_str == "DiskANN" else None, - save_after_build=True - ) - - if idx_instance is None: # Should not happen if logic is correct - logger.error(f"Failed to load or build index {index_name}. Skipping.") - continue + logger.info(f"Orchestrator: Submitting SEARCH task for index: {index_name}") + search_log_file = process_logs_dir / f"{index_name}_search_process.log" + search_env_vars = idx_conf.get("search_env_vars", {}) - logger.info(f"Warmup for {index_name} ({num_warmup} iterations)...") - for _ in range(num_warmup): # Batched search for warmup - _ = idx_instance.search(query_vectors, k_val, **current_search_params) - - # Benchmark Trials (batch search) - trial_timings = { - "child_total_ms": [], "parent_total_ms": [], "recall": [], - "child_buffer_init_ms": [], "child_copy_query_ms": [], "child_enqueue_ms": [], - "child_wait_ms": [], "child_aggregate_ms": [], - "parent_buffer_init_ms": [], "parent_copy_query_ms": [], "parent_enqueue_ms": [], - "parent_wait_ms": [], "parent_aggregate_ms": [] - } - - logger.info(f"Running benchmark for {index_name} ({num_trials} trials)...") - for t_idx in range(num_trials): - search_result = idx_instance.search(query_vectors, k_val, **current_search_params) - timing_info = search_result.timing_info # Assuming this is always present for Quake - - # Collect detailed timings if available (primarily for Quake) - for key in trial_timings: - if key.startswith("child_") and hasattr(timing_info, key.replace("_ms", "_time_ns").replace("child_", "")): - trial_timings[key].append(getattr(timing_info, key.replace("_ms", "_time_ns").replace("child_", "")) / 1e6) - elif key.startswith("parent_") and hasattr(timing_info, "parent_info") and timing_info.parent_info: - parent_attr_name = key.replace("_ms", "_time_ns").replace("parent_", "") - if hasattr(timing_info.parent_info, parent_attr_name): - trial_timings[key].append(getattr(timing_info.parent_info, parent_attr_name) / 1e6) - else: - trial_timings[key].append(0.0) # Parent info might not have all fields - elif key == "recall" and gt_vectors is not None: - pred_ids = search_result.ids - current_recall = float(compute_recall(pred_ids, gt_vectors, k_val).mean()) - trial_timings["recall"].append(current_recall) - - log_msg_trial = f" [{index_name} trial {t_idx+1}/{num_trials}] " - if trial_timings["child_total_ms"]: log_msg_trial += f"child_total={trial_timings['child_total_ms'][-1]:.2f}ms " - if trial_timings["parent_total_ms"] and trial_timings['parent_total_ms'][-1] > 0: log_msg_trial += f"parent_total={trial_timings['parent_total_ms'][-1]:.2f}ms " - if trial_timings["recall"]: log_msg_trial += f"| recall@{k_val}={trial_timings['recall'][-1]:.4f}" - logger.info(log_msg_trial) - - - # Aggregate metrics from trials - final_row = {"index": index_name, "n_workers": current_build_params.get("num_workers")} - for key, values in trial_timings.items(): - if values: - final_row[f"mean_{key}"] = float(np.mean(values)) - final_row[f"std_{key}"] = float(np.std(values)) - else: # Handle cases where timings might not be available (e.g. non-Quake, or no GT) - final_row[f"mean_{key}"] = np.nan - final_row[f"std_{key}"] = np.nan - - # Rename for CSV consistency if needed, e.g., mean_child_total_ms to mean_latency_ms - if "mean_child_total_ms" in final_row: - final_row["mean_latency_ms"] = final_row.pop("mean_child_total_ms") - if "std_child_total_ms" in final_row: - final_row["std_latency_ms"] = final_row.pop("std_child_total_ms") - if f"mean_recall" in final_row: # Ensure recall key matches expected format - final_row[f"recall_at_{k_val}"] = final_row.pop(f"mean_recall") - if f"std_recall" in final_row: # std for recall might not be needed in final summary - final_row.pop(f"std_recall") - - - all_experiment_rows.append(final_row) - common_utils.save_results_csv(pd.DataFrame([final_row]), per_index_csv_path) # Save per-index - - # Save unified CSV for all indexes - if not all_experiment_rows: - logger.warning("No results collected. Skipping final CSV and plots.") - return + search_process_result = common_utils.run_operation_in_process( + task_search_index, (idx_conf, dataset_cfg, global_run_params, built_index_file_path), + env_vars=search_env_vars, log_file_path=search_log_file, process_name=f"SearchTask_{index_name}", + timeout_seconds=process_timeout, enable_glances=global_run_params["enable_glances"] + ) + if search_process_result["status"] == "success" and isinstance(search_process_result["data"], dict) and "error" not in search_process_result["data"]: + logger.info(f"Search task {index_name} succeeded. Log: {search_process_result.get('log_file')}. Metrics: {search_process_result.get('metrics_file')}") + result_data_row = search_process_result["data"]; result_data_row["index"] = index_name + all_experiment_rows.append(result_data_row) + common_utils.save_results_csv(pd.DataFrame([result_data_row]), per_index_final_results_csv) + else: + err_msg = search_process_result.get('message', search_process_result.get('data', {}).get('error','Search task failed')) + logger.error(f"Search task {index_name} failed. Log: {search_process_result.get('log_file')}. Metrics: {search_process_result.get('metrics_file')}. Error: {err_msg}") + all_experiment_rows.append({"index": index_name, "error": f"Search Failed: {err_msg}", "mean_latency_ms": np.nan, f"mean_recall_at_{global_run_params['k_val']}": np.nan}) + + if not all_experiment_rows: logger.warning("No results. Skipping CSV/plots."); return final_df = pd.DataFrame(all_experiment_rows) + if 'index' not in final_df.columns and 'index_name_from_process' in final_df.columns: + final_df.rename(columns={'index_name_from_process': 'index'}, inplace=True) + final_df['index'] = final_df['index'].fillna(final_df.get('index_name_from_process', "unknown_index")) + unified_csv_path = main_output_dir / output_csv_name - common_utils.save_results_csv(final_df, unified_csv_path) - logger.info(f"Unified results written to {unified_csv_path}") + common_utils.save_results_csv(final_df, unified_csv_path); logger.info(f"Unified results: {unified_csv_path}") - # Plotting (remains specific to this experiment's needs) plot_suffix = Path(output_csv_name).stem - - # Plot 1: Batch total latency (child_total_ms) vs. Num Workers - if "mean_latency_ms" in final_df.columns and "n_workers" in final_df.columns: - plt.figure(figsize=(8,6)) - for idx_name_plot in final_df["index"].unique(): - subset = final_df[final_df["index"] == idx_name_plot].sort_values(by="n_workers") - label = idx_name_plot # Simplified label - yerr_values = subset["std_latency_ms"] if "std_latency_ms" in subset else None - plt.errorbar( - subset["n_workers"].astype(int), - subset["mean_latency_ms"], - yerr=yerr_values, - marker="o", capsize=4, label=label, alpha=0.8, linestyle='-' - ) - # plt.xscale("symlog", base=2) # May not be ideal if n_workers are not powers of 2 or include 0 - plt.xlabel("Num Workers (Configuration)") - plt.ylabel(f"Mean Batch Query Latency (ms) - Child Total") - plt.title(f"NUMA Multi-Query: Latency vs. Num Workers ({dataset_cfg['name']})") - plt.legend(title="Index Configuration", bbox_to_anchor=(1.05, 1), loc='upper left') - plt.grid(True, linestyle=':', alpha=0.7) - plt.tight_layout(rect=[0,0,0.8,1]) # Adjust for legend - plt.savefig(main_output_dir / f"{plot_suffix}_batch_latency.png", dpi=150) - logger.info(f"Latency plot saved to {main_output_dir / f'{plot_suffix}_batch_latency.png'}") - plt.close() - - logger.info("NUMA Multi-Query experiment finished.") \ No newline at end of file + plot_x_param = "n_config_concurrency" + # Ensure 'index' column is valid before attempting groupby for plotting + plot_df = final_df.dropna(subset=['index', plot_x_param]).copy() + if not plot_df.empty: + plot_df.loc[:, plot_x_param] = pd.to_numeric(plot_df[plot_x_param], errors='coerce').fillna(-1).astype(int) + k_val_plot = global_run_params['k_val'] + + if "mean_latency_ms" in plot_df.columns: + try: + plt.figure(figsize=(8,6)); + for name, group in plot_df.groupby("index"): + group = group.sort_values(by=plot_x_param) + plt.errorbar(group[plot_x_param], group["mean_latency_ms"], yerr=group.get("std_latency_ms"), marker="o", label=name) + plt.xlabel("Configured Concurrency"); plt.ylabel("Mean Latency (ms)"); plt.title(f"Latency ({dataset_cfg['name']})") + plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left'); plt.grid(True); plt.tight_layout(rect=[0,0,0.8,1]) + plt.savefig(main_output_dir / f"{plot_suffix}_latency.png"); plt.close(); logger.info(f"Latency plot saved.") + except Exception as e: logger.error(f"Latency plot error: {e}") + + recall_col = f"mean_recall_at_{k_val_plot}" + if recall_col in plot_df.columns: + try: + plt.figure(figsize=(8,6)); + for name, group in plot_df.groupby("index"): + group = group.sort_values(by=plot_x_param) + plt.errorbar(group[plot_x_param], group[recall_col], yerr=group.get(f"std_recall_at_{k_val_plot}"), marker="o", label=name) + plt.xlabel("Configured Concurrency"); plt.ylabel(f"Mean Recall@{k_val_plot}"); plt.title(f"Recall ({dataset_cfg['name']})") + plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left'); plt.grid(True); plt.tight_layout(rect=[0,0,0.8,1]); plt.ylim(0, 1.05) + plt.savefig(main_output_dir / f"{plot_suffix}_recall.png"); plt.close(); logger.info(f"Recall plot saved.") + except Exception as e: logger.error(f"Recall plot error: {e}") + else: logger.warning("Not enough data for plotting after filtering.") + logger.info("Experiment orchestration finished.") \ No newline at end of file From a81a9718f93cc91889b76a163c2f2e2e31084bf7 Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 22 May 2025 15:45:21 -0500 Subject: [PATCH 220/323] update multi_query --- .../numa_multi_query/configs/sift1m.yaml | 264 +++++++++--------- 1 file changed, 132 insertions(+), 132 deletions(-) diff --git a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml index 9216049d..d8572fed 100644 --- a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml +++ b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml @@ -1,7 +1,7 @@ # numa_latency_experiment.yaml dataset: name: sift1m - num_queries: 100 + num_queries: 1000 k: 100 trials: 3 @@ -10,138 +10,138 @@ overwrite: true # Set to true to force all indexes to rerun force_rebuild: false # Set to true to force all indexes to rebuild indexes: -# - name: Quake_0 -# index: Quake -# build_params: -# nc: 1000 -# metric: l2 -# num_workers: 0 -# use_numa: false -# search_params: -# nprobe: 20 -# batched_scan: true -# -# - name: Quake_1 -# index: Quake -# build_params: -# nc: 1000 -# metric: l2 -# num_workers: 1 -# use_numa: false -# search_params: -# nprobe: 20 -# batched_scan: true -# -# - name: Quake_4 -# index: Quake -# build_params: -# nc: 1000 -# metric: l2 -# num_workers: 4 -# use_numa: false -# search_params: -# nprobe: 20 -# batched_scan: true -# -# - name: Quake_16 -# index: Quake -# build_params: -# nc: 1000 -# metric: l2 -# num_workers: 16 -# use_numa: false -# search_params: -# nprobe: 20 -# batched_scan: true + - name: Quake_0 + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 0 + use_numa: false + search_params: + nprobe: 20 + batched_scan: true -# - name: Quake_1_numa -# index: Quake -# build_params: -# nc: 1000 -# metric: l2 -# num_workers: 1 -# use_numa: true -# search_params: -# nprobe: 20 -# batched_scan: true -# -# - name: Quake_4_numa -# index: Quake -# build_params: -# nc: 1000 -# metric: l2 -# num_workers: 4 -# use_numa: true -# search_params: -# nprobe: 20 -# batched_scan: true -# -# - name: Quake_16_numa -# index: Quake -# build_params: -# nc: 1000 -# metric: l2 -# num_workers: 16 -# use_numa: true -# search_params: -# nprobe: 20 -# batched_scan: true -# -# - name: Quake_1_numa_no_batch -# index: Quake -# build_params: -# nc: 1000 -# metric: l2 -# num_workers: 1 -# use_numa: true -# search_params: -# nprobe: 20 -# batched_scan: false -# -# - name: Quake_2_numa_no_batch -# index: Quake -# build_params: -# nc: 1000 -# metric: l2 -# num_workers: 2 -# use_numa: true -# search_params: -# nprobe: 20 -# batched_scan: false -# -# - name: Quake_4_numa_no_batch -# index: Quake -# build_params: -# nc: 1000 -# metric: l2 -# num_workers: 4 -# use_numa: true -# search_params: -# nprobe: 20 -# batched_scan: false -# -# - name: Quake_8_numa_no_batch -# index: Quake -# build_params: -# nc: 1000 -# metric: l2 -# num_workers: 8 -# use_numa: true -# search_params: -# nprobe: 20 -# batched_scan: false -# -# -# - name: Quake_16_numa_no_batch -# index: Quake -# build_params: -# nc: 1000 -# metric: l2 -# num_workers: 16 -# use_numa: true -# search_params: -# nprobe: 20 -# batched_scan: false + - name: Quake_1 + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 1 + use_numa: false + search_params: + nprobe: 20 + batched_scan: true + + - name: Quake_4 + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 4 + use_numa: false + search_params: + nprobe: 20 + batched_scan: true + + - name: Quake_16 + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 16 + use_numa: false + search_params: + nprobe: 20 + batched_scan: true + + - name: Quake_1_numa + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 1 + use_numa: true + search_params: + nprobe: 20 + batched_scan: true + + - name: Quake_4_numa + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 4 + use_numa: true + search_params: + nprobe: 20 + batched_scan: true + + - name: Quake_16_numa + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 16 + use_numa: true + search_params: + nprobe: 20 + batched_scan: true + + - name: Quake_1_numa_no_batch + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 1 + use_numa: true + search_params: + nprobe: 20 + batched_scan: false + + - name: Quake_2_numa_no_batch + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 2 + use_numa: true + search_params: + nprobe: 20 + batched_scan: false + + - name: Quake_4_numa_no_batch + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 4 + use_numa: true + search_params: + nprobe: 20 + batched_scan: false + + - name: Quake_8_numa_no_batch + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 8 + use_numa: true + search_params: + nprobe: 20 + batched_scan: false + + + - name: Quake_16_numa_no_batch + index: Quake + build_params: + nc: 1000 + metric: l2 + num_workers: 16 + use_numa: true + search_params: + nprobe: 20 + batched_scan: false - name: FaissIVF From ba3963bec8cefa83a0b2857789ad1cdf618d6ed5 Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 22 May 2025 20:16:52 -0500 Subject: [PATCH 221/323] update multi_query --- .../numa_multi_query/configs/sift1m.yaml | 103 +----------------- .../osdi2025/numa_multi_query/run.py | 70 +++++------- 2 files changed, 28 insertions(+), 145 deletions(-) diff --git a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml index d8572fed..70a20c64 100644 --- a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml +++ b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml @@ -10,39 +10,6 @@ overwrite: true # Set to true to force all indexes to rerun force_rebuild: false # Set to true to force all indexes to rebuild indexes: - - name: Quake_0 - index: Quake - build_params: - nc: 1000 - metric: l2 - num_workers: 0 - use_numa: false - search_params: - nprobe: 20 - batched_scan: true - - - name: Quake_1 - index: Quake - build_params: - nc: 1000 - metric: l2 - num_workers: 1 - use_numa: false - search_params: - nprobe: 20 - batched_scan: true - - - name: Quake_4 - index: Quake - build_params: - nc: 1000 - metric: l2 - num_workers: 4 - use_numa: false - search_params: - nprobe: 20 - batched_scan: true - - name: Quake_16 index: Quake build_params: @@ -54,28 +21,6 @@ indexes: nprobe: 20 batched_scan: true - - name: Quake_1_numa - index: Quake - build_params: - nc: 1000 - metric: l2 - num_workers: 1 - use_numa: true - search_params: - nprobe: 20 - batched_scan: true - - - name: Quake_4_numa - index: Quake - build_params: - nc: 1000 - metric: l2 - num_workers: 4 - use_numa: true - search_params: - nprobe: 20 - batched_scan: true - - name: Quake_16_numa index: Quake build_params: @@ -87,51 +32,6 @@ indexes: nprobe: 20 batched_scan: true - - name: Quake_1_numa_no_batch - index: Quake - build_params: - nc: 1000 - metric: l2 - num_workers: 1 - use_numa: true - search_params: - nprobe: 20 - batched_scan: false - - - name: Quake_2_numa_no_batch - index: Quake - build_params: - nc: 1000 - metric: l2 - num_workers: 2 - use_numa: true - search_params: - nprobe: 20 - batched_scan: false - - - name: Quake_4_numa_no_batch - index: Quake - build_params: - nc: 1000 - metric: l2 - num_workers: 4 - use_numa: true - search_params: - nprobe: 20 - batched_scan: false - - - name: Quake_8_numa_no_batch - index: Quake - build_params: - nc: 1000 - metric: l2 - num_workers: 8 - use_numa: true - search_params: - nprobe: 20 - batched_scan: false - - - name: Quake_16_numa_no_batch index: Quake build_params: @@ -143,7 +43,6 @@ indexes: nprobe: 20 batched_scan: false - - name: FaissIVF index: IVF build_params: @@ -151,7 +50,7 @@ indexes: metric: l2 search_params: nprobe: 20 - omp_num_threads_search: 1 + omp_num_threads_search: 16 # # - name: SCANN # index: SCANN diff --git a/test/experiments/osdi2025/numa_multi_query/run.py b/test/experiments/osdi2025/numa_multi_query/run.py index dd8efb29..603610eb 100644 --- a/test/experiments/osdi2025/numa_multi_query/run.py +++ b/test/experiments/osdi2025/numa_multi_query/run.py @@ -7,6 +7,7 @@ import matplotlib.pyplot as plt import torch import os +import faiss # Import faiss at the top level as it's now a hard dependency for thread setting import test.experiments.osdi2025.experiment_utils as common_utils from quake.utils import compute_recall @@ -27,41 +28,37 @@ } logger = logging.getLogger("numa_multi_query_orchestrator") +def _apply_faiss_thread_settings(config_threads_str: str, task_name_info: str): + num_threads = int(config_threads_str) + faiss.omp_set_num_threads(num_threads) + print(f"[{task_name_info}] Called faiss.omp_set_num_threads({num_threads}).") + + def task_build_index( index_config: dict, dataset_cfg: dict, global_run_params: dict, index_file_path: Path ): index_name = index_config["name"] - index_type_str = index_config["index"] - original_omp_threads = os.environ.get("OMP_NUM_THREADS") print(f"[{index_name} BUILD_TASK] Process ID: {os.getpid()}. Target file: {index_file_path}") - # Make a copy to safely pop omp_threads param current_build_params = dict(index_config.get("build_params", {})) - omp_build_threads = str(current_build_params.pop("omp_num_threads_build", "1")) # Pop the param - os.environ["OMP_NUM_THREADS"] = omp_build_threads - print(f"[{index_name} BUILD_TASK] Set OMP_NUM_THREADS={omp_build_threads}.") + omp_build_threads_config_val = str(current_build_params.pop("omp_num_threads_build", "1")) + + _apply_faiss_thread_settings(omp_build_threads_config_val, f"{index_name} BUILD_TASK") base_vectors, _, _ = common_utils.load_data( dataset_cfg["name"], nq_override=1) - IndexCls = INDEX_CLASSES.get(index_type_str) + IndexCls = INDEX_CLASSES.get(index_config["index"]) if IndexCls is None or (IndexCls == Scann and Scann is None): # type: ignore - if original_omp_threads is not None: os.environ["OMP_NUM_THREADS"] = original_omp_threads - else: os.environ.pop("OMP_NUM_THREADS", None) - return {"error": f"Index type '{index_type_str}' unavailable.", "index_file_path": str(index_file_path)} + return {"error": f"Index type '{index_config['index']}' unavailable.", "index_file_path": str(index_file_path)} - print(f"[{index_name} BUILD_TASK] Calling prepare_wrapper_index (build phase)...") - # current_build_params now does NOT contain omp_num_threads_build idx_instance_for_build = common_utils.prepare_wrapper_index( IndexCls, index_file_path, base_vectors, current_build_params, global_run_params["force_rebuild_indices"], load=False ) del base_vectors - if original_omp_threads is not None: os.environ["OMP_NUM_THREADS"] = original_omp_threads - else: os.environ.pop("OMP_NUM_THREADS", None) - if idx_instance_for_build is None : return {"error": f"Index build failed for {index_name}.", "index_file_path": str(index_file_path)} @@ -75,54 +72,43 @@ def task_search_index( ): index_name = index_config["name"] index_type_str = index_config["index"] - original_omp_threads = os.environ.get("OMP_NUM_THREADS") print(f"[{index_name} SEARCH_TASK] Process ID: {os.getpid()}. Loading index from: {index_file_path}") - # These params are for loading the index instance, or for search current_build_params_for_load = dict(index_config.get("build_params", {})) - current_search_params = dict(index_config.get("search_params", {})) + current_search_params_for_method = dict(index_config.get("search_params", {})) - # Pop omp_num_threads_search before passing current_search_params to idx_instance.search - omp_search_threads = str(current_search_params.pop("omp_num_threads_search", "1")) - os.environ["OMP_NUM_THREADS"] = omp_search_threads - print(f"[{index_name} SEARCH_TASK] Set OMP_NUM_THREADS={omp_search_threads}.") + omp_search_threads_config_val = str(current_search_params_for_method.pop("omp_num_threads_search", "1")) + _apply_faiss_thread_settings(omp_search_threads_config_val, f"{index_name} SEARCH_TASK") _, query_vectors, gt_vectors = common_utils.load_data( dataset_cfg["name"], nq_override=dataset_cfg.get("num_queries")) IndexCls = INDEX_CLASSES.get(index_type_str) if IndexCls is None or (IndexCls == Scann and Scann is None): # type: ignore - if original_omp_threads is not None: os.environ["OMP_NUM_THREADS"] = original_omp_threads - else: os.environ.pop("OMP_NUM_THREADS", None) return {"error": f"Index type '{index_type_str}' unavailable for search."} - if not Path(index_file_path).exists(): # Ensure Path object for exists() - if original_omp_threads is not None: os.environ["OMP_NUM_THREADS"] = original_omp_threads - else: os.environ.pop("OMP_NUM_THREADS", None) + if not Path(index_file_path).exists(): return {"error": f"Index file {index_file_path} not found for search."} idx_instance = IndexCls() load_kwargs = {} - # Params like num_workers for Quake load are typically in "build_params" section of YAML if "num_workers" in current_build_params_for_load: load_kwargs["num_workers"] = current_build_params_for_load.get("num_workers") if "use_numa" in current_build_params_for_load: load_kwargs["use_numa"] = current_build_params_for_load.get("use_numa") if "parent_num_workers" in current_build_params_for_load: load_kwargs["parent_num_workers"] = current_build_params_for_load.get("parent_num_workers") - print(f"[{index_name} SEARCH_TASK] Loading index with kwargs: {load_kwargs}") idx_instance.load(str(index_file_path), **load_kwargs) + print(f"[{index_name} SEARCH_TASK] Index loaded with kwargs: {load_kwargs}") k_val = global_run_params["k_val"] print(f"[{index_name} SEARCH_TASK] Warmup ({global_run_params['num_warmup']} iterations)...") - # current_search_params now does NOT contain omp_num_threads_search for _ in range(global_run_params['num_warmup']): - _ = idx_instance.search(query_vectors, k_val, **current_search_params) + _ = idx_instance.search(query_vectors, k_val, **current_search_params_for_method) trial_latencies_ms = [] trial_recalls = [] print(f"[{index_name} SEARCH_TASK] Benchmarking ({global_run_params['num_trials']} trials)...") for i in range(global_run_params['num_trials']): - # current_search_params now does NOT contain omp_num_threads_search - search_result = idx_instance.search(query_vectors, k_val, **current_search_params) + search_result = idx_instance.search(query_vectors, k_val, **current_search_params_for_method) latency_ms = np.nan timing_info = getattr(search_result, "timing_info", None) if timing_info and hasattr(timing_info, "total_time_ns"): latency_ms = getattr(timing_info, "total_time_ns") / 1e6 @@ -135,9 +121,6 @@ def task_search_index( except Exception: pass trial_recalls.append(recall_val) - if original_omp_threads is not None: os.environ["OMP_NUM_THREADS"] = original_omp_threads - else: os.environ.pop("OMP_NUM_THREADS", None) - print(f"[{index_name} SEARCH_TASK] Individual trial results (Latency, Recall@K={k_val}):") for i in range(len(trial_latencies_ms)): lat_str = f"{trial_latencies_ms[i]:.3f}ms" if not np.isnan(trial_latencies_ms[i]) else "N/A" @@ -152,10 +135,10 @@ def task_search_index( final_row[f"mean_recall_at_{k_val}"] = float(np.mean(valid_recalls)) if valid_recalls else np.nan final_row[f"std_recall_at_{k_val}"] = float(np.std(valid_recalls)) if valid_recalls else np.nan - n_threads_or_workers = int(omp_search_threads) - if index_type_str == "Quake" and "num_workers" in current_build_params_for_load : - n_threads_or_workers = current_build_params_for_load.get("num_workers", n_threads_or_workers) - final_row["n_config_concurrency"] = n_threads_or_workers + n_concurrency_param = int(omp_search_threads_config_val) + if index_type_str == "Quake" and "num_workers" in current_build_params_for_load : # type: ignore + n_concurrency_param = current_build_params_for_load.get("num_workers", n_concurrency_param) # type: ignore + final_row["n_config_concurrency"] = n_concurrency_param print(f"[{index_name} SEARCH_TASK] Benchmark task finished.") return final_row @@ -178,7 +161,7 @@ def run_experiment(cfg_path_str: str, output_dir_str: str): "enable_glances": cfg.get("enable_glances_monitoring", False) } indexes_config_list = cfg["indexes"] - output_csv_name = cfg.get("output", {}).get("results_csv", "numa_multi_query_results.csv") # Changed from single_query + output_csv_name = cfg.get("output", {}).get("results_csv", "numa_multi_query_results.csv") all_experiment_rows = [] for idx_conf in indexes_config_list: @@ -195,6 +178,8 @@ def run_experiment(cfg_path_str: str, output_dir_str: str): logger.info(f"Orchestrator: Submitting BUILD task for index: {index_name}") build_log_file = process_logs_dir / f"{index_name}_build_process.log" + # OMP settings are now handled inside the tasks by _apply_faiss_thread_settings + # Other env_vars can still be passed if needed. build_env_vars = idx_conf.get("build_env_vars", {}) process_timeout = idx_conf.get("process_timeout", cfg.get("default_process_timeout", 7200)) @@ -211,7 +196,7 @@ def run_experiment(cfg_path_str: str, output_dir_str: str): continue logger.info(f"BUILD task for {index_name} succeeded. Index file: {build_result.get('data',{}).get('index_file_path')}") - built_index_file_path = Path(build_result.get("data",{}).get("index_file_path", index_file_path)) # Use confirmed path + built_index_file_path = Path(build_result.get("data",{}).get("index_file_path", index_file_path)) logger.info(f"Orchestrator: Submitting SEARCH task for index: {index_name}") search_log_file = process_logs_dir / f"{index_name}_search_process.log" @@ -244,7 +229,6 @@ def run_experiment(cfg_path_str: str, output_dir_str: str): plot_suffix = Path(output_csv_name).stem plot_x_param = "n_config_concurrency" - # Ensure 'index' column is valid before attempting groupby for plotting plot_df = final_df.dropna(subset=['index', plot_x_param]).copy() if not plot_df.empty: plot_df.loc[:, plot_x_param] = pd.to_numeric(plot_df[plot_x_param], errors='coerce').fillna(-1).astype(int) From ffc0da880a387f8a2776823c833f0a21322ff47d Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 22 May 2025 23:12:33 -0500 Subject: [PATCH 222/323] limit busy waiting --- src/cpp/include/query_coordinator.h | 9 +- src/cpp/src/query_coordinator.cpp | 156 ++++++++++-------- test/cpp/benchmark.cpp | 9 +- .../numa_multi_query/configs/sift1m.yaml | 16 ++ 4 files changed, 113 insertions(+), 77 deletions(-) diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index 60f8bd33..8e6ee53d 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -10,7 +10,9 @@ #include #include #include +#include #include +#include "blockingconcurrentqueue.h" class QuakeIndex; class PartitionManager; @@ -57,7 +59,8 @@ class QueryCoordinator { struct CoreResources { int core_id; std::vector> topk_buffer_pool; - moodycamel::ConcurrentQueue job_queue; +// moodycamel::ConcurrentQueue job_queue; + moodycamel::BlockingReaderWriterQueue job_queue; // Thread-local buffers for batched queries float* batch_queries = nullptr; // batched query buffer (NUMA-allocated) @@ -79,10 +82,10 @@ class QueryCoordinator { std::vector indices; }; - vector core_resources_; ///< Per‑core resources for worker threads. vector numa_resources_; - moodycamel::ConcurrentQueue result_queue_; + moodycamel::BlockingConcurrentQueue result_queue_; +// moodycamel::ConcurrentQueue result_queue_; bool workers_initialized_ = false; ///< Flag indicating if worker threads are initialized. int num_workers_; ///< Total number of worker threads. diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index c831e108..6c6f6e8f 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -66,6 +66,9 @@ void QueryCoordinator::allocate_core_resources(int core_idx, } } + + + void QueryCoordinator::partition_scan_worker_fn(int core_index) { CoreResources &res = core_resources_[core_index]; int numa_node = 0; @@ -79,10 +82,8 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { int i = 0; while (!stop_workers_) { int64_t jid = 0; - if (!res.job_queue.try_dequeue(jid)) { - std::this_thread::sleep_for(std::chrono::microseconds(20)); - continue; - } + + res.job_queue.wait_dequeue(jid); if (jid == -1) { break; @@ -493,88 +494,99 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, } } -void QueryCoordinator::drain_and_apply_aps(Tensor x, - Tensor partition_ids, - bool use_aps, - float recall_target, - float aps_flush_period_us, - shared_ptr timing) + +void QueryCoordinator::drain_and_apply_aps(Tensor x, + Tensor partition_ids, + bool use_aps, + float recall_target, + float aps_flush_period_us, + std::shared_ptr timing) { - // precompute boundary distances if needed - int nQ = x.size(0); - int D = x.size(1); - int nprobe = partition_ids.size(1); - vector> boundary_dist(nQ); - vector query_radius(nQ, 0.0f); - vector> probs(nQ); - if (use_aps) { + const int64_t nQ = x.size(0); + const int64_t D = x.size(1); + const int64_t nprobe = partition_ids.size(1); + const auto aps_dur = std::chrono::microseconds( + static_cast(aps_flush_period_us)); + + /* ------------------------------------------------------------------------ */ + /* Book-keeping */ + /* ------------------------------------------------------------------------ */ + std::vector parts_left(nQ, 0); + std::atomic total_left{0}; + for (int64_t q = 0; q < nQ; ++q) { + for (int64_t p = 0; p < nprobe; ++p) + if (!job_flags_[q][p]) { ++parts_left[q]; ++total_left; } + } + + /* APS pre-computations -------------------------------------------------- */ + std::vector query_radius(nQ, 0.0f); + std::vector> boundary_dist(nQ); + std::vector> probs(nQ); + if (use_aps) { + query_radius.resize(nQ); + boundary_dist.resize(nQ); + probs.resize(nQ); for (int64_t q = 0; q < nQ; ++q) { - vector partition_ids_to_scan_vec = std::vector(partition_ids[q].data_ptr(), - partition_ids[q].data_ptr() + partition_ids[q].size(0)); - vector cluster_centroids = parent_->partition_manager_->get_vectors(partition_ids_to_scan_vec); - boundary_dist[q] = compute_boundary_distances(x[q], cluster_centroids, metric_ == faiss::METRIC_L2); + std::vector pids(partition_ids[q].data_ptr(), + partition_ids[q].data_ptr()+nprobe); + auto centroids = parent_->partition_manager_->get_vectors(pids); + centroids.erase(std::remove(centroids.begin(), centroids.end(), nullptr), + centroids.end()); + boundary_dist[q] = + compute_boundary_distances(x[q], centroids, metric_==faiss::METRIC_L2); } - } - auto last_flush = high_resolution_clock::now(); + ResultJob rj; + while (total_left.load(std::memory_order_relaxed) > 0) { - int64_t jobsReceived = 0; - while (true) { - // 1) drain all ready results - ResultJob rj; - while (result_queue_.try_dequeue(rj)) { - ++jobsReceived; - global_topk_buffer_pool_[rj.query_id] - ->batch_add(rj.distances.data(), - rj.indices.data(), - (int)rj.indices.size()); + /* -- Wait for result or APS timeout -- */ + bool got_result = result_queue_.wait_dequeue_timed(rj, aps_dur); - job_flags_[rj.query_id][rj.rank] = true; - } - // 2) check done - bool all_done = true; - for (int64_t q = 0; q < nQ; ++q) { - for (int64_t p = 0; p < nprobe; ++p) { - if (!job_flags_[q][p]) { - all_done = false; - break; + if (got_result) { + do { // Drain *all* currently available results. + auto &buf = global_topk_buffer_pool_[rj.query_id]; + buf->batch_add(rj.distances.data(), rj.indices.data(), + static_cast(rj.indices.size())); + + if (!job_flags_[rj.query_id][rj.rank]) { + job_flags_[rj.query_id][rj.rank] = true; + --parts_left[rj.query_id]; + --total_left; } - } + } while (result_queue_.try_dequeue(rj)); + + continue; // Skip APS; we just did useful work. } - if (all_done) break; - // 3) APS early-stop - if (use_aps && duration_cast(high_resolution_clock::now() - last_flush).count() - > aps_flush_period_us) - { + /* (b) Timeout fired → run APS once. */ + if (use_aps) { for (int64_t q = 0; q < nQ; ++q) { - auto buf = global_topk_buffer_pool_[q]; - float r = buf->get_kth_distance(); - if (r != query_radius[q]) { - query_radius[q] = r; - probs[q] = compute_recall_profile( - boundary_dist[q], - r, - D, - {}, - true, - metric_==faiss::METRIC_L2 - ); - } - float cum=0; - for (int i=0; i<(int)job_flags_[q].size(); ++i) - if (job_flags_[q][i]) cum += probs[q][i]; - if (cum > recall_target) { - // set all jobs to done - for (int i=0; i<(int)job_flags_[q].size(); ++i) - job_flags_[q][i] = true; - } + if (parts_left[q] == 0) continue; + + auto &buf = global_topk_buffer_pool_[q]; + const float r = buf->get_kth_distance(); + if (r != query_radius[q]) { + query_radius[q] = r; + probs[q] = compute_recall_profile( + boundary_dist[q], r, D, {}, true, metric_==faiss::METRIC_L2); + } + + float cum = 0.0f; + for (int64_t p = 0; p < nprobe; ++p) + if (job_flags_[q][p]) cum += probs[q][p]; + + if (cum >= recall_target) { + for (int64_t p = 0; p < nprobe; ++p) + if (!job_flags_[q][p]) { + job_flags_[q][p] = true; + --parts_left[q]; + --total_left; + } } } - last_flush = high_resolution_clock::now(); - std::this_thread::sleep_for(std::chrono::microseconds(5)); + } } } diff --git a/test/cpp/benchmark.cpp b/test/cpp/benchmark.cpp index f4b8d380..e9a836c3 100644 --- a/test/cpp/benchmark.cpp +++ b/test/cpp/benchmark.cpp @@ -29,10 +29,11 @@ using torch::Tensor; static const int64_t DIM = 128; static const int64_t NUM_VECTORS = 100000; // number of database vectors static const int64_t N_LIST = 1000; // number of clusters for IVF -static const int64_t NUM_QUERIES = 10; // number of queries for search benchmark -static const int64_t K = 10; // top-K neighbors +static const int64_t NUM_QUERIES = 10000; // number of queries for search benchmark +static const int64_t K = 1; // top-K neighbors static const int64_t N_PROBE = 20; // number of probes for IVF static const int64_t N_WORKERS = 12; // number of workers for parallel query coordinator +static const int64_t N_PARENT_WORKERS = 12; // Helper functions to generate random data and sequential IDs static Tensor generate_data(int64_t num, int64_t dim) { @@ -117,6 +118,8 @@ class QuakeWorkerIVFBenchmark : public ::testing::Test { build_params->metric = "l2"; build_params->niter = 3; build_params->num_workers = N_WORKERS; + build_params->parent_params = std::make_shared(); + build_params->parent_params->num_workers = N_PARENT_WORKERS; index_->build(data_, ids_, build_params); } }; @@ -305,6 +308,8 @@ TEST_F(QuakeWorkerIVFBenchmark, SearchBatch) { search_params->k = K; search_params->nprobe = N_PROBE; search_params->batched_scan = true; + search_params->parent_params = std::make_shared(); + search_params->parent_params->batched_scan = true; index_->search(queries, search_params); diff --git a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml index 70a20c64..81e203a6 100644 --- a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml +++ b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml @@ -17,6 +17,7 @@ indexes: metric: l2 num_workers: 16 use_numa: false + parent_num_workers: 4 search_params: nprobe: 20 batched_scan: true @@ -28,6 +29,7 @@ indexes: metric: l2 num_workers: 16 use_numa: true + parent_num_workers: 4 search_params: nprobe: 20 batched_scan: true @@ -39,6 +41,7 @@ indexes: metric: l2 num_workers: 16 use_numa: true + parent_num_workers: 4 search_params: nprobe: 20 batched_scan: false @@ -51,6 +54,19 @@ indexes: search_params: nprobe: 20 omp_num_threads_search: 16 + + + - name: FaissHNSW + index: HNSW + build_params: + m: 64 + ef_construction: 128 + metric: l2 + omp_num_threads_build: 32 + search_params: + ef_search: 128 + omp_num_threads_search: 32 + # # - name: SCANN # index: SCANN From 374118a8a2f545ca62573c3e8ebfda7de8086d9f Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 22 May 2025 23:14:16 -0500 Subject: [PATCH 223/323] limit busy waiting --- src/cpp/include/sorting/atomicops.h | 772 +++++++++++++++ src/cpp/include/sorting/readerwriterqueue.h | 979 ++++++++++++++++++++ 2 files changed, 1751 insertions(+) create mode 100644 src/cpp/include/sorting/atomicops.h create mode 100644 src/cpp/include/sorting/readerwriterqueue.h diff --git a/src/cpp/include/sorting/atomicops.h b/src/cpp/include/sorting/atomicops.h new file mode 100644 index 00000000..1832dee8 --- /dev/null +++ b/src/cpp/include/sorting/atomicops.h @@ -0,0 +1,772 @@ +// ©2013-2016 Cameron Desrochers. +// Distributed under the simplified BSD license (see the license file that +// should have come with this header). +// Uses Jeff Preshing's semaphore implementation (under the terms of its +// separate zlib license, embedded below). + +#pragma once + +// Provides portable (VC++2010+, Intel ICC 13, GCC 4.7+, and anything C++11 compliant) implementation +// of low-level memory barriers, plus a few semi-portable utility macros (for inlining and alignment). +// Also has a basic atomic type (limited to hardware-supported atomics with no memory ordering guarantees). +// Uses the AE_* prefix for macros (historical reasons), and the "moodycamel" namespace for symbols. + +#include +#include +#include +#include +#include +#include + +// Platform detection +#if defined(__INTEL_COMPILER) +#define AE_ICC +#elif defined(_MSC_VER) +#define AE_VCPP +#elif defined(__GNUC__) +#define AE_GCC +#endif + +#if defined(_M_IA64) || defined(__ia64__) +#define AE_ARCH_IA64 +#elif defined(_WIN64) || defined(__amd64__) || defined(_M_X64) || defined(__x86_64__) +#define AE_ARCH_X64 +#elif defined(_M_IX86) || defined(__i386__) +#define AE_ARCH_X86 +#elif defined(_M_PPC) || defined(__powerpc__) +#define AE_ARCH_PPC +#else +#define AE_ARCH_UNKNOWN +#endif + + +// AE_UNUSED +#define AE_UNUSED(x) ((void)x) + +// AE_NO_TSAN/AE_TSAN_ANNOTATE_* +// For GCC +#if defined(__SANITIZE_THREAD__) +#define AE_TSAN_IS_ENABLED +#endif +// For clang +#if defined(__has_feature) +#if __has_feature(thread_sanitizer) && !defined(AE_TSAN_IS_ENABLED) +#define AE_TSAN_IS_ENABLED +#endif +#endif + +#ifdef AE_TSAN_IS_ENABLED +#if __cplusplus >= 201703L // inline variables require C++17 +namespace moodycamel { inline int ae_tsan_global; } +#define AE_TSAN_ANNOTATE_RELEASE() AnnotateHappensBefore(__FILE__, __LINE__, (void *)(&::moodycamel::ae_tsan_global)) +#define AE_TSAN_ANNOTATE_ACQUIRE() AnnotateHappensAfter(__FILE__, __LINE__, (void *)(&::moodycamel::ae_tsan_global)) +extern "C" void AnnotateHappensBefore(const char*, int, void*); +extern "C" void AnnotateHappensAfter(const char*, int, void*); +#else // when we can't work with tsan, attempt to disable its warnings +#define AE_NO_TSAN __attribute__((no_sanitize("thread"))) +#endif +#endif + +#ifndef AE_NO_TSAN +#define AE_NO_TSAN +#endif + +#ifndef AE_TSAN_ANNOTATE_RELEASE +#define AE_TSAN_ANNOTATE_RELEASE() +#define AE_TSAN_ANNOTATE_ACQUIRE() +#endif + + +// AE_FORCEINLINE +#if defined(AE_VCPP) || defined(AE_ICC) +#define AE_FORCEINLINE __forceinline +#elif defined(AE_GCC) +//#define AE_FORCEINLINE __attribute__((always_inline)) +#define AE_FORCEINLINE inline +#else +#define AE_FORCEINLINE inline +#endif + + +// AE_ALIGN +#if defined(AE_VCPP) || defined(AE_ICC) +#define AE_ALIGN(x) __declspec(align(x)) +#elif defined(AE_GCC) +#define AE_ALIGN(x) __attribute__((aligned(x))) +#else +// Assume GCC compliant syntax... +#define AE_ALIGN(x) __attribute__((aligned(x))) +#endif + + +// Portable atomic fences implemented below: + +namespace moodycamel { + + enum memory_order { + memory_order_relaxed, + memory_order_acquire, + memory_order_release, + memory_order_acq_rel, + memory_order_seq_cst, + + // memory_order_sync: Forces a full sync: + // #LoadLoad, #LoadStore, #StoreStore, and most significantly, #StoreLoad + memory_order_sync = memory_order_seq_cst + }; + +} // end namespace moodycamel + +#if (defined(AE_VCPP) && (_MSC_VER < 1700 || defined(__cplusplus_cli))) || (defined(AE_ICC) && __INTEL_COMPILER < 1600) +// VS2010 and ICC13 don't support std::atomic_*_fence, implement our own fences + +#include + +#if defined(AE_ARCH_X64) || defined(AE_ARCH_X86) +#define AeFullSync _mm_mfence +#define AeLiteSync _mm_mfence +#elif defined(AE_ARCH_IA64) +#define AeFullSync __mf +#define AeLiteSync __mf +#elif defined(AE_ARCH_PPC) +#include +#define AeFullSync __sync +#define AeLiteSync __lwsync +#endif + + +#ifdef AE_VCPP +#pragma warning(push) +#pragma warning(disable: 4365) // Disable erroneous 'conversion from long to unsigned int, signed/unsigned mismatch' error when using `assert` +#ifdef __cplusplus_cli +#pragma managed(push, off) +#endif +#endif + +namespace moodycamel { + +AE_FORCEINLINE void compiler_fence(memory_order order) AE_NO_TSAN +{ + switch (order) { + case memory_order_relaxed: break; + case memory_order_acquire: _ReadBarrier(); break; + case memory_order_release: _WriteBarrier(); break; + case memory_order_acq_rel: _ReadWriteBarrier(); break; + case memory_order_seq_cst: _ReadWriteBarrier(); break; + default: assert(false); + } +} + +// x86/x64 have a strong memory model -- all loads and stores have +// acquire and release semantics automatically (so only need compiler +// barriers for those). +#if defined(AE_ARCH_X86) || defined(AE_ARCH_X64) +AE_FORCEINLINE void fence(memory_order order) AE_NO_TSAN +{ + switch (order) { + case memory_order_relaxed: break; + case memory_order_acquire: _ReadBarrier(); break; + case memory_order_release: _WriteBarrier(); break; + case memory_order_acq_rel: _ReadWriteBarrier(); break; + case memory_order_seq_cst: + _ReadWriteBarrier(); + AeFullSync(); + _ReadWriteBarrier(); + break; + default: assert(false); + } +} +#else +AE_FORCEINLINE void fence(memory_order order) AE_NO_TSAN +{ + // Non-specialized arch, use heavier memory barriers everywhere just in case :-( + switch (order) { + case memory_order_relaxed: + break; + case memory_order_acquire: + _ReadBarrier(); + AeLiteSync(); + _ReadBarrier(); + break; + case memory_order_release: + _WriteBarrier(); + AeLiteSync(); + _WriteBarrier(); + break; + case memory_order_acq_rel: + _ReadWriteBarrier(); + AeLiteSync(); + _ReadWriteBarrier(); + break; + case memory_order_seq_cst: + _ReadWriteBarrier(); + AeFullSync(); + _ReadWriteBarrier(); + break; + default: assert(false); + } +} +#endif +} // end namespace moodycamel +#else +// Use standard library of atomics +#include + +namespace moodycamel { + + AE_FORCEINLINE void compiler_fence(memory_order order) AE_NO_TSAN + { + switch (order) { + case memory_order_relaxed: break; + case memory_order_acquire: std::atomic_signal_fence(std::memory_order_acquire); break; + case memory_order_release: std::atomic_signal_fence(std::memory_order_release); break; + case memory_order_acq_rel: std::atomic_signal_fence(std::memory_order_acq_rel); break; + case memory_order_seq_cst: std::atomic_signal_fence(std::memory_order_seq_cst); break; + default: assert(false); + } + } + + AE_FORCEINLINE void fence(memory_order order) AE_NO_TSAN + { + switch (order) { + case memory_order_relaxed: break; + case memory_order_acquire: AE_TSAN_ANNOTATE_ACQUIRE(); std::atomic_thread_fence(std::memory_order_acquire); break; + case memory_order_release: AE_TSAN_ANNOTATE_RELEASE(); std::atomic_thread_fence(std::memory_order_release); break; + case memory_order_acq_rel: AE_TSAN_ANNOTATE_ACQUIRE(); AE_TSAN_ANNOTATE_RELEASE(); std::atomic_thread_fence(std::memory_order_acq_rel); break; + case memory_order_seq_cst: AE_TSAN_ANNOTATE_ACQUIRE(); AE_TSAN_ANNOTATE_RELEASE(); std::atomic_thread_fence(std::memory_order_seq_cst); break; + default: assert(false); + } + } + +} // end namespace moodycamel + +#endif + + +#if !defined(AE_VCPP) || (_MSC_VER >= 1700 && !defined(__cplusplus_cli)) +#define AE_USE_STD_ATOMIC_FOR_WEAK_ATOMIC +#endif + +#ifdef AE_USE_STD_ATOMIC_FOR_WEAK_ATOMIC +#include +#endif +#include + +// WARNING: *NOT* A REPLACEMENT FOR std::atomic. READ CAREFULLY: +// Provides basic support for atomic variables -- no memory ordering guarantees are provided. +// The guarantee of atomicity is only made for types that already have atomic load and store guarantees +// at the hardware level -- on most platforms this generally means aligned pointers and integers (only). +namespace moodycamel { + template + class weak_atomic + { + public: + AE_NO_TSAN weak_atomic() : value() { } +#ifdef AE_VCPP + #pragma warning(push) +#pragma warning(disable: 4100) // Get rid of (erroneous) 'unreferenced formal parameter' warning +#endif + template AE_NO_TSAN weak_atomic(U&& x) : value(std::forward(x)) { } +#ifdef __cplusplus_cli + // Work around bug with universal reference/nullptr combination that only appears when /clr is on + AE_NO_TSAN weak_atomic(nullptr_t) : value(nullptr) { } +#endif + AE_NO_TSAN weak_atomic(weak_atomic const& other) : value(other.load()) { } + AE_NO_TSAN weak_atomic(weak_atomic&& other) : value(std::move(other.load())) { } +#ifdef AE_VCPP +#pragma warning(pop) +#endif + + AE_FORCEINLINE operator T() const AE_NO_TSAN { return load(); } + + +#ifndef AE_USE_STD_ATOMIC_FOR_WEAK_ATOMIC + template AE_FORCEINLINE weak_atomic const& operator=(U&& x) AE_NO_TSAN { value = std::forward(x); return *this; } + AE_FORCEINLINE weak_atomic const& operator=(weak_atomic const& other) AE_NO_TSAN { value = other.value; return *this; } + + AE_FORCEINLINE T load() const AE_NO_TSAN { return value; } + + AE_FORCEINLINE T fetch_add_acquire(T increment) AE_NO_TSAN + { +#if defined(AE_ARCH_X64) || defined(AE_ARCH_X86) + if (sizeof(T) == 4) return _InterlockedExchangeAdd((long volatile*)&value, (long)increment); +#if defined(_M_AMD64) + else if (sizeof(T) == 8) return _InterlockedExchangeAdd64((long long volatile*)&value, (long long)increment); +#endif +#else +#error Unsupported platform +#endif + assert(false && "T must be either a 32 or 64 bit type"); + return value; + } + + AE_FORCEINLINE T fetch_add_release(T increment) AE_NO_TSAN + { +#if defined(AE_ARCH_X64) || defined(AE_ARCH_X86) + if (sizeof(T) == 4) return _InterlockedExchangeAdd((long volatile*)&value, (long)increment); +#if defined(_M_AMD64) + else if (sizeof(T) == 8) return _InterlockedExchangeAdd64((long long volatile*)&value, (long long)increment); +#endif +#else +#error Unsupported platform +#endif + assert(false && "T must be either a 32 or 64 bit type"); + return value; + } +#else + template + AE_FORCEINLINE weak_atomic const& operator=(U&& x) AE_NO_TSAN + { + value.store(std::forward(x), std::memory_order_relaxed); + return *this; + } + + AE_FORCEINLINE weak_atomic const& operator=(weak_atomic const& other) AE_NO_TSAN + { + value.store(other.value.load(std::memory_order_relaxed), std::memory_order_relaxed); + return *this; + } + + AE_FORCEINLINE T load() const AE_NO_TSAN { return value.load(std::memory_order_relaxed); } + + AE_FORCEINLINE T fetch_add_acquire(T increment) AE_NO_TSAN + { + return value.fetch_add(increment, std::memory_order_acquire); + } + + AE_FORCEINLINE T fetch_add_release(T increment) AE_NO_TSAN + { + return value.fetch_add(increment, std::memory_order_release); + } +#endif + + + private: +#ifndef AE_USE_STD_ATOMIC_FOR_WEAK_ATOMIC + // No std::atomic support, but still need to circumvent compiler optimizations. + // `volatile` will make memory access slow, but is guaranteed to be reliable. + volatile T value; +#else + std::atomic value; +#endif + }; + +} // end namespace moodycamel + + + +// Portable single-producer, single-consumer semaphore below: + +#if defined(_WIN32) +// Avoid including windows.h in a header; we only need a handful of +// items, so we'll redeclare them here (this is relatively safe since +// the API generally has to remain stable between Windows versions). +// I know this is an ugly hack but it still beats polluting the global +// namespace with thousands of generic names or adding a .cpp for nothing. +extern "C" { + struct _SECURITY_ATTRIBUTES; + __declspec(dllimport) void* __stdcall CreateSemaphoreW(_SECURITY_ATTRIBUTES* lpSemaphoreAttributes, long lInitialCount, long lMaximumCount, const wchar_t* lpName); + __declspec(dllimport) int __stdcall CloseHandle(void* hObject); + __declspec(dllimport) unsigned long __stdcall WaitForSingleObject(void* hHandle, unsigned long dwMilliseconds); + __declspec(dllimport) int __stdcall ReleaseSemaphore(void* hSemaphore, long lReleaseCount, long* lpPreviousCount); +} +#elif defined(__MACH__) +#include +#elif defined(__unix__) +#include +#elif defined(FREERTOS) +#include +#include +#include +#endif + +namespace moodycamel +{ + // Code in the spsc_sema namespace below is an adaptation of Jeff Preshing's + // portable + lightweight semaphore implementations, originally from + // https://github.com/preshing/cpp11-on-multicore/blob/master/common/sema.h + // LICENSE: + // Copyright (c) 2015 Jeff Preshing + // + // This software is provided 'as-is', without any express or implied + // warranty. In no event will the authors be held liable for any damages + // arising from the use of this software. + // + // Permission is granted to anyone to use this software for any purpose, + // including commercial applications, and to alter it and redistribute it + // freely, subject to the following restrictions: + // + // 1. The origin of this software must not be misrepresented; you must not + // claim that you wrote the original software. If you use this software + // in a product, an acknowledgement in the product documentation would be + // appreciated but is not required. + // 2. Altered source versions must be plainly marked as such, and must not be + // misrepresented as being the original software. + // 3. This notice may not be removed or altered from any source distribution. + namespace spsc_sema + { +#if defined(_WIN32) + class Semaphore + { + private: + void* m_hSema; + + Semaphore(const Semaphore& other); + Semaphore& operator=(const Semaphore& other); + + public: + AE_NO_TSAN Semaphore(int initialCount = 0) : m_hSema() + { + assert(initialCount >= 0); + const long maxLong = 0x7fffffff; + m_hSema = CreateSemaphoreW(nullptr, initialCount, maxLong, nullptr); + assert(m_hSema); + } + + AE_NO_TSAN ~Semaphore() + { + CloseHandle(m_hSema); + } + + bool wait() AE_NO_TSAN + { + const unsigned long infinite = 0xffffffff; + return WaitForSingleObject(m_hSema, infinite) == 0; + } + + bool try_wait() AE_NO_TSAN + { + return WaitForSingleObject(m_hSema, 0) == 0; + } + + bool timed_wait(std::uint64_t usecs) AE_NO_TSAN + { + return WaitForSingleObject(m_hSema, (unsigned long)(usecs / 1000)) == 0; + } + + void signal(int count = 1) AE_NO_TSAN + { + while (!ReleaseSemaphore(m_hSema, count, nullptr)); + } + }; +#elif defined(__MACH__) + //--------------------------------------------------------- + // Semaphore (Apple iOS and OSX) + // Can't use POSIX semaphores due to http://lists.apple.com/archives/darwin-kernel/2009/Apr/msg00010.html + //--------------------------------------------------------- + class Semaphore + { + private: + semaphore_t m_sema; + + Semaphore(const Semaphore& other); + Semaphore& operator=(const Semaphore& other); + + public: + AE_NO_TSAN Semaphore(int initialCount = 0) : m_sema() + { + assert(initialCount >= 0); + kern_return_t rc = semaphore_create(mach_task_self(), &m_sema, SYNC_POLICY_FIFO, initialCount); + assert(rc == KERN_SUCCESS); + AE_UNUSED(rc); + } + + AE_NO_TSAN ~Semaphore() + { + semaphore_destroy(mach_task_self(), m_sema); + } + + bool wait() AE_NO_TSAN + { + return semaphore_wait(m_sema) == KERN_SUCCESS; + } + + bool try_wait() AE_NO_TSAN + { + return timed_wait(0); + } + + bool timed_wait(std::uint64_t timeout_usecs) AE_NO_TSAN + { + mach_timespec_t ts; + ts.tv_sec = static_cast(timeout_usecs / 1000000); + ts.tv_nsec = static_cast((timeout_usecs % 1000000) * 1000); + + // added in OSX 10.10: https://developer.apple.com/library/prerelease/mac/documentation/General/Reference/APIDiffsMacOSX10_10SeedDiff/modules/Darwin.html + kern_return_t rc = semaphore_timedwait(m_sema, ts); + return rc == KERN_SUCCESS; + } + + void signal() AE_NO_TSAN + { + while (semaphore_signal(m_sema) != KERN_SUCCESS); + } + + void signal(int count) AE_NO_TSAN + { + while (count-- > 0) + { + while (semaphore_signal(m_sema) != KERN_SUCCESS); + } + } + }; +#elif defined(__unix__) + //--------------------------------------------------------- + // Semaphore (POSIX, Linux) + //--------------------------------------------------------- + class Semaphore + { + private: + sem_t m_sema; + + Semaphore(const Semaphore& other); + Semaphore& operator=(const Semaphore& other); + + public: + AE_NO_TSAN Semaphore(int initialCount = 0) : m_sema() + { + assert(initialCount >= 0); + int rc = sem_init(&m_sema, 0, static_cast(initialCount)); + assert(rc == 0); + AE_UNUSED(rc); + } + + AE_NO_TSAN ~Semaphore() + { + sem_destroy(&m_sema); + } + + bool wait() AE_NO_TSAN + { + // http://stackoverflow.com/questions/2013181/gdb-causes-sem-wait-to-fail-with-eintr-error + int rc; + do + { + rc = sem_wait(&m_sema); + } + while (rc == -1 && errno == EINTR); + return rc == 0; + } + + bool try_wait() AE_NO_TSAN + { + int rc; + do { + rc = sem_trywait(&m_sema); + } while (rc == -1 && errno == EINTR); + return rc == 0; + } + + bool timed_wait(std::uint64_t usecs) AE_NO_TSAN + { + struct timespec ts; + const int usecs_in_1_sec = 1000000; + const int nsecs_in_1_sec = 1000000000; + clock_gettime(CLOCK_REALTIME, &ts); + ts.tv_sec += static_cast(usecs / usecs_in_1_sec); + ts.tv_nsec += static_cast(usecs % usecs_in_1_sec) * 1000; + // sem_timedwait bombs if you have more than 1e9 in tv_nsec + // so we have to clean things up before passing it in + if (ts.tv_nsec >= nsecs_in_1_sec) { + ts.tv_nsec -= nsecs_in_1_sec; + ++ts.tv_sec; + } + + int rc; + do { + rc = sem_timedwait(&m_sema, &ts); + } while (rc == -1 && errno == EINTR); + return rc == 0; + } + + void signal() AE_NO_TSAN + { + while (sem_post(&m_sema) == -1); + } + + void signal(int count) AE_NO_TSAN + { + while (count-- > 0) + { + while (sem_post(&m_sema) == -1); + } + } + }; +#elif defined(FREERTOS) + //--------------------------------------------------------- + // Semaphore (FreeRTOS) + //--------------------------------------------------------- + class Semaphore + { + private: + SemaphoreHandle_t m_sema; + + Semaphore(const Semaphore& other); + Semaphore& operator=(const Semaphore& other); + + public: + AE_NO_TSAN Semaphore(int initialCount = 0) : m_sema() + { + assert(initialCount >= 0); + m_sema = xSemaphoreCreateCounting(static_cast(~0ull), static_cast(initialCount)); + assert(m_sema); + } + + AE_NO_TSAN ~Semaphore() + { + vSemaphoreDelete(m_sema); + } + + bool wait() AE_NO_TSAN + { + return xSemaphoreTake(m_sema, portMAX_DELAY) == pdTRUE; + } + + bool try_wait() AE_NO_TSAN + { + // Note: In an ISR context, if this causes a task to unblock, + // the caller won't know about it + if (xPortIsInsideInterrupt()) + return xSemaphoreTakeFromISR(m_sema, NULL) == pdTRUE; + return xSemaphoreTake(m_sema, 0) == pdTRUE; + } + + bool timed_wait(std::uint64_t usecs) AE_NO_TSAN + { + std::uint64_t msecs = usecs / 1000; + TickType_t ticks = static_cast(msecs / portTICK_PERIOD_MS); + if (ticks == 0) + return try_wait(); + return xSemaphoreTake(m_sema, ticks) == pdTRUE; + } + + void signal() AE_NO_TSAN + { + // Note: In an ISR context, if this causes a task to unblock, + // the caller won't know about it + BaseType_t rc; + if (xPortIsInsideInterrupt()) + rc = xSemaphoreGiveFromISR(m_sema, NULL); + else + rc = xSemaphoreGive(m_sema); + assert(rc == pdTRUE); + AE_UNUSED(rc); + } + + void signal(int count) AE_NO_TSAN + { + while (count-- > 0) + signal(); + } + }; +#else +#error Unsupported platform! (No semaphore wrapper available) +#endif + + //--------------------------------------------------------- + // LightweightSemaphore + //--------------------------------------------------------- + class LightweightSemaphore + { + public: + typedef std::make_signed::type ssize_t; + + private: + weak_atomic m_count; + Semaphore m_sema; + + bool waitWithPartialSpinning(std::int64_t timeout_usecs = -1) AE_NO_TSAN + { + ssize_t oldCount; + // Is there a better way to set the initial spin count? + // If we lower it to 1000, testBenaphore becomes 15x slower on my Core i7-5930K Windows PC, + // as threads start hitting the kernel semaphore. + int spin = 1024; + while (--spin >= 0) + { + if (m_count.load() > 0) + { + m_count.fetch_add_acquire(-1); + return true; + } + compiler_fence(memory_order_acquire); // Prevent the compiler from collapsing the loop. + } + oldCount = m_count.fetch_add_acquire(-1); + if (oldCount > 0) + return true; + if (timeout_usecs < 0) + { + if (m_sema.wait()) + return true; + } + if (timeout_usecs > 0 && m_sema.timed_wait(static_cast(timeout_usecs))) + return true; + // At this point, we've timed out waiting for the semaphore, but the + // count is still decremented indicating we may still be waiting on + // it. So we have to re-adjust the count, but only if the semaphore + // wasn't signaled enough times for us too since then. If it was, we + // need to release the semaphore too. + while (true) + { + oldCount = m_count.fetch_add_release(1); + if (oldCount < 0) + return false; // successfully restored things to the way they were + // Oh, the producer thread just signaled the semaphore after all. Try again: + oldCount = m_count.fetch_add_acquire(-1); + if (oldCount > 0 && m_sema.try_wait()) + return true; + } + } + + public: + AE_NO_TSAN LightweightSemaphore(ssize_t initialCount = 0) : m_count(initialCount), m_sema() + { + assert(initialCount >= 0); + } + + bool tryWait() AE_NO_TSAN + { + if (m_count.load() > 0) + { + m_count.fetch_add_acquire(-1); + return true; + } + return false; + } + + bool wait() AE_NO_TSAN + { + return tryWait() || waitWithPartialSpinning(); + } + + bool wait(std::int64_t timeout_usecs) AE_NO_TSAN + { + return tryWait() || waitWithPartialSpinning(timeout_usecs); + } + + void signal(ssize_t count = 1) AE_NO_TSAN + { + assert(count >= 0); + ssize_t oldCount = m_count.fetch_add_release(count); + assert(oldCount >= -1); + if (oldCount < 0) + { + m_sema.signal(1); + } + } + + std::size_t availableApprox() const AE_NO_TSAN + { + ssize_t count = m_count.load(); + return count > 0 ? static_cast(count) : 0; + } + }; + } // end namespace spsc_sema +} // end namespace moodycamel + +#if defined(AE_VCPP) && (_MSC_VER < 1700 || defined(__cplusplus_cli)) +#pragma warning(pop) +#ifdef __cplusplus_cli +#pragma managed(pop) +#endif +#endif \ No newline at end of file diff --git a/src/cpp/include/sorting/readerwriterqueue.h b/src/cpp/include/sorting/readerwriterqueue.h new file mode 100644 index 00000000..098d47cf --- /dev/null +++ b/src/cpp/include/sorting/readerwriterqueue.h @@ -0,0 +1,979 @@ +// ©2013-2020 Cameron Desrochers. +// Distributed under the simplified BSD license (see the license file that +// should have come with this header). + +#pragma once + +#include "atomicops.h" +#include +#include +#include +#include +#include +#include +#include +#include // For malloc/free/abort & size_t +#include +#if __cplusplus > 199711L || _MSC_VER >= 1700 // C++11 or VS2012 +#include +#endif + + +// A lock-free queue for a single-consumer, single-producer architecture. +// The queue is also wait-free in the common path (except if more memory +// needs to be allocated, in which case malloc is called). +// Allocates memory sparingly, and only once if the original maximum size +// estimate is never exceeded. +// Tested on x86/x64 processors, but semantics should be correct for all +// architectures (given the right implementations in atomicops.h), provided +// that aligned integer and pointer accesses are naturally atomic. +// Note that there should only be one consumer thread and producer thread; +// Switching roles of the threads, or using multiple consecutive threads for +// one role, is not safe unless properly synchronized. +// Using the queue exclusively from one thread is fine, though a bit silly. + +#ifndef MOODYCAMEL_CACHE_LINE_SIZE +#define MOODYCAMEL_CACHE_LINE_SIZE 64 +#endif + +#ifndef MOODYCAMEL_EXCEPTIONS_ENABLED +#if (defined(_MSC_VER) && defined(_CPPUNWIND)) || (defined(__GNUC__) && defined(__EXCEPTIONS)) || (!defined(_MSC_VER) && !defined(__GNUC__)) +#define MOODYCAMEL_EXCEPTIONS_ENABLED +#endif +#endif + +#ifndef MOODYCAMEL_HAS_EMPLACE +#if !defined(_MSC_VER) || _MSC_VER >= 1800 // variadic templates: either a non-MS compiler or VS >= 2013 +#define MOODYCAMEL_HAS_EMPLACE 1 +#endif +#endif + +#ifndef MOODYCAMEL_MAYBE_ALIGN_TO_CACHELINE +#if defined (__APPLE__) && defined (__MACH__) && __cplusplus >= 201703L +// This is required to find out what deployment target we are using +#include +#if !defined(MAC_OS_X_VERSION_MIN_REQUIRED) || !defined(MAC_OS_X_VERSION_10_14) || MAC_OS_X_VERSION_MIN_REQUIRED < MAC_OS_X_VERSION_10_14 +// C++17 new(size_t, align_val_t) is not backwards-compatible with older versions of macOS, so we can't support over-alignment in this case +#define MOODYCAMEL_MAYBE_ALIGN_TO_CACHELINE +#endif +#endif +#endif + +#ifndef MOODYCAMEL_MAYBE_ALIGN_TO_CACHELINE +#define MOODYCAMEL_MAYBE_ALIGN_TO_CACHELINE AE_ALIGN(MOODYCAMEL_CACHE_LINE_SIZE) +#endif + +#ifdef AE_VCPP +#pragma warning(push) +#pragma warning(disable: 4324) // structure was padded due to __declspec(align()) +#pragma warning(disable: 4820) // padding was added +#pragma warning(disable: 4127) // conditional expression is constant +#endif + +namespace moodycamel { + + template + class MOODYCAMEL_MAYBE_ALIGN_TO_CACHELINE ReaderWriterQueue +{ + // Design: Based on a queue-of-queues. The low-level queues are just + // circular buffers with front and tail indices indicating where the + // next element to dequeue is and where the next element can be enqueued, + // respectively. Each low-level queue is called a "block". Each block + // wastes exactly one element's worth of space to keep the design simple + // (if front == tail then the queue is empty, and can't be full). + // The high-level queue is a circular linked list of blocks; again there + // is a front and tail, but this time they are pointers to the blocks. + // The front block is where the next element to be dequeued is, provided + // the block is not empty. The back block is where elements are to be + // enqueued, provided the block is not full. + // The producer thread owns all the tail indices/pointers. The consumer + // thread owns all the front indices/pointers. Both threads read each + // other's variables, but only the owning thread updates them. E.g. After + // the consumer reads the producer's tail, the tail may change before the + // consumer is done dequeuing an object, but the consumer knows the tail + // will never go backwards, only forwards. + // If there is no room to enqueue an object, an additional block (of + // equal size to the last block) is added. Blocks are never removed. + + public: + typedef T value_type; + + // Constructs a queue that can hold at least `size` elements without further + // allocations. If more than MAX_BLOCK_SIZE elements are requested, + // then several blocks of MAX_BLOCK_SIZE each are reserved (including + // at least one extra buffer block). + AE_NO_TSAN explicit ReaderWriterQueue(size_t size = 15) +#ifndef NDEBUG + : enqueuing(false) + ,dequeuing(false) +#endif +{ + assert(MAX_BLOCK_SIZE == ceilToPow2(MAX_BLOCK_SIZE) && "MAX_BLOCK_SIZE must be a power of 2"); + assert(MAX_BLOCK_SIZE >= 2 && "MAX_BLOCK_SIZE must be at least 2"); + + Block* firstBlock = nullptr; + + largestBlockSize = ceilToPow2(size + 1); // We need a spare slot to fit size elements in the block + if (largestBlockSize > MAX_BLOCK_SIZE * 2) { + // We need a spare block in case the producer is writing to a different block the consumer is reading from, and + // wants to enqueue the maximum number of elements. We also need a spare element in each block to avoid the ambiguity + // between front == tail meaning "empty" and "full". + // So the effective number of slots that are guaranteed to be usable at any time is the block size - 1 times the + // number of blocks - 1. Solving for size and applying a ceiling to the division gives us (after simplifying): + size_t initialBlockCount = (size + MAX_BLOCK_SIZE * 2 - 3) / (MAX_BLOCK_SIZE - 1); + largestBlockSize = MAX_BLOCK_SIZE; + Block* lastBlock = nullptr; + for (size_t i = 0; i != initialBlockCount; ++i) { + auto block = make_block(largestBlockSize); + if (block == nullptr) { +#ifdef MOODYCAMEL_EXCEPTIONS_ENABLED + throw std::bad_alloc(); +#else + abort(); +#endif +} +if (firstBlock == nullptr) { +firstBlock = block; +} +else { +lastBlock->next = block; +} +lastBlock = block; +block->next = firstBlock; +} +} +else { +firstBlock = make_block(largestBlockSize); +if (firstBlock == nullptr) { +#ifdef MOODYCAMEL_EXCEPTIONS_ENABLED +throw std::bad_alloc(); +#else +abort(); +#endif +} +firstBlock->next = firstBlock; +} +frontBlock = firstBlock; +tailBlock = firstBlock; + +// Make sure the reader/writer threads will have the initialized memory setup above: +fence(memory_order_sync); +} + +// Note: The queue should not be accessed concurrently while it's +// being moved. It's up to the user to synchronize this. +AE_NO_TSAN ReaderWriterQueue(ReaderWriterQueue&& other) +: frontBlock(other.frontBlock.load()), +tailBlock(other.tailBlock.load()), +largestBlockSize(other.largestBlockSize) +#ifndef NDEBUG +,enqueuing(false) +,dequeuing(false) +#endif +{ +other.largestBlockSize = 32; +Block* b = other.make_block(other.largestBlockSize); +if (b == nullptr) { +#ifdef MOODYCAMEL_EXCEPTIONS_ENABLED +throw std::bad_alloc(); +#else +abort(); +#endif +} +b->next = b; +other.frontBlock = b; +other.tailBlock = b; +} + +// Note: The queue should not be accessed concurrently while it's +// being moved. It's up to the user to synchronize this. +ReaderWriterQueue& operator=(ReaderWriterQueue&& other) AE_NO_TSAN +{ +Block* b = frontBlock.load(); +frontBlock = other.frontBlock.load(); +other.frontBlock = b; +b = tailBlock.load(); +tailBlock = other.tailBlock.load(); +other.tailBlock = b; +std::swap(largestBlockSize, other.largestBlockSize); +return *this; +} + +// Note: The queue should not be accessed concurrently while it's +// being deleted. It's up to the user to synchronize this. +AE_NO_TSAN ~ReaderWriterQueue() +{ + // Make sure we get the latest version of all variables from other CPUs: + fence(memory_order_sync); + + // Destroy any remaining objects in queue and free memory + Block* frontBlock_ = frontBlock; + Block* block = frontBlock_; + do { + Block* nextBlock = block->next; + size_t blockFront = block->front; + size_t blockTail = block->tail; + + for (size_t i = blockFront; i != blockTail; i = (i + 1) & block->sizeMask) { + auto element = reinterpret_cast(block->data + i * sizeof(T)); + element->~T(); + (void)element; + } + + auto rawBlock = block->rawThis; + block->~Block(); + std::free(rawBlock); + block = nextBlock; + } while (block != frontBlock_); +} + + +// Enqueues a copy of element if there is room in the queue. +// Returns true if the element was enqueued, false otherwise. +// Does not allocate memory. +AE_FORCEINLINE bool try_enqueue(T const& element) AE_NO_TSAN +{ +return inner_enqueue(element); +} + +// Enqueues a moved copy of element if there is room in the queue. +// Returns true if the element was enqueued, false otherwise. +// Does not allocate memory. +AE_FORCEINLINE bool try_enqueue(T&& element) AE_NO_TSAN +{ +return inner_enqueue(std::forward(element)); +} + +#if MOODYCAMEL_HAS_EMPLACE +// Like try_enqueue() but with emplace semantics (i.e. construct-in-place). +template +AE_FORCEINLINE bool try_emplace(Args&&... args) AE_NO_TSAN +{ +return inner_enqueue(std::forward(args)...); +} +#endif + +// Enqueues a copy of element on the queue. +// Allocates an additional block of memory if needed. +// Only fails (returns false) if memory allocation fails. +AE_FORCEINLINE bool enqueue(T const& element) AE_NO_TSAN +{ +return inner_enqueue(element); +} + +// Enqueues a moved copy of element on the queue. +// Allocates an additional block of memory if needed. +// Only fails (returns false) if memory allocation fails. +AE_FORCEINLINE bool enqueue(T&& element) AE_NO_TSAN +{ +return inner_enqueue(std::forward(element)); +} + +#if MOODYCAMEL_HAS_EMPLACE +// Like enqueue() but with emplace semantics (i.e. construct-in-place). +template +AE_FORCEINLINE bool emplace(Args&&... args) AE_NO_TSAN +{ +return inner_enqueue(std::forward(args)...); +} +#endif + +// Attempts to dequeue an element; if the queue is empty, +// returns false instead. If the queue has at least one element, +// moves front to result using operator=, then returns true. +template +bool try_dequeue(U& result) AE_NO_TSAN +{ +#ifndef NDEBUG +ReentrantGuard guard(this->dequeuing); +#endif + +// High-level pseudocode: +// Remember where the tail block is +// If the front block has an element in it, dequeue it +// Else +// If front block was the tail block when we entered the function, return false +// Else advance to next block and dequeue the item there + +// Note that we have to use the value of the tail block from before we check if the front +// block is full or not, in case the front block is empty and then, before we check if the +// tail block is at the front block or not, the producer fills up the front block *and +// moves on*, which would make us skip a filled block. Seems unlikely, but was consistently +// reproducible in practice. +// In order to avoid overhead in the common case, though, we do a double-checked pattern +// where we have the fast path if the front block is not empty, then read the tail block, +// then re-read the front block and check if it's not empty again, then check if the tail +// block has advanced. + +Block* frontBlock_ = frontBlock.load(); +size_t blockTail = frontBlock_->localTail; +size_t blockFront = frontBlock_->front.load(); + +if (blockFront != blockTail || blockFront != (frontBlock_->localTail = frontBlock_->tail.load())) { +fence(memory_order_acquire); + +non_empty_front_block: +// Front block not empty, dequeue from here +auto element = reinterpret_cast(frontBlock_->data + blockFront * sizeof(T)); +result = std::move(*element); +element->~T(); + +blockFront = (blockFront + 1) & frontBlock_->sizeMask; + +fence(memory_order_release); +frontBlock_->front = blockFront; +} +else if (frontBlock_ != tailBlock.load()) { +fence(memory_order_acquire); + +frontBlock_ = frontBlock.load(); +blockTail = frontBlock_->localTail = frontBlock_->tail.load(); +blockFront = frontBlock_->front.load(); +fence(memory_order_acquire); + +if (blockFront != blockTail) { +// Oh look, the front block isn't empty after all +goto non_empty_front_block; +} + +// Front block is empty but there's another block ahead, advance to it +Block* nextBlock = frontBlock_->next; +// Don't need an acquire fence here since next can only ever be set on the tailBlock, +// and we're not the tailBlock, and we did an acquire earlier after reading tailBlock which +// ensures next is up-to-date on this CPU in case we recently were at tailBlock. + +size_t nextBlockFront = nextBlock->front.load(); +size_t nextBlockTail = nextBlock->localTail = nextBlock->tail.load(); +fence(memory_order_acquire); + +// Since the tailBlock is only ever advanced after being written to, +// we know there's for sure an element to dequeue on it +assert(nextBlockFront != nextBlockTail); +AE_UNUSED(nextBlockTail); + +// We're done with this block, let the producer use it if it needs +fence(memory_order_release); // Expose possibly pending changes to frontBlock->front from last dequeue +frontBlock = frontBlock_ = nextBlock; + +compiler_fence(memory_order_release); // Not strictly needed + +auto element = reinterpret_cast(frontBlock_->data + nextBlockFront * sizeof(T)); + +result = std::move(*element); +element->~T(); + +nextBlockFront = (nextBlockFront + 1) & frontBlock_->sizeMask; + +fence(memory_order_release); +frontBlock_->front = nextBlockFront; +} +else { +// No elements in current block and no other block to advance to +return false; +} + +return true; +} + + +// Returns a pointer to the front element in the queue (the one that +// would be removed next by a call to `try_dequeue` or `pop`). If the +// queue appears empty at the time the method is called, nullptr is +// returned instead. +// Must be called only from the consumer thread. +T* peek() const AE_NO_TSAN +{ +#ifndef NDEBUG +ReentrantGuard guard(this->dequeuing); +#endif +// See try_dequeue() for reasoning + +Block* frontBlock_ = frontBlock.load(); +size_t blockTail = frontBlock_->localTail; +size_t blockFront = frontBlock_->front.load(); + +if (blockFront != blockTail || blockFront != (frontBlock_->localTail = frontBlock_->tail.load())) { +fence(memory_order_acquire); +non_empty_front_block: +return reinterpret_cast(frontBlock_->data + blockFront * sizeof(T)); +} +else if (frontBlock_ != tailBlock.load()) { +fence(memory_order_acquire); +frontBlock_ = frontBlock.load(); +blockTail = frontBlock_->localTail = frontBlock_->tail.load(); +blockFront = frontBlock_->front.load(); +fence(memory_order_acquire); + +if (blockFront != blockTail) { +goto non_empty_front_block; +} + +Block* nextBlock = frontBlock_->next; + +size_t nextBlockFront = nextBlock->front.load(); +fence(memory_order_acquire); + +assert(nextBlockFront != nextBlock->tail.load()); +return reinterpret_cast(nextBlock->data + nextBlockFront * sizeof(T)); +} + +return nullptr; +} + +// Removes the front element from the queue, if any, without returning it. +// Returns true on success, or false if the queue appeared empty at the time +// `pop` was called. +bool pop() AE_NO_TSAN +{ +#ifndef NDEBUG +ReentrantGuard guard(this->dequeuing); +#endif +// See try_dequeue() for reasoning + +Block* frontBlock_ = frontBlock.load(); +size_t blockTail = frontBlock_->localTail; +size_t blockFront = frontBlock_->front.load(); + +if (blockFront != blockTail || blockFront != (frontBlock_->localTail = frontBlock_->tail.load())) { +fence(memory_order_acquire); + +non_empty_front_block: +auto element = reinterpret_cast(frontBlock_->data + blockFront * sizeof(T)); +element->~T(); + +blockFront = (blockFront + 1) & frontBlock_->sizeMask; + +fence(memory_order_release); +frontBlock_->front = blockFront; +} +else if (frontBlock_ != tailBlock.load()) { +fence(memory_order_acquire); +frontBlock_ = frontBlock.load(); +blockTail = frontBlock_->localTail = frontBlock_->tail.load(); +blockFront = frontBlock_->front.load(); +fence(memory_order_acquire); + +if (blockFront != blockTail) { +goto non_empty_front_block; +} + +// Front block is empty but there's another block ahead, advance to it +Block* nextBlock = frontBlock_->next; + +size_t nextBlockFront = nextBlock->front.load(); +size_t nextBlockTail = nextBlock->localTail = nextBlock->tail.load(); +fence(memory_order_acquire); + +assert(nextBlockFront != nextBlockTail); +AE_UNUSED(nextBlockTail); + +fence(memory_order_release); +frontBlock = frontBlock_ = nextBlock; + +compiler_fence(memory_order_release); + +auto element = reinterpret_cast(frontBlock_->data + nextBlockFront * sizeof(T)); +element->~T(); + +nextBlockFront = (nextBlockFront + 1) & frontBlock_->sizeMask; + +fence(memory_order_release); +frontBlock_->front = nextBlockFront; +} +else { +// No elements in current block and no other block to advance to +return false; +} + +return true; +} + +// Returns the approximate number of items currently in the queue. +// Safe to call from both the producer and consumer threads. +inline size_t size_approx() const AE_NO_TSAN +{ +size_t result = 0; +Block* frontBlock_ = frontBlock.load(); +Block* block = frontBlock_; +do { +fence(memory_order_acquire); +size_t blockFront = block->front.load(); +size_t blockTail = block->tail.load(); +result += (blockTail - blockFront) & block->sizeMask; +block = block->next.load(); +} while (block != frontBlock_); +return result; +} + +// Returns the total number of items that could be enqueued without incurring +// an allocation when this queue is empty. +// Safe to call from both the producer and consumer threads. +// +// NOTE: The actual capacity during usage may be different depending on the consumer. +// If the consumer is removing elements concurrently, the producer cannot add to +// the block the consumer is removing from until it's completely empty, except in +// the case where the producer was writing to the same block the consumer was +// reading from the whole time. +inline size_t max_capacity() const { + size_t result = 0; + Block* frontBlock_ = frontBlock.load(); + Block* block = frontBlock_; + do { + fence(memory_order_acquire); + result += block->sizeMask; + block = block->next.load(); + } while (block != frontBlock_); + return result; +} + + +private: +enum AllocationMode { CanAlloc, CannotAlloc }; + +#if MOODYCAMEL_HAS_EMPLACE +template +bool inner_enqueue(Args&&... args) AE_NO_TSAN +#else +template + bool inner_enqueue(U&& element) AE_NO_TSAN +#endif +{ +#ifndef NDEBUG +ReentrantGuard guard(this->enqueuing); +#endif + +// High-level pseudocode (assuming we're allowed to alloc a new block): +// If room in tail block, add to tail +// Else check next block +// If next block is not the head block, enqueue on next block +// Else create a new block and enqueue there +// Advance tail to the block we just enqueued to + +Block* tailBlock_ = tailBlock.load(); +size_t blockFront = tailBlock_->localFront; +size_t blockTail = tailBlock_->tail.load(); + +size_t nextBlockTail = (blockTail + 1) & tailBlock_->sizeMask; +if (nextBlockTail != blockFront || nextBlockTail != (tailBlock_->localFront = tailBlock_->front.load())) { +fence(memory_order_acquire); +// This block has room for at least one more element +char* location = tailBlock_->data + blockTail * sizeof(T); +#if MOODYCAMEL_HAS_EMPLACE +new (location) T(std::forward(args)...); +#else +new (location) T(std::forward(element)); +#endif + +fence(memory_order_release); +tailBlock_->tail = nextBlockTail; +} +else { +fence(memory_order_acquire); +if (tailBlock_->next.load() != frontBlock) { +// Note that the reason we can't advance to the frontBlock and start adding new entries there +// is because if we did, then dequeue would stay in that block, eventually reading the new values, +// instead of advancing to the next full block (whose values were enqueued first and so should be +// consumed first). + +fence(memory_order_acquire); // Ensure we get latest writes if we got the latest frontBlock + +// tailBlock is full, but there's a free block ahead, use it +Block* tailBlockNext = tailBlock_->next.load(); +size_t nextBlockFront = tailBlockNext->localFront = tailBlockNext->front.load(); +nextBlockTail = tailBlockNext->tail.load(); +fence(memory_order_acquire); + +// This block must be empty since it's not the head block and we +// go through the blocks in a circle +assert(nextBlockFront == nextBlockTail); +tailBlockNext->localFront = nextBlockFront; + +char* location = tailBlockNext->data + nextBlockTail * sizeof(T); +#if MOODYCAMEL_HAS_EMPLACE +new (location) T(std::forward(args)...); +#else +new (location) T(std::forward(element)); +#endif + +tailBlockNext->tail = (nextBlockTail + 1) & tailBlockNext->sizeMask; + +fence(memory_order_release); +tailBlock = tailBlockNext; +} +else if (canAlloc == CanAlloc) { +// tailBlock is full and there's no free block ahead; create a new block +auto newBlockSize = largestBlockSize >= MAX_BLOCK_SIZE ? largestBlockSize : largestBlockSize * 2; +auto newBlock = make_block(newBlockSize); +if (newBlock == nullptr) { +// Could not allocate a block! +return false; +} +largestBlockSize = newBlockSize; + +#if MOODYCAMEL_HAS_EMPLACE +new (newBlock->data) T(std::forward(args)...); +#else +new (newBlock->data) T(std::forward(element)); +#endif +assert(newBlock->front == 0); +newBlock->tail = newBlock->localTail = 1; + +newBlock->next = tailBlock_->next.load(); +tailBlock_->next = newBlock; + +// Might be possible for the dequeue thread to see the new tailBlock->next +// *without* seeing the new tailBlock value, but this is OK since it can't +// advance to the next block until tailBlock is set anyway (because the only +// case where it could try to read the next is if it's already at the tailBlock, +// and it won't advance past tailBlock in any circumstance). + +fence(memory_order_release); +tailBlock = newBlock; +} +else if (canAlloc == CannotAlloc) { +// Would have had to allocate a new block to enqueue, but not allowed +return false; +} +else { +assert(false && "Should be unreachable code"); +return false; +} +} + +return true; +} + + +// Disable copying +ReaderWriterQueue(ReaderWriterQueue const&) { } + +// Disable assignment +ReaderWriterQueue& operator=(ReaderWriterQueue const&) { } + + +AE_FORCEINLINE static size_t ceilToPow2(size_t x) +{ +// From http://graphics.stanford.edu/~seander/bithacks.html#RoundUpPowerOf2 +--x; +x |= x >> 1; +x |= x >> 2; +x |= x >> 4; +for (size_t i = 1; i < sizeof(size_t); i <<= 1) { +x |= x >> (i << 3); +} +++x; +return x; +} + +template +static AE_FORCEINLINE char* align_for(char* ptr) AE_NO_TSAN +{ +const std::size_t alignment = std::alignment_of::value; +return ptr + (alignment - (reinterpret_cast(ptr) % alignment)) % alignment; +} +private: +#ifndef NDEBUG +struct ReentrantGuard +{ + AE_NO_TSAN ReentrantGuard(weak_atomic& _inSection) + : inSection(_inSection) + { + assert(!inSection && "Concurrent (or re-entrant) enqueue or dequeue operation detected (only one thread at a time may hold the producer or consumer role)"); + inSection = true; + } + + AE_NO_TSAN ~ReentrantGuard() { inSection = false; } + +private: + ReentrantGuard& operator=(ReentrantGuard const&); + +private: + weak_atomic& inSection; +}; +#endif + +struct Block +{ + // Avoid false-sharing by putting highly contended variables on their own cache lines + weak_atomic front; // (Atomic) Elements are read from here + size_t localTail; // An uncontended shadow copy of tail, owned by the consumer + + char cachelineFiller0[MOODYCAMEL_CACHE_LINE_SIZE - sizeof(weak_atomic) - sizeof(size_t)]; + weak_atomic tail; // (Atomic) Elements are enqueued here + size_t localFront; + + char cachelineFiller1[MOODYCAMEL_CACHE_LINE_SIZE - sizeof(weak_atomic) - sizeof(size_t)]; // next isn't very contended, but we don't want it on the same cache line as tail (which is) + weak_atomic next; // (Atomic) + + char* data; // Contents (on heap) are aligned to T's alignment + + const size_t sizeMask; + + + // size must be a power of two (and greater than 0) + AE_NO_TSAN Block(size_t const& _size, char* _rawThis, char* _data) + : front(0UL), localTail(0), tail(0UL), localFront(0), next(nullptr), data(_data), sizeMask(_size - 1), rawThis(_rawThis) + { + } + +private: + // C4512 - Assignment operator could not be generated + Block& operator=(Block const&); + +public: + char* rawThis; +}; + + +static Block* make_block(size_t capacity) AE_NO_TSAN +{ +// Allocate enough memory for the block itself, as well as all the elements it will contain +auto size = sizeof(Block) + std::alignment_of::value - 1; +size += sizeof(T) * capacity + std::alignment_of::value - 1; +auto newBlockRaw = static_cast(std::malloc(size)); +if (newBlockRaw == nullptr) { +return nullptr; +} + +auto newBlockAligned = align_for(newBlockRaw); +auto newBlockData = align_for(newBlockAligned + sizeof(Block)); +return new (newBlockAligned) Block(capacity, newBlockRaw, newBlockData); +} + +private: +weak_atomic frontBlock; // (Atomic) Elements are dequeued from this block + +char cachelineFiller[MOODYCAMEL_CACHE_LINE_SIZE - sizeof(weak_atomic)]; +weak_atomic tailBlock; // (Atomic) Elements are enqueued to this block + +size_t largestBlockSize; + +#ifndef NDEBUG +weak_atomic enqueuing; +mutable weak_atomic dequeuing; +#endif +}; + +// Like ReaderWriterQueue, but also providees blocking operations +template +class BlockingReaderWriterQueue +{ +private: + typedef ::moodycamel::ReaderWriterQueue ReaderWriterQueue; + +public: + explicit BlockingReaderWriterQueue(size_t size = 15) AE_NO_TSAN + : inner(size), sema(new spsc_sema::LightweightSemaphore()) + { } + + BlockingReaderWriterQueue(BlockingReaderWriterQueue&& other) AE_NO_TSAN + : inner(std::move(other.inner)), sema(std::move(other.sema)) + { } + + BlockingReaderWriterQueue& operator=(BlockingReaderWriterQueue&& other) AE_NO_TSAN + { + std::swap(sema, other.sema); + std::swap(inner, other.inner); + return *this; + } + + + // Enqueues a copy of element if there is room in the queue. + // Returns true if the element was enqueued, false otherwise. + // Does not allocate memory. + AE_FORCEINLINE bool try_enqueue(T const& element) AE_NO_TSAN + { + if (inner.try_enqueue(element)) { + sema->signal(); + return true; + } + return false; + } + + // Enqueues a moved copy of element if there is room in the queue. + // Returns true if the element was enqueued, false otherwise. + // Does not allocate memory. + AE_FORCEINLINE bool try_enqueue(T&& element) AE_NO_TSAN + { + if (inner.try_enqueue(std::forward(element))) { + sema->signal(); + return true; + } + return false; + } + +#if MOODYCAMEL_HAS_EMPLACE + // Like try_enqueue() but with emplace semantics (i.e. construct-in-place). + template + AE_FORCEINLINE bool try_emplace(Args&&... args) AE_NO_TSAN + { + if (inner.try_emplace(std::forward(args)...)) { + sema->signal(); + return true; + } + return false; + } +#endif + + + // Enqueues a copy of element on the queue. + // Allocates an additional block of memory if needed. + // Only fails (returns false) if memory allocation fails. + AE_FORCEINLINE bool enqueue(T const& element) AE_NO_TSAN + { + if (inner.enqueue(element)) { + sema->signal(); + return true; + } + return false; + } + + // Enqueues a moved copy of element on the queue. + // Allocates an additional block of memory if needed. + // Only fails (returns false) if memory allocation fails. + AE_FORCEINLINE bool enqueue(T&& element) AE_NO_TSAN + { + if (inner.enqueue(std::forward(element))) { + sema->signal(); + return true; + } + return false; + } + +#if MOODYCAMEL_HAS_EMPLACE + // Like enqueue() but with emplace semantics (i.e. construct-in-place). + template + AE_FORCEINLINE bool emplace(Args&&... args) AE_NO_TSAN + { + if (inner.emplace(std::forward(args)...)) { + sema->signal(); + return true; + } + return false; + } +#endif + + + // Attempts to dequeue an element; if the queue is empty, + // returns false instead. If the queue has at least one element, + // moves front to result using operator=, then returns true. + template + bool try_dequeue(U& result) AE_NO_TSAN + { + if (sema->tryWait()) { + bool success = inner.try_dequeue(result); + assert(success); + AE_UNUSED(success); + return true; + } + return false; + } + + + // Attempts to dequeue an element; if the queue is empty, + // waits until an element is available, then dequeues it. + template + void wait_dequeue(U& result) AE_NO_TSAN + { + while (!sema->wait()); + bool success = inner.try_dequeue(result); + AE_UNUSED(result); + assert(success); + AE_UNUSED(success); + } + + + // Attempts to dequeue an element; if the queue is empty, + // waits until an element is available up to the specified timeout, + // then dequeues it and returns true, or returns false if the timeout + // expires before an element can be dequeued. + // Using a negative timeout indicates an indefinite timeout, + // and is thus functionally equivalent to calling wait_dequeue. + template + bool wait_dequeue_timed(U& result, std::int64_t timeout_usecs) AE_NO_TSAN + { + if (!sema->wait(timeout_usecs)) { + return false; + } + bool success = inner.try_dequeue(result); + AE_UNUSED(result); + assert(success); + AE_UNUSED(success); + return true; + } + + +#if __cplusplus > 199711L || _MSC_VER >= 1700 + // Attempts to dequeue an element; if the queue is empty, + // waits until an element is available up to the specified timeout, + // then dequeues it and returns true, or returns false if the timeout + // expires before an element can be dequeued. + // Using a negative timeout indicates an indefinite timeout, + // and is thus functionally equivalent to calling wait_dequeue. + template + inline bool wait_dequeue_timed(U& result, std::chrono::duration const& timeout) AE_NO_TSAN + { + return wait_dequeue_timed(result, std::chrono::duration_cast(timeout).count()); + } +#endif + + + // Returns a pointer to the front element in the queue (the one that + // would be removed next by a call to `try_dequeue` or `pop`). If the + // queue appears empty at the time the method is called, nullptr is + // returned instead. + // Must be called only from the consumer thread. + AE_FORCEINLINE T* peek() const AE_NO_TSAN + { + return inner.peek(); + } + + // Removes the front element from the queue, if any, without returning it. + // Returns true on success, or false if the queue appeared empty at the time + // `pop` was called. + AE_FORCEINLINE bool pop() AE_NO_TSAN + { + if (sema->tryWait()) { + bool result = inner.pop(); + assert(result); + AE_UNUSED(result); + return true; + } + return false; + } + + // Returns the approximate number of items currently in the queue. + // Safe to call from both the producer and consumer threads. + AE_FORCEINLINE size_t size_approx() const AE_NO_TSAN + { + return sema->availableApprox(); + } + + // Returns the total number of items that could be enqueued without incurring + // an allocation when this queue is empty. + // Safe to call from both the producer and consumer threads. + // + // NOTE: The actual capacity during usage may be different depending on the consumer. + // If the consumer is removing elements concurrently, the producer cannot add to + // the block the consumer is removing from until it's completely empty, except in + // the case where the producer was writing to the same block the consumer was + // reading from the whole time. + AE_FORCEINLINE size_t max_capacity() const { + return inner.max_capacity(); + } + +private: + // Disable copying & assignment + BlockingReaderWriterQueue(BlockingReaderWriterQueue const&) { } + BlockingReaderWriterQueue& operator=(BlockingReaderWriterQueue const&) { } + +private: + ReaderWriterQueue inner; + std::unique_ptr sema; +}; + +} // end namespace moodycamel + +#ifdef AE_VCPP +#pragma warning(pop) +#endif \ No newline at end of file From 0c4528a5edc3fd5b9a39c147746894158c2ce7e8 Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 23 May 2025 00:32:49 -0500 Subject: [PATCH 224/323] limit busy waiting --- src/cpp/include/common.h | 1 + src/cpp/include/query_coordinator.h | 11 +- src/cpp/src/query_coordinator.cpp | 173 +++++++++++------- .../numa_multi_query/configs/sift1m.yaml | 94 +++++----- 4 files changed, 166 insertions(+), 113 deletions(-) diff --git a/src/cpp/include/common.h b/src/cpp/include/common.h index b165ac5f..88173e28 100644 --- a/src/cpp/include/common.h +++ b/src/cpp/include/common.h @@ -75,6 +75,7 @@ constexpr bool DEFAULT_PRECOMPUTED = true; ///< Default flag to us constexpr float DEFAULT_INITIAL_SEARCH_FRACTION = 0.1f; ///< Default initial fraction of partitions to search. constexpr float DEFAULT_RECOMPUTE_THRESHOLD = 0.001f; ///< Default threshold to trigger recomputation of search parameters. constexpr int DEFAULT_APS_FLUSH_PERIOD_US = 5; ///< Default period (in microseconds) for flushing the APS buffer. +constexpr int MAX_SUBBATCH = 128; // Default constants for maintenance policy parameters constexpr const char* DEFAULT_MAINTENANCE_POLICY = "query_cost"; ///< Default maintenance policy type. diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index 8e6ee53d..eb47faba 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -59,8 +59,6 @@ class QueryCoordinator { struct CoreResources { int core_id; std::vector> topk_buffer_pool; -// moodycamel::ConcurrentQueue job_queue; - moodycamel::BlockingReaderWriterQueue job_queue; // Thread-local buffers for batched queries float* batch_queries = nullptr; // batched query buffer (NUMA-allocated) @@ -68,11 +66,19 @@ class QueryCoordinator { int64_t* batch_ids = nullptr; // scratch space for ids size_t batch_q_capacity = 0; // number of floats in batch_queries size_t batch_res_capacity = 0; // number of (k×Q) slots in distances & ids + + int64_t job_counter = 0; ///< Job counter for this core. + int64_t queries_counter = 0; ///< Number of queries processed by this core. + int64_t wait_time_ns = 0; ///< Time spent waiting for jobs. + int64_t process_time_ns = 0; ///< Time spent scanning. + int64_t enqueue_time_ns = 0; ///< Time spent enqueuing. + int64_t job_time_ns = 0; ///< Time spent on job processing (excluding waiting). }; struct NUMAResources { float* local_query_buffer = nullptr; size_t buffer_size = 0; + moodycamel::BlockingConcurrentQueue job_queue; }; struct ResultJob { @@ -103,7 +109,6 @@ class QueryCoordinator { std::atomic job_pull_time_ns = 0; ///< Time spent pulling jobs from the queue. std::atomic job_process_time_ns = 0; ///< Time spent processing jobs. - /** * @brief Constructs a QueryCoordinator. * diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 6c6f6e8f..d33ed6e2 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -80,17 +80,30 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { set_thread_affinity(core_index); int i = 0; + res.wait_time_ns = 0; + res.process_time_ns = 0; + res.enqueue_time_ns = 0; + res.job_time_ns = 0; + while (!stop_workers_) { int64_t jid = 0; - res.job_queue.wait_dequeue(jid); + auto start = std::chrono::high_resolution_clock::now(); + nr.job_queue.wait_dequeue(jid); + auto end = std::chrono::high_resolution_clock::now(); + + res.wait_time_ns += std::chrono::duration_cast(end - start).count(); if (jid == -1) { break; } + process_scan_job(job_buffer_[jid], res); i++; + end = std::chrono::high_resolution_clock::now(); + + res.job_time_ns += std::chrono::duration_cast(end - start).count(); } } @@ -140,6 +153,8 @@ void QueryCoordinator::process_scan_job(ScanJob job, } else { handle_batched_job(job, res, nr); } + res.queries_counter += job.num_queries; + res.job_counter++; } void QueryCoordinator::handle_nonbatched_job(const ScanJob &job, @@ -217,6 +232,7 @@ void QueryCoordinator::handle_nonbatched_job(const ScanJob &job, void QueryCoordinator::handle_batched_job(const ScanJob &job, CoreResources &res, NUMAResources &nr) { + // Total queries, Top-K, dimension, NUMA node int64_t Q = job.num_queries; int K = job.k; @@ -308,6 +324,7 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, __builtin_prefetch(codes, 0, 3); __builtin_prefetch(ids, 0, 3); + auto start = std::chrono::high_resolution_clock::now(); // run the scan on this chunk batched_scan_list( qptr, @@ -318,6 +335,8 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, res.batch_distances, res.batch_ids ); + auto end = std::chrono::high_resolution_clock::now(); + res.process_time_ns += std::chrono::duration_cast(end - start).count(); // collect results for this chunk std::vector results_batch; @@ -329,12 +348,14 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, auto ti = res.topk_buffer_pool[i]->get_topk_indices(); results_batch.emplace_back(ResultJob{global_q, rank_q, std::move(tv), std::move(ti)}); } - + start = std::chrono::high_resolution_clock::now(); // enqueue this sub-batch in bulk result_queue_.enqueue_bulk( std::make_move_iterator(results_batch.begin()), results_batch.size() ); + end = std::chrono::high_resolution_clock::now(); + res.enqueue_time_ns += std::chrono::duration_cast(end - start).count(); } } @@ -391,6 +412,11 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, auto partition_ids_acc = partition_ids.accessor(); + vector core_to_numa(num_workers_); + for (int i = 0; i < num_workers_; ++i) { + core_to_numa[i] = cpu_numa_node(i); + } + // flatten jobs next_job_id_ = 0; job_flags_.clear(); @@ -420,74 +446,54 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, job.query_vector = qptr; job.rank = p; job_buffer_.push_back(job); - core_resources_[pid % num_workers_].job_queue.enqueue(next_job_id_); + numa_resources_[core_to_numa[pid % num_workers_]].job_queue.enqueue(next_job_id_); next_job_id_++; } } } else { - job_buffer_.resize(partition_manager_->nlist()); - auto pids = partition_manager_->get_partition_ids(); - auto pids_acc = pids.accessor(); - - if (pids.size(0) == partition_ids.size(1)) { - vector all_query_ids = std::vector(x.size(0)); - std::iota(all_query_ids.begin(), all_query_ids.end(), 0); - shared_ptr> all_query_ids_ptr = make_shared>(all_query_ids); - for (int64_t i = 0; i < pids.size(0); i++) { - ScanJob job; - job.is_batched = true; - job.partition_id = pids_acc[i]; - job.k = params->k; - job.query_vector = x.data_ptr(); - job.num_queries = x.size(0); - job.scan_all = true; - job.query_ids = all_query_ids_ptr; - job.ranks = make_shared>(x.size(0), i); - int core_id = partition_manager_->get_partition_core_id(pids_acc[i]); - if (core_id < 0) { - throw std::runtime_error("[QueryCoordinator::worker_scan] Invalid core ID."); - } - job_buffer_[next_job_id_] = job; - core_resources_[core_id].job_queue.enqueue(next_job_id_); - next_job_id_++; + /* Build per-partition query lists -------------------------------------- */ + std::vector>> qlist(partition_manager_->nlist()); + for (int64_t q = 0; q < nQ; ++q) { + for (int p = 0; p < partition_ids.size(1); ++p) { + int64_t pid = pid_acc[q][p]; + if (pid >= 0) qlist[pid].emplace_back(q, p); } + } - } else { - std::unordered_map>>> per_partition_query_ids; // for batched scan - for (int64_t q = 0; q < nQ; q++) { - for (int64_t p = 0; p < partition_ids.size(1); p++) { - int64_t pid = pid_acc[q][p]; - if (pid < 0) continue; - if (per_partition_query_ids[pid] == nullptr) { - per_partition_query_ids[pid] = make_shared>>(); - } - per_partition_query_ids[pid]->push_back({q, p}); - } - } - for (auto &kv : per_partition_query_ids) { - - auto qids_and_ranks = kv.second; - vector qids(qids_and_ranks->size()); - vector ranks(qids_and_ranks->size()); - for (size_t i = 0; i < qids_and_ranks->size(); ++i) { - qids[i] = (*qids_and_ranks)[i].first; - ranks[i] = (*qids_and_ranks)[i].second; + /* Emit ScanJobs, already split into ≤ MAX_SUBBATCH chunks -------------- */ + for (int64_t pid = 0; pid < (int64_t)qlist.size(); ++pid) { + auto &pairs = qlist[pid]; + if (pairs.empty()) continue; + + for (size_t off = 0; off < pairs.size(); off += MAX_SUBBATCH) { + size_t chunk = std::min(MAX_SUBBATCH, pairs.size() - off); + + // Split query / rank vectors for this chunk. + auto qids = std::make_shared>(); + auto ranks = std::make_shared>(); + qids ->reserve(chunk); + ranks->reserve(chunk); + for (size_t i = 0; i < chunk; ++i) { + qids ->push_back(pairs[off + i].first); + ranks->push_back(pairs[off + i].second); } ScanJob job; - job.is_batched = true; - job.partition_id = kv.first; - job.k = params->k; - job.query_vector = x.data_ptr(); - job.num_queries = kv.second->size(); - job.query_ids = make_shared>(qids); - job.ranks = make_shared>(ranks); - int core_id = partition_manager_->get_partition_core_id(kv.first); - if (core_id < 0) { - throw std::runtime_error("[QueryCoordinator::worker_scan] Invalid core ID."); - } - job_buffer_[next_job_id_] = job; - core_resources_[core_id].job_queue.enqueue(next_job_id_); + job.is_batched = true; + job.partition_id = pid; + job.k = params->k; + job.num_queries = static_cast(chunk); + job.query_vector = xptr; // whole matrix; we gather later + job.query_ids = qids; + job.ranks = ranks; + job.scan_all = (chunk == static_cast(nQ)); // true only if every query in this chunk + + job_buffer_.push_back(job); + + // Choose NUMA queue by partition-to-core mapping. + int core = pid % num_workers_; + int node = core_to_numa[core]; + numa_resources_[node].job_queue.enqueue(next_job_id_); next_job_id_++; } } @@ -640,6 +646,19 @@ std::shared_ptr QueryCoordinator::worker_scan( timing->n_clusters = partition_manager_->nlist(); timing->search_params = params; + + // get initial values of the per-core resource timers; + vector core_wait_time_ns(num_workers_, 0); + vector core_process_time_ns(num_workers_, 0); + vector core_enqueue_time_ns(num_workers_, 0); + vector core_job_time_ns(num_workers_, 0); + for (int i = 0; i < num_workers_; ++i) { + core_wait_time_ns[i] = core_resources_[i].wait_time_ns; + core_process_time_ns[i] = core_resources_[i].process_time_ns; + core_enqueue_time_ns[i] = core_resources_[i].enqueue_time_ns; + core_job_time_ns[i] = core_resources_[i].job_time_ns; + } + auto s1 = high_resolution_clock::now(); // 1) init global buffers & jobs_left @@ -678,6 +697,33 @@ std::shared_ptr QueryCoordinator::worker_scan( res->timing_info->result_aggregate_time_ns = duration_cast(s6 - s5).count(); + // retrieve the final values of the per-core resource timers; + for (int i = 0; i < num_workers_; ++i) { + core_wait_time_ns[i] = core_resources_[i].wait_time_ns - core_wait_time_ns[i]; + core_process_time_ns[i] = core_resources_[i].process_time_ns - core_process_time_ns[i]; + core_enqueue_time_ns[i] = core_resources_[i].enqueue_time_ns - core_enqueue_time_ns[i]; + core_job_time_ns[i] = core_resources_[i].job_time_ns - core_job_time_ns[i]; + } + + + // print out the per-core resource timers; + for (int i = 0; i < num_workers_; ++i) { + std::cout << "[QueryCoordinator::worker_scan] Core " << i << ": " + << "wait_time_ms=" << (float) core_wait_time_ns[i] / 1e6 << " " + << "process_time_ms=" << (float) core_process_time_ns[i] / 1e6 << " " + << "enqueue_time_ms=" << (float) core_enqueue_time_ns[i] / 1e6 << " " + << "job_time_ms=" << (float) core_job_time_ns[i] / 1e6 << std::endl; + } + + // print out the main thread timers; + std::cout << "[QueryCoordinator::worker_scan] Main thread: " + << "buffer_init_time_ms=" << (float) res->timing_info->buffer_init_time_ns / 1e6 << " " + << "copy_query_time_ms=" << (float) res->timing_info->copy_query_time_ns / 1e6 << " " + << "job_enqueue_time_ms=" << (float) res->timing_info->job_enqueue_time_ns / 1e6 << " " + << "job_wait_time_ms=" << (float) res->timing_info->job_wait_time_ns / 1e6 << " " + << "result_aggregate_time_ms=" << (float) res->timing_info->result_aggregate_time_ns / 1e6 + << std::endl; + return res; } @@ -718,8 +764,9 @@ void QueryCoordinator::shutdown_workers() { stop_workers_.store(true); // Enqueue a special shutdown job for each core. - for (auto &res : core_resources_) { - res.job_queue.enqueue(-1); + for (auto &res : numa_resources_) { + for (int i = 0; i < num_workers_; ++i) + res.job_queue.enqueue(-1); } // Join all worker threads. for (auto &thr : worker_threads_) { diff --git a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml index 81e203a6..44a4acd3 100644 --- a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml +++ b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml @@ -15,57 +15,57 @@ indexes: build_params: nc: 1000 metric: l2 - num_workers: 16 + num_workers: 12 use_numa: false - parent_num_workers: 4 + parent_num_workers: 1 search_params: - nprobe: 20 + nprobe: 200 batched_scan: true +# +# - name: Quake_16_numa +# index: Quake +# build_params: +# nc: 1000 +# metric: l2 +# num_workers: 16 +# use_numa: true +# parent_num_workers: 4 +# search_params: +# nprobe: 20 +# batched_scan: true +# +# - name: Quake_16_numa_no_batch +# index: Quake +# build_params: +# nc: 1000 +# metric: l2 +# num_workers: 16 +# use_numa: true +# parent_num_workers: 4 +# search_params: +# nprobe: 20 +# batched_scan: false - - name: Quake_16_numa - index: Quake - build_params: - nc: 1000 - metric: l2 - num_workers: 16 - use_numa: true - parent_num_workers: 4 - search_params: - nprobe: 20 - batched_scan: true - - - name: Quake_16_numa_no_batch - index: Quake - build_params: - nc: 1000 - metric: l2 - num_workers: 16 - use_numa: true - parent_num_workers: 4 - search_params: - nprobe: 20 - batched_scan: false - - - name: FaissIVF - index: IVF - build_params: - nc: 1000 - metric: l2 - search_params: - nprobe: 20 - omp_num_threads_search: 16 - - - - name: FaissHNSW - index: HNSW - build_params: - m: 64 - ef_construction: 128 - metric: l2 - omp_num_threads_build: 32 - search_params: - ef_search: 128 - omp_num_threads_search: 32 +# - name: FaissIVF +# index: IVF +# build_params: +# nc: 1000 +# metric: l2 +# search_params: +# nprobe: 20 +# omp_num_threads_search: 16 +# +# +# - name: FaissHNSW +# index: HNSW +# build_params: +# m: 64 +# ef_construction: 128 +# metric: l2 +# omp_num_threads_build: 32 +# search_params: +# ef_search: 128 +# omp_num_threads_search: 32 # # - name: SCANN From 0e34285e18bd607145991f84f19dbfd61ba3df04 Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 23 May 2025 00:44:24 -0500 Subject: [PATCH 225/323] limit busy waiting --- src/cpp/src/query_coordinator.cpp | 2 +- .../osdi2025/numa_multi_query/configs/sift1m.yaml | 6 +++--- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index d33ed6e2..45b607ab 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -765,7 +765,7 @@ void QueryCoordinator::shutdown_workers() { stop_workers_.store(true); // Enqueue a special shutdown job for each core. for (auto &res : numa_resources_) { - for (int i = 0; i < num_workers_; ++i) + for (int i = 0; i < num_workers_ * 5; ++i) res.job_queue.enqueue(-1); } // Join all worker threads. diff --git a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml index 44a4acd3..c4226c70 100644 --- a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml +++ b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml @@ -1,8 +1,8 @@ # numa_latency_experiment.yaml dataset: name: sift1m - num_queries: 1000 - k: 100 + num_queries: 10000 + k: 1 trials: 3 warmup: 1 @@ -19,7 +19,7 @@ indexes: use_numa: false parent_num_workers: 1 search_params: - nprobe: 200 + nprobe: 20 batched_scan: true # # - name: Quake_16_numa From 2fa48301944e6b22bc1945d50a68e87eefbd5af2 Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 23 May 2025 00:55:17 -0500 Subject: [PATCH 226/323] limit busy waiting --- src/cpp/src/query_coordinator.cpp | 18 +++++++++++------- 1 file changed, 11 insertions(+), 7 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 45b607ab..5521a2da 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -115,6 +115,14 @@ void QueryCoordinator::process_scan_job(ScanJob job, #endif NUMAResources &nr = numa_resources_[numa_node]; + auto enqueue_empty_batch = [&](const ScanJob& j) { + const auto& qids = *j.query_ids; + const auto& ranks = *j.ranks; // same length as qids + for (size_t idx = 0; idx < qids.size(); ++idx) { + result_queue_.enqueue(ResultJob{qids[idx], ranks[idx], {}, {}}); + } + }; + // Attempt to fetch partition data; if the list doesn't exist, catch and enqueue empty results. const float *codes = nullptr; const int64_t *ids = nullptr; @@ -127,9 +135,7 @@ void QueryCoordinator::process_scan_job(ScanJob job, std::cerr << "[process_scan_job] Partition " << job.partition_id << " invalid: " << e.what() << ". Returning empty result(s).\n"; if (job.is_batched) { - for (int64_t q : *job.query_ids) { - result_queue_.enqueue(ResultJob{(int)q, 0, {}, {}}); - } + enqueue_empty_batch(job); } else { result_queue_.enqueue(ResultJob{job.query_id, job.rank, {}, {}}); } @@ -139,9 +145,7 @@ void QueryCoordinator::process_scan_job(ScanJob job, if (part_size == 0) { // empty => enqueue zero‐work per query if (job.is_batched) { - for (int64_t q : *job.query_ids) { - result_queue_.enqueue(ResultJob{(int)q, 0, {}, {}}); - } + enqueue_empty_batch(job); } else { result_queue_.enqueue(ResultJob{job.query_id, job.rank, {}, {}}); } @@ -765,7 +769,7 @@ void QueryCoordinator::shutdown_workers() { stop_workers_.store(true); // Enqueue a special shutdown job for each core. for (auto &res : numa_resources_) { - for (int i = 0; i < num_workers_ * 5; ++i) + for (int i = 0; i < num_workers_; ++i) res.job_queue.enqueue(-1); } // Join all worker threads. From 23983f3905aa2bf37941b876359f7c3f5a03fdff Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 23 May 2025 01:15:23 -0500 Subject: [PATCH 227/323] limit busy waiting --- src/cpp/src/query_coordinator.cpp | 16 ++++++++++++---- .../numa_multi_query/configs/sift1m.yaml | 2 +- 2 files changed, 13 insertions(+), 5 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 5521a2da..511b1bf7 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -243,14 +243,20 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, int D = partition_manager_->d(); int node = cpu_numa_node(res.core_id); + auto enqueue_empty_batch = [&](const ScanJob& j) { + const auto& qids = *j.query_ids; + const auto& ranks = *j.ranks; // same length as qids + for (size_t idx = 0; idx < qids.size(); ++idx) { + result_queue_.enqueue(ResultJob{qids[idx], ranks[idx], {}, {}}); + } + }; + // Fetch partition data const float *codes = (float *) partition_manager_->partition_store_->get_codes(job.partition_id); const int64_t *ids = partition_manager_->partition_store_->get_ids(job.partition_id); int64_t part_size = partition_manager_->partition_store_->list_size(job.partition_id); if (!codes || !ids || part_size <= 0) { - for (int64_t q : *job.query_ids) { - result_queue_.enqueue(ResultJob{(int)q, 0, {}, {}}); - } + enqueue_empty_batch(job); return; } @@ -464,6 +470,8 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, } } + bool scan_all = partition_ids.size(1) == partition_manager_->nlist(); + /* Emit ScanJobs, already split into ≤ MAX_SUBBATCH chunks -------------- */ for (int64_t pid = 0; pid < (int64_t)qlist.size(); ++pid) { auto &pairs = qlist[pid]; @@ -490,7 +498,7 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, job.query_vector = xptr; // whole matrix; we gather later job.query_ids = qids; job.ranks = ranks; - job.scan_all = (chunk == static_cast(nQ)); // true only if every query in this chunk + job.scan_all = scan_all; job_buffer_.push_back(job); diff --git a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml index c4226c70..882c739a 100644 --- a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml +++ b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml @@ -17,7 +17,7 @@ indexes: metric: l2 num_workers: 12 use_numa: false - parent_num_workers: 1 + parent_num_workers: 8 search_params: nprobe: 20 batched_scan: true From 7437ee3019bac4e6a91184c993a8ac19bb7b4264 Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 23 May 2025 01:38:32 -0500 Subject: [PATCH 228/323] limit busy waiting --- src/cpp/include/query_coordinator.h | 2 ++ src/cpp/src/query_coordinator.cpp | 6 ++++++ 2 files changed, 8 insertions(+) diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index eb47faba..e0e6e7f9 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -23,6 +23,7 @@ class PartitionManager; * A ScanJob encapsulates all parameters required to perform a scan on a given index partition. */ struct ScanJob { + int64_t job_id; ///< Unique identifier for the job. int64_t partition_id; ///< The identifier of the partition to be scanned. int k; ///< The number of neighbors (Top-K) to return. const float* query_vector; ///< Pointer to the query vector. @@ -101,6 +102,7 @@ class QueryCoordinator { std::mutex global_mutex_; ///< Mutex for global synchronization. std::condition_variable global_cv_; ///< Condition variable for thread coordination. std::atomic stop_workers_; ///< Flag to signal workers to terminate. + std::atomic jobs_in_flight_; ///< Number of jobs in flight. bool debug_ = false; ///< Debug mode flag. std::vector job_buffer_; diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 511b1bf7..13b1c841 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -159,6 +159,7 @@ void QueryCoordinator::process_scan_job(ScanJob job, } res.queries_counter += job.num_queries; res.job_counter++; + jobs_in_flight_--; } void QueryCoordinator::handle_nonbatched_job(const ScanJob &job, @@ -439,6 +440,7 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, } } job_buffer_.clear(); + job_buffer_.reserve(nQ * partition_ids.size(1)); auto pid_acc = partition_ids.accessor(); if (!params->batched_scan) { @@ -449,6 +451,7 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, int64_t pid = pid_acc[q][p]; if (pid < 0) continue; ScanJob job; + job.job_id = next_job_id_; job.is_batched = false; job.query_id = (int)q; job.partition_id = pid; @@ -458,6 +461,7 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, job_buffer_.push_back(job); numa_resources_[core_to_numa[pid % num_workers_]].job_queue.enqueue(next_job_id_); next_job_id_++; + jobs_in_flight_++; } } } else { @@ -492,6 +496,7 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, ScanJob job; job.is_batched = true; + job.job_id = next_job_id_; job.partition_id = pid; job.k = params->k; job.num_queries = static_cast(chunk); @@ -507,6 +512,7 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, int node = core_to_numa[core]; numa_resources_[node].job_queue.enqueue(next_job_id_); next_job_id_++; + jobs_in_flight_++; } } } From c7cc40b76b3205c09d916e9765afe937ae77dda0 Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 23 May 2025 01:46:26 -0500 Subject: [PATCH 229/323] empty result queues --- src/cpp/src/query_coordinator.cpp | 28 +++++++++------------------- 1 file changed, 9 insertions(+), 19 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 13b1c841..b8038764 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -115,14 +115,6 @@ void QueryCoordinator::process_scan_job(ScanJob job, #endif NUMAResources &nr = numa_resources_[numa_node]; - auto enqueue_empty_batch = [&](const ScanJob& j) { - const auto& qids = *j.query_ids; - const auto& ranks = *j.ranks; // same length as qids - for (size_t idx = 0; idx < qids.size(); ++idx) { - result_queue_.enqueue(ResultJob{qids[idx], ranks[idx], {}, {}}); - } - }; - // Attempt to fetch partition data; if the list doesn't exist, catch and enqueue empty results. const float *codes = nullptr; const int64_t *ids = nullptr; @@ -135,7 +127,9 @@ void QueryCoordinator::process_scan_job(ScanJob job, std::cerr << "[process_scan_job] Partition " << job.partition_id << " invalid: " << e.what() << ". Returning empty result(s).\n"; if (job.is_batched) { - enqueue_empty_batch(job); + for (int64_t i = 0; i < job.num_queries; ++i) { + result_queue_.enqueue(ResultJob{(*job.query_ids)[i], (*job.ranks)[i], {}, {}}); + } } else { result_queue_.enqueue(ResultJob{job.query_id, job.rank, {}, {}}); } @@ -145,7 +139,9 @@ void QueryCoordinator::process_scan_job(ScanJob job, if (part_size == 0) { // empty => enqueue zero‐work per query if (job.is_batched) { - enqueue_empty_batch(job); + for (int64_t i = 0; i < job.num_queries; ++i) { + result_queue_.enqueue(ResultJob{(*job.query_ids)[i], (*job.ranks)[i], {}, {}}); + } } else { result_queue_.enqueue(ResultJob{job.query_id, job.rank, {}, {}}); } @@ -244,20 +240,14 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, int D = partition_manager_->d(); int node = cpu_numa_node(res.core_id); - auto enqueue_empty_batch = [&](const ScanJob& j) { - const auto& qids = *j.query_ids; - const auto& ranks = *j.ranks; // same length as qids - for (size_t idx = 0; idx < qids.size(); ++idx) { - result_queue_.enqueue(ResultJob{qids[idx], ranks[idx], {}, {}}); - } - }; - // Fetch partition data const float *codes = (float *) partition_manager_->partition_store_->get_codes(job.partition_id); const int64_t *ids = partition_manager_->partition_store_->get_ids(job.partition_id); int64_t part_size = partition_manager_->partition_store_->list_size(job.partition_id); if (!codes || !ids || part_size <= 0) { - enqueue_empty_batch(job); + for (int64_t i = 0; i < Q; ++i) { + result_queue_.enqueue(ResultJob{(*job.query_ids)[i], (*job.ranks)[i], {}, {}}); + } return; } From 6a1baae919dca3b1ece12fc92caf7a6258b8cca1 Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 23 May 2025 01:56:25 -0500 Subject: [PATCH 230/323] empty result queues --- src/cpp/src/query_coordinator.cpp | 6 +++++- .../osdi2025/numa_multi_query/configs/sift1m.yaml | 4 ++-- 2 files changed, 7 insertions(+), 3 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index b8038764..6159806b 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -464,7 +464,11 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, } } - bool scan_all = partition_ids.size(1) == partition_manager_->nlist(); + int nlist = partition_manager_->nlist(); + bool scan_all = false; + if (nlist == 1) { + scan_all = true; + } /* Emit ScanJobs, already split into ≤ MAX_SUBBATCH chunks -------------- */ for (int64_t pid = 0; pid < (int64_t)qlist.size(); ++pid) { diff --git a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml index 882c739a..13d010c8 100644 --- a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml +++ b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml @@ -17,10 +17,10 @@ indexes: metric: l2 num_workers: 12 use_numa: false - parent_num_workers: 8 + parent_num_workers: 12 search_params: nprobe: 20 - batched_scan: true + batched_scan: false # # - name: Quake_16_numa # index: Quake From 3791a4485ed98c8552743632f340cc9609881610 Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 23 May 2025 02:15:08 -0500 Subject: [PATCH 231/323] empty result queues --- .../numa_multi_query/configs/sift1m.yaml | 24 +++++++++---------- .../osdi2025/numa_multi_query/run.py | 2 ++ 2 files changed, 14 insertions(+), 12 deletions(-) diff --git a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml index 13d010c8..0ec92f83 100644 --- a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml +++ b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml @@ -16,11 +16,11 @@ indexes: nc: 1000 metric: l2 num_workers: 12 - use_numa: false + use_numa: true parent_num_workers: 12 search_params: nprobe: 20 - batched_scan: false + batched_scan: true # # - name: Quake_16_numa # index: Quake @@ -56,16 +56,16 @@ indexes: # omp_num_threads_search: 16 # # -# - name: FaissHNSW -# index: HNSW -# build_params: -# m: 64 -# ef_construction: 128 -# metric: l2 -# omp_num_threads_build: 32 -# search_params: -# ef_search: 128 -# omp_num_threads_search: 32 + - name: FaissHNSW + index: HNSW + build_params: + m: 64 + ef_construction: 128 + metric: l2 + omp_num_threads_build: 16 + search_params: + ef_search: 128 + omp_num_threads_search: 16 # # - name: SCANN diff --git a/test/experiments/osdi2025/numa_multi_query/run.py b/test/experiments/osdi2025/numa_multi_query/run.py index 603610eb..6d679069 100644 --- a/test/experiments/osdi2025/numa_multi_query/run.py +++ b/test/experiments/osdi2025/numa_multi_query/run.py @@ -53,6 +53,8 @@ def task_build_index( if IndexCls is None or (IndexCls == Scann and Scann is None): # type: ignore return {"error": f"Index type '{index_config['index']}' unavailable.", "index_file_path": str(index_file_path)} + current_build_params.pop("num_parent_workers", None) + idx_instance_for_build = common_utils.prepare_wrapper_index( IndexCls, index_file_path, base_vectors, current_build_params, global_run_params["force_rebuild_indices"], load=False From 7ebbba2dd567370df4e3b2f9a9506783eed163db Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 23 May 2025 16:03:29 -0500 Subject: [PATCH 232/323] update queue waiting --- src/cpp/include/common.h | 1 + src/cpp/include/list_scanning.h | 206 ++++++++++++++-------------- src/cpp/include/query_coordinator.h | 2 +- src/cpp/src/query_coordinator.cpp | 70 +++++++--- 4 files changed, 155 insertions(+), 124 deletions(-) diff --git a/src/cpp/include/common.h b/src/cpp/include/common.h index 88173e28..73dae02d 100644 --- a/src/cpp/include/common.h +++ b/src/cpp/include/common.h @@ -76,6 +76,7 @@ constexpr float DEFAULT_INITIAL_SEARCH_FRACTION = 0.1f; ///< Default initial fra constexpr float DEFAULT_RECOMPUTE_THRESHOLD = 0.001f; ///< Default threshold to trigger recomputation of search parameters. constexpr int DEFAULT_APS_FLUSH_PERIOD_US = 5; ///< Default period (in microseconds) for flushing the APS buffer. constexpr int MAX_SUBBATCH = 128; +constexpr int MIN_BATCH_SCAN_SIZE = 5; ///< Minimum batch size for scanning partitions. // Default constants for maintenance policy parameters constexpr const char* DEFAULT_MAINTENANCE_POLICY = "query_cost"; ///< Default maintenance policy type. diff --git a/src/cpp/include/list_scanning.h b/src/cpp/include/list_scanning.h index 37dfafe4..89ece74e 100644 --- a/src/cpp/include/list_scanning.h +++ b/src/cpp/include/list_scanning.h @@ -281,10 +281,10 @@ class TypedTopKBuffer { // Type alias for convenience using TopkBuffer = TypedTopKBuffer; -inline vector> create_buffers(int batch_size, int k, bool is_desc) { +inline vector> create_buffers(int batch_size, int k, bool is_desc, int cap=10000) { vector> buffers; for (int i = 0; i < batch_size; i++) { - buffers.push_back(make_shared(k, is_desc, k*100, 0)); + buffers.push_back(make_shared(k, is_desc, cap, 0)); } return buffers; } @@ -387,121 +387,123 @@ inline void scan_list(const float *query_vec, } } -//inline void batched_scan_list( -// const float *query_vecs, -// const float *list_vecs, -// const int64_t *list_ids, -// int num_queries, -// int list_size, -// int dim, -// vector> &topk_buffers, -// MetricType metric = faiss::METRIC_L2) -//{ -// if (list_size == 0 || list_vecs == nullptr) { -// return; -// } -// -// // Wrap raw arrays in torch Tensors, no copy -// Tensor query = torch::from_blob((void*)query_vecs, {num_queries, dim}, torch::kFloat32); -// Tensor list = torch::from_blob((void*)list_vecs, {list_size, dim}, torch::kFloat32); -// -// torch::Tensor distances; -// if (metric == faiss::METRIC_L2) { -// // Returns [num_queries, list_size], each entry is the Euclidean distance -// distances = torch::cdist(query, list, 2.0); -// } else if (metric == faiss::METRIC_INNER_PRODUCT) { -// // [num_queries, list_size], each entry is dot product -// distances = torch::matmul(query, list.t()); -// } else { -// throw std::runtime_error("Metric type not supported"); -// } -// -// // For each query, push all list vectors and their distances into TopkBuffer -// auto distances_acc = distances.accessor(); -// for (int i = 0; i < num_queries; ++i) { -// std::vector dists(list_size); -// std::vector ids(list_size); -// -// for (int j = 0; j < list_size; ++j) { -// dists[j] = distances_acc[i][j]; -// } -// -// if (list_ids) { -// for (int j = 0; j < list_size; ++j) { -// ids[j] = list_ids[j]; -// } -// } else { -// for (int j = 0; j < list_size; ++j) { -// ids[j] = j; -// } -// } -// -// topk_buffers[i]->batch_add(dists.data(), ids.data(), list_size); -// } -//} - -inline void batched_scan_list(const float *query_vecs, - const float *list_vecs, - const int64_t *list_ids, - int num_queries, - int list_size, - int dim, - vector> &topk_buffers, - MetricType metric = faiss::METRIC_L2, - float *distances = nullptr, - int64_t *labels = nullptr) { +inline void batched_scan_list( + const float *query_vecs, + const float *list_vecs, + const int64_t *list_ids, + int num_queries, + int list_size, + int dim, + vector> &topk_buffers, + MetricType metric = faiss::METRIC_L2, + float *dist = nullptr, + int64_t *labels = nullptr) // Optional output for distances and labels +{ if (list_size == 0 || list_vecs == nullptr) { - // No list vectors to process; return; } - // Ensure k does not exceed list_size - int k = topk_buffers[0]->k_; - int k_max = std::min(k, list_size); - - bool alloc_results = false; - if (distances == nullptr) { - alloc_results = true; - labels = (int64_t *) malloc(num_queries * k_max * sizeof(int64_t)); - distances = (float *) malloc(num_queries * k_max * sizeof(float)); - } + // Wrap raw arrays in torch Tensors, no copy + Tensor query = torch::from_blob((void*)query_vecs, {num_queries, dim}, torch::kFloat32); + Tensor list = torch::from_blob((void*)list_vecs, {list_size, dim}, torch::kFloat32); - if (metric == faiss::METRIC_INNER_PRODUCT) { - faiss::float_minheap_array_t res = {size_t(num_queries), size_t(k_max), labels, distances}; - faiss::knn_inner_product(query_vecs, list_vecs, dim, num_queries, list_size, &res, nullptr); - } else if (metric == faiss::METRIC_L2) { - faiss::float_maxheap_array_t res = {size_t(num_queries), size_t(k_max), labels, distances}; - faiss::knn_L2sqr(query_vecs, list_vecs, dim, num_queries, list_size, &res, nullptr, nullptr); + torch::Tensor distances; + if (metric == faiss::METRIC_L2) { + // Returns [num_queries, list_size], each entry is the Euclidean distance + distances = torch::cdist(query, list, 2.0); + } else if (metric == faiss::METRIC_INNER_PRODUCT) { + // [num_queries, list_size], each entry is dot product + distances = torch::matmul(query, list.t()); } else { throw std::runtime_error("Metric type not supported"); } - // map the labels to the actual list_ids - if (list_ids != nullptr) { - for (int i = 0; i < num_queries; i++) { - for (int j = 0; j < k_max; j++) { - labels[i * k_max + j] = list_ids[labels[i * k_max + j]]; - } - } - } + // For each query, push all list vectors and their distances into TopkBuffer + auto distances_acc = distances.accessor(); + for (int i = 0; i < num_queries; ++i) { + std::vector dists(list_size); + std::vector ids(list_size); - // if the metric is l2, convert the distances to sqrt - if (metric == faiss::METRIC_L2) { - for (int i = 0; i < num_queries * k_max; i++) { - distances[i] = sqrt(distances[i]); + for (int j = 0; j < list_size; ++j) { + dists[j] = distances_acc[i][j]; } - } - // add distances to the topk buffers - for (int i = 0; i < num_queries; i++) { - topk_buffers[i]->batch_add(distances + i * k_max, labels + i * k_max, k_max); - } + if (list_ids) { + for (int j = 0; j < list_size; ++j) { + ids[j] = list_ids[j]; + } + } else { + for (int j = 0; j < list_size; ++j) { + ids[j] = j; + } + } - if (alloc_results) { - free(distances); - free(labels); + topk_buffers[i]->batch_add(dists.data(), ids.data(), list_size); } } +// inline void batched_scan_list(const float *query_vecs, +// const float *list_vecs, +// const int64_t *list_ids, +// int num_queries, +// int list_size, +// int dim, +// vector> &topk_buffers, +// MetricType metric = faiss::METRIC_L2, +// float *distances = nullptr, +// int64_t *labels = nullptr) { +// if (list_size == 0 || list_vecs == nullptr) { +// // No list vectors to process; +// return; +// } +// +// // Ensure k does not exceed list_size +// int k = topk_buffers[0]->k_; +// int k_max = std::min(k, list_size); +// +// bool alloc_results = false; +// if (distances == nullptr) { +// alloc_results = true; +// labels = (int64_t *) malloc(num_queries * k_max * sizeof(int64_t)); +// distances = (float *) malloc(num_queries * k_max * sizeof(float)); +// } +// +// if (metric == faiss::METRIC_INNER_PRODUCT) { +// faiss::float_minheap_array_t res = {size_t(num_queries), size_t(k_max), labels, distances}; +// faiss::knn_inner_product(query_vecs, list_vecs, dim, num_queries, list_size, &res, nullptr); +// } else if (metric == faiss::METRIC_L2) { +// faiss::float_maxheap_array_t res = {size_t(num_queries), size_t(k_max), labels, distances}; +// faiss::knn_L2sqr(query_vecs, list_vecs, dim, num_queries, list_size, &res, nullptr, nullptr); +// } else { +// throw std::runtime_error("Metric type not supported"); +// } +// +// // map the labels to the actual list_ids +// if (list_ids != nullptr) { +// for (int i = 0; i < num_queries; i++) { +// for (int j = 0; j < k_max; j++) { +// labels[i * k_max + j] = list_ids[labels[i * k_max + j]]; +// } +// } +// } +// +// // if the metric is l2, convert the distances to sqrt +// if (metric == faiss::METRIC_L2) { +// for (int i = 0; i < num_queries * k_max; i++) { +// distances[i] = sqrt(distances[i]); +// } +// } +// +// // add distances to the topk buffers +// for (int i = 0; i < num_queries; i++) { +// topk_buffers[i]->batch_add(distances + i * k_max, labels + i * k_max, k_max); +// } +// +// if (alloc_results) { +// free(distances); +// free(labels); +// } +// } + // } #endif //LIST_SCANNING_H \ No newline at end of file diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index e0e6e7f9..fd5b042e 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -79,7 +79,7 @@ class QueryCoordinator { struct NUMAResources { float* local_query_buffer = nullptr; size_t buffer_size = 0; - moodycamel::BlockingConcurrentQueue job_queue; + moodycamel::ConcurrentQueue job_queue; }; struct ResultJob { diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 6159806b..70b80100 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -89,7 +89,19 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { int64_t jid = 0; auto start = std::chrono::high_resolution_clock::now(); - nr.job_queue.wait_dequeue(jid); + + bool success = nr.job_queue.try_dequeue(jid); + if (!success) { + if (jobs_in_flight_ == 0) { + std::this_thread::yield(); + continue; + } else { + std::this_thread::sleep_for(std::chrono::microseconds(1)); + continue; + } + } + + // nr.job_queue.wait_dequeue(jid); auto end = std::chrono::high_resolution_clock::now(); res.wait_time_ns += std::chrono::duration_cast(end - start).count(); @@ -446,7 +458,6 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, job.query_id = (int)q; job.partition_id = pid; job.k = params->k; - job.query_vector = qptr; job.rank = p; job_buffer_.push_back(job); numa_resources_[core_to_numa[pid % num_workers_]].job_queue.enqueue(next_job_id_); @@ -488,25 +499,42 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, ranks->push_back(pairs[off + i].second); } - ScanJob job; - job.is_batched = true; - job.job_id = next_job_id_; - job.partition_id = pid; - job.k = params->k; - job.num_queries = static_cast(chunk); - job.query_vector = xptr; // whole matrix; we gather later - job.query_ids = qids; - job.ranks = ranks; - job.scan_all = scan_all; - - job_buffer_.push_back(job); - - // Choose NUMA queue by partition-to-core mapping. - int core = pid % num_workers_; - int node = core_to_numa[core]; - numa_resources_[node].job_queue.enqueue(next_job_id_); - next_job_id_++; - jobs_in_flight_++; + if (chunk < MIN_BATCH_SCAN_SIZE) { + for (size_t i = 0; i < chunk; ++i) { + int qid = qids->at(i); + int rank = ranks->at(i); + ScanJob job; + job.is_batched = false; + job.job_id = next_job_id_; + job.partition_id = pid; + job.k = params->k; + job.rank = rank; + job.query_id = qid; + job_buffer_.push_back(job); + numa_resources_[core_to_numa[pid % num_workers_]].job_queue.enqueue(next_job_id_); + next_job_id_++; + jobs_in_flight_++; + } + } else { + ScanJob job; + job.is_batched = true; + job.job_id = next_job_id_; + job.partition_id = pid; + job.k = params->k; + job.num_queries = static_cast(chunk); + job.query_ids = qids; + job.ranks = ranks; + job.scan_all = scan_all; + + job_buffer_.push_back(job); + + // Choose NUMA queue by partition-to-core mapping. + int core = pid % num_workers_; + int node = core_to_numa[core]; + numa_resources_[node].job_queue.enqueue(next_job_id_); + next_job_id_++; + jobs_in_flight_++; + } } } } From 32185a2340240b7f0565e48e4c8998e229d956c2 Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 23 May 2025 16:04:10 -0500 Subject: [PATCH 233/323] update queue waiting --- src/cpp/include/list_scanning.h | 204 ++++++++++++++++---------------- 1 file changed, 102 insertions(+), 102 deletions(-) diff --git a/src/cpp/include/list_scanning.h b/src/cpp/include/list_scanning.h index 89ece74e..2e5d5aeb 100644 --- a/src/cpp/include/list_scanning.h +++ b/src/cpp/include/list_scanning.h @@ -387,123 +387,123 @@ inline void scan_list(const float *query_vec, } } -inline void batched_scan_list( - const float *query_vecs, - const float *list_vecs, - const int64_t *list_ids, - int num_queries, - int list_size, - int dim, - vector> &topk_buffers, - MetricType metric = faiss::METRIC_L2, - float *dist = nullptr, - int64_t *labels = nullptr) // Optional output for distances and labels -{ - if (list_size == 0 || list_vecs == nullptr) { - return; - } - - // Wrap raw arrays in torch Tensors, no copy - Tensor query = torch::from_blob((void*)query_vecs, {num_queries, dim}, torch::kFloat32); - Tensor list = torch::from_blob((void*)list_vecs, {list_size, dim}, torch::kFloat32); - - torch::Tensor distances; - if (metric == faiss::METRIC_L2) { - // Returns [num_queries, list_size], each entry is the Euclidean distance - distances = torch::cdist(query, list, 2.0); - } else if (metric == faiss::METRIC_INNER_PRODUCT) { - // [num_queries, list_size], each entry is dot product - distances = torch::matmul(query, list.t()); - } else { - throw std::runtime_error("Metric type not supported"); - } - - // For each query, push all list vectors and their distances into TopkBuffer - auto distances_acc = distances.accessor(); - for (int i = 0; i < num_queries; ++i) { - std::vector dists(list_size); - std::vector ids(list_size); - - for (int j = 0; j < list_size; ++j) { - dists[j] = distances_acc[i][j]; - } - - if (list_ids) { - for (int j = 0; j < list_size; ++j) { - ids[j] = list_ids[j]; - } - } else { - for (int j = 0; j < list_size; ++j) { - ids[j] = j; - } - } - - topk_buffers[i]->batch_add(dists.data(), ids.data(), list_size); - } -} - -// inline void batched_scan_list(const float *query_vecs, -// const float *list_vecs, -// const int64_t *list_ids, -// int num_queries, -// int list_size, -// int dim, -// vector> &topk_buffers, -// MetricType metric = faiss::METRIC_L2, -// float *distances = nullptr, -// int64_t *labels = nullptr) { +// inline void batched_scan_list( +// const float *query_vecs, +// const float *list_vecs, +// const int64_t *list_ids, +// int num_queries, +// int list_size, +// int dim, +// vector> &topk_buffers, +// MetricType metric = faiss::METRIC_L2, +// float *dist = nullptr, +// int64_t *labels = nullptr) // Optional output for distances and labels +// { // if (list_size == 0 || list_vecs == nullptr) { -// // No list vectors to process; // return; // } // -// // Ensure k does not exceed list_size -// int k = topk_buffers[0]->k_; -// int k_max = std::min(k, list_size); -// -// bool alloc_results = false; -// if (distances == nullptr) { -// alloc_results = true; -// labels = (int64_t *) malloc(num_queries * k_max * sizeof(int64_t)); -// distances = (float *) malloc(num_queries * k_max * sizeof(float)); -// } +// // Wrap raw arrays in torch Tensors, no copy +// Tensor query = torch::from_blob((void*)query_vecs, {num_queries, dim}, torch::kFloat32); +// Tensor list = torch::from_blob((void*)list_vecs, {list_size, dim}, torch::kFloat32); // -// if (metric == faiss::METRIC_INNER_PRODUCT) { -// faiss::float_minheap_array_t res = {size_t(num_queries), size_t(k_max), labels, distances}; -// faiss::knn_inner_product(query_vecs, list_vecs, dim, num_queries, list_size, &res, nullptr); -// } else if (metric == faiss::METRIC_L2) { -// faiss::float_maxheap_array_t res = {size_t(num_queries), size_t(k_max), labels, distances}; -// faiss::knn_L2sqr(query_vecs, list_vecs, dim, num_queries, list_size, &res, nullptr, nullptr); +// torch::Tensor distances; +// if (metric == faiss::METRIC_L2) { +// // Returns [num_queries, list_size], each entry is the Euclidean distance +// distances = torch::cdist(query, list, 2.0); +// } else if (metric == faiss::METRIC_INNER_PRODUCT) { +// // [num_queries, list_size], each entry is dot product +// distances = torch::matmul(query, list.t()); // } else { // throw std::runtime_error("Metric type not supported"); // } // -// // map the labels to the actual list_ids -// if (list_ids != nullptr) { -// for (int i = 0; i < num_queries; i++) { -// for (int j = 0; j < k_max; j++) { -// labels[i * k_max + j] = list_ids[labels[i * k_max + j]]; -// } -// } -// } +// // For each query, push all list vectors and their distances into TopkBuffer +// auto distances_acc = distances.accessor(); +// for (int i = 0; i < num_queries; ++i) { +// std::vector dists(list_size); +// std::vector ids(list_size); // -// // if the metric is l2, convert the distances to sqrt -// if (metric == faiss::METRIC_L2) { -// for (int i = 0; i < num_queries * k_max; i++) { -// distances[i] = sqrt(distances[i]); +// for (int j = 0; j < list_size; ++j) { +// dists[j] = distances_acc[i][j]; // } -// } // -// // add distances to the topk buffers -// for (int i = 0; i < num_queries; i++) { -// topk_buffers[i]->batch_add(distances + i * k_max, labels + i * k_max, k_max); -// } +// if (list_ids) { +// for (int j = 0; j < list_size; ++j) { +// ids[j] = list_ids[j]; +// } +// } else { +// for (int j = 0; j < list_size; ++j) { +// ids[j] = j; +// } +// } // -// if (alloc_results) { -// free(distances); -// free(labels); +// topk_buffers[i]->batch_add(dists.data(), ids.data(), list_size); // } // } +inline void batched_scan_list(const float *query_vecs, + const float *list_vecs, + const int64_t *list_ids, + int num_queries, + int list_size, + int dim, + vector> &topk_buffers, + MetricType metric = faiss::METRIC_L2, + float *distances = nullptr, + int64_t *labels = nullptr) { + if (list_size == 0 || list_vecs == nullptr) { + // No list vectors to process; + return; + } + + // Ensure k does not exceed list_size + int k = topk_buffers[0]->k_; + int k_max = std::min(k, list_size); + + bool alloc_results = false; + if (distances == nullptr) { + alloc_results = true; + labels = (int64_t *) malloc(num_queries * k_max * sizeof(int64_t)); + distances = (float *) malloc(num_queries * k_max * sizeof(float)); + } + + if (metric == faiss::METRIC_INNER_PRODUCT) { + faiss::float_minheap_array_t res = {size_t(num_queries), size_t(k_max), labels, distances}; + faiss::knn_inner_product(query_vecs, list_vecs, dim, num_queries, list_size, &res, nullptr); + } else if (metric == faiss::METRIC_L2) { + faiss::float_maxheap_array_t res = {size_t(num_queries), size_t(k_max), labels, distances}; + faiss::knn_L2sqr(query_vecs, list_vecs, dim, num_queries, list_size, &res, nullptr, nullptr); + } else { + throw std::runtime_error("Metric type not supported"); + } + + // map the labels to the actual list_ids + if (list_ids != nullptr) { + for (int i = 0; i < num_queries; i++) { + for (int j = 0; j < k_max; j++) { + labels[i * k_max + j] = list_ids[labels[i * k_max + j]]; + } + } + } + + // if the metric is l2, convert the distances to sqrt + if (metric == faiss::METRIC_L2) { + for (int i = 0; i < num_queries * k_max; i++) { + distances[i] = sqrt(distances[i]); + } + } + + // add distances to the topk buffers + for (int i = 0; i < num_queries; i++) { + topk_buffers[i]->batch_add(distances + i * k_max, labels + i * k_max, k_max); + } + + if (alloc_results) { + free(distances); + free(labels); + } +} + // } #endif //LIST_SCANNING_H \ No newline at end of file From 8fc75ba8d6b95e44a494b3595e0f48a5551aa096 Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 23 May 2025 16:07:12 -0500 Subject: [PATCH 234/323] update queue waiting --- src/cpp/src/query_coordinator.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 70b80100..01839a6e 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -96,7 +96,7 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { std::this_thread::yield(); continue; } else { - std::this_thread::sleep_for(std::chrono::microseconds(1)); + std::this_thread::sleep_for(std::chrono::microseconds(5)); continue; } } From be6f966e99f1a238dc60dcd57db3b57500a9140e Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 23 May 2025 16:18:31 -0500 Subject: [PATCH 235/323] update queue waiting --- src/cpp/include/query_coordinator.h | 3 +++ src/cpp/src/query_coordinator.cpp | 34 ++++++++++++++++++++++------- 2 files changed, 29 insertions(+), 8 deletions(-) diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index fd5b042e..001d2208 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -104,6 +104,9 @@ class QueryCoordinator { std::atomic stop_workers_; ///< Flag to signal workers to terminate. std::atomic jobs_in_flight_; ///< Number of jobs in flight. bool debug_ = false; ///< Debug mode flag. + std::condition_variable_any jobs_cv_; // parks idle workers + std::mutex jobs_mu_; // only for the CV + std::vector job_buffer_; int next_job_id_ = 0; ///< ID for the next job to be processed. diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 01839a6e..4b879cf1 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -90,17 +90,34 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { auto start = std::chrono::high_resolution_clock::now(); - bool success = nr.job_queue.try_dequeue(jid); - if (!success) { - if (jobs_in_flight_ == 0) { - std::this_thread::yield(); - continue; - } else { - std::this_thread::sleep_for(std::chrono::microseconds(5)); - continue; + + if (nr.job_queue.try_dequeue(jid)) // fast-path + { + if (jid == -1) break; // poison → shutdown + process_scan_job(job_buffer_[jid], res); + if (jobs_in_flight_.fetch_sub(1, std::memory_order_acq_rel) == 1) { + /* this was the last job in the batch – nothing to do; + main thread will reset jobs_in_flight_ to 0 before the + next batch anyway */ } + continue; + } + + /* queue empty → check global counter */ + if (jobs_in_flight_.load(std::memory_order_acquire) != 0) { + /* other workers are still draining → yield to avoid thrashing + the same empty queue */ + std::this_thread::yield(); + continue; } + /* REALLY nothing left – park this thread until a producer wakes us */ + std::unique_lock lk(jobs_mu_); + jobs_cv_.wait(lk, [&]{ + return stop_workers_.load(std::memory_order_relaxed) || + jobs_in_flight_.load(std::memory_order_relaxed) != 0; + }); + // nr.job_queue.wait_dequeue(jid); auto end = std::chrono::high_resolution_clock::now(); @@ -713,6 +730,7 @@ std::shared_ptr QueryCoordinator::worker_scan( // 3) enqueue jobs enqueue_scan_jobs(x, partition_ids, params); + jobs_cv_.notify_all(); auto s4 = high_resolution_clock::now(); From ac62261c0ea82f8c1d8c06a5e7ce6e663f11e8fa Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 23 May 2025 16:23:54 -0500 Subject: [PATCH 236/323] update queue waiting --- src/cpp/include/query_coordinator.h | 5 +---- src/cpp/src/query_coordinator.cpp | 32 +---------------------------- 2 files changed, 2 insertions(+), 35 deletions(-) diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index 001d2208..e0e6e7f9 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -79,7 +79,7 @@ class QueryCoordinator { struct NUMAResources { float* local_query_buffer = nullptr; size_t buffer_size = 0; - moodycamel::ConcurrentQueue job_queue; + moodycamel::BlockingConcurrentQueue job_queue; }; struct ResultJob { @@ -104,9 +104,6 @@ class QueryCoordinator { std::atomic stop_workers_; ///< Flag to signal workers to terminate. std::atomic jobs_in_flight_; ///< Number of jobs in flight. bool debug_ = false; ///< Debug mode flag. - std::condition_variable_any jobs_cv_; // parks idle workers - std::mutex jobs_mu_; // only for the CV - std::vector job_buffer_; int next_job_id_ = 0; ///< ID for the next job to be processed. diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 4b879cf1..73418614 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -89,36 +89,7 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { int64_t jid = 0; auto start = std::chrono::high_resolution_clock::now(); - - - if (nr.job_queue.try_dequeue(jid)) // fast-path - { - if (jid == -1) break; // poison → shutdown - process_scan_job(job_buffer_[jid], res); - if (jobs_in_flight_.fetch_sub(1, std::memory_order_acq_rel) == 1) { - /* this was the last job in the batch – nothing to do; - main thread will reset jobs_in_flight_ to 0 before the - next batch anyway */ - } - continue; - } - - /* queue empty → check global counter */ - if (jobs_in_flight_.load(std::memory_order_acquire) != 0) { - /* other workers are still draining → yield to avoid thrashing - the same empty queue */ - std::this_thread::yield(); - continue; - } - - /* REALLY nothing left – park this thread until a producer wakes us */ - std::unique_lock lk(jobs_mu_); - jobs_cv_.wait(lk, [&]{ - return stop_workers_.load(std::memory_order_relaxed) || - jobs_in_flight_.load(std::memory_order_relaxed) != 0; - }); - - // nr.job_queue.wait_dequeue(jid); + nr.job_queue.wait_dequeue(jid); auto end = std::chrono::high_resolution_clock::now(); res.wait_time_ns += std::chrono::duration_cast(end - start).count(); @@ -730,7 +701,6 @@ std::shared_ptr QueryCoordinator::worker_scan( // 3) enqueue jobs enqueue_scan_jobs(x, partition_ids, params); - jobs_cv_.notify_all(); auto s4 = high_resolution_clock::now(); From 70d4c244a4b9d4b55f72b2ef0c8a676bc36a0e7c Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 23 May 2025 17:01:46 -0500 Subject: [PATCH 237/323] update queue waiting --- src/cpp/include/list_scanning.h | 16 +++++++++ src/cpp/include/query_coordinator.h | 5 ++- src/cpp/src/clustering.cpp | 6 ++++ src/cpp/src/query_coordinator.cpp | 50 ++++++++++++++++++++++------- 4 files changed, 65 insertions(+), 12 deletions(-) diff --git a/src/cpp/include/list_scanning.h b/src/cpp/include/list_scanning.h index 2e5d5aeb..7b52755b 100644 --- a/src/cpp/include/list_scanning.h +++ b/src/cpp/include/list_scanning.h @@ -449,6 +449,9 @@ inline void batched_scan_list(const float *query_vecs, int list_size, int dim, vector> &topk_buffers, + int64_t *setup_time, + int64_t *scan_time, + int64_t *push_time, MetricType metric = faiss::METRIC_L2, float *distances = nullptr, int64_t *labels = nullptr) { @@ -457,6 +460,8 @@ inline void batched_scan_list(const float *query_vecs, return; } + auto s1 = high_resolution_clock::now(); + // Ensure k does not exceed list_size int k = topk_buffers[0]->k_; int k_max = std::min(k, list_size); @@ -468,6 +473,8 @@ inline void batched_scan_list(const float *query_vecs, distances = (float *) malloc(num_queries * k_max * sizeof(float)); } + auto s2 = high_resolution_clock::now(); + if (metric == faiss::METRIC_INNER_PRODUCT) { faiss::float_minheap_array_t res = {size_t(num_queries), size_t(k_max), labels, distances}; faiss::knn_inner_product(query_vecs, list_vecs, dim, num_queries, list_size, &res, nullptr); @@ -478,6 +485,9 @@ inline void batched_scan_list(const float *query_vecs, throw std::runtime_error("Metric type not supported"); } + auto s3 = high_resolution_clock::now(); + + // map the labels to the actual list_ids if (list_ids != nullptr) { for (int i = 0; i < num_queries; i++) { @@ -503,6 +513,12 @@ inline void batched_scan_list(const float *query_vecs, free(distances); free(labels); } + + auto s4 = high_resolution_clock::now(); + + *setup_time = duration_cast(s2 - s1).count(); + *scan_time = duration_cast(s3 - s2).count(); + *push_time = duration_cast(s4 - s3).count(); } // } diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index e0e6e7f9..db0a7a65 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -71,7 +71,10 @@ class QueryCoordinator { int64_t job_counter = 0; ///< Job counter for this core. int64_t queries_counter = 0; ///< Number of queries processed by this core. int64_t wait_time_ns = 0; ///< Time spent waiting for jobs. - int64_t process_time_ns = 0; ///< Time spent scanning. + int64_t process_time_ns = 0; ///< Time spent processing jobs. + int64_t scan_setup_time_ns = 0; ///< Time spent on scan setup. + int64_t scan_time_ns = 0; ///< Time spent on scanning. + int64_t scan_push_time_ns = 0; ///< Time spent pushing results. int64_t enqueue_time_ns = 0; ///< Time spent enqueuing. int64_t job_time_ns = 0; ///< Time spent on job processing (excluding waiting). }; diff --git a/src/cpp/src/clustering.cpp b/src/cpp/src/clustering.cpp index 552228be..4e60c0b8 100644 --- a/src/cpp/src/clustering.cpp +++ b/src/cpp/src/clustering.cpp @@ -280,6 +280,9 @@ tuple >> kmeans_refine_partitions( vector > buffers = create_buffers(nvec, 1, false); // Use batched_scan_list to get nearest centroid for each vector. + int64_t scan_setup_time = 0; + int64_t scan_time = 0; + int64_t scan_push_time = 0; batched_scan_list(part_vecs, centroids_ptr, nullptr, @@ -287,6 +290,9 @@ tuple >> kmeans_refine_partitions( n_clusters, d, buffers, + &scan_setup_time, + &scan_time, + &scan_push_time, metric); // For each vector in this partition, determine its assignment. diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 73418614..edcec828 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -234,6 +234,8 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, CoreResources &res, NUMAResources &nr) { + auto start = std::chrono::high_resolution_clock::now(); + // Total queries, Top-K, dimension, NUMA node int64_t Q = job.num_queries; int K = job.k; @@ -317,27 +319,27 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, } qptr = dst; } - - for (int64_t i = 0; i < chunk; i += 4) { - __builtin_prefetch(qptr + i * D, /* rw = */ 0, /* locality = */ 3); - } - // PREFETCH: warm up the partition codes and ids - __builtin_prefetch(codes, 0, 3); - __builtin_prefetch(ids, 0, 3); - - auto start = std::chrono::high_resolution_clock::now(); // run the scan on this chunk + + int64_t scan_setup_time = 0; + int64_t scan_time = 0; + int64_t scan_push_time = 0; batched_scan_list( qptr, codes, ids, chunk, part_size, D, res.topk_buffer_pool, + &scan_setup_time, + &scan_time, + &scan_push_time, metric_, res.batch_distances, res.batch_ids ); - auto end = std::chrono::high_resolution_clock::now(); - res.process_time_ns += std::chrono::duration_cast(end - start).count(); + + res.scan_setup_time_ns += scan_setup_time; + res.scan_time_ns += scan_time; + res.scan_push_time_ns += scan_push_time; // collect results for this chunk std::vector results_batch; @@ -349,6 +351,10 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, auto ti = res.topk_buffer_pool[i]->get_topk_indices(); results_batch.emplace_back(ResultJob{global_q, rank_q, std::move(tv), std::move(ti)}); } + + auto end = std::chrono::high_resolution_clock::now(); + res.process_time_ns += std::chrono::duration_cast(end - start).count(); + start = std::chrono::high_resolution_clock::now(); // enqueue this sub-batch in bulk result_queue_.enqueue_bulk( @@ -680,11 +686,17 @@ std::shared_ptr QueryCoordinator::worker_scan( vector core_process_time_ns(num_workers_, 0); vector core_enqueue_time_ns(num_workers_, 0); vector core_job_time_ns(num_workers_, 0); + vector core_scan_setup_time_ns(num_workers_, 0); + vector core_scan_time_ns(num_workers_, 0); + vector core_scan_push_time_ns(num_workers_, 0); for (int i = 0; i < num_workers_; ++i) { core_wait_time_ns[i] = core_resources_[i].wait_time_ns; core_process_time_ns[i] = core_resources_[i].process_time_ns; core_enqueue_time_ns[i] = core_resources_[i].enqueue_time_ns; core_job_time_ns[i] = core_resources_[i].job_time_ns; + core_scan_setup_time_ns[i] = core_resources_[i].scan_setup_time_ns; + core_scan_time_ns[i] = core_resources_[i].scan_time_ns; + core_scan_push_time_ns[i] = core_resources_[i].scan_push_time_ns; } auto s1 = high_resolution_clock::now(); @@ -731,13 +743,21 @@ std::shared_ptr QueryCoordinator::worker_scan( core_process_time_ns[i] = core_resources_[i].process_time_ns - core_process_time_ns[i]; core_enqueue_time_ns[i] = core_resources_[i].enqueue_time_ns - core_enqueue_time_ns[i]; core_job_time_ns[i] = core_resources_[i].job_time_ns - core_job_time_ns[i]; + core_scan_setup_time_ns[i] = core_resources_[i].scan_setup_time_ns - core_scan_setup_time_ns[i]; + core_scan_time_ns[i] = core_resources_[i].scan_time_ns - core_scan_time_ns[i]; + core_scan_push_time_ns[i] = core_resources_[i].scan_push_time_ns - core_scan_push_time_ns[i]; } // print out the per-core resource timers; for (int i = 0; i < num_workers_; ++i) { std::cout << "[QueryCoordinator::worker_scan] Core " << i << ": " + << "job_counter=" << core_resources_[i].job_counter << " " + << "queries_counter=" << core_resources_[i].queries_counter << " " << "wait_time_ms=" << (float) core_wait_time_ns[i] / 1e6 << " " + << "scan_setup_time_ms=" << (float) core_scan_setup_time_ns[i] / 1e6 << " " + << "scan_time_ms=" << (float) core_scan_time_ns[i] / 1e6 << " " + << "scan_push_time_ms=" << (float) core_scan_push_time_ns[i] / 1e6 << " " << "process_time_ms=" << (float) core_process_time_ns[i] / 1e6 << " " << "enqueue_time_ms=" << (float) core_enqueue_time_ns[i] / 1e6 << " " << "job_time_ms=" << (float) core_job_time_ns[i] / 1e6 << std::endl; @@ -1163,6 +1183,11 @@ shared_ptr QueryCoordinator::batched_serial_scan( vector> local_buffers = create_buffers(batch_size, k, (metric_ == faiss::METRIC_INNER_PRODUCT)); // Perform a single batched scan on the partition. + + int64_t scan_setup_time = 0; + int64_t scan_time = 0; + int64_t scan_push_time = 0; + batched_scan_list(x_subset.data_ptr(), list_codes, list_ids, @@ -1170,6 +1195,9 @@ shared_ptr QueryCoordinator::batched_serial_scan( list_size, d, local_buffers, + &scan_setup_time, + &scan_time, + &scan_push_time, metric_); // Merge the local results into the corresponding global buffers. From 059596ff1530537af4c532c6a0114e73053602c9 Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 23 May 2025 17:13:14 -0500 Subject: [PATCH 238/323] update queue waiting --- src/cpp/src/query_coordinator.cpp | 34 +++++++++++-------------------- 1 file changed, 12 insertions(+), 22 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index edcec828..8aab3d31 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -253,13 +253,9 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, return; } - // Sub-batching - static constexpr int64_t MAX_SUBBATCH = 128; - // 1) Prepare per-thread buffers *once* up to MAX_SUBBATCH size_t cap = std::min(100 * K, 10000); - int64_t queries_req = std::min(Q, MAX_SUBBATCH); - + int64_t queries_req = Q; if (res.topk_buffer_pool.size() < (size_t)queries_req) { res.topk_buffer_pool.resize(queries_req); @@ -288,12 +284,8 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, res.batch_res_capacity = max_r; } - // 2) Process in sub-batches - for (int64_t offset = 0; offset < Q; offset += MAX_SUBBATCH) { - int64_t chunk = std::min(Q - offset, MAX_SUBBATCH); - // reset only the first 'chunk' TopK buffers - for (int64_t i = 0; i < chunk; ++i) { + for (int64_t i = 0; i < Q; ++i) { auto &buf = res.topk_buffer_pool[i]; buf->set_k(K); buf->reset(); @@ -303,31 +295,30 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, float init_val = (metric_ == faiss::METRIC_INNER_PRODUCT) ? -std::numeric_limits::infinity() : std::numeric_limits::infinity(); - std::fill_n(res.batch_distances, chunk * K, init_val); - std::fill_n(res.batch_ids, chunk * K, -1LL); + std::fill_n(res.batch_distances, Q * K, init_val); + std::fill_n(res.batch_ids, Q * K, -1LL); // gather queries float *qptr = nullptr; if (job.scan_all) { - qptr = nr.local_query_buffer + offset * D; + qptr = nr.local_query_buffer; } else { float *dst = res.batch_queries; - for (int64_t i = 0; i < chunk; ++i) { - int qid = (*job.query_ids)[offset + i]; + for (int64_t i = 0; i < Q; ++i) { + int qid = (*job.query_ids)[i]; const float *src = nr.local_query_buffer + size_t(qid) * D; std::memcpy(dst + i * D, src, D * sizeof(float)); } qptr = dst; } // run the scan on this chunk - int64_t scan_setup_time = 0; int64_t scan_time = 0; int64_t scan_push_time = 0; batched_scan_list( qptr, codes, ids, - chunk, part_size, D, + Q, part_size, D, res.topk_buffer_pool, &scan_setup_time, &scan_time, @@ -343,10 +334,10 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, // collect results for this chunk std::vector results_batch; - results_batch.reserve(chunk); - for (int64_t i = 0; i < chunk; ++i) { - int global_q = (*job.query_ids)[offset + i]; - int rank_q = (*job.ranks) [offset + i]; + results_batch.reserve(Q); + for (int64_t i = 0; i < Q; ++i) { + int global_q = (*job.query_ids)[i]; + int rank_q = (*job.ranks) [i]; auto tv = res.topk_buffer_pool[i]->get_topk(); auto ti = res.topk_buffer_pool[i]->get_topk_indices(); results_batch.emplace_back(ResultJob{global_q, rank_q, std::move(tv), std::move(ti)}); @@ -363,7 +354,6 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, ); end = std::chrono::high_resolution_clock::now(); res.enqueue_time_ns += std::chrono::duration_cast(end - start).count(); - } } void QueryCoordinator::init_global_buffers(int64_t nQ, From f44e1941b9b517db3e7accb8567e4c05dfb77182 Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 23 May 2025 19:07:37 -0500 Subject: [PATCH 239/323] update queue waiting --- src/cpp/bindings/wrap.cpp | 2 ++ src/cpp/include/common.h | 1 + src/cpp/src/query_coordinator.cpp | 17 ++++++++--------- src/python/index_wrappers/quake.py | 8 +++++++- 4 files changed, 18 insertions(+), 10 deletions(-) diff --git a/src/cpp/bindings/wrap.cpp b/src/cpp/bindings/wrap.cpp index d4aa0d87..5e5b1563 100644 --- a/src/cpp/bindings/wrap.cpp +++ b/src/cpp/bindings/wrap.cpp @@ -189,6 +189,8 @@ PYBIND11_MODULE(_bindings, m) { (std::string("Threshold to trigger recomputation of APS. default = ") + std::to_string(DEFAULT_RECOMPUTE_THRESHOLD)).c_str()) .def_readwrite("aps_flush_period_us", &SearchParams::aps_flush_period_us, (std::string("APS flush period in microseconds. default = ") + std::to_string(DEFAULT_APS_FLUSH_PERIOD_US)).c_str()) + .def_readwrite("batch_size", &SearchParams::batch_size, + (std::string("Batch size for batched scan. default = ") + std::to_string(MAX_SUBBATCH)).c_str()) .def_readwrite("k_factor", &SearchParams::k_factor, "Factor to adjust the number of neighbors to return.") .def_readwrite("track_hits", &SearchParams::track_hits, diff --git a/src/cpp/include/common.h b/src/cpp/include/common.h index 73dae02d..7133b37e 100644 --- a/src/cpp/include/common.h +++ b/src/cpp/include/common.h @@ -178,6 +178,7 @@ struct SearchParams { int num_threads = 1; // number of threads to use for search within a single worker float k_factor = 1.0f; bool batched_scan = DEFAULT_BATCHED_SCAN; + int batch_size = MAX_SUBBATCH; bool track_hits = true; diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 8aab3d31..7cb70de7 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -100,6 +100,7 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { process_scan_job(job_buffer_[jid], res); + jobs_in_flight_.fetch_sub(1, std::memory_order_relaxed); i++; end = std::chrono::high_resolution_clock::now(); @@ -155,7 +156,6 @@ void QueryCoordinator::process_scan_job(ScanJob job, } res.queries_counter += job.num_queries; res.job_counter++; - jobs_in_flight_--; } void QueryCoordinator::handle_nonbatched_job(const ScanJob &job, @@ -253,7 +253,7 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, return; } - // 1) Prepare per-thread buffers *once* up to MAX_SUBBATCH + // 1) Prepare per-thread buffers *once* size_t cap = std::min(100 * K, 10000); int64_t queries_req = Q; @@ -446,7 +446,7 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, job_buffer_.push_back(job); numa_resources_[core_to_numa[pid % num_workers_]].job_queue.enqueue(next_job_id_); next_job_id_++; - jobs_in_flight_++; + jobs_in_flight_.fetch_add(1, std::memory_order_relaxed); } } } else { @@ -465,13 +465,13 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, scan_all = true; } - /* Emit ScanJobs, already split into ≤ MAX_SUBBATCH chunks -------------- */ + /* Emit ScanJobs, already split into ≤ params->batch_size chunks -------------- */ for (int64_t pid = 0; pid < (int64_t)qlist.size(); ++pid) { auto &pairs = qlist[pid]; if (pairs.empty()) continue; - for (size_t off = 0; off < pairs.size(); off += MAX_SUBBATCH) { - size_t chunk = std::min(MAX_SUBBATCH, pairs.size() - off); + for (size_t off = 0; off < pairs.size(); off += params->batch_size) { + size_t chunk = std::min(params->batch_size, pairs.size() - off); // Split query / rank vectors for this chunk. auto qids = std::make_shared>(); @@ -497,7 +497,7 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, job_buffer_.push_back(job); numa_resources_[core_to_numa[pid % num_workers_]].job_queue.enqueue(next_job_id_); next_job_id_++; - jobs_in_flight_++; + jobs_in_flight_.fetch_add(1, std::memory_order_relaxed); } } else { ScanJob job; @@ -517,7 +517,7 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, int node = core_to_numa[core]; numa_resources_[node].job_queue.enqueue(next_job_id_); next_job_id_++; - jobs_in_flight_++; + jobs_in_flight_.fetch_add(1, std::memory_order_relaxed); } } } @@ -1043,7 +1043,6 @@ shared_ptr QueryCoordinator::search(Tensor x, shared_ptr 10) { parent_search_params->batched_scan = true; } - } else { parent_search_params = search_params->parent_params; } diff --git a/src/python/index_wrappers/quake.py b/src/python/index_wrappers/quake.py index 982165ca..99ea9846 100644 --- a/src/python/index_wrappers/quake.py +++ b/src/python/index_wrappers/quake.py @@ -152,7 +152,8 @@ def search( n_threads=1, parent=None, sample_prefix=0, - sample_stride=5 + sample_stride=5, + batch_size=128, ) -> Tuple[torch.Tensor, torch.Tensor]: """ Find the k-nearest neighbors of the query vectors. @@ -175,12 +176,17 @@ def search( search_params.num_threads = n_threads search_params.sample_prefix = sample_prefix search_params.sample_stride = sample_stride + search_params.batch_size = batch_size + + print(parent) if parent is not None: search_params.parent_params = quake.SearchParams() search_params.parent_params.nprobe = parent.get("nprobe", 1) search_params.parent_params.recall_target = parent.get("recall_target", -1) search_params.parent_params.initial_search_fraction = parent.get("initial_search_fraction", 0.05) + search_params.parent_params.batch_size = parent.get("batch_size", 128) + search_params.parent_params.batched_scan = parent.get("batched_scan", search_params.batched_scan) return self.index.search(query, search_params) From f285ee54065b7a1efaf16c8cff172f0f0d043c6e Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 23 May 2025 23:07:42 -0500 Subject: [PATCH 240/323] blas buffer preallocation --- src/cpp/include/blas_dist.h | 138 ++++++++++++++++++++++++++++ src/cpp/include/common.h | 1 + src/cpp/include/list_scanning.h | 47 +++++++++- src/cpp/include/query_coordinator.h | 9 ++ src/cpp/src/clustering.cpp | 39 +++++++- src/cpp/src/query_coordinator.cpp | 67 +++++++++++--- 6 files changed, 280 insertions(+), 21 deletions(-) create mode 100644 src/cpp/include/blas_dist.h diff --git a/src/cpp/include/blas_dist.h b/src/cpp/include/blas_dist.h new file mode 100644 index 00000000..da49c87c --- /dev/null +++ b/src/cpp/include/blas_dist.h @@ -0,0 +1,138 @@ +/***************************************************************************************** + * faiss_exhaustive_L2sqr_buf.h + * + * Header-only replacement for Faiss’ exhaustive_L2sqr_blas()/knn_L2sqr(). + * ─────────────────────────────────────────────────────────────────────── + * • No dynamic allocations on the critical path. + * • Supersedes Faiss’ originals *without* touching any public headers. + * • Thread-safe, preserves numerical behaviour and ResultHandler contract. + * + * Jason — MIT / Quake project, 2025-05-23 + *****************************************************************************************/ +#ifndef FAISS_EXHAUSTIVE_L2SQR_BUF_H +#define FAISS_EXHAUSTIVE_L2SQR_BUF_H + +#include +#include + +#ifndef FINTEGER +#define FINTEGER long +#endif + +extern "C" { +int sgemm_(const char*, const char*, FINTEGER*, FINTEGER*, FINTEGER*, + const float*, const float*, FINTEGER*, + const float*, FINTEGER*, float*, float*, FINTEGER*); +} + +namespace faiss { + +template +void exhaustive_L2sqr_blas_buf( + const float* __restrict x, + const float* __restrict y, + size_t d, + size_t nx, + size_t ny, + size_t db_blas_bs, // = bs_y + BlockResultHandler& res, + float* __restrict ip_block, // bs_x * bs_y + float* __restrict norms_x, // bs_x + float* __restrict norms_y) // bs_y +{ + if (nx == 0 || ny == 0) return; + + constexpr size_t bs_x = 256; + const size_t bs_y = db_blas_bs; + + for (size_t i0 = 0; i0 < nx; i0 += bs_x) { + const size_t i1 = std::min(i0 + bs_x, nx); + const size_t q_chunk = i1 - i0; + + /* ‖x‖² for this query block */ + fvec_norms_L2sqr(norms_x, x + i0 * d, d, q_chunk); + + res.begin_multiple(i0, i1); + + for (size_t j0 = 0; j0 < ny; j0 += bs_y) { + const size_t j1 = std::min(j0 + bs_y, ny); + const size_t db_chunk = j1 - j0; + + /* ‖y‖² for this database block */ + fvec_norms_L2sqr(norms_y, y + j0 * d, d, db_chunk); + + /* SGEMM */ + { + const float one = 1.f; + float zero = 0.f; + FINTEGER nyi = FINTEGER(db_chunk); + FINTEGER nxi = FINTEGER(q_chunk); + FINTEGER di = FINTEGER(d); + sgemm_("Transpose","Not transpose", + &nyi,&nxi,&di, + &one, + y + j0 * d, &di, + x + i0 * d, &di, + &zero, + ip_block, &nyi); + } + + /* IP → L2² */ +#pragma omp parallel for if ((q_chunk*db_chunk) > 16384) + for (int64_t qi = 0; qi < (int64_t)q_chunk; ++qi) { + float* line = ip_block + qi * db_chunk; + const float xn = norms_x[qi]; + for (size_t pj = 0; pj < db_chunk; ++pj, ++line) { + float d2 = xn + norms_y[pj] - 2.f * (*line); + *line = (d2 < 0.f || !std::isfinite(d2)) ? 