From 9c7d71a33ec4236d67d33a945629768ef818a4ea Mon Sep 17 00:00:00 2001 From: lkuffo Date: Mon, 23 Feb 2026 11:40:17 +0100 Subject: [PATCH 01/39] Bye BOND and NARY --- .gitignore | 15 -- BENCHMARKING.md | 8 - CMakeLists.txt | 16 +- README.md | 2 +- benchmarks/CMakeLists.txt | 7 - .../bench_adsampling/pdx_ivf2_adsampling.cpp | 2 +- .../pdx_ivf2_adsampling_u8.cpp | 2 +- .../pdx_ivf2_adsampling_u8_filtered.cpp | 2 +- .../bench_adsampling/pdx_ivf_adsampling.cpp | 2 +- .../pdx_ivf_adsampling_filtered.cpp | 2 +- .../pdx_ivf_adsampling_u8.cpp | 2 +- benchmarks/bench_bond/pdx_bond.cpp | 108 --------- benchmarks/bench_bond/pdx_bond_ivf.cpp | 126 ---------- benchmarks/python_scripts/exact_faiss.py | 41 ---- benchmarks/python_scripts/exact_usearch.py | 39 --- benchmarks/python_scripts/ivf_lorann.py | 113 --------- benchmarks/python_scripts/setup_adsampling.py | 8 +- benchmarks/python_scripts/setup_bond.py | 91 ------- benchmarks/python_scripts/setup_data.py | 21 +- benchmarks/python_scripts/setup_settings.py | 4 - benchmarks/python_scripts/setup_utils.py | 2 - .../results/DEFAULT/IVF_PDX_ADSAMPLING.csv | 11 + .../results/DEFAULT/U8_IVF_PDX_ADSAMPLING.csv | 10 + examples/README.md | 1 - examples/pdx_noindex_bond.py | 54 ----- examples/pdx_simple.py | 2 +- include/lib/lib.hpp | 224 ------------------ include/pdxearch.hpp | 5 - include/pruners/bond.hpp | 42 ---- include/utils/benchmark_utils.hpp | 2 - python/lib.cpp | 19 -- python/pdxearch/constants.py | 2 +- python/pdxearch/index_factory.py | 115 --------- 33 files changed, 48 insertions(+), 1052 deletions(-) delete mode 100644 benchmarks/bench_bond/pdx_bond.cpp delete mode 100644 benchmarks/bench_bond/pdx_bond_ivf.cpp delete mode 100644 benchmarks/python_scripts/exact_faiss.py delete mode 100644 benchmarks/python_scripts/exact_usearch.py delete mode 100644 benchmarks/python_scripts/ivf_lorann.py delete mode 100644 benchmarks/python_scripts/setup_bond.py create mode 100644 benchmarks/results/DEFAULT/IVF_PDX_ADSAMPLING.csv create mode 100644 benchmarks/results/DEFAULT/U8_IVF_PDX_ADSAMPLING.csv delete mode 100644 examples/pdx_noindex_bond.py delete mode 100644 include/pruners/bond.hpp diff --git a/.gitignore b/.gitignore index 7235362..7ff288c 100644 --- a/.gitignore +++ b/.gitignore @@ -62,10 +62,8 @@ pdxearch.egg-info /benchmarks/core_indexes/faiss_l0/* !/benchmarks/core_indexes/faiss_l0/*.json -/benchmarks/datasets/adsampling_nary /benchmarks/datasets/adsampling_pdx /benchmarks/datasets/downloaded -/benchmarks/datasets/nary /benchmarks/datasets/pdx /benchmarks/datasets/purescan /benchmarks/datasets/queries @@ -91,25 +89,12 @@ cmake_install.cmake /benchmarks/milvus/volumes/ /benchmarks/python_scripts/indexes -/benchmarks/BenchmarkNaryIVFADSampling -/benchmarks/BenchmarkNaryIVFADSamplingSIMD /benchmarks/BenchmarkPDXADSampling /benchmarks/BenchmarkIVF2ADSampling /benchmarks/FilteredBenchmarkPDXADSampling /benchmarks/FilteredBenchmarkU8IVF2ADSampling -/benchmarks/BenchmarkASYM_U8PDXADSampling /benchmarks/BenchmarkU8PDXADSampling -/benchmarks/BenchmarkLEP8PDXADSampling -/benchmarks/BenchmarkPDXIVFBOND -/benchmarks/BenchmarkPDXBOND /benchmarks/BenchmarkPDXLinearScan /benchmarks/BenchmarkU* /benchmarks/G4* -/benchmarks/BenchmarkNaryIVFLinearScan -/benchmarks/KernelPDXL1 -/benchmarks/KernelPDXL2 -/benchmarks/KernelPDXIP -/benchmarks/KernelNaryL1 -/benchmarks/KernelNaryL2 -/benchmarks/KernelNaryIP /benchmarks/BenchmarkU8* \ No newline at end of file diff --git a/BENCHMARKING.md b/BENCHMARKING.md index 5a66415..06de31e 100644 --- a/BENCHMARKING.md +++ b/BENCHMARKING.md @@ -67,21 +67,16 @@ On the [/benchmarks/CMakeLists.txt](/benchmarks/CMakeLists.txt) file, you can fi - PDX IVF ADSampling + SQ8: `/benchmarks/BenchmarkU8PDXADSampling` - PDX