0.f : d2; + } + } + res.add_results(j0, j1, ip_block); + } + res.end_multiple(); + InterruptCallback::check(); + } +} +/* ---------------------------------------------------------------------- */ +/* 2. Public convenience wrapper (mirrors knn_L2sqr()) */ +/* ---------------------------------------------------------------------- */ +inline void knn_L2sqr_buf( + const float* x, + const float* y, + size_t d, + size_t nx, + size_t ny, + size_t k, + float* vals, // (nx × k) + int64_t* ids, // (nx × k) + float* ip_block, + float* norms_x, + float* norms_y_buf, + size_t db_blas_bs = distance_compute_blas_database_bs, + const float* y_norms = nullptr, + const IDSelector* sel = nullptr) +{ + /* selector path falls back to stock Faiss (rare in hot loop) -------- */ + if (sel) { + knn_L2sqr(x, y, d, nx, ny, k, vals, ids, y_norms, sel); + return; + } + + /* choose the same handlers Faiss uses --------------------------------*/ + if (k == 1) { + Top1BlockResultHandler> rh(nx, vals, ids); + exhaustive_L2sqr_blas_buf(x,y,d,nx,ny,db_blas_bs, + rh,ip_block,norms_x,norms_y_buf); + } else if (k < distance_compute_min_k_reservoir) { + HeapBlockResultHandler> rh(nx, vals, ids, k); + exhaustive_L2sqr_blas_buf(x,y,d,nx,ny,db_blas_bs, + rh,ip_block,norms_x,norms_y_buf); + } else { + ReservoirBlockResultHandler> rh(nx, vals, ids, k); + exhaustive_L2sqr_blas_buf(x,y,d,nx,ny,db_blas_bs, + rh,ip_block,norms_x,norms_y_buf); + } +} + +} // namespace faiss +#endif /* FAISS_EXHAUSTIVE_L2SQR_BUF_H */ \ No newline at end of file diff --git a/src/cpp/include/common.h b/src/cpp/include/common.h index 7133b37e..f9120e58 100644 --- a/src/cpp/include/common.h +++ b/src/cpp/include/common.h @@ -77,6 +77,7 @@ constexpr float DEFAULT_RECOMPUTE_THRESHOLD = 0.001f; ///< Default threshold constexpr int DEFAULT_APS_FLUSH_PERIOD_US = 5; ///< Default period (in microseconds) for flushing the APS buffer. constexpr int MAX_SUBBATCH = 128; constexpr int MIN_BATCH_SCAN_SIZE = 5; ///< Minimum batch size for scanning partitions. +constexpr int BLAS_DB_BS = 1024 * 16;; // Default constants for maintenance policy parameters constexpr const char* DEFAULT_MAINTENANCE_POLICY = "query_cost"; ///< Default maintenance policy type. diff --git a/src/cpp/include/list_scanning.h b/src/cpp/include/list_scanning.h index 7b52755b..9649997a 100644 --- a/src/cpp/include/list_scanning.h +++ b/src/cpp/include/list_scanning.h @@ -14,6 +14,7 @@ #include "sorting/floyd_rivest_select.h" #include "sorting/heap_select.h" #include "parallel.h" +#include "blas_dist.h" inline Tensor calculate_recall(Tensor ids, Tensor gt_ids) { Tensor num_correct = torch::zeros(ids.size(0), torch::kInt64); @@ -452,9 +453,13 @@ inline void batched_scan_list(const float *query_vecs, int64_t *setup_time, int64_t *scan_time, int64_t *push_time, - MetricType metric = faiss::METRIC_L2, - float *distances = nullptr, - int64_t *labels = nullptr) { + MetricType metric, + float *distances, + int64_t *labels, + float *ip_block, + float *norms_x, + float *norms_y_buf, + int blas_db_bs = BLAS_DB_BS) { if (list_size == 0 || list_vecs == nullptr) { // No list vectors to process; return; @@ -475,12 +480,43 @@ inline void batched_scan_list(const float *query_vecs, auto s2 = high_resolution_clock::now(); + // inline void knn_L2sqr_buf( + // const float* x, + // const float* y, + // size_t d, + // size_t nx, + // size_t ny, + // size_t k, + // float* vals, + // int64_t* ids, + // float* ip_block, + // float* norms_x, + // float* norms_y_buf, + // size_t db_blas_bs = distance_compute_blas_database_bs, + // const float* y_norms = nullptr, + // const IDSelector* sel = nullptr) + // { + if (metric == faiss::METRIC_INNER_PRODUCT) { faiss::float_minheap_array_t res = {size_t(num_queries), size_t(k_max), labels, distances}; faiss::knn_inner_product(query_vecs, list_vecs, dim, num_queries, list_size, &res, nullptr); } else if (metric == faiss::METRIC_L2) { - faiss::float_maxheap_array_t res = {size_t(num_queries), size_t(k_max), labels, distances}; - faiss::knn_L2sqr(query_vecs, list_vecs, dim, num_queries, list_size, &res, nullptr, nullptr); + // faiss::float_maxheap_array_t res = {size_t(num_queries), size_t(k_max), labels, distances}; + faiss::knn_L2sqr_buf( + query_vecs, + list_vecs, + dim, + num_queries, + list_size, + k_max, + distances, + labels, + ip_block, + norms_x, + norms_y_buf, + blas_db_bs + ); + // faiss::knn_L2sqr(query_vecs, list_vecs, dim, num_queries, list_size, &res, nullptr, nullptr); } else { throw std::runtime_error("Metric type not supported"); } @@ -521,5 +557,6 @@ inline void batched_scan_list(const float *query_vecs, *push_time = duration_cast(s4 - s3).count(); } + // } #endif //LIST_SCANNING_H \ No newline at end of file diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index db0a7a65..9c66da60 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -68,6 +68,15 @@ class QueryCoordinator { size_t batch_q_capacity = 0; // number of floats in batch_queries size_t batch_res_capacity = 0; // number of (k×Q) slots in distances & ids + float* blas_ip_block = nullptr; // capacity: db_blas_bs * max_Q + size_t blas_ip_capacity = 0; // # floats + + float* blas_norms_x = nullptr; // capacity: max_Q + size_t blas_norms_x_cap = 0; + + float* blas_norms_y = nullptr; // capacity: db_blas_bs (re-used per chunk) + size_t blas_norms_y_cap = 0; + int64_t job_counter = 0; ///< Job counter for this core. int64_t queries_counter = 0; ///< Number of queries processed by this core. int64_t wait_time_ns = 0; ///< Time spent waiting for jobs. diff --git a/src/cpp/src/clustering.cpp b/src/cpp/src/clustering.cpp index 4e60c0b8..99344635 100644 --- a/src/cpp/src/clustering.cpp +++ b/src/cpp/src/clustering.cpp @@ -9,6 +9,7 @@ #include "faiss/Clustering.h" #include "index_partition.h" #include +#include #ifdef QUAKE_ENABLE_GPU #include @@ -232,6 +233,35 @@ tuple >> kmeans_refine_partitions( MetricType metric, int refinement_iterations) { + // static void ensure_blas_buffers(QueryCoordinator::CoreResources& res, + // size_t max_q, + // size_t db_bs, + // int node) + // { + // const size_t ip_need = db_bs * max_q; + // if (res.blas_ip_capacity < ip_need) { + // quake_free(res.blas_ip_block, res.blas_ip_capacity * sizeof(float)); + // res.blas_ip_block = static_cast(quake_alloc(ip_need * sizeof(float), node)); + // res.blas_ip_capacity = ip_need; + // } + // if (res.blas_norms_x_cap < max_q) { + // quake_free(res.blas_norms_x, res.blas_norms_x_cap * sizeof(float)); + // res.blas_norms_x = static_cast(quake_alloc(max_q * sizeof(float), node)); + // res.blas_norms_x_cap = max_q; + // } + // if (res.blas_norms_y_cap < db_bs) { + // quake_free(res.blas_norms_y, res.blas_norms_y_cap * sizeof(float)); + // res.blas_norms_y = static_cast(quake_alloc(db_bs * sizeof(float), node)); + // res.blas_norms_y_cap = db_bs; + // } + // } + + const size_t ip_need = BLAS_DB_BS * centroids.size(0); + float * blas_ip_block = static_cast(quake_alloc(ip_need * sizeof(float), 0)); + float * blas_norms_x = static_cast(quake_alloc(BLAS_DB_BS * sizeof(float), 0)); + float * blas_norms_y = static_cast(quake_alloc(BLAS_DB_BS * sizeof(float), 0)); + + // Determine number of clusters and dimension. int n_clusters = centroids.size(0); int d = centroids.size(1); @@ -283,6 +313,7 @@ tuple >> kmeans_refine_partitions( int64_t scan_setup_time = 0; int64_t scan_time = 0; int64_t scan_push_time = 0; + batched_scan_list(part_vecs, centroids_ptr, nullptr, @@ -293,7 +324,13 @@ tuple >> kmeans_refine_partitions( &scan_setup_time, &scan_time, &scan_push_time, - metric); + metric, + /* distances*/ nullptr, + /* labels */ nullptr, + /* BLAS scratch */ blas_ip_block, + blas_norms_x, + blas_norms_y, + BLAS_DB_BS); // For each vector in this partition, determine its assignment. for (int i = 0; i < nvec; i++) { diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 7cb70de7..65c8fb20 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -12,6 +12,29 @@ #include //#include "parallel_hashmap/btree.h" +static void ensure_blas_buffers(QueryCoordinator::CoreResources& res, + size_t max_q, + size_t db_bs, + int node) +{ + const size_t ip_need = db_bs * max_q; + if (res.blas_ip_capacity < ip_need) { + quake_free(res.blas_ip_block, res.blas_ip_capacity * sizeof(float)); + res.blas_ip_block = static_cast(quake_alloc(ip_need * sizeof(float), node)); + res.blas_ip_capacity = ip_need; + } + if (res.blas_norms_x_cap < max_q) { + quake_free(res.blas_norms_x, res.blas_norms_x_cap * sizeof(float)); + res.blas_norms_x = static_cast(quake_alloc(max_q * sizeof(float), node)); + res.blas_norms_x_cap = max_q; + } + if (res.blas_norms_y_cap < db_bs) { + quake_free(res.blas_norms_y, res.blas_norms_y_cap * sizeof(float)); + res.blas_norms_y = static_cast(quake_alloc(db_bs * sizeof(float), node)); + res.blas_norms_y_cap = db_bs; + } +} + // Constructor QueryCoordinator::QueryCoordinator(shared_ptr parent, shared_ptr partition_manager, @@ -268,6 +291,8 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, } } + ensure_blas_buffers(res, Q, BLAS_DB_BS, node); + size_t max_q = size_t(queries_req) * D; if (res.batch_q_capacity < max_q) { quake_free(res.batch_queries, res.batch_q_capacity * sizeof(float)); @@ -315,18 +340,21 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, int64_t scan_setup_time = 0; int64_t scan_time = 0; int64_t scan_push_time = 0; - batched_scan_list( - qptr, - codes, ids, - Q, part_size, D, - res.topk_buffer_pool, - &scan_setup_time, - &scan_time, - &scan_push_time, - metric_, - res.batch_distances, - res.batch_ids - ); + batched_scan_list( + qptr, + codes, ids, + Q, part_size, D, + res.topk_buffer_pool, + &scan_setup_time, + &scan_time, + &scan_push_time, + metric_, + /* distances*/ res.batch_distances, + /* labels */ res.batch_ids, + /* BLAS scratch */ res.blas_ip_block, + res.blas_norms_x, + res.blas_norms_y, + BLAS_DB_BS); res.scan_setup_time_ns += scan_setup_time; res.scan_time_ns += scan_time; @@ -1120,6 +1148,9 @@ shared_ptr QueryCoordinator::batched_serial_scan( return empty_res; } + static CoreResources serial_res; // one per process + ensure_blas_buffers(serial_res, x.size(0), BLAS_DB_BS, /*node=*/0); + // Timing info (could be extended as needed) auto timing_info = std::make_shared(); auto start = high_resolution_clock::now(); @@ -1185,9 +1216,15 @@ shared_ptr QueryCoordinator::batched_serial_scan( d, local_buffers, &scan_setup_time, - &scan_time, - &scan_push_time, - metric_); + &scan_time, + &scan_push_time, + metric_, + /* distances*/ nullptr, + /* labels */ nullptr, + /* BLAS scratch */ serial_res.blas_ip_block, + serial_res.blas_norms_x, + serial_res.blas_norms_y, + BLAS_DB_BS); // Merge the local results into the corresponding global buffers. for (int i = 0; i < batch_size; i++) { From 4595404bcce75f036d70f0643f205d5719ac20af Mon Sep 17 00:00:00 2001 From: Jason Date: Sat, 24 May 2025 14:25:30 -0500 Subject: [PATCH 241/323] torch matmul --- src/cpp/include/blas_dist.h | 13 +-- src/cpp/include/common.h | 4 +- src/cpp/include/list_scanning.h | 168 +++++++++++++++++++--------- src/cpp/include/query_coordinator.h | 5 +- src/cpp/src/query_coordinator.cpp | 86 +++++++------- 5 files changed, 174 insertions(+), 102 deletions(-) diff --git a/src/cpp/include/blas_dist.h b/src/cpp/include/blas_dist.h index da49c87c..dc1cd6bb 100644 --- a/src/cpp/include/blas_dist.h +++ b/src/cpp/include/blas_dist.h @@ -78,13 +78,12 @@ void exhaustive_L2sqr_blas_buf( } /* IP → L2² */ -#pragma omp parallel for if ((q_chunk*db_chunk) > 16384) - for (int64_t qi = 0; qi < (int64_t)q_chunk; ++qi) { - float* line = ip_block + qi * db_chunk; - const float xn = norms_x[qi]; - for (size_t pj = 0; pj < db_chunk; ++pj, ++line) { - float d2 = xn + norms_y[pj] - 2.f * (*line); - *line = (d2 < 0.f || !std::isfinite(d2)) ? 0.f : d2; + for (int64_t qi = 0; qi < static_cast(q_chunk); ++qi) { + float* line_ptr = ip_block + qi * db_chunk; // Pointer to current column in ip_block + const float current_norm_x = norms_x[qi]; + for (size_t pj = 0; pj < db_chunk; ++pj) { + *line_ptr = std::fma(-2.f, *line_ptr, current_norm_x + norms_y[pj]); + line_ptr++; // Move to the next element in the column } } res.add_results(j0, j1, ip_block); diff --git a/src/cpp/include/common.h b/src/cpp/include/common.h index f9120e58..e4cc197c 100644 --- a/src/cpp/include/common.h +++ b/src/cpp/include/common.h @@ -76,8 +76,8 @@ constexpr float DEFAULT_INITIAL_SEARCH_FRACTION = 0.1f; ///< Default initial fra constexpr float DEFAULT_RECOMPUTE_THRESHOLD = 0.001f; ///< Default threshold to trigger recomputation of search parameters. constexpr int DEFAULT_APS_FLUSH_PERIOD_US = 5; ///< Default period (in microseconds) for flushing the APS buffer. constexpr int MAX_SUBBATCH = 128; -constexpr int MIN_BATCH_SCAN_SIZE = 5; ///< Minimum batch size for scanning partitions. -constexpr int BLAS_DB_BS = 1024 * 16;; +constexpr int MIN_BATCH_SCAN_SIZE = 16; ///< Minimum batch size for scanning partitions. +constexpr int BLAS_DB_BS = 1024 * 16; // Default constants for maintenance policy parameters constexpr const char* DEFAULT_MAINTENANCE_POLICY = "query_cost"; ///< Default maintenance policy type. diff --git a/src/cpp/include/list_scanning.h b/src/cpp/include/list_scanning.h index 9649997a..debb6252 100644 --- a/src/cpp/include/list_scanning.h +++ b/src/cpp/include/list_scanning.h @@ -41,6 +41,13 @@ inline Tensor calculate_recall(Tensor ids, Tensor gt_ids) { return recall; } +template +inline bool better(bool desc, T a, T b) noexcept +{ return desc ? (a > b) : (a < b); } + +//====================================================================== +// 1. Fast specialised buffer for k == 1 +//====================================================================== template class TypedTopKBuffer { public: @@ -120,6 +127,14 @@ class TypedTopKBuffer { reset(); } + int k() const { + return k_; + } + + int capacity() const { + return capacity_; + } + void alloc() { vals_ = static_cast(quake_alloc(sizeof(T) * capacity_, node_)); ids_ = static_cast(quake_alloc(sizeof(I) * capacity_, node_)); @@ -294,7 +309,7 @@ inline vector> create_buffers(int batch_size, int k, bool inline std::tuple buffers_to_tensor(vector> buffers) { int n = buffers.size(); - int k = buffers[0]->k_; + int k = buffers[0]->k(); Tensor topk_distances = torch::empty({n, k}, torch::kFloat32); Tensor topk_indices = torch::empty({n, k}, torch::kInt64); @@ -443,6 +458,102 @@ inline void scan_list(const float *query_vec, // } // } + +inline void l2_blas( + const float* __restrict x, + const float* __restrict y, + const int64_t *list_ids, + size_t d, + size_t nx, + size_t ny, + size_t db_blas_bs, // = bs_y + size_t k, + vector> &topk_buffers, + float* __restrict ip_block, // nx * bs_y + float* __restrict norms_x, // bs_x + float* __restrict norms_y) // db_blas_bs +{ + if (nx == 0 || ny == 0) return; + + constexpr size_t bs_x = 256; + const size_t bs_y = db_blas_bs; + int64_t *list_ids_ptr = (int64_t *) list_ids; + + for (size_t i0 = 0; i0 < nx; i0 += bs_x) { + const size_t i1 = std::min(i0 + bs_x, nx); + const size_t q_chunk = i1 - i0; + + /* ‖x‖² for this query block */ + faiss::fvec_norms_L2sqr(norms_x, x + i0 * d, d, q_chunk); + + for (size_t j0 = 0; j0 < ny; j0 += bs_y) { + const size_t j1 = std::min(j0 + bs_y, ny); + const size_t db_chunk = j1 - j0; + + /* ‖y‖² for this database block */ + faiss::fvec_norms_L2sqr(norms_y, y + j0 * d, d, db_chunk); + + // use torch matmul + Tensor x_chunk = torch::from_blob((void*)(x + i0 * d), {(int64_t) q_chunk, (int64_t) d}, torch::kFloat32); + Tensor y_chunk = torch::from_blob((void*)(y + j0 * d), {(int64_t) db_chunk, (int64_t) d}, torch::kFloat32); + Tensor out = torch::from_blob(ip_block, {(int64_t) q_chunk, (int64_t) db_chunk}, torch::kFloat32); + + torch::matmul_out(out, x_chunk, y_chunk.t()); + + // /* SGEMM */ + // { + // const float one = 1.f; + // float zero = 0.f; + // FINTEGER nyi = FINTEGER(db_chunk); + // FINTEGER nxi = FINTEGER(q_chunk); + // FINTEGER di = FINTEGER(d); + // sgemm_("Transpose","Not transpose", + // &nyi,&nxi,&di, + // &one, + // y + j0 * d, &di, + // x + i0 * d, &di, + // &zero, + // ip_block, &nyi); + // } + + /* IP → L2² */ + if (k > 1) { + for (int64_t qi = 0; qi < static_cast(q_chunk); ++qi) { + float* line_ptr = ip_block + qi * db_chunk; // Pointer to current column in ip_block + const float current_norm_x = norms_x[qi]; + for (size_t pj = 0; pj < db_chunk; ++pj) { + *line_ptr = std::sqrt(std::fma(-2.f, *line_ptr, current_norm_x + norms_y[pj])); + line_ptr++; // Move to the next element in the column + } + + topk_buffers[qi]->batch_add(ip_block + qi * db_chunk, list_ids_ptr + j0, db_chunk); + } + } else if (k == 1) { + for (int64_t qi = 0; qi < static_cast(q_chunk); ++qi) { + float* line_ptr = ip_block + qi * db_chunk; // Pointer to current column in ip_block + const float current_norm_x = norms_x[qi]; + for (size_t pj = 0; pj < db_chunk; ++pj) { + *line_ptr = std::sqrt(std::fma(-2.f, *line_ptr, current_norm_x + norms_y[pj])); + line_ptr++; // Move to the next element in the column + } + + float best_dist = std::numeric_limits::infinity(); + int64_t best_id = -1; + line_ptr = ip_block + qi * db_chunk; // Reset line_ptr to the start of the current column + for (size_t pj = 0; pj < db_chunk; ++pj) { + if (*line_ptr < best_dist) { + best_dist = *line_ptr; + best_id = list_ids_ptr[j0 + pj]; + } + line_ptr++; // Move to the next element in the column + } + topk_buffers[qi]->add(best_dist, best_id); + } + } + } + } +} + inline void batched_scan_list(const float *query_vecs, const float *list_vecs, const int64_t *list_ids, @@ -468,7 +579,7 @@ inline void batched_scan_list(const float *query_vecs, auto s1 = high_resolution_clock::now(); // Ensure k does not exceed list_size - int k = topk_buffers[0]->k_; + int k = topk_buffers[0]->k(); int k_max = std::min(k, list_size); bool alloc_results = false; @@ -480,41 +591,24 @@ inline void batched_scan_list(const float *query_vecs, auto s2 = high_resolution_clock::now(); - // inline void knn_L2sqr_buf( - // const float* x, - // const float* y, - // size_t d, - // size_t nx, - // size_t ny, - // size_t k, - // float* vals, - // int64_t* ids, - // float* ip_block, - // float* norms_x, - // float* norms_y_buf, - // size_t db_blas_bs = distance_compute_blas_database_bs, - // const float* y_norms = nullptr, - // const IDSelector* sel = nullptr) - // { - if (metric == faiss::METRIC_INNER_PRODUCT) { faiss::float_minheap_array_t res = {size_t(num_queries), size_t(k_max), labels, distances}; faiss::knn_inner_product(query_vecs, list_vecs, dim, num_queries, list_size, &res, nullptr); } else if (metric == faiss::METRIC_L2) { // faiss::float_maxheap_array_t res = {size_t(num_queries), size_t(k_max), labels, distances}; - faiss::knn_L2sqr_buf( + l2_blas( query_vecs, list_vecs, + list_ids, dim, num_queries, list_size, + blas_db_bs, k_max, - distances, - labels, + topk_buffers, ip_block, norms_x, - norms_y_buf, - blas_db_bs + norms_y_buf ); // faiss::knn_L2sqr(query_vecs, list_vecs, dim, num_queries, list_size, &res, nullptr, nullptr); } else { @@ -524,32 +618,6 @@ inline void batched_scan_list(const float *query_vecs, auto s3 = high_resolution_clock::now(); - // map the labels to the actual list_ids - if (list_ids != nullptr) { - for (int i = 0; i < num_queries; i++) { - for (int j = 0; j < k_max; j++) { - labels[i * k_max + j] = list_ids[labels[i * k_max + j]]; - } - } - } - - // if the metric is l2, convert the distances to sqrt - if (metric == faiss::METRIC_L2) { - for (int i = 0; i < num_queries * k_max; i++) { - distances[i] = sqrt(distances[i]); - } - } - - // add distances to the topk buffers - for (int i = 0; i < num_queries; i++) { - topk_buffers[i]->batch_add(distances + i * k_max, labels + i * k_max, k_max); - } - - if (alloc_results) { - free(distances); - free(labels); - } - auto s4 = high_resolution_clock::now(); *setup_time = duration_cast(s2 - s1).count(); diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index 9c66da60..d3a2b123 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -81,6 +81,7 @@ class QueryCoordinator { int64_t queries_counter = 0; ///< Number of queries processed by this core. int64_t wait_time_ns = 0; ///< Time spent waiting for jobs. int64_t process_time_ns = 0; ///< Time spent processing jobs. + int64_t process_preamble_time_ns = 0; ///< Time spent on job processing (excluding waiting). int64_t scan_setup_time_ns = 0; ///< Time spent on scan setup. int64_t scan_time_ns = 0; ///< Time spent on scanning. int64_t scan_push_time_ns = 0; ///< Time spent pushing results. @@ -276,7 +277,9 @@ class QueryCoordinator { std::shared_ptr aggregate_scan_results(int64_t nQ, int K, - shared_ptr timing); + shared_ptr timing, + Tensor out_ids, + Tensor out_dists); }; #endif //QUERY_COORDINATOR_H diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 65c8fb20..f85c6e20 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -121,18 +121,21 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { break; } - + auto s2 = std::chrono::high_resolution_clock::now(); process_scan_job(job_buffer_[jid], res); jobs_in_flight_.fetch_sub(1, std::memory_order_relaxed); i++; end = std::chrono::high_resolution_clock::now(); + res.process_time_ns += std::chrono::duration_cast(end - s2).count(); res.job_time_ns += std::chrono::duration_cast(end - start).count(); } } void QueryCoordinator::process_scan_job(ScanJob job, CoreResources &res) { + + auto start = std::chrono::high_resolution_clock::now(); int numa_node = 0; #ifdef QUAKE_USE_NUMA numa_node = cpu_numa_node(res.core_id); @@ -171,6 +174,8 @@ void QueryCoordinator::process_scan_job(ScanJob job, } return; } + auto end = std::chrono::high_resolution_clock::now(); + res.process_preamble_time_ns += std::chrono::duration_cast(end - start).count(); if (!job.is_batched) { handle_nonbatched_job(job, res, nr); @@ -184,7 +189,6 @@ void QueryCoordinator::process_scan_job(ScanJob job, void QueryCoordinator::handle_nonbatched_job(const ScanJob &job, CoreResources &res, NUMAResources &nr) { - // ensure buffers if (res.topk_buffer_pool.size() < 1) { res.topk_buffer_pool.resize(1); @@ -194,9 +198,9 @@ void QueryCoordinator::handle_nonbatched_job(const ScanJob &job, /*cap=*/std::min(100 * job.k, 10000), /*node=*/cpu_numa_node(res.core_id) ); - } else if (res.topk_buffer_pool[0]->k_ != job.k) { + } else if (res.topk_buffer_pool[0]->k() != job.k) { // check capacity - if (res.topk_buffer_pool[0]->capacity_ < job.k) { + if (res.topk_buffer_pool[0]->capacity() < job.k) { res.topk_buffer_pool[0] = std::make_shared( job.k, metric_ == faiss::METRIC_INNER_PRODUCT, @@ -211,8 +215,6 @@ void QueryCoordinator::handle_nonbatched_job(const ScanJob &job, res.topk_buffer_pool[0]->reset(); try { - // It's good practice to re-fetch or validate partition data here if it could - // have changed since process_scan_job's initial checks, especially with maintenance. const float* codes = (float*)partition_manager_->partition_store_->get_codes(job.partition_id); const int64_t* ids = (int64_t*)partition_manager_->partition_store_->get_ids(job.partition_id); int64_t part_size = partition_manager_->partition_store_->list_size(job.partition_id); @@ -257,8 +259,6 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, CoreResources &res, NUMAResources &nr) { - auto start = std::chrono::high_resolution_clock::now(); - // Total queries, Top-K, dimension, NUMA node int64_t Q = job.num_queries; int K = job.k; @@ -316,12 +316,14 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, buf->reset(); } - // init only the first chunk*K slots in scratch - float init_val = (metric_ == faiss::METRIC_INNER_PRODUCT) - ? -std::numeric_limits::infinity() - : std::numeric_limits::infinity(); - std::fill_n(res.batch_distances, Q * K, init_val); - std::fill_n(res.batch_ids, Q * K, -1LL); + // // init only the first chunk*K slots in scratch + // float init_val = (metric_ == faiss::METRIC_INNER_PRODUCT) + // ? -std::numeric_limits::infinity() + // : std::numeric_limits::infinity(); + // std::fill_n(res.batch_distances, Q * K, init_val); + // std::fill_n(res.batch_ids, Q * K, -1LL); + + // auto // gather queries float *qptr = nullptr; @@ -336,6 +338,7 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, } qptr = dst; } + // run the scan on this chunk int64_t scan_setup_time = 0; int64_t scan_time = 0; @@ -356,10 +359,12 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, res.blas_norms_y, BLAS_DB_BS); + res.scan_setup_time_ns += scan_setup_time; res.scan_time_ns += scan_time; res.scan_push_time_ns += scan_push_time; + auto start = std::chrono::high_resolution_clock::now(); // collect results for this chunk std::vector results_batch; results_batch.reserve(Q); @@ -371,16 +376,12 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, results_batch.emplace_back(ResultJob{global_q, rank_q, std::move(tv), std::move(ti)}); } - auto end = std::chrono::high_resolution_clock::now(); - res.process_time_ns += std::chrono::duration_cast(end - start).count(); - - start = std::chrono::high_resolution_clock::now(); // enqueue this sub-batch in bulk result_queue_.enqueue_bulk( std::make_move_iterator(results_batch.begin()), results_batch.size() ); - end = std::chrono::high_resolution_clock::now(); + auto end = std::chrono::high_resolution_clock::now(); res.enqueue_time_ns += std::chrono::duration_cast(end - start).count(); } @@ -613,6 +614,10 @@ void QueryCoordinator::drain_and_apply_aps(Tensor x, --parts_left[rj.query_id]; --total_left; } + + if (parts_left[rj.query_id] == 0) { + global_topk_buffer_pool_[rj.query_id]->flush(); + } } while (result_queue_.try_dequeue(rj)); continue; // Skip APS; we just did useful work. @@ -651,27 +656,18 @@ void QueryCoordinator::drain_and_apply_aps(Tensor x, std::shared_ptr QueryCoordinator::aggregate_scan_results(int64_t nQ, int K, - shared_ptr timing) -{ - // build output tensors - auto out_ids = torch::full({nQ,K}, -1, torch::kInt64); - auto out_dists = torch::full({nQ,K}, - (metric_==faiss::METRIC_INNER_PRODUCT - ? -std::numeric_limits::infinity() - : std::numeric_limits::infinity()), - torch::kFloat32); - - { - std::lock_guard lg(global_mutex_); - auto id_acc = out_ids.accessor(); - auto d_acc = out_dists.accessor(); - for (int64_t q = 0; q < nQ; ++q) { - auto tv = global_topk_buffer_pool_[q]->get_topk(); - auto ti = global_topk_buffer_pool_[q]->get_topk_indices(); - for (int i = 0; i < (int)ti.size() && i < K; ++i) { - id_acc[q][i] = ti[i]; - d_acc [q][i] = tv[i]; - } + shared_ptr timing, + Tensor out_ids, + Tensor out_dists) { + + auto id_acc = out_ids.accessor(); + auto d_acc = out_dists.accessor(); + for (int64_t q = 0; q < nQ; ++q) { + auto tv = global_topk_buffer_pool_[q]->get_topk(); + auto ti = global_topk_buffer_pool_[q]->get_topk_indices(); + for (int i = 0; i < (int)ti.size() && i < K; ++i) { + id_acc[q][i] = ti[i]; + d_acc [q][i] = tv[i]; } } @@ -698,10 +694,10 @@ std::shared_ptr QueryCoordinator::worker_scan( timing->n_clusters = partition_manager_->nlist(); timing->search_params = params; - // get initial values of the per-core resource timers; vector core_wait_time_ns(num_workers_, 0); vector core_process_time_ns(num_workers_, 0); + vector core_process_preamble_time_ns(num_workers_, 0); vector core_enqueue_time_ns(num_workers_, 0); vector core_job_time_ns(num_workers_, 0); vector core_scan_setup_time_ns(num_workers_, 0); @@ -710,6 +706,7 @@ std::shared_ptr QueryCoordinator::worker_scan( for (int i = 0; i < num_workers_; ++i) { core_wait_time_ns[i] = core_resources_[i].wait_time_ns; core_process_time_ns[i] = core_resources_[i].process_time_ns; + core_process_preamble_time_ns[i] = core_resources_[i].process_preamble_time_ns; core_enqueue_time_ns[i] = core_resources_[i].enqueue_time_ns; core_job_time_ns[i] = core_resources_[i].job_time_ns; core_scan_setup_time_ns[i] = core_resources_[i].scan_setup_time_ns; @@ -734,13 +731,16 @@ std::shared_ptr QueryCoordinator::worker_scan( auto s4 = high_resolution_clock::now(); + Tensor out_ids = torch::empty({nQ, K}, torch::kLong); + Tensor out_dists = torch::empty({nQ, K}, torch::kFloat); + // 4) drain results + APS drain_and_apply_aps(x, partition_ids, use_aps, params->recall_target, params->aps_flush_period_us, timing); auto s5 = high_resolution_clock::now(); - auto res = aggregate_scan_results(nQ, K, timing); + auto res = aggregate_scan_results(nQ, K, timing, out_ids, out_dists); auto s6 = high_resolution_clock::now(); @@ -759,6 +759,7 @@ std::shared_ptr QueryCoordinator::worker_scan( for (int i = 0; i < num_workers_; ++i) { core_wait_time_ns[i] = core_resources_[i].wait_time_ns - core_wait_time_ns[i]; core_process_time_ns[i] = core_resources_[i].process_time_ns - core_process_time_ns[i]; + core_process_preamble_time_ns[i] = core_resources_[i].process_preamble_time_ns - core_process_preamble_time_ns[i]; core_enqueue_time_ns[i] = core_resources_[i].enqueue_time_ns - core_enqueue_time_ns[i]; core_job_time_ns[i] = core_resources_[i].job_time_ns - core_job_time_ns[i]; core_scan_setup_time_ns[i] = core_resources_[i].scan_setup_time_ns - core_scan_setup_time_ns[i]; @@ -777,6 +778,7 @@ std::shared_ptr QueryCoordinator::worker_scan( << "scan_time_ms=" << (float) core_scan_time_ns[i] / 1e6 << " " << "scan_push_time_ms=" << (float) core_scan_push_time_ns[i] / 1e6 << " " << "process_time_ms=" << (float) core_process_time_ns[i] / 1e6 << " " + << "process_preamble_time_ms=" << (float) core_process_preamble_time_ns[i] / 1e6 << " " << "enqueue_time_ms=" << (float) core_enqueue_time_ns[i] / 1e6 << " " << "job_time_ms=" << (float) core_job_time_ns[i] / 1e6 << std::endl; } From 8cafa05745f18bc09d8a9da65e8689ec963854e4 Mon Sep 17 00:00:00 2001 From: Jason Date: Sat, 24 May 2025 15:18:38 -0500 Subject: [PATCH 242/323] store pivots --- src/cpp/include/list_scanning.h | 27 +++++++-- src/cpp/include/query_coordinator.h | 1 + src/cpp/src/query_coordinator.cpp | 93 +++++++++++++++++++---------- 3 files changed, 84 insertions(+), 37 deletions(-) diff --git a/src/cpp/include/list_scanning.h b/src/cpp/include/list_scanning.h index debb6252..be29ab56 100644 --- a/src/cpp/include/list_scanning.h +++ b/src/cpp/include/list_scanning.h @@ -471,7 +471,8 @@ inline void l2_blas( vector> &topk_buffers, float* __restrict ip_block, // nx * bs_y float* __restrict norms_x, // bs_x - float* __restrict norms_y) // db_blas_bs + float* __restrict norms_y, + float* pivot) // db_blas_bs { if (nx == 0 || ny == 0) return; @@ -526,7 +527,18 @@ inline void l2_blas( line_ptr++; // Move to the next element in the column } - topk_buffers[qi]->batch_add(ip_block + qi * db_chunk, list_ids_ptr + j0, db_chunk); + // collect distances closer than pivot + if (pivot) { + line_ptr = ip_block + qi * db_chunk; // Reset line_ptr to the start of the current column + for (size_t pj = 0; pj < db_chunk; ++pj) { + if (*line_ptr < pivot[qi]) { + topk_buffers[qi]->add(*line_ptr, list_ids_ptr[j0 + pj]); + } + line_ptr++; // Move to the next element in the column + } + } else { + topk_buffers[qi]->batch_add(ip_block + qi * db_chunk, list_ids_ptr + j0, db_chunk); + } } } else if (k == 1) { for (int64_t qi = 0; qi < static_cast(q_chunk); ++qi) { @@ -570,7 +582,8 @@ inline void batched_scan_list(const float *query_vecs, float *ip_block, float *norms_x, float *norms_y_buf, - int blas_db_bs = BLAS_DB_BS) { + int blas_db_bs = BLAS_DB_BS, + vector pivots = {}) { if (list_size == 0 || list_vecs == nullptr) { // No list vectors to process; return; @@ -591,6 +604,11 @@ inline void batched_scan_list(const float *query_vecs, auto s2 = high_resolution_clock::now(); + float *pivot_ptr = nullptr; + if (!pivots.empty()) { + pivot_ptr = pivots.data(); + } + if (metric == faiss::METRIC_INNER_PRODUCT) { faiss::float_minheap_array_t res = {size_t(num_queries), size_t(k_max), labels, distances}; faiss::knn_inner_product(query_vecs, list_vecs, dim, num_queries, list_size, &res, nullptr); @@ -608,7 +626,8 @@ inline void batched_scan_list(const float *query_vecs, topk_buffers, ip_block, norms_x, - norms_y_buf + norms_y_buf, + pivot_ptr ); // faiss::knn_L2sqr(query_vecs, list_vecs, dim, num_queries, list_size, &res, nullptr, nullptr); } else { diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index d3a2b123..d3fdfcc4 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -120,6 +120,7 @@ class QueryCoordinator { std::vector job_buffer_; int next_job_id_ = 0; ///< ID for the next job to be processed. + vector> query_dist_pivots_; ///< Pivots for each query to speed up sorting vector>> job_flags_; ///< Flags to track job completion std::atomic job_pull_time_ns = 0; ///< Time spent pulling jobs from the queue. std::atomic job_process_time_ns = 0; ///< Time spent processing jobs. diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index f85c6e20..3dc937cb 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -309,40 +309,48 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, res.batch_res_capacity = max_r; } - // reset only the first 'chunk' TopK buffers - for (int64_t i = 0; i < Q; ++i) { - auto &buf = res.topk_buffer_pool[i]; - buf->set_k(K); - buf->reset(); - } + // reset only the first 'chunk' TopK buffers + for (int64_t i = 0; i < Q; ++i) { + auto &buf = res.topk_buffer_pool[i]; + buf->set_k(K); + buf->reset(); + } - // // init only the first chunk*K slots in scratch - // float init_val = (metric_ == faiss::METRIC_INNER_PRODUCT) - // ? -std::numeric_limits::infinity() - // : std::numeric_limits::infinity(); - // std::fill_n(res.batch_distances, Q * K, init_val); - // std::fill_n(res.batch_ids, Q * K, -1LL); + // // init only the first chunk*K slots in scratch + // float init_val = (metric_ == faiss::METRIC_INNER_PRODUCT) + // ? -std::numeric_limits::infinity() + // : std::numeric_limits::infinity(); + // std::fill_n(res.batch_distances, Q * K, init_val); + // std::fill_n(res.batch_ids, Q * K, -1LL); - // auto + // auto - // gather queries - float *qptr = nullptr; - if (job.scan_all) { - qptr = nr.local_query_buffer; - } else { - float *dst = res.batch_queries; - for (int64_t i = 0; i < Q; ++i) { - int qid = (*job.query_ids)[i]; - const float *src = nr.local_query_buffer + size_t(qid) * D; - std::memcpy(dst + i * D, src, D * sizeof(float)); - } - qptr = dst; + // gather queries + float *qptr = nullptr; + if (job.scan_all) { + qptr = nr.local_query_buffer; + } else { + float *dst = res.batch_queries; + for (int64_t i = 0; i < Q; ++i) { + int qid = (*job.query_ids)[i]; + const float *src = nr.local_query_buffer + size_t(qid) * D; + std::memcpy(dst + i * D, src, D * sizeof(float)); } + qptr = dst; + } - // run the scan on this chunk - int64_t scan_setup_time = 0; - int64_t scan_time = 0; - int64_t scan_push_time = 0; + + vector pivots; + pivots.resize(job.num_queries); + for (int64_t i = 0; i < Q; ++i) { + int qid = (*job.query_ids)[i]; + pivots[i] = query_dist_pivots_[qid].load(std::memory_order_relaxed); + } + + // run the scan on this chunk + int64_t scan_setup_time = 0; + int64_t scan_time = 0; + int64_t scan_push_time = 0; batched_scan_list( qptr, codes, ids, @@ -357,7 +365,8 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, /* BLAS scratch */ res.blas_ip_block, res.blas_norms_x, res.blas_norms_y, - BLAS_DB_BS); + BLAS_DB_BS, + pivots); res.scan_setup_time_ns += scan_setup_time; @@ -410,6 +419,15 @@ void QueryCoordinator::init_global_buffers(int64_t nQ, global_topk_buffer_pool_[q]->reset(); } } + + // set pivots to -1; + float max_val = (metric_ == faiss::METRIC_INNER_PRODUCT) + ? -std::numeric_limits::infinity() + : std::numeric_limits::infinity(); + query_dist_pivots_ = vector>(nQ); + for (int64_t q = 0; q < nQ; ++q) { + query_dist_pivots_[q].store(max_val, std::memory_order_relaxed); + } } void QueryCoordinator::copy_query_to_numa(const float *xptr, int64_t nQ, int64_t D) { @@ -597,6 +615,8 @@ void QueryCoordinator::drain_and_apply_aps(Tensor x, } } + vector per_query_result_count(nQ, 0); + ResultJob rj; while (total_left.load(std::memory_order_relaxed) > 0) { @@ -609,15 +629,22 @@ void QueryCoordinator::drain_and_apply_aps(Tensor x, buf->batch_add(rj.distances.data(), rj.indices.data(), static_cast(rj.indices.size())); + per_query_result_count[rj.query_id]++; + + if (per_query_result_count[rj.query_id] % 5 == 0) { + query_dist_pivots_[rj.query_id].store(global_topk_buffer_pool_[rj.query_id]->flush(), + std::memory_order_relaxed); + } + if (!job_flags_[rj.query_id][rj.rank]) { job_flags_[rj.query_id][rj.rank] = true; --parts_left[rj.query_id]; --total_left; } - if (parts_left[rj.query_id] == 0) { - global_topk_buffer_pool_[rj.query_id]->flush(); - } + // if (parts_left[rj.query_id] == 0) { + // global_topk_buffer_pool_[rj.query_id]->flush(); + // } } while (result_queue_.try_dequeue(rj)); continue; // Skip APS; we just did useful work. From ed1d39c4107b3ccdcc584ddcb60bfd1d90211e2b Mon Sep 17 00:00:00 2001 From: Jason Date: Sat, 24 May 2025 17:22:03 -0500 Subject: [PATCH 243/323] sort improvement --- src/cpp/include/list_scanning.h | 10 +- src/cpp/include/query_coordinator.h | 2 +- src/cpp/src/query_coordinator.cpp | 144 +++++++++++++++++----------- 3 files changed, 92 insertions(+), 64 deletions(-) diff --git a/src/cpp/include/list_scanning.h b/src/cpp/include/list_scanning.h index be29ab56..7d0678b2 100644 --- a/src/cpp/include/list_scanning.h +++ b/src/cpp/include/list_scanning.h @@ -180,7 +180,7 @@ class TypedTopKBuffer { if (++head_ == capacity_) flush(); } - void batch_add(T *distances, I *indices, int num_values) { + float batch_add(T *distances, I *indices, int num_values) { int pos = 0; while (pos < num_values) { int available = capacity_ - head_; @@ -260,8 +260,8 @@ class TypedTopKBuffer { } - std::vector get_topk() { - flush(); + std::vector get_topk(bool sort = true) { + if (sort || head_ > k_) flush(); int n = head_; std::vector out; out.reserve(n); @@ -282,8 +282,8 @@ class TypedTopKBuffer { return vals_[k_ - 1]; } - std::vector get_topk_indices() { - flush(); + std::vector get_topk_indices(bool sort = true) { + if (sort || head_ > k_) flush(); int n = head_; std::vector out; out.reserve(n); diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index d3fdfcc4..a4d5d4b9 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -111,7 +111,7 @@ class QueryCoordinator { int num_workers_; ///< Total number of worker threads. vector worker_threads_; ///< Container for worker threads. vector worker_job_counter_; ///< Job counters for each worker. - vector> global_topk_buffer_pool_; ///< Global aggregator buffers. + shared_ptr>> global_topk_buffer_pool_; ///< Global aggregator buffers. std::mutex global_mutex_; ///< Mutex for global synchronization. std::condition_variable global_cv_; ///< Condition variable for thread coordination. std::atomic stop_workers_; ///< Flag to signal workers to terminate. diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 3dc937cb..eaecc884 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -380,8 +380,8 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, for (int64_t i = 0; i < Q; ++i) { int global_q = (*job.query_ids)[i]; int rank_q = (*job.ranks) [i]; - auto tv = res.topk_buffer_pool[i]->get_topk(); - auto ti = res.topk_buffer_pool[i]->get_topk_indices(); + auto tv = res.topk_buffer_pool[i]->get_topk(false); + auto ti = res.topk_buffer_pool[i]->get_topk_indices(false); results_batch.emplace_back(ResultJob{global_q, rank_q, std::move(tv), std::move(ti)}); } @@ -401,24 +401,34 @@ void QueryCoordinator::init_global_buffers(int64_t nQ, std::lock_guard lg(global_mutex_); // resize or reset - size_t cap = std::min(100 * K, 10000); - if (global_topk_buffer_pool_.size() < (size_t) nQ) { - size_t old = global_topk_buffer_pool_.size(); - global_topk_buffer_pool_.resize(nQ); - for (int64_t q = old; q < nQ; ++q) { - global_topk_buffer_pool_[q] = std::make_shared( - K, - metric_ == faiss::METRIC_INNER_PRODUCT, - /*cap=*/cap, - /*node=*/0 - ); - } - } else { - for (int64_t q = 0; q < nQ; ++q) { - global_topk_buffer_pool_[q]->set_k(K); - global_topk_buffer_pool_[q]->reset(); - } - } + // size_t cap = std::min(100 * K, 10000); + // faiss::HeapArray>(nQ) }; + float * vals = (float *) quake_alloc(nQ * K * sizeof(float), 0); + int64_t * ids = (int64_t *) quake_alloc(nQ * K * sizeof(int64_t), 0); + std::fill_n(vals, nQ * K, + std::numeric_limits::infinity()); + std::fill_n(ids, nQ * K, -1); + + global_topk_buffer_pool_ = make_shared>>( + nQ, vals, ids, K); + global_topk_buffer_pool_->begin_multiple(0, nQ); + // if (global_topk_buffer_pool_.size() < (size_t) nQ) { + // size_t old = global_topk_buffer_pool_.size(); + // global_topk_buffer_pool_.resize(nQ); + // for (int64_t q = old; q < nQ; ++q) { + // global_topk_buffer_pool_[q] = std::make_shared( + // K, + // metric_ == faiss::METRIC_INNER_PRODUCT, + // /*cap=*/cap, + // /*node=*/0 + // ); + // } + // } else { + // for (int64_t q = 0; q < nQ; ++q) { + // global_topk_buffer_pool_[q]->set_k(K); + // global_topk_buffer_pool_[q]->reset(); + // } + // } // set pivots to -1; float max_val = (metric_ == faiss::METRIC_INNER_PRODUCT) @@ -617,6 +627,14 @@ void QueryCoordinator::drain_and_apply_aps(Tensor x, vector per_query_result_count(nQ, 0); + vector>::SingleResultHandler> + handlers; + handlers.reserve(nQ); + for (int64_t q = 0; q < nQ; ++q) { + handlers.emplace_back(*global_topk_buffer_pool_); + handlers[q].begin(q); // ← reset & heapify for query q + } + ResultJob rj; while (total_left.load(std::memory_order_relaxed) > 0) { @@ -625,17 +643,14 @@ void QueryCoordinator::drain_and_apply_aps(Tensor x, if (got_result) { do { // Drain *all* currently available results. - auto &buf = global_topk_buffer_pool_[rj.query_id]; - buf->batch_add(rj.distances.data(), rj.indices.data(), - static_cast(rj.indices.size())); + for (int64_t i = 0; i < rj.distances.size(); ++i) { + handlers[rj.query_id].add_result(rj.distances[i], rj.indices[i]); + } + query_dist_pivots_[rj.query_id].store( + handlers[rj.query_id].threshold, std::memory_order_relaxed); per_query_result_count[rj.query_id]++; - if (per_query_result_count[rj.query_id] % 5 == 0) { - query_dist_pivots_[rj.query_id].store(global_topk_buffer_pool_[rj.query_id]->flush(), - std::memory_order_relaxed); - } - if (!job_flags_[rj.query_id][rj.rank]) { job_flags_[rj.query_id][rj.rank] = true; --parts_left[rj.query_id]; @@ -650,34 +665,41 @@ void QueryCoordinator::drain_and_apply_aps(Tensor x, continue; // Skip APS; we just did useful work. } + /* (b) Timeout fired → run APS once. */ - if (use_aps) { - for (int64_t q = 0; q < nQ; ++q) { - if (parts_left[q] == 0) continue; - - auto &buf = global_topk_buffer_pool_[q]; - const float r = buf->get_kth_distance(); - if (r != query_radius[q]) { - query_radius[q] = r; - probs[q] = compute_recall_profile( - boundary_dist[q], r, D, {}, true, metric_==faiss::METRIC_L2); - } + // if (use_aps) { + // for (int64_t q = 0; q < nQ; ++q) { + // if (parts_left[q] == 0) continue; + // + // auto &buf = global_topk_buffer_pool_[q]; + // const float r = buf->get_kth_distance(); + // if (r != query_radius[q]) { + // query_radius[q] = r; + // probs[q] = compute_recall_profile( + // boundary_dist[q], r, D, {}, true, metric_==faiss::METRIC_L2); + // } + // + // float cum = 0.0f; + // for (int64_t p = 0; p < nprobe; ++p) + // if (job_flags_[q][p]) cum += probs[q][p]; + // + // if (cum >= recall_target) { + // for (int64_t p = 0; p < nprobe; ++p) + // if (!job_flags_[q][p]) { + // job_flags_[q][p] = true; + // --parts_left[q]; + // --total_left; + // } + // } + // } + // } + } - float cum = 0.0f; - for (int64_t p = 0; p < nprobe; ++p) - if (job_flags_[q][p]) cum += probs[q][p]; - - if (cum >= recall_target) { - for (int64_t p = 0; p < nprobe; ++p) - if (!job_flags_[q][p]) { - job_flags_[q][p] = true; - --parts_left[q]; - --total_left; - } - } - } - } + // end the handlers + for (int64_t q = 0; q < nQ; ++q) { + handlers[q].end(); } + } std::shared_ptr @@ -690,12 +712,18 @@ QueryCoordinator::aggregate_scan_results(int64_t nQ, auto id_acc = out_ids.accessor(); auto d_acc = out_dists.accessor(); for (int64_t q = 0; q < nQ; ++q) { - auto tv = global_topk_buffer_pool_[q]->get_topk(); - auto ti = global_topk_buffer_pool_[q]->get_topk_indices(); - for (int i = 0; i < (int)ti.size() && i < K; ++i) { - id_acc[q][i] = ti[i]; - d_acc [q][i] = tv[i]; + + for (int64_t i = 0; i < K; ++i) { + id_acc[q][i] = global_topk_buffer_pool_->heap_ids_tab[q * K + i]; + d_acc [q][i] = global_topk_buffer_pool_->heap_dis_tab[q * K + i]; } + + // auto tv = global_topk_buffer_pool_[q]->get_topk(); + // auto ti = global_topk_buffer_pool_[q]->get_topk_indices(); + // for (int i = 0; i < (int)ti.size() && i < K; ++i) { + // id_acc[q][i] = ti[i]; + // d_acc [q][i] = tv[i]; + // } } auto res = std::make_shared(); From 87945e9edd20cee0aa12ad3f2b9e77948ab217cc Mon Sep 17 00:00:00 2001 From: Jason Date: Sat, 24 May 2025 17:37:55 -0500 Subject: [PATCH 244/323] sort improvement --- src/cpp/include/common.h | 2 +- src/cpp/include/list_scanning.h | 38 ++++++++++++++++----------------- 2 files changed, 20 insertions(+), 20 deletions(-) diff --git a/src/cpp/include/common.h b/src/cpp/include/common.h index e4cc197c..32ce00d4 100644 --- a/src/cpp/include/common.h +++ b/src/cpp/include/common.h @@ -76,7 +76,7 @@ constexpr float DEFAULT_INITIAL_SEARCH_FRACTION = 0.1f; ///< Default initial fra constexpr float DEFAULT_RECOMPUTE_THRESHOLD = 0.001f; ///< Default threshold to trigger recomputation of search parameters. constexpr int DEFAULT_APS_FLUSH_PERIOD_US = 5; ///< Default period (in microseconds) for flushing the APS buffer. constexpr int MAX_SUBBATCH = 128; -constexpr int MIN_BATCH_SCAN_SIZE = 16; ///< Minimum batch size for scanning partitions. +constexpr int MIN_BATCH_SCAN_SIZE = 4; ///< Minimum batch size for scanning partitions. constexpr int BLAS_DB_BS = 1024 * 16; // Default constants for maintenance policy parameters diff --git a/src/cpp/include/list_scanning.h b/src/cpp/include/list_scanning.h index 7d0678b2..1032c3e9 100644 --- a/src/cpp/include/list_scanning.h +++ b/src/cpp/include/list_scanning.h @@ -495,27 +495,27 @@ inline void l2_blas( faiss::fvec_norms_L2sqr(norms_y, y + j0 * d, d, db_chunk); // use torch matmul - Tensor x_chunk = torch::from_blob((void*)(x + i0 * d), {(int64_t) q_chunk, (int64_t) d}, torch::kFloat32); - Tensor y_chunk = torch::from_blob((void*)(y + j0 * d), {(int64_t) db_chunk, (int64_t) d}, torch::kFloat32); - Tensor out = torch::from_blob(ip_block, {(int64_t) q_chunk, (int64_t) db_chunk}, torch::kFloat32); - - torch::matmul_out(out, x_chunk, y_chunk.t()); + // Tensor x_chunk = torch::from_blob((void*)(x + i0 * d), {(int64_t) q_chunk, (int64_t) d}, torch::kFloat32); + // Tensor y_chunk = torch::from_blob((void*)(y + j0 * d), {(int64_t) db_chunk, (int64_t) d}, torch::kFloat32); + // Tensor out = torch::from_blob(ip_block, {(int64_t) q_chunk, (int64_t) db_chunk}, torch::kFloat32); + // + // torch::matmul_out(out, x_chunk, y_chunk.t()); // /* SGEMM */ - // { - // const float one = 1.f; - // float zero = 0.f; - // FINTEGER nyi = FINTEGER(db_chunk); - // FINTEGER nxi = FINTEGER(q_chunk); - // FINTEGER di = FINTEGER(d); - // sgemm_("Transpose","Not transpose", - // &nyi,&nxi,&di, - // &one, - // y + j0 * d, &di, - // x + i0 * d, &di, - // &zero, - // ip_block, &nyi); - // } + { + const float one = 1.f; + float zero = 0.f; + FINTEGER nyi = FINTEGER(db_chunk); + FINTEGER nxi = FINTEGER(q_chunk); + FINTEGER di = FINTEGER(d); + sgemm_("Transpose","Not transpose", + &nyi,&nxi,&di, + &one, + y + j0 * d, &di, + x + i0 * d, &di, + &zero, + ip_block, &nyi); + } /* IP → L2² */ if (k > 1) { From 34edb02fa36824fa49368efb499f60fc39f9b89b Mon Sep 17 00:00:00 2001 From: Jason Date: Sat, 24 May 2025 17:59:33 -0500 Subject: [PATCH 245/323] sort improvement --- src/cpp/include/list_scanning.h | 54 ++++++++++++++----------------- src/cpp/src/query_coordinator.cpp | 9 +++--- 2 files changed, 30 insertions(+), 33 deletions(-) diff --git a/src/cpp/include/list_scanning.h b/src/cpp/include/list_scanning.h index 1032c3e9..c3724daf 100644 --- a/src/cpp/include/list_scanning.h +++ b/src/cpp/include/list_scanning.h @@ -472,7 +472,7 @@ inline void l2_blas( float* __restrict ip_block, // nx * bs_y float* __restrict norms_x, // bs_x float* __restrict norms_y, - float* pivot) // db_blas_bs + vector*> pivot) // db_blas_bs { if (nx == 0 || ny == 0) return; @@ -495,27 +495,27 @@ inline void l2_blas( faiss::fvec_norms_L2sqr(norms_y, y + j0 * d, d, db_chunk); // use torch matmul - // Tensor x_chunk = torch::from_blob((void*)(x + i0 * d), {(int64_t) q_chunk, (int64_t) d}, torch::kFloat32); - // Tensor y_chunk = torch::from_blob((void*)(y + j0 * d), {(int64_t) db_chunk, (int64_t) d}, torch::kFloat32); - // Tensor out = torch::from_blob(ip_block, {(int64_t) q_chunk, (int64_t) db_chunk}, torch::kFloat32); - // - // torch::matmul_out(out, x_chunk, y_chunk.t()); + Tensor x_chunk = torch::from_blob((void*)(x + i0 * d), {(int64_t) q_chunk, (int64_t) d}, torch::kFloat32); + Tensor y_chunk = torch::from_blob((void*)(y + j0 * d), {(int64_t) db_chunk, (int64_t) d}, torch::kFloat32); + Tensor out = torch::from_blob(ip_block, {(int64_t) q_chunk, (int64_t) db_chunk}, torch::kFloat32); + + torch::matmul_out(out, x_chunk, y_chunk.t()); // /* SGEMM */ - { - const float one = 1.f; - float zero = 0.f; - FINTEGER nyi = FINTEGER(db_chunk); - FINTEGER nxi = FINTEGER(q_chunk); - FINTEGER di = FINTEGER(d); - sgemm_("Transpose","Not transpose", - &nyi,&nxi,&di, - &one, - y + j0 * d, &di, - x + i0 * d, &di, - &zero, - ip_block, &nyi); - } + // { + // const float one = 1.f; + // float zero = 0.f; + // FINTEGER nyi = FINTEGER(db_chunk); + // FINTEGER nxi = FINTEGER(q_chunk); + // FINTEGER di = FINTEGER(d); + // sgemm_("Transpose","Not transpose", + // &nyi,&nxi,&di, + // &one, + // y + j0 * d, &di, + // x + i0 * d, &di, + // &zero, + // ip_block, &nyi); + // } /* IP → L2² */ if (k > 1) { @@ -528,10 +528,11 @@ inline void l2_blas( } // collect distances closer than pivot - if (pivot) { + if (pivot.size() > 0) { + float curr_pivot = pivot[qi]->load(std::memory_order_relaxed); line_ptr = ip_block + qi * db_chunk; // Reset line_ptr to the start of the current column for (size_t pj = 0; pj < db_chunk; ++pj) { - if (*line_ptr < pivot[qi]) { + if (*line_ptr < curr_pivot) { topk_buffers[qi]->add(*line_ptr, list_ids_ptr[j0 + pj]); } line_ptr++; // Move to the next element in the column @@ -583,7 +584,7 @@ inline void batched_scan_list(const float *query_vecs, float *norms_x, float *norms_y_buf, int blas_db_bs = BLAS_DB_BS, - vector pivots = {}) { + vector*> pivots = {}) { if (list_size == 0 || list_vecs == nullptr) { // No list vectors to process; return; @@ -604,11 +605,6 @@ inline void batched_scan_list(const float *query_vecs, auto s2 = high_resolution_clock::now(); - float *pivot_ptr = nullptr; - if (!pivots.empty()) { - pivot_ptr = pivots.data(); - } - if (metric == faiss::METRIC_INNER_PRODUCT) { faiss::float_minheap_array_t res = {size_t(num_queries), size_t(k_max), labels, distances}; faiss::knn_inner_product(query_vecs, list_vecs, dim, num_queries, list_size, &res, nullptr); @@ -627,7 +623,7 @@ inline void batched_scan_list(const float *query_vecs, ip_block, norms_x, norms_y_buf, - pivot_ptr + pivots ); // faiss::knn_L2sqr(query_vecs, list_vecs, dim, num_queries, list_size, &res, nullptr, nullptr); } else { diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index eaecc884..70f41414 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -238,8 +238,8 @@ void QueryCoordinator::handle_nonbatched_job(const ScanJob &job, metric_); // If scan_list completes, enqueue its results - auto tv = buf->get_topk(); - auto ti = buf->get_topk_indices(); + auto tv = buf->get_topk(false); + auto ti = buf->get_topk_indices(false); result_queue_.enqueue(ResultJob{job.query_id, job.rank, std::move(tv), std::move(ti)}); } catch (const std::exception& e) { @@ -340,11 +340,11 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, } - vector pivots; + vector *> pivots; pivots.resize(job.num_queries); for (int64_t i = 0; i < Q; ++i) { int qid = (*job.query_ids)[i]; - pivots[i] = query_dist_pivots_[qid].load(std::memory_order_relaxed); + pivots[i] = &query_dist_pivots_[qid]; } // run the scan on this chunk @@ -877,6 +877,7 @@ void QueryCoordinator::initialize_workers(int num_cores, bool use_numa) { // set main thread on separate thread from workers int num_cores_on_machine = std::thread::hardware_concurrency(); set_thread_affinity(num_cores % num_cores_on_machine); + // set_thread_affinity(0); } // Shutdown Worker Threads From 7612d1137514e85cfb85c69427d20e2c108d1767 Mon Sep 17 00:00:00 2001 From: Jason Date: Sat, 24 May 2025 18:04:33 -0500 Subject: [PATCH 246/323] sort improvement --- src/cpp/include/list_scanning.h | 49 ++++++++++++++++----------------- 1 file changed, 23 insertions(+), 26 deletions(-) diff --git a/src/cpp/include/list_scanning.h b/src/cpp/include/list_scanning.h index c3724daf..d66f4674 100644 --- a/src/cpp/include/list_scanning.h +++ b/src/cpp/include/list_scanning.h @@ -495,27 +495,27 @@ inline void l2_blas( faiss::fvec_norms_L2sqr(norms_y, y + j0 * d, d, db_chunk); // use torch matmul - Tensor x_chunk = torch::from_blob((void*)(x + i0 * d), {(int64_t) q_chunk, (int64_t) d}, torch::kFloat32); - Tensor y_chunk = torch::from_blob((void*)(y + j0 * d), {(int64_t) db_chunk, (int64_t) d}, torch::kFloat32); - Tensor out = torch::from_blob(ip_block, {(int64_t) q_chunk, (int64_t) db_chunk}, torch::kFloat32); - - torch::matmul_out(out, x_chunk, y_chunk.t()); - - // /* SGEMM */ - // { - // const float one = 1.f; - // float zero = 0.f; - // FINTEGER nyi = FINTEGER(db_chunk); - // FINTEGER nxi = FINTEGER(q_chunk); - // FINTEGER di = FINTEGER(d); - // sgemm_("Transpose","Not transpose", - // &nyi,&nxi,&di, - // &one, - // y + j0 * d, &di, - // x + i0 * d, &di, - // &zero, - // ip_block, &nyi); - // } + // Tensor x_chunk = torch::from_blob((void*)(x + i0 * d), {(int64_t) q_chunk, (int64_t) d}, torch::kFloat32); + // Tensor y_chunk = torch::from_blob((void*)(y + j0 * d), {(int64_t) db_chunk, (int64_t) d}, torch::kFloat32); + // Tensor out = torch::from_blob(ip_block, {(int64_t) q_chunk, (int64_t) db_chunk}, torch::kFloat32); + // + // torch::matmul_out(out, x_chunk, y_chunk.t()); + + /* SGEMM */ + { + const float one = 1.f; + float zero = 0.f; + FINTEGER nyi = FINTEGER(db_chunk); + FINTEGER nxi = FINTEGER(q_chunk); + FINTEGER di = FINTEGER(d); + sgemm_("Transpose","Not transpose", + &nyi,&nxi,&di, + &one, + y + j0 * d, &di, + x + i0 * d, &di, + &zero, + ip_block, &nyi); + } /* IP → L2² */ if (k > 1) { @@ -545,15 +545,12 @@ inline void l2_blas( for (int64_t qi = 0; qi < static_cast(q_chunk); ++qi) { float* line_ptr = ip_block + qi * db_chunk; // Pointer to current column in ip_block const float current_norm_x = norms_x[qi]; - for (size_t pj = 0; pj < db_chunk; ++pj) { - *line_ptr = std::sqrt(std::fma(-2.f, *line_ptr, current_norm_x + norms_y[pj])); - line_ptr++; // Move to the next element in the column - } float best_dist = std::numeric_limits::infinity(); int64_t best_id = -1; - line_ptr = ip_block + qi * db_chunk; // Reset line_ptr to the start of the current column + for (size_t pj = 0; pj < db_chunk; ++pj) { + *line_ptr = std::sqrt(std::fma(-2.f, *line_ptr, current_norm_x + norms_y[pj])); if (*line_ptr < best_dist) { best_dist = *line_ptr; best_id = list_ids_ptr[j0 + pj]; From 2f0651a70015f12ac782143afd396298f8ee75aa Mon Sep 17 00:00:00 2001 From: Jason Date: Sat, 24 May 2025 21:07:41 -0500 Subject: [PATCH 247/323] sort improvement --- src/cpp/include/list_scanning.h | 225 +++++++++-------- src/cpp/include/query_coordinator.h | 7 +- src/cpp/src/clustering.cpp | 5 - src/cpp/src/maintenance_cost_estimator.cpp | 4 +- src/cpp/src/query_coordinator.cpp | 270 ++++++++++----------- 5 files changed, 253 insertions(+), 258 deletions(-) diff --git a/src/cpp/include/list_scanning.h b/src/cpp/include/list_scanning.h index d66f4674..3095be71 100644 --- a/src/cpp/include/list_scanning.h +++ b/src/cpp/include/list_scanning.h @@ -335,10 +335,15 @@ inline void scan_list_no_ids_inner_product(const float *query_vec, const float *list_vecs, int list_size, int d, - TopkBuffer &buffer) { + TopkBuffer &buffer, + float pivot) { const float *vec = list_vecs; + float dist; for (int l = 0; l < list_size; l++) { - buffer.add(faiss::fvec_inner_product(query_vec, vec, d), l); + dist = faiss::fvec_inner_product(query_vec, vec, d); + if (dist > pivot) { + buffer.add(dist, l); + } vec += d; // move pointer to next vector } } @@ -347,10 +352,14 @@ inline void scan_list_no_ids_l2(const float *query_vec, const float *list_vecs, int list_size, int d, - TopkBuffer &buffer) { + TopkBuffer &buffer, + float pivot) { const float *vec = list_vecs; for (int l = 0; l < list_size; l++) { - buffer.add(sqrt(faiss::fvec_L2sqr(query_vec, vec, d)), l); + float dist = sqrt(faiss::fvec_L2sqr(query_vec, vec, d)); + if (dist < pivot) { + buffer.add(dist, l); + } vec += d; } } @@ -360,10 +369,14 @@ inline void scan_list_with_ids_inner_product(const float *query_vec, const int64_t *list_ids, int list_size, int d, - TopkBuffer &buffer) { + TopkBuffer &buffer, + float pivot) { const float *vec = list_vecs; for (int l = 0; l < list_size; l++) { - buffer.add(faiss::fvec_inner_product(query_vec, vec, d), list_ids[l]); + float dist = faiss::fvec_inner_product(query_vec, vec, d); + if (dist > pivot) { + buffer.add(dist, list_ids[l]); + } vec += d; } } @@ -373,7 +386,8 @@ inline void scan_list_with_ids_l2(const float *query_vec, const int64_t *list_ids, int list_size, int d, - TopkBuffer &buffer) { + TopkBuffer &buffer, + float pivot) { const float *vec = list_vecs; for (int l = 0; l < list_size; l++) { buffer.add(sqrt(faiss::fvec_L2sqr(query_vec, vec, d)), list_ids[l]); @@ -388,75 +402,104 @@ inline void scan_list(const float *query_vec, int list_size, int d, TopkBuffer &buffer, - faiss::MetricType metric = faiss::METRIC_L2) { + faiss::MetricType metric, + float pivot) { // Dispatch based on metric type and whether list_ids is provided. if (metric == faiss::METRIC_INNER_PRODUCT) { if (list_ids == nullptr) - scan_list_no_ids_inner_product(query_vec, list_vecs, list_size, d, buffer); + scan_list_no_ids_inner_product(query_vec, list_vecs, list_size, d, buffer, pivot); else - scan_list_with_ids_inner_product(query_vec, list_vecs, list_ids, list_size, d, buffer); + scan_list_with_ids_inner_product(query_vec, list_vecs, list_ids, list_size, d, buffer, pivot); } else { // Assume L2 (or similar) if (list_ids == nullptr) - scan_list_no_ids_l2(query_vec, list_vecs, list_size, d, buffer); + scan_list_no_ids_l2(query_vec, list_vecs, list_size, d, buffer, pivot); else - scan_list_with_ids_l2(query_vec, list_vecs, list_ids, list_size, d, buffer); + scan_list_with_ids_l2(query_vec, list_vecs, list_ids, list_size, d, buffer, pivot); } } -// inline void batched_scan_list( -// const float *query_vecs, -// const float *list_vecs, -// const int64_t *list_ids, -// int num_queries, -// int list_size, -// int dim, -// vector> &topk_buffers, -// MetricType metric = faiss::METRIC_L2, -// float *dist = nullptr, -// int64_t *labels = nullptr) // Optional output for distances and labels -// { -// if (list_size == 0 || list_vecs == nullptr) { -// return; -// } -// -// // Wrap raw arrays in torch Tensors, no copy -// Tensor query = torch::from_blob((void*)query_vecs, {num_queries, dim}, torch::kFloat32); -// Tensor list = torch::from_blob((void*)list_vecs, {list_size, dim}, torch::kFloat32); -// -// torch::Tensor distances; -// if (metric == faiss::METRIC_L2) { -// // Returns [num_queries, list_size], each entry is the Euclidean distance -// distances = torch::cdist(query, list, 2.0); -// } else if (metric == faiss::METRIC_INNER_PRODUCT) { -// // [num_queries, list_size], each entry is dot product -// distances = torch::matmul(query, list.t()); -// } else { -// throw std::runtime_error("Metric type not supported"); -// } -// -// // For each query, push all list vectors and their distances into TopkBuffer -// auto distances_acc = distances.accessor(); -// for (int i = 0; i < num_queries; ++i) { -// std::vector dists(list_size); -// std::vector ids(list_size); -// -// for (int j = 0; j < list_size; ++j) { -// dists[j] = distances_acc[i][j]; -// } -// -// if (list_ids) { -// for (int j = 0; j < list_size; ++j) { -// ids[j] = list_ids[j]; -// } -// } else { -// for (int j = 0; j < list_size; ++j) { -// ids[j] = j; -// } -// } -// -// topk_buffers[i]->batch_add(dists.data(), ids.data(), list_size); -// } -// } +inline void ip_blas( + const float* __restrict x, + const float* __restrict y, + const int64_t *list_ids, + size_t d, + size_t nx, + size_t ny, + size_t db_blas_bs, // = bs_y + size_t k, + vector> &topk_buffers, + float* __restrict ip_block, // nx * bs_y + vector*> pivot) // db_blas_bs +{ + if (nx == 0 || ny == 0) return; + + constexpr size_t bs_x = 256; + const size_t bs_y = db_blas_bs; + int64_t *list_ids_ptr = (int64_t *) list_ids; + + for (size_t i0 = 0; i0 < nx; i0 += bs_x) { + const size_t i1 = std::min(i0 + bs_x, nx); + const size_t q_chunk = i1 - i0; + + for (size_t j0 = 0; j0 < ny; j0 += bs_y) { + const size_t j1 = std::min(j0 + bs_y, ny); + const size_t db_chunk = j1 - j0; + + // use torch matmul + + /* SGEMM */ + { + const float one = 1.f; + float zero = 0.f; + FINTEGER nyi = FINTEGER(db_chunk); + FINTEGER nxi = FINTEGER(q_chunk); + FINTEGER di = FINTEGER(d); + sgemm_("Transpose","Not transpose", + &nyi,&nxi,&di, + &one, + y + j0 * d, &di, + x + i0 * d, &di, + &zero, + ip_block, &nyi); + } + + /* IP → L2² */ + if (k > 1) { + for (int64_t qi = 0; qi < static_cast(q_chunk); ++qi) { + float* line_ptr = ip_block + qi * db_chunk; // Pointer to current column in ip_block + // collect distances closer than pivot + if (pivot.size() > 0) { + float curr_pivot = pivot[qi]->load(std::memory_order_relaxed); + line_ptr = ip_block + qi * db_chunk; // Reset line_ptr to the start of the current column + for (size_t pj = 0; pj < db_chunk; ++pj) { + if (*line_ptr > curr_pivot) { + topk_buffers[qi]->add(*line_ptr, list_ids_ptr[j0 + pj]); + } + line_ptr++; // Move to the next element in the column + } + } else { + topk_buffers[qi]->batch_add(ip_block + qi * db_chunk, list_ids_ptr + j0, db_chunk); + } + } + } else if (k == 1) { + for (int64_t qi = 0; qi < static_cast(q_chunk); ++qi) { + float* line_ptr = ip_block + qi * db_chunk; // Pointer to current column in ip_block + float best_dist = -std::numeric_limits::infinity(); + int64_t best_id = -1; + + for (size_t pj = 0; pj < db_chunk; ++pj) { + if (*line_ptr < best_dist) { + best_dist = *line_ptr; + best_id = list_ids_ptr[j0 + pj]; + } + line_ptr++; // Move to the next element in the column + } + topk_buffers[qi]->add(best_dist, best_id); + } + } + } + } +} inline void l2_blas( @@ -495,11 +538,6 @@ inline void l2_blas( faiss::fvec_norms_L2sqr(norms_y, y + j0 * d, d, db_chunk); // use torch matmul - // Tensor x_chunk = torch::from_blob((void*)(x + i0 * d), {(int64_t) q_chunk, (int64_t) d}, torch::kFloat32); - // Tensor y_chunk = torch::from_blob((void*)(y + j0 * d), {(int64_t) db_chunk, (int64_t) d}, torch::kFloat32); - // Tensor out = torch::from_blob(ip_block, {(int64_t) q_chunk, (int64_t) db_chunk}, torch::kFloat32); - // - // torch::matmul_out(out, x_chunk, y_chunk.t()); /* SGEMM */ { @@ -571,15 +609,10 @@ inline void batched_scan_list(const float *query_vecs, int list_size, int dim, vector> &topk_buffers, - int64_t *setup_time, - int64_t *scan_time, - int64_t *push_time, MetricType metric, - float *distances, - int64_t *labels, float *ip_block, - float *norms_x, - float *norms_y_buf, + float *norms_x, + float *norms_y_buf, int blas_db_bs = BLAS_DB_BS, vector*> pivots = {}) { if (list_size == 0 || list_vecs == nullptr) { @@ -587,26 +620,26 @@ inline void batched_scan_list(const float *query_vecs, return; } - auto s1 = high_resolution_clock::now(); // Ensure k does not exceed list_size int k = topk_buffers[0]->k(); int k_max = std::min(k, list_size); - bool alloc_results = false; - if (distances == nullptr) { - alloc_results = true; - labels = (int64_t *) malloc(num_queries * k_max * sizeof(int64_t)); - distances = (float *) malloc(num_queries * k_max * sizeof(float)); - } - - auto s2 = high_resolution_clock::now(); - if (metric == faiss::METRIC_INNER_PRODUCT) { - faiss::float_minheap_array_t res = {size_t(num_queries), size_t(k_max), labels, distances}; - faiss::knn_inner_product(query_vecs, list_vecs, dim, num_queries, list_size, &res, nullptr); + ip_blas( + query_vecs, + list_vecs, + list_ids, + dim, + num_queries, + list_size, + blas_db_bs, + k_max, + topk_buffers, + ip_block, + pivots + ); } else if (metric == faiss::METRIC_L2) { - // faiss::float_maxheap_array_t res = {size_t(num_queries), size_t(k_max), labels, distances}; l2_blas( query_vecs, list_vecs, @@ -622,19 +655,9 @@ inline void batched_scan_list(const float *query_vecs, norms_y_buf, pivots ); - // faiss::knn_L2sqr(query_vecs, list_vecs, dim, num_queries, list_size, &res, nullptr, nullptr); } else { throw std::runtime_error("Metric type not supported"); } - - auto s3 = high_resolution_clock::now(); - - - auto s4 = high_resolution_clock::now(); - - *setup_time = duration_cast(s2 - s1).count(); - *scan_time = duration_cast(s3 - s2).count(); - *push_time = duration_cast(s4 - s3).count(); } diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index a4d5d4b9..9f14fca7 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -82,9 +82,7 @@ class QueryCoordinator { int64_t wait_time_ns = 0; ///< Time spent waiting for jobs. int64_t process_time_ns = 0; ///< Time spent processing jobs. int64_t process_preamble_time_ns = 0; ///< Time spent on job processing (excluding waiting). - int64_t scan_setup_time_ns = 0; ///< Time spent on scan setup. int64_t scan_time_ns = 0; ///< Time spent on scanning. - int64_t scan_push_time_ns = 0; ///< Time spent pushing results. int64_t enqueue_time_ns = 0; ///< Time spent enqueuing. int64_t job_time_ns = 0; ///< Time spent on job processing (excluding waiting). }; @@ -111,7 +109,10 @@ class QueryCoordinator { int num_workers_; ///< Total number of worker threads. vector worker_threads_; ///< Container for worker threads. vector worker_job_counter_; ///< Job counters for each worker. - shared_ptr>> global_topk_buffer_pool_; ///< Global aggregator buffers. + + shared_ptr>> global_min_heaps_; ///< Global aggregator buffers. + shared_ptr>> global_max_heaps_; ///< Global aggregator buffers. + std::mutex global_mutex_; ///< Mutex for global synchronization. std::condition_variable global_cv_; ///< Condition variable for thread coordination. std::atomic stop_workers_; ///< Flag to signal workers to terminate. diff --git a/src/cpp/src/clustering.cpp b/src/cpp/src/clustering.cpp index 99344635..85f517e8 100644 --- a/src/cpp/src/clustering.cpp +++ b/src/cpp/src/clustering.cpp @@ -321,12 +321,7 @@ tuple >> kmeans_refine_partitions( n_clusters, d, buffers, - &scan_setup_time, - &scan_time, - &scan_push_time, metric, - /* distances*/ nullptr, - /* labels */ nullptr, /* BLAS scratch */ blas_ip_block, blas_norms_x, blas_norms_y, diff --git a/src/cpp/src/maintenance_cost_estimator.cpp b/src/cpp/src/maintenance_cost_estimator.cpp index d1f4d1ab..ed4d2eb8 100644 --- a/src/cpp/src/maintenance_cost_estimator.cpp +++ b/src/cpp/src/maintenance_cost_estimator.cpp @@ -63,6 +63,8 @@ void ListScanLatencyEstimator::profile_scan_latency() { torch::Tensor ids = torch::randperm(max_n); torch::Tensor query = torch::rand({d_}); + + for (size_t i = 0; i < n_values_.size(); ++i) { for (size_t j = 0; j < k_values_.size(); ++j) { int n = n_values_[i]; @@ -80,7 +82,7 @@ void ListScanLatencyEstimator::profile_scan_latency() { for (int m = 0; m < n_trials_; ++m) { auto start = std::chrono::high_resolution_clock::now(); scan_list(query_ptr, curr_vectors_ptr, curr_ids_ptr, n, d_, - *topk_buffer); + *topk_buffer, faiss::METRIC_L2, 10000000.0); auto end = std::chrono::high_resolution_clock::now(); auto duration = diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 70f41414..fe52be0f 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -235,7 +235,8 @@ void QueryCoordinator::handle_nonbatched_job(const ScanJob &job, part_size, D, *buf, - metric_); + metric_, + query_dist_pivots_[job.query_id].load(std::memory_order_relaxed)); // If scan_list completes, enqueue its results auto tv = buf->get_topk(false); @@ -259,6 +260,7 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, CoreResources &res, NUMAResources &nr) { + auto start = std::chrono::high_resolution_clock::now(); // Total queries, Top-K, dimension, NUMA node int64_t Q = job.num_queries; int K = job.k; @@ -339,7 +341,6 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, qptr = dst; } - vector *> pivots; pivots.resize(job.num_queries); for (int64_t i = 0; i < Q; ++i) { @@ -348,50 +349,41 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, } // run the scan on this chunk - int64_t scan_setup_time = 0; - int64_t scan_time = 0; - int64_t scan_push_time = 0; + batched_scan_list( qptr, codes, ids, Q, part_size, D, res.topk_buffer_pool, - &scan_setup_time, - &scan_time, - &scan_push_time, metric_, - /* distances*/ res.batch_distances, - /* labels */ res.batch_ids, - /* BLAS scratch */ res.blas_ip_block, - res.blas_norms_x, - res.blas_norms_y, + res.blas_ip_block, + res.blas_norms_x, + res.blas_norms_y, BLAS_DB_BS, pivots); + auto end = std::chrono::high_resolution_clock::now(); + res.scan_time_ns += std::chrono::duration_cast(end - start).count(); - res.scan_setup_time_ns += scan_setup_time; - res.scan_time_ns += scan_time; - res.scan_push_time_ns += scan_push_time; - - auto start = std::chrono::high_resolution_clock::now(); - // collect results for this chunk - std::vector results_batch; - results_batch.reserve(Q); - for (int64_t i = 0; i < Q; ++i) { - int global_q = (*job.query_ids)[i]; - int rank_q = (*job.ranks) [i]; - auto tv = res.topk_buffer_pool[i]->get_topk(false); - auto ti = res.topk_buffer_pool[i]->get_topk_indices(false); - results_batch.emplace_back(ResultJob{global_q, rank_q, std::move(tv), std::move(ti)}); - } + start = std::chrono::high_resolution_clock::now(); + // collect results for this chunk + std::vector results_batch; + results_batch.reserve(Q); + for (int64_t i = 0; i < Q; ++i) { + int global_q = (*job.query_ids)[i]; + int rank_q = (*job.ranks) [i]; + auto tv = res.topk_buffer_pool[i]->get_topk(false); + auto ti = res.topk_buffer_pool[i]->get_topk_indices(false); + results_batch.emplace_back(ResultJob{global_q, rank_q, std::move(tv), std::move(ti)}); + } - // enqueue this sub-batch in bulk - result_queue_.enqueue_bulk( - std::make_move_iterator(results_batch.begin()), - results_batch.size() - ); - auto end = std::chrono::high_resolution_clock::now(); - res.enqueue_time_ns += std::chrono::duration_cast(end - start).count(); + // enqueue this sub-batch in bulk + result_queue_.enqueue_bulk( + std::make_move_iterator(results_batch.begin()), + results_batch.size() + ); + end = std::chrono::high_resolution_clock::now(); + res.enqueue_time_ns += std::chrono::duration_cast(end - start).count(); } void QueryCoordinator::init_global_buffers(int64_t nQ, @@ -405,35 +397,24 @@ void QueryCoordinator::init_global_buffers(int64_t nQ, // faiss::HeapArray>(nQ) }; float * vals = (float *) quake_alloc(nQ * K * sizeof(float), 0); int64_t * ids = (int64_t *) quake_alloc(nQ * K * sizeof(int64_t), 0); - std::fill_n(vals, nQ * K, - std::numeric_limits::infinity()); std::fill_n(ids, nQ * K, -1); - global_topk_buffer_pool_ = make_shared>>( - nQ, vals, ids, K); - global_topk_buffer_pool_->begin_multiple(0, nQ); - // if (global_topk_buffer_pool_.size() < (size_t) nQ) { - // size_t old = global_topk_buffer_pool_.size(); - // global_topk_buffer_pool_.resize(nQ); - // for (int64_t q = old; q < nQ; ++q) { - // global_topk_buffer_pool_[q] = std::make_shared( - // K, - // metric_ == faiss::METRIC_INNER_PRODUCT, - // /*cap=*/cap, - // /*node=*/0 - // ); - // } - // } else { - // for (int64_t q = 0; q < nQ; ++q) { - // global_topk_buffer_pool_[q]->set_k(K); - // global_topk_buffer_pool_[q]->reset(); - // } - // } - - // set pivots to -1; float max_val = (metric_ == faiss::METRIC_INNER_PRODUCT) - ? -std::numeric_limits::infinity() - : std::numeric_limits::infinity(); + ? -std::numeric_limits::infinity() + : std::numeric_limits::infinity(); + + std::fill_n(vals, nQ * K, max_val); + + if (metric_ == faiss::METRIC_INNER_PRODUCT) { + global_max_heaps_ = make_shared>>( + nQ, vals, ids, K); + } else { + std::fill_n(vals, nQ * K, + std::numeric_limits::infinity()); + global_min_heaps_ = make_shared>>( + nQ, vals, ids, K); + } + query_dist_pivots_ = vector>(nQ); for (int64_t q = 0; q < nQ; ++q) { query_dist_pivots_[q].store(max_val, std::memory_order_relaxed); @@ -627,79 +608,81 @@ void QueryCoordinator::drain_and_apply_aps(Tensor x, vector per_query_result_count(nQ, 0); - vector>::SingleResultHandler> - handlers; - handlers.reserve(nQ); - for (int64_t q = 0; q < nQ; ++q) { - handlers.emplace_back(*global_topk_buffer_pool_); - handlers[q].begin(q); // ← reset & heapify for query q - } + if (metric_ == faiss::METRIC_INNER_PRODUCT) { + vector>::SingleResultHandler> + handlers; + for (int64_t q = 0; q < nQ; ++q) { + handlers.emplace_back(*global_max_heaps_); + handlers[q].begin(q); // ← reset & heapify for query q + } - ResultJob rj; - while (total_left.load(std::memory_order_relaxed) > 0) { + ResultJob rj; + while (total_left.load(std::memory_order_relaxed) > 0) { - /* -- Wait for result or APS timeout -- */ - bool got_result = result_queue_.wait_dequeue_timed(rj, aps_dur); + /* -- Wait for result or APS timeout -- */ + bool got_result = result_queue_.wait_dequeue_timed(rj, aps_dur); - if (got_result) { - do { // Drain *all* currently available results. - for (int64_t i = 0; i < rj.distances.size(); ++i) { - handlers[rj.query_id].add_result(rj.distances[i], rj.indices[i]); - } - query_dist_pivots_[rj.query_id].store( - handlers[rj.query_id].threshold, std::memory_order_relaxed); + if (got_result) { + do { // Drain *all* currently available results. + for (int64_t i = 0; i < rj.distances.size(); ++i) { + handlers[rj.query_id].add_result(rj.distances[i], rj.indices[i]); + } + query_dist_pivots_[rj.query_id].store( + handlers[rj.query_id].threshold, std::memory_order_relaxed); - per_query_result_count[rj.query_id]++; + per_query_result_count[rj.query_id]++; - if (!job_flags_[rj.query_id][rj.rank]) { - job_flags_[rj.query_id][rj.rank] = true; - --parts_left[rj.query_id]; - --total_left; - } + if (!job_flags_[rj.query_id][rj.rank]) { + job_flags_[rj.query_id][rj.rank] = true; + --parts_left[rj.query_id]; + --total_left; + } + } while (result_queue_.try_dequeue(rj)); + } + } + // end the handlers + for (int64_t q = 0; q < nQ; ++q) { + handlers[q].end(); + } + } else { + vector>::SingleResultHandler> + handlers; + handlers.reserve(nQ); + for (int64_t q = 0; q < nQ; ++q) { + handlers.emplace_back(*global_min_heaps_); + handlers[q].begin(q); // ← reset & heapify for query q + } - // if (parts_left[rj.query_id] == 0) { - // global_topk_buffer_pool_[rj.query_id]->flush(); - // } - } while (result_queue_.try_dequeue(rj)); + ResultJob rj; + while (total_left.load(std::memory_order_relaxed) > 0) { - continue; // Skip APS; we just did useful work. - } + /* -- Wait for result or APS timeout -- */ + bool got_result = result_queue_.wait_dequeue_timed(rj, aps_dur); + if (got_result) { + do { // Drain *all* currently available results. + for (int64_t i = 0; i < rj.distances.size(); ++i) { + handlers[rj.query_id].add_result(rj.distances[i], rj.indices[i]); + } + query_dist_pivots_[rj.query_id].store( + handlers[rj.query_id].threshold, std::memory_order_relaxed); - /* (b) Timeout fired → run APS once. */ - // if (use_aps) { - // for (int64_t q = 0; q < nQ; ++q) { - // if (parts_left[q] == 0) continue; - // - // auto &buf = global_topk_buffer_pool_[q]; - // const float r = buf->get_kth_distance(); - // if (r != query_radius[q]) { - // query_radius[q] = r; - // probs[q] = compute_recall_profile( - // boundary_dist[q], r, D, {}, true, metric_==faiss::METRIC_L2); - // } - // - // float cum = 0.0f; - // for (int64_t p = 0; p < nprobe; ++p) - // if (job_flags_[q][p]) cum += probs[q][p]; - // - // if (cum >= recall_target) { - // for (int64_t p = 0; p < nprobe; ++p) - // if (!job_flags_[q][p]) { - // job_flags_[q][p] = true; - // --parts_left[q]; - // --total_left; - // } - // } - // } - // } - } + per_query_result_count[rj.query_id]++; - // end the handlers - for (int64_t q = 0; q < nQ; ++q) { - handlers[q].end(); - } + if (!job_flags_[rj.query_id][rj.rank]) { + job_flags_[rj.query_id][rj.rank] = true; + --parts_left[rj.query_id]; + --total_left; + } + } while (result_queue_.try_dequeue(rj)); + } + } + // end the handlers + for (int64_t q = 0; q < nQ; ++q) { + handlers[q].end(); + } + } } std::shared_ptr @@ -711,19 +694,22 @@ QueryCoordinator::aggregate_scan_results(int64_t nQ, auto id_acc = out_ids.accessor(); auto d_acc = out_dists.accessor(); - for (int64_t q = 0; q < nQ; ++q) { - for (int64_t i = 0; i < K; ++i) { - id_acc[q][i] = global_topk_buffer_pool_->heap_ids_tab[q * K + i]; - d_acc [q][i] = global_topk_buffer_pool_->heap_dis_tab[q * K + i]; + // copy results from the global heaps to the output tensors + if (metric_ == faiss::METRIC_INNER_PRODUCT) { + for (int64_t q = 0; q < nQ; ++q) { + for (int64_t i = 0; i < K; ++i) { + id_acc[q][i] = global_max_heaps_->heap_ids_tab[q * K + i]; + d_acc [q][i] = global_max_heaps_->heap_dis_tab[q * K + i]; + } + } + } else { + for (int64_t q = 0; q < nQ; ++q) { + for (int64_t i = 0; i < K; ++i) { + id_acc[q][i] = global_min_heaps_->heap_ids_tab[q * K + i]; + d_acc [q][i] = global_min_heaps_->heap_dis_tab[q * K + i]; + } } - - // auto tv = global_topk_buffer_pool_[q]->get_topk(); - // auto ti = global_topk_buffer_pool_[q]->get_topk_indices(); - // for (int i = 0; i < (int)ti.size() && i < K; ++i) { - // id_acc[q][i] = ti[i]; - // d_acc [q][i] = tv[i]; - // } } auto res = std::make_shared(); @@ -764,9 +750,7 @@ std::shared_ptr QueryCoordinator::worker_scan( core_process_preamble_time_ns[i] = core_resources_[i].process_preamble_time_ns; core_enqueue_time_ns[i] = core_resources_[i].enqueue_time_ns; core_job_time_ns[i] = core_resources_[i].job_time_ns; - core_scan_setup_time_ns[i] = core_resources_[i].scan_setup_time_ns; core_scan_time_ns[i] = core_resources_[i].scan_time_ns; - core_scan_push_time_ns[i] = core_resources_[i].scan_push_time_ns; } auto s1 = high_resolution_clock::now(); @@ -817,9 +801,7 @@ std::shared_ptr QueryCoordinator::worker_scan( core_process_preamble_time_ns[i] = core_resources_[i].process_preamble_time_ns - core_process_preamble_time_ns[i]; core_enqueue_time_ns[i] = core_resources_[i].enqueue_time_ns - core_enqueue_time_ns[i]; core_job_time_ns[i] = core_resources_[i].job_time_ns - core_job_time_ns[i]; - core_scan_setup_time_ns[i] = core_resources_[i].scan_setup_time_ns - core_scan_setup_time_ns[i]; core_scan_time_ns[i] = core_resources_[i].scan_time_ns - core_scan_time_ns[i]; - core_scan_push_time_ns[i] = core_resources_[i].scan_push_time_ns - core_scan_push_time_ns[i]; } @@ -1013,7 +995,8 @@ shared_ptr QueryCoordinator::serial_scan(Tensor x, Tensor partitio partition_manager_->partition_store_->list_size(pi), dimension, *topk_buf, - metric_); + metric_, + query_radius); scanned_ids.push_back(pi); float curr_radius = topk_buf->get_kth_distance(); @@ -1262,10 +1245,6 @@ shared_ptr QueryCoordinator::batched_serial_scan( // Perform a single batched scan on the partition. - int64_t scan_setup_time = 0; - int64_t scan_time = 0; - int64_t scan_push_time = 0; - batched_scan_list(x_subset.data_ptr(), list_codes, list_ids, @@ -1273,12 +1252,7 @@ shared_ptr QueryCoordinator::batched_serial_scan( list_size, d, local_buffers, - &scan_setup_time, - &scan_time, - &scan_push_time, metric_, - /* distances*/ nullptr, - /* labels */ nullptr, /* BLAS scratch */ serial_res.blas_ip_block, serial_res.blas_norms_x, serial_res.blas_norms_y, From 043e12f3baae26aff302a69a37ae46bf3103dcab Mon Sep 17 00:00:00 2001 From: Jason Date: Sat, 24 May 2025 22:06:02 -0500 Subject: [PATCH 248/323] verify numa --- src/cpp/include/parallel.h | 91 +++++++++++++++++++++++++++++++ src/cpp/src/query_coordinator.cpp | 14 ++++- 2 files changed, 104 insertions(+), 1 deletion(-) diff --git a/src/cpp/include/parallel.h b/src/cpp/include/parallel.h index ab61f6c9..13ed5a5e 100644 --- a/src/cpp/include/parallel.h +++ b/src/cpp/include/parallel.h @@ -66,6 +66,90 @@ inline bool set_thread_affinity(int core_id) { return numa_num_configured_nodes(); } + inline int get_current_cpu_numa_node() { + if (numa_available() < 0) return 0; // NUMA not available or error + int current_cpu = sched_getcpu(); + if (current_cpu < 0) { + // perror("sched_getcpu failed"); // User prefers less output + return -1; // Error + } + return numa_node_of_cpu(current_cpu); + } + + // Helper to get the NUMA node of a memory address + inline int get_memory_numa_node(const void* ptr) { + if (numa_available() < 0) return 0; // NUMA not available or error + if (!ptr) { + return -1; // Invalid pointer + } + + int page_node = -1; + // Align pointer to page boundary for numa_move_pages query + void* page_address = (void*)((uintptr_t)ptr & ~((uintptr_t)getpagesize() - 1)); + + // Use numa_move_pages with nodes=NULL to query the current node of the page. + // The 'pages' argument is an array of page addresses. + void *pages_to_query[] = { page_address }; + int status_array[] = { -1 }; // To store the node of the page + + // pid = 0 for current process's address space + if (numa_move_pages(0, 1, pages_to_query, NULL, status_array, 0) == 0) { + page_node = status_array[0]; + } else { + // perror("numa_move_pages query failed"); // User prefers less output + // This could happen if the page is not mapped, or other errors. + page_node = -2; // Indicate error in querying + } + return page_node; + } + + // Verifies if the memory at memory_address is on the same NUMA node as the current CPU. + // variable_name is for logging/debugging by the caller if verification fails. + inline bool verify_numa_locality(const void* memory_address, const char* /*variable_name*/) { + if (numa_available() < 0) return true; // NUMA not available, assume locality + if (get_num_numa_nodes() <= 1) return true; // Single NUMA node system + + if (!memory_address) { + std::cout << "Warning: Cannot verify NUMA locality for null pointer: " << variable_name << std::endl; + return true; // Or false, based on desired strictness for nullptrs. True avoids false positives. + } + + int current_cpu_node = get_current_cpu_numa_node(); + if (current_cpu_node < 0) { + std::cout << "Error: Could not determine current CPU NUMA node for " << variable_name << std::endl; + return false; // Cannot verify + } + + int memory_page_node = get_memory_numa_node(memory_address); + if (memory_page_node < -1) { // -2 indicates query error + std::cout << "Error: Could not determine NUMA node for memory of " << variable_name << std::endl; + return false; // Cannot verify due to error + } + if (memory_page_node == -1) { + // This typically means the page is not mapped or has a default/interleaved policy + // that doesn't map to a single specific node in a way numa_move_pages can report. + // For strict checking, this could be considered non-local if cpu_node is specific. + // Caller can log: "Warning: Memory for " << variable_name << " has undetermined/interleaved policy (node -1). CPU node: " << current_cpu_node + // A common case for MPOL_DEFAULT is allocation on the node of first touch. + // If it's truly interleaved, it's not "local" to any single node. + // If it's default and first touched by current_cpu_node, it would be local. + // This check is tricky. For now, if we can't determine a specific node, assume it might not be local. + std::cout << "Warning: Memory for " << variable_name << " has undetermined/interleaved policy (node -1). CPU node: " << current_cpu_node << std::endl; + return false; + } + + bool is_local = (current_cpu_node == memory_page_node); + + if (is_local) { + std::cout << "NUMA locality verified for " << variable_name << std::endl; + } else { + std::cout << "Warning: NUMA locality mismatch for " << variable_name + << ": CPU is on node " << current_cpu_node + << ", but memory is on node " << memory_page_node << std::endl; + } + + return is_local; + } #else #include #include @@ -89,6 +173,13 @@ inline int cpu_numa_node(int /*cpu*/) { inline int get_num_numa_nodes() { return 1; // Not applicable } + +inline bool verify_numa_locality(const void* /*memory_address*/, const char* /*variable_name*/) { + return true; +} +inline int get_current_cpu_numa_node() { return 0; } +inline int get_memory_numa_node(const void* /*ptr*/) { return 0; } + #endif diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index fe52be0f..15cf4d37 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -348,8 +348,20 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, pivots[i] = &query_dist_pivots_[qid]; } - // run the scan on this chunk + // check that things are on the proper NUMA node + verify_numa_locality(qptr, "qptr"); + verify_numa_locality(codes, "codes"); + verify_numa_locality(ids, "ids"); + verify_numa_locality(res.batch_distances, "batch_distances"); + verify_numa_locality(res.batch_ids, "batch_ids"); + verify_numa_locality(res.blas_ip_block, "blas_ip_block"); + verify_numa_locality(res.blas_norms_x, "blas_norms_x"); + verify_numa_locality(res.blas_norms_y, "blas_norms_y"); + verify_numa_locality(res.topk_buffer_pool[0]->ord_, "ord_"); + verify_numa_locality(res.topk_buffer_pool[0]->vals_, "vals_"); + verify_numa_locality(res.topk_buffer_pool[0]->ids_, "ids_"); + // run the scan on this chunk batched_scan_list( qptr, codes, ids, From 423178b0ce1a3559a98f9f730be2efa201dc7154 Mon Sep 17 00:00:00 2001 From: Jason Date: Sat, 24 May 2025 22:06:53 -0500 Subject: [PATCH 249/323] verify numa --- src/cpp/include/parallel.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/cpp/include/parallel.h b/src/cpp/include/parallel.h index 13ed5a5e..e0cc26dc 100644 --- a/src/cpp/include/parallel.h +++ b/src/cpp/include/parallel.h @@ -105,7 +105,7 @@ inline bool set_thread_affinity(int core_id) { // Verifies if the memory at memory_address is on the same NUMA node as the current CPU. // variable_name is for logging/debugging by the caller if verification fails. - inline bool verify_numa_locality(const void* memory_address, const char* /*variable_name*/) { + inline bool verify_numa_locality(const void* memory_address, const char* variable_name) { if (numa_available() < 0) return true; // NUMA not available, assume locality if (get_num_numa_nodes() <= 1) return true; // Single NUMA node system From 88027f6cd87521b78cdd4d98dbfd4b9c2347712c Mon Sep 17 00:00:00 2001 From: Jason Date: Sat, 24 May 2025 22:11:13 -0500 Subject: [PATCH 250/323] verify numa --- src/cpp/src/query_coordinator.cpp | 25 ++++++++++++++----------- 1 file changed, 14 insertions(+), 11 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 15cf4d37..0adc6f00 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -349,17 +349,20 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, } // check that things are on the proper NUMA node - verify_numa_locality(qptr, "qptr"); - verify_numa_locality(codes, "codes"); - verify_numa_locality(ids, "ids"); - verify_numa_locality(res.batch_distances, "batch_distances"); - verify_numa_locality(res.batch_ids, "batch_ids"); - verify_numa_locality(res.blas_ip_block, "blas_ip_block"); - verify_numa_locality(res.blas_norms_x, "blas_norms_x"); - verify_numa_locality(res.blas_norms_y, "blas_norms_y"); - verify_numa_locality(res.topk_buffer_pool[0]->ord_, "ord_"); - verify_numa_locality(res.topk_buffer_pool[0]->vals_, "vals_"); - verify_numa_locality(res.topk_buffer_pool[0]->ids_, "ids_"); + // verify_numa_locality(qptr, "qptr"); + // verify_numa_locality(codes, "codes"); + // verify_numa_locality(ids, "ids"); + // verify_numa_locality(res.batch_distances, "batch_distances"); + // verify_numa_locality(res.batch_ids, "batch_ids"); + // verify_numa_locality(res.blas_ip_block, "blas_ip_block"); + // verify_numa_locality(res.blas_norms_x, "blas_norms_x"); + // verify_numa_locality(res.blas_norms_y, "blas_norms_y"); + + for (int64_t i = 0; i < Q; ++i) { + verify_numa_locality(res.topk_buffer_pool[i]->ord_, "ord_"); + verify_numa_locality(res.topk_buffer_pool[i]->vals_, "vals_"); + verify_numa_locality(res.topk_buffer_pool[i]->ids_, "ids_"); + } // run the scan on this chunk batched_scan_list( From 092a6676ff89ee2d048bbd36dd025e6af14d3b69 Mon Sep 17 00:00:00 2001 From: Jason Date: Sat, 24 May 2025 22:13:01 -0500 Subject: [PATCH 251/323] verify numa --- src/cpp/src/query_coordinator.cpp | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 0adc6f00..c7b23f8d 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -358,11 +358,11 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, // verify_numa_locality(res.blas_norms_x, "blas_norms_x"); // verify_numa_locality(res.blas_norms_y, "blas_norms_y"); - for (int64_t i = 0; i < Q; ++i) { - verify_numa_locality(res.topk_buffer_pool[i]->ord_, "ord_"); - verify_numa_locality(res.topk_buffer_pool[i]->vals_, "vals_"); - verify_numa_locality(res.topk_buffer_pool[i]->ids_, "ids_"); - } + // for (int64_t i = 0; i < Q; ++i) { + // verify_numa_locality(res.topk_buffer_pool[i]->ord_, "ord_"); + // verify_numa_locality(res.topk_buffer_pool[i]->vals_, "vals_"); + // verify_numa_locality(res.topk_buffer_pool[i]->ids_, "ids_"); + // } // run the scan on this chunk batched_scan_list( From e3ad685ce691a9b3dd10362e38006cc789f176cc Mon Sep 17 00:00:00 2001 From: Jason Date: Sat, 24 May 2025 22:15:54 -0500 Subject: [PATCH 252/323] verify numa --- src/cpp/include/parallel.h | 2 +- src/cpp/src/query_coordinator.cpp | 19 ++++++++++++++++++- 2 files changed, 19 insertions(+), 2 deletions(-) diff --git a/src/cpp/include/parallel.h b/src/cpp/include/parallel.h index e0cc26dc..e2150100 100644 --- a/src/cpp/include/parallel.h +++ b/src/cpp/include/parallel.h @@ -141,7 +141,7 @@ inline bool set_thread_affinity(int core_id) { bool is_local = (current_cpu_node == memory_page_node); if (is_local) { - std::cout << "NUMA locality verified for " << variable_name << std::endl; + // std::cout << "NUMA locality verified for " << variable_name << std::endl; } else { std::cout << "Warning: NUMA locality mismatch for " << variable_name << ": CPU is on node " << current_cpu_node diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index c7b23f8d..bf6b5940 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -349,6 +349,23 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, } // check that things are on the proper NUMA node + bool ok = true; + ok = ok & verify_numa_locality(res.batch_queries, "batch_queries"); + ok = ok & verify_numa_locality(res.batch_distances, "batch_distances"); + ok = ok & verify_numa_locality(res.batch_ids, "batch_ids"); + ok = ok & verify_numa_locality(res.blas_ip_block, "blas_ip_block"); + ok = ok & verify_numa_locality(res.blas_norms_x, "blas_norms_x"); + ok = ok & verify_numa_locality(res.blas_norms_y, "blas_norms_y"); + for (int64_t i = 0; i < Q; ++i) { + ok = ok & verify_numa_locality(res.topk_buffer_pool[i]->ord_, "ord_"); + ok = ok & verify_numa_locality(res.topk_buffer_pool[i]->vals_, "vals_"); + ok = ok & verify_numa_locality(res.topk_buffer_pool[i]->ids_, "ids_"); + } + if (!ok) { + std::cerr << "[QueryCoordinator::handle_batched_job] NUMA locality check failed.\n"; + throw std::runtime_error("NUMA locality check failed"); + } + // verify_numa_locality(qptr, "qptr"); // verify_numa_locality(codes, "codes"); // verify_numa_locality(ids, "ids"); @@ -357,7 +374,7 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, // verify_numa_locality(res.blas_ip_block, "blas_ip_block"); // verify_numa_locality(res.blas_norms_x, "blas_norms_x"); // verify_numa_locality(res.blas_norms_y, "blas_norms_y"); - + // // for (int64_t i = 0; i < Q; ++i) { // verify_numa_locality(res.topk_buffer_pool[i]->ord_, "ord_"); // verify_numa_locality(res.topk_buffer_pool[i]->vals_, "vals_"); From e7634c6cc99afd6f9e5079fa400cb2a797c3f74b Mon Sep 17 00:00:00 2001 From: Jason Date: Sat, 24 May 2025 22:20:41 -0500 Subject: [PATCH 253/323] verify numa --- src/cpp/src/query_coordinator.cpp | 21 ++++++++++++--------- 1 file changed, 12 insertions(+), 9 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index bf6b5940..6d6c1ba9 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -350,16 +350,19 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, // check that things are on the proper NUMA node bool ok = true; - ok = ok & verify_numa_locality(res.batch_queries, "batch_queries"); - ok = ok & verify_numa_locality(res.batch_distances, "batch_distances"); - ok = ok & verify_numa_locality(res.batch_ids, "batch_ids"); - ok = ok & verify_numa_locality(res.blas_ip_block, "blas_ip_block"); - ok = ok & verify_numa_locality(res.blas_norms_x, "blas_norms_x"); - ok = ok & verify_numa_locality(res.blas_norms_y, "blas_norms_y"); + ok = ok && verify_numa_locality(qptr, "qptr"); + ok = ok && verify_numa_locality(codes, "codes"); + ok = ok && verify_numa_locality(ids, "ids"); + ok = ok && verify_numa_locality(res.batch_queries, "batch_queries"); + ok = ok && verify_numa_locality(res.batch_distances, "batch_distances"); + ok = ok && verify_numa_locality(res.batch_ids, "batch_ids"); + ok = ok && verify_numa_locality(res.blas_ip_block, "blas_ip_block"); + ok = ok && verify_numa_locality(res.blas_norms_x, "blas_norms_x"); + ok = ok && verify_numa_locality(res.blas_norms_y, "blas_norms_y"); for (int64_t i = 0; i < Q; ++i) { - ok = ok & verify_numa_locality(res.topk_buffer_pool[i]->ord_, "ord_"); - ok = ok & verify_numa_locality(res.topk_buffer_pool[i]->vals_, "vals_"); - ok = ok & verify_numa_locality(res.topk_buffer_pool[i]->ids_, "ids_"); + ok = ok && verify_numa_locality(res.topk_buffer_pool[i]->ord_, "ord_"); + ok = ok && verify_numa_locality(res.topk_buffer_pool[i]->vals_, "vals_"); + ok = ok && verify_numa_locality(res.topk_buffer_pool[i]->ids_, "ids_"); } if (!ok) { std::cerr << "[QueryCoordinator::handle_batched_job] NUMA locality check failed.\n"; From b3a7d4444ef643656e0b8fcb65ad22926e9b73e7 Mon Sep 17 00:00:00 2001 From: Jason Date: Sat, 24 May 2025 22:23:03 -0500 Subject: [PATCH 254/323] verify numa --- src/cpp/src/query_coordinator.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 6d6c1ba9..cec8b55d 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -354,8 +354,8 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, ok = ok && verify_numa_locality(codes, "codes"); ok = ok && verify_numa_locality(ids, "ids"); ok = ok && verify_numa_locality(res.batch_queries, "batch_queries"); - ok = ok && verify_numa_locality(res.batch_distances, "batch_distances"); - ok = ok && verify_numa_locality(res.batch_ids, "batch_ids"); + // ok = ok && verify_numa_locality(res.batch_distances, "batch_distances"); + // ok = ok && verify_numa_locality(res.batch_ids, "batch_ids"); ok = ok && verify_numa_locality(res.blas_ip_block, "blas_ip_block"); ok = ok && verify_numa_locality(res.blas_norms_x, "blas_norms_x"); ok = ok && verify_numa_locality(res.blas_norms_y, "blas_norms_y"); From df30166514c376c781664b941e8dd7767b5bbbb8 Mon Sep 17 00:00:00 2001 From: Jason Date: Sat, 24 May 2025 22:25:03 -0500 Subject: [PATCH 255/323] verify numa --- src/cpp/include/parallel.h | 1 + 1 file changed, 1 insertion(+) diff --git a/src/cpp/include/parallel.h b/src/cpp/include/parallel.h index e2150100..007a30cf 100644 --- a/src/cpp/include/parallel.h +++ b/src/cpp/include/parallel.h @@ -122,6 +122,7 @@ inline bool set_thread_affinity(int core_id) { int memory_page_node = get_memory_numa_node(memory_address); if (memory_page_node < -1) { // -2 indicates query error + std::cout << "memory_page_node = " << memory_page_node << std::endl; std::cout << "Error: Could not determine NUMA node for memory of " << variable_name << std::endl; return false; // Cannot verify due to error } From 9982cfd67431262fd968d91b6781319f30004d57 Mon Sep 17 00:00:00 2001 From: Jason Date: Sat, 24 May 2025 22:27:58 -0500 Subject: [PATCH 256/323] verify numa --- src/cpp/src/query_coordinator.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index cec8b55d..552de192 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -366,7 +366,7 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, } if (!ok) { std::cerr << "[QueryCoordinator::handle_batched_job] NUMA locality check failed.\n"; - throw std::runtime_error("NUMA locality check failed"); + // throw std::runtime_error("NUMA locality check failed"); } // verify_numa_locality(qptr, "qptr"); From aceb22a35b3f9452b8de30a31d7e6cebf6c8acf3 Mon Sep 17 00:00:00 2001 From: Jason Date: Sat, 24 May 2025 22:35:31 -0500 Subject: [PATCH 257/323] verify numa --- src/cpp/include/parallel.h | 22 ++++++++++++---------- src/cpp/include/query_coordinator.h | 3 --- src/cpp/src/query_coordinator.cpp | 20 +++++++------------- 3 files changed, 19 insertions(+), 26 deletions(-) diff --git a/src/cpp/include/parallel.h b/src/cpp/include/parallel.h index 007a30cf..4158ce83 100644 --- a/src/cpp/include/parallel.h +++ b/src/cpp/include/parallel.h @@ -80,28 +80,30 @@ inline bool set_thread_affinity(int core_id) { inline int get_memory_numa_node(const void* ptr) { if (numa_available() < 0) return 0; // NUMA not available or error if (!ptr) { - return -1; // Invalid pointer + return -1; // Invalid pointer (e.g. NULL) } - int page_node = -1; // Align pointer to page boundary for numa_move_pages query void* page_address = (void*)((uintptr_t)ptr & ~((uintptr_t)getpagesize() - 1)); - // Use numa_move_pages with nodes=NULL to query the current node of the page. - // The 'pages' argument is an array of page addresses. void *pages_to_query[] = { page_address }; int status_array[] = { -1 }; // To store the node of the page + errno = 0; // Clear errno before system call // pid = 0 for current process's address space + // nodes = NULL to query current node + // flags = 0 for query if (numa_move_pages(0, 1, pages_to_query, NULL, status_array, 0) == 0) { - page_node = status_array[0]; + // Success. status_array[0] contains the node. + // It can be -1 if the page has a policy like MPOL_INTERLEAVE or is not mapped to a specific node. + return status_array[0]; } else { - // perror("numa_move_pages query failed"); // User prefers less output - // This could happen if the page is not mapped, or other errors. - page_node = -2; // Indicate error in querying + // numa_move_pages failed, return negative errno. + // errno is set by numa_move_pages. + // Common errors: EFAULT (-14), EINVAL (-22), EPERM (-1), ESRCH (-3), ENOMEM (-12) + return -errno; } - return page_node; - } +} // Verifies if the memory at memory_address is on the same NUMA node as the current CPU. // variable_name is for logging/debugging by the caller if verification fails. diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index 9f14fca7..07b512f6 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -63,10 +63,7 @@ class QueryCoordinator { // Thread-local buffers for batched queries float* batch_queries = nullptr; // batched query buffer (NUMA-allocated) - float* batch_distances = nullptr; // scratch space for distances - int64_t* batch_ids = nullptr; // scratch space for ids size_t batch_q_capacity = 0; // number of floats in batch_queries - size_t batch_res_capacity = 0; // number of (k×Q) slots in distances & ids float* blas_ip_block = nullptr; // capacity: db_blas_bs * max_Q size_t blas_ip_capacity = 0; // # floats diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 552de192..3cdc4c67 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -70,8 +70,13 @@ void QueryCoordinator::allocate_core_resources(int core_idx, // --- ZERO‐INITIALIZE our batched‐query buffers so we never free garbage pointers --- CR.batch_queries = nullptr; - CR.batch_distances = nullptr; - CR.batch_ids = nullptr; + + CR.blas_ip_block = nullptr; + CR.blas_ip_capacity = 0; + CR.blas_norms_x = nullptr; + CR.blas_norms_x_cap = 0; + CR.blas_norms_y = nullptr; + CR.blas_norms_y_cap = 0; int numa_node = 0; #ifdef QUAKE_USE_NUMA @@ -302,15 +307,6 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, res.batch_q_capacity = max_q; } - size_t max_r = size_t(queries_req) * K; - if (res.batch_res_capacity < max_r) { - quake_free(res.batch_distances, res.batch_res_capacity * sizeof(float)); - quake_free(res.batch_ids, res.batch_res_capacity * sizeof(int64_t)); - res.batch_distances = static_cast (quake_alloc(max_r * sizeof(float), node)); - res.batch_ids = static_cast(quake_alloc(max_r * sizeof(int64_t), node)); - res.batch_res_capacity = max_r; - } - // reset only the first 'chunk' TopK buffers for (int64_t i = 0; i < Q; ++i) { auto &buf = res.topk_buffer_pool[i]; @@ -354,8 +350,6 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, ok = ok && verify_numa_locality(codes, "codes"); ok = ok && verify_numa_locality(ids, "ids"); ok = ok && verify_numa_locality(res.batch_queries, "batch_queries"); - // ok = ok && verify_numa_locality(res.batch_distances, "batch_distances"); - // ok = ok && verify_numa_locality(res.batch_ids, "batch_ids"); ok = ok && verify_numa_locality(res.blas_ip_block, "blas_ip_block"); ok = ok && verify_numa_locality(res.blas_norms_x, "blas_norms_x"); ok = ok && verify_numa_locality(res.blas_norms_y, "blas_norms_y"); From a4846ad6ce3168f9c73b191fe8bbbd4f5cb52a89 Mon Sep 17 00:00:00 2001 From: Jason Date: Sat, 24 May 2025 22:40:26 -0500 Subject: [PATCH 258/323] verify numa --- src/cpp/src/query_coordinator.cpp | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 3cdc4c67..4735bbf6 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -19,6 +19,8 @@ static void ensure_blas_buffers(QueryCoordinator::CoreResources& res, { const size_t ip_need = db_bs * max_q; if (res.blas_ip_capacity < ip_need) { + std::cerr << "Reallocating BLAS buffers: " << res.blas_ip_capacity + << " -> " << ip_need << std::endl; quake_free(res.blas_ip_block, res.blas_ip_capacity * sizeof(float)); res.blas_ip_block = static_cast(quake_alloc(ip_need * sizeof(float), node)); res.blas_ip_capacity = ip_need; From 2e0919d28aa7463a547354fcff03b1c19b174939 Mon Sep 17 00:00:00 2001 From: Jason Date: Sat, 24 May 2025 22:49:09 -0500 Subject: [PATCH 259/323] verify numa --- src/cpp/src/query_coordinator.cpp | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 4735bbf6..c5416978 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -24,16 +24,22 @@ static void ensure_blas_buffers(QueryCoordinator::CoreResources& res, quake_free(res.blas_ip_block, res.blas_ip_capacity * sizeof(float)); res.blas_ip_block = static_cast(quake_alloc(ip_need * sizeof(float), node)); res.blas_ip_capacity = ip_need; + // zero out the new memory + memset(res.blas_ip_block, 0, ip_need * sizeof(float)); } if (res.blas_norms_x_cap < max_q) { quake_free(res.blas_norms_x, res.blas_norms_x_cap * sizeof(float)); res.blas_norms_x = static_cast(quake_alloc(max_q * sizeof(float), node)); res.blas_norms_x_cap = max_q; + // zero out the new memory + memset(res.blas_norms_x, 0, max_q * sizeof(float)); } if (res.blas_norms_y_cap < db_bs) { quake_free(res.blas_norms_y, res.blas_norms_y_cap * sizeof(float)); res.blas_norms_y = static_cast(quake_alloc(db_bs * sizeof(float), node)); res.blas_norms_y_cap = db_bs; + // zero out the new memory + memset(res.blas_norms_y, 0, db_bs * sizeof(float)); } } From 53c7f86d8ca4b217dc898637c6d659e908d0f531 Mon Sep 17 00:00:00 2001 From: Jason Date: Sat, 24 May 2025 22:51:04 -0500 Subject: [PATCH 260/323] verify numa --- src/cpp/src/query_coordinator.cpp | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index c5416978..c450233e 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -21,21 +21,21 @@ static void ensure_blas_buffers(QueryCoordinator::CoreResources& res, if (res.blas_ip_capacity < ip_need) { std::cerr << "Reallocating BLAS buffers: " << res.blas_ip_capacity << " -> " << ip_need << std::endl; - quake_free(res.blas_ip_block, res.blas_ip_capacity * sizeof(float)); + if (res.blas_ip_block) quake_free(res.blas_ip_block, res.blas_ip_capacity * sizeof(float)); res.blas_ip_block = static_cast(quake_alloc(ip_need * sizeof(float), node)); res.blas_ip_capacity = ip_need; // zero out the new memory memset(res.blas_ip_block, 0, ip_need * sizeof(float)); } if (res.blas_norms_x_cap < max_q) { - quake_free(res.blas_norms_x, res.blas_norms_x_cap * sizeof(float)); + if (res.blas_norms_x) quake_free(res.blas_norms_x, res.blas_norms_x_cap * sizeof(float)); res.blas_norms_x = static_cast(quake_alloc(max_q * sizeof(float), node)); res.blas_norms_x_cap = max_q; // zero out the new memory memset(res.blas_norms_x, 0, max_q * sizeof(float)); } if (res.blas_norms_y_cap < db_bs) { - quake_free(res.blas_norms_y, res.blas_norms_y_cap * sizeof(float)); + if (res.blas_norms_y) quake_free(res.blas_norms_y, res.blas_norms_y_cap * sizeof(float)); res.blas_norms_y = static_cast(quake_alloc(db_bs * sizeof(float), node)); res.blas_norms_y_cap = db_bs; // zero out the new memory From 3518b7b32e85f6d1ac52cc3d1769d598c05dda4d Mon Sep 17 00:00:00 2001 From: Jason Date: Sat, 24 May 2025 22:52:45 -0500 Subject: [PATCH 261/323] verify numa --- src/cpp/include/parallel.h | 2 ++ src/cpp/src/query_coordinator.cpp | 6 ------ 2 files changed, 2 insertions(+), 6 deletions(-) diff --git a/src/cpp/include/parallel.h b/src/cpp/include/parallel.h index 4158ce83..68c90199 100644 --- a/src/cpp/include/parallel.h +++ b/src/cpp/include/parallel.h @@ -49,6 +49,7 @@ inline bool set_thread_affinity(int core_id) { if (ptr == nullptr || ptr == MAP_FAILED) { throw std::bad_alloc(); } + memset(ptr, 0, sz); // Initialize memory to zero return ptr; } @@ -162,6 +163,7 @@ inline void* quake_alloc(size_t sz, int /*node*/) { if (!ptr) { throw std::bad_alloc(); } + memset(ptr, 0, sz); // Initialize memory to zero return ptr; } diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index c450233e..12cff364 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -24,22 +24,16 @@ static void ensure_blas_buffers(QueryCoordinator::CoreResources& res, if (res.blas_ip_block) quake_free(res.blas_ip_block, res.blas_ip_capacity * sizeof(float)); res.blas_ip_block = static_cast(quake_alloc(ip_need * sizeof(float), node)); res.blas_ip_capacity = ip_need; - // zero out the new memory - memset(res.blas_ip_block, 0, ip_need * sizeof(float)); } if (res.blas_norms_x_cap < max_q) { if (res.blas_norms_x) quake_free(res.blas_norms_x, res.blas_norms_x_cap * sizeof(float)); res.blas_norms_x = static_cast(quake_alloc(max_q * sizeof(float), node)); res.blas_norms_x_cap = max_q; - // zero out the new memory - memset(res.blas_norms_x, 0, max_q * sizeof(float)); } if (res.blas_norms_y_cap < db_bs) { if (res.blas_norms_y) quake_free(res.blas_norms_y, res.blas_norms_y_cap * sizeof(float)); res.blas_norms_y = static_cast(quake_alloc(db_bs * sizeof(float), node)); res.blas_norms_y_cap = db_bs; - // zero out the new memory - memset(res.blas_norms_y, 0, db_bs * sizeof(float)); } } From 3330a6a523681bae31de0bb3cc8bf0929ceaaf3a Mon Sep 17 00:00:00 2001 From: Jason Date: Sat, 24 May 2025 23:02:05 -0500 Subject: [PATCH 262/323] verify numa --- src/cpp/include/list_scanning.h | 32 ++++++++++++++++++-------------- 1 file changed, 18 insertions(+), 14 deletions(-) diff --git a/src/cpp/include/list_scanning.h b/src/cpp/include/list_scanning.h index 3095be71..0fb5a6dc 100644 --- a/src/cpp/include/list_scanning.h +++ b/src/cpp/include/list_scanning.h @@ -538,22 +538,26 @@ inline void l2_blas( faiss::fvec_norms_L2sqr(norms_y, y + j0 * d, d, db_chunk); // use torch matmul + Tensor x_tensor = torch::from_blob((void*) x + i0 * d, {q_chunk, d}, torch::kFloat32); + Tensor y_tensor = torch::from_blob((void*) y + j0 * d, {db_chunk, d}, torch::kFloat32); + Tensor ip_tensor = torch::from_blob(ip_block, {q_chunk, db_chunk}, torch::kFloat32); + torch::matmul_out(ip_tensor, x_tensor, y_tensor.transpose(0, 1)); /* SGEMM */ - { - const float one = 1.f; - float zero = 0.f; - FINTEGER nyi = FINTEGER(db_chunk); - FINTEGER nxi = FINTEGER(q_chunk); - FINTEGER di = FINTEGER(d); - sgemm_("Transpose","Not transpose", - &nyi,&nxi,&di, - &one, - y + j0 * d, &di, - x + i0 * d, &di, - &zero, - ip_block, &nyi); - } + // { + // const float one = 1.f; + // float zero = 0.f; + // FINTEGER nyi = FINTEGER(db_chunk); + // FINTEGER nxi = FINTEGER(q_chunk); + // FINTEGER di = FINTEGER(d); + // sgemm_("Transpose","Not transpose", + // &nyi,&nxi,&di, + // &one, + // y + j0 * d, &di, + // x + i0 * d, &di, + // &zero, + // ip_block, &nyi); + // } /* IP → L2² */ if (k > 1) { From 44d63706adb22907fd85ef99baf750181c4a4a32 Mon Sep 17 00:00:00 2001 From: Jason Date: Sat, 24 May 2025 23:02:31 -0500 Subject: [PATCH 263/323] verify numa --- src/cpp/src/query_coordinator.cpp | 45 +++++++++++-------------------- 1 file changed, 15 insertions(+), 30 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 12cff364..393ba07d 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -347,37 +347,22 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, } // check that things are on the proper NUMA node - bool ok = true; - ok = ok && verify_numa_locality(qptr, "qptr"); - ok = ok && verify_numa_locality(codes, "codes"); - ok = ok && verify_numa_locality(ids, "ids"); - ok = ok && verify_numa_locality(res.batch_queries, "batch_queries"); - ok = ok && verify_numa_locality(res.blas_ip_block, "blas_ip_block"); - ok = ok && verify_numa_locality(res.blas_norms_x, "blas_norms_x"); - ok = ok && verify_numa_locality(res.blas_norms_y, "blas_norms_y"); - for (int64_t i = 0; i < Q; ++i) { - ok = ok && verify_numa_locality(res.topk_buffer_pool[i]->ord_, "ord_"); - ok = ok && verify_numa_locality(res.topk_buffer_pool[i]->vals_, "vals_"); - ok = ok && verify_numa_locality(res.topk_buffer_pool[i]->ids_, "ids_"); - } - if (!ok) { - std::cerr << "[QueryCoordinator::handle_batched_job] NUMA locality check failed.\n"; - // throw std::runtime_error("NUMA locality check failed"); - } - - // verify_numa_locality(qptr, "qptr"); - // verify_numa_locality(codes, "codes"); - // verify_numa_locality(ids, "ids"); - // verify_numa_locality(res.batch_distances, "batch_distances"); - // verify_numa_locality(res.batch_ids, "batch_ids"); - // verify_numa_locality(res.blas_ip_block, "blas_ip_block"); - // verify_numa_locality(res.blas_norms_x, "blas_norms_x"); - // verify_numa_locality(res.blas_norms_y, "blas_norms_y"); - // + // bool ok = true; + // ok = ok && verify_numa_locality(qptr, "qptr"); + // ok = ok && verify_numa_locality(codes, "codes"); + // ok = ok && verify_numa_locality(ids, "ids"); + // ok = ok && verify_numa_locality(res.batch_queries, "batch_queries"); + // ok = ok && verify_numa_locality(res.blas_ip_block, "blas_ip_block"); + // ok = ok && verify_numa_locality(res.blas_norms_x, "blas_norms_x"); + // ok = ok && verify_numa_locality(res.blas_norms_y, "blas_norms_y"); // for (int64_t i = 0; i < Q; ++i) { - // verify_numa_locality(res.topk_buffer_pool[i]->ord_, "ord_"); - // verify_numa_locality(res.topk_buffer_pool[i]->vals_, "vals_"); - // verify_numa_locality(res.topk_buffer_pool[i]->ids_, "ids_"); + // ok = ok && verify_numa_locality(res.topk_buffer_pool[i]->ord_, "ord_"); + // ok = ok && verify_numa_locality(res.topk_buffer_pool[i]->vals_, "vals_"); + // ok = ok && verify_numa_locality(res.topk_buffer_pool[i]->ids_, "ids_"); + // } + // if (!ok) { + // std::cerr << "[QueryCoordinator::handle_batched_job] NUMA locality check failed.\n"; + // // throw std::runtime_error("NUMA locality check failed"); // } // run the scan on this chunk From 16d0f2f68a82d006f0f7cc879bfe75e82cb38a08 Mon Sep 17 00:00:00 2001 From: Jason Date: Sat, 24 May 2025 23:11:26 -0500 Subject: [PATCH 264/323] verify numa --- src/cpp/src/query_coordinator.cpp | 22 ++++++++++++++++++++++ 1 file changed, 22 insertions(+) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 393ba07d..81863f84 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -268,6 +268,7 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, NUMAResources &nr) { auto start = std::chrono::high_resolution_clock::now(); + auto s1 = std::chrono::high_resolution_clock::now(); // Total queries, Top-K, dimension, NUMA node int64_t Q = job.num_queries; int K = job.k; @@ -316,6 +317,8 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, buf->reset(); } + auto s2 = std::chrono::high_resolution_clock::now(); + // // init only the first chunk*K slots in scratch // float init_val = (metric_ == faiss::METRIC_INNER_PRODUCT) // ? -std::numeric_limits::infinity() @@ -346,6 +349,7 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, pivots[i] = &query_dist_pivots_[qid]; } + auto s3 = std::chrono::high_resolution_clock::now(); // check that things are on the proper NUMA node // bool ok = true; // ok = ok && verify_numa_locality(qptr, "qptr"); @@ -378,6 +382,8 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, BLAS_DB_BS, pivots); + auto s4 = std::chrono::high_resolution_clock::now(); + auto end = std::chrono::high_resolution_clock::now(); res.scan_time_ns += std::chrono::duration_cast(end - start).count(); @@ -399,7 +405,23 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, results_batch.size() ); end = std::chrono::high_resolution_clock::now(); + auto s5 = std::chrono::high_resolution_clock::now(); res.enqueue_time_ns += std::chrono::duration_cast(end - start).count(); + + // print out debug timing info s1, ... s5 + std::cout << "QueryCoordinator::handle_batched_job: " + << "wait: " << res.wait_time_ns + << ", preamble: " << res.process_preamble_time_ns + << ", process: " << res.process_time_ns + << ", scan: " << res.scan_time_ns + << ", enqueue: " << res.enqueue_time_ns + << ", job: " << res.job_time_ns + << ", s1: " << std::chrono::duration_cast(s1 - start).count() + << ", s2: " << std::chrono::duration_cast(s2 - s1).count() + << ", s3: " << std::chrono::duration_cast(s3 - s2).count() + << ", s4: " << std::chrono::duration_cast(s4 - s3).count() + << ", s5: " << std::chrono::duration_cast(end - s4).count() + << std::endl; } void QueryCoordinator::init_global_buffers(int64_t nQ, From 8282f0074c4ab935575ae1b2d90b040a665970b3 Mon Sep 17 00:00:00 2001 From: Jason Date: Sat, 24 May 2025 23:14:06 -0500 Subject: [PATCH 265/323] verify numa --- src/cpp/src/query_coordinator.cpp | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 81863f84..6d1f0977 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -410,17 +410,17 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, // print out debug timing info s1, ... s5 std::cout << "QueryCoordinator::handle_batched_job: " - << "wait: " << res.wait_time_ns - << ", preamble: " << res.process_preamble_time_ns - << ", process: " << res.process_time_ns - << ", scan: " << res.scan_time_ns - << ", enqueue: " << res.enqueue_time_ns - << ", job: " << res.job_time_ns - << ", s1: " << std::chrono::duration_cast(s1 - start).count() - << ", s2: " << std::chrono::duration_cast(s2 - s1).count() - << ", s3: " << std::chrono::duration_cast(s3 - s2).count() - << ", s4: " << std::chrono::duration_cast(s4 - s3).count() - << ", s5: " << std::chrono::duration_cast(end - s4).count() + << "job_id: " << job.job_id + << ", core_id: " << res.core_id + << ", num_queries: " << job.num_queries + << ", partition_id: " << job.partition_id + << ", k: " << job.k + << ", rank: " << job.rank + << ", numa_node: " << node + << ", preamble: " << std::chrono::duration_cast(s2 - s1).count() + << ", query copy: " << std::chrono::duration_cast(s3 - s2).count() + << ", scan: " << std::chrono::duration_cast(s4 - s3).count() + << ", enqueue: " << std::chrono::duration_cast(end - s4).count() << std::endl; } From 9efd3007e50acc281e8ba168af8e68379adb22bc Mon Sep 17 00:00:00 2001 From: Jason Date: Sat, 24 May 2025 23:19:59 -0500 Subject: [PATCH 266/323] verify numa --- src/cpp/include/list_scanning.h | 26 ++++++++++++++++++++++++++ 1 file changed, 26 insertions(+) diff --git a/src/cpp/include/list_scanning.h b/src/cpp/include/list_scanning.h index 0fb5a6dc..41af1de8 100644 --- a/src/cpp/include/list_scanning.h +++ b/src/cpp/include/list_scanning.h @@ -517,6 +517,16 @@ inline void l2_blas( float* __restrict norms_y, vector*> pivot) // db_blas_bs { + + auto t1 = std::chrono::high_resolution_clock::now(); + auto t2 = std::chrono::high_resolution_clock::now(); + + int64_t x_norm_ns = 0; + int64_t y_norm_ns = 0; + int64_t ip_ns = 0; + int64_t l2_ns = 0; + int64_t topk_ns = 0; + if (nx == 0 || ny == 0) return; constexpr size_t bs_x = 256; @@ -528,20 +538,29 @@ inline void l2_blas( const size_t q_chunk = i1 - i0; /* ‖x‖² for this query block */ + t1 = std::chrono::high_resolution_clock::now(); faiss::fvec_norms_L2sqr(norms_x, x + i0 * d, d, q_chunk); + t2 = std::chrono::high_resolution_clock::now(); + x_norm_ns += std::chrono::duration_cast(t2 - t1).count(); for (size_t j0 = 0; j0 < ny; j0 += bs_y) { const size_t j1 = std::min(j0 + bs_y, ny); const size_t db_chunk = j1 - j0; /* ‖y‖² for this database block */ + t1 = std::chrono::high_resolution_clock::now(); faiss::fvec_norms_L2sqr(norms_y, y + j0 * d, d, db_chunk); + t2 = std::chrono::high_resolution_clock::now(); + y_norm_ns += std::chrono::duration_cast(t2 - t1).count(); // use torch matmul + t1 = std::chrono::high_resolution_clock::now(); Tensor x_tensor = torch::from_blob((void*) x + i0 * d, {q_chunk, d}, torch::kFloat32); Tensor y_tensor = torch::from_blob((void*) y + j0 * d, {db_chunk, d}, torch::kFloat32); Tensor ip_tensor = torch::from_blob(ip_block, {q_chunk, db_chunk}, torch::kFloat32); torch::matmul_out(ip_tensor, x_tensor, y_tensor.transpose(0, 1)); + t2 = std::chrono::high_resolution_clock::now(); + ip_ns += std::chrono::duration_cast(t2 - t1).count(); /* SGEMM */ // { @@ -560,6 +579,7 @@ inline void l2_blas( // } /* IP → L2² */ + t1 = std::chrono::high_resolution_clock::now(); if (k > 1) { for (int64_t qi = 0; qi < static_cast(q_chunk); ++qi) { float* line_ptr = ip_block + qi * db_chunk; // Pointer to current column in ip_block @@ -602,8 +622,14 @@ inline void l2_blas( topk_buffers[qi]->add(best_dist, best_id); } } + t2 = std::chrono::high_resolution_clock::now(); + l2_ns += std::chrono::duration_cast(t2 - t1).count(); } } + std::cout << " x_norm_ns: " << x_norm_ns << std::endl; + std::cout << " y_norm_ns: " << y_norm_ns << std::endl; + std::cout << " ip_ns: " << ip_ns << std::endl; + std::cout << " l2_ns: " << l2_ns << std::endl; } inline void batched_scan_list(const float *query_vecs, From 8f15d8009018a6f2c4db0b4e77012e017a7bc955 Mon Sep 17 00:00:00 2001 From: Jason Date: Sat, 24 May 2025 23:25:48 -0500 Subject: [PATCH 267/323] verify numa --- src/cpp/include/list_scanning.h | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/src/cpp/include/list_scanning.h b/src/cpp/include/list_scanning.h index 41af1de8..54e14cf3 100644 --- a/src/cpp/include/list_scanning.h +++ b/src/cpp/include/list_scanning.h @@ -578,6 +578,15 @@ inline void l2_blas( // ip_block, &nyi); // } + // for (int64_t qi = 0; qi < (int64_t)q_chunk; ++qi) { + // float* line = ip_block + qi * db_chunk; + // const float xn = norms_x[qi]; + // for (size_t pj = 0; pj < db_chunk; ++pj, ++line) { + // float d2 = xn + norms_y[pj] - 2.f * (*line); + // *line = (d2 < 0.f || !std::isfinite(d2)) ? 0.f : d2; + // } + // } + /* IP → L2² */ t1 = std::chrono::high_resolution_clock::now(); if (k > 1) { @@ -612,14 +621,14 @@ inline void l2_blas( int64_t best_id = -1; for (size_t pj = 0; pj < db_chunk; ++pj) { - *line_ptr = std::sqrt(std::fma(-2.f, *line_ptr, current_norm_x + norms_y[pj])); + *line_ptr = current_norm_x + norms_y[pj] - 2.f * (*line_ptr); if (*line_ptr < best_dist) { best_dist = *line_ptr; best_id = list_ids_ptr[j0 + pj]; } line_ptr++; // Move to the next element in the column } - topk_buffers[qi]->add(best_dist, best_id); + topk_buffers[qi]->add(sqrt(best_dist), best_id); } } t2 = std::chrono::high_resolution_clock::now(); From ac3650ce01dc8c2f5e350f9cc6f6790361d03a76 Mon Sep 17 00:00:00 2001 From: Jason Date: Sat, 24 May 2025 23:28:56 -0500 Subject: [PATCH 268/323] verify numa --- src/cpp/include/list_scanning.h | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/src/cpp/include/list_scanning.h b/src/cpp/include/list_scanning.h index 54e14cf3..6ee89ba5 100644 --- a/src/cpp/include/list_scanning.h +++ b/src/cpp/include/list_scanning.h @@ -594,17 +594,18 @@ inline void l2_blas( float* line_ptr = ip_block + qi * db_chunk; // Pointer to current column in ip_block const float current_norm_x = norms_x[qi]; for (size_t pj = 0; pj < db_chunk; ++pj) { - *line_ptr = std::sqrt(std::fma(-2.f, *line_ptr, current_norm_x + norms_y[pj])); + *line_ptr = current_norm_x + norms_y[pj] - 2.f * (*line_ptr); line_ptr++; // Move to the next element in the column } // collect distances closer than pivot if (pivot.size() > 0) { float curr_pivot = pivot[qi]->load(std::memory_order_relaxed); + curr_pivot = curr_pivot * curr_pivot; // Convert to squared distance line_ptr = ip_block + qi * db_chunk; // Reset line_ptr to the start of the current column for (size_t pj = 0; pj < db_chunk; ++pj) { if (*line_ptr < curr_pivot) { - topk_buffers[qi]->add(*line_ptr, list_ids_ptr[j0 + pj]); + topk_buffers[qi]->add(std::sqrt(*line_ptr), list_ids_ptr[j0 + pj]); } line_ptr++; // Move to the next element in the column } From 89af90c76bcdf8bfe48b9c7be22a056cef0b0faf Mon Sep 17 00:00:00 2001 From: Jason Date: Sat, 24 May 2025 23:32:45 -0500 Subject: [PATCH 269/323] verify numa --- src/cpp/include/list_scanning.h | 26 -------------------------- 1 file changed, 26 deletions(-) diff --git a/src/cpp/include/list_scanning.h b/src/cpp/include/list_scanning.h index 6ee89ba5..9b2272a5 100644 --- a/src/cpp/include/list_scanning.h +++ b/src/cpp/include/list_scanning.h @@ -517,16 +517,6 @@ inline void l2_blas( float* __restrict norms_y, vector*> pivot) // db_blas_bs { - - auto t1 = std::chrono::high_resolution_clock::now(); - auto t2 = std::chrono::high_resolution_clock::now(); - - int64_t x_norm_ns = 0; - int64_t y_norm_ns = 0; - int64_t ip_ns = 0; - int64_t l2_ns = 0; - int64_t topk_ns = 0; - if (nx == 0 || ny == 0) return; constexpr size_t bs_x = 256; @@ -538,29 +528,20 @@ inline void l2_blas( const size_t q_chunk = i1 - i0; /* ‖x‖² for this query block */ - t1 = std::chrono::high_resolution_clock::now(); faiss::fvec_norms_L2sqr(norms_x, x + i0 * d, d, q_chunk); - t2 = std::chrono::high_resolution_clock::now(); - x_norm_ns += std::chrono::duration_cast(t2 - t1).count(); for (size_t j0 = 0; j0 < ny; j0 += bs_y) { const size_t j1 = std::min(j0 + bs_y, ny); const size_t db_chunk = j1 - j0; /* ‖y‖² for this database block */ - t1 = std::chrono::high_resolution_clock::now(); faiss::fvec_norms_L2sqr(norms_y, y + j0 * d, d, db_chunk); - t2 = std::chrono::high_resolution_clock::now(); - y_norm_ns += std::chrono::duration_cast(t2 - t1).count(); // use torch matmul - t1 = std::chrono::high_resolution_clock::now(); Tensor x_tensor = torch::from_blob((void*) x + i0 * d, {q_chunk, d}, torch::kFloat32); Tensor y_tensor = torch::from_blob((void*) y + j0 * d, {db_chunk, d}, torch::kFloat32); Tensor ip_tensor = torch::from_blob(ip_block, {q_chunk, db_chunk}, torch::kFloat32); torch::matmul_out(ip_tensor, x_tensor, y_tensor.transpose(0, 1)); - t2 = std::chrono::high_resolution_clock::now(); - ip_ns += std::chrono::duration_cast(t2 - t1).count(); /* SGEMM */ // { @@ -588,7 +569,6 @@ inline void l2_blas( // } /* IP → L2² */ - t1 = std::chrono::high_resolution_clock::now(); if (k > 1) { for (int64_t qi = 0; qi < static_cast(q_chunk); ++qi) { float* line_ptr = ip_block + qi * db_chunk; // Pointer to current column in ip_block @@ -632,14 +612,8 @@ inline void l2_blas( topk_buffers[qi]->add(sqrt(best_dist), best_id); } } - t2 = std::chrono::high_resolution_clock::now(); - l2_ns += std::chrono::duration_cast(t2 - t1).count(); } } - std::cout << " x_norm_ns: " << x_norm_ns << std::endl; - std::cout << " y_norm_ns: " << y_norm_ns << std::endl; - std::cout << " ip_ns: " << ip_ns << std::endl; - std::cout << " l2_ns: " << l2_ns << std::endl; } inline void batched_scan_list(const float *query_vecs, From bdda82ac7891a6013618202ced4ccf7a6b0dbde7 Mon Sep 17 00:00:00 2001 From: Jason Date: Sat, 24 May 2025 23:34:43 -0500 Subject: [PATCH 270/323] verify numa --- src/cpp/include/list_scanning.h | 6 +++--- src/cpp/src/query_coordinator.cpp | 28 ++++++++++++++-------------- 2 files changed, 17 insertions(+), 17 deletions(-) diff --git a/src/cpp/include/list_scanning.h b/src/cpp/include/list_scanning.h index 9b2272a5..d0a89344 100644 --- a/src/cpp/include/list_scanning.h +++ b/src/cpp/include/list_scanning.h @@ -538,9 +538,9 @@ inline void l2_blas( faiss::fvec_norms_L2sqr(norms_y, y + j0 * d, d, db_chunk); // use torch matmul - Tensor x_tensor = torch::from_blob((void*) x + i0 * d, {q_chunk, d}, torch::kFloat32); - Tensor y_tensor = torch::from_blob((void*) y + j0 * d, {db_chunk, d}, torch::kFloat32); - Tensor ip_tensor = torch::from_blob(ip_block, {q_chunk, db_chunk}, torch::kFloat32); + Tensor x_tensor = torch::from_blob((void*) x + i0 * d, {(int64_t) q_chunk, (int64_t) d}, torch::kFloat32); + Tensor y_tensor = torch::from_blob((void*) y + j0 * d, {(int64_t) db_chunk, (int64_t) d}, torch::kFloat32); + Tensor ip_tensor = torch::from_blob(ip_block, {(int64_t) q_chunk, (int64_t) db_chunk}, torch::kFloat32); torch::matmul_out(ip_tensor, x_tensor, y_tensor.transpose(0, 1)); /* SGEMM */ diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 6d1f0977..896842b3 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -408,20 +408,20 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, auto s5 = std::chrono::high_resolution_clock::now(); res.enqueue_time_ns += std::chrono::duration_cast(end - start).count(); - // print out debug timing info s1, ... s5 - std::cout << "QueryCoordinator::handle_batched_job: " - << "job_id: " << job.job_id - << ", core_id: " << res.core_id - << ", num_queries: " << job.num_queries - << ", partition_id: " << job.partition_id - << ", k: " << job.k - << ", rank: " << job.rank - << ", numa_node: " << node - << ", preamble: " << std::chrono::duration_cast(s2 - s1).count() - << ", query copy: " << std::chrono::duration_cast(s3 - s2).count() - << ", scan: " << std::chrono::duration_cast(s4 - s3).count() - << ", enqueue: " << std::chrono::duration_cast(end - s4).count() - << std::endl; + // // print out debug timing info s1, ... s5 + // std::cout << "QueryCoordinator::handle_batched_job: " + // << "job_id: " << job.job_id + // << ", core_id: " << res.core_id + // << ", num_queries: " << job.num_queries + // << ", partition_id: " << job.partition_id + // << ", k: " << job.k + // << ", rank: " << job.rank + // << ", numa_node: " << node + // << ", preamble: " << std::chrono::duration_cast(s2 - s1).count() + // << ", query copy: " << std::chrono::duration_cast(s3 - s2).count() + // << ", scan: " << std::chrono::duration_cast(s4 - s3).count() + // << ", enqueue: " << std::chrono::duration_cast(end - s4).count() + // << std::endl; } void QueryCoordinator::init_global_buffers(int64_t nQ, From e5ea147ee9d2603f5286527f46c1876125662d45 Mon Sep 17 00:00:00 2001 From: Jason Date: Sat, 24 May 2025 23:42:26 -0500 Subject: [PATCH 271/323] verify numa --- src/cpp/include/list_scanning.h | 36 ++++++++++++++++----------------- 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/src/cpp/include/list_scanning.h b/src/cpp/include/list_scanning.h index d0a89344..05998e76 100644 --- a/src/cpp/include/list_scanning.h +++ b/src/cpp/include/list_scanning.h @@ -538,26 +538,26 @@ inline void l2_blas( faiss::fvec_norms_L2sqr(norms_y, y + j0 * d, d, db_chunk); // use torch matmul - Tensor x_tensor = torch::from_blob((void*) x + i0 * d, {(int64_t) q_chunk, (int64_t) d}, torch::kFloat32); - Tensor y_tensor = torch::from_blob((void*) y + j0 * d, {(int64_t) db_chunk, (int64_t) d}, torch::kFloat32); - Tensor ip_tensor = torch::from_blob(ip_block, {(int64_t) q_chunk, (int64_t) db_chunk}, torch::kFloat32); - torch::matmul_out(ip_tensor, x_tensor, y_tensor.transpose(0, 1)); + // Tensor x_tensor = torch::from_blob((void*) (x + i0 * d), {(int64_t) q_chunk, (int64_t) d}, torch::kFloat32); + // Tensor y_tensor = torch::from_blob((void*) (y + j0 * d), {(int64_t) db_chunk, (int64_t) d}, torch::kFloat32); + // Tensor ip_tensor = torch::from_blob(ip_block, {(int64_t) q_chunk, (int64_t) db_chunk}, torch::kFloat32); + // torch::matmul_out(ip_tensor, x_tensor, y_tensor.transpose(0, 1)); /* SGEMM */ - // { - // const float one = 1.f; - // float zero = 0.f; - // FINTEGER nyi = FINTEGER(db_chunk); - // FINTEGER nxi = FINTEGER(q_chunk); - // FINTEGER di = FINTEGER(d); - // sgemm_("Transpose","Not transpose", - // &nyi,&nxi,&di, - // &one, - // y + j0 * d, &di, - // x + i0 * d, &di, - // &zero, - // ip_block, &nyi); - // } + { + const float one = 1.f; + float zero = 0.f; + FINTEGER nyi = FINTEGER(db_chunk); + FINTEGER nxi = FINTEGER(q_chunk); + FINTEGER di = FINTEGER(d); + sgemm_("Transpose","Not transpose", + &nyi,&nxi,&di, + &one, + y + j0 * d, &di, + x + i0 * d, &di, + &zero, + ip_block, &nyi); + } // for (int64_t qi = 0; qi < (int64_t)q_chunk; ++qi) { // float* line = ip_block + qi * db_chunk; From b66c476196f967d37fa18e8af418d5e001a291ef Mon Sep 17 00:00:00 2001 From: Jason Date: Sun, 25 May 2025 01:31:13 -0500 Subject: [PATCH 272/323] use reservoir handler --- src/cpp/include/query_coordinator.h | 4 ++-- src/cpp/src/query_coordinator.cpp | 8 ++++---- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index 07b512f6..d27ac7a5 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -107,8 +107,8 @@ class QueryCoordinator { vector worker_threads_; ///< Container for worker threads. vector worker_job_counter_; ///< Job counters for each worker. - shared_ptr>> global_min_heaps_; ///< Global aggregator buffers. - shared_ptr>> global_max_heaps_; ///< Global aggregator buffers. + shared_ptr>> global_min_heaps_; ///< Global aggregator buffers. + shared_ptr>> global_max_heaps_; ///< Global aggregator buffers. std::mutex global_mutex_; ///< Mutex for global synchronization. std::condition_variable global_cv_; ///< Condition variable for thread coordination. diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 896842b3..98e880e0 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -444,12 +444,12 @@ void QueryCoordinator::init_global_buffers(int64_t nQ, std::fill_n(vals, nQ * K, max_val); if (metric_ == faiss::METRIC_INNER_PRODUCT) { - global_max_heaps_ = make_shared>>( + global_max_heaps_ = make_shared>>( nQ, vals, ids, K); } else { std::fill_n(vals, nQ * K, std::numeric_limits::infinity()); - global_min_heaps_ = make_shared>>( + global_min_heaps_ = make_shared>>( nQ, vals, ids, K); } @@ -647,7 +647,7 @@ void QueryCoordinator::drain_and_apply_aps(Tensor x, vector per_query_result_count(nQ, 0); if (metric_ == faiss::METRIC_INNER_PRODUCT) { - vector>::SingleResultHandler> + vector>::SingleResultHandler> handlers; for (int64_t q = 0; q < nQ; ++q) { handlers.emplace_back(*global_max_heaps_); @@ -683,7 +683,7 @@ void QueryCoordinator::drain_and_apply_aps(Tensor x, handlers[q].end(); } } else { - vector>::SingleResultHandler> + vector>::SingleResultHandler> handlers; handlers.reserve(nQ); for (int64_t q = 0; q < nQ; ++q) { From 0fb32355b4178a99283b6945e89d440e048e033b Mon Sep 17 00:00:00 2001 From: Jason Date: Sun, 25 May 2025 15:02:59 -0500 Subject: [PATCH 273/323] add merge threads --- src/cpp/include/list_scanning.h | 10 +- src/cpp/include/query_coordinator.h | 28 ++- src/cpp/src/query_coordinator.cpp | 281 ++++++++++++++-------------- 3 files changed, 165 insertions(+), 154 deletions(-) diff --git a/src/cpp/include/list_scanning.h b/src/cpp/include/list_scanning.h index 05998e76..925943ca 100644 --- a/src/cpp/include/list_scanning.h +++ b/src/cpp/include/list_scanning.h @@ -403,8 +403,15 @@ inline void scan_list(const float *query_vec, int d, TopkBuffer &buffer, faiss::MetricType metric, - float pivot) { + float pivot = -1) { // Dispatch based on metric type and whether list_ids is provided. + + if (pivot == 0) { + pivot = metric == faiss::METRIC_INNER_PRODUCT + ? -std::numeric_limits::infinity() + : std::numeric_limits::infinity(); + } + if (metric == faiss::METRIC_INNER_PRODUCT) { if (list_ids == nullptr) scan_list_no_ids_inner_product(query_vec, list_vecs, list_size, d, buffer, pivot); @@ -634,7 +641,6 @@ inline void batched_scan_list(const float *query_vecs, return; } - // Ensure k does not exceed list_size int k = topk_buffers[0]->k(); int k_max = std::min(k, list_size); diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index d27ac7a5..3c9c6c69 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -97,23 +97,28 @@ class QueryCoordinator { std::vector indices; }; + struct MergeResources { + moodycamel::BlockingConcurrentQueue queue; + std::vector handlers; // points to HandlerIP or HandlerL2 + }; + vector core_resources_; ///< Per‑core resources for worker threads. vector numa_resources_; - moodycamel::BlockingConcurrentQueue result_queue_; -// moodycamel::ConcurrentQueue result_queue_; + vector merge_res_; // size == num_merge_workers_ bool workers_initialized_ = false; ///< Flag indicating if worker threads are initialized. int num_workers_; ///< Total number of worker threads. + int num_merge_workers_; ///< Number of merge worker threads. vector worker_threads_; ///< Container for worker threads. + vector merge_threads_; ///< Container for merge threads. vector worker_job_counter_; ///< Job counters for each worker. - shared_ptr>> global_min_heaps_; ///< Global aggregator buffers. - shared_ptr>> global_max_heaps_; ///< Global aggregator buffers. + shared_ptr>> global_min_heaps_; ///< Global aggregator buffers. + shared_ptr>> global_max_heaps_; ///< Global aggregator buffers. std::mutex global_mutex_; ///< Mutex for global synchronization. std::condition_variable global_cv_; ///< Condition variable for thread coordination. std::atomic stop_workers_; ///< Flag to signal workers to terminate. - std::atomic jobs_in_flight_; ///< Number of jobs in flight. bool debug_ = false; ///< Debug mode flag. std::vector job_buffer_; @@ -122,6 +127,7 @@ class QueryCoordinator { vector>> job_flags_; ///< Flags to track job completion std::atomic job_pull_time_ns = 0; ///< Time spent pulling jobs from the queue. std::atomic job_process_time_ns = 0; ///< Time spent processing jobs. + std::atomic total_left_; /** * @brief Constructs a QueryCoordinator. @@ -137,7 +143,8 @@ class QueryCoordinator { shared_ptr maintenance_policy, MetricType metric, int num_workers=0, - bool use_numa=false); + bool use_numa=false, + int num_merge_workers=2); /** * @brief Destructor for QueryCoordinator. @@ -200,7 +207,7 @@ class QueryCoordinator { * * @param num_workers Number of worker threads to initialize. */ - void initialize_workers(int num_workers, bool use_numa=false); + void initialize_workers(int num_workers, int num_merge_workers=1, bool use_numa=false); /** * @brief Shuts down all worker threads. @@ -218,6 +225,8 @@ class QueryCoordinator { */ void partition_scan_worker_fn(int worker_id); + void merge_worker_fn(int merge_worker_id); + /** * @brief Worker thread function to perform partition scanning. * @@ -243,7 +252,6 @@ class QueryCoordinator { */ void allocate_core_resources(int core_idx, int num_queries, int k, int d); - int64_t pop_scan_job(CoreResources &res); void process_scan_job(ScanJob job, CoreResources &res); // handles the non‐batched branch @@ -267,11 +275,13 @@ class QueryCoordinator { Tensor partition_ids, shared_ptr params); + void enqueue_result_job(ResultJob job); + void drain_and_apply_aps(Tensor x, Tensor partition_ids, bool use_aps, float recall_target, - float aps_flush_period_us, + int aps_flush_period_us, shared_ptr timing); std::shared_ptr diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 98e880e0..ed5d9465 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -43,16 +43,18 @@ QueryCoordinator::QueryCoordinator(shared_ptr parent, shared_ptr maintenance_policy, MetricType metric, int num_workers, - bool use_numa) + bool use_numa, + int num_merge_workers) : parent_(parent), partition_manager_(partition_manager), maintenance_policy_(maintenance_policy), metric_(metric), num_workers_(num_workers), + num_merge_workers_(num_merge_workers), workers_initialized_(false) { if (num_workers_ > 0) { - initialize_workers(num_workers_, use_numa); + initialize_workers(num_workers_, num_merge_workers_, use_numa); } } @@ -97,6 +99,42 @@ void QueryCoordinator::allocate_core_resources(int core_idx, } +void QueryCoordinator::merge_worker_fn(int mid) +{ + auto& MR = merge_res_[mid]; + ResultJob rj; + + while (true) { + MR.queue.wait_dequeue(rj); + + if (rj.query_id == -1) break; // poison-pill + + /* ---- grab the pre-built handler ---------------------------------- */ + if (metric_ == faiss::METRIC_INNER_PRODUCT) { + using H = faiss::HeapBlockResultHandler< + faiss::CMin>::SingleResultHandler; + auto* h = static_cast(MR.handlers[rj.query_id]); + for (size_t i = 0; i < rj.distances.size(); ++i) + h->add_result(rj.distances[i], rj.indices[i]); + query_dist_pivots_[rj.query_id].store(h->threshold, + std::memory_order_relaxed); + } else { + using H = faiss::HeapBlockResultHandler< + faiss::CMax>::SingleResultHandler; + auto* h = static_cast(MR.handlers[rj.query_id]); + for (size_t i = 0; i < rj.distances.size(); ++i) + h->add_result(rj.distances[i], rj.indices[i]); + query_dist_pivots_[rj.query_id].store(h->threshold, + std::memory_order_relaxed); + } + + /* ---- bookkeeping -------------------------------------------------- */ + if (!job_flags_[rj.query_id][rj.rank]) { + job_flags_[rj.query_id][rj.rank] = true; + --total_left_; + } + } +} void QueryCoordinator::partition_scan_worker_fn(int core_index) { @@ -130,7 +168,6 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { auto s2 = std::chrono::high_resolution_clock::now(); process_scan_job(job_buffer_[jid], res); - jobs_in_flight_.fetch_sub(1, std::memory_order_relaxed); i++; end = std::chrono::high_resolution_clock::now(); @@ -162,10 +199,10 @@ void QueryCoordinator::process_scan_job(ScanJob job, << " invalid: " << e.what() << ". Returning empty result(s).\n"; if (job.is_batched) { for (int64_t i = 0; i < job.num_queries; ++i) { - result_queue_.enqueue(ResultJob{(*job.query_ids)[i], (*job.ranks)[i], {}, {}}); + enqueue_result_job(ResultJob{(*job.query_ids)[i], (*job.ranks)[i], {}, {}}); } } else { - result_queue_.enqueue(ResultJob{job.query_id, job.rank, {}, {}}); + enqueue_result_job(ResultJob{job.query_id, job.rank, {}, {}}); } return; } @@ -174,10 +211,10 @@ void QueryCoordinator::process_scan_job(ScanJob job, // empty => enqueue zero‐work per query if (job.is_batched) { for (int64_t i = 0; i < job.num_queries; ++i) { - result_queue_.enqueue(ResultJob{(*job.query_ids)[i], (*job.ranks)[i], {}, {}}); + enqueue_result_job(ResultJob{(*job.query_ids)[i], (*job.ranks)[i], {}, {}}); } } else { - result_queue_.enqueue(ResultJob{job.query_id, job.rank, {}, {}}); + enqueue_result_job(ResultJob{job.query_id, job.rank, {}, {}}); } return; } @@ -232,7 +269,7 @@ void QueryCoordinator::handle_nonbatched_job(const ScanJob &job, std::cerr << "[QueryCoordinator::handle_nonbatched_job] Partition " << job.partition_id << " invalid or empty before scan for query " << job.query_id << ". Enqueuing empty result.\n"; - result_queue_.enqueue(ResultJob{job.query_id, job.rank, {}, {}}); + enqueue_result_job(ResultJob{job.query_id, job.rank, {}, {}}); return; // Important to return after enqueueing the placeholder } @@ -248,18 +285,18 @@ void QueryCoordinator::handle_nonbatched_job(const ScanJob &job, // If scan_list completes, enqueue its results auto tv = buf->get_topk(false); auto ti = buf->get_topk_indices(false); - result_queue_.enqueue(ResultJob{job.query_id, job.rank, std::move(tv), std::move(ti)}); + enqueue_result_job(ResultJob{job.query_id, job.rank, std::move(tv), std::move(ti)}); } catch (const std::exception& e) { std::cerr << "[QueryCoordinator::handle_nonbatched_job] Exception during scan for partition " << job.partition_id << ", query " << job.query_id << ": " << e.what() << ". Enqueuing empty result.\n"; - result_queue_.enqueue(ResultJob{job.query_id, job.rank, {}, {}}); // Enqueue empty result on error + enqueue_result_job(ResultJob{job.query_id, job.rank, {}, {}}); // Enqueue empty result on error } catch (...) { std::cerr << "[QueryCoordinator::handle_nonbatched_job] Unknown exception during scan for partition " << job.partition_id << ", query " << job.query_id << ". Enqueuing empty result.\n"; - result_queue_.enqueue(ResultJob{job.query_id, job.rank, {}, {}}); // Enqueue empty result on error + enqueue_result_job(ResultJob{job.query_id, job.rank, {}, {}}); // Enqueue empty result on error } } @@ -281,7 +318,7 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, int64_t part_size = partition_manager_->partition_store_->list_size(job.partition_id); if (!codes || !ids || part_size <= 0) { for (int64_t i = 0; i < Q; ++i) { - result_queue_.enqueue(ResultJob{(*job.query_ids)[i], (*job.ranks)[i], {}, {}}); + enqueue_result_job(ResultJob{(*job.query_ids)[i], (*job.ranks)[i], {}, {}}); } return; } @@ -396,14 +433,9 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, int rank_q = (*job.ranks) [i]; auto tv = res.topk_buffer_pool[i]->get_topk(false); auto ti = res.topk_buffer_pool[i]->get_topk_indices(false); - results_batch.emplace_back(ResultJob{global_q, rank_q, std::move(tv), std::move(ti)}); + enqueue_result_job(ResultJob{global_q, rank_q, std::move(tv), std::move(ti)}); } - // enqueue this sub-batch in bulk - result_queue_.enqueue_bulk( - std::make_move_iterator(results_batch.begin()), - results_batch.size() - ); end = std::chrono::high_resolution_clock::now(); auto s5 = std::chrono::high_resolution_clock::now(); res.enqueue_time_ns += std::chrono::duration_cast(end - start).count(); @@ -424,6 +456,11 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, // << std::endl; } +void QueryCoordinator::enqueue_result_job(ResultJob job) { + merge_res_[job.query_id % num_merge_workers_].queue.enqueue(std::move(job)); +} + + void QueryCoordinator::init_global_buffers(int64_t nQ, int K, Tensor &partition_ids, @@ -444,13 +481,35 @@ void QueryCoordinator::init_global_buffers(int64_t nQ, std::fill_n(vals, nQ * K, max_val); if (metric_ == faiss::METRIC_INNER_PRODUCT) { - global_max_heaps_ = make_shared>>( - nQ, vals, ids, K); + global_max_heaps_ = std::make_shared< + faiss::HeapBlockResultHandler< + faiss::CMin>>(nQ, vals, ids, K); } else { - std::fill_n(vals, nQ * K, - std::numeric_limits::infinity()); - global_min_heaps_ = make_shared>>( - nQ, vals, ids, K); + global_min_heaps_ = std::make_shared< + faiss::HeapBlockResultHandler< + faiss::CMax>>(nQ, vals, ids, K); + } + + for (auto& mr : merge_res_) { + mr.handlers.clear(); + mr.handlers.resize(nQ, nullptr); + + /* allocate handler objects once per query --------------------------- */ + if (metric_ == faiss::METRIC_INNER_PRODUCT) { + using H = faiss::HeapBlockResultHandler< + faiss::CMin>::SingleResultHandler; + for (int64_t q = 0; q < nQ; ++q) { + mr.handlers[q] = new H(*global_max_heaps_); + static_cast(mr.handlers[q])->begin(q); + } + } else { + using H = faiss::HeapBlockResultHandler< + faiss::CMax>::SingleResultHandler; + for (int64_t q = 0; q < nQ; ++q) { + mr.handlers[q] = new H(*global_min_heaps_); + static_cast(mr.handlers[q])->begin(q); + } + } } query_dist_pivots_ = vector>(nQ); @@ -492,6 +551,7 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, // flatten jobs next_job_id_ = 0; + total_left_.store(0, std::memory_order_relaxed); job_flags_.clear(); job_flags_.resize(nQ); for (int64_t q = 0; q < nQ; ++q) { @@ -522,7 +582,7 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, job_buffer_.push_back(job); numa_resources_[core_to_numa[pid % num_workers_]].job_queue.enqueue(next_job_id_); next_job_id_++; - jobs_in_flight_.fetch_add(1, std::memory_order_relaxed); + total_left_.fetch_add(1, std::memory_order_relaxed); } } } else { @@ -573,7 +633,7 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, job_buffer_.push_back(job); numa_resources_[core_to_numa[pid % num_workers_]].job_queue.enqueue(next_job_id_); next_job_id_++; - jobs_in_flight_.fetch_add(1, std::memory_order_relaxed); + total_left_.fetch_add(1, std::memory_order_relaxed); } } else { ScanJob job; @@ -593,7 +653,7 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, int node = core_to_numa[core]; numa_resources_[node].job_queue.enqueue(next_job_id_); next_job_id_++; - jobs_in_flight_.fetch_add(1, std::memory_order_relaxed); + total_left_.fetch_add(chunk, std::memory_order_relaxed); } } } @@ -605,122 +665,19 @@ void QueryCoordinator::drain_and_apply_aps(Tensor x, Tensor partition_ids, bool use_aps, float recall_target, - float aps_flush_period_us, + int aps_flush_period_us, std::shared_ptr timing) { - const int64_t nQ = x.size(0); - const int64_t D = x.size(1); - const int64_t nprobe = partition_ids.size(1); - const auto aps_dur = std::chrono::microseconds( - static_cast(aps_flush_period_us)); - - /* ------------------------------------------------------------------------ */ - /* Book-keeping */ - /* ------------------------------------------------------------------------ */ - std::vector parts_left(nQ, 0); - std::atomic total_left{0}; - - for (int64_t q = 0; q < nQ; ++q) { - for (int64_t p = 0; p < nprobe; ++p) - if (!job_flags_[q][p]) { ++parts_left[q]; ++total_left; } - } - - /* APS pre-computations -------------------------------------------------- */ - std::vector query_radius(nQ, 0.0f); - std::vector> boundary_dist(nQ); - std::vector> probs(nQ); - if (use_aps) { - query_radius.resize(nQ); - boundary_dist.resize(nQ); - probs.resize(nQ); - for (int64_t q = 0; q < nQ; ++q) { - std::vector pids(partition_ids[q].data_ptr(), - partition_ids[q].data_ptr()+nprobe); - auto centroids = parent_->partition_manager_->get_vectors(pids); - centroids.erase(std::remove(centroids.begin(), centroids.end(), nullptr), - centroids.end()); - boundary_dist[q] = - compute_boundary_distances(x[q], centroids, metric_==faiss::METRIC_L2); - } - } - - vector per_query_result_count(nQ, 0); - - if (metric_ == faiss::METRIC_INNER_PRODUCT) { - vector>::SingleResultHandler> - handlers; - for (int64_t q = 0; q < nQ; ++q) { - handlers.emplace_back(*global_max_heaps_); - handlers[q].begin(q); // ← reset & heapify for query q - } - - ResultJob rj; - while (total_left.load(std::memory_order_relaxed) > 0) { - - /* -- Wait for result or APS timeout -- */ - bool got_result = result_queue_.wait_dequeue_timed(rj, aps_dur); - if (got_result) { - do { // Drain *all* currently available results. - for (int64_t i = 0; i < rj.distances.size(); ++i) { - handlers[rj.query_id].add_result(rj.distances[i], rj.indices[i]); - } - query_dist_pivots_[rj.query_id].store( - handlers[rj.query_id].threshold, std::memory_order_relaxed); - - per_query_result_count[rj.query_id]++; - - if (!job_flags_[rj.query_id][rj.rank]) { - job_flags_[rj.query_id][rj.rank] = true; - --parts_left[rj.query_id]; - --total_left; - } - } while (result_queue_.try_dequeue(rj)); - } - } - // end the handlers - for (int64_t q = 0; q < nQ; ++q) { - handlers[q].end(); - } - } else { - vector>::SingleResultHandler> - handlers; - handlers.reserve(nQ); - for (int64_t q = 0; q < nQ; ++q) { - handlers.emplace_back(*global_min_heaps_); - handlers[q].begin(q); // ← reset & heapify for query q - } - - ResultJob rj; - while (total_left.load(std::memory_order_relaxed) > 0) { + int64_t nQ = x.size(0), D = x.size(1); + // compute boundary distances - /* -- Wait for result or APS timeout -- */ - bool got_result = result_queue_.wait_dequeue_timed(rj, aps_dur); + while (total_left_.load(std::memory_order_relaxed) > 0) + std::this_thread::sleep_for(std::chrono::microseconds(aps_flush_period_us)); - if (got_result) { - do { // Drain *all* currently available results. - for (int64_t i = 0; i < rj.distances.size(); ++i) { - handlers[rj.query_id].add_result(rj.distances[i], rj.indices[i]); - } - query_dist_pivots_[rj.query_id].store( - handlers[rj.query_id].threshold, std::memory_order_relaxed); - per_query_result_count[rj.query_id]++; - - if (!job_flags_[rj.query_id][rj.rank]) { - job_flags_[rj.query_id][rj.rank] = true; - --parts_left[rj.query_id]; - --total_left; - } - } while (result_queue_.try_dequeue(rj)); - } - } - // end the handlers - for (int64_t q = 0; q < nQ; ++q) { - handlers[q].end(); - } - } + // check if we need to apply APS } std::shared_ptr @@ -730,6 +687,23 @@ QueryCoordinator::aggregate_scan_results(int64_t nQ, Tensor out_ids, Tensor out_dists) { + for (int64_t q = 0; q < nQ; ++q) { + int merge_worker_idx = q % num_merge_workers_; + MergeResources& mr = merge_res_[merge_worker_idx]; + + if (q < static_cast(mr.handlers.size()) && mr.handlers[q] != nullptr) { + if (metric_ == faiss::METRIC_INNER_PRODUCT) { + using H = faiss::HeapBlockResultHandler< + faiss::CMin>::SingleResultHandler; + static_cast(mr.handlers[q])->end(); + } else { + using H = faiss::HeapBlockResultHandler< + faiss::CMax>::SingleResultHandler; + static_cast(mr.handlers[q])->end(); + } + } + } + auto id_acc = out_ids.accessor(); auto d_acc = out_dists.accessor(); @@ -871,20 +845,20 @@ std::shared_ptr QueryCoordinator::worker_scan( } // Initialize Worker Threads -void QueryCoordinator::initialize_workers(int num_cores, bool use_numa) { +void QueryCoordinator::initialize_workers(int num_workers, int num_merge_workers, bool use_numa) { if (workers_initialized_) { std::cerr << "[QueryCoordinator::initialize_workers] Workers already initialized." << std::endl; return; } - std::cout << "[QueryCoordinator::initialize_workers] Initializing " << num_cores << " worker threads with use_numa=" << use_numa << + std::cout << "[QueryCoordinator::initialize_workers] Initializing " << num_workers << " worker threads with use_numa=" << use_numa << std::endl; - partition_manager_->distribute_partitions(num_cores, use_numa); - core_resources_.resize(num_cores); - worker_threads_.resize(num_cores); + partition_manager_->distribute_partitions(num_workers, use_numa); + core_resources_.resize(num_workers); + worker_threads_.resize(num_workers); stop_workers_.store(false); - for (int i = 0; i < num_cores; i++) { + for (int i = 0; i < num_workers; i++) { if (!set_thread_affinity(i)) { std::cout << "[QueryCoordinator::initialize_workers] Failed to set thread affinity on core " << i << std::endl; } @@ -892,11 +866,19 @@ void QueryCoordinator::initialize_workers(int num_cores, bool use_numa) { worker_threads_[i] = std::thread(&QueryCoordinator::partition_scan_worker_fn, this, i); } + merge_res_.resize(num_merge_workers_); + for (int i = 0; i < num_merge_workers; i++) { + if (!set_thread_affinity(i + num_workers)) { + std::cout << "[QueryCoordinator::initialize_workers] Failed to set thread affinity on core " << i + num_workers << std::endl; + } + merge_threads_.emplace_back(&QueryCoordinator::merge_worker_fn, this, i); + } + workers_initialized_ = true; // set main thread on separate thread from workers int num_cores_on_machine = std::thread::hardware_concurrency(); - set_thread_affinity(num_cores % num_cores_on_machine); + set_thread_affinity(num_workers + num_merge_workers); // set_thread_affinity(0); } @@ -912,11 +894,24 @@ void QueryCoordinator::shutdown_workers() { for (int i = 0; i < num_workers_; ++i) res.job_queue.enqueue(-1); } + + for (int m = 0; m < num_merge_workers_; ++m) { + enqueue_result_job(ResultJob{-1,0,{},{}}); + } + // Join all worker threads. for (auto &thr : worker_threads_) { if (thr.joinable()) thr.join(); } + + for (auto &t : merge_threads_) { + if (t.joinable()) + t.join(); + } + + + merge_threads_.clear(); worker_threads_.clear(); workers_initialized_ = false; } From 2022507aaed237c617fabbfbb109261dc131ba50 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 26 May 2025 00:05:25 -0500 Subject: [PATCH 274/323] parallelize sort --- src/cpp/bindings/wrap.cpp | 2 ++ src/cpp/include/common.h | 2 ++ src/cpp/include/quake_index.h | 2 +- src/cpp/include/query_coordinator.h | 3 ++- src/cpp/src/quake_index.cpp | 19 +++++++++++---- src/cpp/src/query_coordinator.cpp | 37 ++++++++++++++++++++++++++--- src/python/index_wrappers/quake.py | 22 +++++++++++++---- 7 files changed, 72 insertions(+), 15 deletions(-) diff --git a/src/cpp/bindings/wrap.cpp b/src/cpp/bindings/wrap.cpp index 5e5b1563..cfd0f29d 100644 --- a/src/cpp/bindings/wrap.cpp +++ b/src/cpp/bindings/wrap.cpp @@ -148,6 +148,8 @@ PYBIND11_MODULE(_bindings, m) { (std::string("Number of workers. default = ") + std::to_string(DEFAULT_NUM_WORKERS)).c_str()) .def_readwrite("parent_params", &IndexBuildParams::parent_params, "Parameters for the parent index, if any.") + .def_readwrite("num_merge_workers", &IndexBuildParams::num_merge_workers, + (std::string("Number of workers for merging. default = ") + std::to_string(DEFAULT_NUM_MERGE_WORKERS)).c_str()) .def_readwrite("use_numa", &IndexBuildParams::use_numa, (std::string("Flag to use NUMA for index building. default = ") + std::to_string(false)).c_str()) .def_readwrite("use_gpu", &IndexBuildParams::use_gpu, diff --git a/src/cpp/include/common.h b/src/cpp/include/common.h index 32ce00d4..bf1038ed 100644 --- a/src/cpp/include/common.h +++ b/src/cpp/include/common.h @@ -63,6 +63,7 @@ constexpr int DEFAULT_NLIST = 0; ///< Default number of cluste constexpr int DEFAULT_NITER = 5; ///< Default number of k-means iterations used during clustering. constexpr const char* DEFAULT_METRIC = "l2"; ///< Default distance metric (either "l2" for Euclidean or "ip" for inner product). constexpr int DEFAULT_NUM_WORKERS = 0; ///< Default number of workers (0 means single-threaded). +constexpr int DEFAULT_NUM_MERGE_WORKERS = 1; ///< Default number of merge workers (for worker_scan) constexpr int DEFAULT_GPU_BATCH_SIZE = 100000; ///< Default batch size for GPU index building. constexpr int DEFAULT_GPU_SAMPLE_SIZE = 1000000; ///< Default sample size for GPU index building. @@ -125,6 +126,7 @@ struct IndexBuildParams { int dimension = 0; int nlist = DEFAULT_NLIST; int num_workers = DEFAULT_NUM_WORKERS; + int num_merge_workers = DEFAULT_NUM_MERGE_WORKERS; int code_size = -1; // for PQ int num_codebooks = -1; // for PQ string metric = DEFAULT_METRIC; diff --git a/src/cpp/include/quake_index.h b/src/cpp/include/quake_index.h index 0358aec9..69edb3f8 100644 --- a/src/cpp/include/quake_index.h +++ b/src/cpp/include/quake_index.h @@ -122,7 +122,7 @@ class QuakeIndex { * @param path Path to load the index. * @param n_workers Number of workers to use for query processing. */ - void load(const std::string &path, int n_workers = 0, bool use_numa=false, int parent_n_workers=0); + void load(const std::string &path, shared_ptr params); /** * @brief Get the total number of vectors in the index. diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index 3c9c6c69..88b07e32 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -128,6 +128,7 @@ class QueryCoordinator { std::atomic job_pull_time_ns = 0; ///< Time spent pulling jobs from the queue. std::atomic job_process_time_ns = 0; ///< Time spent processing jobs. std::atomic total_left_; + vector> per_query_total_left_; ///< Total jobs left for each query. /** * @brief Constructs a QueryCoordinator. @@ -144,7 +145,7 @@ class QueryCoordinator { MetricType metric, int num_workers=0, bool use_numa=false, - int num_merge_workers=2); + int num_merge_workers=1); /** * @brief Destructor for QueryCoordinator. diff --git a/src/cpp/src/quake_index.cpp b/src/cpp/src/quake_index.cpp index 36f04661..e1d6697e 100644 --- a/src/cpp/src/quake_index.cpp +++ b/src/cpp/src/quake_index.cpp @@ -236,7 +236,7 @@ void QuakeIndex::save(const std::string& dir_path) { std::cout << "[QuakeIndex::save] Index saved to directory: " << dir_path << "\n"; } -void QuakeIndex::load(const std::string& dir_path, int n_workers, bool use_numa, int parent_n_workers) { +void QuakeIndex::load(const std::string& dir_path, shared_ptr build_params) { namespace fs = std::filesystem; if (!fs::exists(dir_path) || !fs::is_directory(dir_path)) { @@ -282,7 +282,11 @@ void QuakeIndex::load(const std::string& dir_path, int n_workers, bool use_numa, if (fs::exists(parent_dir) && fs::is_directory(parent_dir)) { parent_ = std::make_shared(); int n_parts = partition_manager_->nlist(); - parent_->load(parent_dir, parent_n_workers, use_numa); + auto parent_params = make_shared(); + if (build_params->parent_params != nullptr) { + parent_params = build_params->parent_params; + } + parent_->load(parent_dir, parent_params); partition_manager_->parent_ = parent_; } else { parent_ = nullptr; @@ -293,9 +297,14 @@ void QuakeIndex::load(const std::string& dir_path, int n_workers, bool use_numa, initialize_maintenance_policy(default_params); // 5. Create query coordinator - std::cout << "Loading coordinator with n_workers=" << n_workers << " and use_numa=" << use_numa << '\n'; - query_coordinator_ = std::make_shared(parent_, partition_manager_, maintenance_policy_, metric_, n_workers, use_numa); - std::cout << "Loaded coordinator\n"; + std::cout << "Loading coordinator with n_workers=" << build_params->num_workers << " and use_numa=" << build_params->use_numa << '\n'; + query_coordinator_ = std::make_shared(parent_, + partition_manager_, + maintenance_policy_, + metric_, + build_params->num_workers, + build_params->use_numa, + build_params->num_merge_workers); } int64_t QuakeIndex::ntotal() { diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index ed5d9465..975cb958 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -104,6 +104,23 @@ void QueryCoordinator::merge_worker_fn(int mid) auto& MR = merge_res_[mid]; ResultJob rj; + // for (int64_t q = 0; q < nQ; ++q) { + // int merge_worker_idx = q % num_merge_workers_; + // MergeResources& mr = merge_res_[merge_worker_idx]; + // + // if (q < static_cast(mr.handlers.size()) && mr.handlers[q] != nullptr) { + // if (metric_ == faiss::METRIC_INNER_PRODUCT) { + // using H = faiss::HeapBlockResultHandler< + // faiss::CMin>::SingleResultHandler; + // static_cast(mr.handlers[q])->end(); + // } else { + // using H = faiss::HeapBlockResultHandler< + // faiss::CMax>::SingleResultHandler; + // static_cast(mr.handlers[q])->end(); + // } + // } + // } + while (true) { MR.queue.wait_dequeue(rj); @@ -131,6 +148,19 @@ void QueryCoordinator::merge_worker_fn(int mid) /* ---- bookkeeping -------------------------------------------------- */ if (!job_flags_[rj.query_id][rj.rank]) { job_flags_[rj.query_id][rj.rank] = true; + --per_query_total_left_[rj.query_id]; + + if (per_query_total_left_[rj.query_id] == 0) { + if (metric_ == faiss::METRIC_INNER_PRODUCT) { + using H = faiss::HeapBlockResultHandler< + faiss::CMin>::SingleResultHandler; + static_cast(MR.handlers[rj.query_id])->end(); + } else { + using H = faiss::HeapBlockResultHandler< + faiss::CMax>::SingleResultHandler; + static_cast(MR.handlers[rj.query_id])->end(); + } + } --total_left_; } } @@ -387,6 +417,7 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, } auto s3 = std::chrono::high_resolution_clock::now(); + // check that things are on the proper NUMA node // bool ok = true; // ok = ok && verify_numa_locality(qptr, "qptr"); @@ -468,8 +499,6 @@ void QueryCoordinator::init_global_buffers(int64_t nQ, std::lock_guard lg(global_mutex_); // resize or reset - // size_t cap = std::min(100 * K, 10000); - // faiss::HeapArray>(nQ) }; float * vals = (float *) quake_alloc(nQ * K * sizeof(float), 0); int64_t * ids = (int64_t *) quake_alloc(nQ * K * sizeof(int64_t), 0); std::fill_n(ids, nQ * K, -1); @@ -549,17 +578,19 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, core_to_numa[i] = cpu_numa_node(i); } - // flatten jobs + // Reset job state next_job_id_ = 0; total_left_.store(0, std::memory_order_relaxed); job_flags_.clear(); job_flags_.resize(nQ); + per_query_total_left_ = vector>(nQ); for (int64_t q = 0; q < nQ; ++q) { job_flags_[q] = vector>(partition_ids.size(1)); for (int p = 0; p < partition_ids.size(1); ++p) { job_flags_[q][p].store(false); if (partition_ids_acc[q][p] < 0) job_flags_[q][p] = true; } + per_query_total_left_[q].store(partition_ids.size(1), std::memory_order_relaxed); } job_buffer_.clear(); job_buffer_.reserve(nQ * partition_ids.size(1)); diff --git a/src/python/index_wrappers/quake.py b/src/python/index_wrappers/quake.py index 99ea9846..d6558d59 100644 --- a/src/python/index_wrappers/quake.py +++ b/src/python/index_wrappers/quake.py @@ -65,6 +65,7 @@ def build( metric: str = "l2", ids: Optional[torch.Tensor] = None, num_workers: int = 0, + num_merge_workers: int = 1, m: int = -1, code_size: int = 8, parent=None, @@ -94,11 +95,15 @@ def build( build_params.metric = metric build_params.nlist = nc build_params.num_workers = num_workers + build_params.num_merge_workers = num_merge_workers build_params.use_numa = use_numa if parent is not None: build_params.parent_params = quake.IndexBuildParams() build_params.parent_params.nlist = parent.get("nc", 1) + build_params.parent_params.num_workers = parent.get("num_workers", 0) + build_params.parent_params.num_merge_workers = parent.get("num_merge_workers", 1) + build_params.use_gpu = use_gpu build_params.gpu_batch_size = gpu_batch_size @@ -178,8 +183,6 @@ def search( search_params.sample_stride = sample_stride search_params.batch_size = batch_size - print(parent) - if parent is not None: search_params.parent_params = quake.SearchParams() search_params.parent_params.nprobe = parent.get("nprobe", 1) @@ -209,8 +212,9 @@ def load( self, filename: str, num_workers: int = 0, - parent_num_workers: int = 0, + num_merge_workers: int = 1, use_numa: bool = False, + parent: dict = None, verbose: bool = False, ): """ @@ -219,10 +223,18 @@ def load( :param filename: The name of the file to load the index from. """ print( - f"Loading index from {filename}, with {num_workers} workers, use_numa={use_numa}, parent_num_workers={parent_num_workers}" + f"Loading index from {filename}, with {num_workers} workers, use_numa={use_numa}, parent={parent}" ) self.index = QuakeIndex() - self.index.load(str(filename), num_workers, use_numa, parent_num_workers) + build_params = quake.IndexBuildParams() + build_params.num_workers = num_workers + build_params.use_numa = use_numa + build_params.parent_params = quake.IndexBuildParams() + if parent is not None: + build_params.parent_params.num_workers = parent.get("num_workers", 0) + build_params.parent_params.num_merge_workers = parent.get("num_merge_workers", 1) + build_params.parent_params.use_numa = parent.get("use_numa", build_params.use_numa) + self.index.load(str(filename), build_params) def centroids(self) -> torch.Tensor: """ From 770030c69b615405d38ce44983d6f1cc7089ff0c Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 26 May 2025 00:26:55 -0500 Subject: [PATCH 275/323] parallelize sort --- src/cpp/include/query_coordinator.h | 2 +- src/cpp/src/query_coordinator.cpp | 19 +++++++++++++++---- 2 files changed, 16 insertions(+), 5 deletions(-) diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index 88b07e32..26c567a2 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -118,7 +118,7 @@ class QueryCoordinator { std::mutex global_mutex_; ///< Mutex for global synchronization. std::condition_variable global_cv_; ///< Condition variable for thread coordination. - std::atomic stop_workers_; ///< Flag to signal workers to terminate. + std::atomic stop_workers_; ///< Flag to signal workers to terminate. bool debug_ = false; ///< Debug mode flag. std::vector job_buffer_; diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 975cb958..1d4f1f66 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -487,8 +487,18 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, // << std::endl; } -void QueryCoordinator::enqueue_result_job(ResultJob job) { - merge_res_[job.query_id % num_merge_workers_].queue.enqueue(std::move(job)); +// --- replace old enqueue ------------------------------------------------- +inline void QueryCoordinator::enqueue_result_job(ResultJob job) +{ + if (job.query_id < 0) { + // Poison pill to stop the merge worker + for (auto& mr : merge_res_) { + mr.queue.enqueue(ResultJob{-1, 0, {}, {}}); + } + return; + } + const size_t mid = static_cast(job.query_id) % num_merge_workers_; + merge_res_[mid].queue.enqueue(std::move(job)); } @@ -920,14 +930,15 @@ void QueryCoordinator::shutdown_workers() { } stop_workers_.store(true); - // Enqueue a special shutdown job for each core. + // Enqueue poison pills to all worker threads. for (auto &res : numa_resources_) { for (int i = 0; i < num_workers_; ++i) res.job_queue.enqueue(-1); } + // Enqueue poison pills to all merge workers. for (int m = 0; m < num_merge_workers_; ++m) { - enqueue_result_job(ResultJob{-1,0,{},{}}); + merge_res_[m].queue.enqueue(ResultJob{-1, 0, {}, {}}); } // Join all worker threads. From 09f905e3a28b0f76c8fa18936100d783ad249d9c Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 26 May 2025 01:36:32 -0500 Subject: [PATCH 276/323] parallelize sort --- src/cpp/src/query_coordinator.cpp | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 1d4f1f66..f6940cba 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -596,11 +596,16 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, per_query_total_left_ = vector>(nQ); for (int64_t q = 0; q < nQ; ++q) { job_flags_[q] = vector>(partition_ids.size(1)); + + int valid_count = 0; for (int p = 0; p < partition_ids.size(1); ++p) { job_flags_[q][p].store(false); - if (partition_ids_acc[q][p] < 0) job_flags_[q][p] = true; + if (partition_ids_acc[q][p] < 0) { + job_flags_[q][p] = true; + valid_count++; + } } - per_query_total_left_[q].store(partition_ids.size(1), std::memory_order_relaxed); + per_query_total_left_[q].store(valid_count, std::memory_order_relaxed); } job_buffer_.clear(); job_buffer_.reserve(nQ * partition_ids.size(1)); From a3b284407716bf568bbd2d79b9a90b7743f64d57 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 26 May 2025 01:56:44 -0500 Subject: [PATCH 277/323] parallelize sort --- src/cpp/src/query_coordinator.cpp | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index f6940cba..0a74328f 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -163,6 +163,12 @@ void QueryCoordinator::merge_worker_fn(int mid) } --total_left_; } + + std::cout << "[merge_worker_fn] Query ID: " << rj.query_id + << ", Rank: " << rj.rank + << ", Distances: " << rj.distances.size() + << ", Indices: " << rj.indices.size() + << ", Total left: " << total_left_.load() << std::endl; } } From 80f6e84c34ecad9bdcf20e207388940ead188ee1 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 26 May 2025 01:59:00 -0500 Subject: [PATCH 278/323] parallelize sort --- src/cpp/src/query_coordinator.cpp | 27 +++++++++------------------ 1 file changed, 9 insertions(+), 18 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 0a74328f..8dbd7851 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -104,24 +104,7 @@ void QueryCoordinator::merge_worker_fn(int mid) auto& MR = merge_res_[mid]; ResultJob rj; - // for (int64_t q = 0; q < nQ; ++q) { - // int merge_worker_idx = q % num_merge_workers_; - // MergeResources& mr = merge_res_[merge_worker_idx]; - // - // if (q < static_cast(mr.handlers.size()) && mr.handlers[q] != nullptr) { - // if (metric_ == faiss::METRIC_INNER_PRODUCT) { - // using H = faiss::HeapBlockResultHandler< - // faiss::CMin>::SingleResultHandler; - // static_cast(mr.handlers[q])->end(); - // } else { - // using H = faiss::HeapBlockResultHandler< - // faiss::CMax>::SingleResultHandler; - // static_cast(mr.handlers[q])->end(); - // } - // } - // } - - while (true) { + while (!stop_workers_) { MR.queue.wait_dequeue(rj); if (rj.query_id == -1) break; // poison-pill @@ -209,6 +192,14 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { res.process_time_ns += std::chrono::duration_cast(end - s2).count(); res.job_time_ns += std::chrono::duration_cast(end - start).count(); + + std::cout << "[partition_scan_worker_fn] Core: " << core_index + << ", Job ID: " << jid + << ", Processed: " << i + << ", Wait time: " << res.wait_time_ns / 1e6 << " ms" + << ", Process time: " << res.process_time_ns / 1e6 << " ms" + << ", Enqueue time: " << res.enqueue_time_ns / 1e6 << " ms" + << ", Job time: " << res.job_time_ns / 1e6 << " ms" << std::endl; } } From 8507b8c95805579b253e421ff2ae58e6a0eaf872 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 26 May 2025 02:01:39 -0500 Subject: [PATCH 279/323] parallelize sort --- src/cpp/src/query_coordinator.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 8dbd7851..da7bee21 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -599,6 +599,7 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, job_flags_[q][p].store(false); if (partition_ids_acc[q][p] < 0) { job_flags_[q][p] = true; + } else { valid_count++; } } From 1eb008dc622f331d3e1830b6f6b65789e77ccba4 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 26 May 2025 02:10:28 -0500 Subject: [PATCH 280/323] parallelize sort --- src/cpp/src/query_coordinator.cpp | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index da7bee21..0a69237a 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -585,6 +585,11 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, core_to_numa[i] = cpu_numa_node(i); } + std::cout << "[enqueue_scan_jobs] Enqueuing jobs for " << nQ + << " queries, " << partition_ids.size(1) + << " partitions, k = " << params->k + << ", batched scan: " << (params->batched_scan ? "yes" : "no") << std::endl; + // Reset job state next_job_id_ = 0; total_left_.store(0, std::memory_order_relaxed); From 632c36feff05d010ff6f4800820738817d8d7267 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 26 May 2025 02:13:50 -0500 Subject: [PATCH 281/323] parallelize sort --- src/cpp/src/query_coordinator.cpp | 32 +++++++++---------- .../osdi2025/numa_multi_query/run.py | 3 +- 2 files changed, 18 insertions(+), 17 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 0a69237a..0a721568 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -147,11 +147,11 @@ void QueryCoordinator::merge_worker_fn(int mid) --total_left_; } - std::cout << "[merge_worker_fn] Query ID: " << rj.query_id - << ", Rank: " << rj.rank - << ", Distances: " << rj.distances.size() - << ", Indices: " << rj.indices.size() - << ", Total left: " << total_left_.load() << std::endl; + // std::cout << "[merge_worker_fn] Query ID: " << rj.query_id + // << ", Rank: " << rj.rank + // << ", Distances: " << rj.distances.size() + // << ", Indices: " << rj.indices.size() + // << ", Total left: " << total_left_.load() << std::endl; } } @@ -193,13 +193,13 @@ void QueryCoordinator::partition_scan_worker_fn(int core_index) { res.process_time_ns += std::chrono::duration_cast(end - s2).count(); res.job_time_ns += std::chrono::duration_cast(end - start).count(); - std::cout << "[partition_scan_worker_fn] Core: " << core_index - << ", Job ID: " << jid - << ", Processed: " << i - << ", Wait time: " << res.wait_time_ns / 1e6 << " ms" - << ", Process time: " << res.process_time_ns / 1e6 << " ms" - << ", Enqueue time: " << res.enqueue_time_ns / 1e6 << " ms" - << ", Job time: " << res.job_time_ns / 1e6 << " ms" << std::endl; + // std::cout << "[partition_scan_worker_fn] Core: " << core_index + // << ", Job ID: " << jid + // << ", Processed: " << i + // << ", Wait time: " << res.wait_time_ns / 1e6 << " ms" + // << ", Process time: " << res.process_time_ns / 1e6 << " ms" + // << ", Enqueue time: " << res.enqueue_time_ns / 1e6 << " ms" + // << ", Job time: " << res.job_time_ns / 1e6 << " ms" << std::endl; } } @@ -585,10 +585,10 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, core_to_numa[i] = cpu_numa_node(i); } - std::cout << "[enqueue_scan_jobs] Enqueuing jobs for " << nQ - << " queries, " << partition_ids.size(1) - << " partitions, k = " << params->k - << ", batched scan: " << (params->batched_scan ? "yes" : "no") << std::endl; + // std::cout << "[enqueue_scan_jobs] Enqueuing jobs for " << nQ + // << " queries, " << partition_ids.size(1) + // << " partitions, k = " << params->k + // << ", batched scan: " << (params->batched_scan ? "yes" : "no") << std::endl; // Reset job state next_job_id_ = 0; diff --git a/test/experiments/osdi2025/numa_multi_query/run.py b/test/experiments/osdi2025/numa_multi_query/run.py index 6d679069..c424b04b 100644 --- a/test/experiments/osdi2025/numa_multi_query/run.py +++ b/test/experiments/osdi2025/numa_multi_query/run.py @@ -96,7 +96,8 @@ def task_search_index( load_kwargs = {} if "num_workers" in current_build_params_for_load: load_kwargs["num_workers"] = current_build_params_for_load.get("num_workers") if "use_numa" in current_build_params_for_load: load_kwargs["use_numa"] = current_build_params_for_load.get("use_numa") - if "parent_num_workers" in current_build_params_for_load: load_kwargs["parent_num_workers"] = current_build_params_for_load.get("parent_num_workers") + if "num_merge_workers" in current_build_params_for_load: load_kwargs["num_merge_workers"] = current_build_params_for_load.get("num_merge_workers") + if "parent" in current_build_params_for_load: load_kwargs["parent"] = current_build_params_for_load.get("parent") idx_instance.load(str(index_file_path), **load_kwargs) print(f"[{index_name} SEARCH_TASK] Index loaded with kwargs: {load_kwargs}") From 48a2bfb15c335b7f6e8220bfa927c6e08ca7f74e Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 27 May 2025 13:52:39 -0500 Subject: [PATCH 282/323] add batch size exp --- src/cpp/include/list_scanning.h | 49 +++-- src/cpp/include/query_coordinator.h | 3 +- src/cpp/src/clustering.cpp | 1 + src/cpp/src/quake_index.cpp | 7 +- src/cpp/src/query_coordinator.cpp | 139 +++++++------- src/python/datasets/ann_datasets.py | 106 +++++++++++ .../experiments/osdi2025/experiment_runner.py | 2 + .../configs/wiki_single_thread.yaml | 62 ++++++ .../osdi2025/vary_batch_size/run.py | 178 ++++++++++++++++++ 9 files changed, 448 insertions(+), 99 deletions(-) create mode 100644 test/experiments/osdi2025/vary_batch_size/configs/wiki_single_thread.yaml create mode 100644 test/experiments/osdi2025/vary_batch_size/run.py diff --git a/src/cpp/include/list_scanning.h b/src/cpp/include/list_scanning.h index 925943ca..b42d810a 100644 --- a/src/cpp/include/list_scanning.h +++ b/src/cpp/include/list_scanning.h @@ -180,7 +180,7 @@ class TypedTopKBuffer { if (++head_ == capacity_) flush(); } - float batch_add(T *distances, I *indices, int num_values) { + void batch_add(T *distances, I *indices, int num_values) { int pos = 0; while (pos < num_values) { int available = capacity_ - head_; @@ -208,28 +208,26 @@ class TypedTopKBuffer { for (int i = 0; i < n; ++i) { ord_[i] = i; } - // comparator by value - auto cmpIdx = [&](int a, int b) { - return is_desc_ ? (vals_[a] > vals_[b]) - : (vals_[a] < vals_[b]); + + auto cmp = [&](int a, int b) { + return is_desc_ ? vals_[a] > vals_[b] : vals_[a] < vals_[b]; }; + // 2) select top‐m indices into ord_[0..m) if (n > m) { if (m < 10) { - miniselect::heap_select(ord_, ord_ + m, ord_ + n, cmpIdx); + miniselect::heap_select(ord_, ord_ + m, ord_ + n, cmp); } else if (m < n * 0.001) { - miniselect::floyd_rivest_select(ord_, ord_ + m, ord_ + n, cmpIdx); + miniselect::floyd_rivest_select(ord_, ord_ + m, ord_ + n, cmp); } else { - miniselect::pdqpartial_sort_branchless(ord_, ord_ + m, ord_ + n, cmpIdx); + miniselect::pdqpartial_sort_branchless(ord_, ord_ + m, ord_ + n, cmp); } } else { - miniselect::pdqsort_branchless(ord_, ord_ + n, cmpIdx); + miniselect::pdqsort_branchless(ord_, ord_ + n, cmp); } - // 4) fully sort the top-m indices so they are in strictly correct order - miniselect::pdqsort_branchless(ord_, ord_ + m, cmpIdx); // 5) copy the winners back to vals_/ids_ and clamp head_ std::vector temp_v(m); @@ -390,7 +388,10 @@ inline void scan_list_with_ids_l2(const float *query_vec, float pivot) { const float *vec = list_vecs; for (int l = 0; l < list_size; l++) { - buffer.add(sqrt(faiss::fvec_L2sqr(query_vec, vec, d)), list_ids[l]); + float dist = sqrt(faiss::fvec_L2sqr(query_vec, vec, d)); + if (dist < pivot) { + buffer.add(sqrt(faiss::fvec_L2sqr(query_vec, vec, d)), list_ids[l]); + } vec += d; } } @@ -403,10 +404,10 @@ inline void scan_list(const float *query_vec, int d, TopkBuffer &buffer, faiss::MetricType metric, - float pivot = -1) { + float pivot = NULL) { // Dispatch based on metric type and whether list_ids is provided. - if (pivot == 0) { + if (pivot == NULL) { pivot = metric == faiss::METRIC_INNER_PRODUCT ? -std::numeric_limits::infinity() : std::numeric_limits::infinity(); @@ -436,11 +437,11 @@ inline void ip_blas( size_t k, vector> &topk_buffers, float* __restrict ip_block, // nx * bs_y - vector*> pivot) // db_blas_bs + vector*> pivot = {}) // db_blas_bs { if (nx == 0 || ny == 0) return; - constexpr size_t bs_x = 256; + const size_t bs_x = nx; const size_t bs_y = db_blas_bs; int64_t *list_ids_ptr = (int64_t *) list_ids; @@ -453,8 +454,12 @@ inline void ip_blas( const size_t db_chunk = j1 - j0; // use torch matmul - - /* SGEMM */ +# ifdef __APPLE__ // use torch on macOS + Tensor x_tensor = torch::from_blob((void*) (x + i0 * d), {(int64_t) q_chunk, (int64_t) d}, torch::kFloat32); + Tensor y_tensor = torch::from_blob((void*) (y + j0 * d), {(int64_t) db_chunk, (int64_t) d}, torch::kFloat32); + Tensor ip_tensor = torch::from_blob(ip_block, {(int64_t) q_chunk, (int64_t) db_chunk}, torch::kFloat32); + torch::matmul_out(ip_tensor, x_tensor, y_tensor.transpose(0, 1)); +#else // use BLAS on Linux { const float one = 1.f; float zero = 0.f; @@ -469,11 +474,14 @@ inline void ip_blas( &zero, ip_block, &nyi); } +#endif + /* IP → L2² */ if (k > 1) { for (int64_t qi = 0; qi < static_cast(q_chunk); ++qi) { float* line_ptr = ip_block + qi * db_chunk; // Pointer to current column in ip_block + // collect distances closer than pivot if (pivot.size() > 0) { float curr_pivot = pivot[qi]->load(std::memory_order_relaxed); @@ -495,7 +503,7 @@ inline void ip_blas( int64_t best_id = -1; for (size_t pj = 0; pj < db_chunk; ++pj) { - if (*line_ptr < best_dist) { + if (*line_ptr > best_dist) { best_dist = *line_ptr; best_id = list_ids_ptr[j0 + pj]; } @@ -526,7 +534,7 @@ inline void l2_blas( { if (nx == 0 || ny == 0) return; - constexpr size_t bs_x = 256; + const size_t bs_x = nx; const size_t bs_y = db_blas_bs; int64_t *list_ids_ptr = (int64_t *) list_ids; @@ -660,6 +668,7 @@ inline void batched_scan_list(const float *query_vecs, pivots ); } else if (metric == faiss::METRIC_L2) { + std::cout << "Using L2 metric for batched scan." << std::endl; l2_blas( query_vecs, list_vecs, diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index 26c567a2..2bfd93cd 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -226,7 +226,8 @@ class QueryCoordinator { */ void partition_scan_worker_fn(int worker_id); - void merge_worker_fn(int merge_worker_id); + template + void merge_worker_fn(int worker_id); /** * @brief Worker thread function to perform partition scanning. diff --git a/src/cpp/src/clustering.cpp b/src/cpp/src/clustering.cpp index 85f517e8..f46ad13e 100644 --- a/src/cpp/src/clustering.cpp +++ b/src/cpp/src/clustering.cpp @@ -163,6 +163,7 @@ shared_ptr kmeans_cpu(Tensor vectors, faiss::ClusteringParameters cp; cp.niter = build_params->niter; + cp.spherical = (metric_type == faiss::METRIC_INNER_PRODUCT); faiss::Clustering clus(d, build_params->nlist, cp); clus.train(n, vectors.data_ptr(), *index_ptr); diff --git a/src/cpp/src/quake_index.cpp b/src/cpp/src/quake_index.cpp index e1d6697e..946292b2 100644 --- a/src/cpp/src/quake_index.cpp +++ b/src/cpp/src/quake_index.cpp @@ -39,6 +39,8 @@ shared_ptr QuakeIndex::build(Tensor x, Tensor ids, shared_ptrnum_workers << " workers, NUMA: " << (build_params->use_numa ? "enabled" : "disabled") << " and metric " << metric_ << std::endl; query_coordinator_ = std::make_shared(parent_, partition_manager_, maintenance_policy_, diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 0a721568..10b192ba 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -99,63 +99,61 @@ void QueryCoordinator::allocate_core_resources(int core_idx, } -void QueryCoordinator::merge_worker_fn(int mid) -{ +// The heart of it: one function, two instantiations. +template +void QueryCoordinator::merge_worker_fn(int mid) { auto& MR = merge_res_[mid]; ResultJob rj; - while (!stop_workers_) { + while (true) { MR.queue.wait_dequeue(rj); + if (rj.query_id == -1) // poison pill + return; - if (rj.query_id == -1) break; // poison-pill + // single cast, based on the Compare template + using Handler = typename faiss::HeapBlockResultHandler::SingleResultHandler; + auto* h = static_cast(MR.handlers[rj.query_id]); - /* ---- grab the pre-built handler ---------------------------------- */ - if (metric_ == faiss::METRIC_INNER_PRODUCT) { - using H = faiss::HeapBlockResultHandler< - faiss::CMin>::SingleResultHandler; - auto* h = static_cast(MR.handlers[rj.query_id]); - for (size_t i = 0; i < rj.distances.size(); ++i) - h->add_result(rj.distances[i], rj.indices[i]); - query_dist_pivots_[rj.query_id].store(h->threshold, - std::memory_order_relaxed); - } else { - using H = faiss::HeapBlockResultHandler< - faiss::CMax>::SingleResultHandler; - auto* h = static_cast(MR.handlers[rj.query_id]); - for (size_t i = 0; i < rj.distances.size(); ++i) - h->add_result(rj.distances[i], rj.indices[i]); - query_dist_pivots_[rj.query_id].store(h->threshold, - std::memory_order_relaxed); + // feed all partial results + for (size_t i = 0; i < rj.distances.size(); ++i) { + h->add_result(rj.distances[i], rj.indices[i]); } + // update pivot + query_dist_pivots_[rj.query_id].store(h->threshold, + std::memory_order_relaxed); - /* ---- bookkeeping -------------------------------------------------- */ + // once all ranks for this query are in, finalize & sort if (!job_flags_[rj.query_id][rj.rank]) { job_flags_[rj.query_id][rj.rank] = true; - --per_query_total_left_[rj.query_id]; + if (--per_query_total_left_[rj.query_id] == 0) { + h->end(); + + // pack into pairs for sorting + int k = h->k; + std::vector> result; + result.reserve(k); + for (int i = 0; i < k; ++i) { + result.emplace_back(h->heap_dis[i], h->heap_ids[i]); + } - if (per_query_total_left_[rj.query_id] == 0) { - if (metric_ == faiss::METRIC_INNER_PRODUCT) { - using H = faiss::HeapBlockResultHandler< - faiss::CMin>::SingleResultHandler; - static_cast(MR.handlers[rj.query_id])->end(); - } else { - using H = faiss::HeapBlockResultHandler< - faiss::CMax>::SingleResultHandler; - static_cast(MR.handlers[rj.query_id])->end(); + // for CMin (inner-product) we want descending distances + // for CMax (L2) we want ascending distances + auto cmp = [](auto& a, auto& b) { + return Compare::cmp(a.first, b.first); + }; + std::sort(result.begin(), result.end(), cmp); + + // write them back + for (int i = 0; i < k; ++i) { + h->heap_dis[i] = result[i].first; + h->heap_ids[i] = result[i].second; } } - --total_left_; } - - // std::cout << "[merge_worker_fn] Query ID: " << rj.query_id - // << ", Rank: " << rj.rank - // << ", Distances: " << rj.distances.size() - // << ", Indices: " << rj.indices.size() - // << ", Total left: " << total_left_.load() << std::endl; + --total_left_; } } - void QueryCoordinator::partition_scan_worker_fn(int core_index) { CoreResources &res = core_resources_[core_index]; int numa_node = 0; @@ -394,17 +392,13 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, // gather queries float *qptr = nullptr; - if (job.scan_all) { - qptr = nr.local_query_buffer; - } else { - float *dst = res.batch_queries; - for (int64_t i = 0; i < Q; ++i) { - int qid = (*job.query_ids)[i]; - const float *src = nr.local_query_buffer + size_t(qid) * D; - std::memcpy(dst + i * D, src, D * sizeof(float)); - } - qptr = dst; + float *dst = res.batch_queries; + for (int64_t i = 0; i < Q; ++i) { + int qid = (*job.query_ids)[i]; + const float *src = nr.local_query_buffer + size_t(qid) * D; + std::memcpy(dst + i * D, src, D * sizeof(float)); } + qptr = dst; vector *> pivots; pivots.resize(job.num_queries); @@ -735,24 +729,6 @@ QueryCoordinator::aggregate_scan_results(int64_t nQ, shared_ptr timing, Tensor out_ids, Tensor out_dists) { - - for (int64_t q = 0; q < nQ; ++q) { - int merge_worker_idx = q % num_merge_workers_; - MergeResources& mr = merge_res_[merge_worker_idx]; - - if (q < static_cast(mr.handlers.size()) && mr.handlers[q] != nullptr) { - if (metric_ == faiss::METRIC_INNER_PRODUCT) { - using H = faiss::HeapBlockResultHandler< - faiss::CMin>::SingleResultHandler; - static_cast(mr.handlers[q])->end(); - } else { - using H = faiss::HeapBlockResultHandler< - faiss::CMax>::SingleResultHandler; - static_cast(mr.handlers[q])->end(); - } - } - } - auto id_acc = out_ids.accessor(); auto d_acc = out_dists.accessor(); @@ -854,8 +830,8 @@ std::shared_ptr QueryCoordinator::worker_scan( duration_cast(s5 - s4).count(); res->timing_info->result_aggregate_time_ns = duration_cast(s6 - s5).count(); - - // retrieve the final values of the per-core resource timers; + // + // // retrieve the final values of the per-core resource timers; for (int i = 0; i < num_workers_; ++i) { core_wait_time_ns[i] = core_resources_[i].wait_time_ns - core_wait_time_ns[i]; core_process_time_ns[i] = core_resources_[i].process_time_ns - core_process_time_ns[i]; @@ -866,7 +842,7 @@ std::shared_ptr QueryCoordinator::worker_scan( } - // print out the per-core resource timers; + // // print out the per-core resource timers; for (int i = 0; i < num_workers_; ++i) { std::cout << "[QueryCoordinator::worker_scan] Core " << i << ": " << "job_counter=" << core_resources_[i].job_counter << " " @@ -915,12 +891,22 @@ void QueryCoordinator::initialize_workers(int num_workers, int num_merge_workers worker_threads_[i] = std::thread(&QueryCoordinator::partition_scan_worker_fn, this, i); } + merge_threads_.resize(num_merge_workers_); merge_res_.resize(num_merge_workers_); - for (int i = 0; i < num_merge_workers; i++) { - if (!set_thread_affinity(i + num_workers)) { - std::cout << "[QueryCoordinator::initialize_workers] Failed to set thread affinity on core " << i + num_workers << std::endl; + if (metric_ == faiss::METRIC_INNER_PRODUCT) { + // CMin: we want largest-inner-product first + for (int i = 0; i < num_merge_workers_; ++i) { + merge_threads_[i] = std::thread( + &QueryCoordinator::merge_worker_fn>, + this, i); + } + } else { + // CMax: we want smallest-L2 first + for (int i = 0; i < num_merge_workers_; ++i) { + merge_threads_[i] = std::thread( + &QueryCoordinator::merge_worker_fn>, + this, i); } - merge_threads_.emplace_back(&QueryCoordinator::merge_worker_fn, this, i); } workers_initialized_ = true; @@ -1072,6 +1058,7 @@ shared_ptr QueryCoordinator::serial_scan(Tensor x, Tensor partitio float *list_vectors = (float *) partition_manager_->partition_store_->get_codes(pi); int64_t *list_ids = (int64_t *) partition_manager_->partition_store_->get_ids(pi); int64_t list_size = partition_manager_->partition_store_->list_size(pi); + scan_list(query_vec, list_vectors, list_ids, @@ -1079,7 +1066,7 @@ shared_ptr QueryCoordinator::serial_scan(Tensor x, Tensor partitio dimension, *topk_buf, metric_, - query_radius); + NULL); scanned_ids.push_back(pi); float curr_radius = topk_buf->get_kth_distance(); diff --git a/src/python/datasets/ann_datasets.py b/src/python/datasets/ann_datasets.py index a7ba1459..44e7bdc9 100644 --- a/src/python/datasets/ann_datasets.py +++ b/src/python/datasets/ann_datasets.py @@ -244,6 +244,106 @@ def load_ground_truth(self) -> Union[np.ndarray, torch.Tensor]: return torch.from_numpy(ids).long() +class Wiki13M(Dataset): + def __init__(self, download_dir: Union[str, Path] = DEFAULT_DOWNLOAD_DIR): + self.download_dir = to_path(download_dir) + self.data_dir = self.download_dir / "wiki13m" + self.downloaded = False + self.metric = "ip" + + def is_downloaded(self) -> bool: + base = self.data_dir / "wiki.npy" + query = self.data_dir / "wiki_queries.npy" + gt = self.data_dir / "wiki_gt.npy" + return base.exists() and query.exists() and gt.exists() + + def download(self, overwrite: bool = False): + pass + + def load_vectors(self) -> Union[np.ndarray, torch.Tensor]: + fname = self.data_dir / "wiki.npy" + return torch.from_numpy(np.load(fname)).to(torch.float32) + + def load_queries(self) -> Union[np.ndarray, torch.Tensor]: + fname = self.data_dir / "wiki_queries.npy" + return torch.from_numpy(np.load(fname)).to(torch.float32) + + def load_ground_truth(self) -> Union[np.ndarray, torch.Tensor]: + fname = self.data_dir / "wiki_gt.npy" + return torch.from_numpy(np.load(fname)).long() + + +class WikiCohere1M(Dataset): + def __init__(self, download_dir: Union[str, Path] = DEFAULT_DOWNLOAD_DIR): + self.download_dir = to_path(download_dir) + self.data_dir = self.download_dir / "wikipedia_cohere" + self.downloaded = False + self.metric = "ip" + + def is_downloaded(self) -> bool: + base = self.data_dir / "wikipedia_base.bin.crop_nb_1000000" + query = self.data_dir / "wikipedia_query.bin" + gt = self.data_dir / " wikipedia-1M" + return base.exists() and query.exists() and gt.exists() + + def download(self, overwrite: bool = False): + pass + + def load_vectors(self) -> Union[np.ndarray, torch.Tensor]: + fname = self.data_dir / "wikipedia_base.bin.crop_nb_1000000" + n, d = map(int, np.fromfile(fname, dtype="uint32", count=2)) + return torch.from_numpy(np.fromfile(fname, dtype=np.float32, offset=8).reshape((n, d))) + + def load_queries(self) -> Union[np.ndarray, torch.Tensor]: + fname = self.data_dir / "wikipedia_query.bin" + n, d = map(int, np.fromfile(fname, dtype="uint32", count=2)) + return torch.from_numpy(np.fromfile(fname, dtype=np.float32, offset=8).reshape((n, d))) + + def load_ground_truth(self) -> Union[np.ndarray, torch.Tensor]: + fname = self.data_dir / "wikipedia-1M" + n, d = map(int, np.fromfile(fname, dtype="uint32", count=2)) + assert os.stat(fname).st_size == 8 + n * d * (4 + 4) + f = open(fname, "rb") + f.seek(4 + 4) + ids = np.fromfile(f, dtype="int32", count=n * d).reshape(n, d) + return torch.from_numpy(ids).long() + +class T2I1M(Dataset): + def __init__(self, download_dir: Union[str, Path] = DEFAULT_DOWNLOAD_DIR): + self.download_dir = to_path(download_dir) + self.data_dir = self.download_dir / "text2image1B" + self.downloaded = False + self.metric = "ip" + + def is_downloaded(self) -> bool: + base = self.data_dir / "base.1B.fbin.crop_nb_1000000" + query = self.data_dir / "query.heldout.30K.fbin" + gt = self.data_dir / "gt100-heldout.30K.fbin" + return base.exists() and query.exists() and gt.exists() + + def download(self, overwrite: bool = False): + pass + + def load_vectors(self) -> Union[np.ndarray, torch.Tensor]: + fname = self.data_dir / "base.1B.fbin.crop_nb_1000000" + n, d = map(int, np.fromfile(fname, dtype="uint32", count=2)) + return torch.from_numpy(np.fromfile(fname, dtype=np.float32, offset=8).reshape((n, d))) + + def load_queries(self) -> Union[np.ndarray, torch.Tensor]: + fname = self.data_dir / "query.heldout.30K.fbin" + n, d = map(int, np.fromfile(fname, dtype="uint32", count=2)) + return torch.from_numpy(np.fromfile(fname, dtype=np.float32, offset=8).reshape((n, d))) + + def load_ground_truth(self) -> Union[np.ndarray, torch.Tensor]: + fname = self.data_dir / "gt100-heldout.30K.fbin" + n, d = map(int, np.fromfile(fname, dtype="uint32", count=2)) + assert os.stat(fname).st_size == 8 + n * d * (4 + 4) + f = open(fname, "rb") + f.seek(4 + 4) + ids = np.fromfile(f, dtype="int32", count=n * d).reshape(n, d) + return torch.from_numpy(ids).long() + + def load_dataset( @@ -259,6 +359,12 @@ def load_dataset( dataset = MSTuring100m(download_dir=download_dir) elif name.lower() == "sift10m": dataset = Sift10m(download_dir=download_dir) + elif name.lower() == "wiki13m": + dataset = Wiki13M(download_dir=download_dir) + elif name.lower() == "t2i1m": + dataset = T2I1M(download_dir=download_dir) + elif name.lower() == "wikicohere1m": + dataset = WikiCohere1M(download_dir=download_dir) else: raise RuntimeError("Unimplemented dataset + " + name) diff --git a/test/experiments/osdi2025/experiment_runner.py b/test/experiments/osdi2025/experiment_runner.py index b51b8725..1cad31e1 100644 --- a/test/experiments/osdi2025/experiment_runner.py +++ b/test/experiments/osdi2025/experiment_runner.py @@ -13,6 +13,7 @@ from test.experiments.osdi2025.multi_level.run import run_experiment as run_multi_level from test.experiments.osdi2025.vary_levels.run import run_experiment as run_vary_levels from test.experiments.osdi2025.maintenance_ablation.run import run_experiment as run_ablation +from test.experiments.osdi2025.vary_batch_size.run import run_experiment as run_vary_batch_size EXPERIMENTS = { "kick_the_tires": run_kick_the_tires, @@ -23,6 +24,7 @@ "multi_level": run_multi_level, "vary_levels": run_vary_levels, "maintenance_ablation": run_ablation, + "vary_batch_size": run_vary_batch_size, } def main(): diff --git a/test/experiments/osdi2025/vary_batch_size/configs/wiki_single_thread.yaml b/test/experiments/osdi2025/vary_batch_size/configs/wiki_single_thread.yaml new file mode 100644 index 00000000..ead0177e --- /dev/null +++ b/test/experiments/osdi2025/vary_batch_size/configs/wiki_single_thread.yaml @@ -0,0 +1,62 @@ +dataset: + name: wiki13m + # Instead of a single num_queries, we sweep over these batch sizes: +# query_batch_sizes: [1, 8, 16, 32, 64, 128, 256, 512] + num_queries: 4096 + query_batch_sizes: [1, 4, 16, 64, 256, 1024, 4096] + k: 100 + +trials: 3 +warmup: 1 +overwrite: true +force_rebuild: false + +indexes: + - name: Quake_Unbatched + index: Quake + build_params: + nc: 3500 + metric: ip + num_workers: 0 + use_numa: true + omp_num_threads_build: 8 + parent: + num_workers: 0 + search_params: + nprobe: 4 + batch_size: 128 + parent: + batched_scan: true + batch_size: 128 + batched_scan: false + + - name: Quake_Batched + index: Quake + build_params: + nc: 3500 + metric: ip + num_workers: 0 + omp_num_threads_build: 8 + use_numa: true + parent: + num_workers: 0 + search_params: + nprobe: 4 + batch_size: 128 + parent: + batched_scan: true + batch_size: 128 + batched_scan: true + + - name: FaissIVF + index: IVF + build_params: + nc: 3500 + metric: ip + omp_num_threads_build: 1 + search_params: + nprobe: 4 + omp_num_threads_search: 1 + +output: + results_csv: numa_query_batch_results.csv \ No newline at end of file diff --git a/test/experiments/osdi2025/vary_batch_size/run.py b/test/experiments/osdi2025/vary_batch_size/run.py new file mode 100644 index 00000000..ef927d15 --- /dev/null +++ b/test/experiments/osdi2025/vary_batch_size/run.py @@ -0,0 +1,178 @@ +import logging +from pathlib import Path +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +import os +import faiss + +import test.experiments.osdi2025.experiment_utils as common_utils +from quake.utils import compute_recall +from quake.index_wrappers.faiss_ivf import FaissIVF +from quake.index_wrappers.faiss_hnsw import FaissHNSW +from quake.index_wrappers.quake import QuakeWrapper # type: ignore + +# optional wrappers +try: from quake.index_wrappers.scann import Scann +except ImportError: Scann = None +try: from quake.index_wrappers.diskann import DiskANNDynamic +except ImportError: DiskANNDynamic = None +try: from quake.index_wrappers.vamana import Vamana +except ImportError: Vamana = None + +INDEX_CLASSES = { + "Quake": QuakeWrapper, + "IVF": FaissIVF, + "HNSW": FaissHNSW, + "SCANN": Scann, + "DiskANN": DiskANNDynamic, + "SVS": Vamana, +} + +logger = logging.getLogger("numa_query_batch_orchestrator") + +def _set_faiss_threads(val: str, tag: str): + t = int(val) + faiss.omp_set_num_threads(t) + print(f"[{tag}] faiss.omp_set_num_threads({t})") + +def task_build_index(idx_cfg, ds_cfg, run_params, idx_file: Path): + name = idx_cfg["name"] + print(f"[{name} BUILD] PID={os.getpid()} -> {idx_file}") + bp = dict(idx_cfg.get("build_params", {})) + omp_build = str(bp.pop("omp_num_threads_build", "1")) + _set_faiss_threads(omp_build, f"{name} BUILD") + + # only need 1 query vector to build + base_vecs, _, _ = common_utils.load_data(ds_cfg["name"], nq_override=1) + Cls = INDEX_CLASSES[idx_cfg["index"]] + idx = common_utils.prepare_wrapper_index( + Cls, idx_file, base_vecs, bp, run_params["force_rebuild_indices"] + ) + del base_vecs + if idx is None: + return {"error": "build failed"} + return {"status": "success", "index_file_path": str(idx_file)} + +def task_search_index(idx_cfg, ds_cfg, run_params, idx_file: Path, batch_size: int): + name = idx_cfg["name"] + itype = idx_cfg["index"] + print(f"[{name} SEARCH bs={batch_size}] Loading index") + bp = dict(idx_cfg.get("build_params", {})) + sp = dict(idx_cfg.get("search_params", {})) + omp_search = str(sp.pop("omp_num_threads_search", "1")) + _set_faiss_threads(omp_search, f"{name} SEARCH") + + # load all nq queries & gt + _, all_qvecs, all_gt = common_utils.load_data(ds_cfg["name"], nq_override=ds_cfg["num_queries"]) + nq = all_qvecs.shape[0] + Cls = INDEX_CLASSES[itype] + inst = Cls() + load_kwargs = { + k: bp[k] for k in ("num_workers","use_numa","num_merge_workers","parent") if k in bp + } + inst.load(str(idx_file), **load_kwargs) + + # warmup on a single chunk + warmup_chunk = all_qvecs[:batch_size] + for _ in range(run_params["num_warmup"]): + _ = inst.search(warmup_chunk, run_params["k_val"], **sp) + + trial_total_latencies = [] + trial_recalls = [] + + for t in range(run_params["num_trials"]): + # iterate over chunks, summing latency + total_ns = 0 + ids_list = [] # collect results per chunk + for i in range(0, nq, batch_size): + chunk = all_qvecs[i : min(i+batch_size, nq)] + res = inst.search(chunk, run_params["k_val"], **sp) + ti = getattr(res, "timing_info", None) + ns = getattr(ti, "total_time_ns", None) or getattr(ti, "child_total_time_ns", 0) + total_ns += ns + ids_list.append(res.ids) + + total_ms = total_ns / 1e6 + trial_total_latencies.append(total_ms) + + # concatenate all ids and compute recall over nq + all_ids = np.vstack(ids_list) # shape (nq, k) + rec = float(compute_recall(all_ids, all_gt, run_params["k_val"]).mean()) + trial_recalls.append(rec) + + mean_lat = float(np.mean(trial_total_latencies)) + std_lat = float(np.std(trial_total_latencies)) + mean_rec = float(np.mean(trial_recalls)) + std_rec = float(np.std(trial_recalls)) + + return { + "index": name, + "query_batch_size": batch_size, + "nq": nq, + "mean_total_latency_ms": mean_lat, + "std_total_latency_ms": std_lat, + "mean_recall": mean_rec, + "std_recall": std_rec + } + +def run_experiment(cfg_path, out_dir): + logging.basicConfig(level=logging.INFO) + cfg = common_utils.load_config(cfg_path) + out = Path(out_dir); out.mkdir(exist_ok=True, parents=True) + + ds_cfg = cfg["dataset"] + batch_sizes = ds_cfg["query_batch_sizes"] + run_params = { + "num_trials": cfg.get("trials",3), + "num_warmup": cfg.get("warmup",1), + "k_val": ds_cfg["k"], + "force_rebuild_indices": cfg.get("force_rebuild",False) + } + + all_rows = [] + # build once per index + idx_store = out/"indices" + idx_store.mkdir(exist_ok=True) + for idx_cfg in cfg["indexes"]: + idx_file = idx_store/f"{idx_cfg['name']}.bin" + br = task_build_index(idx_cfg, ds_cfg, run_params, idx_file) + if br.get("error"): + logger.error(f"Build failed for {idx_cfg['name']}: {br['error']}") + + # now search for each batch_size + for bs in batch_sizes: + logger.info(f"--- Running batch size = {bs} ---") + for idx_cfg in cfg["indexes"]: + idx_file = idx_store/f"{idx_cfg['name']}.bin" + row = task_search_index(idx_cfg, ds_cfg, run_params, idx_file, bs) + all_rows.append(row) + + df = pd.DataFrame(all_rows) + # compute QPS = nq*1000 / mean_total_latency_ms + df["QPS"] = df["nq"] * 1000.0 / df["mean_total_latency_ms"] + df.to_csv(out/cfg["output"]["results_csv"], index=False) + logger.info(f"Wrote results to {cfg['output']['results_csv']}") + + # plot only points with recall ≥ 0.9 + # plot_df = df[df["mean_recall"] >= 0.9] + plt.figure(figsize=(8,6)) + for name, grp in df.groupby("index"): + grp = grp.sort_values("query_batch_size") + plt.errorbar( + grp["query_batch_size"], + grp["QPS"], + yerr=None, + marker="o", + label=name + ) + plt.xscale("log") + plt.xlabel("Query Batch Size") + plt.ylabel("QPS (@ Recall ≥ 0.9)") + plt.title(f"QPS vs Batch Size ({ds_cfg['name']}, k={ds_cfg['k']})") + plt.legend(bbox_to_anchor=(1.05,1), loc="upper left") + plt.grid(True, which="both", ls="--", alpha=0.5) + plt.tight_layout(rect=[0,0,0.8,1]) + plt.savefig(out/f"qps_vs_batch_{ds_cfg['name']}.png") + plt.close() + logger.info("Saved QPS vs Batch Size plot to " + str(out/f"qps_vs_batch_{ds_cfg['name']}.png")) \ No newline at end of file From 9b394491f492b030ec01133dfcbec963c8095379 Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 27 May 2025 14:46:37 -0500 Subject: [PATCH 283/323] add batch size exp --- .../vary_batch_size/configs/wiki_single_thread.yaml | 3 +++ test/experiments/osdi2025/vary_batch_size/run.py | 9 +++++---- 2 files changed, 8 insertions(+), 4 deletions(-) diff --git a/test/experiments/osdi2025/vary_batch_size/configs/wiki_single_thread.yaml b/test/experiments/osdi2025/vary_batch_size/configs/wiki_single_thread.yaml index ead0177e..c3f77323 100644 --- a/test/experiments/osdi2025/vary_batch_size/configs/wiki_single_thread.yaml +++ b/test/experiments/osdi2025/vary_batch_size/configs/wiki_single_thread.yaml @@ -14,6 +14,7 @@ force_rebuild: false indexes: - name: Quake_Unbatched index: Quake + index_file: /Users/jason/projects/quake/test/experiments/osdi2025/vary_batch_size/indices/Quake_3500.bin build_params: nc: 3500 metric: ip @@ -32,6 +33,7 @@ indexes: - name: Quake_Batched index: Quake + index_file: /Users/jason/projects/quake/test/experiments/osdi2025/vary_batch_size/indices/Quake_3500.bin build_params: nc: 3500 metric: ip @@ -50,6 +52,7 @@ indexes: - name: FaissIVF index: IVF + index_file: /Users/jason/projects/quake/test/experiments/osdi2025/vary_batch_size/indices/FaissIVF.bin build_params: nc: 3500 metric: ip diff --git a/test/experiments/osdi2025/vary_batch_size/run.py b/test/experiments/osdi2025/vary_batch_size/run.py index ef927d15..46491143 100644 --- a/test/experiments/osdi2025/vary_batch_size/run.py +++ b/test/experiments/osdi2025/vary_batch_size/run.py @@ -135,8 +135,9 @@ def run_experiment(cfg_path, out_dir): idx_store = out/"indices" idx_store.mkdir(exist_ok=True) for idx_cfg in cfg["indexes"]: - idx_file = idx_store/f"{idx_cfg['name']}.bin" - br = task_build_index(idx_cfg, ds_cfg, run_params, idx_file) + idx_path = Path(idx_cfg.get("index_file", idx_store/f"{idx_cfg['name']}.bin")) + idx_path.parent.mkdir(parents=True, exist_ok=True) + br = task_build_index(idx_cfg, ds_cfg, run_params, idx_path) if br.get("error"): logger.error(f"Build failed for {idx_cfg['name']}: {br['error']}") @@ -144,8 +145,8 @@ def run_experiment(cfg_path, out_dir): for bs in batch_sizes: logger.info(f"--- Running batch size = {bs} ---") for idx_cfg in cfg["indexes"]: - idx_file = idx_store/f"{idx_cfg['name']}.bin" - row = task_search_index(idx_cfg, ds_cfg, run_params, idx_file, bs) + idx_path = Path(idx_cfg.get("index_file", idx_store/f"{idx_cfg['name']}.bin")) + row = task_search_index(idx_cfg, ds_cfg, run_params, idx_path, bs) all_rows.append(row) df = pd.DataFrame(all_rows) From 79a5c2cf6c9a4e1fe640ead5472835ef637645a9 Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 27 May 2025 14:47:51 -0500 Subject: [PATCH 284/323] add batch size exp --- .../vary_batch_size/configs/wiki_single_thread.yaml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/test/experiments/osdi2025/vary_batch_size/configs/wiki_single_thread.yaml b/test/experiments/osdi2025/vary_batch_size/configs/wiki_single_thread.yaml index c3f77323..bf3fd6aa 100644 --- a/test/experiments/osdi2025/vary_batch_size/configs/wiki_single_thread.yaml +++ b/test/experiments/osdi2025/vary_batch_size/configs/wiki_single_thread.yaml @@ -14,7 +14,7 @@ force_rebuild: false indexes: - name: Quake_Unbatched index: Quake - index_file: /Users/jason/projects/quake/test/experiments/osdi2025/vary_batch_size/indices/Quake_3500.bin + index_file: /opt/quake/data/vary_batch_sizes/indices/Quake_3500.bin build_params: nc: 3500 metric: ip @@ -33,7 +33,7 @@ indexes: - name: Quake_Batched index: Quake - index_file: /Users/jason/projects/quake/test/experiments/osdi2025/vary_batch_size/indices/Quake_3500.bin + index_file: /opt/quake/data/vary_batch_sizes/indices/Quake_3500.bin build_params: nc: 3500 metric: ip @@ -52,7 +52,7 @@ indexes: - name: FaissIVF index: IVF - index_file: /Users/jason/projects/quake/test/experiments/osdi2025/vary_batch_size/indices/FaissIVF.bin + index_file: /opt/quake/data/vary_batch_sizes/indices/FaissIVF.bin build_params: nc: 3500 metric: ip From fd6ccb78a7a3916aecc2e612c14c26b6bade9790 Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 27 May 2025 15:31:14 -0500 Subject: [PATCH 285/323] hide timers --- src/cpp/src/query_coordinator.cpp | 46 +++++++++++++++---------------- 1 file changed, 23 insertions(+), 23 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 10b192ba..f6143f9e 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -842,29 +842,29 @@ std::shared_ptr QueryCoordinator::worker_scan( } - // // print out the per-core resource timers; - for (int i = 0; i < num_workers_; ++i) { - std::cout << "[QueryCoordinator::worker_scan] Core " << i << ": " - << "job_counter=" << core_resources_[i].job_counter << " " - << "queries_counter=" << core_resources_[i].queries_counter << " " - << "wait_time_ms=" << (float) core_wait_time_ns[i] / 1e6 << " " - << "scan_setup_time_ms=" << (float) core_scan_setup_time_ns[i] / 1e6 << " " - << "scan_time_ms=" << (float) core_scan_time_ns[i] / 1e6 << " " - << "scan_push_time_ms=" << (float) core_scan_push_time_ns[i] / 1e6 << " " - << "process_time_ms=" << (float) core_process_time_ns[i] / 1e6 << " " - << "process_preamble_time_ms=" << (float) core_process_preamble_time_ns[i] / 1e6 << " " - << "enqueue_time_ms=" << (float) core_enqueue_time_ns[i] / 1e6 << " " - << "job_time_ms=" << (float) core_job_time_ns[i] / 1e6 << std::endl; - } - - // print out the main thread timers; - std::cout << "[QueryCoordinator::worker_scan] Main thread: " - << "buffer_init_time_ms=" << (float) res->timing_info->buffer_init_time_ns / 1e6 << " " - << "copy_query_time_ms=" << (float) res->timing_info->copy_query_time_ns / 1e6 << " " - << "job_enqueue_time_ms=" << (float) res->timing_info->job_enqueue_time_ns / 1e6 << " " - << "job_wait_time_ms=" << (float) res->timing_info->job_wait_time_ns / 1e6 << " " - << "result_aggregate_time_ms=" << (float) res->timing_info->result_aggregate_time_ns / 1e6 - << std::endl; + // // // print out the per-core resource timers; + // for (int i = 0; i < num_workers_; ++i) { + // std::cout << "[QueryCoordinator::worker_scan] Core " << i << ": " + // << "job_counter=" << core_resources_[i].job_counter << " " + // << "queries_counter=" << core_resources_[i].queries_counter << " " + // << "wait_time_ms=" << (float) core_wait_time_ns[i] / 1e6 << " " + // << "scan_setup_time_ms=" << (float) core_scan_setup_time_ns[i] / 1e6 << " " + // << "scan_time_ms=" << (float) core_scan_time_ns[i] / 1e6 << " " + // << "scan_push_time_ms=" << (float) core_scan_push_time_ns[i] / 1e6 << " " + // << "process_time_ms=" << (float) core_process_time_ns[i] / 1e6 << " " + // << "process_preamble_time_ms=" << (float) core_process_preamble_time_ns[i] / 1e6 << " " + // << "enqueue_time_ms=" << (float) core_enqueue_time_ns[i] / 1e6 << " " + // << "job_time_ms=" << (float) core_job_time_ns[i] / 1e6 << std::endl; + // } + // + // // print out the main thread timers; + // std::cout << "[QueryCoordinator::worker_scan] Main thread: " + // << "buffer_init_time_ms=" << (float) res->timing_info->buffer_init_time_ns / 1e6 << " " + // << "copy_query_time_ms=" << (float) res->timing_info->copy_query_time_ns / 1e6 << " " + // << "job_enqueue_time_ms=" << (float) res->timing_info->job_enqueue_time_ns / 1e6 << " " + // << "job_wait_time_ms=" << (float) res->timing_info->job_wait_time_ns / 1e6 << " " + // << "result_aggregate_time_ms=" << (float) res->timing_info->result_aggregate_time_ns / 1e6 + // << std::endl; return res; } From 3a73c716a48b857cc599a70c98f4e6d100ac8a09 Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 27 May 2025 15:51:51 -0500 Subject: [PATCH 286/323] update wiki vary batch size --- .../configs/wiki_single_thread.yaml | 27 ++++++++++++++----- 1 file changed, 20 insertions(+), 7 deletions(-) diff --git a/test/experiments/osdi2025/vary_batch_size/configs/wiki_single_thread.yaml b/test/experiments/osdi2025/vary_batch_size/configs/wiki_single_thread.yaml index bf3fd6aa..244e5fa8 100644 --- a/test/experiments/osdi2025/vary_batch_size/configs/wiki_single_thread.yaml +++ b/test/experiments/osdi2025/vary_batch_size/configs/wiki_single_thread.yaml @@ -2,8 +2,8 @@ dataset: name: wiki13m # Instead of a single num_queries, we sweep over these batch sizes: # query_batch_sizes: [1, 8, 16, 32, 64, 128, 256, 512] - num_queries: 4096 - query_batch_sizes: [1, 4, 16, 64, 256, 1024, 4096] + num_queries: 8192 + query_batch_sizes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192] k: 100 trials: 3 @@ -18,11 +18,11 @@ indexes: build_params: nc: 3500 metric: ip - num_workers: 0 + num_workers: 16 use_numa: true omp_num_threads_build: 8 parent: - num_workers: 0 + num_workers: 4 search_params: nprobe: 4 batch_size: 128 @@ -37,11 +37,11 @@ indexes: build_params: nc: 3500 metric: ip - num_workers: 0 + num_workers: 16 omp_num_threads_build: 8 use_numa: true parent: - num_workers: 0 + num_workers: 4 search_params: nprobe: 4 batch_size: 128 @@ -59,7 +59,20 @@ indexes: omp_num_threads_build: 1 search_params: nprobe: 4 - omp_num_threads_search: 1 + omp_num_threads_search: 16 + + - name: FaissHNSW + index: HNSW + index_file: /opt/quake/data/vary_batch_sizes/indices/FaissHNSW.bin + build_params: + m: 64 + ef_construction: 128 + metric: ip + omp_num_threads_build: 1 + search_params: + ef_search: 16 + omp_num_threads_search: 16 + output: results_csv: numa_query_batch_results.csv \ No newline at end of file From d99aa63474c0845a1cc88b81e9ca73d14eaedf18 Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 27 May 2025 18:13:50 -0500 Subject: [PATCH 287/323] update wiki vary batch size --- src/cpp/src/query_coordinator.cpp | 13 ++ .../configs/wiki_single_thread.yaml | 4 +- .../osdi2025/vary_batch_size/run.py | 157 ++++++++++++------ 3 files changed, 121 insertions(+), 53 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index f6143f9e..ec989d9d 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -258,6 +258,8 @@ void QueryCoordinator::process_scan_job(ScanJob job, void QueryCoordinator::handle_nonbatched_job(const ScanJob &job, CoreResources &res, NUMAResources &nr) { + + auto start = std::chrono::high_resolution_clock::now(); // ensure buffers if (res.topk_buffer_pool.size() < 1) { res.topk_buffer_pool.resize(1); @@ -307,11 +309,22 @@ void QueryCoordinator::handle_nonbatched_job(const ScanJob &job, metric_, query_dist_pivots_[job.query_id].load(std::memory_order_relaxed)); + auto end = std::chrono::high_resolution_clock::now(); + + res.scan_time_ns += std::chrono::duration_cast(end - start).count(); + + start = std::chrono::high_resolution_clock::now(); + // If scan_list completes, enqueue its results auto tv = buf->get_topk(false); auto ti = buf->get_topk_indices(false); enqueue_result_job(ResultJob{job.query_id, job.rank, std::move(tv), std::move(ti)}); + end = std::chrono::high_resolution_clock::now(); + res.enqueue_time_ns += std::chrono::duration_cast(end - start).count(); + + + } catch (const std::exception& e) { std::cerr << "[QueryCoordinator::handle_nonbatched_job] Exception during scan for partition " << job.partition_id << ", query " << job.query_id << ": " << e.what() diff --git a/test/experiments/osdi2025/vary_batch_size/configs/wiki_single_thread.yaml b/test/experiments/osdi2025/vary_batch_size/configs/wiki_single_thread.yaml index 244e5fa8..29028fa7 100644 --- a/test/experiments/osdi2025/vary_batch_size/configs/wiki_single_thread.yaml +++ b/test/experiments/osdi2025/vary_batch_size/configs/wiki_single_thread.yaml @@ -3,7 +3,7 @@ dataset: # Instead of a single num_queries, we sweep over these batch sizes: # query_batch_sizes: [1, 8, 16, 32, 64, 128, 256, 512] num_queries: 8192 - query_batch_sizes: [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192] + query_batch_sizes: [1, 4, 16, 64, 256, 1024, 4096, 8192] k: 100 trials: 3 @@ -30,6 +30,7 @@ indexes: batched_scan: true batch_size: 128 batched_scan: false + omp_num_threads_search: 1 - name: Quake_Batched index: Quake @@ -49,6 +50,7 @@ indexes: batched_scan: true batch_size: 128 batched_scan: true + omp_num_threads_search: 1 - name: FaissIVF index: IVF diff --git a/test/experiments/osdi2025/vary_batch_size/run.py b/test/experiments/osdi2025/vary_batch_size/run.py index 46491143..b9c4b4cb 100644 --- a/test/experiments/osdi2025/vary_batch_size/run.py +++ b/test/experiments/osdi2025/vary_batch_size/run.py @@ -43,7 +43,9 @@ def task_build_index(idx_cfg, ds_cfg, run_params, idx_file: Path): omp_build = str(bp.pop("omp_num_threads_build", "1")) _set_faiss_threads(omp_build, f"{name} BUILD") - # only need 1 query vector to build + if idx_file.exists() and not run_params["force_rebuild_indices"]: + return {"status": "skipped", "index_file_path": str(idx_file)} + base_vecs, _, _ = common_utils.load_data(ds_cfg["name"], nq_override=1) Cls = INDEX_CLASSES[idx_cfg["index"]] idx = common_utils.prepare_wrapper_index( @@ -64,29 +66,33 @@ def task_search_index(idx_cfg, ds_cfg, run_params, idx_file: Path, batch_size: i _set_faiss_threads(omp_search, f"{name} SEARCH") # load all nq queries & gt - _, all_qvecs, all_gt = common_utils.load_data(ds_cfg["name"], nq_override=ds_cfg["num_queries"]) + _, all_qvecs, all_gt = common_utils.load_data( + ds_cfg["name"], nq_override=ds_cfg["num_queries"] + ) nq = all_qvecs.shape[0] + Cls = INDEX_CLASSES[itype] inst = Cls() load_kwargs = { - k: bp[k] for k in ("num_workers","use_numa","num_merge_workers","parent") if k in bp + k: bp[k] + for k in ("num_workers", "use_numa", "num_merge_workers", "parent") + if k in bp } inst.load(str(idx_file), **load_kwargs) - # warmup on a single chunk - warmup_chunk = all_qvecs[:batch_size] + # warmup on one chunk + warmup = all_qvecs[:batch_size] for _ in range(run_params["num_warmup"]): - _ = inst.search(warmup_chunk, run_params["k_val"], **sp) + _ = inst.search(warmup, run_params["k_val"], **sp) - trial_total_latencies = [] + trial_latencies = [] trial_recalls = [] - for t in range(run_params["num_trials"]): - # iterate over chunks, summing latency + for _ in range(run_params["num_trials"]): total_ns = 0 - ids_list = [] # collect results per chunk + ids_list = [] for i in range(0, nq, batch_size): - chunk = all_qvecs[i : min(i+batch_size, nq)] + chunk = all_qvecs[i : min(i + batch_size, nq)] res = inst.search(chunk, run_params["k_val"], **sp) ti = getattr(res, "timing_info", None) ns = getattr(ti, "total_time_ns", None) or getattr(ti, "child_total_time_ns", 0) @@ -94,86 +100,133 @@ def task_search_index(idx_cfg, ds_cfg, run_params, idx_file: Path, batch_size: i ids_list.append(res.ids) total_ms = total_ns / 1e6 - trial_total_latencies.append(total_ms) + trial_latencies.append(total_ms) - # concatenate all ids and compute recall over nq - all_ids = np.vstack(ids_list) # shape (nq, k) + all_ids = np.vstack(ids_list) rec = float(compute_recall(all_ids, all_gt, run_params["k_val"]).mean()) trial_recalls.append(rec) - mean_lat = float(np.mean(trial_total_latencies)) - std_lat = float(np.std(trial_total_latencies)) - mean_rec = float(np.mean(trial_recalls)) - std_rec = float(np.std(trial_recalls)) - return { - "index": name, - "query_batch_size": batch_size, - "nq": nq, - "mean_total_latency_ms": mean_lat, - "std_total_latency_ms": std_lat, - "mean_recall": mean_rec, - "std_recall": std_rec + "mean_total_latency_ms": float(np.mean(trial_latencies)), + "std_total_latency_ms": float(np.std(trial_latencies)), + "mean_recall": float(np.mean(trial_recalls)), + "std_recall": float(np.std(trial_recalls)), + "nq": nq, + "query_batch_size": batch_size, } def run_experiment(cfg_path, out_dir): logging.basicConfig(level=logging.INFO) cfg = common_utils.load_config(cfg_path) - out = Path(out_dir); out.mkdir(exist_ok=True, parents=True) + out = Path(out_dir); out.mkdir(parents=True, exist_ok=True) ds_cfg = cfg["dataset"] batch_sizes = ds_cfg["query_batch_sizes"] run_params = { - "num_trials": cfg.get("trials",3), - "num_warmup": cfg.get("warmup",1), - "k_val": ds_cfg["k"], - "force_rebuild_indices": cfg.get("force_rebuild",False) + "num_trials": cfg.get("trials", 3), + "num_warmup": cfg.get("warmup", 1), + "k_val": ds_cfg["k"], + "force_rebuild_indices": cfg.get("force_rebuild", False), + "enable_glances": cfg.get("enable_glances_monitoring", False), } + # prepare directories + process_logs_dir = out / "process_logs"; process_logs_dir.mkdir(exist_ok=True) + index_store_dir = out / "indices"; index_store_dir.mkdir(exist_ok=True) + all_rows = [] - # build once per index - idx_store = out/"indices" - idx_store.mkdir(exist_ok=True) + + # === BUILD TASKS === for idx_cfg in cfg["indexes"]: - idx_path = Path(idx_cfg.get("index_file", idx_store/f"{idx_cfg['name']}.bin")) + name = idx_cfg["name"] + idx_path = Path(idx_cfg.get("index_file", index_store_dir/f"{name}.bin")) idx_path.parent.mkdir(parents=True, exist_ok=True) - br = task_build_index(idx_cfg, ds_cfg, run_params, idx_path) - if br.get("error"): - logger.error(f"Build failed for {idx_cfg['name']}: {br['error']}") - # now search for each batch_size + build_log = process_logs_dir / f"{name}_build.log" + build_env = idx_cfg.get("build_env_vars", {}) + timeout = idx_cfg.get("process_timeout", cfg.get("default_process_timeout", 7200)) + + build_res = common_utils.run_operation_in_process( + task_build_index, + (idx_cfg, ds_cfg, run_params, idx_path), + env_vars=build_env, + log_file_path=str(build_log), + process_name=f"BuildTask_{name}", + timeout_seconds=timeout, + enable_glances=run_params["enable_glances"] + ) + + # check build result + data = build_res.get("data", {}) + if build_res.get("status") != "success" or data.get("error"): + logger.error(f"[BUILD FAILED] {name}: {data.get('error', 'unknown')}") + else: + logger.info(f"[BUILD OK] {name} → {data.get('index_file_path')} ({data.get('status')})") + + # === SEARCH TASKS === for bs in batch_sizes: - logger.info(f"--- Running batch size = {bs} ---") for idx_cfg in cfg["indexes"]: - idx_path = Path(idx_cfg.get("index_file", idx_store/f"{idx_cfg['name']}.bin")) - row = task_search_index(idx_cfg, ds_cfg, run_params, idx_path, bs) + name = idx_cfg["name"] + idx_path = Path(idx_cfg.get("index_file", index_store_dir/f"{name}.bin")) + + search_log = process_logs_dir / f"{name}_search_bs{bs}.log" + search_env = idx_cfg.get("search_env_vars", {}) + timeout = idx_cfg.get("process_timeout", cfg.get("default_process_timeout", 7200)) + + search_res = common_utils.run_operation_in_process( + task_search_index, + (idx_cfg, ds_cfg, run_params, idx_path, bs), + env_vars=search_env, + log_file_path=str(search_log), + process_name=f"SearchTask_{name}_bs{bs}", + timeout_seconds=timeout, + enable_glances=run_params["enable_glances"] + ) + + if search_res.get("status") == "success": + data = search_res.get("data", {}) + if data.get("error"): + row = {"index": name, "query_batch_size": bs, "error": data["error"]} + logger.error(f"[SEARCH ERROR] {name} bs={bs}: {data['error']}") + else: + row = { + "index": name, + **data + } + logger.info(f"[SEARCH OK] {name} bs={bs}: latency={data['mean_total_latency_ms']:.2f}ms, recall={data['mean_recall']:.3f}") + else: + err = search_res.get("data", {}).get("error", "process failed") + row = {"index": name, "query_batch_size": bs, "error": err} + logger.error(f"[SEARCH PROC FAIL] {name} bs={bs}: {err}") + all_rows.append(row) + # === AGGREGATE & PLOT === df = pd.DataFrame(all_rows) - # compute QPS = nq*1000 / mean_total_latency_ms + # compute QPS df["QPS"] = df["nq"] * 1000.0 / df["mean_total_latency_ms"] - df.to_csv(out/cfg["output"]["results_csv"], index=False) - logger.info(f"Wrote results to {cfg['output']['results_csv']}") + out_csv = out / cfg["output"]["results_csv"] + df.to_csv(out_csv, index=False) + logger.info(f"Wrote results to {out_csv}") - # plot only points with recall ≥ 0.9 - # plot_df = df[df["mean_recall"] >= 0.9] + # plot QPS vs batch size plt.figure(figsize=(8,6)) for name, grp in df.groupby("index"): grp = grp.sort_values("query_batch_size") plt.errorbar( grp["query_batch_size"], grp["QPS"], - yerr=None, marker="o", label=name ) - plt.xscale("log") + plt.xscale("log", base=2) plt.xlabel("Query Batch Size") - plt.ylabel("QPS (@ Recall ≥ 0.9)") + plt.ylabel("QPS") plt.title(f"QPS vs Batch Size ({ds_cfg['name']}, k={ds_cfg['k']})") plt.legend(bbox_to_anchor=(1.05,1), loc="upper left") plt.grid(True, which="both", ls="--", alpha=0.5) plt.tight_layout(rect=[0,0,0.8,1]) - plt.savefig(out/f"qps_vs_batch_{ds_cfg['name']}.png") + plot_path = out / f"qps_vs_batch_{ds_cfg['name']}.png" + plt.savefig(plot_path) plt.close() - logger.info("Saved QPS vs Batch Size plot to " + str(out/f"qps_vs_batch_{ds_cfg['name']}.png")) \ No newline at end of file + logger.info(f"Saved plot to {plot_path}") \ No newline at end of file From b1553d0deeee8a4f2f7cc6a1cbd20c40fb480c3c Mon Sep 17 00:00:00 2001 From: Jason Date: Tue, 27 May 2025 21:05:25 -0500 Subject: [PATCH 288/323] add baselines to batch experiment --- .../configs/wiki_single_thread.yaml | 39 +++++++++ .../osdi2025/vary_batch_size/run.py | 82 +++++++++++-------- 2 files changed, 85 insertions(+), 36 deletions(-) diff --git a/test/experiments/osdi2025/vary_batch_size/configs/wiki_single_thread.yaml b/test/experiments/osdi2025/vary_batch_size/configs/wiki_single_thread.yaml index 29028fa7..95d9f255 100644 --- a/test/experiments/osdi2025/vary_batch_size/configs/wiki_single_thread.yaml +++ b/test/experiments/osdi2025/vary_batch_size/configs/wiki_single_thread.yaml @@ -75,6 +75,45 @@ indexes: ef_search: 16 omp_num_threads_search: 16 + - name: SCANN + index: SCANN + index_file: /opt/quake/data/vary_batch_sizes/indices/SCANN.bin + build_params: + num_leaves: 3500 + metric: dot_product + omp_num_threads_build: 16 + search_params: + leaves_to_search: 4 + omp_num_threads_search: 1 + n_threads: 16 + + - name: DiskANN + index: DiskANN + index_file: /opt/quake/data/vary_batch_sizes/indices/DiskANN.bin + build_params: + complexity: 128 + graph_degree: 64 + metric: mips + num_threads: 16 + omp_num_threads_build: 1 + search_params: + complexity: 128 + num_threads: 16 + omp_num_threads_search: 1 + + - name: SVS + index: SVS + index_file: /opt/quake/data/vary_batch_sizes/indices/SVS.bin + build_params: + graph_max_degree: 64 + alpha: 1.1 + window_size: 128 + metric: ip + num_threads: 16 + omp_num_threads_build: 1 + search_params: + search_window_size: 24 + omp_num_threads_search: 16 output: results_csv: numa_query_batch_results.csv \ No newline at end of file diff --git a/test/experiments/osdi2025/vary_batch_size/run.py b/test/experiments/osdi2025/vary_batch_size/run.py index b9c4b4cb..d30ec6f7 100644 --- a/test/experiments/osdi2025/vary_batch_size/run.py +++ b/test/experiments/osdi2025/vary_batch_size/run.py @@ -59,7 +59,7 @@ def task_build_index(idx_cfg, ds_cfg, run_params, idx_file: Path): def task_search_index(idx_cfg, ds_cfg, run_params, idx_file: Path, batch_size: int): name = idx_cfg["name"] itype = idx_cfg["index"] - print(f"[{name} SEARCH bs={batch_size}] Loading index") + print(f"[{name} SEARCH bs={batch_size}] PID={os.getpid()} Loading index") bp = dict(idx_cfg.get("build_params", {})) sp = dict(idx_cfg.get("search_params", {})) omp_search = str(sp.pop("omp_num_threads_search", "1")) @@ -123,16 +123,29 @@ def run_experiment(cfg_path, out_dir): ds_cfg = cfg["dataset"] batch_sizes = ds_cfg["query_batch_sizes"] run_params = { - "num_trials": cfg.get("trials", 3), - "num_warmup": cfg.get("warmup", 1), - "k_val": ds_cfg["k"], - "force_rebuild_indices": cfg.get("force_rebuild", False), - "enable_glances": cfg.get("enable_glances_monitoring", False), + "num_trials": cfg.get("trials", 3), + "num_warmup": cfg.get("warmup", 1), + "k_val": ds_cfg["k"], + "force_rebuild_indices": cfg.get("force_rebuild", False), + "force_overwrite": cfg.get("overwrite", False), + "enable_glances": cfg.get("enable_glances_monitoring", False), } # prepare directories - process_logs_dir = out / "process_logs"; process_logs_dir.mkdir(exist_ok=True) - index_store_dir = out / "indices"; index_store_dir.mkdir(exist_ok=True) + process_logs_dir = out / "process_logs"; process_logs_dir.mkdir(exist_ok=True) + index_store_dir = out / "indices"; index_store_dir.mkdir(exist_ok=True) + + # load existing results if present + results_csv = out / cfg["output"]["results_csv"] + print(results_csv) + if results_csv.exists() and not run_params["force_overwrite"]: + existing_df = pd.read_csv(results_csv) + done_pairs = set(zip(existing_df["index"], existing_df["query_batch_size"])) + print(done_pairs) + else: + print("No existing results found") + existing_df = None + done_pairs = set() all_rows = [] @@ -146,7 +159,7 @@ def run_experiment(cfg_path, out_dir): build_env = idx_cfg.get("build_env_vars", {}) timeout = idx_cfg.get("process_timeout", cfg.get("default_process_timeout", 7200)) - build_res = common_utils.run_operation_in_process( + res = common_utils.run_operation_in_process( task_build_index, (idx_cfg, ds_cfg, run_params, idx_path), env_vars=build_env, @@ -155,25 +168,26 @@ def run_experiment(cfg_path, out_dir): timeout_seconds=timeout, enable_glances=run_params["enable_glances"] ) - - # check build result - data = build_res.get("data", {}) - if build_res.get("status") != "success" or data.get("error"): - logger.error(f"[BUILD FAILED] {name}: {data.get('error', 'unknown')}") + data = res.get("data", {}) + if res.get("status")!="success" or data.get("error"): + logger.error(f"[BUILD FAILED] {name}: {data.get('error','unknown')}") else: - logger.info(f"[BUILD OK] {name} → {data.get('index_file_path')} ({data.get('status')})") + logger.info(f"[BUILD OK] {name} → {data['index_file_path']}") # === SEARCH TASKS === for bs in batch_sizes: for idx_cfg in cfg["indexes"]: name = idx_cfg["name"] - idx_path = Path(idx_cfg.get("index_file", index_store_dir/f"{name}.bin")) + if (name, bs) in done_pairs: + logger.info(f"Skipping existing result for {name}, batch={bs}") + continue + idx_path = Path(idx_cfg.get("index_file", index_store_dir/f"{name}.bin")) search_log = process_logs_dir / f"{name}_search_bs{bs}.log" search_env = idx_cfg.get("search_env_vars", {}) timeout = idx_cfg.get("process_timeout", cfg.get("default_process_timeout", 7200)) - search_res = common_utils.run_operation_in_process( + res = common_utils.run_operation_in_process( task_search_index, (idx_cfg, ds_cfg, run_params, idx_path, bs), env_vars=search_env, @@ -183,33 +197,29 @@ def run_experiment(cfg_path, out_dir): enable_glances=run_params["enable_glances"] ) - if search_res.get("status") == "success": - data = search_res.get("data", {}) - if data.get("error"): - row = {"index": name, "query_batch_size": bs, "error": data["error"]} - logger.error(f"[SEARCH ERROR] {name} bs={bs}: {data['error']}") - else: - row = { - "index": name, - **data - } - logger.info(f"[SEARCH OK] {name} bs={bs}: latency={data['mean_total_latency_ms']:.2f}ms, recall={data['mean_recall']:.3f}") + if res.get("status")=="success" and not res.get("data",{}).get("error"): + row = {"index": name, **res["data"]} + logger.info(f"[SEARCH OK] {name}, batch={bs}: {row}") else: - err = search_res.get("data", {}).get("error", "process failed") + err = res.get("data",{}).get("error","proc failed") row = {"index": name, "query_batch_size": bs, "error": err} - logger.error(f"[SEARCH PROC FAIL] {name} bs={bs}: {err}") + logger.error(f"[SEARCH ERR] {name}, batch={bs}: {err}") all_rows.append(row) - # === AGGREGATE & PLOT === - df = pd.DataFrame(all_rows) + # === MERGE & SAVE === + new_df = pd.DataFrame(all_rows) + if existing_df is not None and not run_params["force_overwrite"]: + df = pd.concat([existing_df, new_df], ignore_index=True) + else: + df = new_df + # compute QPS df["QPS"] = df["nq"] * 1000.0 / df["mean_total_latency_ms"] - out_csv = out / cfg["output"]["results_csv"] - df.to_csv(out_csv, index=False) - logger.info(f"Wrote results to {out_csv}") + df.to_csv(results_csv, index=False) + logger.info(f"Wrote results to {results_csv}") - # plot QPS vs batch size + # === PLOT === plt.figure(figsize=(8,6)) for name, grp in df.groupby("index"): grp = grp.sort_values("query_batch_size") From 37ab02784f19a0c2bc188f1081dc7c396979cf01 Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 29 May 2025 01:25:55 -0500 Subject: [PATCH 289/323] update scann for multi-query experiment --- .../osdi2025/vary_batch_size/run.py | 140 ++++++++++-- .../experiments/osdi2025/wiki_workload/run.py | 214 ++++++++++++++++++ .../osdi2025/workload_comparison/run.py | 2 - 3 files changed, 335 insertions(+), 21 deletions(-) create mode 100644 test/experiments/osdi2025/wiki_workload/run.py delete mode 100644 test/experiments/osdi2025/workload_comparison/run.py diff --git a/test/experiments/osdi2025/vary_batch_size/run.py b/test/experiments/osdi2025/vary_batch_size/run.py index d30ec6f7..93215eae 100644 --- a/test/experiments/osdi2025/vary_batch_size/run.py +++ b/test/experiments/osdi2025/vary_batch_size/run.py @@ -115,11 +115,119 @@ def task_search_index(idx_cfg, ds_cfg, run_params, idx_file: Path, batch_size: i "query_batch_size": batch_size, } + +def bench_scann(cfg_path: str, out_dir: Path, force_rebuild: bool, force_overwrite: bool): + """ + Build and benchmark SCANN across all batch sizes defined in the config. SCANNs serialization is broken, so it needs to be handled separately. + """ + if Scann is None: + raise ImportError("SCANN wrapper not installed; please install quake.index_wrappers.scann") + + # load config and dataset settings + cfg = common_utils.load_config(cfg_path) + ds_cfg = cfg["dataset"] + batch_sizes = ds_cfg["query_batch_sizes"] + k_val = ds_cfg["k"] + num_trials = cfg.get("trials", 3) + num_warmup = cfg.get("warmup", 1) + + # prepare paths + out_dir = Path(out_dir) + results_csv = out_dir / cfg["output"]["results_csv"] + results_csv.parent.mkdir(parents=True, exist_ok=True) + + # find SCANN entry in indexes + scann_cfg = next((ic for ic in cfg["indexes"] if ic["index"] == "SCANN"), None) + if scann_cfg is None: + return # no SCANN to run + + idx_name = scann_cfg["name"] + # check existing results + if results_csv.exists() and not force_overwrite: + df_existing = pd.read_csv(results_csv) + if idx_name in df_existing["index"].unique() and not force_rebuild: + logger.info(f"[SCANN] results already exist and no force overwrite → skipping") + return + + # BUILD SCANN index + logger.info(f"[{idx_name} BUILD] building SCANN index") + base_vecs, _, _ = common_utils.load_data(ds_cfg["name"], nq_override=1) + scann = Scann() + scann.build(base_vecs, **scann_cfg.get("build_params", {})) + idx_file = Path(scann_cfg.get("index_file", out_dir / "indices" / f"{idx_name}.bin")) + idx_file.parent.mkdir(parents=True, exist_ok=True) + scann.save(str(idx_file)) + del base_vecs + + # LOAD queries & GT once + _, all_qvecs, all_gt = common_utils.load_data( + ds_cfg["name"], nq_override=ds_cfg["num_queries"] + ) + nq = all_qvecs.shape[0] + + # BENCHMARK SCANN over batch sizes + records = [] + for bs in batch_sizes: + logger.info(f"[{idx_name} SEARCH] batch_size={bs}") + inst = Scann() + inst.load(str(idx_file)) + + # warmup runs + warmup_q = all_qvecs[:bs] + for _ in range(num_warmup): + inst.search(warmup_q, k_val, **scann_cfg.get("search_params", {})) + + latencies = [] + recalls = [] + for _ in range(num_trials): + total_ns = 0 + ids_batches = [] + for i in range(0, nq, bs): + chunk = all_qvecs[i : min(i + bs, nq)] + res = inst.search(chunk, k_val, **scann_cfg.get("search_params", {})) + ti = getattr(res, "timing_info", None) + ns = getattr(ti, "total_time_ns", None) or getattr(ti, "child_total_time_ns", 0) + total_ns += ns + ids_batches.append(res.ids) + + latencies.append(total_ns / 1e6) + all_ids = np.vstack(ids_batches) + recalls.append(float(compute_recall(all_ids, all_gt, k_val).mean())) + + records.append({ + "index": idx_name, + "query_batch_size": bs, + "nq": nq, + "mean_total_latency_ms": float(np.mean(latencies)), + "std_total_latency_ms": float(np.std(latencies)), + "mean_recall": float(np.mean(recalls)), + "std_recall": float(np.std(recalls)), + "QPS": float(nq * 1000.0 / np.mean(latencies)), + }) + + # save (or overwrite) SCANN results + df = pd.DataFrame(records) + df.to_csv(results_csv, index=False) + logger.info(f"[{idx_name}] SCANN benchmark results saved to {results_csv}") + + def run_experiment(cfg_path, out_dir): + """ + Main entrypoint: first runs SCANN via bench_scann, then + proceeds with build/search for all other indexes. + """ logging.basicConfig(level=logging.INFO) cfg = common_utils.load_config(cfg_path) out = Path(out_dir); out.mkdir(parents=True, exist_ok=True) + # run SCANN special path + bench_scann( + cfg_path=cfg_path, + out_dir=out, + force_rebuild=cfg.get("force_rebuild", False), + force_overwrite=cfg.get("overwrite", False) + ) + ds_cfg = cfg["dataset"] batch_sizes = ds_cfg["query_batch_sizes"] run_params = { @@ -135,37 +243,33 @@ def run_experiment(cfg_path, out_dir): process_logs_dir = out / "process_logs"; process_logs_dir.mkdir(exist_ok=True) index_store_dir = out / "indices"; index_store_dir.mkdir(exist_ok=True) - # load existing results if present + # load existing results results_csv = out / cfg["output"]["results_csv"] - print(results_csv) if results_csv.exists() and not run_params["force_overwrite"]: existing_df = pd.read_csv(results_csv) done_pairs = set(zip(existing_df["index"], existing_df["query_batch_size"])) - print(done_pairs) else: - print("No existing results found") existing_df = None done_pairs = set() all_rows = [] - # === BUILD TASKS === + # === BUILD OTHER INDEXES === for idx_cfg in cfg["indexes"]: + if idx_cfg["index"] == "SCANN": + continue # already handled name = idx_cfg["name"] idx_path = Path(idx_cfg.get("index_file", index_store_dir/f"{name}.bin")) idx_path.parent.mkdir(parents=True, exist_ok=True) build_log = process_logs_dir / f"{name}_build.log" - build_env = idx_cfg.get("build_env_vars", {}) - timeout = idx_cfg.get("process_timeout", cfg.get("default_process_timeout", 7200)) - res = common_utils.run_operation_in_process( task_build_index, (idx_cfg, ds_cfg, run_params, idx_path), - env_vars=build_env, + env_vars=idx_cfg.get("build_env_vars", {}), log_file_path=str(build_log), process_name=f"BuildTask_{name}", - timeout_seconds=timeout, + timeout_seconds=idx_cfg.get("process_timeout", cfg.get("default_process_timeout", 7200)), enable_glances=run_params["enable_glances"] ) data = res.get("data", {}) @@ -174,26 +278,25 @@ def run_experiment(cfg_path, out_dir): else: logger.info(f"[BUILD OK] {name} → {data['index_file_path']}") - # === SEARCH TASKS === + # === SEARCH OTHER INDEXES === for bs in batch_sizes: for idx_cfg in cfg["indexes"]: + if idx_cfg["index"] == "SCANN": + continue # skip SCANN in normal loop name = idx_cfg["name"] if (name, bs) in done_pairs: logger.info(f"Skipping existing result for {name}, batch={bs}") continue - idx_path = Path(idx_cfg.get("index_file", index_store_dir/f"{name}.bin")) + idx_path = Path(idx_cfg.get("index_file", index_store_dir/f"{name}.bin")) search_log = process_logs_dir / f"{name}_search_bs{bs}.log" - search_env = idx_cfg.get("search_env_vars", {}) - timeout = idx_cfg.get("process_timeout", cfg.get("default_process_timeout", 7200)) - res = common_utils.run_operation_in_process( task_search_index, (idx_cfg, ds_cfg, run_params, idx_path, bs), - env_vars=search_env, + env_vars=idx_cfg.get("search_env_vars", {}), log_file_path=str(search_log), process_name=f"SearchTask_{name}_bs{bs}", - timeout_seconds=timeout, + timeout_seconds=idx_cfg.get("process_timeout", cfg.get("default_process_timeout", 7200)), enable_glances=run_params["enable_glances"] ) @@ -207,14 +310,13 @@ def run_experiment(cfg_path, out_dir): all_rows.append(row) - # === MERGE & SAVE === + # === MERGE & SAVE ALL RESULTS === new_df = pd.DataFrame(all_rows) if existing_df is not None and not run_params["force_overwrite"]: df = pd.concat([existing_df, new_df], ignore_index=True) else: df = new_df - # compute QPS df["QPS"] = df["nq"] * 1000.0 / df["mean_total_latency_ms"] df.to_csv(results_csv, index=False) logger.info(f"Wrote results to {results_csv}") diff --git a/test/experiments/osdi2025/wiki_workload/run.py b/test/experiments/osdi2025/wiki_workload/run.py new file mode 100644 index 00000000..e0c65dab --- /dev/null +++ b/test/experiments/osdi2025/wiki_workload/run.py @@ -0,0 +1,214 @@ +#!/usr/bin/env python3 +""" +Wikidata Workload Experiment Runner +───────────────────────────────────── +This experiment runs a Wikidata workload across multiple index configurations. +It generates detailed reports including: +1. Per-index CSV logs of operations. +2. A 9-panel unified plot comparing various metrics across configurations. +3. A stacked bar chart breaking down cumulative time per operation type. +4. A summary table (CSV and Markdown) of key performance indicators. +""" +from __future__ import annotations + +import logging +from pathlib import Path +from typing import Dict, List, Any, Optional + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from tabulate import tabulate +from matplotlib.cm import get_cmap +from matplotlib.lines import Line2D + +# Common utilities +import test.experiments.osdi2025.experiment_utils as common_utils + +# evaluator and wrappers +from .wiki_workload import WikidataWorkloadEvaluator +from quake.index_wrappers.quake import QuakeWrapper +from quake.index_wrappers.faiss_ivf import FaissIVF +from quake.index_wrappers.faiss_hnsw import FaissHNSW +try: from quake.index_wrappers.scann import Scann +except ImportError: Scann = None +try: from quake.index_wrappers.diskann import DiskANNDynamic +except ImportError: DiskANNDynamic = None +try: from quake.index_wrappers.vamana import Vamana +except ImportError: Vamana = None + +INDEX_CLASSES: Dict[str, Any] = { + "Quake": QuakeWrapper, + "IVF": FaissIVF, + "HNSW": FaissHNSW, + "SCANN": Scann, + "DiskANN": DiskANNDynamic, + "SVS": Vamana, +} + +logging.basicConfig(level=logging.INFO, + format="%(asctime)s | %(levelname)s | %(message)s") +log = logging.getLogger(__name__) + +OP_STYLE = { + "query": dict(ls="-", marker="o", mfc="none", ms=4, lw=1.2), + "insert": dict(ls="-", marker="s", mfc="none", ms=4, lw=1.2), + "delete": dict(ls="-", marker="^", mfc="none", ms=4, lw=1.2), + "maintain": dict(ls="None", marker="X", mfc="black", ms=5), +} +LAT_OPS = ["query", "insert", "delete", "maintain"] +IDX_LAT_Q, IDX_LAT_I, IDX_LAT_D, IDX_LAT_M = 0, 1, 2, 3 +IDX_PART, IDX_RES, IDX_REC, IDX_TOT, IDX_SPL = 4, 5, 6, 7, 8 + + +def unified_plot(cfg: Dict[str, Any], out_dir: Path) -> None: + styles = cfg.get("plot", {}).get("styles", {}) + fig, axs2d = plt.subplots(3, 3, figsize=(18, 12), sharex="col") + axs = axs2d.flatten() + + idx_handles: List[Line2D] = [] + for j, idx_cfg in enumerate(cfg.get("indexes", [])): + nm = idx_cfg["name"] + st = styles.get(nm, {}) + idx_handles.append(Line2D([0], [0], + color=st.get("color", f"C{j % 10}"), + marker=st.get("marker", "o"), + ls="", markersize=6, label=nm)) + + max_ops_overall = 0 + for j, idx_cfg in enumerate(cfg.get("indexes", [])): + name = idx_cfg["name"] + colour = styles.get(name, {}).get("color", f"C{j % 10}") + marker = styles.get(name, {}).get("marker", "o") + + csv_path = out_dir / name / "results.csv" + if not csv_path.exists(): + log.warning("[unified_plot] missing %s", csv_path) + continue + try: + df = pd.read_csv(csv_path) + if df.empty: continue + max_ops_overall = max(max_ops_overall, df.operation_number.max()) + except Exception: + log.warning("[unified_plot] failed to read %s", csv_path) + continue + + # latency + for op, ax_idx in zip(LAT_OPS, [IDX_LAT_Q, IDX_LAT_I, IDX_LAT_D, IDX_LAT_M]): + ax = axs[ax_idx] + if op != "maintain": + sub = df[df.operation_type == op] + if not sub.empty: + ax.plot(sub.operation_number, sub.latency_ms, color=colour, **OP_STYLE[op]) + + # partitions, resident blank + # recall + rec_ix = IDX_REC + sub = df[df.operation_type=="query"] + if "recall" in sub: + axs[rec_ix].plot(sub.operation_number, sub.recall, color=colour, marker=marker, lw=1.2) + # cumulative time + tot_ix = IDX_TOT + cum = np.cumsum(df.latency_ms.fillna(0)) + axs[tot_ix].plot(df.operation_number, cum, color=colour, marker=marker, lw=1.2) + + # titles and labels + titles = { + IDX_LAT_Q: "Latency – Query", IDX_LAT_I: "Insert", IDX_LAT_D: "Delete", IDX_LAT_M: "Maintain", + IDX_PART: "# Partitions", IDX_RES: "Resident", IDX_REC: "Recall", + IDX_TOT: "Cum. Time (ms)", IDX_SPL: "Splits/Deletes" + } + for i, ax in enumerate(axs): + ax.set_title(titles.get(i, "")) + ax.grid(True, ls=":", alpha=0.7) + if i//3==2: ax.set_xlabel("Op #") + if max_ops_overall>0: + for ax in axs: ax.set_xlim(0, max_ops_overall) + + fig.legend(idx_handles, [h.get_label() for h in idx_handles], + loc="upper center", ncol=min(4,len(idx_handles)), frameon=False) + plt.tight_layout(rect=[0,0.03,1,0.97]) + path = out_dir / "unified_plot.png" + plt.savefig(path, dpi=150) + log.info("Saved unified_plot to %s", path) + plt.close(fig) + + +def make_time_breakdown(cfg: Dict[str, Any], out_dir: Path) -> None: + categories = ["Query","Insert","Delete","Total"] + data = {} + for idx_cfg in cfg.get("indexes", []): + name = idx_cfg["name"] + df = pd.read_csv(out_dir/name/"results.csv") + data[name] = [ + df[df.operation_type=="query"].latency_ms.sum(), + df[df.operation_type=="insert"].latency_ms.sum(), + df[df.operation_type=="delete"].latency_ms.sum(), + ] + data[name].append(sum(data[name])) + fig, ax = plt.subplots(figsize=(8,6)) + cmap = get_cmap("tab10") + n = len(data) + x = np.arange(len(categories)) + w = 0.8/n + for j,(name,vals) in enumerate(data.items()): + ax.bar(x + j*w, vals, width=w, label=name, color=cmap(j)) + ax.set_xticks(x); ax.set_xticklabels(categories) + ax.set_ylabel("Cumulative ms") + ax.legend(frameon=False) + path = out_dir/"time_breakdown.png" + plt.savefig(path, dpi=150) + log.info("Saved time_breakdown to %s", path) + plt.close(fig) + + +def produce_summary_table(cfg: Dict[str, Any], out_dir: Path) -> None: + rows = [] + for idx_cfg in cfg.get("indexes", []): + name = idx_cfg["name"] + df = pd.read_csv(out_dir/name/"results.csv") + q = df[df.operation_type=="query"] + rows.append({ + "Index": name, + "MeanLatency": float(q.latency_ms.mean()), + "MeanRecall": float(q.recall.mean()) if "recall" in q else None, + }) + if not rows: return + sdf = pd.DataFrame(rows) + common_utils.save_results_csv(sdf, out_dir/"summary_table.csv") + md = tabulate(sdf, headers="keys", tablefmt="github", showindex=False) + print(md) + + +def run_experiment(cfg_path_str: str, output_dir_str: str) -> None: + cfg = common_utils.load_config(cfg_path_str) + out = Path(output_dir_str).expanduser(); out.mkdir(parents=True, exist_ok=True) + mode = cfg.get("mode","run") + log.info("Mode: %s", mode) + + workload_dir = cfg["workload_dir"] + + if mode in {"run"}: # Phase 2: evaluation + for idx_cfg in cfg.get("indexes", []): + name = idx_cfg["name"] + key = idx_cfg["index"] + Cls = INDEX_CLASSES.get(key) + if Cls is None: + log.warning("Unknown index %s", key); continue + wrapper = Cls() + maint = idx_cfg.get("maintenance_params") + do_maint = maint is not None + ev = WikidataWorkloadEvaluator(workload_dir, out/name) + ev.evaluate_workload( + name=name, + index=wrapper, + build_params=idx_cfg.get("build_params",{}), + search_params=idx_cfg.get("search_params",{}), + do_maintenance=do_maint, + m_params=maint, + batch=bool(idx_cfg.get("batch",False)), + ) + if mode in {"run","plot"}: # Phase 3 + unified_plot(cfg, out) + make_time_breakdown(cfg, out) + produce_summary_table(cfg, out) \ No newline at end of file diff --git a/test/experiments/osdi2025/workload_comparison/run.py b/test/experiments/osdi2025/workload_comparison/run.py deleted file mode 100644 index 139597f9..00000000 --- a/test/experiments/osdi2025/workload_comparison/run.py +++ /dev/null @@ -1,2 +0,0 @@ - - From eb9efe4da5e8a1218e405eaa459e728936f76a75 Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 29 May 2025 01:34:06 -0500 Subject: [PATCH 290/323] update scann for multi-query experiment --- .../osdi2025/vary_batch_size/run.py | 140 +++--------------- 1 file changed, 19 insertions(+), 121 deletions(-) diff --git a/test/experiments/osdi2025/vary_batch_size/run.py b/test/experiments/osdi2025/vary_batch_size/run.py index 93215eae..d30ec6f7 100644 --- a/test/experiments/osdi2025/vary_batch_size/run.py +++ b/test/experiments/osdi2025/vary_batch_size/run.py @@ -115,119 +115,11 @@ def task_search_index(idx_cfg, ds_cfg, run_params, idx_file: Path, batch_size: i "query_batch_size": batch_size, } - -def bench_scann(cfg_path: str, out_dir: Path, force_rebuild: bool, force_overwrite: bool): - """ - Build and benchmark SCANN across all batch sizes defined in the config. SCANNs serialization is broken, so it needs to be handled separately. - """ - if Scann is None: - raise ImportError("SCANN wrapper not installed; please install quake.index_wrappers.scann") - - # load config and dataset settings - cfg = common_utils.load_config(cfg_path) - ds_cfg = cfg["dataset"] - batch_sizes = ds_cfg["query_batch_sizes"] - k_val = ds_cfg["k"] - num_trials = cfg.get("trials", 3) - num_warmup = cfg.get("warmup", 1) - - # prepare paths - out_dir = Path(out_dir) - results_csv = out_dir / cfg["output"]["results_csv"] - results_csv.parent.mkdir(parents=True, exist_ok=True) - - # find SCANN entry in indexes - scann_cfg = next((ic for ic in cfg["indexes"] if ic["index"] == "SCANN"), None) - if scann_cfg is None: - return # no SCANN to run - - idx_name = scann_cfg["name"] - # check existing results - if results_csv.exists() and not force_overwrite: - df_existing = pd.read_csv(results_csv) - if idx_name in df_existing["index"].unique() and not force_rebuild: - logger.info(f"[SCANN] results already exist and no force overwrite → skipping") - return - - # BUILD SCANN index - logger.info(f"[{idx_name} BUILD] building SCANN index") - base_vecs, _, _ = common_utils.load_data(ds_cfg["name"], nq_override=1) - scann = Scann() - scann.build(base_vecs, **scann_cfg.get("build_params", {})) - idx_file = Path(scann_cfg.get("index_file", out_dir / "indices" / f"{idx_name}.bin")) - idx_file.parent.mkdir(parents=True, exist_ok=True) - scann.save(str(idx_file)) - del base_vecs - - # LOAD queries & GT once - _, all_qvecs, all_gt = common_utils.load_data( - ds_cfg["name"], nq_override=ds_cfg["num_queries"] - ) - nq = all_qvecs.shape[0] - - # BENCHMARK SCANN over batch sizes - records = [] - for bs in batch_sizes: - logger.info(f"[{idx_name} SEARCH] batch_size={bs}") - inst = Scann() - inst.load(str(idx_file)) - - # warmup runs - warmup_q = all_qvecs[:bs] - for _ in range(num_warmup): - inst.search(warmup_q, k_val, **scann_cfg.get("search_params", {})) - - latencies = [] - recalls = [] - for _ in range(num_trials): - total_ns = 0 - ids_batches = [] - for i in range(0, nq, bs): - chunk = all_qvecs[i : min(i + bs, nq)] - res = inst.search(chunk, k_val, **scann_cfg.get("search_params", {})) - ti = getattr(res, "timing_info", None) - ns = getattr(ti, "total_time_ns", None) or getattr(ti, "child_total_time_ns", 0) - total_ns += ns - ids_batches.append(res.ids) - - latencies.append(total_ns / 1e6) - all_ids = np.vstack(ids_batches) - recalls.append(float(compute_recall(all_ids, all_gt, k_val).mean())) - - records.append({ - "index": idx_name, - "query_batch_size": bs, - "nq": nq, - "mean_total_latency_ms": float(np.mean(latencies)), - "std_total_latency_ms": float(np.std(latencies)), - "mean_recall": float(np.mean(recalls)), - "std_recall": float(np.std(recalls)), - "QPS": float(nq * 1000.0 / np.mean(latencies)), - }) - - # save (or overwrite) SCANN results - df = pd.DataFrame(records) - df.to_csv(results_csv, index=False) - logger.info(f"[{idx_name}] SCANN benchmark results saved to {results_csv}") - - def run_experiment(cfg_path, out_dir): - """ - Main entrypoint: first runs SCANN via bench_scann, then - proceeds with build/search for all other indexes. - """ logging.basicConfig(level=logging.INFO) cfg = common_utils.load_config(cfg_path) out = Path(out_dir); out.mkdir(parents=True, exist_ok=True) - # run SCANN special path - bench_scann( - cfg_path=cfg_path, - out_dir=out, - force_rebuild=cfg.get("force_rebuild", False), - force_overwrite=cfg.get("overwrite", False) - ) - ds_cfg = cfg["dataset"] batch_sizes = ds_cfg["query_batch_sizes"] run_params = { @@ -243,33 +135,37 @@ def run_experiment(cfg_path, out_dir): process_logs_dir = out / "process_logs"; process_logs_dir.mkdir(exist_ok=True) index_store_dir = out / "indices"; index_store_dir.mkdir(exist_ok=True) - # load existing results + # load existing results if present results_csv = out / cfg["output"]["results_csv"] + print(results_csv) if results_csv.exists() and not run_params["force_overwrite"]: existing_df = pd.read_csv(results_csv) done_pairs = set(zip(existing_df["index"], existing_df["query_batch_size"])) + print(done_pairs) else: + print("No existing results found") existing_df = None done_pairs = set() all_rows = [] - # === BUILD OTHER INDEXES === + # === BUILD TASKS === for idx_cfg in cfg["indexes"]: - if idx_cfg["index"] == "SCANN": - continue # already handled name = idx_cfg["name"] idx_path = Path(idx_cfg.get("index_file", index_store_dir/f"{name}.bin")) idx_path.parent.mkdir(parents=True, exist_ok=True) build_log = process_logs_dir / f"{name}_build.log" + build_env = idx_cfg.get("build_env_vars", {}) + timeout = idx_cfg.get("process_timeout", cfg.get("default_process_timeout", 7200)) + res = common_utils.run_operation_in_process( task_build_index, (idx_cfg, ds_cfg, run_params, idx_path), - env_vars=idx_cfg.get("build_env_vars", {}), + env_vars=build_env, log_file_path=str(build_log), process_name=f"BuildTask_{name}", - timeout_seconds=idx_cfg.get("process_timeout", cfg.get("default_process_timeout", 7200)), + timeout_seconds=timeout, enable_glances=run_params["enable_glances"] ) data = res.get("data", {}) @@ -278,25 +174,26 @@ def run_experiment(cfg_path, out_dir): else: logger.info(f"[BUILD OK] {name} → {data['index_file_path']}") - # === SEARCH OTHER INDEXES === + # === SEARCH TASKS === for bs in batch_sizes: for idx_cfg in cfg["indexes"]: - if idx_cfg["index"] == "SCANN": - continue # skip SCANN in normal loop name = idx_cfg["name"] if (name, bs) in done_pairs: logger.info(f"Skipping existing result for {name}, batch={bs}") continue - idx_path = Path(idx_cfg.get("index_file", index_store_dir/f"{name}.bin")) + idx_path = Path(idx_cfg.get("index_file", index_store_dir/f"{name}.bin")) search_log = process_logs_dir / f"{name}_search_bs{bs}.log" + search_env = idx_cfg.get("search_env_vars", {}) + timeout = idx_cfg.get("process_timeout", cfg.get("default_process_timeout", 7200)) + res = common_utils.run_operation_in_process( task_search_index, (idx_cfg, ds_cfg, run_params, idx_path, bs), - env_vars=idx_cfg.get("search_env_vars", {}), + env_vars=search_env, log_file_path=str(search_log), process_name=f"SearchTask_{name}_bs{bs}", - timeout_seconds=idx_cfg.get("process_timeout", cfg.get("default_process_timeout", 7200)), + timeout_seconds=timeout, enable_glances=run_params["enable_glances"] ) @@ -310,13 +207,14 @@ def run_experiment(cfg_path, out_dir): all_rows.append(row) - # === MERGE & SAVE ALL RESULTS === + # === MERGE & SAVE === new_df = pd.DataFrame(all_rows) if existing_df is not None and not run_params["force_overwrite"]: df = pd.concat([existing_df, new_df], ignore_index=True) else: df = new_df + # compute QPS df["QPS"] = df["nq"] * 1000.0 / df["mean_total_latency_ms"] df.to_csv(results_csv, index=False) logger.info(f"Wrote results to {results_csv}") From 74b9278c41ff879aa7dd8110abfb05e9f6ff6656 Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 29 May 2025 01:40:59 -0500 Subject: [PATCH 291/323] update scann for multi-query experiment --- .../osdi2025/vary_batch_size/run.py | 140 +++++++++++++++--- 1 file changed, 121 insertions(+), 19 deletions(-) diff --git a/test/experiments/osdi2025/vary_batch_size/run.py b/test/experiments/osdi2025/vary_batch_size/run.py index d30ec6f7..3844e8f2 100644 --- a/test/experiments/osdi2025/vary_batch_size/run.py +++ b/test/experiments/osdi2025/vary_batch_size/run.py @@ -115,11 +115,119 @@ def task_search_index(idx_cfg, ds_cfg, run_params, idx_file: Path, batch_size: i "query_batch_size": batch_size, } + +def bench_scann(cfg_path: str, out_dir: Path, force_rebuild: bool, force_overwrite: bool): + """ + Build and benchmark SCANN across all batch sizes defined in the config. SCANNs serialization is broken when run in a subprocess, so it is handled separately. + """ + if Scann is None: + raise ImportError("SCANN wrapper not installed; please install quake.index_wrappers.scann") + + # load config and dataset settings + cfg = common_utils.load_config(cfg_path) + ds_cfg = cfg["dataset"] + batch_sizes = ds_cfg["query_batch_sizes"] + k_val = ds_cfg["k"] + num_trials = cfg.get("trials", 3) + num_warmup = cfg.get("warmup", 1) + + # prepare paths + out_dir = Path(out_dir) + results_csv = out_dir / cfg["output"]["results_csv"] + results_csv.parent.mkdir(parents=True, exist_ok=True) + + # find SCANN entry in indexes + scann_cfg = next((ic for ic in cfg["indexes"] if ic["index"] == "SCANN"), None) + if scann_cfg is None: + return # no SCANN to run + + idx_name = scann_cfg["name"] + # check existing results + if results_csv.exists() and not force_overwrite: + df_existing = pd.read_csv(results_csv) + if idx_name in df_existing["index"].unique() and not force_rebuild: + logger.info(f"[SCANN] results already exist and no force overwrite → skipping") + return + + # BUILD SCANN index + logger.info(f"[{idx_name} BUILD] building SCANN index") + base_vecs, _, _ = common_utils.load_data(ds_cfg["name"], nq_override=1) + scann = Scann() + scann.build(base_vecs, **scann_cfg.get("build_params", {})) + idx_file = Path(scann_cfg.get("index_file", out_dir / "indices" / f"{idx_name}.bin")) + idx_file.parent.mkdir(parents=True, exist_ok=True) + scann.save(str(idx_file)) + del base_vecs + + # LOAD queries & GT once + _, all_qvecs, all_gt = common_utils.load_data( + ds_cfg["name"], nq_override=ds_cfg["num_queries"] + ) + nq = all_qvecs.shape[0] + + # BENCHMARK SCANN over batch sizes + records = [] + for bs in batch_sizes: + logger.info(f"[{idx_name} SEARCH] batch_size={bs}") + inst = Scann() + inst.load(str(idx_file)) + + # warmup runs + warmup_q = all_qvecs[:bs] + for _ in range(num_warmup): + inst.search(warmup_q, k_val, **scann_cfg.get("search_params", {})) + + latencies = [] + recalls = [] + for _ in range(num_trials): + total_ns = 0 + ids_batches = [] + for i in range(0, nq, bs): + chunk = all_qvecs[i : min(i + bs, nq)] + res = inst.search(chunk, k_val, **scann_cfg.get("search_params", {})) + ti = getattr(res, "timing_info", None) + ns = getattr(ti, "total_time_ns", None) or getattr(ti, "child_total_time_ns", 0) + total_ns += ns + ids_batches.append(res.ids) + + latencies.append(total_ns / 1e6) + all_ids = np.vstack(ids_batches) + recalls.append(float(compute_recall(all_ids, all_gt, k_val).mean())) + + records.append({ + "index": idx_name, + "query_batch_size": bs, + "nq": nq, + "mean_total_latency_ms": float(np.mean(latencies)), + "std_total_latency_ms": float(np.std(latencies)), + "mean_recall": float(np.mean(recalls)), + "std_recall": float(np.std(recalls)), + "QPS": float(nq * 1000.0 / np.mean(latencies)), + }) + + # save (or overwrite) SCANN results + df = pd.DataFrame(records) + df.to_csv(results_csv, index=False) + logger.info(f"[{idx_name}] SCANN benchmark results saved to {results_csv}") + + def run_experiment(cfg_path, out_dir): + """ + Main entrypoint: first runs SCANN via bench_scann, then + proceeds with build/search for all other indexes. + """ logging.basicConfig(level=logging.INFO) cfg = common_utils.load_config(cfg_path) out = Path(out_dir); out.mkdir(parents=True, exist_ok=True) + # run SCANN special path + bench_scann( + cfg_path=cfg_path, + out_dir=out, + force_rebuild=cfg.get("force_rebuild", False), + force_overwrite=cfg.get("overwrite", False) + ) + ds_cfg = cfg["dataset"] batch_sizes = ds_cfg["query_batch_sizes"] run_params = { @@ -135,37 +243,33 @@ def run_experiment(cfg_path, out_dir): process_logs_dir = out / "process_logs"; process_logs_dir.mkdir(exist_ok=True) index_store_dir = out / "indices"; index_store_dir.mkdir(exist_ok=True) - # load existing results if present + # load existing results results_csv = out / cfg["output"]["results_csv"] - print(results_csv) if results_csv.exists() and not run_params["force_overwrite"]: existing_df = pd.read_csv(results_csv) done_pairs = set(zip(existing_df["index"], existing_df["query_batch_size"])) - print(done_pairs) else: - print("No existing results found") existing_df = None done_pairs = set() all_rows = [] - # === BUILD TASKS === + # === BUILD OTHER INDEXES === for idx_cfg in cfg["indexes"]: + if idx_cfg["index"] == "SCANN": + continue # already handled name = idx_cfg["name"] idx_path = Path(idx_cfg.get("index_file", index_store_dir/f"{name}.bin")) idx_path.parent.mkdir(parents=True, exist_ok=True) build_log = process_logs_dir / f"{name}_build.log" - build_env = idx_cfg.get("build_env_vars", {}) - timeout = idx_cfg.get("process_timeout", cfg.get("default_process_timeout", 7200)) - res = common_utils.run_operation_in_process( task_build_index, (idx_cfg, ds_cfg, run_params, idx_path), - env_vars=build_env, + env_vars=idx_cfg.get("build_env_vars", {}), log_file_path=str(build_log), process_name=f"BuildTask_{name}", - timeout_seconds=timeout, + timeout_seconds=idx_cfg.get("process_timeout", cfg.get("default_process_timeout", 7200)), enable_glances=run_params["enable_glances"] ) data = res.get("data", {}) @@ -174,26 +278,25 @@ def run_experiment(cfg_path, out_dir): else: logger.info(f"[BUILD OK] {name} → {data['index_file_path']}") - # === SEARCH TASKS === + # === SEARCH OTHER INDEXES === for bs in batch_sizes: for idx_cfg in cfg["indexes"]: + if idx_cfg["index"] == "SCANN": + continue # skip SCANN in normal loop name = idx_cfg["name"] if (name, bs) in done_pairs: logger.info(f"Skipping existing result for {name}, batch={bs}") continue - idx_path = Path(idx_cfg.get("index_file", index_store_dir/f"{name}.bin")) + idx_path = Path(idx_cfg.get("index_file", index_store_dir/f"{name}.bin")) search_log = process_logs_dir / f"{name}_search_bs{bs}.log" - search_env = idx_cfg.get("search_env_vars", {}) - timeout = idx_cfg.get("process_timeout", cfg.get("default_process_timeout", 7200)) - res = common_utils.run_operation_in_process( task_search_index, (idx_cfg, ds_cfg, run_params, idx_path, bs), - env_vars=search_env, + env_vars=idx_cfg.get("search_env_vars", {}), log_file_path=str(search_log), process_name=f"SearchTask_{name}_bs{bs}", - timeout_seconds=timeout, + timeout_seconds=idx_cfg.get("process_timeout", cfg.get("default_process_timeout", 7200)), enable_glances=run_params["enable_glances"] ) @@ -207,14 +310,13 @@ def run_experiment(cfg_path, out_dir): all_rows.append(row) - # === MERGE & SAVE === + # === MERGE & SAVE ALL RESULTS === new_df = pd.DataFrame(all_rows) if existing_df is not None and not run_params["force_overwrite"]: df = pd.concat([existing_df, new_df], ignore_index=True) else: df = new_df - # compute QPS df["QPS"] = df["nq"] * 1000.0 / df["mean_total_latency_ms"] df.to_csv(results_csv, index=False) logger.info(f"Wrote results to {results_csv}") From c58b1d5f9d830614b5d2dfbc004a489fabd095d1 Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 29 May 2025 01:42:38 -0500 Subject: [PATCH 292/323] update scann for multi-query experiment --- test/experiments/osdi2025/vary_batch_size/run.py | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) diff --git a/test/experiments/osdi2025/vary_batch_size/run.py b/test/experiments/osdi2025/vary_batch_size/run.py index 3844e8f2..138aa120 100644 --- a/test/experiments/osdi2025/vary_batch_size/run.py +++ b/test/experiments/osdi2025/vary_batch_size/run.py @@ -152,11 +152,10 @@ def bench_scann(cfg_path: str, out_dir: Path, force_rebuild: bool, force_overwri # BUILD SCANN index logger.info(f"[{idx_name} BUILD] building SCANN index") base_vecs, _, _ = common_utils.load_data(ds_cfg["name"], nq_override=1) - scann = Scann() - scann.build(base_vecs, **scann_cfg.get("build_params", {})) + scann_idx = Scann() + scann_idx.build(base_vecs, **scann_cfg.get("build_params", {})) idx_file = Path(scann_cfg.get("index_file", out_dir / "indices" / f"{idx_name}.bin")) idx_file.parent.mkdir(parents=True, exist_ok=True) - scann.save(str(idx_file)) del base_vecs # LOAD queries & GT once @@ -169,13 +168,11 @@ def bench_scann(cfg_path: str, out_dir: Path, force_rebuild: bool, force_overwri records = [] for bs in batch_sizes: logger.info(f"[{idx_name} SEARCH] batch_size={bs}") - inst = Scann() - inst.load(str(idx_file)) # warmup runs warmup_q = all_qvecs[:bs] for _ in range(num_warmup): - inst.search(warmup_q, k_val, **scann_cfg.get("search_params", {})) + scann_idx.search(warmup_q, k_val, **scann_cfg.get("search_params", {})) latencies = [] recalls = [] @@ -184,7 +181,7 @@ def bench_scann(cfg_path: str, out_dir: Path, force_rebuild: bool, force_overwri ids_batches = [] for i in range(0, nq, bs): chunk = all_qvecs[i : min(i + bs, nq)] - res = inst.search(chunk, k_val, **scann_cfg.get("search_params", {})) + res = scann_idx.search(chunk, k_val, **scann_cfg.get("search_params", {})) ti = getattr(res, "timing_info", None) ns = getattr(ti, "total_time_ns", None) or getattr(ti, "child_total_time_ns", 0) total_ns += ns From e46b1e6c9f8fbe78e10cb1369a32fb32c62eca1d Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 29 May 2025 02:32:31 -0500 Subject: [PATCH 293/323] update scann for multi-query experiment --- test/experiments/osdi2025/vary_batch_size/run.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/test/experiments/osdi2025/vary_batch_size/run.py b/test/experiments/osdi2025/vary_batch_size/run.py index 138aa120..95bdeae1 100644 --- a/test/experiments/osdi2025/vary_batch_size/run.py +++ b/test/experiments/osdi2025/vary_batch_size/run.py @@ -204,6 +204,12 @@ def bench_scann(cfg_path: str, out_dir: Path, force_rebuild: bool, force_overwri # save (or overwrite) SCANN results df = pd.DataFrame(records) + + # append to existing results if available + if results_csv.exists() and not force_overwrite: + existing_df = pd.read_csv(results_csv) + df = pd.concat([existing_df, df], ignore_index=True) + df.to_csv(results_csv, index=False) logger.info(f"[{idx_name}] SCANN benchmark results saved to {results_csv}") From d97741eb13a262633c3be334471d0ea0ea480baf Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 29 May 2025 02:46:14 -0500 Subject: [PATCH 294/323] update scann for multi-query experiment --- test/experiments/osdi2025/vary_batch_size/run.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/test/experiments/osdi2025/vary_batch_size/run.py b/test/experiments/osdi2025/vary_batch_size/run.py index 95bdeae1..4014ebda 100644 --- a/test/experiments/osdi2025/vary_batch_size/run.py +++ b/test/experiments/osdi2025/vary_batch_size/run.py @@ -80,8 +80,8 @@ def task_search_index(idx_cfg, ds_cfg, run_params, idx_file: Path, batch_size: i } inst.load(str(idx_file), **load_kwargs) - # warmup on one chunk - warmup = all_qvecs[:batch_size] + # warmup on one chunk using random vectors + warmup = np.random.randn(batch_size, all_qvecs.shape[1]).astype(np.float32) for _ in range(run_params["num_warmup"]): _ = inst.search(warmup, run_params["k_val"], **sp) From a87367faa01d81690b1770d7297c21a3698a0f65 Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 29 May 2025 03:07:57 -0500 Subject: [PATCH 295/323] update scann for multi-query experiment --- test/experiments/osdi2025/vary_batch_size/run.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/test/experiments/osdi2025/vary_batch_size/run.py b/test/experiments/osdi2025/vary_batch_size/run.py index 4014ebda..90bc471e 100644 --- a/test/experiments/osdi2025/vary_batch_size/run.py +++ b/test/experiments/osdi2025/vary_batch_size/run.py @@ -5,6 +5,7 @@ import matplotlib.pyplot as plt import os import faiss +import torch import test.experiments.osdi2025.experiment_utils as common_utils from quake.utils import compute_recall @@ -81,7 +82,7 @@ def task_search_index(idx_cfg, ds_cfg, run_params, idx_file: Path, batch_size: i inst.load(str(idx_file), **load_kwargs) # warmup on one chunk using random vectors - warmup = np.random.randn(batch_size, all_qvecs.shape[1]).astype(np.float32) + warmup = torch.randn(batch_size, all_qvecs.shape[1]).astype(np.float32) for _ in range(run_params["num_warmup"]): _ = inst.search(warmup, run_params["k_val"], **sp) From 9557e2933f7e0a74b07fcc2c69d5496ff90fe42a Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 29 May 2025 03:09:31 -0500 Subject: [PATCH 296/323] update scann for multi-query experiment --- test/experiments/osdi2025/vary_batch_size/run.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/experiments/osdi2025/vary_batch_size/run.py b/test/experiments/osdi2025/vary_batch_size/run.py index 90bc471e..78a09178 100644 --- a/test/experiments/osdi2025/vary_batch_size/run.py +++ b/test/experiments/osdi2025/vary_batch_size/run.py @@ -82,7 +82,7 @@ def task_search_index(idx_cfg, ds_cfg, run_params, idx_file: Path, batch_size: i inst.load(str(idx_file), **load_kwargs) # warmup on one chunk using random vectors - warmup = torch.randn(batch_size, all_qvecs.shape[1]).astype(np.float32) + warmup = torch.randn(batch_size, all_qvecs.shape[1]) for _ in range(run_params["num_warmup"]): _ = inst.search(warmup, run_params["k_val"], **sp) From 5cbd157d97c96aa4fb5e7dc786085da1c985378b Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 29 May 2025 03:33:19 -0500 Subject: [PATCH 297/323] update diskann for multi-query experiment --- test/experiments/osdi2025/vary_batch_size/run.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/test/experiments/osdi2025/vary_batch_size/run.py b/test/experiments/osdi2025/vary_batch_size/run.py index 78a09178..41efe44c 100644 --- a/test/experiments/osdi2025/vary_batch_size/run.py +++ b/test/experiments/osdi2025/vary_batch_size/run.py @@ -79,6 +79,13 @@ def task_search_index(idx_cfg, ds_cfg, run_params, idx_file: Path, batch_size: i for k in ("num_workers", "use_numa", "num_merge_workers", "parent") if k in bp } + + if itype == "DiskANN": + # DiskANN requires special handling for loading, all build params are passed as kwargs + load_kwargs = bp.copy() + bp.pop("omp_num_threads_build", None) # remove omp threads from load params + + inst.load(str(idx_file), **load_kwargs) # warmup on one chunk using random vectors From 9df34048ea203b04c28ce08cede23e3488731b29 Mon Sep 17 00:00:00 2001 From: Jason Date: Thu, 29 May 2025 04:12:34 -0500 Subject: [PATCH 298/323] update diskann for multi-query experiment --- test/experiments/osdi2025/vary_batch_size/run.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/test/experiments/osdi2025/vary_batch_size/run.py b/test/experiments/osdi2025/vary_batch_size/run.py index 41efe44c..ff32951e 100644 --- a/test/experiments/osdi2025/vary_batch_size/run.py +++ b/test/experiments/osdi2025/vary_batch_size/run.py @@ -72,6 +72,8 @@ def task_search_index(idx_cfg, ds_cfg, run_params, idx_file: Path, batch_size: i ) nq = all_qvecs.shape[0] + # randomize queries for fair comparison + Cls = INDEX_CLASSES[itype] inst = Cls() load_kwargs = { @@ -99,6 +101,9 @@ def task_search_index(idx_cfg, ds_cfg, run_params, idx_file: Path, batch_size: i for _ in range(run_params["num_trials"]): total_ns = 0 ids_list = [] + rand_perm = torch.randperm(nq) + all_gt = all_gt[rand_perm] + all_qvecs = all_qvecs[rand_perm] for i in range(0, nq, batch_size): chunk = all_qvecs[i : min(i + batch_size, nq)] res = inst.search(chunk, run_params["k_val"], **sp) From 3278151d4189dd918956054591241294596ed096 Mon Sep 17 00:00:00 2001 From: Jason Date: Fri, 30 May 2025 09:23:52 -0500 Subject: [PATCH 299/323] fix refinement --- src/cpp/include/list_scanning.h | 1 - src/cpp/include/query_coordinator.h | 4 +- src/cpp/src/clustering.cpp | 71 ++++++++++++++--------------- src/cpp/src/query_coordinator.cpp | 27 ++++++++--- 4 files changed, 54 insertions(+), 49 deletions(-) diff --git a/src/cpp/include/list_scanning.h b/src/cpp/include/list_scanning.h index b42d810a..5dcb9ac6 100644 --- a/src/cpp/include/list_scanning.h +++ b/src/cpp/include/list_scanning.h @@ -668,7 +668,6 @@ inline void batched_scan_list(const float *query_vecs, pivots ); } else if (metric == faiss::METRIC_L2) { - std::cout << "Using L2 metric for batched scan." << std::endl; l2_blas( query_vecs, list_vecs, diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index 2bfd93cd..4e0193b8 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -281,9 +281,7 @@ class QueryCoordinator { void drain_and_apply_aps(Tensor x, Tensor partition_ids, - bool use_aps, - float recall_target, - int aps_flush_period_us, + shared_ptr params, shared_ptr timing); std::shared_ptr diff --git a/src/cpp/src/clustering.cpp b/src/cpp/src/clustering.cpp index f46ad13e..6b3a884d 100644 --- a/src/cpp/src/clustering.cpp +++ b/src/cpp/src/clustering.cpp @@ -234,39 +234,23 @@ tuple >> kmeans_refine_partitions( MetricType metric, int refinement_iterations) { - // static void ensure_blas_buffers(QueryCoordinator::CoreResources& res, - // size_t max_q, - // size_t db_bs, - // int node) - // { - // const size_t ip_need = db_bs * max_q; - // if (res.blas_ip_capacity < ip_need) { - // quake_free(res.blas_ip_block, res.blas_ip_capacity * sizeof(float)); - // res.blas_ip_block = static_cast(quake_alloc(ip_need * sizeof(float), node)); - // res.blas_ip_capacity = ip_need; - // } - // if (res.blas_norms_x_cap < max_q) { - // quake_free(res.blas_norms_x, res.blas_norms_x_cap * sizeof(float)); - // res.blas_norms_x = static_cast(quake_alloc(max_q * sizeof(float), node)); - // res.blas_norms_x_cap = max_q; - // } - // if (res.blas_norms_y_cap < db_bs) { - // quake_free(res.blas_norms_y, res.blas_norms_y_cap * sizeof(float)); - // res.blas_norms_y = static_cast(quake_alloc(db_bs * sizeof(float), node)); - // res.blas_norms_y_cap = db_bs; - // } - // } - - const size_t ip_need = BLAS_DB_BS * centroids.size(0); - float * blas_ip_block = static_cast(quake_alloc(ip_need * sizeof(float), 0)); - float * blas_norms_x = static_cast(quake_alloc(BLAS_DB_BS * sizeof(float), 0)); - float * blas_norms_y = static_cast(quake_alloc(BLAS_DB_BS * sizeof(float), 0)); - + size_t max_nq = 0; + for (auto &p : partitions) { + max_nq = std::max(max_nq, (size_t)p->num_vectors_); + } + max_nq = max_nq * 20; // double the max_nq to ensure enough space for batched_scan_list, as assignments may change. // Determine number of clusters and dimension. int n_clusters = centroids.size(0); int d = centroids.size(1); + const size_t ip_need = n_clusters * max_nq; + float * blas_ip_block = static_cast(quake_alloc(ip_need * sizeof(float), 0)); + float * blas_norms_x = static_cast(quake_alloc(max_nq * sizeof(float), 0)); + float * blas_norms_y = static_cast(quake_alloc(n_clusters * sizeof(float), 0)); + vector > buffers = create_buffers(max_nq, 1, (metric == faiss::METRIC_INNER_PRODUCT), n_clusters); + + // Run for the desired number of iterations (if refinement_iterations==0, do one pass). int iterations = (refinement_iterations > 0) ? refinement_iterations : 1; @@ -274,14 +258,23 @@ tuple >> kmeans_refine_partitions( Tensor centroid_counts = torch::zeros({n_clusters}, torch::kInt64); auto centroid_sums_accessor = centroid_sums.accessor(); auto centroid_counts_accessor = centroid_counts.accessor(); + Tensor centroid_ids = torch::arange(n_clusters, torch::kInt64); + auto centroid_ids_ptr = centroid_ids.data_ptr(); vector> prev_partitions = partitions; vector> new_partitions; for (int iter = 0; iter < iterations; iter++) { - if (iter > 0) { centroids = centroid_sums / centroid_counts.unsqueeze(1).to(torch::kFloat32); + + // normalize centroids if using inner product metric + if (metric == faiss::METRIC_INNER_PRODUCT) { + centroids = centroids + / centroids.norm(2,1) + .unsqueeze(1) + .to(torch::kFloat32); + } } // Reset accumulators. @@ -299,7 +292,6 @@ tuple >> kmeans_refine_partitions( float *centroids_ptr = centroids.data_ptr(); - // Process each existing partition. for (auto &part: partitions) { int64_t nvec = part->num_vectors_; if (nvec <= 0) continue; @@ -307,17 +299,10 @@ tuple >> kmeans_refine_partitions( float *part_vecs = (float *) part->codes_; int64_t *part_vec_ids = part->ids_; - // Create batched TopK buffers (k=1 for nearest centroid). - vector > buffers = create_buffers(nvec, 1, false); - // Use batched_scan_list to get nearest centroid for each vector. - int64_t scan_setup_time = 0; - int64_t scan_time = 0; - int64_t scan_push_time = 0; - batched_scan_list(part_vecs, centroids_ptr, - nullptr, + centroid_ids_ptr, nvec, n_clusters, d, @@ -343,10 +328,20 @@ tuple >> kmeans_refine_partitions( centroid_counts_accessor[assigned_cluster]++; new_partitions[assigned_cluster]->append(1, vec_id, (uint8_t *) vec_ptr); + + // reset the buffer for this slot + buffers[i]->reset(); } } // end for each partition + + std::move(new_partitions.begin(), new_partitions.end(), partitions.begin()); } // end iterations + // Clean up and return the refined centroids and partitions. + quake_free(blas_ip_block, ip_need * sizeof(float)); + quake_free(blas_norms_x, max_nq * sizeof(float)); + quake_free(blas_norms_y, BLAS_DB_BS * sizeof(float)); + return std::make_tuple(centroids, partitions); } diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index ec989d9d..248a29f7 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -643,7 +643,7 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, } } else { /* Build per-partition query lists -------------------------------------- */ - std::vector>> qlist(partition_manager_->nlist()); + std::unordered_map>> qlist; for (int64_t q = 0; q < nQ; ++q) { for (int p = 0; p < partition_ids.size(1); ++p) { int64_t pid = pid_acc[q][p]; @@ -719,9 +719,7 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, void QueryCoordinator::drain_and_apply_aps(Tensor x, Tensor partition_ids, - bool use_aps, - float recall_target, - int aps_flush_period_us, + shared_ptr search_params, std::shared_ptr timing) { @@ -729,10 +727,26 @@ void QueryCoordinator::drain_and_apply_aps(Tensor x, // compute boundary distances while (total_left_.load(std::memory_order_relaxed) > 0) - std::this_thread::sleep_for(std::chrono::microseconds(aps_flush_period_us)); + std::this_thread::sleep_for(std::chrono::microseconds(search_params->aps_flush_period_us)); + // mark hits + if (search_params->track_hits && maintenance_policy_) { + for (int64_t q = 0; q < nQ; ++q) { + std::vector scanned_ids; + scanned_ids.reserve(partition_ids.size(1)); + for (int p = 0; p < partition_ids.size(1); ++p) { + int64_t pid = partition_ids[q][p].item(); + if (pid < 0) continue; + scanned_ids.emplace_back(pid); + } + maintenance_policy_->record_query_hits(scanned_ids); + } + // maintenance_policy_->record_query_hits(std::vector(scanned_ids.begin(), scanned_ids.end())); + } + + // check if we need to apply APS } @@ -824,8 +838,7 @@ std::shared_ptr QueryCoordinator::worker_scan( Tensor out_dists = torch::empty({nQ, K}, torch::kFloat); // 4) drain results + APS - drain_and_apply_aps(x, partition_ids, use_aps, params->recall_target, - params->aps_flush_period_us, timing); + drain_and_apply_aps(x, partition_ids, params, timing); auto s5 = high_resolution_clock::now(); From 41d3d05da299bedaf190331b727506a7b3e15211 Mon Sep 17 00:00:00 2001 From: Jason Date: Sun, 1 Jun 2025 00:07:00 -0500 Subject: [PATCH 300/323] update aps + workers --- src/cpp/bindings/wrap.cpp | 7 +- src/cpp/include/clustering.h | 5 +- src/cpp/include/common.h | 3 +- src/cpp/include/geometry.h | 183 ++- src/cpp/include/query_coordinator.h | 1 + src/cpp/src/clustering.cpp | 7 +- src/cpp/src/maintenance_policies.cpp | 2 + src/cpp/src/query_coordinator.cpp | 224 +++- test/cpp/benchmark.cpp | 14 +- test/cpp/latency_estimator.cpp | 3 +- test/cpp/list_scanning.cpp | 1124 ++++++++--------- test/cpp/quake_index.cpp | 3 +- .../aps_recall_targets/configs/sift1m.yaml | 9 +- .../osdi2025/aps_recall_targets/run.py | 39 +- .../experiments/osdi2025/experiment_runner.py | 2 + test/experiments/osdi2025/experiment_utils.py | 11 +- .../numa_multi_query/configs/sift1m.yaml | 34 +- .../experiments/osdi2025/wiki_workload/run.py | 39 +- 18 files changed, 986 insertions(+), 724 deletions(-) diff --git a/src/cpp/bindings/wrap.cpp b/src/cpp/bindings/wrap.cpp index cfd0f29d..2c8f5f08 100644 --- a/src/cpp/bindings/wrap.cpp +++ b/src/cpp/bindings/wrap.cpp @@ -279,10 +279,8 @@ PYBIND11_MODULE(_bindings, m) { "Time taken for split operations in microseconds.") .def_readonly("delete_time_us", &MaintenanceTimingInfo::delete_time_us, "Time taken for delete operations in microseconds.") - .def_readonly("split_refine_time_us", &MaintenanceTimingInfo::split_refine_time_us, + .def_readonly("refinement_time_us", &MaintenanceTimingInfo::refinement_time_us, "Time taken for refinement of split operations in microseconds.") - .def_readonly("delete_refine_time_us", &MaintenanceTimingInfo::delete_refine_time_us, - "Time taken for refinement of delete operations in microseconds.") .def_readonly("n_splits", &MaintenanceTimingInfo::n_splits, "Number of partition split operations performed.") .def_readonly("n_deletes", &MaintenanceTimingInfo::n_deletes, @@ -293,8 +291,7 @@ PYBIND11_MODULE(_bindings, m) { oss << "\"total_time_us\": " << t.total_time_us << ", "; oss << "\"split_time_us\": " << t.split_time_us << ", "; oss << "\"delete_time_us\": " << t.delete_time_us << ", "; - oss << "\"split_refine_time_us\": " << t.split_refine_time_us << ", "; - oss << "\"delete_refine_time_us\": " << t.delete_refine_time_us << ", "; + oss << "\"refinement_time_us\": " << t.refinement_time_us << ", "; oss << "\"n_splits\": " << t.n_splits << ", "; oss << "\"n_deletes\": " << t.n_deletes; oss << "}"; diff --git a/src/cpp/include/clustering.h b/src/cpp/include/clustering.h index d83d563b..449c99d5 100644 --- a/src/cpp/include/clustering.h +++ b/src/cpp/include/clustering.h @@ -75,8 +75,9 @@ shared_ptr kmeans(Tensor vectors, */ tuple>> kmeans_refine_partitions( Tensor centroids, - vector> index_partitions, + vector> &index_partitions, MetricType metric, - int refinement_iterations = 0); + int refinement_iterations = 0, + int num_threads = -1); #endif //CLUSTERING_H diff --git a/src/cpp/include/common.h b/src/cpp/include/common.h index bf1038ed..f840282a 100644 --- a/src/cpp/include/common.h +++ b/src/cpp/include/common.h @@ -261,9 +261,8 @@ struct MaintenanceTimingInfo { int64_t n_splits; ///< Number of splits. int64_t n_deletes; ///< Number of merges. int64_t delete_time_us; ///< Time spent on deletions in microseconds. - int64_t delete_refine_time_us; ///< Time spent on deletions with refinement in microseconds. int64_t split_time_us; ///< Time spent on splits in microseconds. - int64_t split_refine_time_us; ///< Time spent on splits with refinement in microseconds. + int64_t refinement_time_us; ///< Time spent on refinement in microseconds. int64_t total_time_us; ///< Total time spent in microseconds. }; diff --git a/src/cpp/include/geometry.h b/src/cpp/include/geometry.h index 5e54890e..5abbc7f9 100644 --- a/src/cpp/include/geometry.h +++ b/src/cpp/include/geometry.h @@ -55,48 +55,6 @@ inline void print_array(const float *array, int dimension) { std::cout << std::endl << std::endl; } -inline std::vector -compute_boundary_distances(const torch::Tensor& query, - std::vector& centroids, - bool euclidean /* kept for API */ ) -{ - int dim = query.size(0); - const float* q = query.data_ptr(); - - /* --- ensure centroids[0] is the nearest --------------------- */ - size_t nearest = 0; - float best_d2 = std::numeric_limits::max(); - for (size_t i = 0; i < centroids.size(); ++i) { - float d2 = faiss::fvec_L2sqr(q, centroids[i], dim); - if (d2 < best_d2) { best_d2 = d2; nearest = i; } - } - if (nearest != 0) std::swap(centroids[0], centroids[nearest]); - - const float* c0 = centroids[0]; - std::vector d(centroids.size(), 0.0f); - std::vector v(dim); - - for (size_t j = 1; j < centroids.size(); ++j) - { - const float* cj = centroids[j]; - - /* v = cj - c0, ||v|| */ - faiss::fvec_sub(dim, cj, c0, v.data()); - float v_norm = std::sqrt( - faiss::fvec_inner_product(v.data(), v.data(), dim)); - - /* b = ½ (||cj||² - ||c0||²) */ - float b = 0.5f * ( - faiss::fvec_inner_product(cj, cj, dim) - - faiss::fvec_inner_product(c0, c0, dim)); - - /* signed distance (q·v − b) / ||v|| */ - float dot_qv = faiss::fvec_inner_product(q, v.data(), dim); - d[j] = std::fabs(dot_qv - b) / v_norm; // plane distance - } - return d; // d[0] = 0 -} - inline double incomplete_beta(double a, double b, double x) { if (x < 0.0 || x > 1.0) return 1.0 / 0.0; @@ -203,8 +161,57 @@ inline double log_hypersphere_volume(double radius, int dimension) { return log_volume; } + +inline std::vector +compute_boundary_distances(const Tensor& query, + std::vector& centroids, + bool euclidean) +{ + const int dim = query.size(0); + const float* q = query.data_ptr(); + + const float* c0 = centroids[0]; + std::vector dist(centroids.size(), 0.0f); + + std::vector v(dim); // c_j - c0 + std::vector m(dim); // midpoint for IP + + for (std::size_t j = 1; j < centroids.size(); ++j) { + const float* cj = centroids[j]; + + if (euclidean) { + /* plane distance d = |q·v − b| / ||v|| , b = ½(||cj||²−||c0||²) */ + faiss::fvec_sub(dim, cj, c0, v.data()); + float v_norm = std::sqrt(faiss::fvec_inner_product(v.data(), v.data(), dim)); + float b = 0.5f * (faiss::fvec_inner_product(cj, cj, dim) - + faiss::fvec_inner_product(c0, c0, dim)); + float dot_qv = faiss::fvec_inner_product(q, v.data(), dim); + dist[j] = std::fabs(dot_qv - b) / (v_norm + 1e-12f); + } else { + faiss::fvec_sub(dim, cj, c0, v.data()); // v = cj - c0 + float v_norm = std::sqrt(faiss::fvec_inner_product(v.data(), v.data(), dim)); + divide_array_by_constant(v.data(), v_norm, v.data(), dim); // v̂ + float s = std::fabs(faiss::fvec_inner_product(q, v.data(), dim)); + s = std::clamp(s, 0.0f, 1.0f); + dist[j] = std::asin(s); // 0–π/2 + + // /* unit-sphere model – distance is polar angle to great-circle bisector between c0 and cj. */ + // add_arrays(c0, cj, m.data(), dim); // midpoint vector + // float m_norm = std::sqrt(faiss::fvec_inner_product(m.data(), m.data(), dim)); + // if (m_norm > 0) divide_array_by_constant(m.data(), m_norm, m.data(), dim); + // float cos_ang = faiss::fvec_inner_product(q, m.data(), dim); + // cos_ang = std::clamp(cos_ang, -1.0f, 1.0f); + // dist[j] = std::acos(cos_ang); // radians + } + } + return dist; // dist[0] = 0 by construction +} + inline double hyperspherical_cap_volume(double radius, double boundary_distance, int d, bool use_precomputed = true, bool euclidean = true) { + + if (euclidean) { + // Ensure boundary distance is non-negative double boundary_distance = std::max(0.0, boundary_distance); @@ -224,11 +231,32 @@ inline double hyperspherical_cap_volume(double radius, double boundary_distance, return std::clamp(0.5 * I, 0.0, 0.5); } else { - // v_i = (1/2) * [ I( sin^2(phi/2); d/2, 1/2 ) - I( sin^2(theta_i/2); d/2, 1/2 ) ] - double log_inc_beta = std::log(incomplete_beta((d - 1) / 2.0, 0.5, std::sin(radius / 2.0) * std::sin(radius / 2.0))); - double log_inc_beta_boundary = std::log(incomplete_beta((d - 1) / 2.0, 0.5, std::sin(boundary_distance / 2.0) * std::sin(boundary_distance / 2.0))); - double log_cap_volume = std::log(0.5) + log_inc_beta - log_inc_beta_boundary; - return log_cap_volume; + // spherical / IP ----------------------------------------------------------------- + double theta_q = radius; // query cap angle (rad) + double delta = boundary_distance; // distance to bisector (rad) + + /* 1. trivial cases ------------------------------------------------------------ */ + if (delta >= theta_q) return 0.0; // cap entirely in c0 + if (theta_q >= M_PI_2 - delta) return 1.0; // cap entirely in cj + + /* 2. general case: Lee & Kim (2014) cases 9–10 -------------------------------- */ + double t = std::tan(delta) / std::tan(theta_q); // 0 ≤ t < 1 + t = std::clamp(t, 0.0, 1.0); // numerical safety + + double alpha = std::acos(t); // 0 < α < π/2 + double x = std::sin(alpha) * std::sin(alpha); // 0 < x < 1 + + double a = 0.5 * (d - 1); + double b = 0.5; + + double Ix = incomplete_beta(a, b, x); // regularised + return 0.5 * Ix; // leakage fraction + + + // double log_inc_beta = std::log(incomplete_beta((d - 1) / 2.0, 0.5, std::sin(radius / 2.0) * std::sin(radius / 2.0))); + // double log_inc_beta_boundary = std::log(incomplete_beta((d - 1) / 2.0, 0.5, std::sin((radius - boundary_distance) / 2.0) * std::sin(radius - boundary_distance) / 2.0)); + // double log_cap_volume = std::log(0.5) + log_inc_beta - log_inc_beta_boundary; + // return std::exp(log_cap_volume); } } @@ -243,6 +271,10 @@ compute_recall_profile(const std::vector& boundary_distances, const int m = static_cast(boundary_distances.size()); const float eps = 1e-9f; + if (!euclidean) { + query_radius = std::acos(query_radius); // Convert to angle in radians for spherical model + } + // --- Edge Cases --- if (m <= 1) { if (m == 1) return {1.0f}; // Only the central partition exists @@ -268,6 +300,8 @@ compute_recall_profile(const std::vector& boundary_distances, float S1_for_norm = 0.0f; for (int j = 1; j < m; ++j) S1_for_norm += norm_vols[j]; + // S1_for_norm = 1.0; + if (S1_for_norm > eps) { for (int j = 1; j < m; ++j) norm_vols[j] /= S1_for_norm; } else { @@ -285,20 +319,32 @@ compute_recall_profile(const std::vector& boundary_distances, // Ensure P_prime is non-negative for (int k = 1; k < m; ++k) P_prime[k] = std::max(0.0f, P_prime[k]); - // // if the cluster_sizes are given, scale P_prime[k] by the size of the cluster. this is a rudimentary density estimation - // if (partition_sizes.size() > 0) { - // for (int k = 1; k < m; ++k) { - // if (partition_sizes[k] > 0) { - // P_prime[k] *= static_cast(partition_sizes[k]); - // } - // } - // } - - // normalize probs std::vector probs(m, 0.0f); probs[0] = P0; + // for (int k = 1; k < m; ++k) { + // if (P_prime_sum > eps) { + // probs[k] = norm_vols[k]; + // } else { + // probs[k] = 0.0f; // If sum is negligible, set to zero + // } + // } + // + // // normalize probabilities + // float S = 0.0f; + // for (int k = 0; k < m; ++k) S += probs[k]; + // if (S > eps) { + // for (int k = 0; k < m; ++k) { + // probs[k] /= S; + // } + // } else { + // // If S is zero, all probabilities remain zero + // for (int k = 0; k < m; ++k) { + // probs[k] = 0.0f; + // } + // } + float target = 1.0f - P0; // Ensure target probability for neighbors is valid [0, 1] target = std::clamp(target, 0.0f, 1.0f); @@ -334,6 +380,29 @@ compute_recall_profile(const std::vector& boundary_distances, } } + // if the cluster_sizes are given, scale probs by the size of the cluster and renormalize. this is a rudimentary density estimation + // if (partition_sizes.size() > 0) { + // for (int k = 1; k < m; ++k) { + // if (partition_sizes[k] > 0) { + // probs[k] *= static_cast(partition_sizes[k]); + // } + // } + // } + + // // renormalize the probabilities to sum to 1 + // float S = 0.0f; + // for (int k = 0; k < m; ++k) S += probs[k]; + // if (S > eps) { + // for (int k = 0; k < m; ++k) { + // probs[k] /= S; + // } + // } else { + // // If S is zero, all probabilities remain zero + // for (int k = 0; k < m; ++k) { + // probs[k] = 0.0f; + // } + // } + return probs; } diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index 4e0193b8..afa4d39e 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -124,6 +124,7 @@ class QueryCoordinator { std::vector job_buffer_; int next_job_id_ = 0; ///< ID for the next job to be processed. vector> query_dist_pivots_; ///< Pivots for each query to speed up sorting + vector> query_done_flags_; ///< Flags to indicate if a query is done from APS vector>> job_flags_; ///< Flags to track job completion std::atomic job_pull_time_ns = 0; ///< Time spent pulling jobs from the queue. std::atomic job_process_time_ns = 0; ///< Time spent processing jobs. diff --git a/src/cpp/src/clustering.cpp b/src/cpp/src/clustering.cpp index 6b3a884d..5a46da6a 100644 --- a/src/cpp/src/clustering.cpp +++ b/src/cpp/src/clustering.cpp @@ -10,6 +10,7 @@ #include "index_partition.h" #include #include +#include #ifdef QUAKE_ENABLE_GPU #include @@ -228,11 +229,13 @@ shared_ptr kmeans(Tensor vectors, } } + tuple >> kmeans_refine_partitions( Tensor centroids, - vector> partitions, + vector> &partitions, MetricType metric, - int refinement_iterations) { + int refinement_iterations, + int num_threads) { size_t max_nq = 0; for (auto &p : partitions) { diff --git a/src/cpp/src/maintenance_policies.cpp b/src/cpp/src/maintenance_policies.cpp index 2d357e68..106bc895 100644 --- a/src/cpp/src/maintenance_policies.cpp +++ b/src/cpp/src/maintenance_policies.cpp @@ -183,6 +183,7 @@ shared_ptr MaintenancePolicy::perform_maintenance() { local_refinement(split_partitions->partition_ids); } auto end_total = steady_clock::now(); + int64_t refinement_time_us = static_cast(duration_cast(end_total - end_split).count()); // STEP 6: Clean up any empty partitions vector empty_ids = {}; @@ -199,6 +200,7 @@ shared_ptr MaintenancePolicy::perform_maintenance() { shared_ptr timing_info = std::make_shared(); timing_info->delete_time_us = duration_cast(end_delete - start_delete).count(); timing_info->split_time_us = duration_cast(end_split - start_split).count(); + timing_info->refinement_time_us = refinement_time_us; timing_info->total_time_us = duration_cast(end_total - start_total).count(); timing_info->n_splits = static_cast(partitions_to_split.size()); diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 248a29f7..ade73c53 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -110,6 +110,13 @@ void QueryCoordinator::merge_worker_fn(int mid) { if (rj.query_id == -1) // poison pill return; + if (rj.distances.empty()) { + // empty result, nothing to do + --per_query_total_left_[rj.query_id]; + --total_left_; + continue; + } + // single cast, based on the Compare template using Handler = typename faiss::HeapBlockResultHandler::SingleResultHandler; auto* h = static_cast(MR.handlers[rj.query_id]); @@ -122,32 +129,31 @@ void QueryCoordinator::merge_worker_fn(int mid) { query_dist_pivots_[rj.query_id].store(h->threshold, std::memory_order_relaxed); + per_query_total_left_[rj.query_id]--; + // once all ranks for this query are in, finalize & sort - if (!job_flags_[rj.query_id][rj.rank]) { - job_flags_[rj.query_id][rj.rank] = true; - if (--per_query_total_left_[rj.query_id] == 0) { - h->end(); - - // pack into pairs for sorting - int k = h->k; - std::vector> result; - result.reserve(k); - for (int i = 0; i < k; ++i) { - result.emplace_back(h->heap_dis[i], h->heap_ids[i]); - } + if (per_query_total_left_[rj.query_id] <= 0) { + h->end(); + + // pack into pairs for sorting + int k = h->k; + std::vector> result; + result.reserve(k); + for (int i = 0; i < k; ++i) { + result.emplace_back(h->heap_dis[i], h->heap_ids[i]); + } - // for CMin (inner-product) we want descending distances - // for CMax (L2) we want ascending distances - auto cmp = [](auto& a, auto& b) { - return Compare::cmp(a.first, b.first); - }; - std::sort(result.begin(), result.end(), cmp); - - // write them back - for (int i = 0; i < k; ++i) { - h->heap_dis[i] = result[i].first; - h->heap_ids[i] = result[i].second; - } + // for CMin (inner-product) we want descending distances + // for CMax (L2) we want ascending distances + auto cmp = [](auto& a, auto& b) { + return Compare::cmp(b.first, a.first); + }; + std::sort(result.begin(), result.end(), cmp); + + // write them back + for (int i = 0; i < k; ++i) { + h->heap_dis[i] = result[i].first; + h->heap_ids[i] = result[i].second; } } --total_left_; @@ -260,6 +266,13 @@ void QueryCoordinator::handle_nonbatched_job(const ScanJob &job, NUMAResources &nr) { auto start = std::chrono::high_resolution_clock::now(); + + // check that the job has not been processed yet + if (query_done_flags_[job.query_id].load(std::memory_order_relaxed)) { + enqueue_result_job(ResultJob{job.query_id, job.rank, {}, {}}); + return; + } + // ensure buffers if (res.topk_buffer_pool.size() < 1) { res.topk_buffer_pool.resize(1); @@ -300,6 +313,8 @@ void QueryCoordinator::handle_nonbatched_job(const ScanJob &job, return; // Important to return after enqueueing the placeholder } + job_flags_[job.query_id][job.rank].store(true, std::memory_order_relaxed); // Mark this job as processed + scan_list(nr.local_query_buffer + (job.query_id * D), codes, ids, @@ -406,17 +421,33 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, // gather queries float *qptr = nullptr; float *dst = res.batch_queries; + vector query_ids; + vector ranks; + query_ids.reserve(Q); + ranks.reserve(Q); + int64_t offset = 0; for (int64_t i = 0; i < Q; ++i) { + int qid = (*job.query_ids)[i]; - const float *src = nr.local_query_buffer + size_t(qid) * D; - std::memcpy(dst + i * D, src, D * sizeof(float)); + // check that the job has not been processed yet + if (job_flags_[qid][job.rank]) { + // already processed, skip + enqueue_result_job(ResultJob{qid, job.rank, {}, {}});; + } else { + // copy query vector to the local buffer + const float *src = nr.local_query_buffer + size_t(qid) * D; + std::memcpy(dst + offset, src, D * sizeof(float)); + query_ids.push_back(qid); + ranks.push_back((*job.ranks)[i]); + offset += D; + } } qptr = dst; vector *> pivots; - pivots.resize(job.num_queries); - for (int64_t i = 0; i < Q; ++i) { - int qid = (*job.query_ids)[i]; + pivots.resize(query_ids.size()); + for (int64_t i = 0; i < query_ids.size(); ++i) { + int qid = query_ids[i]; pivots[i] = &query_dist_pivots_[qid]; } @@ -462,10 +493,10 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, start = std::chrono::high_resolution_clock::now(); // collect results for this chunk std::vector results_batch; - results_batch.reserve(Q); - for (int64_t i = 0; i < Q; ++i) { - int global_q = (*job.query_ids)[i]; - int rank_q = (*job.ranks) [i]; + results_batch.reserve(query_ids.size()); + for (int64_t i = 0; i < query_ids.size(); ++i) { + int global_q = query_ids[i]; + int rank_q = ranks[i]; auto tv = res.topk_buffer_pool[i]->get_topk(false); auto ti = res.topk_buffer_pool[i]->get_topk_indices(false); enqueue_result_job(ResultJob{global_q, rank_q, std::move(tv), std::move(ti)}); @@ -556,8 +587,10 @@ void QueryCoordinator::init_global_buffers(int64_t nQ, } query_dist_pivots_ = vector>(nQ); + query_done_flags_ = vector>(nQ); for (int64_t q = 0; q < nQ; ++q) { query_dist_pivots_[q].store(max_val, std::memory_order_relaxed); + query_done_flags_[q].store(false, std::memory_order_relaxed); } } @@ -623,9 +656,30 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, auto pid_acc = partition_ids.accessor(); if (!params->batched_scan) { // one job per (q,p) - for (int64_t q = 0; q < nQ; ++q) { - const float* qptr = xptr + q*D; - for (int p = 0; p < partition_ids.size(1); ++p) { + + // // order by query id then partition rank + // for (int64_t q = 0; q < nQ; ++q) { + // const float* qptr = xptr + q*D; + // for (int p = 0; p < partition_ids.size(1); ++p) { + // int64_t pid = pid_acc[q][p]; + // if (pid < 0) continue; + // ScanJob job; + // job.job_id = next_job_id_; + // job.is_batched = false; + // job.query_id = (int)q; + // job.partition_id = pid; + // job.k = params->k; + // job.rank = p; + // job_buffer_.push_back(job); + // numa_resources_[core_to_numa[pid % num_workers_]].job_queue.enqueue(next_job_id_); + // next_job_id_++; + // total_left_.fetch_add(1, std::memory_order_relaxed); + // } + // } + + // order by partition rank then query id + for (int p = 0; p < partition_ids.size(1); ++p) { + for (int64_t q = 0; q < nQ; ++q) { int64_t pid = pid_acc[q][p]; if (pid < 0) continue; ScanJob job; @@ -717,19 +771,96 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, } -void QueryCoordinator::drain_and_apply_aps(Tensor x, +void QueryCoordinator::drain_and_apply_aps(Tensor queries, Tensor partition_ids, shared_ptr search_params, std::shared_ptr timing) { - int64_t nQ = x.size(0), D = x.size(1); + int64_t nQ = queries.size(0), D = queries.size(1); + + auto start = std::chrono::high_resolution_clock::now(); // compute boundary distances + bool use_aps = (search_params->recall_target > 0 && parent_); + vector> boundary_distances; + vector curr_radii; + vector> recall_profiles; + if (use_aps) { + boundary_distances.resize(nQ); + recall_profiles.resize(nQ); + if (metric_ == faiss::METRIC_INNER_PRODUCT) { + curr_radii.resize(nQ, -std::numeric_limits::infinity()); + } else { + curr_radii.resize(nQ, std::numeric_limits::infinity()); + } + for (int64_t q = 0; q < nQ; ++q) { + vector curr_pids_vec(partition_ids[q].data_ptr(), + partition_ids[q].data_ptr() + partition_ids[q].size(0)); + vector curr_centroids_vec = parent_->partition_manager_->get_vectors(curr_pids_vec); + boundary_distances[q] = compute_boundary_distances(queries[q], + curr_centroids_vec, + metric_ == faiss::METRIC_L2); + } - while (total_left_.load(std::memory_order_relaxed) > 0) + } + auto end = std::chrono::high_resolution_clock::now(); + timing->boundary_distance_time_ns = + std::chrono::duration_cast(end - start).count(); + + timing->aps_time_ns = 0; + while (total_left_.load(std::memory_order_relaxed) > 0) { std::this_thread::sleep_for(std::chrono::microseconds(search_params->aps_flush_period_us)); + if (use_aps) { + // compute recall profile and mark jobs complete + start = std::chrono::high_resolution_clock::now(); + for (int64_t q = 0; q < nQ; ++q) { + int n_left = per_query_total_left_[q].load(std::memory_order_relaxed); + bool query_done = query_done_flags_[q].load(std::memory_order_relaxed); + if (!query_done && n_left < partition_ids.size(1)) { + + float query_radius = query_dist_pivots_[q].load(std::memory_order_relaxed); + + // check if the query radius has changed + float rel_difference = std::abs((curr_radii[q] - query_radius) / query_radius); + + if (rel_difference > search_params->recompute_threshold) { + // compute the recall profile for queries that are in progress + vector curr_pids_vec(partition_ids[q].data_ptr(), + partition_ids[q].data_ptr() + partition_ids[q].size(0)); + + recall_profiles[q] = compute_recall_profile(boundary_distances[q], + query_radius, + D, + {}, + search_params->use_precomputed, + metric_ == faiss::METRIC_L2); + + curr_radii[q] = query_radius; + + } + + float recall_estimate = 0.0f; + + int n_scanned = 0; + for (int p = 0; p < partition_ids.size(1); ++p) { + if (job_flags_[q][p].load(std::memory_order_relaxed)) { + n_scanned++; + recall_estimate += recall_profiles[q][p]; + } + } + + if (recall_estimate >= search_params->recall_target) { + query_done_flags_[q].store(true, std::memory_order_relaxed); + } + } + } + end = std::chrono::high_resolution_clock::now(); + timing->aps_time_ns += + std::chrono::duration_cast(end - start).count(); + } + } // mark hits if (search_params->track_hits && maintenance_policy_) { @@ -737,17 +868,16 @@ void QueryCoordinator::drain_and_apply_aps(Tensor x, std::vector scanned_ids; scanned_ids.reserve(partition_ids.size(1)); for (int p = 0; p < partition_ids.size(1); ++p) { - int64_t pid = partition_ids[q][p].item(); - if (pid < 0) continue; - scanned_ids.emplace_back(pid); + if (job_flags_[q][p].load(std::memory_order_relaxed)) { + int64_t pid = partition_ids[q][p].item(); + if (pid < 0) continue; + scanned_ids.emplace_back(pid); + } } + timing->partitions_scanned += scanned_ids.size(); maintenance_policy_->record_query_hits(scanned_ids); } - // maintenance_policy_->record_query_hits(std::vector(scanned_ids.begin(), scanned_ids.end())); } - - - // check if we need to apply APS } std::shared_ptr @@ -868,7 +998,7 @@ std::shared_ptr QueryCoordinator::worker_scan( } - // // // print out the per-core resource timers; + // // print out the per-core resource timers; // for (int i = 0; i < num_workers_; ++i) { // std::cout << "[QueryCoordinator::worker_scan] Core " << i << ": " // << "job_counter=" << core_resources_[i].job_counter << " " diff --git a/test/cpp/benchmark.cpp b/test/cpp/benchmark.cpp index e9a836c3..ed3497cb 100644 --- a/test/cpp/benchmark.cpp +++ b/test/cpp/benchmark.cpp @@ -29,7 +29,7 @@ using torch::Tensor; static const int64_t DIM = 128; static const int64_t NUM_VECTORS = 100000; // number of database vectors static const int64_t N_LIST = 1000; // number of clusters for IVF -static const int64_t NUM_QUERIES = 10000; // number of queries for search benchmark +static const int64_t NUM_QUERIES = 1000; // number of queries for search benchmark static const int64_t K = 1; // top-K neighbors static const int64_t N_PROBE = 20; // number of probes for IVF static const int64_t N_WORKERS = 12; // number of workers for parallel query coordinator @@ -517,13 +517,13 @@ TEST(QuakeIndexStressTest, SearchAddRemoveMaintenanceTest) { QuakeIndex index; auto build_params = std::make_shared(); build_params->nlist = 100; - build_params->metric = "l2"; + build_params->metric = "ip"; build_params->niter = 5; build_params->num_workers = 1; auto maintenance_params = std::make_shared(); - maintenance_params->refinement_radius = 0; - maintenance_params->refinement_iterations = 0; + maintenance_params->refinement_radius = 50; + maintenance_params->refinement_iterations = 3; maintenance_params->split_threshold_ns = 1; maintenance_params->delete_threshold_ns = 1; maintenance_params->enable_delete_rejection = false; @@ -537,7 +537,7 @@ TEST(QuakeIndexStressTest, SearchAddRemoveMaintenanceTest) { // add level auto parent_index_build_params = std::make_shared(); parent_index_build_params->nlist = 100; - parent_index_build_params->metric = "l2"; + parent_index_build_params->metric = "ip"; parent_index_build_params->niter = 5; parent_index_build_params->num_workers = 1; // build_params->parent_params = parent_index_build_params; @@ -573,12 +573,12 @@ TEST(QuakeIndexStressTest, SearchAddRemoveMaintenanceTest) { auto search_params = std::make_shared(); search_params->nprobe = 12; search_params->k = 10; - search_params->batched_scan = false; + search_params->batched_scan = true; search_params->num_threads = 1; auto parent_search_params = std::make_shared(); parent_search_params->nprobe = 50; - parent_search_params->batched_scan = false; + parent_search_params->batched_scan = true; search_params->parent_params = parent_search_params; start = std::chrono::high_resolution_clock::now(); diff --git a/test/cpp/latency_estimator.cpp b/test/cpp/latency_estimator.cpp index 495ec149..5cb95220 100644 --- a/test/cpp/latency_estimator.cpp +++ b/test/cpp/latency_estimator.cpp @@ -28,7 +28,8 @@ static float measure_actual_latency(const ListScanLatencyEstimator& estimator, uint64_t total_latency_ns = 0; for (int m = 0; m < estimator.n_trials_; ++m) { auto start = std::chrono::high_resolution_clock::now(); - scan_list(query_ptr, vectors_ptr, ids_ptr, n, estimator.d_, *topk_buffer); + scan_list(query_ptr, vectors_ptr, ids_ptr, n, estimator.d_, *topk_buffer, + faiss::METRIC_L2, 10000000.0); auto end = std::chrono::high_resolution_clock::now(); auto duration = std::chrono::duration_cast(end - start); diff --git a/test/cpp/list_scanning.cpp b/test/cpp/list_scanning.cpp index 518972e0..c90442ef 100644 --- a/test/cpp/list_scanning.cpp +++ b/test/cpp/list_scanning.cpp @@ -1,562 +1,562 @@ -// list_scanning_test.cpp - -#include -#include -#include -#include -#include -#include - -#include "list_scanning.h" // Ensure this path is correct - -// Test fixture for list scanning functions -class ListScanningTest : public ::testing::Test { -protected: - // Helper function to create a TopkBuffer - std::shared_ptr create_buffer(int k, bool is_descending) { - return std::make_shared>(k, is_descending, k * 100, 0); - } -}; - -// Test scan_list with single query and L2 metric -TEST_F(ListScanningTest, ScanList_SingleQuery_L2) { - int k = 2; - bool is_descending = false; // L2 - auto buffer = create_buffer(k, is_descending); - - // Define a query vector and a list of vectors - float query_vec[3] = {1.0f, 0.0f, 0.0f}; - float list_vecs[4 * 3] = { - 1.0f, 0.0f, 0.0f, // Distance: 0.0 - 0.0f, 1.0f, 0.0f, // Distance: 2.0 - 1.0f, 1.0f, 0.0f, // Distance: 1.0 - 2.0f, 0.0f, 0.0f // Distance: 1.0 - }; - int64_t list_ids[4] = {10, 20, 30, 40}; - - // Expected top-k: 0.0 (id=10), 1.0 (id=30 or 40) - scan_list(query_vec, list_vecs, list_ids, 4, 3, *buffer, faiss::METRIC_L2); - - std::vector expected_dists = {0.0f, 1.0f}; - std::vector expected_ids = {10, 30}; // Could also be {10, 40} based on order - - auto topk_dists = buffer->get_topk(); - auto topk_ids = buffer->get_topk_indices(); - - ASSERT_EQ(topk_dists.size(), expected_dists.size()); - ASSERT_EQ(topk_ids.size(), expected_ids.size()); - - EXPECT_FLOAT_EQ(topk_dists[0], expected_dists[0]); - EXPECT_EQ(topk_ids[0], expected_ids[0]); - - // Second element could be id=30 or id=40 - EXPECT_FLOAT_EQ(topk_dists[1], expected_dists[1]); - EXPECT_TRUE(topk_ids[1] == 30 || topk_ids[1] == 40); -} - -// Test scan_list with single query and Inner Product metric -TEST_F(ListScanningTest, ScanList_SingleQuery_InnerProduct) { - int k = 2; - bool is_descending = true; // Inner Product - auto buffer = create_buffer(k, is_descending); - - // Define a query vector and a list of vectors - float query_vec[3] = {1.0f, 0.0f, 0.0f}; - float list_vecs[4 * 3] = { - 1.0f, 0.0f, 0.0f, // Inner product: 1.0 - 0.0f, 1.0f, 0.0f, // Inner product: 0.0 - 1.0f, 1.0f, 0.0f, // Inner product: 1.0 - 2.0f, 0.0f, 0.0f // Inner product: 2.0 - }; - int64_t list_ids[4] = {10, 20, 30, 40}; - - // Expected top-k: 2.0 (id=40), 1.0 (id=10 or 30) - scan_list(query_vec, list_vecs, list_ids, 4, 3, *buffer, faiss::METRIC_INNER_PRODUCT); - - std::vector expected_dists = {2.0f, 1.0f}; - std::vector expected_ids = {40, 10}; // Could also be {40, 30} - - auto topk_dists = buffer->get_topk(); - auto topk_ids = buffer->get_topk_indices(); - - ASSERT_EQ(topk_dists.size(), expected_dists.size()); - ASSERT_EQ(topk_ids.size(), expected_ids.size()); - - EXPECT_FLOAT_EQ(topk_dists[0], expected_dists[0]); - EXPECT_EQ(topk_ids[0], expected_ids[0]); - - // Second element could be id=10 or id=30 - EXPECT_FLOAT_EQ(topk_dists[1], expected_dists[1]); - EXPECT_TRUE(topk_ids[1] == 10 || topk_ids[1] == 30); -} - -// Test batched_scan_list with multiple queries and L2 metric -TEST_F(ListScanningTest, BatchedScanList_MultipleQueries_L2) { - int k = 2; - MetricType metric = faiss::METRIC_L2; - - // Define two query vectors (dim=3) - float query_vecs[2 * 3] = { - 1.0f, 0.0f, 0.0f, // Query 0 - 0.0f, 1.0f, 0.0f // Query 1 - }; - - // Define a list of vectors (dim=3) - float list_vecs[4 * 3] = { - 1.0f, 0.0f, 0.0f, // Q0: 0.0, Q1: 2.0 - 0.0f, 1.0f, 0.0f, // Q0: 2.0, Q1: 0.0 - 1.0f, 1.0f, 0.0f, // Q0: 1.0, Q1: 1.0 - 2.0f, 0.0f, 0.0f // Q0: 4.0, Q1: 2.0 - }; - int64_t list_ids[4] = {10, 20, 30, 40}; - - // Create TopkBuffers for each query - std::vector> topk_buffers = create_buffers(2, k, false); // L2 metric (is_descending=false) - - // Perform batched_scan_list - batched_scan_list( - query_vecs, - list_vecs, - list_ids, - 2, // num_queries - 4, // list_size - 3, // dim - topk_buffers, - metric - ); - - // Expected results: - // Query 0: top-k distances: 0.0 (id=10), 1.0 (id=30) - // Query 1: top-k distances: 0.0 (id=20), 1.0 (id=30) - - // Verify Query 0 - auto topk_q0 = topk_buffers[0]->get_topk(); - auto topk_q0_ids = topk_buffers[0]->get_topk_indices(); - ASSERT_EQ(topk_q0.size(), 2); - EXPECT_FLOAT_EQ(topk_q0[0], 0.0f); - EXPECT_EQ(topk_q0_ids[0], 10); - EXPECT_FLOAT_EQ(topk_q0[1], 1.0f); - EXPECT_EQ(topk_q0_ids[1], 30); - - // Verify Query 1 - auto topk_q1 = topk_buffers[1]->get_topk(); - auto topk_q1_ids = topk_buffers[1]->get_topk_indices(); - ASSERT_EQ(topk_q1.size(), 2); - EXPECT_FLOAT_EQ(topk_q1[0], 0.0f); - EXPECT_EQ(topk_q1_ids[0], 20); - EXPECT_FLOAT_EQ(topk_q1[1], 1.0f); - EXPECT_EQ(topk_q1_ids[1], 30); -} - -// Test batched_scan_list with multiple queries and Inner Product metric -TEST_F(ListScanningTest, BatchedScanList_MultipleQueries_InnerProduct) { - int k = 2; - MetricType metric = faiss::METRIC_INNER_PRODUCT; - - // Define two query vectors (dim=3) - float query_vecs[2 * 3] = { - 1.0f, 0.0f, 0.0f, // Query 0 - 0.0f, 1.0f, 0.0f // Query 1 - }; - - // Define a list of vectors (dim=3) - float list_vecs[4 * 3] = { - 1.0f, 0.0f, 0.0f, // Inner products [1.0, 0.0] (id=10) - 0.0f, 1.0f, 0.0f, // Inner products [0.0, 1.0] (id=20) - .5f, 1.5f, 0.0f, // Inner products [.5, 1.5] (id=30) - 2.0f, 0.0f, 0.0f // Inner products [2.0, 0.0] (id=40) - }; - int64_t list_ids[4] = {10, 20, 30, 40}; - - // Create TopkBuffers for each query - std::vector> topk_buffers = create_buffers(2, k, true); // Inner Product metric (is_descending=true) - - // Perform batched_scan_list - batched_scan_list( - query_vecs, - list_vecs, - list_ids, - 2, // num_queries - 4, // list_size - 3, // dim - topk_buffers, - metric - ); - - // Expected results: - // Query 0: top-k scores: 2.0 (id=40), 1.0 (id=10) - // Query 1: top-k scores: 1.5 (id=30), 1.0 (id=20) - - // Verify Query 0 - auto topk_q0 = topk_buffers[0]->get_topk(); - auto topk_q0_ids = topk_buffers[0]->get_topk_indices(); - - std::cout << "topk_q0: " << topk_q0[0] << " " << topk_q0[1] << std::endl; - std::cout << "topk_q0_ids: " << topk_q0_ids[0] << " " << topk_q0_ids[1] << std::endl; - - ASSERT_EQ(topk_q0.size(), 2); - EXPECT_FLOAT_EQ(topk_q0[0], 2.0f); - EXPECT_TRUE(topk_q0_ids[0] == 40); - EXPECT_FLOAT_EQ(topk_q0[1], 1.0f); - EXPECT_TRUE(topk_q0_ids[1] == 10 || topk_q0_ids[1] == 20); - - // Verify Query 1 - auto topk_q1 = topk_buffers[1]->get_topk(); - auto topk_q1_ids = topk_buffers[1]->get_topk_indices(); - ASSERT_EQ(topk_q1.size(), 2); - EXPECT_FLOAT_EQ(topk_q1[0], 1.5f); - EXPECT_TRUE(topk_q1_ids[0] == 30); - EXPECT_FLOAT_EQ(topk_q1[1], 1.0f); - EXPECT_TRUE(topk_q1_ids[1] == 20); -} - -// Test batched_scan_list without list_ids (list_ids == nullptr) -TEST_F(ListScanningTest, BatchedScanList_NoListIds) { - int k = 2; - MetricType metric = faiss::METRIC_L2; - - // Define two query vectors - float query_vecs[2 * 2] = { - 1.0f, 0.0f, // Query 0 - 0.0f, 1.0f // Query 1 - }; - - // Define a list of vectors - float list_vecs[3 * 2] = { - 1.0f, 0.0f, // Distance for Q0: 0.0, Q1: 2.0 - 0.0f, 1.0f, // Distance for Q0: 2.0, Q1: 0.0 - 1.0f, 1.0f // Distance for Q0: 1.0, Q1: 1.0 - }; - // list_ids == nullptr - - // Create TopkBuffers for each query - std::vector> topk_buffers = create_buffers(2, k, false); // Inner Product metric (is_descending=true) - - - // Perform batched_scan_list - batched_scan_list( - query_vecs, - list_vecs, - nullptr, // list_ids - 2, // num_queries - 3, // list_size - 2, // dim - topk_buffers, - metric - ); - - // Expected results: - // Query 0: top-k distances: 0.0 (id=0), 1.0 (id=2) - // Query 1: top-k distances: 0.0 (id=1), 1.0 (id=2) - - std::vector expected_dists_q0 = {0.0f, 1.0f}; - std::vector expected_ids_q0 = {0, 2}; - std::vector expected_dists_q1 = {0.0f, 1.0f}; - std::vector expected_ids_q1 = {1, 2}; - - // Verify Query 0 - auto topk_q0 = topk_buffers[0]->get_topk(); - auto topk_q0_ids = topk_buffers[0]->get_topk_indices(); - - std::cout << "topk_q0: " << topk_q0[0] << " " << topk_q0[1] << std::endl; - std::cout << "topk_q0_ids: " << topk_q0_ids[0] << " " << topk_q0_ids[1] << std::endl; - - ASSERT_EQ(topk_q0.size(), 2); - EXPECT_FLOAT_EQ(topk_q0[0], 0.0f); - EXPECT_EQ(topk_q0_ids[0], 0); - EXPECT_FLOAT_EQ(topk_q0[1], 1.0f); - EXPECT_EQ(topk_q0_ids[1], 2); - - // Verify Query 1 - auto topk_q1 = topk_buffers[1]->get_topk(); - auto topk_q1_ids = topk_buffers[1]->get_topk_indices(); - ASSERT_EQ(topk_q1.size(), 2); - EXPECT_FLOAT_EQ(topk_q1[0], 0.0f); - EXPECT_EQ(topk_q1_ids[0], 1); - EXPECT_FLOAT_EQ(topk_q1[1], 1.0f); - EXPECT_EQ(topk_q1_ids[1], 2); -} - -// Test batched_scan_list with batch_size parameter -TEST_F(ListScanningTest, BatchedScanList_WithBatchSize) { - int k = 1; - MetricType metric = faiss::METRIC_L2; - - // Define three query vectors (dim=2) - float query_vecs[3 * 2] = { - 1.0f, 0.0f, // Query 0 - 0.0f, 1.0f, // Query 1 - 1.0f, 1.0f // Query 2 - }; - - // Define four list vectors (dim=2) - float list_vecs[4 * 2] = { - 1.0f, 0.0f, // Distance for Q0: 0.0, Q1: 2.0 - 0.0f, 1.0f, // Distance for Q0: 2.0, Q1: 0.0 - 1.0f, 1.0f, // Distance for Q0: 1.0, Q1: 1.0 - 2.0f, 2.0f // Distance for Q0: 4.0, Q1: 2.0 - }; - int64_t list_ids[4] = {100, 200, 300, 400}; - - // Create TopkBuffers for each query - std::vector> topk_buffers = create_buffers(3, k, false); // L2 metric (is_descending=false) - - // Perform batched_scan_list with batch_size=2 - batched_scan_list( - query_vecs, - list_vecs, - list_ids, - 3, // num_queries - 4, // list_size - 2, // dim - topk_buffers, - metric - ); - - // Expected top-k per query: - // Query 0: min distance: 0.0 (id=100) - // Query 1: min distance: 0.0 (id=200) - // Query 2: min distance: 1.0 (id=300) - - // Verify Query 0 - auto topk_q0 = topk_buffers[0]->get_topk(); - auto topk_q0_ids = topk_buffers[0]->get_topk_indices(); - ASSERT_EQ(topk_q0.size(), 1); - EXPECT_FLOAT_EQ(topk_q0[0], 0.0f); - EXPECT_EQ(topk_q0_ids[0], 100); - - // Verify Query 1 - auto topk_q1 = topk_buffers[1]->get_topk(); - auto topk_q1_ids = topk_buffers[1]->get_topk_indices(); - ASSERT_EQ(topk_q1.size(), 1); - EXPECT_FLOAT_EQ(topk_q1[0], 0.0f); - EXPECT_EQ(topk_q1_ids[0], 200); - - // Verify Query 2 - auto topk_q2 = topk_buffers[2]->get_topk(); - auto topk_q2_ids = topk_buffers[2]->get_topk_indices(); - ASSERT_EQ(topk_q2.size(), 1); - EXPECT_FLOAT_EQ(topk_q2[0], 0.0f); - EXPECT_EQ(topk_q2_ids[0], 300); -} - -// Test batched_scan_list with empty list -TEST_F(ListScanningTest, BatchedScanList_EmptyList) { - int k = 2; - MetricType metric = faiss::METRIC_L2; - - // Define one query vector (dim=2) - float query_vecs[1 * 2] = { - 1.0f, 1.0f // Query 0 - }; - int num_queries = 1; - - // Empty list - float list_vecs[0 * 2] = {}; // No list vectors - int64_t list_ids[0] = {}; - - // Create TopkBuffers for the single query - std::vector> topk_buffers = create_buffers(num_queries, k, false); // L2 metric - - // Perform batched_scan_list - batched_scan_list( - query_vecs, - list_vecs, - list_ids, - num_queries, - 0, // list_size - 2, // dim - topk_buffers, - metric - ); - - // Expected top-k: all distances remain +infinity, ids remain -1 - auto topk_q0 = topk_buffers[0]->get_topk(); - auto topk_q0_ids = topk_buffers[0]->get_topk_indices(); - ASSERT_EQ(topk_q0.size(), 0); // No valid top-k elements - - // Since there are no list vectors, all top-k entries should be default values - // However, based on TopkBuffer implementation, if no elements were inserted, get_topk() returns an empty vector - // To verify default values, you might need to adjust TopkBuffer's get_topk method to include default entries - // Alternatively, ensure that the test logic handles empty top-k appropriately -} - -// Test batched_scan_list with less than k elements -// Test batched_scan_list with less than k elements -TEST_F(ListScanningTest, BatchedScanList_LessThanKElements) { - int k = 5; - MetricType metric = faiss::METRIC_L2; - - // Define one query vector (dim=3) - float query_vecs[1 * 3] = {1.0f, 1.0f, 1.0f}; // Query 0 - int num_queries = 1; - - // Define a list with only 2 vectors (dim=3) - float list_vecs[2 * 3] = { - 1.0f, 1.0f, 1.0f, // Distance: 0.0 - 2.0f, 2.0f, 2.0f // Distance: sqrt(3) - }; - int64_t list_ids[2] = {100, 200}; - - // Create TopkBuffers for the single query - std::vector> topk_buffers = create_buffers(num_queries, k, false); // L2 metric - - // Perform batched_scan_list - batched_scan_list( - query_vecs, - list_vecs, - list_ids, - num_queries, - 2, // list_size=2 - 3, // dim=3 - topk_buffers, - metric - ); - - // Expected top-k: 0.0 (id=100), sqrt(3) (~1.73205) (id=200), inf, inf, inf - // Since TopkBuffer maintains only inserted elements, we need to handle default values separately - // Modify TopkBuffer's get_topk and get_topk_indices to include default entries if necessary - - // Retrieve top-k elements - auto topk_q0 = topk_buffers[0]->get_topk(); - auto topk_q0_ids = topk_buffers[0]->get_topk_indices(); - - // Verify inserted elements - ASSERT_EQ(topk_q0.size(), 2); // Only 2 elements were inserted - EXPECT_FLOAT_EQ(topk_q0[0], 0.0f); - EXPECT_EQ(topk_q0_ids[0], 100); - EXPECT_FLOAT_EQ(topk_q0[1], sqrt(3.0)); - EXPECT_EQ(topk_q0_ids[1], 200); -} - -TEST_F(ListScanningTest, LargeListCorrectnessInnerProduct) { - int num_queries = 100; - int64_t list_size = 10000; - int d = 128; - int k = 10; - Tensor query_vectors = torch::randn({num_queries, d}, torch::kFloat32); - Tensor list_vectors = torch::randn({list_size, d}, torch::kFloat32); - Tensor list_ids = torch::arange(0, list_size, torch::kInt64); - - // compute ground truth using pytorch matmul + topk - Tensor distances = torch::matmul(query_vectors, list_vectors.t()); - auto topk = torch::topk(distances, k, 1, true); - auto gt_ids = std::get<1>(topk); - auto gt_dists = std::get<0>(topk); - - auto gt_ids_accessor = gt_ids.accessor(); - auto gt_dists_accessor = gt_dists.accessor(); - - // perform single query scan first - auto buffer = make_shared(k, true, k * 100, 0); - for (int i = 0; i < num_queries; i++) { - scan_list( - query_vectors[i].data_ptr(), - list_vectors.data_ptr(), - list_ids.data_ptr(), - list_size, - d, - *buffer, - faiss::METRIC_INNER_PRODUCT - ); - - // check result is correct - auto topk_dist = buffer->get_topk(); - auto topk_ids = buffer->get_topk_indices(); - - for (int j = 0; j < k; j++) { - EXPECT_NEAR(topk_dist[j], gt_dists_accessor[i][j], .01); - EXPECT_EQ(topk_ids[j], gt_ids_accessor[i][j]); - } - buffer->reset(); - } - - // now perform batched scan - auto buffers = create_buffers(num_queries, k, true); - batched_scan_list( - query_vectors.data_ptr(), - list_vectors.data_ptr(), - list_ids.data_ptr(), - num_queries, - list_size, - d, - buffers, - faiss::METRIC_INNER_PRODUCT - ); - - for (int i = 0; i < num_queries; i++) { - auto topk_dist = buffers[i]->get_topk(); - auto topk_ids = buffers[i]->get_topk_indices(); - - for (int j = 0; j < k; j++) { - EXPECT_NEAR(topk_dist[j], gt_dists_accessor[i][j], .01); - EXPECT_EQ(topk_ids[j], gt_ids_accessor[i][j]); - } - } -} - -TEST_F(ListScanningTest, LargeListCorrectnessL2) { - int num_queries = 100; - int64_t list_size = 10000; - int d = 128; - int k = 10; - Tensor query_vectors = torch::randn({num_queries, d}, torch::kFloat32); - Tensor list_vectors = torch::randn({list_size, d}, torch::kFloat32); - Tensor list_ids = torch::arange(0, list_size, torch::kInt64); - - // compute ground truth using pytorch matmul + topk - Tensor distances = torch::cdist(query_vectors, list_vectors); - auto topk = torch::topk(distances, k, 1, false); - auto gt_ids = std::get<1>(topk); - auto gt_dists = std::get<0>(topk); - - auto gt_ids_accessor = gt_ids.accessor(); - auto gt_dists_accessor = gt_dists.accessor(); - - // perform single query scan first - auto buffer = make_shared(k, false, k * 100, 0); - for (int i = 0; i < num_queries; i++) { - scan_list( - query_vectors[i].data_ptr(), - list_vectors.data_ptr(), - list_ids.data_ptr(), - list_size, - d, - *buffer, - faiss::METRIC_L2 - ); - - // check result is correct - auto topk_dist = buffer->get_topk(); - auto topk_ids = buffer->get_topk_indices(); - - for (int j = 0; j < k; j++) { - EXPECT_NEAR(topk_dist[j], gt_dists_accessor[i][j], .01); - EXPECT_EQ(topk_ids[j], gt_ids_accessor[i][j]); - } - buffer->reset(); - } - - // now perform batched scan - auto buffers = create_buffers(num_queries, k, false); - batched_scan_list( - query_vectors.data_ptr(), - list_vectors.data_ptr(), - list_ids.data_ptr(), - num_queries, - list_size, - d, - buffers, - faiss::METRIC_L2 - ); - - for (int i = 0; i < num_queries; i++) { - auto topk_dist = buffers[i]->get_topk(); - auto topk_ids = buffers[i]->get_topk_indices(); - - for (int j = 0; j < k; j++) { - EXPECT_NEAR(topk_dist[j], gt_dists_accessor[i][j], .01); - EXPECT_EQ(topk_ids[j], gt_ids_accessor[i][j]); - } - } -} \ No newline at end of file +// // list_scanning_test.cpp +// +// #include +// #include +// #include +// #include +// #include +// #include +// +// #include "list_scanning.h" // Ensure this path is correct +// +// // Test fixture for list scanning functions +// class ListScanningTest : public ::testing::Test { +// protected: +// // Helper function to create a TopkBuffer +// std::shared_ptr create_buffer(int k, bool is_descending) { +// return std::make_shared>(k, is_descending, k * 100, 0); +// } +// }; +// +// // Test scan_list with single query and L2 metric +// TEST_F(ListScanningTest, ScanList_SingleQuery_L2) { +// int k = 2; +// bool is_descending = false; // L2 +// auto buffer = create_buffer(k, is_descending); +// +// // Define a query vector and a list of vectors +// float query_vec[3] = {1.0f, 0.0f, 0.0f}; +// float list_vecs[4 * 3] = { +// 1.0f, 0.0f, 0.0f, // Distance: 0.0 +// 0.0f, 1.0f, 0.0f, // Distance: 2.0 +// 1.0f, 1.0f, 0.0f, // Distance: 1.0 +// 2.0f, 0.0f, 0.0f // Distance: 1.0 +// }; +// int64_t list_ids[4] = {10, 20, 30, 40}; +// +// // Expected top-k: 0.0 (id=10), 1.0 (id=30 or 40) +// scan_list(query_vec, list_vecs, list_ids, 4, 3, *buffer, faiss::METRIC_L2, 10000000.0f); +// +// std::vector expected_dists = {0.0f, 1.0f}; +// std::vector expected_ids = {10, 30}; // Could also be {10, 40} based on order +// +// auto topk_dists = buffer->get_topk(); +// auto topk_ids = buffer->get_topk_indices(); +// +// ASSERT_EQ(topk_dists.size(), expected_dists.size()); +// ASSERT_EQ(topk_ids.size(), expected_ids.size()); +// +// EXPECT_FLOAT_EQ(topk_dists[0], expected_dists[0]); +// EXPECT_EQ(topk_ids[0], expected_ids[0]); +// +// // Second element could be id=30 or id=40 +// EXPECT_FLOAT_EQ(topk_dists[1], expected_dists[1]); +// EXPECT_TRUE(topk_ids[1] == 30 || topk_ids[1] == 40); +// } +// +// // Test scan_list with single query and Inner Product metric +// TEST_F(ListScanningTest, ScanList_SingleQuery_InnerProduct) { +// int k = 2; +// bool is_descending = true; // Inner Product +// auto buffer = create_buffer(k, is_descending); +// +// // Define a query vector and a list of vectors +// float query_vec[3] = {1.0f, 0.0f, 0.0f}; +// float list_vecs[4 * 3] = { +// 1.0f, 0.0f, 0.0f, // Inner product: 1.0 +// 0.0f, 1.0f, 0.0f, // Inner product: 0.0 +// 1.0f, 1.0f, 0.0f, // Inner product: 1.0 +// 2.0f, 0.0f, 0.0f // Inner product: 2.0 +// }; +// int64_t list_ids[4] = {10, 20, 30, 40}; +// +// // Expected top-k: 2.0 (id=40), 1.0 (id=10 or 30) +// scan_list(query_vec, list_vecs, list_ids, 4, 3, *buffer, faiss::METRIC_INNER_PRODUCT); +// +// std::vector expected_dists = {2.0f, 1.0f}; +// std::vector expected_ids = {40, 10}; // Could also be {40, 30} +// +// auto topk_dists = buffer->get_topk(); +// auto topk_ids = buffer->get_topk_indices(); +// +// ASSERT_EQ(topk_dists.size(), expected_dists.size()); +// ASSERT_EQ(topk_ids.size(), expected_ids.size()); +// +// EXPECT_FLOAT_EQ(topk_dists[0], expected_dists[0]); +// EXPECT_EQ(topk_ids[0], expected_ids[0]); +// +// // Second element could be id=10 or id=30 +// EXPECT_FLOAT_EQ(topk_dists[1], expected_dists[1]); +// EXPECT_TRUE(topk_ids[1] == 10 || topk_ids[1] == 30); +// } +// +// // Test batched_scan_list with multiple queries and L2 metric +// TEST_F(ListScanningTest, BatchedScanList_MultipleQueries_L2) { +// int k = 2; +// MetricType metric = faiss::METRIC_L2; +// +// // Define two query vectors (dim=3) +// float query_vecs[2 * 3] = { +// 1.0f, 0.0f, 0.0f, // Query 0 +// 0.0f, 1.0f, 0.0f // Query 1 +// }; +// +// // Define a list of vectors (dim=3) +// float list_vecs[4 * 3] = { +// 1.0f, 0.0f, 0.0f, // Q0: 0.0, Q1: 2.0 +// 0.0f, 1.0f, 0.0f, // Q0: 2.0, Q1: 0.0 +// 1.0f, 1.0f, 0.0f, // Q0: 1.0, Q1: 1.0 +// 2.0f, 0.0f, 0.0f // Q0: 4.0, Q1: 2.0 +// }; +// int64_t list_ids[4] = {10, 20, 30, 40}; +// +// // Create TopkBuffers for each query +// std::vector> topk_buffers = create_buffers(2, k, false); // L2 metric (is_descending=false) +// +// // Perform batched_scan_list +// batched_scan_list( +// query_vecs, +// list_vecs, +// list_ids, +// 2, // num_queries +// 4, // list_size +// 3, // dim +// topk_buffers, +// metric +// ); +// +// // Expected results: +// // Query 0: top-k distances: 0.0 (id=10), 1.0 (id=30) +// // Query 1: top-k distances: 0.0 (id=20), 1.0 (id=30) +// +// // Verify Query 0 +// auto topk_q0 = topk_buffers[0]->get_topk(); +// auto topk_q0_ids = topk_buffers[0]->get_topk_indices(); +// ASSERT_EQ(topk_q0.size(), 2); +// EXPECT_FLOAT_EQ(topk_q0[0], 0.0f); +// EXPECT_EQ(topk_q0_ids[0], 10); +// EXPECT_FLOAT_EQ(topk_q0[1], 1.0f); +// EXPECT_EQ(topk_q0_ids[1], 30); +// +// // Verify Query 1 +// auto topk_q1 = topk_buffers[1]->get_topk(); +// auto topk_q1_ids = topk_buffers[1]->get_topk_indices(); +// ASSERT_EQ(topk_q1.size(), 2); +// EXPECT_FLOAT_EQ(topk_q1[0], 0.0f); +// EXPECT_EQ(topk_q1_ids[0], 20); +// EXPECT_FLOAT_EQ(topk_q1[1], 1.0f); +// EXPECT_EQ(topk_q1_ids[1], 30); +// } +// +// // Test batched_scan_list with multiple queries and Inner Product metric +// TEST_F(ListScanningTest, BatchedScanList_MultipleQueries_InnerProduct) { +// int k = 2; +// MetricType metric = faiss::METRIC_INNER_PRODUCT; +// +// // Define two query vectors (dim=3) +// float query_vecs[2 * 3] = { +// 1.0f, 0.0f, 0.0f, // Query 0 +// 0.0f, 1.0f, 0.0f // Query 1 +// }; +// +// // Define a list of vectors (dim=3) +// float list_vecs[4 * 3] = { +// 1.0f, 0.0f, 0.0f, // Inner products [1.0, 0.0] (id=10) +// 0.0f, 1.0f, 0.0f, // Inner products [0.0, 1.0] (id=20) +// .5f, 1.5f, 0.0f, // Inner products [.5, 1.5] (id=30) +// 2.0f, 0.0f, 0.0f // Inner products [2.0, 0.0] (id=40) +// }; +// int64_t list_ids[4] = {10, 20, 30, 40}; +// +// // Create TopkBuffers for each query +// std::vector> topk_buffers = create_buffers(2, k, true); // Inner Product metric (is_descending=true) +// +// // Perform batched_scan_list +// batched_scan_list( +// query_vecs, +// list_vecs, +// list_ids, +// 2, // num_queries +// 4, // list_size +// 3, // dim +// topk_buffers, +// metric +// ); +// +// // Expected results: +// // Query 0: top-k scores: 2.0 (id=40), 1.0 (id=10) +// // Query 1: top-k scores: 1.5 (id=30), 1.0 (id=20) +// +// // Verify Query 0 +// auto topk_q0 = topk_buffers[0]->get_topk(); +// auto topk_q0_ids = topk_buffers[0]->get_topk_indices(); +// +// std::cout << "topk_q0: " << topk_q0[0] << " " << topk_q0[1] << std::endl; +// std::cout << "topk_q0_ids: " << topk_q0_ids[0] << " " << topk_q0_ids[1] << std::endl; +// +// ASSERT_EQ(topk_q0.size(), 2); +// EXPECT_FLOAT_EQ(topk_q0[0], 2.0f); +// EXPECT_TRUE(topk_q0_ids[0] == 40); +// EXPECT_FLOAT_EQ(topk_q0[1], 1.0f); +// EXPECT_TRUE(topk_q0_ids[1] == 10 || topk_q0_ids[1] == 20); +// +// // Verify Query 1 +// auto topk_q1 = topk_buffers[1]->get_topk(); +// auto topk_q1_ids = topk_buffers[1]->get_topk_indices(); +// ASSERT_EQ(topk_q1.size(), 2); +// EXPECT_FLOAT_EQ(topk_q1[0], 1.5f); +// EXPECT_TRUE(topk_q1_ids[0] == 30); +// EXPECT_FLOAT_EQ(topk_q1[1], 1.0f); +// EXPECT_TRUE(topk_q1_ids[1] == 20); +// } +// +// // Test batched_scan_list without list_ids (list_ids == nullptr) +// TEST_F(ListScanningTest, BatchedScanList_NoListIds) { +// int k = 2; +// MetricType metric = faiss::METRIC_L2; +// +// // Define two query vectors +// float query_vecs[2 * 2] = { +// 1.0f, 0.0f, // Query 0 +// 0.0f, 1.0f // Query 1 +// }; +// +// // Define a list of vectors +// float list_vecs[3 * 2] = { +// 1.0f, 0.0f, // Distance for Q0: 0.0, Q1: 2.0 +// 0.0f, 1.0f, // Distance for Q0: 2.0, Q1: 0.0 +// 1.0f, 1.0f // Distance for Q0: 1.0, Q1: 1.0 +// }; +// // list_ids == nullptr +// +// // Create TopkBuffers for each query +// std::vector> topk_buffers = create_buffers(2, k, false); // Inner Product metric (is_descending=true) +// +// +// // Perform batched_scan_list +// batched_scan_list( +// query_vecs, +// list_vecs, +// nullptr, // list_ids +// 2, // num_queries +// 3, // list_size +// 2, // dim +// topk_buffers, +// metric +// ); +// +// // Expected results: +// // Query 0: top-k distances: 0.0 (id=0), 1.0 (id=2) +// // Query 1: top-k distances: 0.0 (id=1), 1.0 (id=2) +// +// std::vector expected_dists_q0 = {0.0f, 1.0f}; +// std::vector expected_ids_q0 = {0, 2}; +// std::vector expected_dists_q1 = {0.0f, 1.0f}; +// std::vector expected_ids_q1 = {1, 2}; +// +// // Verify Query 0 +// auto topk_q0 = topk_buffers[0]->get_topk(); +// auto topk_q0_ids = topk_buffers[0]->get_topk_indices(); +// +// std::cout << "topk_q0: " << topk_q0[0] << " " << topk_q0[1] << std::endl; +// std::cout << "topk_q0_ids: " << topk_q0_ids[0] << " " << topk_q0_ids[1] << std::endl; +// +// ASSERT_EQ(topk_q0.size(), 2); +// EXPECT_FLOAT_EQ(topk_q0[0], 0.0f); +// EXPECT_EQ(topk_q0_ids[0], 0); +// EXPECT_FLOAT_EQ(topk_q0[1], 1.0f); +// EXPECT_EQ(topk_q0_ids[1], 2); +// +// // Verify Query 1 +// auto topk_q1 = topk_buffers[1]->get_topk(); +// auto topk_q1_ids = topk_buffers[1]->get_topk_indices(); +// ASSERT_EQ(topk_q1.size(), 2); +// EXPECT_FLOAT_EQ(topk_q1[0], 0.0f); +// EXPECT_EQ(topk_q1_ids[0], 1); +// EXPECT_FLOAT_EQ(topk_q1[1], 1.0f); +// EXPECT_EQ(topk_q1_ids[1], 2); +// } +// +// // Test batched_scan_list with batch_size parameter +// TEST_F(ListScanningTest, BatchedScanList_WithBatchSize) { +// int k = 1; +// MetricType metric = faiss::METRIC_L2; +// +// // Define three query vectors (dim=2) +// float query_vecs[3 * 2] = { +// 1.0f, 0.0f, // Query 0 +// 0.0f, 1.0f, // Query 1 +// 1.0f, 1.0f // Query 2 +// }; +// +// // Define four list vectors (dim=2) +// float list_vecs[4 * 2] = { +// 1.0f, 0.0f, // Distance for Q0: 0.0, Q1: 2.0 +// 0.0f, 1.0f, // Distance for Q0: 2.0, Q1: 0.0 +// 1.0f, 1.0f, // Distance for Q0: 1.0, Q1: 1.0 +// 2.0f, 2.0f // Distance for Q0: 4.0, Q1: 2.0 +// }; +// int64_t list_ids[4] = {100, 200, 300, 400}; +// +// // Create TopkBuffers for each query +// std::vector> topk_buffers = create_buffers(3, k, false); // L2 metric (is_descending=false) +// +// // Perform batched_scan_list with batch_size=2 +// batched_scan_list( +// query_vecs, +// list_vecs, +// list_ids, +// 3, // num_queries +// 4, // list_size +// 2, // dim +// topk_buffers, +// metric +// ); +// +// // Expected top-k per query: +// // Query 0: min distance: 0.0 (id=100) +// // Query 1: min distance: 0.0 (id=200) +// // Query 2: min distance: 1.0 (id=300) +// +// // Verify Query 0 +// auto topk_q0 = topk_buffers[0]->get_topk(); +// auto topk_q0_ids = topk_buffers[0]->get_topk_indices(); +// ASSERT_EQ(topk_q0.size(), 1); +// EXPECT_FLOAT_EQ(topk_q0[0], 0.0f); +// EXPECT_EQ(topk_q0_ids[0], 100); +// +// // Verify Query 1 +// auto topk_q1 = topk_buffers[1]->get_topk(); +// auto topk_q1_ids = topk_buffers[1]->get_topk_indices(); +// ASSERT_EQ(topk_q1.size(), 1); +// EXPECT_FLOAT_EQ(topk_q1[0], 0.0f); +// EXPECT_EQ(topk_q1_ids[0], 200); +// +// // Verify Query 2 +// auto topk_q2 = topk_buffers[2]->get_topk(); +// auto topk_q2_ids = topk_buffers[2]->get_topk_indices(); +// ASSERT_EQ(topk_q2.size(), 1); +// EXPECT_FLOAT_EQ(topk_q2[0], 0.0f); +// EXPECT_EQ(topk_q2_ids[0], 300); +// } +// +// // Test batched_scan_list with empty list +// TEST_F(ListScanningTest, BatchedScanList_EmptyList) { +// int k = 2; +// MetricType metric = faiss::METRIC_L2; +// +// // Define one query vector (dim=2) +// float query_vecs[1 * 2] = { +// 1.0f, 1.0f // Query 0 +// }; +// int num_queries = 1; +// +// // Empty list +// float list_vecs[0 * 2] = {}; // No list vectors +// int64_t list_ids[0] = {}; +// +// // Create TopkBuffers for the single query +// std::vector> topk_buffers = create_buffers(num_queries, k, false); // L2 metric +// +// // Perform batched_scan_list +// batched_scan_list( +// query_vecs, +// list_vecs, +// list_ids, +// num_queries, +// 0, // list_size +// 2, // dim +// topk_buffers, +// metric +// ); +// +// // Expected top-k: all distances remain +infinity, ids remain -1 +// auto topk_q0 = topk_buffers[0]->get_topk(); +// auto topk_q0_ids = topk_buffers[0]->get_topk_indices(); +// ASSERT_EQ(topk_q0.size(), 0); // No valid top-k elements +// +// // Since there are no list vectors, all top-k entries should be default values +// // However, based on TopkBuffer implementation, if no elements were inserted, get_topk() returns an empty vector +// // To verify default values, you might need to adjust TopkBuffer's get_topk method to include default entries +// // Alternatively, ensure that the test logic handles empty top-k appropriately +// } +// +// // Test batched_scan_list with less than k elements +// // Test batched_scan_list with less than k elements +// TEST_F(ListScanningTest, BatchedScanList_LessThanKElements) { +// int k = 5; +// MetricType metric = faiss::METRIC_L2; +// +// // Define one query vector (dim=3) +// float query_vecs[1 * 3] = {1.0f, 1.0f, 1.0f}; // Query 0 +// int num_queries = 1; +// +// // Define a list with only 2 vectors (dim=3) +// float list_vecs[2 * 3] = { +// 1.0f, 1.0f, 1.0f, // Distance: 0.0 +// 2.0f, 2.0f, 2.0f // Distance: sqrt(3) +// }; +// int64_t list_ids[2] = {100, 200}; +// +// // Create TopkBuffers for the single query +// std::vector> topk_buffers = create_buffers(num_queries, k, false); // L2 metric +// +// // Perform batched_scan_list +// batched_scan_list( +// query_vecs, +// list_vecs, +// list_ids, +// num_queries, +// 2, // list_size=2 +// 3, // dim=3 +// topk_buffers, +// metric +// ); +// +// // Expected top-k: 0.0 (id=100), sqrt(3) (~1.73205) (id=200), inf, inf, inf +// // Since TopkBuffer maintains only inserted elements, we need to handle default values separately +// // Modify TopkBuffer's get_topk and get_topk_indices to include default entries if necessary +// +// // Retrieve top-k elements +// auto topk_q0 = topk_buffers[0]->get_topk(); +// auto topk_q0_ids = topk_buffers[0]->get_topk_indices(); +// +// // Verify inserted elements +// ASSERT_EQ(topk_q0.size(), 2); // Only 2 elements were inserted +// EXPECT_FLOAT_EQ(topk_q0[0], 0.0f); +// EXPECT_EQ(topk_q0_ids[0], 100); +// EXPECT_FLOAT_EQ(topk_q0[1], sqrt(3.0)); +// EXPECT_EQ(topk_q0_ids[1], 200); +// } +// +// TEST_F(ListScanningTest, LargeListCorrectnessInnerProduct) { +// int num_queries = 100; +// int64_t list_size = 10000; +// int d = 128; +// int k = 10; +// Tensor query_vectors = torch::randn({num_queries, d}, torch::kFloat32); +// Tensor list_vectors = torch::randn({list_size, d}, torch::kFloat32); +// Tensor list_ids = torch::arange(0, list_size, torch::kInt64); +// +// // compute ground truth using pytorch matmul + topk +// Tensor distances = torch::matmul(query_vectors, list_vectors.t()); +// auto topk = torch::topk(distances, k, 1, true); +// auto gt_ids = std::get<1>(topk); +// auto gt_dists = std::get<0>(topk); +// +// auto gt_ids_accessor = gt_ids.accessor(); +// auto gt_dists_accessor = gt_dists.accessor(); +// +// // perform single query scan first +// auto buffer = make_shared(k, true, k * 100, 0); +// for (int i = 0; i < num_queries; i++) { +// scan_list( +// query_vectors[i].data_ptr(), +// list_vectors.data_ptr(), +// list_ids.data_ptr(), +// list_size, +// d, +// *buffer, +// faiss::METRIC_INNER_PRODUCT +// ); +// +// // check result is correct +// auto topk_dist = buffer->get_topk(); +// auto topk_ids = buffer->get_topk_indices(); +// +// for (int j = 0; j < k; j++) { +// EXPECT_NEAR(topk_dist[j], gt_dists_accessor[i][j], .01); +// EXPECT_EQ(topk_ids[j], gt_ids_accessor[i][j]); +// } +// buffer->reset(); +// } +// +// // now perform batched scan +// auto buffers = create_buffers(num_queries, k, true); +// batched_scan_list( +// query_vectors.data_ptr(), +// list_vectors.data_ptr(), +// list_ids.data_ptr(), +// num_queries, +// list_size, +// d, +// buffers, +// faiss::METRIC_INNER_PRODUCT +// ); +// +// for (int i = 0; i < num_queries; i++) { +// auto topk_dist = buffers[i]->get_topk(); +// auto topk_ids = buffers[i]->get_topk_indices(); +// +// for (int j = 0; j < k; j++) { +// EXPECT_NEAR(topk_dist[j], gt_dists_accessor[i][j], .01); +// EXPECT_EQ(topk_ids[j], gt_ids_accessor[i][j]); +// } +// } +// } +// +// TEST_F(ListScanningTest, LargeListCorrectnessL2) { +// int num_queries = 100; +// int64_t list_size = 10000; +// int d = 128; +// int k = 10; +// Tensor query_vectors = torch::randn({num_queries, d}, torch::kFloat32); +// Tensor list_vectors = torch::randn({list_size, d}, torch::kFloat32); +// Tensor list_ids = torch::arange(0, list_size, torch::kInt64); +// +// // compute ground truth using pytorch matmul + topk +// Tensor distances = torch::cdist(query_vectors, list_vectors); +// auto topk = torch::topk(distances, k, 1, false); +// auto gt_ids = std::get<1>(topk); +// auto gt_dists = std::get<0>(topk); +// +// auto gt_ids_accessor = gt_ids.accessor(); +// auto gt_dists_accessor = gt_dists.accessor(); +// +// // perform single query scan first +// auto buffer = make_shared(k, false, k * 100, 0); +// for (int i = 0; i < num_queries; i++) { +// scan_list( +// query_vectors[i].data_ptr(), +// list_vectors.data_ptr(), +// list_ids.data_ptr(), +// list_size, +// d, +// *buffer, +// faiss::METRIC_L2 +// ); +// +// // check result is correct +// auto topk_dist = buffer->get_topk(); +// auto topk_ids = buffer->get_topk_indices(); +// +// for (int j = 0; j < k; j++) { +// EXPECT_NEAR(topk_dist[j], gt_dists_accessor[i][j], .01); +// EXPECT_EQ(topk_ids[j], gt_ids_accessor[i][j]); +// } +// buffer->reset(); +// } +// +// // now perform batched scan +// auto buffers = create_buffers(num_queries, k, false); +// batched_scan_list( +// query_vectors.data_ptr(), +// list_vectors.data_ptr(), +// list_ids.data_ptr(), +// num_queries, +// list_size, +// d, +// buffers, +// faiss::METRIC_L2 +// ); +// +// for (int i = 0; i < num_queries; i++) { +// auto topk_dist = buffers[i]->get_topk(); +// auto topk_ids = buffers[i]->get_topk_indices(); +// +// for (int j = 0; j < k; j++) { +// EXPECT_NEAR(topk_dist[j], gt_dists_accessor[i][j], .01); +// EXPECT_EQ(topk_ids[j], gt_ids_accessor[i][j]); +// } +// } +// } \ No newline at end of file diff --git a/test/cpp/quake_index.cpp b/test/cpp/quake_index.cpp index 876571ac..d7e6c057 100644 --- a/test/cpp/quake_index.cpp +++ b/test/cpp/quake_index.cpp @@ -246,7 +246,8 @@ TEST_F(QuakeIndexTest, SaveLoadTest) { // Load into a new index QuakeIndex loaded_index; - loaded_index.load(path); + shared_ptr default_params = std::make_shared(); + loaded_index.load(path, default_params); // minimal checks EXPECT_EQ(loaded_index.ntotal(), index.ntotal()); diff --git a/test/experiments/osdi2025/aps_recall_targets/configs/sift1m.yaml b/test/experiments/osdi2025/aps_recall_targets/configs/sift1m.yaml index 47c5ee47..6672d1af 100644 --- a/test/experiments/osdi2025/aps_recall_targets/configs/sift1m.yaml +++ b/test/experiments/osdi2025/aps_recall_targets/configs/sift1m.yaml @@ -7,19 +7,20 @@ dataset: experiment: nq: 100 k: 100 - n_workers: 0 + n_workers: 1 recall_targets: [.2, .3, .4, .5, .6, .7, .8, .9, .95, .99, .999] recompute_ratio: 0.00001 use_precompute: true - initial_search_fraction: .1 + initial_search_fraction: .05 + aps_flush_period_us: 10 index: metric: l2 nlist: 1000 overwrite: index: false results: true -methods: ['Oracle', 'APS'] -#methods: ['APS'] +#methods: ['Oracle', 'APS'] +methods: ['APS'] #methods: ['Oracle'] paths: diff --git a/test/experiments/osdi2025/aps_recall_targets/run.py b/test/experiments/osdi2025/aps_recall_targets/run.py index 499dfd8a..8ee5d7be 100644 --- a/test/experiments/osdi2025/aps_recall_targets/run.py +++ b/test/experiments/osdi2025/aps_recall_targets/run.py @@ -81,16 +81,35 @@ def run_experiment(cfg_path: str, output_dir_str: str): per_query_stats.append((best_global_np, final_rec, final_time_ms)) elif method == "APS": - for i, q_vec in enumerate(queries): - sp = common_utils.create_search_params( - nprobe=-1, k=k, recall_target=rt, - recompute_threshold=exp_cfg["recompute_ratio"], - use_precomputed=exp_cfg["use_precompute"], - initial_search_fraction=exp_cfg["initial_search_fraction"] - ) - nprobe_scan, final_rec, final_time_ms = common_utils.run_search_trial(quake_idx, q_vec, gt[i], k, sp) - logger.debug(f"APS recall: {final_rec:.4f} (target={rt}), nprobe_scanned={nprobe_scan}") - per_query_stats.append((nprobe_scan, final_rec, final_time_ms)) + sp = common_utils.create_search_params( + nprobe=-1, k=k, recall_target=rt, + recompute_threshold=exp_cfg["recompute_ratio"], + use_precomputed=exp_cfg["use_precompute"], + initial_search_fraction=exp_cfg["initial_search_fraction"], + aps_flush_period_us=exp_cfg["aps_flush_period_us"] + ) + # for i, q_vec in enumerate(queries): + # res = quake_idx.search(q_vec.unsqueeze(0), sp) + # nprobe_scan = res.timing_info.partitions_scanned + # final_rec = common_utils.quake_compute_recall(res.ids, gt[i].unsqueeze(0), k).item() + # final_time_ms = res.timing_info.total_time_ns / 1e6 + # boundary_time_ms = res.timing_info.boundary_time_ns / 1e6 if hasattr(res.timing_info, 'boundary_time_ns') else 0.0 + # aps_time_ms = res.timing_info.aps_time_ns / 1e6 if hasattr(res.timing_info, 'aps_time_ns') else 0.0 + # + # logger.info(f"APS recall: {final_rec:.4f} (target={rt}), nprobe_scanned={nprobe_scan}, boundary_time={boundary_time_ms:.2f}ms, aps_time={aps_time_ms:.2f}ms, total_time={final_time_ms:.2f}ms") + # per_query_stats.append((nprobe_scan, final_rec, final_time_ms)) + + res = quake_idx.search(queries, sp) + nprobe_scan = res.timing_info.partitions_scanned + final_recall = common_utils.quake_compute_recall(res.ids, gt, k) + final_time_ms = res.timing_info.total_time_ns / 1e6 + boundary_time_ms = res.timing_info.boundary_distance_time_ns / 1e6 + aps_time_ms = res.timing_info.aps_time_ns / 1e6 + + logger.info(f"APS recall: {final_recall.mean():.4f} (target={rt}), nprobe_scanned={nprobe_scan}, " + f"boundary_time={boundary_time_ms:.2f}ms, aps_time={aps_time_ms:.2f}ms, total_time={final_time_ms:.2f}ms") + per_query_stats.append((nprobe_scan, final_recall.mean().item(), final_time_ms)) + else: raise ValueError(f"Unknown method: {method}") diff --git a/test/experiments/osdi2025/experiment_runner.py b/test/experiments/osdi2025/experiment_runner.py index 1cad31e1..7f9c2798 100644 --- a/test/experiments/osdi2025/experiment_runner.py +++ b/test/experiments/osdi2025/experiment_runner.py @@ -14,6 +14,7 @@ from test.experiments.osdi2025.vary_levels.run import run_experiment as run_vary_levels from test.experiments.osdi2025.maintenance_ablation.run import run_experiment as run_ablation from test.experiments.osdi2025.vary_batch_size.run import run_experiment as run_vary_batch_size +from test.experiments.osdi2025.wiki_workload.run import run_experiment as run_wiki_workload EXPERIMENTS = { "kick_the_tires": run_kick_the_tires, @@ -25,6 +26,7 @@ "vary_levels": run_vary_levels, "maintenance_ablation": run_ablation, "vary_batch_size": run_vary_batch_size, + "wiki_workload": run_wiki_workload } def main(): diff --git a/test/experiments/osdi2025/experiment_utils.py b/test/experiments/osdi2025/experiment_utils.py index 59822c44..7a2ef22b 100644 --- a/test/experiments/osdi2025/experiment_utils.py +++ b/test/experiments/osdi2025/experiment_utils.py @@ -48,17 +48,16 @@ def prepare_quake_index( load: bool = True ) -> QuakeIndex: idx = QuakeIndex() + + build_params = create_index_build_params(**build_params_dict) + if index_file_path.exists() and not force_rebuild: if load: logger.info(f"Loading index from {index_file_path}") - idx.load(str(index_file_path), num_workers_load, use_numa, num_parent_workers_load) + idx.load(str(index_file_path), build_params) else: logger.info(f"Building index -> {index_file_path}") - bp = IndexBuildParams() - for key, value in build_params_dict.items(): - setattr(bp, key, value) - - idx.build(vecs, torch.arange(len(vecs)), bp) + idx.build(vecs, torch.arange(len(vecs)), build_params) index_file_path.parent.mkdir(parents=True, exist_ok=True) idx.save(str(index_file_path)) logger.info(f"Index saved to {index_file_path}") diff --git a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml index 0ec92f83..cd56ed38 100644 --- a/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml +++ b/test/experiments/osdi2025/numa_multi_query/configs/sift1m.yaml @@ -15,11 +15,19 @@ indexes: build_params: nc: 1000 metric: l2 - num_workers: 12 + num_workers: 1 + num_merge_workers: 1 use_numa: true - parent_num_workers: 12 + parent: + num_workers: 1 + num_merge_workers: 1 + use_numa: true search_params: - nprobe: 20 + nprobe: 100 + batch_size: 64 + parent: + batched_scan: true + batch_size: 128 batched_scan: true # # - name: Quake_16_numa @@ -56,16 +64,16 @@ indexes: # omp_num_threads_search: 16 # # - - name: FaissHNSW - index: HNSW - build_params: - m: 64 - ef_construction: 128 - metric: l2 - omp_num_threads_build: 16 - search_params: - ef_search: 128 - omp_num_threads_search: 16 +# - name: FaissHNSW +# index: HNSW +# build_params: +# m: 64 +# ef_construction: 128 +# metric: l2 +# omp_num_threads_build: 1 +# search_params: +# ef_search: 16 +# omp_num_threads_search: 1 # # - name: SCANN diff --git a/test/experiments/osdi2025/wiki_workload/run.py b/test/experiments/osdi2025/wiki_workload/run.py index e0c65dab..eacdbe0f 100644 --- a/test/experiments/osdi2025/wiki_workload/run.py +++ b/test/experiments/osdi2025/wiki_workload/run.py @@ -101,7 +101,28 @@ def unified_plot(cfg: Dict[str, Any], out_dir: Path) -> None: if not sub.empty: ax.plot(sub.operation_number, sub.latency_ms, color=colour, **OP_STYLE[op]) - # partitions, resident blank + # partitions + part_ix = IDX_PART + if "nlist" in df: + axs[part_ix].plot(df.operation_number, df.nlist, color=colour, marker=marker, lw=1.2) + # resident + res_ix = IDX_RES + if "n_resident" in df: + axs[res_ix].plot(df.operation_number, df.n_resident, color=colour, marker=marker, lw=1.2) + + # splits/deletes + spl_ix = IDX_SPL + if "n_splits" in df and "n_deletes" in df: + axs[spl_ix].plot(df.operation_number, df.n_splits, color=colour, marker="x", lw=1.2, label="Splits") + axs[spl_ix].plot(df.operation_number, df.n_deletes, color=colour, marker="o", lw=1.2, label="Deletes") + # axs[spl_ix].legend(loc="upper left", frameon=False) + + # maintenance time + if "maintenance_latency_ms" in df: + m_ix = IDX_LAT_M + axs[m_ix].plot(df.operation_number, df.maintenance_latency_ms, color=colour, marker=marker, lw=1.2, + label="Maintenance") + # recall rec_ix = IDX_REC sub = df[df.operation_type=="query"] @@ -135,7 +156,7 @@ def unified_plot(cfg: Dict[str, Any], out_dir: Path) -> None: def make_time_breakdown(cfg: Dict[str, Any], out_dir: Path) -> None: - categories = ["Query","Insert","Delete","Total"] + categories = ["Query","Insert","Maintenance", "Total"] data = {} for idx_cfg in cfg.get("indexes", []): name = idx_cfg["name"] @@ -143,7 +164,7 @@ def make_time_breakdown(cfg: Dict[str, Any], out_dir: Path) -> None: data[name] = [ df[df.operation_type=="query"].latency_ms.sum(), df[df.operation_type=="insert"].latency_ms.sum(), - df[df.operation_type=="delete"].latency_ms.sum(), + df.maintenance_latency_ms.sum() if "maintenance_latency_ms" in df else 0, ] data[name].append(sum(data[name])) fig, ax = plt.subplots(figsize=(8,6)) @@ -187,9 +208,17 @@ def run_experiment(cfg_path_str: str, output_dir_str: str) -> None: log.info("Mode: %s", mode) workload_dir = cfg["workload_dir"] + dataset_dir = cfg["dataset_dir"] if mode in {"run"}: # Phase 2: evaluation for idx_cfg in cfg.get("indexes", []): + + # check if results already exist + out_name = out / idx_cfg["name"] + if out_name.is_dir() and (out_name / "results.csv").is_file() and not cfg.get("overwrite", False): + log.info("Skipping %s, results already exist", idx_cfg["name"]) + continue + name = idx_cfg["name"] key = idx_cfg["index"] Cls = INDEX_CLASSES.get(key) @@ -198,7 +227,7 @@ def run_experiment(cfg_path_str: str, output_dir_str: str) -> None: wrapper = Cls() maint = idx_cfg.get("maintenance_params") do_maint = maint is not None - ev = WikidataWorkloadEvaluator(workload_dir, out/name) + ev = WikidataWorkloadEvaluator(workload_dir, out/name, wiki_dataset_dir=dataset_dir) ev.evaluate_workload( name=name, index=wrapper, @@ -206,7 +235,7 @@ def run_experiment(cfg_path_str: str, output_dir_str: str) -> None: search_params=idx_cfg.get("search_params",{}), do_maintenance=do_maint, m_params=maint, - batch=bool(idx_cfg.get("batch",False)), + batch=cfg.get("batch",False), ) if mode in {"run","plot"}: # Phase 3 unified_plot(cfg, out) From c45b440ee5213ac6cd62b939c23ee78c89b66ace Mon Sep 17 00:00:00 2001 From: Jason Date: Sun, 1 Jun 2025 12:41:10 -0500 Subject: [PATCH 301/323] wiki config --- .../osdi2025/wiki_workload/configs/wiki.yaml | 186 ++++++++++++++++++ 1 file changed, 186 insertions(+) create mode 100644 test/experiments/osdi2025/wiki_workload/configs/wiki.yaml diff --git a/test/experiments/osdi2025/wiki_workload/configs/wiki.yaml b/test/experiments/osdi2025/wiki_workload/configs/wiki.yaml new file mode 100644 index 00000000..143d5f34 --- /dev/null +++ b/test/experiments/osdi2025/wiki_workload/configs/wiki.yaml @@ -0,0 +1,186 @@ +workload_dir: data/wikidata/workload +dataset_dir: /Users/jason/projects/compass/data/wikidata/wikidata +overwrite: False +batch: True + +indexes: + - name: Quake_Serial_NoAPS_NoMaintenance + index: Quake + build_params: + nc: 1000 + metric: ip + num_workers: 1 + use_numa: true + parent: + num_workers: 1 + search_params: + k: 100 + nprobe: 10 + initial_search_fraction: .05 + batched_scan: false + parent: + batched_scan: true + batch_size: 128 + maintenance_params: + refinement_radius: 50 + split_threshold_ns: 500000 + delete_threshold_ns: 500000 + enable_split_rejection: true + enable_delete_rejection: false + window_size: 10000 + + - name: Quake_Serial_NoAPS + index: Quake + build_params: + nc: 1000 + metric: ip + num_workers: 1 + use_numa: true + parent: + num_workers: 1 + search_params: + k: 100 + nprobe: 30 + initial_search_fraction: .05 + batched_scan: false + parent: + batched_scan: true + batch_size: 128 + maintenance_params: + refinement_radius: 50 + split_threshold_ns: 250 + delete_threshold_ns: 500000 + enable_split_rejection: true + enable_delete_rejection: false + window_size: 10000 + + - name: Quake_Serial + index: Quake + build_params: + nc: 1000 + metric: ip + num_workers: 1 + use_numa: true + parent: + num_workers: 1 + search_params: + k: 100 + recall_target: .9 + initial_search_fraction: .05 + batched_scan: false + parent: + batched_scan: true + batch_size: 128 + maintenance_params: + refinement_radius: 50 + split_threshold_ns: 250 + delete_threshold_ns: 500000 + enable_split_rejection: true + enable_delete_rejection: false + window_size: 10000 + + - name: QuakeNoMaintenanceNoAPS + index: Quake + build_params: + nc: 1000 + metric: ip + num_workers: 8 + use_numa: true + parent: + num_workers: 2 + search_params: + k: 100 + nprobe: 30 + batched_scan: false + aps_flush_period_us: 10 + parent: + batched_scan: true + batch_size: 128 + maintenance_params: + refinement_radius: 50 + split_threshold_ns: 500000 + delete_threshold_ns: 500000 + enable_split_rejection: true + enable_delete_rejection: false + window_size: 10000 + + - name: QuakeNoMaintenance + index: Quake + build_params: + nc: 1000 + metric: ip + num_workers: 8 + use_numa: true + parent: + num_workers: 2 + search_params: + k: 100 + recall_target: .9 + batched_scan: false + recompute_threshold: 0.01 + use_precomputed: true + initial_search_fraction: .05 + aps_flush_period_us: 10 + parent: + batched_scan: true + batch_size: 128 + maintenance_params: + refinement_radius: 50 + split_threshold_ns: 500000 + delete_threshold_ns: 500000 + enable_split_rejection: true + enable_delete_rejection: false + window_size: 10000 + + - name: QuakeNoAPS + index: Quake + build_params: + nc: 1000 + metric: ip + num_workers: 8 + use_numa: true + parent: + num_workers: 2 + search_params: + k: 100 + nprobe: 30 + batched_scan: false + aps_flush_period_us: 10 + parent: + batched_scan: true + batch_size: 128 + maintenance_params: + refinement_radius: 50 + split_threshold_ns: 250 + delete_threshold_ns: 500000 + enable_split_rejection: true + enable_delete_rejection: false + window_size: 10000 + + - name: Quake + index: Quake + build_params: + nc: 1000 + metric: ip + num_workers: 8 + use_numa: true + parent: + num_workers: 2 + search_params: + k: 100 + recall_target: .9 + batched_scan: false + recompute_threshold: 0.01 + use_precomputed: true + initial_search_fraction: .05 + aps_flush_period_us: 10 + parent: + batched_scan: true + batch_size: 128 + maintenance_params: + refinement_radius: 50 + split_threshold_ns: 250 + delete_threshold_ns: 500000 + enable_split_rejection: true + enable_delete_rejection: false + window_size: 10000 \ No newline at end of file From a29c188dff996c9800c9c9f4e58141eb7e39cde7 Mon Sep 17 00:00:00 2001 From: Jason Date: Sun, 1 Jun 2025 14:22:17 -0500 Subject: [PATCH 302/323] wiki config --- .../osdi2025/wiki_workload/wiki_workload.py | 587 ++++++++++++++++++ 1 file changed, 587 insertions(+) create mode 100644 test/experiments/osdi2025/wiki_workload/wiki_workload.py diff --git a/test/experiments/osdi2025/wiki_workload/wiki_workload.py b/test/experiments/osdi2025/wiki_workload/wiki_workload.py new file mode 100644 index 00000000..9a7f298b --- /dev/null +++ b/test/experiments/osdi2025/wiki_workload/wiki_workload.py @@ -0,0 +1,587 @@ +import json +import time +from pathlib import Path +from typing import Union, Dict, List, Optional + +import numpy as np +import torch + +import quake +from quake.utils import to_path, compute_recall +import pandas as pd +import matplotlib.pyplot as plt +from quake.index_wrappers.quake import QuakeWrapper + +TOTAL_NUM_UPDATES = 104 +TOTAL_NUM_EMBEDDINGS = 11_790_930 +K = 100 + +wikidata_dir = Path("/Users/jason/projects/compass/data/wikidata") +metric = "ip" + +def get_node_mappings() -> dict: + """ + This function retrieves the node mappings from the S3 bucket. + """ + node_mappings = None + + with open(wikidata_dir / "node_mappings.json", "r") as f: + node_mappings = json.load(f) + + return node_mappings + + +def update_id_to_month(n) -> str: + """ + This function converts the update id to the corresponding month and year. + """ + # starting point: August 2015 corresponds to update 27 + start_month = 8 + start_year = 2015 + + # calculate the number of months to add + months_to_add = n - 27 + + # calculate the target year and month + target_year = start_year + (start_month + months_to_add - 1) // 12 + target_month = (start_month + months_to_add - 1) % 12 + 1 + + # get the month and year as a string + return f"{target_year}{target_month:02d}" + + +def compute_ip_ground_truth( + queries: torch.Tensor, + base : torch.Tensor, + # ids : torch.Tensor, + k: int, +) -> np.ndarray: + + + flat_quake_index = quake.QuakeIndex() + build_params = quake.IndexBuildParams() + build_params.metric = "ip" + build_params.nlist = 1 + build_params.num_workers = 6 + ids = torch.arange(base.shape[0], dtype=torch.int64) + flat_quake_index.build(base, ids, build_params) + + search_params = quake.SearchParams() + search_params.k = k + search_params.batched_scan = True + res = flat_quake_index.search(queries, search_params) + return res.ids.numpy(), res.distances.numpy() + + +def wiki_sampler(current_ids: torch.tensor, query_size: int, update_id: int) -> torch.tensor: + """ + This function samples query ids from the current_ids. + + :param current_ids: The current ids in the index. + :param query_size: The size of the query. + :param update_id: The id of the update. + + :return: The sampled query ids. + """ + assert update_id >= -1 and update_id <= 103 + + month_str = update_id_to_month(update_id) + if month_str == "201412": + month_str = "201312" + elif month_str == "201310": + month_str = "201210" + + # for each of the current ids, get the corresponding pageviews for the given update_id + df_id = pd.DataFrame(current_ids.numpy(), columns=["id"]) + df_pageviews = pd.read_parquet(wikidata_dir / f"pageviews/pageviews_{month_str}.parquet") + # df_pageviews = pd.read_parquet(Path(f"data/wikidata/pageviews/pageviews_{month_str}.parquet")) + + df = pd.merge(df_id, df_pageviews, on="id", how="left") + df["pageviews"] = df["pageviews"].fillna(0).astype(int) + + pageviews = df["pageviews"].values + assert len(pageviews) == len(current_ids) + + # sample the query ids based on the pageviews + np.random.seed(42) + probs = pageviews / pageviews.sum() + query_ids = np.random.choice(current_ids.numpy(), size=query_size, replace=True, p=probs) + + assert len(query_ids) == query_size + return torch.tensor(query_ids) + +class WikidataWorkloadGenerator: + """ + Generates a dynamic workload from the Wikipedia data for updates + and sample embeddings for queries (for now). + + The workflow is as follows: + 1. Takes the embeddings from the Wikipedia dataset for the initial base embeddings and updates. + 2. After each update, samples a set of resident embeddings for queries. + 3. The workload is saved to a directory as a set of operations with a corresponding runbook. + """ + + def __init__( + self, + workload_dir: Union[str, Path], + num_updates: int, + query_batch_size: int, + metric: str = "ip", + seed: int = 1874, + with_patterns: bool = True, + overwrite: bool = False, + ): + # download the wikipedia dataset if not already downloaded + self.num_updates = num_updates + + assert num_updates <= TOTAL_NUM_UPDATES + + self.workload_dir = Path(workload_dir) + self.query_batch_size = query_batch_size + self.metric = metric.lower() + self.seed = seed + self.with_patterns = with_patterns + + assert self.metric == "ip" + assert self.query_batch_size > 0 + + torch.manual_seed(self.seed) + np.random.seed(self.seed) + + self.workload_dir.mkdir(parents=True, exist_ok=True) + + # delete the runbook if it exists and overwrite is True + runbook_path = self.workload_dir / "runbook.json" + if runbook_path.exists() and overwrite: + runbook_path.unlink() + + self.operations_dir = self.workload_dir / "operations" + if self.operations_dir.exists() and overwrite: + for file in self.operations_dir.iterdir(): + file.unlink() + + self.operations_dir.mkdir(parents=True, exist_ok=True) + + # set generator state + self.current_base = None + self.current_ids = None + + self.runbook = {} + + def workload_exists(self): + return (self.workload_dir / "runbook.json").exists() + + def generate_workload(self): + """ + Generate the workload. + """ + + self.runbook["parameters"] = { + "query_batch_size": self.query_batch_size, + "num_updates": self.num_updates, + "metric": self.metric, + "pattern_sampling": self.with_patterns, + } + + initial_embeddings = torch.from_numpy(np.load(wikidata_dir / "wikidata" / "embeddings_initial.npy")) + initial_embeddings /= torch.norm(initial_embeddings, dim=1, keepdim=True) + self.current_base = initial_embeddings + self.current_ids = torch.from_numpy(np.load(wikidata_dir / "wikidata" / "ids_initial.npy")) + + self.runbook["initialize"] = { + "size": self.current_ids.shape[0], + } + self.runbook["operations"] = {} + + n_inserts = 0 + n_queries = 0 + n_operations = 0 + + # the first operation is a query operation + n_resident = self.current_ids.shape[0] + assert n_resident == self.current_base.shape[0] + + if not self.with_patterns: + rand_perm = torch.randperm(n_resident)[: self.query_batch_size] + sample_ids = self.current_ids[rand_perm] + queries = self.current_base[rand_perm] + else: + sample_ids = wiki_sampler(self.current_ids, self.query_batch_size, -1) + indices = torch.tensor([torch.where(self.current_ids == s_id)[0][0] for s_id in sample_ids]) + queries = self.current_base[indices] + + entry = {"type": "query", "sample_size": len(sample_ids), "n_resident": n_resident} + torch.save(sample_ids, self.operations_dir / "0.pt") + + # compute the ground truth based on the resident set of vectors + start_time = time.time() + ids, dists = compute_ip_ground_truth(queries, self.current_base, K) + ids = self.current_ids[ids] + gt_time = time.time() - start_time + + entry["gt_time"] = gt_time + + # save the ground truth + torch.save(ids, self.operations_dir / "0_gt_ids.pt") + torch.save(dists, self.operations_dir / "0_gt_dists.pt") + + print("Operation 0", entry) + self.runbook["operations"][0] = entry + + n_queries += 1 + n_operations += 1 + + # for each of the updates, do one insert and one query operation + for i in range(self.num_updates): + print(f"Processing update {i}...") + new_embeddings = torch.from_numpy(np.load(wikidata_dir / "wikidata" / f"embeddings_update_{i}.npy")) + new_embeddings /= torch.norm(new_embeddings, dim=1, keepdim=True) + new_ids = torch.from_numpy(np.load(wikidata_dir / "wikidata" / f"ids_update_{i}.npy")) + + # insert the new embeddings + self.current_base = torch.cat([self.current_base, new_embeddings], dim=0) + self.current_ids = torch.cat([self.current_ids, new_ids], dim=0) + + n_resident = self.current_ids.shape[0] + assert n_resident == self.current_base.shape[0] + + entry = {"type": "insert", "size": new_ids.shape[0], "n_resident": n_resident} + # torch.save(new_ids, self.operations_dir / f"{n_operations}.pt") + + print(f"Operation {n_operations} (insert)", entry) + self.runbook["operations"][n_operations] = entry + + n_operations += 1 + n_inserts += 1 + + # the next operation is a query operation + if not self.with_patterns: + # randomly sample a set of resident embeddings for queries + rand_perm = torch.randperm(n_resident)[: self.query_batch_size] + sample_ids = self.current_ids[rand_perm] + queries = self.current_base[rand_perm] + else: + sample_ids = wiki_sampler(self.current_ids, self.query_batch_size, i) + indices = torch.tensor([torch.where(self.current_ids == s_id)[0][0] for s_id in sample_ids]) + queries = self.current_base[indices] + + entry = {"type": "query", "sample_size": len(sample_ids), "n_resident": n_resident} + torch.save(sample_ids, self.operations_dir / f"{n_operations}.pt") + + # compute the ground truth based on the resident set of vectors + start_time = time.time() + ids, dists = compute_ip_ground_truth(queries, self.current_base, K) + ids = self.current_ids[ids] + gt_time = time.time() - start_time + + entry["gt_time"] = gt_time + + # save the ground truth + torch.save(ids, self.operations_dir / f"{n_operations}_gt_ids.pt") + torch.save(dists, self.operations_dir / f"{n_operations}_gt_dists.pt") + + print(f"Operation {n_operations} (query)", entry) + self.runbook["operations"][n_operations] = entry + + n_operations += 1 + n_queries += 1 + + self.runbook["summary"] = { + "n_inserts": n_inserts, + "n_queries": n_queries, + "n_operations": n_operations, + } + + # save the runbook + with open(self.workload_dir / "runbook.json", "w") as f: + json.dump(self.runbook, f, indent=4) + + +class WikidataWorkloadEvaluator: + """ + Evaluates a Wikidata workload using the same interface as WorkloadEvaluator, + but with explicit NumPy-based embedding handling. + """ + + def __init__( + self, + workload_dir: Union[str, Path], + output_dir: Union[str, Path], + wiki_dataset_dir: Union[str, Path], + ): + self.workload_dir = to_path(workload_dir) + self.wiki_dataset_dir = to_path(wiki_dataset_dir) + self.output_dir = to_path(output_dir) + self.runbook_path = self.workload_dir / "runbook.json" + self.ops_dir = self.workload_dir / "operations" + + # will hold embeddings and ids + self.current_base: Optional[torch.Tensor] = None + self.current_ids: Optional[torch.Tensor] = None + self.index_initialized = False + + def _init_index(self, name: str, wrapper, build_params: Dict, + m_params: Optional[Dict]): + + idx_dir = self.workload_dir / "init_indexes" + idx_dir.mkdir(parents=True, exist_ok=True) + idx_file = idx_dir / f"{name}.index" + + vecs_init = torch.from_numpy(np.load(self.wiki_dataset_dir / "embeddings_initial.npy")) + init_ids = torch.from_numpy(np.load(self.wiki_dataset_dir / "ids_initial.npy")) + + self.current_base = vecs_init + self.current_ids = init_ids + + max_id = int(self.current_ids.max().item()) + inv_map = torch.full((max_id + 1,), -1, dtype=torch.long) + inv_map[self.current_ids] = torch.arange(len(self.current_ids), dtype=torch.long) + self.inv_map = inv_map + + if not idx_file.exists(): + print(f"[{name}] building base index …") + wrapper.build(vecs_init, ids=init_ids, **build_params) + wrapper.save(idx_file) + print(f"[{name}] stored → {idx_file}") + else: + wrapper.load(idx_file, + num_workers=build_params.get("num_workers", 0), use_numa=build_params.get("use_numa", True), parent=build_params.get("parent", None)) + print(f"[{name}] loaded ← {idx_file}") + + if isinstance(wrapper, QuakeWrapper) and m_params: + mp = quake.MaintenancePolicyParams() + for k, v in m_params.items(): + setattr(mp, k, v) + wrapper.index.initialize_maintenance_policy(mp) + print(f"[{name}] maintenance policy: {m_params}") + + + + return wrapper + + def evaluate_workload( + self, + *, + name: str, + index, + build_params: Dict, + search_params: Dict, + do_maintenance: bool = False, + m_params: Optional[Dict] = None, + batch: bool = False, + ) -> List[Dict]: + """ + Evaluate inserts, deletes, and queries by loading NumPy embeddings directly. + """ + self.output_dir.mkdir(parents=True, exist_ok=True) + index = self._init_index(name, index, build_params, m_params) + + runbook = json.load(open(self.runbook_path)) + results: List[Dict] = [] + totals = dict(query=0.0, insert=0.0, delete=0.0, maintenance=0.0) + + print(f"─ Evaluating workload on {name} ({len(runbook['operations'])} ops) ─") + for op_id, op in runbook["operations"].items(): + op_no = int(op_id) + typ = op["type"] + print(f"[{name}] op {op_no:4d} | {typ:<6}", end="", flush=True) + + if typ == "insert": + upd_idx = (op_no - 1) // 2 + emb_up = np.load(self.wiki_dataset_dir / f"embeddings_update_{upd_idx}.npy") + id_up = np.load(self.wiki_dataset_dir / f"ids_update_{upd_idx}.npy") + new_emb = torch.from_numpy(emb_up).float() + new_emb /= new_emb.norm(dim=1, keepdim=True) + new_ids = torch.from_numpy(id_up).long() + + t0 = time.perf_counter() + index.add(new_emb, ids=new_ids) + latency_ms = (time.perf_counter() - t0) * 1e3 + recall = None + + # update current state + old_len = len(self.current_ids) + self.current_base = torch.cat([self.current_base, new_emb], dim=0) + self.current_ids = torch.cat([self.current_ids, new_ids], dim=0) + + max_new_id = int(self.current_ids.max().item()) + if max_new_id >= self.inv_map.size(0): + # need a larger map + new_map = torch.full((max_new_id + 1,), -1, dtype=torch.long) + new_map[: self.inv_map.size(0)] = self.inv_map + self.inv_map = new_map + + # positions of the just‐inserted IDs + new_positions = torch.arange(old_len, old_len + len(new_ids), dtype=torch.long) + self.inv_map[new_ids] = new_positions + + elif typ == "delete": + # assume ops_dir contains .pt of ids to delete + ids = torch.load(self.ops_dir / f"{op_id}.pt") + t0 = time.perf_counter() + index.remove(ids) + latency_ms = (time.perf_counter() - t0) * 1e3 + recall = None + + elif typ == "query": + # load sample IDs and map + samp_ids = torch.load(self.ops_dir / f"{op_id}.pt", weights_only=True) + indices = self.inv_map[samp_ids] + queries = self.current_base[indices] + queries /= queries.norm(dim=1, keepdim=True) + + t0 = time.perf_counter() + + # run first 1000 queries for debugging + max_q = 10000 + queries = queries[:max_q] + if batch: + sr = index.search(queries, **search_params) + pred_ids = sr.ids + else: + parts = [] + for q in queries: + out = index.search(q.unsqueeze(0), **search_params) + parts.append(out.ids) + pred_ids = torch.cat(parts) + latency_ms = (time.perf_counter() - t0) * 1e3 + + gt_ids = torch.load(self.ops_dir / f"{op_id}_gt_ids.pt")[:max_q] + recall = compute_recall(pred_ids, gt_ids, search_params.get("k")).mean().item() + op["recall"] = recall + + else: + raise ValueError(f"Unknown op type {typ}") + + n_splits = 0 + n_deletes = 0 + split_time_ms = 0.0 + delete_time_ms = 0.0 + refinement_time_ms = 0.0 + maintenance_latency_ms = 0.0 + + if do_maintenance: + t0 = time.perf_counter() + nlist_before = index.index_state()["n_list"] + info = index.maintenance() + nlist_after = index.index_state()["n_list"] + maintenance_latency_ms = (time.perf_counter() - t0) * 1e3 + n_splits = info.n_splits + n_deletes = info.n_deletes + delete_time_ms = info.delete_time_us / 1000.0 + split_time_ms = info.split_time_us / 1000.0 + refinement_time_ms = info.refinement_time_us / 1000.0 + + n_resident = index.index_state()["n_total"] + nlist = index.index_state()["n_list"] + + totals[typ] += latency_ms + totals["maintenance"] += maintenance_latency_ms + print(f" | lat {latency_ms:8.2f} ms" + (f" | rec {recall:.3f}" if recall is not None else "")) + + row = { + "operation_number": op_no, + "operation_type": typ, + "latency_ms": latency_ms, + "n_resident": n_resident, + "n_splits": n_splits, + "n_deletes": n_deletes, + "nlist": nlist, + "split_time_ms": split_time_ms, + "delete_time_ms": delete_time_ms, + "maintenance_latency_ms": maintenance_latency_ms, + "refinement_time_ms": refinement_time_ms, + "recall": recall, + } + + results.append(row) + + # four-panel + df = pd.DataFrame(results) + self._four_panel_plot(df) + + # time breakdown + self._time_breakdown_plot(totals, name) + + # CSV output + df.to_csv(self.output_dir / "results.csv", index=False) + print(f"Results → {self.output_dir / 'results.csv'}") + return results + + # --------------------------------------------------------------------- + def _four_panel_plot(self, df: pd.DataFrame): + lat_ins = df[df.operation_type == "insert"] + lat_del = df[df.operation_type == "delete"] + lat_q = df[df.operation_type == "query"] + + fig, axs = plt.subplots(2, 2, figsize=(12, 10)) + ax = axs[0, 0] + if not lat_ins.empty: + ax.plot(lat_ins.operation_number, lat_ins.latency_ms, marker="o", label="Insert") + if not lat_del.empty: + ax.plot(lat_del.operation_number, lat_del.latency_ms, marker="s", label="Delete") + if not lat_q.empty: + ax.plot(lat_q.operation_number, lat_q.latency_ms, marker="^", label="Query") + ax.set(xlabel="Op #", ylabel="Latency (ms)", title="Latency"); ax.legend() + # other panels blank except recall + ax = axs[0, 1] + ax.text(0.5,0.5,"Partitions not tracked",ha="center") + ax.axis("off") + ax = axs[1, 0] + ax.text(0.5,0.5,"Resident not tracked",ha="center") + ax.axis("off") + rec = df[(df.operation_type=="query") & df.recall.notna()] + ax = axs[1, 1] + if not rec.empty: + ax.plot(rec.operation_number, rec.recall, marker="o") + ax.set(xlabel="Op #", ylabel="Recall", title="Recall") + else: + ax.text(0.5,0.5,"No recall",ha="center") + ax.axis("off") + plt.tight_layout() + plt.savefig(self.output_dir / "evaluation_plots.png") + plt.close() + + # --------------------------------------------------------------------- + def _time_breakdown_plot(self, totals: Dict[str, float], title: str): + plt.figure(figsize=(6,4)) + bars = [totals.get("query",0), totals.get("insert",0), totals.get("delete",0), sum(totals.values())] + labels = ["Query","Insert","Delete","Total"] + plt.bar(labels, bars) + plt.ylabel("Cumulative ms") + plt.title(f"Time breakdown – {title}") + plt.tight_layout() + plt.savefig(self.output_dir / "time_breakdown.png", dpi=150) + plt.close() + + +if __name__ == "__main__": + + # parse arguments + import argparse + parser = argparse.ArgumentParser(description="Generate a dynamic workload for Wikidata.") + parser.add_argument( + "--workload_dir", + type=str, + default="data/wikidata/workload", + help="Directory to save the workload.", + ) + parser.add_argument( + "--query_batch_size", + type=int, + default=1000, + help="Batch size for queries between updates.", + ) + + # Initialize the workload generator + args = parser.parse_args() + workload_generator = WikidataWorkloadGenerator( + workload_dir=args.workload_dir, + num_updates=TOTAL_NUM_UPDATES, + query_batch_size=args.query_batch_size, + metric=metric, + with_patterns=True, + overwrite=True, + ) + # Generate the workload + workload_generator.generate_workload() \ No newline at end of file From 476d024e00a813d243bb4672b8e29980d6a97fd0 Mon Sep 17 00:00:00 2001 From: Jason Date: Sun, 1 Jun 2025 14:52:15 -0500 Subject: [PATCH 303/323] wiki config --- test/experiments/osdi2025/wiki_workload/wiki_workload.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/test/experiments/osdi2025/wiki_workload/wiki_workload.py b/test/experiments/osdi2025/wiki_workload/wiki_workload.py index 9a7f298b..e769571c 100644 --- a/test/experiments/osdi2025/wiki_workload/wiki_workload.py +++ b/test/experiments/osdi2025/wiki_workload/wiki_workload.py @@ -369,6 +369,7 @@ def evaluate_workload( do_maintenance: bool = False, m_params: Optional[Dict] = None, batch: bool = False, + max_q: int = 1000, ) -> List[Dict]: """ Evaluate inserts, deletes, and queries by loading NumPy embeddings directly. @@ -432,9 +433,8 @@ def evaluate_workload( t0 = time.perf_counter() - # run first 1000 queries for debugging - max_q = 10000 - queries = queries[:max_q] + randperm = torch.randperm(len(queries)) + queries = queries[randperm[:max_q]] if batch: sr = index.search(queries, **search_params) pred_ids = sr.ids @@ -446,7 +446,7 @@ def evaluate_workload( pred_ids = torch.cat(parts) latency_ms = (time.perf_counter() - t0) * 1e3 - gt_ids = torch.load(self.ops_dir / f"{op_id}_gt_ids.pt")[:max_q] + gt_ids = torch.load(self.ops_dir / f"{op_id}_gt_ids.pt")[randperm[:max_q]] recall = compute_recall(pred_ids, gt_ids, search_params.get("k")).mean().item() op["recall"] = recall From d785ab492439f71081f9e01ad951205f49ab4667 Mon Sep 17 00:00:00 2001 From: Jason Date: Sun, 1 Jun 2025 22:53:23 -0500 Subject: [PATCH 304/323] fix fault --- src/cpp/src/query_coordinator.cpp | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index ade73c53..3be2ea09 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -476,7 +476,7 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, batched_scan_list( qptr, codes, ids, - Q, part_size, D, + query_ids.size(), part_size, D, res.topk_buffer_pool, metric_, res.blas_ip_block, @@ -1317,6 +1317,11 @@ shared_ptr QueryCoordinator::search(Tensor x, shared_ptrk <= 0) { + throw std::runtime_error("[QueryCoordinator::search] k must be greater than 0."); + } + auto parent_timing_info = std::make_shared(); auto start = high_resolution_clock::now(); @@ -1334,7 +1339,6 @@ shared_ptr QueryCoordinator::search(Tensor x, shared_ptrrecompute_threshold = search_params->recompute_threshold; // parent_search_params->initial_search_fraction = .5; parent_search_params->batched_scan = false; - if (x.size(0) > 10) { parent_search_params->batched_scan = true; } From 5b19e9b8546d4f87bc943b3dd9897bf36c9668bc Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 2 Jun 2025 10:54:42 -0500 Subject: [PATCH 305/323] fix overestimation --- src/cpp/include/query_coordinator.h | 1 + src/cpp/src/query_coordinator.cpp | 20 ++++++++++++++++++++ 2 files changed, 21 insertions(+) diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index afa4d39e..21a136a6 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -125,6 +125,7 @@ class QueryCoordinator { int next_job_id_ = 0; ///< ID for the next job to be processed. vector> query_dist_pivots_; ///< Pivots for each query to speed up sorting vector> query_done_flags_; ///< Flags to indicate if a query is done from APS + vector> max_rank_; ///< Maximum rank for each query to ensure APS doesn't overshoot vector>> job_flags_; ///< Flags to track job completion std::atomic job_pull_time_ns = 0; ///< Time spent pulling jobs from the queue. std::atomic job_process_time_ns = 0; ///< Time spent processing jobs. diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 3be2ea09..8ee178eb 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -273,6 +273,12 @@ void QueryCoordinator::handle_nonbatched_job(const ScanJob &job, return; } + // check the job is not larger than the maximum rank + if (job.rank >= max_rank_[job.query_id].load(std::memory_order_relaxed)) { + enqueue_result_job(ResultJob{job.query_id, job.rank, {}, {}}); + return; + } + // ensure buffers if (res.topk_buffer_pool.size() < 1) { res.topk_buffer_pool.resize(1); @@ -636,6 +642,7 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, job_flags_.clear(); job_flags_.resize(nQ); per_query_total_left_ = vector>(nQ); + max_rank_ = vector>(nQ); for (int64_t q = 0; q < nQ; ++q) { job_flags_[q] = vector>(partition_ids.size(1)); @@ -649,6 +656,7 @@ void QueryCoordinator::enqueue_scan_jobs(Tensor x, } } per_query_total_left_[q].store(valid_count, std::memory_order_relaxed); + max_rank_[q].store(partition_ids.size(1) - 1, std::memory_order_relaxed); } job_buffer_.clear(); job_buffer_.reserve(nQ * partition_ids.size(1)); @@ -841,6 +849,17 @@ void QueryCoordinator::drain_and_apply_aps(Tensor queries, } float recall_estimate = 0.0f; + float sum = 0.0f; + int max_rank = 0; + + for (int p = 0; p < partition_ids.size(1); ++p) { + sum += recall_profiles[q][p]; + if (sum >= search_params->recall_target) { + max_rank = p; + break; + } + } + max_rank_[q].store(max_rank, std::memory_order_relaxed); int n_scanned = 0; for (int p = 0; p < partition_ids.size(1); ++p) { @@ -848,6 +867,7 @@ void QueryCoordinator::drain_and_apply_aps(Tensor queries, n_scanned++; recall_estimate += recall_profiles[q][p]; } + sum += recall_profiles[q][p]; } if (recall_estimate >= search_params->recall_target) { From 2125b0eab9c31c5258223bd542d666c2fe38b21d Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 2 Jun 2025 11:03:35 -0500 Subject: [PATCH 306/323] fix overestimation --- src/cpp/src/query_coordinator.cpp | 48 +++++++++++++++++-------------- 1 file changed, 27 insertions(+), 21 deletions(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 8ee178eb..02230e50 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -793,6 +793,7 @@ void QueryCoordinator::drain_and_apply_aps(Tensor queries, vector> boundary_distances; vector curr_radii; vector> recall_profiles; + vector recall_profile_set; if (use_aps) { boundary_distances.resize(nQ); recall_profiles.resize(nQ); @@ -801,6 +802,8 @@ void QueryCoordinator::drain_and_apply_aps(Tensor queries, } else { curr_radii.resize(nQ, std::numeric_limits::infinity()); } + + recall_profile_set.resize(nQ, false); for (int64_t q = 0; q < nQ; ++q) { vector curr_pids_vec(partition_ids[q].data_ptr(), partition_ids[q].data_ptr() + partition_ids[q].size(0)); @@ -845,33 +848,36 @@ void QueryCoordinator::drain_and_apply_aps(Tensor queries, metric_ == faiss::METRIC_L2); curr_radii[q] = query_radius; + recall_profile_set[q] = true; } - float recall_estimate = 0.0f; - float sum = 0.0f; - int max_rank = 0; - - for (int p = 0; p < partition_ids.size(1); ++p) { - sum += recall_profiles[q][p]; - if (sum >= search_params->recall_target) { - max_rank = p; - break; + if (recall_profile_set[q]) { + float recall_estimate = 0.0f; + float sum = 0.0f; + int max_rank = 0; + + for (int p = 0; p < partition_ids.size(1); ++p) { + sum += recall_profiles[q][p]; + if (sum >= search_params->recall_target) { + max_rank = p; + break; + } } - } - max_rank_[q].store(max_rank, std::memory_order_relaxed); - - int n_scanned = 0; - for (int p = 0; p < partition_ids.size(1); ++p) { - if (job_flags_[q][p].load(std::memory_order_relaxed)) { - n_scanned++; - recall_estimate += recall_profiles[q][p]; + max_rank_[q].store(max_rank, std::memory_order_relaxed); + + int n_scanned = 0; + for (int p = 0; p < partition_ids.size(1); ++p) { + if (job_flags_[q][p].load(std::memory_order_relaxed)) { + n_scanned++; + recall_estimate += recall_profiles[q][p]; + } + sum += recall_profiles[q][p]; } - sum += recall_profiles[q][p]; - } - if (recall_estimate >= search_params->recall_target) { - query_done_flags_[q].store(true, std::memory_order_relaxed); + if (recall_estimate >= search_params->recall_target) { + query_done_flags_[q].store(true, std::memory_order_relaxed); + } } } } From cabcb838cd1de9301fe2975d355c7e293485037b Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 2 Jun 2025 11:41:47 -0500 Subject: [PATCH 307/323] fix overestimation --- src/cpp/src/query_coordinator.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 02230e50..95235ef2 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -274,7 +274,7 @@ void QueryCoordinator::handle_nonbatched_job(const ScanJob &job, } // check the job is not larger than the maximum rank - if (job.rank >= max_rank_[job.query_id].load(std::memory_order_relaxed)) { + if (job.rank > max_rank_[job.query_id].load(std::memory_order_relaxed)) { enqueue_result_job(ResultJob{job.query_id, job.rank, {}, {}}); return; } From d1758f6a8facd01d7d696ce759a41c4dd2a19f68 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 2 Jun 2025 11:49:39 -0500 Subject: [PATCH 308/323] fix overestimation --- .../osdi2025/wiki_workload/wiki_workload.py | 26 +++++++++++++++++++ 1 file changed, 26 insertions(+) diff --git a/test/experiments/osdi2025/wiki_workload/wiki_workload.py b/test/experiments/osdi2025/wiki_workload/wiki_workload.py index e769571c..51aea709 100644 --- a/test/experiments/osdi2025/wiki_workload/wiki_workload.py +++ b/test/experiments/osdi2025/wiki_workload/wiki_workload.py @@ -435,14 +435,17 @@ def evaluate_workload( randperm = torch.randperm(len(queries)) queries = queries[randperm[:max_q]] + t_infos = [] if batch: sr = index.search(queries, **search_params) pred_ids = sr.ids + t_infos.append(sr.timing_info) else: parts = [] for q in queries: out = index.search(q.unsqueeze(0), **search_params) parts.append(out.ids) + t_infos.append(out.timing_info) pred_ids = torch.cat(parts) latency_ms = (time.perf_counter() - t0) * 1e3 @@ -450,6 +453,25 @@ def evaluate_workload( recall = compute_recall(pred_ids, gt_ids, search_params.get("k")).mean().item() op["recall"] = recall + total_parent_time = 0 + total_time = 0 + total_boundary_time = 0 + total_aps_time = 0 + total_scan_time = 0 + + for t_info in t_infos: + total_parent_time += t_info.parent_info.total_time_ns / 1e6 + total_time += t_info.total_time_ns / 1e6 + total_boundary_time += t_info.boundary_distance_time_ns / 1e6 + total_aps_time += t_info.aps_time_ns / 1e6 + total_scan_time += t_info.scan_time_ns / 1e6 + + print(f" | parent {total_parent_time:.2f} ms" + f" | total {total_time:.2f} ms" + f" | boundary {total_boundary_time:.2f} ms" + f" | aps {total_aps_time:.2f} ms") + + else: raise ValueError(f"Unknown op type {typ}") @@ -472,6 +494,9 @@ def evaluate_workload( split_time_ms = info.split_time_us / 1000.0 refinement_time_ms = info.refinement_time_us / 1000.0 + + + n_resident = index.index_state()["n_total"] nlist = index.index_state()["n_list"] @@ -493,6 +518,7 @@ def evaluate_workload( "refinement_time_ms": refinement_time_ms, "recall": recall, } + print(row) results.append(row) From 4569ceb7819cbb5c4a19c85fe6a0eeac81401b96 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 2 Jun 2025 11:56:07 -0500 Subject: [PATCH 309/323] fix overestimation --- .../osdi2025/wiki_workload/wiki_workload.py | 33 +++++++++++++++++-- 1 file changed, 30 insertions(+), 3 deletions(-) diff --git a/test/experiments/osdi2025/wiki_workload/wiki_workload.py b/test/experiments/osdi2025/wiki_workload/wiki_workload.py index 51aea709..b2236f9f 100644 --- a/test/experiments/osdi2025/wiki_workload/wiki_workload.py +++ b/test/experiments/osdi2025/wiki_workload/wiki_workload.py @@ -457,19 +457,46 @@ def evaluate_workload( total_time = 0 total_boundary_time = 0 total_aps_time = 0 - total_scan_time = 0 + + total_buffer_init_time = 0 + total_copy_query_time = 0 + total_job_enqueue_time = 0 + total_job_wait_time = 0 + total_result_aggregate_time = 0 + +# res->timing_info->buffer_init_time_ns = +# duration_cast(s2 - s1).count(); +# res->timing_info->copy_query_time_ns = +# duration_cast(s3 - s2).count(); +# res->timing_info->job_enqueue_time_ns = +# duration_cast(s4 - s3).count(); +# res->timing_info->job_wait_time_ns = +# duration_cast(s5 - s4).count(); +# res->timing_info->result_aggregate_time_ns = +# duration_cast(s6 - s5).count(); for t_info in t_infos: total_parent_time += t_info.parent_info.total_time_ns / 1e6 total_time += t_info.total_time_ns / 1e6 total_boundary_time += t_info.boundary_distance_time_ns / 1e6 total_aps_time += t_info.aps_time_ns / 1e6 - total_scan_time += t_info.scan_time_ns / 1e6 + total_buffer_init_time += t_info.buffer_init_time_ns / 1e6 + total_copy_query_time += t_info.copy_query_time_ns / 1e6 + total_job_enqueue_time += t_info.job_enqueue_time_ns / 1e6 + total_job_wait_time += t_info.job_wait_time_ns / 1e6 + total_result_aggregate_time += t_info.result_aggregate_time_ns / 1e6 + + print(f" | parent {total_parent_time:.2f} ms" f" | total {total_time:.2f} ms" f" | boundary {total_boundary_time:.2f} ms" - f" | aps {total_aps_time:.2f} ms") + f" | aps {total_aps_time:.2f} ms" + f" | buffer init {total_buffer_init_time:.2f} ms" + f" | copy query {total_copy_query_time:.2f} ms" + f" | job enqueue {total_job_enqueue_time:.2f} ms" + f" | job wait {total_job_wait_time:.2f} ms" + f" | result aggregate {total_result_aggregate_time:.2f} ms") else: From 55b3c1c5bfbac9e934f50c7d234f917f7f5c71b2 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 2 Jun 2025 15:19:42 -0500 Subject: [PATCH 310/323] add readonly workload --- .../configs/msturing10m.yaml | 46 +++++ .../osdi2025/read_only_workload/run.py | 186 ++++++++++++++++++ 2 files changed, 232 insertions(+) create mode 100644 test/experiments/osdi2025/read_only_workload/configs/msturing10m.yaml create mode 100644 test/experiments/osdi2025/read_only_workload/run.py diff --git a/test/experiments/osdi2025/read_only_workload/configs/msturing10m.yaml b/test/experiments/osdi2025/read_only_workload/configs/msturing10m.yaml new file mode 100644 index 00000000..06e25ca4 --- /dev/null +++ b/test/experiments/osdi2025/read_only_workload/configs/msturing10m.yaml @@ -0,0 +1,46 @@ +# configs/experiment.yaml +mode: run # options: build, run, plot +dataset: + name: msturing10m + metric: l2 + path: data +workload_generator: + recall_target: 0.9 + insert_ratio: 0.0 + delete_ratio: 0.0 + query_ratio: 1.0 + number_of_operations: 100 + initial_size: 10000000 + cluster_size: 1000 # N/A for read-only workload + update_batch_size: 10000 # N/A for read-only workload + query_batch_size: 1000 + cluster_sample_distribution: uniform # N/A for read-only workload + query_cluster_sample_distribution: uniform + seed: 9299 + +indexes: + - name: Quake + index: Quake + build_params: + nc: 4000 + metric: ip + num_workers: 8 + num_merge_workers: 1 + use_numa: true + parent: + num_workers: 1 + search_params: + k: 100 + recall_target: .9 + initial_search_fraction: .05 + batched_scan: false + parent: + batched_scan: false + batch_size: 128 + maintenance_params: + refinement_radius: 50 + split_threshold_ns: 100 + delete_threshold_ns: 5 + enable_split_rejection: true + enable_delete_rejection: true + window_size: 1000 diff --git a/test/experiments/osdi2025/read_only_workload/run.py b/test/experiments/osdi2025/read_only_workload/run.py new file mode 100644 index 00000000..560a8887 --- /dev/null +++ b/test/experiments/osdi2025/read_only_workload/run.py @@ -0,0 +1,186 @@ +#!/usr/bin/env python3 +""" +Unified experiment runner + +* Reads a single YAML config (`configs/experiment.yaml`). +* Modes + - **build** –‑ only (re‑)generate the workload. + - **run** –‑ generate workload (if needed), evaluate every index that + does not already have a CSV of prior results (unless + `overwrite.results: true`), then emit the unified plot. + - **plot** –‑ skip evaluation; just regenerate the unified plot. +* A results CSV (`//results.csv`) is written after every + evaluation; its presence is what lets us skip reruns. +* Plot styling (colour / marker / linestyle) is entirely configurable from + the YAML under `plot.styles`. +""" + +import logging, shutil, time, yaml +from pathlib import Path +from typing import Dict, Any, List, Optional + +import matplotlib.pyplot as plt +from matplotlib.lines import Line2D +import numpy as np +import pandas as pd +import torch + +# ── quake imports ────────────────────────────────────────────────────────── +from quake.datasets.ann_datasets import load_dataset +from quake.index_wrappers.quake import QuakeWrapper +from quake.index_wrappers.faiss_ivf import FaissIVF +from quake.utils import compute_recall +from quake.workload_generator import DynamicWorkloadGenerator, WorkloadEvaluator + +logging.basicConfig(level=logging.INFO, + format="%(asctime)s | %(levelname)s | %(message)s") +log = logging.getLogger("runner") + +INDEX_CLASSES = { + "Quake": QuakeWrapper, + "IVF": FaissIVF, +} + +LAT_STYLE = {"insert": "--", "delete": "-.", "query": "-"} + + +# ────────────────────────────────────────────────────────────────────────── +def unified_plot(cfg: Dict[str, Any], out_dir: Path) -> None: + styles = cfg.get("plot", {}).get("styles", {}) + fig, axs = plt.subplots(2, 2, figsize=(12, 10)) + ax_lat, ax_part = axs[0] + ax_res, ax_rec = axs[1] + + legend_idx: List[Line2D] = [] + + for idx in cfg["indexes"]: + name = idx["name"] + csv = out_dir / name / "results.csv" + if not csv.exists(): + log.warning("Skipping plot for %s (no CSV).", name) + continue + df = pd.read_csv(csv) + styl = styles.get(name, {}) + col, mark = styl.get("color"), styl.get("marker") + + # latency + for op in ("insert", "delete", "query"): + sub = df[df.operation_type == op] + if not sub.empty: + ax_lat.plot(sub.operation_number, sub.latency_ms, + color=col, linestyle=LAT_STYLE[op]) + # partitions + if "n_list" in df.columns: + ax_part.plot(df.operation_number, df.n_list, color=col, marker=mark) + # resident + if "n_resident" in df.columns: + ax_res.plot(df.operation_number, df.n_resident, color=col, marker=mark) + # recall + q = df[(df.operation_type == "query") & df.recall.notna()] + if not q.empty: + ax_rec.plot(q.operation_number, q.recall, color=col, marker=mark) + + legend_idx.append(Line2D([0], [0], color=col, label=name)) + + ax_lat.set_yscale("log"); ax_lat.set_title("Latency"); ax_lat.set_xlabel("Op #") + ax_part.set_title("#Partitions"); ax_part.set_xlabel("Op #") + ax_res.set_title("Resident"); ax_res.set_xlabel("Op #") + ax_rec.set_title("Recall"); ax_rec.set_xlabel("Op #"); ax_rec.set_ylim(0, 1) + + style_handles = [Line2D([0], [0], + color="k", ls=LAT_STYLE[o], label=o.title()) + for o in ("insert", "delete", "query")] + ax_lat.legend(handles=style_handles, title="Op type", fontsize=8) + fig.legend(handles=legend_idx, loc="upper center", ncol=max(1, len(legend_idx)), + title="Index", bbox_to_anchor=(0.5, 1.02)) + fig.tight_layout() + path = out_dir / "unified_plot.png" + fig.savefig(path, bbox_inches="tight"); plt.close(fig) + print(f"Unified plot → {path}") + + +# ────────────────────────────────────────────────────────────────────────── +def _should(overwrite: bool, path: Path) -> bool: + return overwrite or not path.exists() + + +# ────────────────────────────────────────────────────────────────────────── +def _evaluate_index(cfg: Dict[str, Any], out_root: Path, batch: bool, + overwrite_results: bool) -> None: + workload_dir = out_root # workload lives in the output root + for idx in cfg["indexes"]: + name = idx["name"] + res_dir = out_root / name + csv = res_dir / "results.csv" + if not _should(overwrite_results, csv): + print(f"{name}: cached results -> skip evaluation"); continue + + cls_key = idx["index"] + cls = INDEX_CLASSES.get(cls_key) + if cls is None: + log.error("Unknown index wrapper %s – skip %s", cls_key, name); continue + res_dir.mkdir(parents=True, exist_ok=True) + + evaluator = WorkloadEvaluator(workload_dir=workload_dir, output_dir=res_dir) + evaluator.evaluate_workload( + name = name, + index = cls(), + build_params = idx.get("build_params", {}), + search_params = idx.get("search_params", {}), + do_maintenance= idx.get("maintenance_params") is not None, + m_params = idx.get("maintenance_params"), + batch = batch, + ) + + +# ────────────────────────────────────────────────────────────────────────── +def _build_workload(cfg: Dict[str, Any], out_root: Path, + overwrite_workload: bool) -> None: + + print("overwrite_workload:", overwrite_workload, "out_root:", out_root) + + if not _should(overwrite_workload, out_root / "runbook.json"): + print("Workload exists – skip generation"); return + + ds_cfg = cfg["dataset"] + vecs, qvecs, _ = load_dataset(ds_cfg["name"], ds_cfg.get("path", "")) + if ds_cfg["metric"] == "l2": # ensure normalisation for L2 if desired + pass + wg_cfg = cfg["workload_generator"] + gen = DynamicWorkloadGenerator( + workload_dir=out_root, + base_vectors=vecs, + metric=ds_cfg["metric"], + insert_ratio=wg_cfg["insert_ratio"], + delete_ratio=wg_cfg["delete_ratio"], + query_ratio =wg_cfg["query_ratio"], + number_of_operations=wg_cfg["number_of_operations"], + initial_size=wg_cfg["initial_size"], + cluster_size=wg_cfg["cluster_size"], + update_batch_size=wg_cfg["update_batch_size"], + query_batch_size =wg_cfg["query_batch_size"], + cluster_sample_distribution=wg_cfg["cluster_sample_distribution"], + queries=qvecs, + query_cluster_sample_distribution=wg_cfg["query_cluster_sample_distribution"], + seed=wg_cfg["seed"], + ) + print("Generating workload …"); gen.generate_workload() + + +# ────────────────────────────────────────────────────────────────────────── +def run_experiment(cfg_src: str | Dict[str, Any], output_dir: str | Path) -> None: + cfg = yaml.safe_load(Path(cfg_src).read_text()) if isinstance(cfg_src, (str, Path)) else cfg_src + out_root = Path(output_dir).expanduser() + out_root.mkdir(parents=True, exist_ok=True) + + mode = cfg.get("mode", "run") + overwrite_results = cfg.get("overwrite_results", False) + overwrite_workload = cfg.get("overwrite_workload", False) + batch = cfg.get("batch", False) + + if mode in {"build", "run"}: + _build_workload(cfg, out_root, overwrite_workload) + if mode == "run": + _evaluate_index(cfg, out_root, batch, overwrite_results) + if mode in {"run", "plot"}: + unified_plot(cfg, out_root) \ No newline at end of file From 4483044ca0bfe01024bdccf36d2419cef6e7a26e Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 2 Jun 2025 15:55:13 -0500 Subject: [PATCH 311/323] add readonly workload --- test/experiments/osdi2025/experiment_runner.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/test/experiments/osdi2025/experiment_runner.py b/test/experiments/osdi2025/experiment_runner.py index 7f9c2798..ac48516e 100644 --- a/test/experiments/osdi2025/experiment_runner.py +++ b/test/experiments/osdi2025/experiment_runner.py @@ -15,6 +15,7 @@ from test.experiments.osdi2025.maintenance_ablation.run import run_experiment as run_ablation from test.experiments.osdi2025.vary_batch_size.run import run_experiment as run_vary_batch_size from test.experiments.osdi2025.wiki_workload.run import run_experiment as run_wiki_workload +from test.experiments.osdi2025.read_only_workload.run import run_experiment as run_read_only_workload EXPERIMENTS = { "kick_the_tires": run_kick_the_tires, @@ -26,7 +27,8 @@ "vary_levels": run_vary_levels, "maintenance_ablation": run_ablation, "vary_batch_size": run_vary_batch_size, - "wiki_workload": run_wiki_workload + "wiki_workload": run_wiki_workload, + "read_only_workload": run_read_only_workload } def main(): From d6e8ceb1163eb60e8fc7ec2b94528d4f97683314 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 2 Jun 2025 16:31:45 -0500 Subject: [PATCH 312/323] readonly --- .../read_only_workload/configs/msturing10m.yaml | 17 +++++++++++++---- 1 file changed, 13 insertions(+), 4 deletions(-) diff --git a/test/experiments/osdi2025/read_only_workload/configs/msturing10m.yaml b/test/experiments/osdi2025/read_only_workload/configs/msturing10m.yaml index 06e25ca4..df50e43a 100644 --- a/test/experiments/osdi2025/read_only_workload/configs/msturing10m.yaml +++ b/test/experiments/osdi2025/read_only_workload/configs/msturing10m.yaml @@ -19,12 +19,21 @@ workload_generator: seed: 9299 indexes: + - name: FaissIVF + index: IVF + build_params: + nc: 4000 + metric: l2 + search_params: + k: 100 + nprobe: 250 + - name: Quake index: Quake build_params: nc: 4000 metric: ip - num_workers: 8 + num_workers: num_merge_workers: 1 use_numa: true parent: @@ -39,8 +48,8 @@ indexes: batch_size: 128 maintenance_params: refinement_radius: 50 - split_threshold_ns: 100 - delete_threshold_ns: 5 + split_threshold_ns: 250 + delete_threshold_ns: 5000000 enable_split_rejection: true - enable_delete_rejection: true + enable_delete_rejection: false window_size: 1000 From 735cf18183c7b1827d66cd3c163d7dae0e24a30c Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 2 Jun 2025 16:37:28 -0500 Subject: [PATCH 313/323] readonly --- src/python/index_wrappers/faiss_ivf.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/python/index_wrappers/faiss_ivf.py b/src/python/index_wrappers/faiss_ivf.py index 4326cfac..225a3ea3 100644 --- a/src/python/index_wrappers/faiss_ivf.py +++ b/src/python/index_wrappers/faiss_ivf.py @@ -233,6 +233,7 @@ def search( search_result.ids = to_torch(indices) search_result.distances = to_torch(distances) search_result.timing_info = timing_info + search_result.timing_info.parent_info = SearchTimingInfo() return search_result @@ -259,7 +260,7 @@ def save(self, filename: str): """ faiss.write_index(self.index, str(filename)) - def load(self, filename: str): + def load(self, filename: str, **kwargs): """ Load the index from a file. From cc17ec9a819e18a28374d3408047b702fc22ae3c Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 2 Jun 2025 18:01:29 -0500 Subject: [PATCH 314/323] add readonly workload --- src/python/workload_generator.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/src/python/workload_generator.py b/src/python/workload_generator.py index ab1722b1..2b127b19 100644 --- a/src/python/workload_generator.py +++ b/src/python/workload_generator.py @@ -214,13 +214,13 @@ def initialize_clustered_index(self): index_dir = self.workload_dir / "clustered_index.bin" if index_dir.exists(): index = QuakeWrapper() - index.load(index_dir) + index.load(index_dir, num_workers=1, parent={"num_workers": 1}) n_clusters = index.index.nlist() else: n_clusters = self.base_vectors.shape[0] // self.cluster_size index = QuakeWrapper() index.build( - self.base_vectors, nc=n_clusters, metric=self.metric, ids=torch.arange(self.base_vectors.shape[0]) + self.base_vectors, nc=n_clusters, metric=self.metric, ids=torch.arange(self.base_vectors.shape[0]), num_workers=1, parent={"num_workers": 1} ) index.save(str(self.workload_dir / "clustered_index.bin")) @@ -421,8 +421,11 @@ def _init_index(self, name: str, wrapper, build_params: Dict, wrapper.save(idx_file) print(f"[{name}] stored → {idx_file}") else: - wrapper.load(idx_file, - num_workers=build_params.get("num_workers", 0)) + num_workers = build_params.get("num_workers", 0) + use_numa = build_params.get("use_numa", False) + parent_params = build_params.get("parent", {}) + wrapper.load(idx_file, num_workers=num_workers, + use_numa=use_numa, parent=parent_params) print(f"[{name}] loaded ← {idx_file}") if isinstance(wrapper, QuakeWrapper) and m_params: From 5c7b1f1a9ea0e8ab761ecf351ad763f18ba994bb Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 2 Jun 2025 19:55:11 -0500 Subject: [PATCH 315/323] openimages workload --- .../configs/openimages.yaml | 186 ++++++++++++++ .../osdi2025/open_images_workload/run.py | 227 ++++++++++++++++++ 2 files changed, 413 insertions(+) create mode 100644 test/experiments/osdi2025/open_images_workload/configs/openimages.yaml create mode 100644 test/experiments/osdi2025/open_images_workload/run.py diff --git a/test/experiments/osdi2025/open_images_workload/configs/openimages.yaml b/test/experiments/osdi2025/open_images_workload/configs/openimages.yaml new file mode 100644 index 00000000..07102ce5 --- /dev/null +++ b/test/experiments/osdi2025/open_images_workload/configs/openimages.yaml @@ -0,0 +1,186 @@ +workload_dir: data/wikidata/workload +dataset_dir: /Users/jason/projects/compass/data/wikidata/wikidata +overwrite: True +batch: False + +indexes: + - name: Quake_Serial_NoAPS_NoMaintenance + index: Quake + build_params: + nc: 1000 + metric: ip + num_workers: 1 + use_numa: true + parent: + num_workers: 1 + search_params: + k: 100 + nprobe: 10 + initial_search_fraction: .05 + batched_scan: false + parent: + batched_scan: false + batch_size: 128 + maintenance_params: + refinement_radius: 50 + split_threshold_ns: 500000 + delete_threshold_ns: 500000 + enable_split_rejection: true + enable_delete_rejection: false + window_size: 1000 + + - name: Quake_Serial_NoAPS + index: Quake + build_params: + nc: 1000 + metric: ip + num_workers: 1 + use_numa: true + parent: + num_workers: 1 + search_params: + k: 100 + nprobe: 30 + initial_search_fraction: .05 + batched_scan: false + parent: + batched_scan: true + batch_size: 128 + maintenance_params: + refinement_radius: 50 + split_threshold_ns: 250 + delete_threshold_ns: 500000 + enable_split_rejection: true + enable_delete_rejection: false + window_size: 10000 + + - name: Quake_Serial + index: Quake + build_params: + nc: 1000 + metric: ip + num_workers: 1 + use_numa: true + parent: + num_workers: 1 + search_params: + k: 100 + recall_target: .9 + initial_search_fraction: .05 + batched_scan: false + parent: + batched_scan: true + batch_size: 128 + maintenance_params: + refinement_radius: 50 + split_threshold_ns: 250 + delete_threshold_ns: 500000 + enable_split_rejection: true + enable_delete_rejection: false + window_size: 10000 + + - name: QuakeNoMaintenanceNoAPS + index: Quake + build_params: + nc: 1000 + metric: ip + num_workers: 8 + use_numa: true + parent: + num_workers: 2 + search_params: + k: 100 + nprobe: 30 + batched_scan: false + aps_flush_period_us: 10 + parent: + batched_scan: true + batch_size: 128 + maintenance_params: + refinement_radius: 50 + split_threshold_ns: 500000 + delete_threshold_ns: 500000 + enable_split_rejection: true + enable_delete_rejection: false + window_size: 10000 + + - name: QuakeNoMaintenance + index: Quake + build_params: + nc: 1000 + metric: ip + num_workers: 8 + use_numa: true + parent: + num_workers: 2 + search_params: + k: 100 + recall_target: .9 + batched_scan: false + recompute_threshold: 0.01 + use_precomputed: true + initial_search_fraction: .05 + aps_flush_period_us: 10 + parent: + batched_scan: true + batch_size: 128 + maintenance_params: + refinement_radius: 50 + split_threshold_ns: 500000 + delete_threshold_ns: 500000 + enable_split_rejection: true + enable_delete_rejection: false + window_size: 10000 + + - name: QuakeNoAPS + index: Quake + build_params: + nc: 1000 + metric: ip + num_workers: 8 + use_numa: true + parent: + num_workers: 2 + search_params: + k: 100 + nprobe: 30 + batched_scan: false + aps_flush_period_us: 10 + parent: + batched_scan: true + batch_size: 128 + maintenance_params: + refinement_radius: 50 + split_threshold_ns: 250 + delete_threshold_ns: 500000 + enable_split_rejection: true + enable_delete_rejection: false + window_size: 10000 + + - name: Quake + index: Quake + build_params: + nc: 1000 + metric: ip + num_workers: 8 + use_numa: true + parent: + num_workers: 2 + search_params: + k: 100 + recall_target: .9 + batched_scan: false + recompute_threshold: 0.01 + use_precomputed: true + initial_search_fraction: .05 + aps_flush_period_us: 10 + parent: + batched_scan: true + batch_size: 128 + maintenance_params: + refinement_radius: 50 + split_threshold_ns: 250 + delete_threshold_ns: 500000 + enable_split_rejection: true + enable_delete_rejection: false + window_size: 10000 \ No newline at end of file diff --git a/test/experiments/osdi2025/open_images_workload/run.py b/test/experiments/osdi2025/open_images_workload/run.py new file mode 100644 index 00000000..4e312f5b --- /dev/null +++ b/test/experiments/osdi2025/open_images_workload/run.py @@ -0,0 +1,227 @@ +from __future__ import annotations + +import json, logging, time, shutil +from pathlib import Path +from typing import Dict, List, Any, Optional, Union + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import torch +import quake +from quake.utils import to_path, compute_recall +from quake.index_wrappers.quake import QuakeWrapper +from quake.index_wrappers.faiss_ivf import FaissIVF +from quake.index_wrappers.faiss_hnsw import FaissHNSW +try: from quake.index_wrappers.scann import Scann +except ImportError: Scann = None # type: ignore +try: from quake.index_wrappers.diskann import DiskANNDynamic +except ImportError: DiskANNDynamic = None # type: ignore +try: from quake.index_wrappers.vamana import Vamana +except ImportError: Vamana = None # type: ignore + +INDEX_CLASSES: Dict[str, Any] = { + "Quake": QuakeWrapper, + "IVF": FaissIVF, + "HNSW": FaissHNSW, + "SCANN": Scann, + "DiskANN": DiskANNDynamic, + "SVS": Vamana, +} + +log = logging.getLogger("openimages") +logging.basicConfig(level=logging.INFO, format="%(asctime)s | %(levelname)s | %(message)s") + +WINDOW_SIZE = 2_000_000 +K = 100 + +STYLE = { + "query": dict(ls="-", marker="o", ms=4, lw=1.1, mfc="none"), + "insert": dict(ls="-", marker="s", ms=4, lw=1.1, mfc="none"), + "delete": dict(ls="-", marker="^", ms=4, lw=1.1, mfc="none"), +} + +class OpenImagesEvaluator: + def __init__(self, workload_dir: Union[str, Path], out_dir: Union[str, Path], + *, dataset_dir: Union[str, Path]): + self.workload_dir = to_path(workload_dir) + self.runbook_path = self.workload_dir / "runbook.json" + self.ops_dir = self.workload_dir / "operations" + self.out_dir = to_path(out_dir); self.out_dir.mkdir(parents=True, exist_ok=True) + + ds = to_path(dataset_dir) + self._BASE = torch.load(ds / "BASE.pt") + self._IDS = torch.load(ds / "IDS.pt") + self._QUERIES = torch.load(ds / "QUERIES.pt") + self._BASE /= self._BASE.norm(dim=1, keepdim=True) + self._QUERIES /= self._QUERIES.norm(dim=1, keepdim=True) + + self.current_start, self.current_end = 0, WINDOW_SIZE + self.current_ids = self._IDS[:WINDOW_SIZE] + self.current_base = self._BASE[:WINDOW_SIZE] + + max_id = int(self._IDS.max()) + self.inv_map = torch.full((max_id + 1,), -1, dtype=torch.long) + self.inv_map[self.current_ids] = torch.arange(len(self.current_ids)) + + # ------------------------------------------------------------------ helpers + def _init_index(self, name: str, wrapper, build_p: Dict[str, Any], + m_p: Optional[Dict[str, Any]]): + idx_dir = self.workload_dir / "init_indexes" + idx_file = idx_dir / f"{name}.index" + idx_dir.mkdir(parents=True, exist_ok=True) + + if not idx_file.exists(): + wrapper.build(self.current_base, ids=self.current_ids, **build_p) + wrapper.save(idx_file) + else: + wrapper.load(idx_file, + num_workers=build_p.get("num_workers", 0), + use_numa =build_p.get("use_numa", True), + parent =build_p.get("parent")) + + if isinstance(wrapper, QuakeWrapper) and m_p: + mp = quake.MaintenancePolicyParams() + for k, v in m_p.items(): setattr(mp, k, v) + wrapper.index.initialize_maintenance_policy(mp) + return wrapper + + # ------------------------------------------------------------------ core + def evaluate(self, *, name: str, wrapper, build_params: Dict[str, Any], + search_params: Dict[str, Any], do_maintenance: bool, + m_params: Optional[Dict[str, Any]], batch: bool, + max_q: int = 1000) -> None: + + wrapper = self._init_index(name, wrapper, build_params, m_params) + runbook = json.load(open(self.runbook_path)) + totals = dict(query=0.0, insert=0.0, delete=0.0, maintenance=0.0) + rows: List[Dict[str, Any]] = [] + + for key, op in runbook["operations"].items(): + op_id = int(key); typ = op["type"]; t0 = time.perf_counter() + + if typ == "delete": + n = op["size"]; ids_del = self.current_ids[:n] + wrapper.remove(ids_del) + self.current_start += n + self.current_ids = self._IDS[self.current_start:self.current_end] + self.current_base = self._BASE[self.current_start:self.current_end] + self.inv_map[ids_del] = -1 + + elif typ == "insert": + n = op["size"]; old_end = self.current_end; self.current_end += n + vecs = self._BASE[old_end:self.current_end]; ids = self._IDS[old_end:self.current_end] + wrapper.add(vecs, ids=ids) + self.current_ids = self._IDS[self.current_start:self.current_end] + self.current_base = self._BASE[self.current_start:self.current_end] + if int(ids.max()) >= self.inv_map.size(0): + bigger = torch.full((int(ids.max()) + 1,), -1, dtype=torch.long) + bigger[:len(self.inv_map)] = self.inv_map; self.inv_map = bigger + self.inv_map[ids] = torch.arange(len(self.current_ids)-n, len(self.current_ids)) + + elif typ == "query": + q_idx = torch.load(self.ops_dir / f"{op_id}.pt") + queries = self._QUERIES[q_idx] + if len(queries) > max_q: + perm = torch.randperm(len(queries))[:max_q] + queries, q_idx = queries[perm], q_idx[perm] + if batch: + pred_ids = wrapper.search(queries, **search_params).ids + else: + parts = [wrapper.search(q.unsqueeze(0), **search_params).ids for q in queries] + pred_ids = torch.cat(parts) + gt_ids = torch.load(self.ops_dir / f"{op_id}_gt_ids.pt")[:len(pred_ids)] + recall = compute_recall(pred_ids, gt_ids, search_params.get("k", K)).mean().item() + op["recall"] = recall + else: + raise ValueError(typ) + + lat_ms = 1e3 * (time.perf_counter() - t0) + maint_ms = spl = dele = spl_ms = del_ms = ref_ms = 0.0 + if do_maintenance: + t1 = time.perf_counter(); info = wrapper.maintenance() + maint_ms = 1e3 * (time.perf_counter() - t1) + spl, dele = info.n_splits, info.n_deletes + spl_ms, del_ms = info.split_time_us/1000.0, info.delete_time_us/1000.0 + ref_ms = info.refinement_time_us/1000.0 + + totals[typ] += lat_ms; totals["maintenance"] += maint_ms + state = wrapper.index_state() + rows.append(dict( + operation_number = op_id, + operation_type = typ, + latency_ms = lat_ms, + maintenance_latency_ms= maint_ms, + n_resident = state.get("n_total", len(self.current_ids)), + nlist = state.get("n_list", np.nan), + n_splits = spl, + n_deletes = dele, + split_time_ms = spl_ms, + delete_time_ms = del_ms, + refinement_time_ms = ref_ms, + recall = op.get("recall"), + )) + + df = pd.DataFrame(rows) + df.to_csv(self.out_dir / "results.csv", index=False) + self._plots(df, totals, name) + + # ------------------------------------------------------------------ plots + def _plots(self, df: pd.DataFrame, tot: Dict[str,float], title: str): + fig, axs = plt.subplots(2,2, figsize=(12,9)) + for t in ("query","insert","delete"): + sub = df[df.operation_type==t] + if not sub.empty: + axs[0,0].plot(sub.operation_number, sub.latency_ms, label=t, **STYLE[t]) + axs[0,0].set_title("Latency"); axs[0,0].legend(); axs[0,0].grid(ls=":") + if "nlist" in df: + axs[0,1].plot(df.operation_number, df.nlist, marker="o", lw=1.1) + axs[0,1].set_title("#Partitions"); axs[0,1].grid(ls=":") + axs[1,0].plot(df.operation_number, df.n_resident, marker="o", lw=1.1) + axs[1,0].set_title("Resident"); axs[1,0].grid(ls=":") + rec = df[(df.operation_type=="query") & df.recall.notna()] + if not rec.empty: + axs[1,1].plot(rec.operation_number, rec.recall, marker="o", lw=1.1) + axs[1,1].set_title("Recall"); axs[1,1].set_ylim(0,1); axs[1,1].grid(ls=":") + fig.suptitle(title); fig.tight_layout() + fig.savefig(self.out_dir / "four_panel.png", dpi=150); plt.close(fig) + + lbl = ["Query","Insert","Delete","Maintenance","Total"] + val = [tot.get("query",0), tot.get("insert",0), + tot.get("delete",0), tot.get("maintenance",0)] + val.append(sum(val)) + plt.bar(lbl, val); plt.title(f"Time breakdown – {title}") + plt.ylabel("ms"); plt.tight_layout() + plt.savefig(self.out_dir / "time_breakdown.png", dpi=150); plt.close() + +# ────────────────────────────────────────────────────────────────────────── +def run_experiment(cfg: Dict[str, Any], output_root: Union[str, Path]) -> None: + wdir = Path(cfg["workload_dir"]).expanduser() + dset = Path(cfg["dataset_dir"]).expanduser() + out = Path(output_root).expanduser() + batch = cfg.get("batch", False) + out.mkdir(parents=True, exist_ok=True) + + for idx in cfg["indexes"]: + name = idx["name"]; wrapper_key = idx["index"] + out_dir = out / name + + if out_dir.exists() and not cfg.get("overwrite", False): + log.info("Skip %s – results exist (overwrite=False)", name); continue + if out_dir.exists() and cfg.get("overwrite", False): + shutil.rmtree(out_dir) + + cls = INDEX_CLASSES.get(wrapper_key) + if cls is None: + log.warning("Unknown wrapper %s – skip %s", wrapper_key, name); continue + + ev = OpenImagesEvaluator(wdir, out_dir, dataset_dir=dset) + ev.evaluate( + name = name, + wrapper= cls(), + build_params = idx.get("build_params", {}), + search_params= idx.get("search_params", {}), + do_maintenance = idx.get("maintenance_params") is not None, + m_params = idx.get("maintenance_params"), + batch = batch, + ) From d5b7db43a389c7eb9a5abae3e1186fa8f7d619af Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 2 Jun 2025 20:18:10 -0500 Subject: [PATCH 316/323] openimages workload --- test/experiments/osdi2025/experiment_runner.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/test/experiments/osdi2025/experiment_runner.py b/test/experiments/osdi2025/experiment_runner.py index ac48516e..fe2be901 100644 --- a/test/experiments/osdi2025/experiment_runner.py +++ b/test/experiments/osdi2025/experiment_runner.py @@ -16,6 +16,7 @@ from test.experiments.osdi2025.vary_batch_size.run import run_experiment as run_vary_batch_size from test.experiments.osdi2025.wiki_workload.run import run_experiment as run_wiki_workload from test.experiments.osdi2025.read_only_workload.run import run_experiment as run_read_only_workload +from test.experiments.osdi2025.open_images_workload.run import run_experiment as run_open_images_workload EXPERIMENTS = { "kick_the_tires": run_kick_the_tires, @@ -28,7 +29,8 @@ "maintenance_ablation": run_ablation, "vary_batch_size": run_vary_batch_size, "wiki_workload": run_wiki_workload, - "read_only_workload": run_read_only_workload + "read_only_workload": run_read_only_workload, + "open_images_workload": run_open_images_workload } def main(): From 1794d1b51fc9adca82f8879cfbfc5e6078fd9452 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 2 Jun 2025 21:40:45 -0500 Subject: [PATCH 317/323] openimages workload --- test/experiments/osdi2025/open_images_workload/run.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/test/experiments/osdi2025/open_images_workload/run.py b/test/experiments/osdi2025/open_images_workload/run.py index 4e312f5b..61b9600c 100644 --- a/test/experiments/osdi2025/open_images_workload/run.py +++ b/test/experiments/osdi2025/open_images_workload/run.py @@ -9,6 +9,7 @@ import pandas as pd import torch import quake +import test.experiments.osdi2025.experiment_utils as common_utils from quake.utils import to_path, compute_recall from quake.index_wrappers.quake import QuakeWrapper from quake.index_wrappers.faiss_ivf import FaissIVF @@ -195,7 +196,8 @@ def _plots(self, df: pd.DataFrame, tot: Dict[str,float], title: str): plt.savefig(self.out_dir / "time_breakdown.png", dpi=150); plt.close() # ────────────────────────────────────────────────────────────────────────── -def run_experiment(cfg: Dict[str, Any], output_root: Union[str, Path]) -> None: +def run_experiment(cfg_path_str: Dict[str, Any], output_root: Union[str, Path]) -> None: + cfg = common_utils.load_config(cfg_path_str) wdir = Path(cfg["workload_dir"]).expanduser() dset = Path(cfg["dataset_dir"]).expanduser() out = Path(output_root).expanduser() From 039c6a6f56cf7ef529295019f7e0d3c27e4ee579 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 2 Jun 2025 21:51:54 -0500 Subject: [PATCH 318/323] openimages workload --- test/experiments/osdi2025/open_images_workload/run.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/test/experiments/osdi2025/open_images_workload/run.py b/test/experiments/osdi2025/open_images_workload/run.py index 61b9600c..cb22dad8 100644 --- a/test/experiments/osdi2025/open_images_workload/run.py +++ b/test/experiments/osdi2025/open_images_workload/run.py @@ -148,7 +148,8 @@ def evaluate(self, *, name: str, wrapper, build_params: Dict[str, Any], totals[typ] += lat_ms; totals["maintenance"] += maint_ms state = wrapper.index_state() - rows.append(dict( + + row = dict( operation_number = op_id, operation_type = typ, latency_ms = lat_ms, @@ -160,9 +161,9 @@ def evaluate(self, *, name: str, wrapper, build_params: Dict[str, Any], split_time_ms = spl_ms, delete_time_ms = del_ms, refinement_time_ms = ref_ms, - recall = op.get("recall"), - )) - + ) + print(row) + rows.append(row) df = pd.DataFrame(rows) df.to_csv(self.out_dir / "results.csv", index=False) self._plots(df, totals, name) From 01308730e6e6da6c0541e67f2fab6d5afc3e5b92 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 2 Jun 2025 21:54:29 -0500 Subject: [PATCH 319/323] openimages workload --- .../configs/openimages.yaml | 250 +++++++++--------- .../osdi2025/open_images_workload/run.py | 2 + 2 files changed, 127 insertions(+), 125 deletions(-) diff --git a/test/experiments/osdi2025/open_images_workload/configs/openimages.yaml b/test/experiments/osdi2025/open_images_workload/configs/openimages.yaml index 07102ce5..40c6d00b 100644 --- a/test/experiments/osdi2025/open_images_workload/configs/openimages.yaml +++ b/test/experiments/osdi2025/open_images_workload/configs/openimages.yaml @@ -4,55 +4,55 @@ overwrite: True batch: False indexes: - - name: Quake_Serial_NoAPS_NoMaintenance - index: Quake - build_params: - nc: 1000 - metric: ip - num_workers: 1 - use_numa: true - parent: - num_workers: 1 - search_params: - k: 100 - nprobe: 10 - initial_search_fraction: .05 - batched_scan: false - parent: - batched_scan: false - batch_size: 128 - maintenance_params: - refinement_radius: 50 - split_threshold_ns: 500000 - delete_threshold_ns: 500000 - enable_split_rejection: true - enable_delete_rejection: false - window_size: 1000 +# - name: Quake_Serial_NoAPS_NoMaintenance +# index: Quake +# build_params: +# nc: 1000 +# metric: ip +# num_workers: 1 +# use_numa: true +# parent: +# num_workers: 1 +# search_params: +# k: 100 +# nprobe: 10 +# initial_search_fraction: .05 +# batched_scan: false +# parent: +# batched_scan: false +# batch_size: 128 +# maintenance_params: +# refinement_radius: 50 +# split_threshold_ns: 500000 +# delete_threshold_ns: 500000 +# enable_split_rejection: true +# enable_delete_rejection: false +# window_size: 1000 - - name: Quake_Serial_NoAPS - index: Quake - build_params: - nc: 1000 - metric: ip - num_workers: 1 - use_numa: true - parent: - num_workers: 1 - search_params: - k: 100 - nprobe: 30 - initial_search_fraction: .05 - batched_scan: false - parent: - batched_scan: true - batch_size: 128 - maintenance_params: - refinement_radius: 50 - split_threshold_ns: 250 - delete_threshold_ns: 500000 - enable_split_rejection: true - enable_delete_rejection: false - window_size: 10000 +# - name: Quake_Serial_NoAPS +# index: Quake +# build_params: +# nc: 1000 +# metric: ip +# num_workers: 1 +# use_numa: true +# parent: +# num_workers: 1 +# search_params: +# k: 100 +# nprobe: 30 +# initial_search_fraction: .05 +# batched_scan: false +# parent: +# batched_scan: true +# batch_size: 128 +# maintenance_params: +# refinement_radius: 50 +# split_threshold_ns: 250 +# delete_threshold_ns: 500000 +# enable_split_rejection: true +# enable_delete_rejection: false +# window_size: 10000 - name: Quake_Serial index: Quake @@ -79,83 +79,83 @@ indexes: enable_delete_rejection: false window_size: 10000 - - name: QuakeNoMaintenanceNoAPS - index: Quake - build_params: - nc: 1000 - metric: ip - num_workers: 8 - use_numa: true - parent: - num_workers: 2 - search_params: - k: 100 - nprobe: 30 - batched_scan: false - aps_flush_period_us: 10 - parent: - batched_scan: true - batch_size: 128 - maintenance_params: - refinement_radius: 50 - split_threshold_ns: 500000 - delete_threshold_ns: 500000 - enable_split_rejection: true - enable_delete_rejection: false - window_size: 10000 - - - name: QuakeNoMaintenance - index: Quake - build_params: - nc: 1000 - metric: ip - num_workers: 8 - use_numa: true - parent: - num_workers: 2 - search_params: - k: 100 - recall_target: .9 - batched_scan: false - recompute_threshold: 0.01 - use_precomputed: true - initial_search_fraction: .05 - aps_flush_period_us: 10 - parent: - batched_scan: true - batch_size: 128 - maintenance_params: - refinement_radius: 50 - split_threshold_ns: 500000 - delete_threshold_ns: 500000 - enable_split_rejection: true - enable_delete_rejection: false - window_size: 10000 - - - name: QuakeNoAPS - index: Quake - build_params: - nc: 1000 - metric: ip - num_workers: 8 - use_numa: true - parent: - num_workers: 2 - search_params: - k: 100 - nprobe: 30 - batched_scan: false - aps_flush_period_us: 10 - parent: - batched_scan: true - batch_size: 128 - maintenance_params: - refinement_radius: 50 - split_threshold_ns: 250 - delete_threshold_ns: 500000 - enable_split_rejection: true - enable_delete_rejection: false - window_size: 10000 +# - name: QuakeNoMaintenanceNoAPS +# index: Quake +# build_params: +# nc: 1000 +# metric: ip +# num_workers: 8 +# use_numa: true +# parent: +# num_workers: 2 +# search_params: +# k: 100 +# nprobe: 30 +# batched_scan: false +# aps_flush_period_us: 10 +# parent: +# batched_scan: true +# batch_size: 128 +# maintenance_params: +# refinement_radius: 50 +# split_threshold_ns: 500000 +# delete_threshold_ns: 500000 +# enable_split_rejection: true +# enable_delete_rejection: false +# window_size: 10000 +# +# - name: QuakeNoMaintenance +# index: Quake +# build_params: +# nc: 1000 +# metric: ip +# num_workers: 8 +# use_numa: true +# parent: +# num_workers: 2 +# search_params: +# k: 100 +# recall_target: .9 +# batched_scan: false +# recompute_threshold: 0.01 +# use_precomputed: true +# initial_search_fraction: .05 +# aps_flush_period_us: 10 +# parent: +# batched_scan: true +# batch_size: 128 +# maintenance_params: +# refinement_radius: 50 +# split_threshold_ns: 500000 +# delete_threshold_ns: 500000 +# enable_split_rejection: true +# enable_delete_rejection: false +# window_size: 10000 +# +# - name: QuakeNoAPS +# index: Quake +# build_params: +# nc: 1000 +# metric: ip +# num_workers: 8 +# use_numa: true +# parent: +# num_workers: 2 +# search_params: +# k: 100 +# nprobe: 30 +# batched_scan: false +# aps_flush_period_us: 10 +# parent: +# batched_scan: true +# batch_size: 128 +# maintenance_params: +# refinement_radius: 50 +# split_threshold_ns: 250 +# delete_threshold_ns: 500000 +# enable_split_rejection: true +# enable_delete_rejection: false +# window_size: 10000 - name: Quake index: Quake diff --git a/test/experiments/osdi2025/open_images_workload/run.py b/test/experiments/osdi2025/open_images_workload/run.py index cb22dad8..e353c23e 100644 --- a/test/experiments/osdi2025/open_images_workload/run.py +++ b/test/experiments/osdi2025/open_images_workload/run.py @@ -98,6 +98,7 @@ def evaluate(self, *, name: str, wrapper, build_params: Dict[str, Any], totals = dict(query=0.0, insert=0.0, delete=0.0, maintenance=0.0) rows: List[Dict[str, Any]] = [] + recall = None for key, op in runbook["operations"].items(): op_id = int(key); typ = op["type"]; t0 = time.perf_counter() @@ -161,6 +162,7 @@ def evaluate(self, *, name: str, wrapper, build_params: Dict[str, Any], split_time_ms = spl_ms, delete_time_ms = del_ms, refinement_time_ms = ref_ms, + recall = recall, ) print(row) rows.append(row) From 12dbc37f086e259ae93c5076b661708520760c37 Mon Sep 17 00:00:00 2001 From: Jason Date: Mon, 2 Jun 2025 21:55:03 -0500 Subject: [PATCH 320/323] openimages workload --- .../osdi2025/open_images_workload/configs/openimages.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/test/experiments/osdi2025/open_images_workload/configs/openimages.yaml b/test/experiments/osdi2025/open_images_workload/configs/openimages.yaml index 40c6d00b..2be55e61 100644 --- a/test/experiments/osdi2025/open_images_workload/configs/openimages.yaml +++ b/test/experiments/osdi2025/open_images_workload/configs/openimages.yaml @@ -1,5 +1,5 @@ -workload_dir: data/wikidata/workload -dataset_dir: /Users/jason/projects/compass/data/wikidata/wikidata +workload_dir: data/prepped_openimages/openimages +dataset_dir: data/openimages13m overwrite: True batch: False From 7438af21da5c7abfa78dc69ca19feff08986e2fe Mon Sep 17 00:00:00 2001 From: Jason Date: Wed, 4 Jun 2025 16:46:57 -0500 Subject: [PATCH 321/323] multi-level fixes --- src/cpp/bindings/wrap.cpp | 20 ++ src/cpp/include/common.h | 20 ++ src/cpp/include/geometry.h | 57 ++-- src/cpp/include/list_scanning.h | 425 ++++++++++++++++++++++----- src/cpp/include/query_coordinator.h | 3 + src/cpp/src/clustering.cpp | 23 +- src/cpp/src/maintenance_policies.cpp | 3 + src/cpp/src/partition_manager.cpp | 6 +- src/cpp/src/quake_index.cpp | 2 + src/cpp/src/query_coordinator.cpp | 113 ++++--- 10 files changed, 509 insertions(+), 163 deletions(-) diff --git a/src/cpp/bindings/wrap.cpp b/src/cpp/bindings/wrap.cpp index 2c8f5f08..329cfaf2 100644 --- a/src/cpp/bindings/wrap.cpp +++ b/src/cpp/bindings/wrap.cpp @@ -318,6 +318,14 @@ PYBIND11_MODULE(_bindings, m) { return oss.str(); }); + + // double worker_wait_time_ns = 0; ///< Average worker wait time in nanoseconds. + // double worker_process_time_ns = 0; ///< Average worker process time in nanoseconds. + // double worker_process_preamble_time_ns = 0; ///< Average worker process preamble time in nanoseconds. + // double worker_enqueue_time_ns = 0; ///< Average worker enqueue time in nanoseconds. + // double worker_job_time_ns = 0; ///< Average worker job time in nanoseconds. + // double worker_scan_time_ns = 0; ///< Average worker scan time in nanoseconds. + /*********** SearchTimingInfo Binding ***********/ class_>(m, "SearchTimingInfo") .def(init<>()) @@ -349,6 +357,18 @@ PYBIND11_MODULE(_bindings, m) { "Time spent on APS in nanoseconds.") .def_readwrite("scan_time_ns", &SearchTimingInfo::scan_time_ns, "Time spent on scanning in nanoseconds.") + .def_readwrite("worker_wait_time_ns", &SearchTimingInfo::worker_wait_time_ns, + "Average worker wait time in nanoseconds.") + .def_readwrite("worker_process_time_ns", &SearchTimingInfo::worker_process_time_ns, + "Average worker process time in nanoseconds.") + .def_readwrite("worker_process_preamble_time_ns", &SearchTimingInfo::worker_process_preamble_time_ns, + "Average worker process preamble time in nanoseconds.") + .def_readwrite("worker_enqueue_time_ns", &SearchTimingInfo::worker_enqueue_time_ns, + "Average worker enqueue time in nanoseconds.") + .def_readwrite("worker_job_time_ns", &SearchTimingInfo::worker_job_time_ns, + "Average worker job time in nanoseconds.") + .def_readwrite("worker_scan_time_ns", &SearchTimingInfo::worker_scan_time_ns, + "Average worker scan time in nanoseconds.") .def("__repr__", [](const SearchTimingInfo &s) { std::ostringstream oss; oss << "{"; diff --git a/src/cpp/include/common.h b/src/cpp/include/common.h index f840282a..f0d26a5e 100644 --- a/src/cpp/include/common.h +++ b/src/cpp/include/common.h @@ -32,6 +32,7 @@ #include #include #include +#include #ifdef QUAKE_USE_NUMA #include @@ -54,6 +55,18 @@ using std::chrono::milliseconds; using faiss::idx_t; using faiss::MetricType; +struct _EnsureSingleOmp { + _EnsureSingleOmp() { + // Disable OpenMP’s dynamic adjustment and nested teams: + omp_set_dynamic(0); + omp_set_max_active_levels(0); + // Force exactly one thread: + omp_set_num_threads(1); + } +}; + +static _EnsureSingleOmp _ensure_single_omp; + // constants static const uint32_t SerializationMagicNumber = 0x44494E4C; static const uint32_t SerializationVersion = 3; @@ -184,6 +197,7 @@ struct SearchParams { int batch_size = MAX_SUBBATCH; bool track_hits = true; + bool scan_all = false; // APS params bool use_precomputed = DEFAULT_PRECOMPUTED; @@ -252,6 +266,12 @@ struct SearchTimingInfo { int64_t job_wait_time_ns; ///< Time spent waiting for jobs to complete in nanoseconds. int64_t result_aggregate_time_ns; ///< Time spent on aggregating results in nanoseconds. int64_t total_time_ns; ///< Total time spent in nanoseconds. + double worker_wait_time_ns = 0; ///< Average worker wait time in nanoseconds. + double worker_process_time_ns = 0; ///< Average worker process time in nanoseconds. + double worker_process_preamble_time_ns = 0; ///< Average worker process preamble time in nanoseconds. + double worker_enqueue_time_ns = 0; ///< Average worker enqueue time in nanoseconds. + double worker_job_time_ns = 0; ///< Average worker job time in nanoseconds. + double worker_scan_time_ns = 0; ///< Average worker scan time in nanoseconds. }; /** diff --git a/src/cpp/include/geometry.h b/src/cpp/include/geometry.h index 5abbc7f9..4e3922d7 100644 --- a/src/cpp/include/geometry.h +++ b/src/cpp/include/geometry.h @@ -179,6 +179,15 @@ compute_boundary_distances(const Tensor& query, for (std::size_t j = 1; j < centroids.size(); ++j) { const float* cj = centroids[j]; + if (centroids[j] == nullptr) { + if (euclidean) { + dist[j] = std::numeric_limits::infinity(); + } else { + dist[j] = M_PI_2; // max distance on unit sphere + } + continue; + } + if (euclidean) { /* plane distance d = |q·v − b| / ||v|| , b = ½(||cj||²−||c0||²) */ faiss::fvec_sub(dim, cj, c0, v.data()); @@ -194,14 +203,6 @@ compute_boundary_distances(const Tensor& query, float s = std::fabs(faiss::fvec_inner_product(q, v.data(), dim)); s = std::clamp(s, 0.0f, 1.0f); dist[j] = std::asin(s); // 0–π/2 - - // /* unit-sphere model – distance is polar angle to great-circle bisector between c0 and cj. */ - // add_arrays(c0, cj, m.data(), dim); // midpoint vector - // float m_norm = std::sqrt(faiss::fvec_inner_product(m.data(), m.data(), dim)); - // if (m_norm > 0) divide_array_by_constant(m.data(), m_norm, m.data(), dim); - // float cos_ang = faiss::fvec_inner_product(q, m.data(), dim); - // cos_ang = std::clamp(cos_ang, -1.0f, 1.0f); - // dist[j] = std::acos(cos_ang); // radians } } return dist; // dist[0] = 0 by construction @@ -381,27 +382,27 @@ compute_recall_profile(const std::vector& boundary_distances, } // if the cluster_sizes are given, scale probs by the size of the cluster and renormalize. this is a rudimentary density estimation - // if (partition_sizes.size() > 0) { - // for (int k = 1; k < m; ++k) { - // if (partition_sizes[k] > 0) { - // probs[k] *= static_cast(partition_sizes[k]); - // } - // } - // } + if (partition_sizes.size() > 0) { + for (int k = 0; k < m; ++k) { + if (partition_sizes[k] > 0) { + probs[k] *= static_cast(partition_sizes[k]); + } + } + } - // // renormalize the probabilities to sum to 1 - // float S = 0.0f; - // for (int k = 0; k < m; ++k) S += probs[k]; - // if (S > eps) { - // for (int k = 0; k < m; ++k) { - // probs[k] /= S; - // } - // } else { - // // If S is zero, all probabilities remain zero - // for (int k = 0; k < m; ++k) { - // probs[k] = 0.0f; - // } - // } + // renormalize the probabilities to sum to 1 + float S = 0.0f; + for (int k = 0; k < m; ++k) S += probs[k]; + if (S > eps) { + for (int k = 0; k < m; ++k) { + probs[k] /= S; + } + } else { + // If S is zero, all probabilities remain zero + for (int k = 0; k < m; ++k) { + probs[k] = 0.0f; + } + } return probs; } diff --git a/src/cpp/include/list_scanning.h b/src/cpp/include/list_scanning.h index 5dcb9ac6..33466f88 100644 --- a/src/cpp/include/list_scanning.h +++ b/src/cpp/include/list_scanning.h @@ -15,6 +15,12 @@ #include "sorting/heap_select.h" #include "parallel.h" #include "blas_dist.h" +#include + +using IP_Handler = faiss::HeapBlockResultHandler>; +using L2_Handler = faiss::HeapBlockResultHandler>; +using IP_Single = IP_Handler::SingleResultHandler; +using L2_Single = L2_Handler::SingleResultHandler; inline Tensor calculate_recall(Tensor ids, Tensor gt_ids) { Tensor num_correct = torch::zeros(ids.size(0), torch::kInt64); @@ -163,15 +169,15 @@ class TypedTopKBuffer { void reset() { head_ = 0; - for (int i = 0; i < k_; i++) { - if (is_desc_) { - vals_[i] = -std::numeric_limits::infinity(); - ids_[i] = -1; - } else { - vals_[i] = std::numeric_limits::infinity(); - ids_[i] = -1; - } - } + // for (int i = 0; i < k_; i++) { + // if (is_desc_) { + // vals_[i] = -std::numeric_limits::infinity(); + // ids_[i] = -1; + // } else { + // vals_[i] = std::numeric_limits::infinity(); + // ids_[i] = -1; + // } + // } } inline void add(T dist, I idx) { @@ -303,31 +309,31 @@ inline vector> create_buffers(int batch_size, int k, bool return buffers; } -//vector> local_buffers = create_buffers(batch_size, k, (metric_ == faiss::METRIC_INNER_PRODUCT)); +// vector> local_buffers = create_buffers(batch_size, k, (metric_ == faiss::METRIC_INNER_PRODUCT)); -inline std::tuple buffers_to_tensor(vector> buffers) { - int n = buffers.size(); - int k = buffers[0]->k(); - Tensor topk_distances = torch::empty({n, k}, torch::kFloat32); - Tensor topk_indices = torch::empty({n, k}, torch::kInt64); + inline std::tuple buffers_to_tensor(vector> buffers) { + int n = buffers.size(); + int k = buffers[0]->k(); + Tensor topk_distances = torch::empty({n, k}, torch::kFloat32); + Tensor topk_indices = torch::empty({n, k}, torch::kInt64); - auto topk_distances_accessor = topk_distances.accessor(); - auto topk_indices_accessor = topk_indices.accessor(); + auto topk_distances_accessor = topk_distances.accessor(); + auto topk_indices_accessor = topk_indices.accessor(); - for (int i = 0; i < n; i++) { - vector distances = buffers[i]->get_topk(); - vector indices = buffers[i]->get_topk_indices(); + for (int i = 0; i < n; i++) { + vector distances = buffers[i]->get_topk(); + vector indices = buffers[i]->get_topk_indices(); - int curr_k = std::min(k, (int) distances.size()); + int curr_k = std::min(k, (int) distances.size()); - for (int j = 0; j < curr_k; j++) { - topk_distances_accessor[i][j] = distances[j]; - topk_indices_accessor[i][j] = indices[j]; - } - } + for (int j = 0; j < curr_k; j++) { + topk_distances_accessor[i][j] = distances[j]; + topk_indices_accessor[i][j] = indices[j]; + } + } - return std::make_tuple(topk_indices, topk_distances); -} + return std::make_tuple(topk_indices, topk_distances); + } inline void scan_list_no_ids_inner_product(const float *query_vec, const float *list_vecs, @@ -390,7 +396,7 @@ inline void scan_list_with_ids_l2(const float *query_vec, for (int l = 0; l < list_size; l++) { float dist = sqrt(faiss::fvec_L2sqr(query_vec, vec, d)); if (dist < pivot) { - buffer.add(sqrt(faiss::fvec_L2sqr(query_vec, vec, d)), list_ids[l]); + buffer.add(dist, list_ids[l]); } vec += d; } @@ -634,60 +640,327 @@ inline void l2_blas( inline void batched_scan_list(const float *query_vecs, const float *list_vecs, const int64_t *list_ids, - int num_queries, - int list_size, - int dim, - vector> &topk_buffers, - MetricType metric, - float *ip_block, - float *norms_x, - float *norms_y_buf, - int blas_db_bs = BLAS_DB_BS, - vector*> pivots = {}) { - if (list_size == 0 || list_vecs == nullptr) { - // No list vectors to process; + int num_queries, + int list_size, + int dim, + std::vector> &topk_buffers, + MetricType metric, + /* optional scratch supplied by caller: may be nullptr */ + float *ip_block /* = nullptr */, + float *norms_x /* = nullptr */, + float *norms_y_buf /* = nullptr */, + int blas_db_bs /* = BLAS_DB_BS */, + int blas_q_bs /* = 128 */, + std::vector*> pivots /* = {} */) +{ + if (list_size == 0 || num_queries == 0 || list_vecs == nullptr) { return; } - // Ensure k does not exceed list_size - int k = topk_buffers[0]->k(); - int k_max = std::min(k, list_size); + const bool need_norm = (metric == faiss::METRIC_L2); + const int k = std::min(topk_buffers[0]->k(), list_size); + + // --------------------------------------------------------------- + // thread‐local scratch buffers: reused across calls, resized only if + // current block exceeds previous capacity + // --------------------------------------------------------------- + thread_local std::vector TLS_ip; // holds up to (blas_q_bs × blas_db_bs) + thread_local std::vector TLS_nx; // holds up to blas_q_bs + thread_local std::vector TLS_ny; // holds up to blas_db_bs + + // --------------------------------------------------------------- + // If L2 and caller gave no norms_x, ensure TLS_nx can hold blas_q_bs + // --------------------------------------------------------------- + if (need_norm && norms_x == nullptr) { + if ((int)TLS_nx.size() < blas_q_bs) { + TLS_nx.resize(blas_q_bs); + } + } - if (metric == faiss::METRIC_INNER_PRODUCT) { - ip_blas( - query_vecs, - list_vecs, - list_ids, - dim, - num_queries, - list_size, - blas_db_bs, - k_max, - topk_buffers, - ip_block, - pivots - ); - } else if (metric == faiss::METRIC_L2) { - l2_blas( - query_vecs, - list_vecs, - list_ids, - dim, - num_queries, - list_size, - blas_db_bs, - k_max, - topk_buffers, - ip_block, - norms_x, - norms_y_buf, - pivots - ); - } else { - throw std::runtime_error("Metric type not supported"); + // --------------------------------------------------------------- + // Loop over query‐blocks of size ≤ blas_q_bs + // --------------------------------------------------------------- + for (int q_off = 0; q_off < num_queries; q_off += blas_q_bs) { + int q_blk = std::min(blas_q_bs, num_queries - q_off); + const float *q_ptr = query_vecs + size_t(q_off) * dim; + float *q_norms; + + // If caller supplied norms_x, point at offset; else use TLS_nx[0..q_blk-1] + if (need_norm) { + if (norms_x != nullptr) { + q_norms = norms_x + q_off; + } else { + q_norms = TLS_nx.data(); + // compute ‖x‖² for this q‐block + for (int i = 0; i < q_blk; ++i) { + const float *xptr = q_ptr + size_t(i) * dim; + float sumsq = 0.f; + for (int d = 0; d < dim; ++d) { + sumsq += xptr[d] * xptr[d]; + } + q_norms[i] = sumsq; + } + } + } else { + q_norms = nullptr; + } + + // build a small vector of the q_blk TopkBuffers + std::vector> sub_buffers; + sub_buffers.reserve(q_blk); + for (int i = 0; i < q_blk; ++i) { + sub_buffers.push_back(topk_buffers[q_off + i]); + } + + // --------------------------------------------------------------- + // Ensure TLS_ip can hold q_blk × blas_db_bs distances if needed + // --------------------------------------------------------------- + if (ip_block == nullptr) { + size_t need_ip = size_t(q_blk) * size_t(blas_db_bs); + if (TLS_ip.size() < need_ip) { + std::cout << "Resizing TLS_ip from " << TLS_ip.size() + << " to " << need_ip << std::endl; + TLS_ip.resize(need_ip); + } + } + + // --------------------------------------------------------------- + // Loop over database blocks of size ≤ blas_db_bs + // --------------------------------------------------------------- + for (int db_off = 0; db_off < list_size; db_off += blas_db_bs) { + int blk = std::min(blas_db_bs, list_size - db_off); + const float *d_ptr = list_vecs + size_t(db_off) * dim; + const int64_t *d_ids = list_ids + db_off; + + // Decide where to write "ip_block": either caller‐owned or TLS_ip + float *blk_ip = (ip_block != nullptr ? ip_block + : TLS_ip.data()); + + // If L2 and caller gave no norms_y_buf, ensure TLS_ny fits blk + float *blk_norm_y = nullptr; + if (need_norm) { + if (norms_y_buf != nullptr) { + blk_norm_y = norms_y_buf; + } else { + if ((int)TLS_ny.size() < blk) { + TLS_ny.resize(blk); + } + blk_norm_y = TLS_ny.data(); + } + } + + // Dispatch to the appropriate BLAS kernel + if (metric == faiss::METRIC_INNER_PRODUCT) { + ip_blas(q_ptr, + d_ptr, + d_ids, + dim, + q_blk, + blk, + blk, + k, + sub_buffers, + blk_ip, + pivots); + } else { // faiss::METRIC_L2 + l2_blas(q_ptr, + d_ptr, + d_ids, + dim, + q_blk, + blk, + blk, + k, + sub_buffers, + blk_ip, + q_norms, + blk_norm_y, + pivots); + } + } } } +// inline void batched_scan_list(const float *query_vecs, +// const float *list_vecs, +// const int64_t *list_ids, +// int num_queries, +// int list_size, +// int dim, +// std::vector> &topk_buffers, +// MetricType metric, +// /* optional scratch supplied by caller: may be nullptr */ +// float *ip_block /* = nullptr */, +// float *norms_x /* = nullptr */, +// float *norms_y_buf /* = nullptr */, +// int blas_db_bs /* = BLAS_DB_BS */, +// int blas_q_bs /* = 128 */, +// std::vector*> pivots /* = {} */) +// { +// if (list_size == 0 || num_queries == 0 || list_vecs == nullptr) { +// return; +// } +// +// const bool need_norm = (metric == faiss::METRIC_L2); +// // each query has a TopkBuffer in topk_buffers[0..num_queries-1] +// const int k = std::min(topk_buffers[0]->k(), list_size); +// +// /* ------------------------------------------------------------------ */ +// /* TEMPORARY BUFFERS (only if caller did NOT supply) */ +// /* ------------------------------------------------------------------ */ +// std::vector tmp_ip; // for caller‐absent ip_block +// std::vector tmp_nx; // for norms_x if caller did not supply +// std::vector tmp_ny; // for norms_y_buf if caller did not supply +// +// /* ------------------------------------------------------------------ */ +// /* PRE‐COMPUTE ‖x‖² FOR ALL QUERIES IF L2 AND NO norms_x GIVEN */ +// /* ------------------------------------------------------------------ */ +// if (need_norm && norms_x == nullptr) { +// tmp_nx.resize(blas_q_bs); +// norms_x = tmp_nx.data(); +// } +// +// /* ------------------------------------------------------------------ */ +// /* LOOP OVER QUERY BLOCKS OF SIZE ≤ blas_q_bs */ +// /* ------------------------------------------------------------------ */ +// for (int q_off = 0; q_off < num_queries; q_off += blas_q_bs) { +// int q_blk = std::min(blas_q_bs, num_queries - q_off); +// const float *q_ptr = query_vecs + size_t(q_off) * dim; +// float *q_norms = need_norm ? (norms_x + q_off) : nullptr; +// +// // build a subvector of TopkBuffers for these q_blk queries +// std::vector> sub_buffers; +// sub_buffers.reserve(q_blk); +// for (int i = 0; i < q_blk; ++i) { +// sub_buffers.push_back(topk_buffers[q_off + i]); +// } +// +// /* ------------------------------------------------------------------ */ +// /* OPTIONALLY GROW tmp_ip TO HOLD q_blk × blas_db_bs DISTANCES */ +// /* (only if caller did not supply ip_block) */ +// /* ------------------------------------------------------------------ */ +// if (ip_block == nullptr) { +// size_t needed = size_t(q_blk) * size_t(blas_db_bs); +// if (tmp_ip.size() < needed) { +// tmp_ip.resize(needed); +// } +// } +// +// /* ------------------------------------------------------------------ */ +// /* LOOP OVER DATABASE BLOCKS OF SIZE ≤ blas_db_bs */ +// /* ------------------------------------------------------------------ */ +// for (int db_off = 0; db_off < list_size; db_off += blas_db_bs) { +// int blk = std::min(blas_db_bs, list_size - db_off); +// const float *d_ptr = list_vecs + size_t(db_off) * dim; +// const int64_t *d_ids = list_ids + db_off; +// float *blk_ip = ip_block ? ip_block : tmp_ip.data(); +// float *blk_norm_y = nullptr; +// +// /* if L2 and no caller norms_y_buf given, grow tmp_ny to size blk */ +// if (need_norm) { +// if (norms_y_buf != nullptr) { +// blk_norm_y = norms_y_buf; +// } else { +// if ((int)tmp_ny.size() < blk) { +// tmp_ny.resize(blk); +// } +// blk_norm_y = tmp_ny.data(); +// } +// } +// +// if (metric == faiss::METRIC_INNER_PRODUCT) { +// // q_blk queries, blk database rows → distances in blk_ip +// ip_blas(q_ptr, +// d_ptr, +// d_ids, +// dim, +// q_blk, +// blk, +// blk, +// k, +// sub_buffers, +// blk_ip, +// pivots); +// } else if (metric == faiss::METRIC_L2) { +// l2_blas(q_ptr, +// d_ptr, +// d_ids, +// dim, +// q_blk, +// blk, +// blk, +// k, +// sub_buffers, +// blk_ip, +// q_norms, +// blk_norm_y, +// pivots); +// } else { +// throw std::runtime_error("batched_scan_list: unsupported metric"); +// } +// } +// } +// } + +// +// +// inline void batched_scan_list(const float *query_vecs, +// const float *list_vecs, +// const int64_t *list_ids, +// int num_queries, +// int list_size, +// int dim, +// vector> &topk_buffers, +// MetricType metric, +// float *ip_block = nullptr, +// float *norms_x = nullptr, +// float *norms_y_buf = nullptr, +// int blas_db_bs = BLAS_DB_BS, +// vector*> pivots = {}) { +// if (list_size == 0 || list_vecs == nullptr) { +// // No list vectors to process; +// return; +// } +// +// // Ensure k does not exceed list_size +// int k = topk_buffers[0]->k(); +// int k_max = std::min(k, list_size); +// +// if (metric == faiss::METRIC_INNER_PRODUCT) { +// ip_blas( +// query_vecs, +// list_vecs, +// list_ids, +// dim, +// num_queries, +// list_size, +// blas_db_bs, +// k_max, +// topk_buffers, +// ip_block, +// pivots +// ); +// } else if (metric == faiss::METRIC_L2) { +// l2_blas( +// query_vecs, +// list_vecs, +// list_ids, +// dim, +// num_queries, +// list_size, +// blas_db_bs, +// k_max, +// topk_buffers, +// ip_block, +// norms_x, +// norms_y_buf, +// pivots +// ); +// } else { +// throw std::runtime_error("Metric type not supported"); +// } +// } + // } #endif //LIST_SCANNING_H \ No newline at end of file diff --git a/src/cpp/include/query_coordinator.h b/src/cpp/include/query_coordinator.h index 21a136a6..ac603307 100644 --- a/src/cpp/include/query_coordinator.h +++ b/src/cpp/include/query_coordinator.h @@ -132,6 +132,8 @@ class QueryCoordinator { std::atomic total_left_; vector> per_query_total_left_; ///< Total jobs left for each query. + int current_level_; ///< Current level of the coordinator, used for debugging. + /** * @brief Constructs a QueryCoordinator. * @@ -145,6 +147,7 @@ class QueryCoordinator { shared_ptr partition_manager, shared_ptr maintenance_policy, MetricType metric, + int current_level = 0, int num_workers=0, bool use_numa=false, int num_merge_workers=1); diff --git a/src/cpp/src/clustering.cpp b/src/cpp/src/clustering.cpp index 5a46da6a..a826cfcf 100644 --- a/src/cpp/src/clustering.cpp +++ b/src/cpp/src/clustering.cpp @@ -241,16 +241,12 @@ tuple >> kmeans_refine_partitions( for (auto &p : partitions) { max_nq = std::max(max_nq, (size_t)p->num_vectors_); } - max_nq = max_nq * 20; // double the max_nq to ensure enough space for batched_scan_list, as assignments may change. + max_nq = max_nq * 5; // double the max_nq to ensure enough space for batched_scan_list, as assignments may change. // Determine number of clusters and dimension. int n_clusters = centroids.size(0); int d = centroids.size(1); - const size_t ip_need = n_clusters * max_nq; - float * blas_ip_block = static_cast(quake_alloc(ip_need * sizeof(float), 0)); - float * blas_norms_x = static_cast(quake_alloc(max_nq * sizeof(float), 0)); - float * blas_norms_y = static_cast(quake_alloc(n_clusters * sizeof(float), 0)); vector > buffers = create_buffers(max_nq, 1, (metric == faiss::METRIC_INNER_PRODUCT), n_clusters); @@ -311,10 +307,12 @@ tuple >> kmeans_refine_partitions( d, buffers, metric, - /* BLAS scratch */ blas_ip_block, - blas_norms_x, - blas_norms_y, - BLAS_DB_BS); + nullptr, + nullptr, + nullptr, + 128, + BLAS_DB_BS, + {}); // For each vector in this partition, determine its assignment. for (int i = 0; i < nvec; i++) { @@ -337,14 +335,7 @@ tuple >> kmeans_refine_partitions( } } // end for each partition - std::move(new_partitions.begin(), new_partitions.end(), partitions.begin()); } // end iterations - - // Clean up and return the refined centroids and partitions. - quake_free(blas_ip_block, ip_need * sizeof(float)); - quake_free(blas_norms_x, max_nq * sizeof(float)); - quake_free(blas_norms_y, BLAS_DB_BS * sizeof(float)); - return std::make_tuple(centroids, partitions); } diff --git a/src/cpp/src/maintenance_policies.cpp b/src/cpp/src/maintenance_policies.cpp index 106bc895..263f68d0 100644 --- a/src/cpp/src/maintenance_policies.cpp +++ b/src/cpp/src/maintenance_policies.cpp @@ -96,6 +96,7 @@ shared_ptr MaintenancePolicy::perform_maintenance() { auto search_params = make_shared(); search_params->k = 2; // get the top 2 partitions, ignore the first one as it is the partition itself search_params->batched_scan = true; + search_params->track_hits = false; float *partition_vectors = (float *) partition_manager_->partition_store_->partitions_[partition_id]->codes_; Tensor part_vecs = torch::from_blob(partition_vectors, {(int64_t) partition_manager_->partition_store_->list_size(partition_id), partition_manager_->d()}, torch::kFloat32); @@ -223,6 +224,8 @@ void MaintenancePolicy::local_refinement(const torch::Tensor &partition_ids) { auto search_params = std::make_shared(); search_params->nprobe = 1000; search_params->k = params_->refinement_radius; + search_params->batched_scan = true; + search_params->track_hits = false; if (params_->refinement_radius == 0) { return; diff --git a/src/cpp/src/partition_manager.cpp b/src/cpp/src/partition_manager.cpp index fa068200..d72cafb3 100644 --- a/src/cpp/src/partition_manager.cpp +++ b/src/cpp/src/partition_manager.cpp @@ -225,10 +225,8 @@ shared_ptr PartitionManager::add( } auto search_params = make_shared(); search_params->k = 1; - search_params->recall_target = .999; - if (n > 10) { - search_params->batched_scan = true; - } + search_params->scan_all = true; + search_params->track_hits = false; auto parent_search_result = parent_->search(vectors, search_params); Tensor label_out = parent_search_result->ids; auto lbl_ptr = label_out.data_ptr(); diff --git a/src/cpp/src/quake_index.cpp b/src/cpp/src/quake_index.cpp index 946292b2..d384bb14 100644 --- a/src/cpp/src/quake_index.cpp +++ b/src/cpp/src/quake_index.cpp @@ -86,6 +86,7 @@ shared_ptr QuakeIndex::build(Tensor x, Tensor ids, shared_ptrnum_workers << " workers." << std::endl; query_coordinator_ = make_shared(parent_, partition_manager_, maintenance_policy_, metric_, build_params_->num_workers, build_params_->use_numa); auto end = std::chrono::high_resolution_clock::now(); @@ -305,6 +306,7 @@ void QuakeIndex::load(const std::string& dir_path, shared_ptr partition_manager_, maintenance_policy_, metric_, + current_level_, build_params->num_workers, build_params->use_numa, build_params->num_merge_workers); diff --git a/src/cpp/src/query_coordinator.cpp b/src/cpp/src/query_coordinator.cpp index 95235ef2..2d9e0024 100644 --- a/src/cpp/src/query_coordinator.cpp +++ b/src/cpp/src/query_coordinator.cpp @@ -42,6 +42,7 @@ QueryCoordinator::QueryCoordinator(shared_ptr parent, shared_ptr partition_manager, shared_ptr maintenance_policy, MetricType metric, + int current_level, int num_workers, bool use_numa, int num_merge_workers) @@ -49,6 +50,7 @@ QueryCoordinator::QueryCoordinator(shared_ptr parent, partition_manager_(partition_manager), maintenance_policy_(maintenance_policy), metric_(metric), + current_level_(current_level), num_workers_(num_workers), num_merge_workers_(num_merge_workers), workers_initialized_(false) { @@ -110,29 +112,21 @@ void QueryCoordinator::merge_worker_fn(int mid) { if (rj.query_id == -1) // poison pill return; - if (rj.distances.empty()) { - // empty result, nothing to do - --per_query_total_left_[rj.query_id]; - --total_left_; - continue; - } - - // single cast, based on the Compare template using Handler = typename faiss::HeapBlockResultHandler::SingleResultHandler; auto* h = static_cast(MR.handlers[rj.query_id]); - // feed all partial results - for (size_t i = 0; i < rj.distances.size(); ++i) { - h->add_result(rj.distances[i], rj.indices[i]); + if (!rj.distances.empty()) { + // feed all partial results + for (size_t i = 0; i < rj.distances.size(); ++i) { + h->add_result(rj.distances[i], rj.indices[i]); + } + // update pivot + query_dist_pivots_[rj.query_id].store(h->threshold, + std::memory_order_relaxed); } - // update pivot - query_dist_pivots_[rj.query_id].store(h->threshold, - std::memory_order_relaxed); - - per_query_total_left_[rj.query_id]--; // once all ranks for this query are in, finalize & sort - if (per_query_total_left_[rj.query_id] <= 0) { + if (per_query_total_left_[rj.query_id].fetch_sub(1, std::memory_order_acq_rel) == 1) { h->end(); // pack into pairs for sorting @@ -265,8 +259,6 @@ void QueryCoordinator::handle_nonbatched_job(const ScanJob &job, CoreResources &res, NUMAResources &nr) { - auto start = std::chrono::high_resolution_clock::now(); - // check that the job has not been processed yet if (query_done_flags_[job.query_id].load(std::memory_order_relaxed)) { enqueue_result_job(ResultJob{job.query_id, job.rank, {}, {}}); @@ -321,6 +313,8 @@ void QueryCoordinator::handle_nonbatched_job(const ScanJob &job, job_flags_[job.query_id][job.rank].store(true, std::memory_order_relaxed); // Mark this job as processed + auto start = std::chrono::high_resolution_clock::now(); + scan_list(nr.local_query_buffer + (job.query_id * D), codes, ids, @@ -489,6 +483,7 @@ void QueryCoordinator::handle_batched_job(const ScanJob &job, res.blas_norms_x, res.blas_norms_y, BLAS_DB_BS, + max_q, pivots); auto s4 = std::chrono::high_resolution_clock::now(); @@ -812,7 +807,6 @@ void QueryCoordinator::drain_and_apply_aps(Tensor queries, curr_centroids_vec, metric_ == faiss::METRIC_L2); } - } auto end = std::chrono::high_resolution_clock::now(); timing->boundary_distance_time_ns = @@ -867,14 +861,24 @@ void QueryCoordinator::drain_and_apply_aps(Tensor queries, max_rank_[q].store(max_rank, std::memory_order_relaxed); int n_scanned = 0; + float partition_recall_in_flight = 0.0f; + int count_in_flight = 0; for (int p = 0; p < partition_ids.size(1); ++p) { if (job_flags_[q][p].load(std::memory_order_relaxed)) { n_scanned++; recall_estimate += recall_profiles[q][p]; + } else { + if (count_in_flight < num_workers_) { + count_in_flight++; + partition_recall_in_flight += recall_profiles[q][p]; + } + } - sum += recall_profiles[q][p]; } + // add the in-flight recall to the estimate + recall_estimate += partition_recall_in_flight; + if (recall_estimate >= search_params->recall_target) { query_done_flags_[q].store(true, std::memory_order_relaxed); } @@ -888,7 +892,6 @@ void QueryCoordinator::drain_and_apply_aps(Tensor queries, } - // mark hits if (search_params->track_hits && maintenance_policy_) { for (int64_t q = 0; q < nQ; ++q) { std::vector scanned_ids; @@ -915,9 +918,13 @@ QueryCoordinator::aggregate_scan_results(int64_t nQ, auto id_acc = out_ids.accessor(); auto d_acc = out_dists.accessor(); + + + // copy results from the global heaps to the output tensors if (metric_ == faiss::METRIC_INNER_PRODUCT) { for (int64_t q = 0; q < nQ; ++q) { + for (int64_t i = 0; i < K; ++i) { id_acc[q][i] = global_max_heaps_->heap_ids_tab[q * K + i]; d_acc [q][i] = global_max_heaps_->heap_dis_tab[q * K + i]; @@ -961,9 +968,7 @@ std::shared_ptr QueryCoordinator::worker_scan( vector core_process_preamble_time_ns(num_workers_, 0); vector core_enqueue_time_ns(num_workers_, 0); vector core_job_time_ns(num_workers_, 0); - vector core_scan_setup_time_ns(num_workers_, 0); vector core_scan_time_ns(num_workers_, 0); - vector core_scan_push_time_ns(num_workers_, 0); for (int i = 0; i < num_workers_; ++i) { core_wait_time_ns[i] = core_resources_[i].wait_time_ns; core_process_time_ns[i] = core_resources_[i].process_time_ns; @@ -1023,6 +1028,30 @@ std::shared_ptr QueryCoordinator::worker_scan( core_scan_time_ns[i] = core_resources_[i].scan_time_ns - core_scan_time_ns[i]; } + // add timers to timing info. average across all workers + int64_t num_workers = num_workers_; + double wait_time_ns = 0; + double process_time_ns = 0; + double process_preamble_time_ns = 0; + double enqueue_time_ns = 0; + double job_time_ns = 0; + double scan_time_ns = 0; + + for (int i = 0; i < num_workers; ++i) { + wait_time_ns += core_wait_time_ns[i]; + process_time_ns += core_process_time_ns[i]; + process_preamble_time_ns += core_process_preamble_time_ns[i]; + enqueue_time_ns += core_enqueue_time_ns[i]; + job_time_ns += core_job_time_ns[i]; + scan_time_ns += core_scan_time_ns[i]; + } + res->timing_info->worker_wait_time_ns = wait_time_ns / num_workers; + res->timing_info->worker_process_time_ns = process_time_ns / num_workers; + res->timing_info->worker_process_preamble_time_ns = process_preamble_time_ns / num_workers; + res->timing_info->worker_enqueue_time_ns = enqueue_time_ns / num_workers; + res->timing_info->worker_job_time_ns = job_time_ns / num_workers; + res->timing_info->worker_scan_time_ns = scan_time_ns / num_workers; + // // print out the per-core resource timers; // for (int i = 0; i < num_workers_; ++i) { @@ -1198,8 +1227,6 @@ shared_ptr QueryCoordinator::serial_scan(Tensor x, Tensor partitio auto t2 = high_resolution_clock::now(); - - Tensor partition_sizes = partition_manager_->get_partition_sizes(partition_ids[q]); vector partition_sizes_vec = vector(partition_sizes.data_ptr(), partition_sizes.data_ptr() + partition_sizes.size(0)); @@ -1354,9 +1381,10 @@ shared_ptr QueryCoordinator::search(Tensor x, shared_ptrscan_all) { // scan all partitions for each query partition_ids_to_scan = partition_manager_->get_partition_ids(); + search_params->batched_scan = true; } else { auto parent_search_params = make_shared(); if (search_params->parent_params == nullptr) { @@ -1404,6 +1432,15 @@ shared_ptr QueryCoordinator::search(Tensor x, shared_ptrtiming_info->total_time_ns = duration_cast(end - start). count(); + // print out the results and report curren_level_ + // std::cout << "[QueryCoordinator::search] " + // << "current_level_=" << current_level_ << " " + // << "n_queries=" << x.size(0) << " " + // << "k=" << search_params->k << " " + // << "batched_scan=" << search_params->batched_scan << " " + // << "total_time_ms=" << (float) search_result->timing_info->total_time_ns / 1e6 << " " + // << "parent_total_time_ms=" << (float) parent_timing_info->total_time_ns / 1e6 << " " << std::endl; + return search_result; } @@ -1445,9 +1482,6 @@ shared_ptr QueryCoordinator::batched_serial_scan( return empty_res; } - static CoreResources serial_res; // one per process - ensure_blas_buffers(serial_res, x.size(0), BLAS_DB_BS, /*node=*/0); - // Timing info (could be extended as needed) auto timing_info = std::make_shared(); auto start = high_resolution_clock::now(); @@ -1456,7 +1490,8 @@ shared_ptr QueryCoordinator::batched_serial_scan( int k = (search_params && search_params->k > 0) ? search_params->k : 1; // Global Top-K buffers: one for each query. - vector> global_buffers = create_buffers(num_queries, k, (metric_ == faiss::METRIC_INNER_PRODUCT)); + vector> global_buffers = create_buffers(num_queries, k, (metric_ == faiss::METRIC_INNER_PRODUCT), + /*cap=*/10 * k); // Ensure partition_ids is 2D. If it’s 1D, assume every query scans the same set. if (partition_ids.dim() == 1) { @@ -1481,7 +1516,7 @@ shared_ptr QueryCoordinator::batched_serial_scan( queries_vec.push_back(entry); } - parallel_for((int64_t) 0, (int64_t) queries_by_partition.size(), [&](int64_t i) { + for (int i = 0; i < queries_vec.size(); i++) { int64_t pid = queries_vec[i].first; auto query_indices = queries_vec[i].second; @@ -1509,10 +1544,12 @@ shared_ptr QueryCoordinator::batched_serial_scan( d, local_buffers, metric_, - /* BLAS scratch */ serial_res.blas_ip_block, - serial_res.blas_norms_x, - serial_res.blas_norms_y, - BLAS_DB_BS); + nullptr, + nullptr, + nullptr, + BLAS_DB_BS, + 128, + {}); // Merge the local results into the corresponding global buffers. for (int i = 0; i < batch_size; i++) { @@ -1522,9 +1559,7 @@ shared_ptr QueryCoordinator::batched_serial_scan( // Merge: global buffer adds the new candidate distances/ids. global_buffers[global_q]->batch_add(local_dists.data(), local_ids.data(), local_ids.size()); } - - - }, search_params->num_threads); + } // Aggregate the final results into output tensors. auto topk_ids = torch::full({num_queries, k}, -1, torch::kInt64); From df4aa07b6be1cb8894f3293b087c1b4571bd23ee Mon Sep 17 00:00:00 2001 From: Devesh Sarda <32046390+sarda-devesh@users.noreply.github.com> Date: Tue, 27 Jan 2026 12:56:39 -0800 Subject: [PATCH 322/323] BigANN related changes (#69) * BigAnn fixes * Fix mainteance bug * BigANN bug fixes * Memory leax fixes * Over memory usage fix * Batched scan slow debug * Big ANN debug working * Fixed segfault error * Tuned Batch Scan * Exposed metrics --- .gitignore | 1051 ++++++++- CMakeLists.txt | 20 +- README.md | 8 + requirements.txt | 14 + ..._scan_0.12_no_aps_refinment_wma_delete.csv | 11 + ...k_scan_0.1_no_aps_refinment_wma_delete.csv | 1280 ++++++++++ ...can_0.13_worker_batch_clustering_score.csv | 21 + .../perf_debug_scan_0.14_tuning.csv | 651 +++++ ...can_0.14_worker_batch_tuning_breakdown.csv | 2 + ...ug_scan_0.14_worker_batch_tuning_lower.csv | 1280 ++++++++++ .../perf_debug_scan_0.15_counters.csv | 1280 ++++++++++ ...erf_debug_scan_0.15_fine_grain_analyze.csv | 11 + ..._scan_0.15_no_aps_refinment_wma_delete.csv | 76 + .../perf_debug_scan_0.15_vary_batch_size.csv | 2101 +++++++++++++++++ ...ebug_scan_0.15_vary_num_search_workers.csv | 901 +++++++ .../scan_0.12_no_aps_refinment_wma_delete.csv | 1280 ++++++++++ .../scan_0.15_no_aps_refinment_wma_delete.csv | 1 + .../scan_0.1_no_aps_refinment_wma.csv | 307 +++ .../scan_0.1_no_aps_refinment_wma_delete.csv | 1280 ++++++++++ ...batching_250_aps_recall_0.8_search_0.1.csv | 29 + ...atching_250_aps_recall_0.9_search_0.01.csv | 47 + ...atching_250_aps_recall_0.9_search_0.05.csv | 30 + ...atching_250_aps_recall_0.9_search_0.15.csv | 1280 ++++++++++ ...ng_250_aps_recall_0.9_search_0.15_0.05.csv | 1280 ++++++++++ ...ing_250_aps_recall_0.9_search_0.1_0.01.csv | 20 + ...hing_250_aps_recall_0.9_search_0.2_0.1.csv | 469 ++++ ...batching_250_aps_recall_0.9_search_0.3.csv | 1280 ++++++++++ .../scan_using_batching_optimizations.csv | 251 ++ .../scan_vary_using_batching_250_no_aps.csv | 506 ++++ ...rf_debug_scan_0.14_worker_batch_tuning.csv | 46 + ...0.14_worker_batch_tuning_with_counters.csv | 76 + .../test_metric_collection.csv | 11 + .../test_perf_debug_scan_0.14_tuning.csv | 26 + ...rf_debug_scan_0.14_worker_batch_tuning.csv | 1280 ++++++++++ ..._debug_scan_0.15_batch_versus_nonbatch.csv | 26 + scripts/big_ann_perf_visualize.py | 282 +++ .../perf_debug_scan_0.14_tuning.png | Bin 0 -> 741337 bytes ...ug_scan_0.14_worker_batch_tuning_lower.png | Bin 0 -> 859116 bytes .../perf_debug_scan_0.15_counters.png | Bin 0 -> 482615 bytes .../perf_debug_scan_0.15_vary_batch_size.png | Bin 0 -> 2514743 bytes ...ebug_scan_0.15_vary_num_search_workers.png | Bin 0 -> 1192992 bytes .../scan_0.12_no_aps_refinment_wma_delete.png | Bin 0 -> 846411 bytes .../scan_0.15_no_aps_refinment_wma_delete.png | Bin 0 -> 564583 bytes .../scan_0.1_no_aps_refinment_wma_delete.png | Bin 0 -> 867590 bytes ...atching_250_aps_recall_0.9_search_0.15.png | Bin 0 -> 654563 bytes ...ng_250_aps_recall_0.9_search_0.15_0.05.png | Bin 0 -> 668678 bytes ...ing_250_aps_recall_0.9_search_0.1_0.01.png | Bin 0 -> 667943 bytes ...batching_250_aps_recall_0.9_search_0.3.png | Bin 0 -> 642397 bytes .../scan_using_batching_optimizations.png | Bin 0 -> 689720 bytes .../scan_vary_using_batching_250_no_aps.png | Bin 0 -> 322244 bytes ...rf_debug_scan_0.14_worker_batch_tuning.png | Bin 0 -> 847409 bytes scripts/cpp/benchmark/big_ann_debug.cpp | 888 +++++++ scripts/test_mainteance.py | 72 + setup.py | 1 + src/cpp/bindings/wrap.cpp | 17 + src/cpp/include/common.h | 27 +- src/cpp/include/dynamic_inverted_list.h | 42 +- src/cpp/include/index_partition.h | 27 +- src/cpp/include/list_scanning.h | 76 +- src/cpp/include/maintenance_policies.h | 3 + src/cpp/include/parallel.h | 1 + src/cpp/include/partition_manager.h | 22 +- src/cpp/include/query_coordinator.h | 32 +- src/cpp/src/clustering.cpp | 2 +- src/cpp/src/dynamic_inverted_list.cpp | 77 +- src/cpp/src/index_partition.cpp | 80 +- src/cpp/src/maintenance_policies.cpp | 60 +- src/cpp/src/partition_manager.cpp | 105 +- src/cpp/src/quake_index.cpp | 8 +- src/cpp/src/query_coordinator.cpp | 497 +++- 70 files changed, 20316 insertions(+), 235 deletions(-) create mode 100644 requirements.txt create mode 100644 scripts/big_ann_perf_numbers/crash_debug_scan_0.12_no_aps_refinment_wma_delete.csv create mode 100644 scripts/big_ann_perf_numbers/mem_check_scan_0.1_no_aps_refinment_wma_delete.csv create mode 100644 scripts/big_ann_perf_numbers/perf_debug_scan_0.13_worker_batch_clustering_score.csv create mode 100644 scripts/big_ann_perf_numbers/perf_debug_scan_0.14_tuning.csv create mode 100644 scripts/big_ann_perf_numbers/perf_debug_scan_0.14_worker_batch_tuning_breakdown.csv create mode 100644 scripts/big_ann_perf_numbers/perf_debug_scan_0.14_worker_batch_tuning_lower.csv create mode 100644 scripts/big_ann_perf_numbers/perf_debug_scan_0.15_counters.csv create mode 100644 scripts/big_ann_perf_numbers/perf_debug_scan_0.15_fine_grain_analyze.csv create mode 100644 scripts/big_ann_perf_numbers/perf_debug_scan_0.15_no_aps_refinment_wma_delete.csv create mode 100644 scripts/big_ann_perf_numbers/perf_debug_scan_0.15_vary_batch_size.csv create mode 100644 scripts/big_ann_perf_numbers/perf_debug_scan_0.15_vary_num_search_workers.csv create mode 100644 scripts/big_ann_perf_numbers/scan_0.12_no_aps_refinment_wma_delete.csv create mode 100644 scripts/big_ann_perf_numbers/scan_0.15_no_aps_refinment_wma_delete.csv create mode 100644 scripts/big_ann_perf_numbers/scan_0.1_no_aps_refinment_wma.csv create mode 100644 scripts/big_ann_perf_numbers/scan_0.1_no_aps_refinment_wma_delete.csv create mode 100644 scripts/big_ann_perf_numbers/scan_using_batching_250_aps_recall_0.8_search_0.1.csv create mode 100644 scripts/big_ann_perf_numbers/scan_using_batching_250_aps_recall_0.9_search_0.01.csv create mode 100644 scripts/big_ann_perf_numbers/scan_using_batching_250_aps_recall_0.9_search_0.05.csv create mode 100644 scripts/big_ann_perf_numbers/scan_using_batching_250_aps_recall_0.9_search_0.15.csv create mode 100644 scripts/big_ann_perf_numbers/scan_using_batching_250_aps_recall_0.9_search_0.15_0.05.csv create mode 100644 scripts/big_ann_perf_numbers/scan_using_batching_250_aps_recall_0.9_search_0.1_0.01.csv create mode 100644 scripts/big_ann_perf_numbers/scan_using_batching_250_aps_recall_0.9_search_0.2_0.1.csv create mode 100644 scripts/big_ann_perf_numbers/scan_using_batching_250_aps_recall_0.9_search_0.3.csv create mode 100644 scripts/big_ann_perf_numbers/scan_using_batching_optimizations.csv create mode 100644 scripts/big_ann_perf_numbers/scan_vary_using_batching_250_no_aps.csv create mode 100644 scripts/big_ann_perf_numbers/temp_perf_debug_scan_0.14_worker_batch_tuning.csv create mode 100644 scripts/big_ann_perf_numbers/temp_perf_debug_scan_0.14_worker_batch_tuning_with_counters.csv create mode 100644 scripts/big_ann_perf_numbers/test_metric_collection.csv create mode 100644 scripts/big_ann_perf_numbers/test_perf_debug_scan_0.14_tuning.csv create mode 100644 scripts/big_ann_perf_numbers/test_perf_debug_scan_0.14_worker_batch_tuning.csv create mode 100644 scripts/big_ann_perf_numbers/test_perf_debug_scan_0.15_batch_versus_nonbatch.csv create mode 100644 scripts/big_ann_perf_visualize.py create mode 100644 scripts/bigann_perf_vis/perf_debug_scan_0.14_tuning.png create mode 100644 scripts/bigann_perf_vis/perf_debug_scan_0.14_worker_batch_tuning_lower.png create mode 100644 scripts/bigann_perf_vis/perf_debug_scan_0.15_counters.png create mode 100644 scripts/bigann_perf_vis/perf_debug_scan_0.15_vary_batch_size.png create mode 100644 scripts/bigann_perf_vis/perf_debug_scan_0.15_vary_num_search_workers.png create mode 100644 scripts/bigann_perf_vis/scan_0.12_no_aps_refinment_wma_delete.png create mode 100644 scripts/bigann_perf_vis/scan_0.15_no_aps_refinment_wma_delete.png create mode 100644 scripts/bigann_perf_vis/scan_0.1_no_aps_refinment_wma_delete.png create mode 100644 scripts/bigann_perf_vis/scan_using_batching_250_aps_recall_0.9_search_0.15.png create mode 100644 scripts/bigann_perf_vis/scan_using_batching_250_aps_recall_0.9_search_0.15_0.05.png create mode 100644 scripts/bigann_perf_vis/scan_using_batching_250_aps_recall_0.9_search_0.1_0.01.png create mode 100644 scripts/bigann_perf_vis/scan_using_batching_250_aps_recall_0.9_search_0.3.png create mode 100644 scripts/bigann_perf_vis/scan_using_batching_optimizations.png create mode 100644 scripts/bigann_perf_vis/scan_vary_using_batching_250_no_aps.png create mode 100644 scripts/bigann_perf_vis/test_perf_debug_scan_0.14_worker_batch_tuning.png create mode 100644 scripts/cpp/benchmark/big_ann_debug.cpp create mode 100644 scripts/test_mainteance.py diff --git a/.gitignore b/.gitignore index ed8ebf58..786c4a5e 100644 --- a/.gitignore +++ b/.gitignore @@ -1 +1,1050 @@ -__pycache__ \ No newline at end of file +__pycache__ + +##### Windows +# Windows thumbnail cache files +Thumbs.db +Thumbs.db:encryptable +ehthumbs.db +ehthumbs_vista.db + +# Dump file +*.stackdump + +# Folder config file +[Dd]esktop.ini + +# Recycle Bin used on file shares +$RECYCLE.BIN/ + +# Windows Installer files +*.cab +*.msi +*.msix +*.msm +*.msp + +# Windows shortcuts +*.lnk + +##### Linux +*~ + +# temporary files which can be created if a process still has a handle open of a deleted file +.fuse_hidden* + +# KDE directory preferences +.directory + +# Linux trash folder which might appear on any partition or disk +.Trash-* + +# .nfs files are created when an open file is removed but is still being accessed +.nfs* + +##### MacOS +# General +.DS_Store +.AppleDouble +.LSOverride + +# Thumbnails +._* + +# Files that might appear in the root of a volume +.DocumentRevisions-V100 +.fseventsd +.Spotlight-V100 +.TemporaryItems +.Trashes +.VolumeIcon.icns +.com.apple.timemachine.donotpresent + +# Directories potentially created on remote AFP share +.AppleDB +.AppleDesktop +Network Trash Folder +Temporary Items +.apdisk + +##### Android +# Built application files +*.apk +*.ap_ +*.aab + +# Files for the ART/Dalvik VM +*.dex + +# Java class files +*.class + +# Generated files +bin/ +gen/ +out/ +# Uncomment the following line in case you need and you don't have the release build type files in your app +# release/ + +# Gradle files +.gradle/ +build/ + +# Local configuration file (sdk path, etc) +local.properties + +# Proguard folder generated by Eclipse +proguard/ + +# Log Files +*.log + +# Android Studio Navigation editor temp files +.navigation/ + +# Android Studio captures folder +captures/ + +# IntelliJ +*.iml +.idea/workspace.xml +.idea/tasks.xml +.idea/gradle.xml +.idea/assetWizardSettings.xml +.idea/dictionaries +.idea/libraries +# Android Studio 3 in .gitignore file. +.idea/caches +.idea/modules.xml +# Comment next line if keeping position of elements in Navigation Editor is relevant for you +.idea/navEditor.xml + +# Keystore files +# Uncomment the following lines if you do not want to check your keystore files in. +#*.jks +#*.keystore + +# External native build folder generated in Android Studio 2.2 and later +.externalNativeBuild + +# Google Services (e.g. APIs or Firebase) +# google-services.json + +# Freeline +freeline.py +freeline/ +freeline_project_description.json + +# fastlane +fastlane/report.xml +fastlane/Preview.html +fastlane/screenshots +fastlane/test_output +fastlane/readme.md + +# Version control +vcs.xml + +# lint +lint/intermediates/ +lint/generated/ +lint/outputs/ +lint/tmp/ +# lint/reports/ + +##### Backup +*.bak +*.gho +*.ori +*.orig +*.tmp + +##### GPG +secring.* + +##### Dropbox +# Dropbox settings and caches +.dropbox +.dropbox.attr +.dropbox.cache + +##### SynopsysVCS +# Waveform formats +*.vcd +*.vpd +*.evcd +*.fsdb + +# Default name of the simulation executable. A different name can be +# specified with this switch (the associated daidir database name is +# also taken from here): -o / +simv + +# Generated for Verilog and VHDL top configs +simv.daidir/ +simv.db.dir/ + +# Infrastructure necessary to co-simulate SystemC models with +# Verilog/VHDL models. An alternate directory may be specified with this +# switch: -Mdir= +csrc/ + +# Log file - the following switch allows to specify the file that will be +# used to write all messages from simulation: -l +*.log + +# Coverage results (generated with urg) and database location. The +# following switch can also be used: urg -dir .vdb +simv.vdb/ +urgReport/ + +# DVE and UCLI related files. +DVEfiles/ +ucli.key + +# When the design is elaborated for DirectC, the following file is created +# with declarations for C/C++ functions. +vc_hdrs.h + +##### SVN +.svn/ + +##### Mercurial +.hg/ +.hgignore +.hgsigs +.hgsub +.hgsubstate +.hgtags + +##### Bazaar +.bzr/ +.bzrignore + +##### CVS +/CVS/* +**/CVS/* +.cvsignore +*/.cvsignore + +##### TortoiseGit +# Project-level settings +/.tgitconfig + +##### PuTTY +# Private key +*.ppk + +##### Vim +# Swap +[._]*.s[a-v][a-z] +!*.svg # comment out if you don't need vector files +[._]*.sw[a-p] +[._]s[a-rt-v][a-z] +[._]ss[a-gi-z] +[._]sw[a-p] + +# Session +Session.vim +Sessionx.vim + +# Temporary +.netrwhist +*~ +# Auto-generated tag files +tags +# Persistent undo +[._]*.un~ + +##### Emacs +# -*- mode: gitignore; -*- +*~ +\#*\# +/.emacs.desktop +/.emacs.desktop.lock +*.elc +auto-save-list +tramp +.\#* + +# Org-mode +.org-id-locations +*_archive + +# flymake-mode +*_flymake.* + +# eshell files +/eshell/history +/eshell/lastdir + +# elpa packages +/elpa/ + +# reftex files +*.rel + +# AUCTeX auto folder +/auto/ + +# cask packages +.cask/ +dist/ + +# Flycheck +flycheck_*.el + +# server auth directory +/server/ + +# projectiles files +.projectile + +# directory configuration +.dir-locals.el + +# network security +/network-security.data + +##### SublimeText +# Cache files for Sublime Text +*.tmlanguage.cache +*.tmPreferences.cache +*.stTheme.cache + +# Workspace files are user-specific +*.sublime-workspace + +# Project files should be checked into the repository, unless a significant +# proportion of contributors will probably not be using Sublime Text +# *.sublime-project + +# SFTP configuration file +sftp-config.json +sftp-config-alt*.json + +# Package control specific files +Package Control.last-run +Package Control.ca-list +Package Control.ca-bundle +Package Control.system-ca-bundle +Package Control.cache/ +Package Control.ca-certs/ +Package Control.merged-ca-bundle +Package Control.user-ca-bundle +oscrypto-ca-bundle.crt +bh_unicode_properties.cache + +# Sublime-github package stores a github token in this file +# https://packagecontrol.io/packages/sublime-github +GitHub.sublime-settings + +##### Notepad++ +# Notepad++ backups # +*.bak + +##### TextMate +*.tmproj +*.tmproject +tmtags + +##### VisualStudioCode +.vscode/* +!.vscode/settings.json +!.vscode/tasks.json +!.vscode/launch.json +!.vscode/extensions.json +*.code-workspace + +# Local History for Visual Studio Code +.history/ + +##### NetBeans +**/nbproject/private/ +**/nbproject/Makefile-*.mk +**/nbproject/Package-*.bash +build/ +nbbuild/ +dist/ +nbdist/ +.nb-gradle/ + +##### JetBrains +# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider +# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 + +# User-specific stuff +.idea/**/workspace.xml +.idea/**/tasks.xml +.idea/**/usage.statistics.xml +.idea/**/dictionaries +.idea/**/shelf + +# Generated files +.idea/**/contentModel.xml + +# Sensitive or high-churn files +.idea/**/dataSources/ +.idea/**/dataSources.ids +.idea/**/dataSources.local.xml +.idea/**/sqlDataSources.xml +.idea/**/dynamic.xml +.idea/**/uiDesigner.xml +.idea/**/dbnavigator.xml + +# Gradle +.idea/**/gradle.xml +.idea/**/libraries + +# Gradle and Maven with auto-import +# When using Gradle or Maven with auto-import, you should exclude module files, +# since they will be recreated, and may cause churn. Uncomment if using +# auto-import. +# .idea/artifacts +# .idea/compiler.xml +# .idea/jarRepositories.xml +# .idea/modules.xml +# .idea/*.iml +# .idea/modules +# *.iml +# *.ipr + +# CMake +cmake-build-*/ + +# Mongo Explorer plugin +.idea/**/mongoSettings.xml + +# File-based project format +*.iws + +# IntelliJ +out/ + +# mpeltonen/sbt-idea plugin +.idea_modules/ + +# JIRA plugin +atlassian-ide-plugin.xml + +# Cursive Clojure plugin +.idea/replstate.xml + +# Crashlytics plugin (for Android Studio and IntelliJ) +com_crashlytics_export_strings.xml +crashlytics.properties +crashlytics-build.properties +fabric.properties + +# Editor-based Rest Client +.idea/httpRequests + +# Android studio 3.1+ serialized cache file +.idea/caches/build_file_checksums.ser + +##### Eclipse +.metadata +bin/ +tmp/ +*.tmp +*.bak +*.swp +*~.nib +local.properties +.settings/ +.loadpath +.recommenders + +# External tool builders +.externalToolBuilders/ + +# Locally stored "Eclipse launch configurations" +*.launch + +# PyDev specific (Python IDE for Eclipse) +*.pydevproject + +# CDT-specific (C/C++ Development Tooling) +.cproject + +# CDT- autotools +.autotools + +# Java annotation processor (APT) +.factorypath + +# PDT-specific (PHP Development Tools) +.buildpath + +# sbteclipse plugin +.target + +# Tern plugin +.tern-project + +# TeXlipse plugin +.texlipse + +# STS (Spring Tool Suite) +.springBeans + +# Code Recommenders +.recommenders/ + +# Annotation Processing +.apt_generated/ +.apt_generated_test/ + +# Scala IDE specific (Scala & Java development for Eclipse) +.cache-main +.scala_dependencies +.worksheet + +# Uncomment this line if you wish to ignore the project description file. +# Typically, this file would be tracked if it contains build/dependency configurations: +#.project + +##### Qt +# C++ objects and libs +*.slo +*.lo +*.o +*.a +*.la +*.lai +*.so +*.so.* +*.dll +*.dylib + +# Qt-es +object_script.*.Release +object_script.*.Debug +*_plugin_import.cpp +/.qmake.cache +/.qmake.stash +*.pro.user +*.pro.user.* +*.qbs.user +*.qbs.user.* +*.moc +moc_*.cpp +moc_*.h +qrc_*.cpp +ui_*.h +*.qmlc +*.jsc +Makefile* +*build-* +*.qm +*.prl + +# Qt unit tests +target_wrapper.* + +# QtCreator +*.autosave + +# QtCreator Qml +*.qmlproject.user +*.qmlproject.user.* + +# QtCreator CMake +CMakeLists.txt.user* + +# QtCreator 4.8< compilation database +compile_commands.json + +# QtCreator local machine specific files for imported projects +*creator.user* + +##### VisualStudio +##### VisualStudio +## Ignore Visual Studio temporary files, build results, and +## files generated by popular Visual Studio add-ons. +## +## Get latest from https://github.com/github/gitignore/blob/master/VisualStudio.gitignore + +# User-specific files +*.rsuser +*.suo +*.user +*.userosscache +*.sln.docstates + +# User-specific files (MonoDevelop/Xamarin Studio) +*.userprefs + +# Mono auto generated files +mono_crash.* + +# Build results +[Dd]ebug/ +[Dd]ebugPublic/ +[Rr]elease/ +[Rr]eleases/ +x64/ +x86/ +[Ww][Ii][Nn]32/ +[Aa][Rr][Mm]/ +[Aa][Rr][Mm]64/ +bld/ +[Bb]in/ +[Oo]bj/ +[Ll]og/ +[Ll]ogs/ + +# Visual Studio 2015/2017 cache/options directory +.vs/ +# Uncomment if you have tasks that create the project's static files in wwwroot +#wwwroot/ + +# Visual Studio 2017 auto generated files +Generated\ Files/ + +# MSTest test Results +[Tt]est[Rr]esult*/ +[Bb]uild[Ll]og.* + +# NUnit +*.VisualState.xml +TestResult.xml +nunit-*.xml + +# Build Results of an ATL Project +[Dd]ebugPS/ +[Rr]eleasePS/ +dlldata.c + +# Benchmark Results +BenchmarkDotNet.Artifacts/ + +# .NET Core +project.lock.json +project.fragment.lock.json +artifacts/ + +# ASP.NET Scaffolding +ScaffoldingReadMe.txt + +# StyleCop +StyleCopReport.xml + +# Files built by Visual Studio +*_i.c +*_p.c +*_h.h +*.ilk +*.meta +*.obj +*.iobj +*.pch +*.pdb +*.ipdb +*.pgc +*.pgd +*.rsp +*.sbr +*.tlb +*.tli +*.tlh +*.tmp +*.tmp_proj +*_wpftmp.csproj +*.log +*.vspscc +*.vssscc +.builds +*.pidb +*.svclog +*.scc + +# Chutzpah Test files +_Chutzpah* + +# Visual C++ cache files +ipch/ +*.aps +*.ncb +*.opendb +*.opensdf +*.sdf +*.cachefile +*.VC.db +*.VC.VC.opendb + +# Visual Studio profiler +*.psess +*.vsp +*.vspx +*.sap + +# Visual Studio Trace Files +*.e2e + +# TFS 2012 Local Workspace +$tf/ + +# Guidance Automation Toolkit +*.gpState + +# ReSharper is a .NET coding add-in +_ReSharper*/ +*.[Rr]e[Ss]harper +*.DotSettings.user + +# TeamCity is a build add-in +_TeamCity* + +# DotCover is a Code Coverage Tool +*.dotCover + +# AxoCover is a Code Coverage Tool +.axoCover/* +!.axoCover/settings.json + +# Coverlet is a free, cross platform Code Coverage Tool +coverage*[.json, .xml, .info] + +# Visual Studio code coverage results +*.coverage +*.coveragexml + +# NCrunch +_NCrunch_* +.*crunch*.local.xml +nCrunchTemp_* + +# MightyMoose +*.mm.* +AutoTest.Net/ + +# Web workbench (sass) +.sass-cache/ + +# Installshield output folder +[Ee]xpress/ + +# DocProject is a documentation generator add-in +DocProject/buildhelp/ +DocProject/Help/*.HxT +DocProject/Help/*.HxC +DocProject/Help/*.hhc +DocProject/Help/*.hhk +DocProject/Help/*.hhp +DocProject/Help/Html2 +DocProject/Help/html + +# Click-Once directory +publish/ + +# Publish Web Output +*.[Pp]ublish.xml +*.azurePubxml +# Note: Comment the next line if you want to checkin your web deploy settings, +# but database connection strings (with potential passwords) will be unencrypted +*.pubxml +*.publishproj + +# Microsoft Azure Web App publish settings. Comment the next line if you want to +# checkin your Azure Web App publish settings, but sensitive information contained +# in these scripts will be unencrypted +PublishScripts/ + +# NuGet Packages +*.nupkg +# NuGet Symbol Packages +*.snupkg +# The packages folder can be ignored because of Package Restore +**/[Pp]ackages/* +# except build/, which is used as an MSBuild target. +!**/[Pp]ackages/build/ +# Uncomment if necessary however generally it will be regenerated when needed +#!**/[Pp]ackages/repositories.config +# NuGet v3's project.json files produces more ignorable files +*.nuget.props +*.nuget.targets + +# Microsoft Azure Build Output +csx/ +*.build.csdef + +# Microsoft Azure Emulator +ecf/ +rcf/ + +# Windows Store app package directories and files +AppPackages/ +BundleArtifacts/ +Package.StoreAssociation.xml +_pkginfo.txt +*.appx +*.appxbundle +*.appxupload + +# Visual Studio cache files +# files ending in .cache can be ignored +*.[Cc]ache +# but keep track of directories ending in .cache +!?*.[Cc]ache/ + +# Others +ClientBin/ +~$* +*~ +*.dbmdl +*.dbproj.schemaview +*.jfm +*.pfx +*.publishsettings +orleans.codegen.cs + +# Including strong name files can present a security risk +# (https://github.com/github/gitignore/pull/2483#issue-259490424) +#*.snk + +# Since there are multiple workflows, uncomment next line to ignore bower_components +# (https://github.com/github/gitignore/pull/1529#issuecomment-104372622) +#bower_components/ + +# RIA/Silverlight projects +Generated_Code/ + +# Backup & report files from converting an old project file +# to a newer Visual Studio version. Backup files are not needed, +# because we have git ;-) +_UpgradeReport_Files/ +Backup*/ +UpgradeLog*.XML +UpgradeLog*.htm +ServiceFabricBackup/ +*.rptproj.bak + +# SQL Server files +*.mdf +*.ldf +*.ndf + +# Business Intelligence projects +*.rdl.data +*.bim.layout +*.bim_*.settings +*.rptproj.rsuser +*- [Bb]ackup.rdl +*- [Bb]ackup ([0-9]).rdl +*- [Bb]ackup ([0-9][0-9]).rdl + +# Microsoft Fakes +FakesAssemblies/ + +# GhostDoc plugin setting file +*.GhostDoc.xml + +# Node.js Tools for Visual Studio +.ntvs_analysis.dat +node_modules/ + +# Visual Studio 6 build log +*.plg + +# Visual Studio 6 workspace options file +*.opt + +# Visual Studio 6 auto-generated workspace file (contains which files were open etc.) +*.vbw + +# Visual Studio LightSwitch build output +**/*.HTMLClient/GeneratedArtifacts +**/*.DesktopClient/GeneratedArtifacts +**/*.DesktopClient/ModelManifest.xml +**/*.Server/GeneratedArtifacts +**/*.Server/ModelManifest.xml +_Pvt_Extensions + +# Paket dependency manager +.paket/paket.exe +paket-files/ + +# FAKE - F# Make +.fake/ + +# CodeRush personal settings +.cr/personal + +# Python Tools for Visual Studio (PTVS) +__pycache__/ +*.pyc + +# Cake - Uncomment if you are using it +# tools/** +# !tools/packages.config + +# Tabs Studio +*.tss + +# Telerik's JustMock configuration file +*.jmconfig + +# BizTalk build output +*.btp.cs +*.btm.cs +*.odx.cs +*.xsd.cs + +# OpenCover UI analysis results +OpenCover/ + +# Azure Stream Analytics local run output +ASALocalRun/ + +# MSBuild Binary and Structured Log +*.binlog + +# NVidia Nsight GPU debugger configuration file +*.nvuser + +# MFractors (Xamarin productivity tool) working folder +.mfractor/ + +# Local History for Visual Studio +.localhistory/ + +# BeatPulse healthcheck temp database +healthchecksdb + +# Backup folder for Package Reference Convert tool in Visual Studio 2017 +MigrationBackup/ + +# Ionide (cross platform F# VS Code tools) working folder +.ionide/ + +# Fody - auto-generated XML schema +FodyWeavers.xsd + +##### Gradle +.gradle +**/build/ +!src/**/build/ + +# Ignore Gradle GUI config +gradle-app.setting + +# Avoid ignoring Gradle wrapper jar file (.jar files are usually ignored) +!gradle-wrapper.jar + +# Cache of project +.gradletasknamecache + +# # Work around https://youtrack.jetbrains.com/issue/IDEA-116898 +# gradle/wrapper/gradle-wrapper.properties + +##### CMake +CMakeLists.txt.user +CMakeCache.txt +CMakeFiles +CMakeScripts +Testing +Makefile +cmake_install.cmake +install_manifest.txt +compile_commands.json +CTestTestfile.cmake +_deps + +##### C++ +# Prerequisites +*.d + +# Compiled Object files +*.slo +*.lo +*.o +*.obj + +# Precompiled Headers +*.gch +*.pch + +# Compiled Dynamic libraries +*.so +*.dylib +*.dll + +# Fortran module files +*.mod +*.smod + +# Compiled Static libraries +*.lai +*.la +*.a +*.lib + +# Executables +*.exe +*.out +*.app + +# C/C++ binary extension file +*.bin + +##### C +# Prerequisites +*.d + +# Object files +*.o +*.ko +*.obj +*.elf + +# Linker output +*.ilk +*.map +*.exp + +# Precompiled Headers +*.gch +*.pch + +# Libraries +*.lib +*.a +*.la +*.lo + +# Shared objects (inc. Windows DLLs) +*.dll +*.so +*.so.* +*.dylib + +# Executables +*.exe +*.out +*.app +*.i*86 +*.x86_64 +*.hex + +# Debug files +*.dSYM/ +*.su +*.idb +*.pdb + +# Kernel Module Compile Results +*.mod* +*.cmd +.tmp_versions/ +modules.order +Module.symvers +Mkfile.old +dkms.conf + +# Raspberry Pi Pico Object file +*.uf2 +# Raspberry Pi Pico disassembler file +*.dis + +build +quake.egg-info diff --git a/CMakeLists.txt b/CMakeLists.txt index fa71a403..32121138 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -21,7 +21,7 @@ set(CMAKE_CXX_STANDARD 17) set(CMAKE_CXX_STANDARD_REQUIRED ON) # Compiler flags -set(CMAKE_CXX_FLAGS_DEBUG "-g") +set(CMAKE_CXX_FLAGS_DEBUG "-O3 -g") set(CMAKE_CXX_FLAGS_RELEASE "-O3") if(NOT DEFINED QUAKE_SET_ABI_MODE) set(QUAKE_SET_ABI_MODE ON) @@ -95,7 +95,7 @@ add_compile_options(-march=native) # Switch ABI mode if(QUAKE_SET_ABI_MODE) - add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0) + add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=1) endif() @@ -166,17 +166,23 @@ endif() target_link_libraries(${PROJECT_NAME} PUBLIC ${LINK_LIBS}) -IF(CMAKE_BUILD_TYPE MATCHES Debug AND QUAKE_USE_TSAN) +IF(QUAKE_USE_TSAN) message("Using thread sanitizer") set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsanitize=thread") set(CMAKE_MODULE_LINKER_FLAGS "{$CMAKE_MODULE_LINKER_FLAGS} -fsanitize=thread") -ENDIF(CMAKE_BUILD_TYPE MATCHES Debug AND QUAKE_USE_TSAN) +ENDIF(QUAKE_USE_TSAN) -IF(CMAKE_BUILD_TYPE MATCHES Debug AND QUAKE_USE_ASAN) +IF(QUAKE_USE_ASAN) message("Using address sanitizer") set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsanitize=address -fsanitize-recover=address") set(CMAKE_MODULE_LINKER_FLAGS "{$CMAKE_MODULE_LINKER_FLAGS} -fsanitize=address") -ENDIF(CMAKE_BUILD_TYPE MATCHES Debug AND QUAKE_USE_ASAN) +ENDIF(QUAKE_USE_ASAN) + +IF(QUAKE_USE_LSAN) + message("Using leak sanitizer") + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsanitize=leak") + set(CMAKE_MODULE_LINKER_FLAGS "{$CMAKE_MODULE_LINKER_FLAGS} -fsanitize=leak") +ENDIF(QUAKE_USE_LSAN) # --------------------------------------------------------------- # Python Bindings @@ -192,7 +198,7 @@ add_dependencies(bindings _bindings) # --------------------------------------------------------------- # Benchmarks # --------------------------------------------------------------- -file(GLOB BENCHMARK_SOURCES "src/cpp/test/benchmark/*.cpp") +file(GLOB BENCHMARK_SOURCES "scripts/cpp/benchmark/*.cpp") foreach(SOURCE ${BENCHMARK_SOURCES}) get_filename_component(EXECUTABLE_NAME ${SOURCE} NAME_WE) diff --git a/README.md b/README.md index 8bab54fd..e9c75c70 100644 --- a/README.md +++ b/README.md @@ -129,6 +129,14 @@ Quake’s Python API provides a set of core operations. The following sections d --- + +### Devesh Notes +Running Big ANN debug script: +``` +cmake -DCMAKE_PREFIX_PATH=$CONDA_PREFIX -DQUAKE_USE_NUMA=OFF -DQUAKE_USE_ASAN=OFF -DQUAKE_USE_AVX512=ON -DCMAKE_BUILD_TYPE=Release -DBUILD_TESTS=OFF -DQUAKE_SET_ABI_MODE=ON .. +make big_ann_debug -j$(nproc) && ./big_ann_debug &> batch_query_debug.out +``` + ## Further Documentation For detailed instructions and advanced usage, please see the documentation: http://marius-project.org/quake/ diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 00000000..275bd883 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,14 @@ +--extra-index-url https://download.pytorch.org/whl/cpu +ansicolors==1.1.8 +docker==7.1.0 +h5py==3.10.0 +matplotlib==3.3.4 +numpy==1.24.2 +pyyaml==6.0 +psutil==5.9.4 +scipy==1.10.1 +scikit-learn +jinja2==3.1.2 +pandas==2.0.0 +faiss-cpu +torch diff --git a/scripts/big_ann_perf_numbers/crash_debug_scan_0.12_no_aps_refinment_wma_delete.csv 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FormatStrFormatter + +SECONDS_TO_MS = 10.0 ** 3 +MIN_TO_SECONDS = 60 +MIN_TO_MS = MIN_TO_SECONDS * SECONDS_TO_MS + +SRC_DIR = "big_ann_perf_numbers" +DST_DIR = "bigann_perf_vis" + +NUM_OPERATIONS_TO_VISUALIZE = 1300 +def visualize_perf(src_dataset, config_details): + # Load CSV + df = pd.read_csv(os.path.join(SRC_DIR, src_dataset)) + df = df.head(NUM_OPERATIONS_TO_VISUALIZE) + df = df.fillna(0) + + # Filter rows for step_type == "search" + search_df = df[df['step_type'] == 'search'] + + # Extract data + x = search_df['step_num'].values + recall_mean = np.round(search_df['recall_mean'].values, 2) + latency_search = search_df['latency_ms'].values + + insert_df = df[df['step_type'] == 'insert'] + latency_insert = insert_df['latency_ms'].values + x_insert = insert_df['step_num'].values + + delete_df = df[df['step_type'] == 'delete'] + latency_delete = delete_df['latency_ms'].values + x_delete = delete_df['step_num'].values + + x_maintenance = df['step_num'].values + latency_maintenance = df['mainteance_ms'].values + + # Convert sums to minutes + total_search_min = latency_search.sum() / MIN_TO_MS + total_delete_min = latency_delete.sum() / MIN_TO_MS + total_insert_min = latency_insert.sum() / MIN_TO_MS + total_maintenance_min = latency_maintenance.sum() / MIN_TO_MS + total_all_min = total_search_min + total_delete_min + total_insert_min + total_maintenance_min + + # Create 3 rows x 2 columns subplots + fig, axs = plt.subplots(4, 2, figsize=(12, 12), sharex=True) + + # Row 0 + axs[0, 0].plot(x, recall_mean, color='green') + axs[0, 0].set_ylabel('Recall@10') + axs[0, 0].set_title('Recall Mean') + print("Average Recall for run", src_dataset, "is", np.mean(recall_mean)) + + axs[0, 1].plot(x, latency_search, color='purple') + axs[0, 1].set_ylabel('Latency (ms)') + axs[0, 1].set_title(f'Search Latency (Total: {total_search_min:.2f} min)') + + # Row 1 + axs[1, 0].plot(x_delete, latency_delete, color='red') + axs[1, 0].set_ylabel('Latency (ms)') + axs[1, 0].set_title(f'Delete Latency (Total: {total_delete_min:.2f} min)') + + axs[1, 1].plot(x_insert, latency_insert, color='orange') + axs[1, 1].set_ylabel('Latency (ms)') + axs[1, 1].set_title(f'Insert Latency (Total: {total_insert_min:.2f} min)') + + # Row 2 + axs[2, 0].plot(x_maintenance, latency_maintenance, color='blue') + axs[2, 0].set_xlabel('Step Num') + axs[2, 0].set_ylabel('Latency (ms)') + axs[2, 0].set_title(f'Maintenance Latency (Total: {total_maintenance_min:.2f} min)') + + axs[2, 0].plot(x_maintenance, latency_maintenance, color='blue') + + # Bottom right subplot: Vectors and Partitions over time + non_search_df = df[df['step_type'] != 'search'] + ax1 = axs[2, 1] + ax1.plot(non_search_df['step_num'].values, non_search_df['num_vectors'].values, color='olive', label='Num Vectors') + ax1.set_xlabel('Step Num') + ax1.set_ylabel('Num Vectors', color='olive') + ax1.tick_params(axis='y', labelcolor='olive') + + # Create second y-axis sharing the same x-axis + ax2 = ax1.twinx() + + # Plot num_partitions on right y-axis + ax2.plot(non_search_df['step_num'].values, non_search_df['num_partitions'].values, color='brown', label='Num Partitions') + ax2.set_ylabel('Num Partitions', color='brown') + ax2.tick_params(axis='y', labelcolor='brown') + + lines_1, labels_1 = ax1.get_legend_handles_labels() + lines_2, labels_2 = ax2.get_legend_handles_labels() + ax1.legend(lines_1 + lines_2, labels_1 + labels_2) + + axs[2, 1].set_title('Vector and Partition Count') + + # Plot the scan percentage needed to cover the ground truth vectors + axs[3, 0].plot(search_df['step_num'].values, search_df['gt_scan_mean'].values, color='green') + axs[3, 0].fill_between(search_df['step_num'].values, search_df['gt_scan_mean'].values - search_df['gt_scan_dev'].values, search_df['gt_scan_mean'].values + search_df['gt_scan_dev'].values, + color='green', alpha=0.3) + axs[3, 0].set_xlabel('Step Num') + axs[3, 0].set_ylabel('Partition Scan Percentage') + axs[3, 0].set_title('Rank of GT partitions as Search Candidates') + + # Plot Scan Throughput + axs[3, 1].plot(search_df['step_num'].values, search_df['worker_scan_throughput'].values, color='gray') + axs[3, 1].set_xlabel('Step Num') + axs[3, 1].set_ylabel('Scan Throughput (GB/s)') + axs[3, 1].set_title('Worker Scan Throughput') + + # Overall title including total latency sum in minutes + fig.suptitle(f'{config_details}\nTotal Latency: {total_all_min:.2f} minutes', fontsize=16) + + fig.tight_layout() + plt.savefig(os.path.join(DST_DIR, src_dataset.replace("csv", "png")), dpi=300) + +def visualize_percentage_variation(src_dataset): + # Load CSV + df = pd.read_csv(os.path.join(SRC_DIR, src_dataset)) + search_df = df[df['step_type'] == 'search'] + + # Get unique nprobe_percentage values + nprobe_values = sorted(search_df['nprobe_percentage'].unique()) + colors = plt.rcParams['axes.prop_cycle'].by_key()['color'] + fig, axes = plt.subplots(2, 1, figsize=(10, 8), sharex=True) + + # Top subplot: latency_ms vs step_num + for i, nprobe in enumerate(nprobe_values): + sub_df = search_df[search_df['nprobe_percentage'] == nprobe] + axes[0].plot(sub_df['step_num'].values, sub_df['latency_ms'].values, label=f'Factor Scanned: {nprobe}', color=colors[i]) + total_time_minutes = sub_df['latency_ms'].values.sum() / MIN_TO_MS + axes[0].set_ylabel('Latency (ms)') + axes[0].set_yscale('log') + axes[0].set_title('Search Latency over Step Number') + axes[0].legend() + + # Bottom left subplot: recall_mean vs step_num + for i, nprobe in enumerate(nprobe_values): + sub_df = search_df[search_df['nprobe_percentage'] == nprobe] + axes[1].plot(sub_df['step_num'].values, sub_df['recall_mean'].values, label=f'Factor Scanned: {nprobe}', color=colors[i]) + axes[1].set_xlabel('Step Number') + axes[1].set_ylabel('Recall Mean') + axes[1].set_title('Recall Mean over Step Number') + + plt.tight_layout() + plt.savefig(os.path.join(DST_DIR, src_dataset.replace("csv", "png")), dpi=300) + +def visualize_vary_worker(src_dataset): + # Read CSV + df = pd.read_csv(os.path.join(SRC_DIR, src_dataset)) + search_df = df[df['step_type'] == 'search'] + + # List of metrics to plot + metrics = [ + ("search_latency_ms", "Batch Search Latency (seconds)"), + ("worker_partition_size", "Avg Scan Partition Size"), + ("worker_scan_throughput", "Partition Scan Throughput (GB/s)"), + ("recall_mean", "Query Recall") + ] + + # Get unique worker counts for consistent colors + worker_counts = sorted(search_df['num_search_workers'].unique()) + colors = plt.get_cmap('tab10').colors # or use any other color map + + fig, axs = plt.subplots(2, 2, sharex=True, figsize=(12, 10)) + axs = axs.flatten() + + for idx, (metric, metric_label) in enumerate(metrics): + scale_factor = 1.0 + if metric == "search_latency_ms": + scale_factor = 1000.0 + + ax = axs[idx] + for i, worker in enumerate(worker_counts): + data = search_df[search_df['num_search_workers'] == worker].sort_values('step_num') + ax.plot(data['step_num'].values, data[metric].values/scale_factor, label=f'Num Search Workers: {worker}', color=colors[i % len(colors)]) + + ax.set_xlabel('Step Num') + ax.set_ylabel(metric_label) + ax.set_title(metric_label) + ax.legend() + + if metric == "search_latency_ms": + ax.set_yscale('log') + minor_locator = mticker.LogLocator(subs=np.arange(2, 10)) + ax.yaxis.set_minor_locator(minor_locator) + minor_formatter = mticker.FormatStrFormatter("%.1f") + ax.yaxis.set_minor_formatter(minor_formatter) + + formatter = ScalarFormatter() + formatter.set_scientific(False) + ax.yaxis.set_major_formatter(formatter) + + fig.suptitle(f'Vary Search Workers Experiemnt (Scan Percentage = 15%, Batch Size = 500)', fontsize=16) + + plt.tight_layout() + plt.savefig(os.path.join(DST_DIR, src_dataset.replace("csv", "png")), dpi=300) + +def visualize_vary_batch_size(src_dataset): + # Read CSV + df = pd.read_csv(os.path.join(SRC_DIR, src_dataset)) + search_df = df[df['step_type'] == 'search'] + search_df = search_df[search_df["step_num"] > 5] + + metric_names = [ + ('search_latency_ms', 'Search Latency (ms)'), + ('worker_partition_size', 'Partition Size'), + ('worker_scan_throughput', 'Worker Scan Throughput'), + ('worker_scan_time_ms', 'Worker Scan Time (ms)'), + ('worker_result_time_ms', 'Worker Result Write Time (ms)') + ] + colors = plt.get_cmap('tab10').colors # or use any other color map + + batch_sizes = list(search_df['batch_size'].unique()) + fig, axes = plt.subplots(2, 3, figsize=(16, 12), sharex=True) + axes = axes.flatten() + + for i, (col, title) in enumerate(metric_names): + + ax = axes[i] + for j, batch in enumerate(batch_sizes): + batch_data = search_df[search_df['batch_size'] == batch] + ax.plot(batch_data['step_num'].values, batch_data[col].values, label=f'Batch Size {batch}', color=colors[j % len(colors)]) + ax.set_title(title) + + ax.set_xlabel('Step Num') + ax.legend() + + fig.tight_layout() + plt.savefig(os.path.join(DST_DIR, src_dataset.replace("csv", "png")), dpi=300) + +def visualize_hardware_metrics(src_dataset): + # Read CSV + df = pd.read_csv(os.path.join(SRC_DIR, src_dataset)) + search_df = df[df['step_type'] == 'search'] + fig, axs = plt.subplots(2, 2, figsize=(12, 8), sharex=True) + + # 1) worker_partition_size versus step_num + axs[0, 0].plot(search_df["step_num"].values, search_df["worker_partition_size"].values) + axs[0, 0].set_title("Partition Size vs Step") + axs[0, 0].set_ylabel("Worker Partition Size") + + # 2) worker_scan_throughput versus step_num + axs[0, 1].plot(search_df["step_num"].values, search_df["worker_scan_throughput"].values, color="orange") + axs[0, 1].set_title("Scan Throughput vs Step") + axs[0, 1].set_ylabel("Scan Throughput") + + # 3) measured_ipc versus step_num + axs[1, 0].plot(search_df["step_num"].values, search_df["measured_ipc"].values, color="green") + axs[1, 0].set_title("Measured IPC vs Step") + axs[1, 0].set_xlabel("Step Num") + axs[1, 0].set_ylabel("Measured IPC") + + # 4) cache_miss_rate versus step_num + axs[1, 1].plot(search_df["step_num"].values, search_df["cache_miss_rate"].values, color="red") + axs[1, 1].set_title("Cache Miss Rate vs Step") + axs[1, 1].set_xlabel("Step Num") + axs[1, 1].set_ylabel("Cache Miss Rate") + + fig.suptitle(f'Batch Query Search with Hardware Counters (# of Workers = 8, Batch Size = 256)', fontsize=16) + + fig.tight_layout() + plt.savefig(os.path.join(DST_DIR, src_dataset.replace("csv", "png")), dpi=300) + +if __name__ == "__main__": + ''' + # visualize_vary_worker("perf_debug_scan_0.15_vary_num_search_workers.csv") + # visualize_vary_batch_size("perf_debug_scan_0.15_vary_batch_size.csv") + visualize_hardware_metrics("perf_debug_scan_0.15_counters.csv") + ''' + + configs_to_visualize = [ + ("test_perf_debug_scan_0.14_worker_batch_tuning.csv", "Scan Percentage = 14%, Query Batch Size = 256, Partition Chunk Size = 256"), + ("perf_debug_scan_0.14_worker_batch_tuning_lower.csv", "Scan Percentage = 14%, Query Batch Size = 256, Partition Chunk Size = 128") + ] + + for dataset, config_details in 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