Two-Level IVF ADSampling: `/benchmarks/BenchmarkIVF2ADSampling` - PDX Two Level IVF ADSampling + SQ8: `/benchmarks/BenchmarkU8IVF2ADSampling` -- PDX IVF BOND: `/benchmarks/BenchmarkPDXIVFBOND` - FAISS IVF: `/benchmarks/python_scripts/ivf_faiss.py` All of these programs have two optional parameters: - `` to specify the name of the dataset to use. If not given, it will try to use all the datasets set in [benchmark_utils.hpp](/include/utils/benchmark_utils.hpp) or [benchmark_utils.py](/benchmarks/python_scripts/benchmark_utils.py) in the Python scripts. - `` to specify the `nprobe` parameter on the IVF index, which controls the recall. If not given or `0`, it will use a series of parameters from 2 to 4096 set in the [benchmark_utils.hpp](/include/utils/benchmark_utils.hpp) or [benchmark_utils.py](/benchmarks/python_scripts/benchmark_utils.py) in the Python scripts. -PDX IVF BOND has an additional third parameter: -- ``: An integer value. On Intel SPR, we use distance-to-means (`1`). For the other microarchitectures, we use dimension-zones (`5`). Refer to Figure 5 of [our publication](https://ir.cwi.nl/pub/35044/35044.pdf). - > [!IMPORTANT] > Recall that the IVF indexes must be created beforehand by the `setup_data.py` script. ### Exact Search -- PDX BOND: ```/benchmarks/BenchmarkPDXBOND``` - USearch: ```python /benchmarks/python_scripts/exact_usearch.py``` - SKLearn: ```python /benchmarks/python_scripts/exact_sklearn.py``` - FAISS: ```python /benchmarks/python_scripts/exact_faiss.py``` @@ -89,9 +84,6 @@ PDX IVF BOND has an additional third parameter: All of these programs have one optional parameter: - `` to specify the name of the dataset to use. If not given, it will try to use all the datasets set in [benchmark_utils.hpp](/include/utils/benchmark_utils.hpp) or [benchmark_utils.py](/benchmarks/python_scripts/benchmark_utils.py) in the Python scripts. -PDX BOND has an additional second parameter: -- ``: An integer value. On exact-search, we always use distance-to-means (`1`). Refer to Figure 5 of [our publication](https://ir.cwi.nl/pub/35044/35044.pdf). - **Notes**: Usearch, SKLearn, and FAISS scripts expect the original `.hdf5` files under the `/downloaded` directory. Furthermore, they require their respective Python packages (`pip install -r ./benchmarks/python_scripts/requirements.txt`). ## Output diff --git a/CMakeLists.txt b/CMakeLists.txt index c8d1d3b..2b3fd0b 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -10,13 +10,13 @@ include(CTest) # include(ExternalProject) set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "extern/findFFTW") -find_package(FFTW QUIET) -if (FFTW_FLOAT_LIB_FOUND) - message(STATUS "FFTW (+float capabilities) found " ${FFTW_INCLUDE_DIRS}) - add_definitions(-DHAS_FFTW) - include_directories(${FFTW_INCLUDE_DIRS}) -else() - message(WARNING "FFTW (+float capabilities) not found, we recommend you install it") +#find_package(FFTW QUIET) +#if (FFTW_FLOAT_LIB_FOUND) +# message(STATUS "FFTW (+float capabilities) found " ${FFTW_INCLUDE_DIRS}) +# add_definitions(-DHAS_FFTW) +# include_directories(${FFTW_INCLUDE_DIRS}) +#else() +# message(WARNING "FFTW (+float capabilities) not found, we recommend you install it") # TODO: Perhaps downloading? # set(FFTW_INSTALL_DIR ${CMAKE_BINARY_DIR}/fftw-install) # ExternalProject_Add(fftw @@ -30,7 +30,7 @@ else() # set(FFTW_FLOAT_LIB ${FFTW_INSTALL_DIR}/lib/libfftw3f.so) # set(FFTW_FOUND TRUE) # add_definitions(-DHAS_FFTW) -endif() +#endif() # Installing FFTW https://www.fftw.org/fftw3_doc/Installation-on-Unix.html --------------------------------------------- # wget https://www.fftw.org/fftw-3.3.10.tar.gz diff --git a/README.md b/README.md index d076894..6471f1d 100644 --- a/README.md +++ b/README.md @@ -117,7 +117,7 @@ An exhaustive search scans all the vectors in the collection. Having an IVF inde The key observation here is that thanks to the underlying IVF index, the exhaustive search starts with the most promising clusters. A tight threshold is found early on, which enables the quick pruning of most candidates. ### Exhaustive search without an index -By creating random clusters with the PDX layout, you can still accelerate exhaustive search without an index. Unlike ADSampling, with BOND (our pruning algorithm), you can use the raw vectors. Gains vary depending on the distribution of the data. +By creating random clusters with the PDX layout, you can still accelerate exhaustive search without an index.

PDX Layout diff --git a/benchmarks/CMakeLists.txt b/benchmarks/CMakeLists.txt index 10506d2..df5bd36 100644 --- a/benchmarks/CMakeLists.txt +++ b/benchmarks/CMakeLists.txt @@ -1,4 +1,3 @@ -# ADSampling add_executable(BenchmarkPDXADSampling bench_adsampling/pdx_ivf_adsampling.cpp) add_executable(BenchmarkIVF2ADSampling bench_adsampling/pdx_ivf2_adsampling.cpp) add_executable(BenchmarkU8PDXADSampling bench_adsampling/pdx_ivf_adsampling_u8.cpp) @@ -6,10 +5,6 @@ add_executable(BenchmarkU8IVF2ADSampling bench_adsampling/pdx_ivf2_adsampling_u8 add_executable(FilteredBenchmarkU8IVF2ADSampling bench_adsampling/pdx_ivf2_adsampling_u8_filtered.cpp) add_executable(FilteredBenchmarkPDXADSampling bench_adsampling/pdx_ivf_adsampling_filtered.cpp) -# BOND -add_executable(BenchmarkPDXIVFBOND ./bench_bond/pdx_bond_ivf.cpp) -add_executable(BenchmarkPDXBOND ./bench_bond/pdx_bond.cpp) - if (FFTW_FOUND) target_link_libraries(BenchmarkPDXADSampling ${FFTW_FLOAT_LIB}) target_link_libraries(FilteredBenchmarkPDXADSampling ${FFTW_FLOAT_LIB}) @@ -17,6 +12,4 @@ if (FFTW_FOUND) target_link_libraries(BenchmarkU8PDXADSampling ${FFTW_FLOAT_LIB}) target_link_libraries(BenchmarkU8IVF2ADSampling ${FFTW_FLOAT_LIB}) target_link_libraries(FilteredBenchmarkU8IVF2ADSampling ${FFTW_FLOAT_LIB}) - target_link_libraries(BenchmarkPDXIVFBOND ${FFTW_FLOAT_LIB}) - target_link_libraries(BenchmarkPDXBOND ${FFTW_FLOAT_LIB}) endif() diff --git a/benchmarks/bench_adsampling/pdx_ivf2_adsampling.cpp b/benchmarks/bench_adsampling/pdx_ivf2_adsampling.cpp index da05572..00ff70b 100644 --- a/benchmarks/bench_adsampling/pdx_ivf2_adsampling.cpp +++ b/benchmarks/bench_adsampling/pdx_ivf2_adsampling.cpp @@ -46,7 +46,7 @@ int main(int argc, char *argv[]) { PDX::IndexPDXIVF2 pdx_data = PDX::IndexPDXIVF2(); pdx_data.Restore(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf2"); - std::unique_ptr _matrix_ptr = MmapFile(BenchmarkUtils::NARY_ADSAMPLING_DATA + dataset + "-ivf2-matrix"); + std::unique_ptr _matrix_ptr = MmapFile(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf2-matrix"); auto *_matrix = reinterpret_cast(_matrix_ptr.get()); std::unique_ptr query_ptr = MmapFile(BenchmarkUtils::QUERIES_DATA + dataset); diff --git a/benchmarks/bench_adsampling/pdx_ivf2_adsampling_u8.cpp b/benchmarks/bench_adsampling/pdx_ivf2_adsampling_u8.cpp index dad758d..d90d7e9 100644 --- a/benchmarks/bench_adsampling/pdx_ivf2_adsampling_u8.cpp +++ b/benchmarks/bench_adsampling/pdx_ivf2_adsampling_u8.cpp @@ -45,7 +45,7 @@ int main(int argc, char *argv[]) { } PDX::IndexPDXIVF2 pdx_data = PDX::IndexPDXIVF2(); pdx_data.Restore(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf2-u8"); - std::unique_ptr _matrix_ptr = MmapFile(BenchmarkUtils::NARY_ADSAMPLING_DATA + dataset + "-ivf2-u8-matrix"); + std::unique_ptr _matrix_ptr = MmapFile(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf2-u8-matrix"); auto *_matrix = reinterpret_cast(_matrix_ptr.get()); std::unique_ptr query_ptr = MmapFile(BenchmarkUtils::QUERIES_DATA + dataset); diff --git a/benchmarks/bench_adsampling/pdx_ivf2_adsampling_u8_filtered.cpp b/benchmarks/bench_adsampling/pdx_ivf2_adsampling_u8_filtered.cpp index e65f246..e65d24e 100644 --- a/benchmarks/bench_adsampling/pdx_ivf2_adsampling_u8_filtered.cpp +++ b/benchmarks/bench_adsampling/pdx_ivf2_adsampling_u8_filtered.cpp @@ -52,7 +52,7 @@ int main(int argc, char *argv[]) { } PDX::IndexPDXIVF2 pdx_data = PDX::IndexPDXIVF2(); pdx_data.Restore(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf2-u8"); - std::unique_ptr _matrix_ptr = MmapFile(BenchmarkUtils::NARY_ADSAMPLING_DATA + dataset + "-ivf2-u8-matrix"); + std::unique_ptr _matrix_ptr = MmapFile(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf2-u8-matrix"); auto *_matrix = reinterpret_cast(_matrix_ptr.get()); std::unique_ptr query_ptr = MmapFile(BenchmarkUtils::QUERIES_DATA + dataset); diff --git a/benchmarks/bench_adsampling/pdx_ivf_adsampling.cpp b/benchmarks/bench_adsampling/pdx_ivf_adsampling.cpp index 0b62a05..0b71da8 100644 --- a/benchmarks/bench_adsampling/pdx_ivf_adsampling.cpp +++ b/benchmarks/bench_adsampling/pdx_ivf_adsampling.cpp @@ -47,7 +47,7 @@ int main(int argc, char *argv[]) { PDX::IndexPDXIVF pdx_data = PDX::IndexPDXIVF(); pdx_data.Restore(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf"); - std::unique_ptr _matrix_ptr = MmapFile(BenchmarkUtils::NARY_ADSAMPLING_DATA + dataset + "-matrix"); + std::unique_ptr _matrix_ptr = MmapFile(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-matrix"); auto *_matrix = reinterpret_cast(_matrix_ptr.get()); std::unique_ptr query_ptr = MmapFile(BenchmarkUtils::QUERIES_DATA + dataset); diff --git a/benchmarks/bench_adsampling/pdx_ivf_adsampling_filtered.cpp b/benchmarks/bench_adsampling/pdx_ivf_adsampling_filtered.cpp index b04401c..1ea749b 100644 --- a/benchmarks/bench_adsampling/pdx_ivf_adsampling_filtered.cpp +++ b/benchmarks/bench_adsampling/pdx_ivf_adsampling_filtered.cpp @@ -52,7 +52,7 @@ int main(int argc, char *argv[]) { PDX::IndexPDXIVF pdx_data = PDX::IndexPDXIVF(); pdx_data.Restore(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf"); - std::unique_ptr _matrix_ptr = MmapFile(BenchmarkUtils::NARY_ADSAMPLING_DATA + dataset + "-matrix"); + std::unique_ptr _matrix_ptr = MmapFile(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-matrix"); auto *_matrix = reinterpret_cast(_matrix_ptr.get()); std::unique_ptr query_ptr = MmapFile(BenchmarkUtils::QUERIES_DATA + dataset); diff --git a/benchmarks/bench_adsampling/pdx_ivf_adsampling_u8.cpp b/benchmarks/bench_adsampling/pdx_ivf_adsampling_u8.cpp index c8c779f..2c2cd24 100644 --- a/benchmarks/bench_adsampling/pdx_ivf_adsampling_u8.cpp +++ b/benchmarks/bench_adsampling/pdx_ivf_adsampling_u8.cpp @@ -45,7 +45,7 @@ int main(int argc, char *argv[]) { } PDX::IndexPDXIVF pdx_data = PDX::IndexPDXIVF(); pdx_data.Restore(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf-u8"); - std::unique_ptr _matrix_ptr = MmapFile(BenchmarkUtils::NARY_ADSAMPLING_DATA + dataset + "-ivf-u8-matrix"); + std::unique_ptr _matrix_ptr = MmapFile(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf-u8-matrix"); auto *_matrix = reinterpret_cast(_matrix_ptr.get()); std::unique_ptr query_ptr = MmapFile(BenchmarkUtils::QUERIES_DATA + dataset); diff --git a/benchmarks/bench_bond/pdx_bond.cpp b/benchmarks/bench_bond/pdx_bond.cpp deleted file mode 100644 index 97330d9..0000000 --- a/benchmarks/bench_bond/pdx_bond.cpp +++ /dev/null @@ -1,108 +0,0 @@ -#ifndef BENCHMARK_TIME -#define BENCHMARK_TIME = true -#endif - -#ifndef PDX_USE_EXPLICIT_SIMD -#define PDX_USE_EXPLICIT_SIMD = true -#endif - -#include -#include -#include "utils/file_reader.hpp" -#include "index_base/pdx_ivf.hpp" -#include "pruners/bond.hpp" -#include "pdxearch.hpp" -#include "utils/benchmark_utils.hpp" - -int main(int argc, char *argv[]) { - std::string arg_dataset; - PDX::DimensionsOrder DIMENSION_ORDER = PDX::DISTANCE_TO_MEANS_IMPROVED; - DIMENSION_ORDER = PDX::DISTANCE_TO_MEANS_IMPROVED; - std::string ALGORITHM = "pdx-bond"; - if (argc > 1){ - arg_dataset = argv[1]; - } - if (argc > 2){ - DIMENSION_ORDER = static_cast(atoi(argv[2])); - if (DIMENSION_ORDER == PDX::DISTANCE_TO_MEANS_IMPROVED){ - ALGORITHM = "pdx-bond"; - } - else if (DIMENSION_ORDER == PDX::DISTANCE_TO_MEANS){ - ALGORITHM = "pdx-bond-dtm"; - } - else if (DIMENSION_ORDER == PDX::DECREASING_IMPROVED){ - ALGORITHM = "pdx-bond-dec"; - } - else if (DIMENSION_ORDER == PDX::DECREASING){ - ALGORITHM = "pdx-bond-dec"; - } - else if (DIMENSION_ORDER == PDX::SEQUENTIAL){ - ALGORITHM = "pdx-bond-sec"; - } - else if (DIMENSION_ORDER == PDX::DIMENSION_ZONES){ - ALGORITHM = "pdx-bond-dz"; - } - } - std::cout << "==> PDX BOND EXACT\n"; - - const bool VERIFY_RESULTS = BenchmarkUtils::VERIFY_RESULTS; - - uint8_t KNN = BenchmarkUtils::KNN; - float SELECTIVITY_THRESHOLD = BenchmarkUtils::SELECTIVITY_THRESHOLD; - size_t NUM_QUERIES; - size_t NUM_MEASURE_RUNS = BenchmarkUtils::NUM_MEASURE_RUNS; - - std::string RESULTS_PATH = BENCHMARK_UTILS.RESULTS_DIR_PATH + "EXACT_PDX_BOND.csv"; - - for (const auto & dataset : BenchmarkUtils::DATASETS) { - if (arg_dataset.size() > 0 && arg_dataset != dataset){ - continue; - } - PDX::IndexPDXIVF pdx_data = PDX::IndexPDXIVF(); - - pdx_data.Restore(BenchmarkUtils::PDX_DATA + dataset + "-flat"); - std::unique_ptr query_ptr = MmapFile(BenchmarkUtils::QUERIES_DATA + dataset); - auto *query = reinterpret_cast(query_ptr.get()); - - NUM_QUERIES = 1000; - std::unique_ptr ground_truth = MmapFile(BenchmarkUtils::GROUND_TRUTH_DATA + dataset + "_100_norm"); - auto *int_ground_truth = reinterpret_cast(ground_truth.get()); - query += 1; // skip number of embeddings - - PDX::IndexPDXIVF nary_data = PDX::IndexPDXIVF(); - - std::vector runtimes; - runtimes.resize(NUM_MEASURE_RUNS * NUM_QUERIES); - - auto pruner = PDX::BondPruner(pdx_data.num_dimensions); - PDX::PDXearch searcher = PDX::PDXearch(pdx_data, pruner, 0, DIMENSION_ORDER); - - float recalls = 0; - if (VERIFY_RESULTS){ - for (size_t l = 0; l < NUM_QUERIES; ++l) { - auto result = searcher.Search(query + l * pdx_data.num_dimensions, KNN); - BenchmarkUtils::VerifyResult(recalls, result, KNN, int_ground_truth, l); - } - } - for (size_t j = 0; j < NUM_MEASURE_RUNS; ++j) { - for (size_t l = 0; l < NUM_QUERIES; ++l) { - searcher.Search(query + l * pdx_data.num_dimensions, KNN); - runtimes[j + l * NUM_MEASURE_RUNS] = { - searcher.end_to_end_clock.accum_time - }; - } - } - float real_selectivity = 1 - SELECTIVITY_THRESHOLD; - BenchmarkMetadata results_metadata = { - dataset, - ALGORITHM, - NUM_MEASURE_RUNS, - NUM_QUERIES, - 0, - KNN, - recalls, - real_selectivity, - }; - BenchmarkUtils::SaveResults(runtimes, RESULTS_PATH, results_metadata); - } -} diff --git a/benchmarks/bench_bond/pdx_bond_ivf.cpp b/benchmarks/bench_bond/pdx_bond_ivf.cpp deleted file mode 100644 index 53c87ad..0000000 --- a/benchmarks/bench_bond/pdx_bond_ivf.cpp +++ /dev/null @@ -1,126 +0,0 @@ -#ifndef BENCHMARK_TIME -#define BENCHMARK_TIME = true -#endif - -#ifndef PDX_USE_EXPLICIT_SIMD -#define PDX_USE_EXPLICIT_SIMD = true -#endif - -#include -#include -#include "utils/file_reader.hpp" -#include "index_base/pdx_ivf.hpp" -#include "pruners/bond.hpp" -#include "pdxearch.hpp" -#include "utils/benchmark_utils.hpp" - -int main(int argc, char *argv[]) { - std::string arg_dataset; - size_t arg_ivf_nprobe = 0; - std::string ALGORITHM = "pdx-bond"; - PDX::DimensionsOrder DIMENSION_ORDER = PDX::SEQUENTIAL; - DIMENSION_ORDER = PDX::DIMENSION_ZONES; - if (argc > 1){ - arg_dataset = argv[1]; - } - if (argc > 2){ - arg_ivf_nprobe = atoi(argv[2]); - } - if (argc > 3){ - // enum PDXearchDimensionsOrder { - // SEQUENTIAL, - // DISTANCE_TO_MEANS, - // DECREASING, - // DISTANCE_TO_MEANS_IMPROVED, - // DECREASING_IMPROVED, - // DIMENSION_ZONES - // }; - DIMENSION_ORDER = static_cast(atoi(argv[3])); - ALGORITHM = "pdx-bond"; - if (DIMENSION_ORDER == PDX::DISTANCE_TO_MEANS){ - ALGORITHM = "pdx-bond-dtm"; - } - else if (DIMENSION_ORDER == PDX::DIMENSION_ZONES){ - ALGORITHM = "pdx-bond-dz"; - } - - } - std::cout << "==> PDX IVF BOND\n"; - - const bool VERIFY_RESULTS = BenchmarkUtils::VERIFY_RESULTS; - - uint8_t KNN = BenchmarkUtils::KNN; - float SELECTIVITY_THRESHOLD = BenchmarkUtils::SELECTIVITY_THRESHOLD; - size_t NUM_QUERIES; - size_t NUM_MEASURE_RUNS = BenchmarkUtils::NUM_MEASURE_RUNS; - - std::string RESULTS_PATH = BENCHMARK_UTILS.RESULTS_DIR_PATH + "IVF_PDX_BOND.csv"; - - - for (const auto & dataset : BenchmarkUtils::DATASETS) { - if (arg_dataset.size() > 0 && arg_dataset != dataset){ - continue; - } - PDX::IndexPDXIVF pdx_data = PDX::IndexPDXIVF(); - - pdx_data.Restore(BenchmarkUtils::PDX_DATA + dataset + "-ivf"); - - std::unique_ptr query_ptr = MmapFile(BenchmarkUtils::QUERIES_DATA + dataset); - auto *query = reinterpret_cast(query_ptr.get()); - - NUM_QUERIES = 1000; - std::unique_ptr ground_truth = MmapFile(BenchmarkUtils::GROUND_TRUTH_DATA + dataset + "_100_norm"); - auto *int_ground_truth = reinterpret_cast(ground_truth.get()); - query += 1; // skip number of embeddings - - auto pruner = PDX::BondPruner(pdx_data.num_dimensions); - PDX::PDXearch searcher = PDX::PDXearch(pdx_data, pruner, 0, DIMENSION_ORDER); - - std::vector nprobes_to_use; - if (arg_ivf_nprobe > 0) { - nprobes_to_use = {arg_ivf_nprobe}; - } else { - nprobes_to_use.assign(std::begin(BenchmarkUtils::IVF_PROBES), std::end(BenchmarkUtils::IVF_PROBES)); - } - - for (size_t ivf_nprobe : nprobes_to_use) { - if (pdx_data.num_clusters < ivf_nprobe) { - continue; - } - if (arg_ivf_nprobe > 0 && ivf_nprobe != arg_ivf_nprobe){ - continue; - } - std::vector runtimes; - runtimes.resize(NUM_MEASURE_RUNS * NUM_QUERIES); - searcher.SetNProbe(ivf_nprobe); - - float recalls = 0; - if (VERIFY_RESULTS){ - for (size_t l = 0; l < NUM_QUERIES; ++l) { - auto result = searcher.Search(query + l * pdx_data.num_dimensions, KNN); - BenchmarkUtils::VerifyResult(recalls, result, KNN, int_ground_truth, l); - } - } - for (size_t j = 0; j < NUM_MEASURE_RUNS; ++j) { - for (size_t l = 0; l < NUM_QUERIES; ++l) { - searcher.Search(query + l * pdx_data.num_dimensions, KNN); - runtimes[j + l * NUM_MEASURE_RUNS] = { - searcher.end_to_end_clock.accum_time - }; - } - } - float real_selectivity = 1 - SELECTIVITY_THRESHOLD; - BenchmarkMetadata results_metadata = { - dataset, - ALGORITHM, - NUM_MEASURE_RUNS, - NUM_QUERIES, - ivf_nprobe, - KNN, - recalls, - real_selectivity, - }; - BenchmarkUtils::SaveResults(runtimes, RESULTS_PATH, results_metadata); - } - } -} diff --git a/benchmarks/python_scripts/exact_faiss.py b/benchmarks/python_scripts/exact_faiss.py deleted file mode 100644 index ece1462..0000000 --- a/benchmarks/python_scripts/exact_faiss.py +++ /dev/null @@ -1,41 +0,0 @@ -import sys -from benchmark_utils import * -from setup_utils import * -from setup_settings import * -from WrapperBruteForce import BruteForceFAISS - -disable_multithreading() - -if __name__ == '__main__': - RESULTS_PATH = os.path.join(RESULTS_DIRECTORY, "EXACT_FAISS.csv") - arg_dataset = "" - if len(sys.argv) > 1: - arg_dataset = sys.argv[1] - for dataset in DATASETS: - if len(arg_dataset) and dataset != arg_dataset: - continue - runtimes = [] - clock = TicToc() - - data, queries = read_hdf5_data(dataset) - data = np.ascontiguousarray(data) - - searcher = BruteForceFAISS("euclidean", len(data[0])) - searcher.fit(data) - - for _ in range(N_MEASURE_RUNS): - for q in queries[:100]: - q = np.ascontiguousarray(q) - clock.tic() - searcher.query(q, KNN) - runtimes.append(clock.toc()) - - metadata = { - 'dataset': dataset, - 'n_queries': len(queries), - 'algorithm': 'faiss', - 'recall': 1.0 - } - save_results(runtimes, RESULTS_PATH, metadata) - - diff --git a/benchmarks/python_scripts/exact_usearch.py b/benchmarks/python_scripts/exact_usearch.py deleted file mode 100644 index 6f25e2d..0000000 --- a/benchmarks/python_scripts/exact_usearch.py +++ /dev/null @@ -1,39 +0,0 @@ -import sys -from benchmark_utils import * -from setup_utils import * -from setup_settings import * -from WrapperBruteForce import BruteForceUsearch, BruteForceSKLearn - -disable_multithreading() - -if __name__ == '__main__': - RESULTS_PATH = os.path.join(RESULTS_DIRECTORY, "EXACT_USEARCH.csv") - arg_dataset = "" - if len(sys.argv) > 1: - arg_dataset = sys.argv[1] - for dataset in DATASETS: - if len(arg_dataset) and dataset != arg_dataset: - continue - runtimes = [] - clock = TicToc() - - data, queries = read_hdf5_data(dataset) - data = np.ascontiguousarray(data) - - searcher = BruteForceUsearch("euclidean") - searcher_gt = BruteForceSKLearn("euclidean") - for _ in range(N_MEASURE_RUNS): - for q in queries: - q = np.ascontiguousarray(q) - clock.tic() - searcher.query(data, q, KNN) - runtimes.append(clock.toc()) - metadata = { - 'dataset': dataset, - 'n_queries': len(queries), - 'algorithm': 'usearch', - 'recall': 1.0 - } - save_results(runtimes, RESULTS_PATH, metadata) - - diff --git a/benchmarks/python_scripts/ivf_lorann.py b/benchmarks/python_scripts/ivf_lorann.py deleted file mode 100644 index 0188a77..0000000 --- a/benchmarks/python_scripts/ivf_lorann.py +++ /dev/null @@ -1,113 +0,0 @@ -import lorann -import json -import sys -from benchmark_utils import * -from setup_utils import * -from setup_settings import * -from sklearn import preprocessing - -disable_multithreading() - -BUILD = True -DATASETS_TO_USE = [ - 'openai-1536-angular', - 'agnews-mxbai-1024-euclidean', - 'instructorxl-arxiv-768', - 'simplewiki-openai-3072-normalized', - 'msong-420', - 'llama-128-ip', -] -if __name__ == '__main__': - RESULTS_PATH = os.path.join(RESULTS_DIRECTORY, "IVF_LORANN.csv") - arg_dataset = "" - IVF_NPROBE = 0 - if len(sys.argv) > 1: - arg_dataset = sys.argv[1] - if len(sys.argv) > 2: - IVF_NPROBE = int(sys.argv[2]) # controls recall of search - if not len(DATASETS_TO_USE): DATASETS_TO_USE = DATASETS - for dataset in DATASETS_TO_USE: - if len(arg_dataset) and dataset != arg_dataset: - continue - - dimensionality = DIMENSIONALITIES[dataset] - index_name = os.path.join(CORE_INDEXES_LORANN, get_core_index_filename(dataset)) - gt_name = os.path.join(SEMANTIC_GROUND_TRUTH_PATH, get_ground_truth_filename(dataset, 100)) - - if BUILD: - print('Building LoRANN index for', dataset) - print('Loading data') - data = read_hdf5_train_data(dataset) - print('Normalizing') - data = preprocessing.normalize(data, axis=1, norm='l2') - num_embeddings = len(data) - if dataset == "simplewiki-openai-3072-normalized": # Special case because it has too many dimensions! - nbuckets = 2048 - elif num_embeddings < 500_000: - nbuckets = math.ceil(2 * math.sqrt(num_embeddings)) - elif num_embeddings < 2_500_000: - nbuckets = math.ceil(4 * math.sqrt(num_embeddings)) - else: # Deep with 10m - nbuckets = math.ceil(8 * math.sqrt(num_embeddings)) - print('Instantiating') - index = lorann.LorannIndex( - data=data, - n_clusters=nbuckets, - global_dim=192, - quantization_bits=8, - euclidean=True, - ) - print('Building') - index.build(verbose=False) - print('Saving') - index.save(str(index_name)) - continue - - queries = read_hdf5_test_data(dataset) - queries = preprocessing.normalize(queries, axis=1, norm='l2') - - print('Restoring Lorann index...') - index = lorann.LorannIndex.load(str(index_name)) - print('Index restored...') - - nprobes_to_use = [] - if IVF_NPROBE: - nprobes_to_use = [IVF_NPROBE] - else : - nprobes_to_use = IVF_NPROBES - - for ivf_nprobe in nprobes_to_use: - print('Nprobe: ', ivf_nprobe) - if IVF_NPROBE > 0 and IVF_NPROBE != ivf_nprobe: - continue - if ivf_nprobe > index.n_clusters: - continue - runtimes = [] - recalls = [] - clock = TicToc() - print('Querying Measure...') - for i in range(N_MEASURE_RUNS): - for q in queries: - q = np.ascontiguousarray(np.array([q])) - clock.tic() - index.search(q, KNN, clusters_to_search=ivf_nprobe, points_to_rerank=200, return_distances=False, n_threads=1) - runtimes.append(clock.toc()) - - # Measure recall afterwards to not affect cache - gt = json.load(open(gt_name, 'r')) - query_i = 0 - for q in queries: - matches = index.search(np.ascontiguousarray(np.array([q])), KNN, clusters_to_search=ivf_nprobe, points_to_rerank=200, return_distances=False, n_threads=-1) - recalls.append(float(len(set(matches[0]).intersection(set(gt[str(query_i)][:KNN])))) / KNN) - query_i += 1 - - metadata = { - 'dataset': dataset, - 'n_queries': len(queries), - 'algorithm': 'ivf_faiss', - 'recall': sum(recalls) / float(len(recalls)), - 'ivf_nprobe': ivf_nprobe - } - save_results(runtimes, RESULTS_PATH, metadata) - - diff --git a/benchmarks/python_scripts/setup_adsampling.py b/benchmarks/python_scripts/setup_adsampling.py index d417c02..cb26d74 100644 --- a/benchmarks/python_scripts/setup_adsampling.py +++ b/benchmarks/python_scripts/setup_adsampling.py @@ -26,7 +26,7 @@ def generate_adsampling_ivf(dataset_name: str, _type='pdx', normalize=True): base_idx._persist(os.path.join(PDX_ADSAMPLING_DATA, dataset_name + '-ivf')) # Store metadata needed by ADSampling - preprocessor.store_metadata(os.path.join(NARY_ADSAMPLING_DATA, dataset_name + '-matrix')) + preprocessor.store_metadata(os.path.join(PDX_ADSAMPLING_DATA, dataset_name + '-matrix')) def generate_adsampling_ivf_global8(dataset_name: str, normalize=True): base_idx = BaseIndexPDXIVF(DIMENSIONALITIES[dataset_name], 'l2sq') @@ -43,7 +43,7 @@ def generate_adsampling_ivf_global8(dataset_name: str, normalize=True): base_idx._to_pdx(data, _type='pdx-v4-h', quantize=True, centroids_preprocessor=preprocessor, use_original_centroids=True) base_idx._persist(os.path.join(PDX_ADSAMPLING_DATA, dataset_name + '-ivf-u8')) - preprocessor.store_metadata(os.path.join(NARY_ADSAMPLING_DATA, dataset_name + '-ivf-u8-matrix')) + preprocessor.store_metadata(os.path.join(PDX_ADSAMPLING_DATA, dataset_name + '-ivf-u8-matrix')) def generate_adsampling_ivf2(dataset_name: str, normalize=True): base_idx = BaseIndexPDXIVF2(DIMENSIONALITIES[dataset_name], 'l2sq') @@ -61,7 +61,7 @@ def generate_adsampling_ivf2(dataset_name: str, normalize=True): base_idx._persist(os.path.join(PDX_ADSAMPLING_DATA, dataset_name + '-ivf2')) # Store metadata needed by ADSampling - preprocessor.store_metadata(os.path.join(NARY_ADSAMPLING_DATA, dataset_name + '-ivf2-matrix')) + preprocessor.store_metadata(os.path.join(PDX_ADSAMPLING_DATA, dataset_name + '-ivf2-matrix')) def generate_adsampling_ivf2_global8(dataset_name: str, normalize=True): base_idx = BaseIndexPDXIVF2(DIMENSIONALITIES[dataset_name], 'l2sq') @@ -82,7 +82,7 @@ def generate_adsampling_ivf2_global8(dataset_name: str, normalize=True): base_idx._to_pdx(data, _type='pdx-v4-h', quantize=True, centroids_preprocessor=preprocessor, use_original_centroids=True) base_idx._persist(os.path.join(PDX_ADSAMPLING_DATA, dataset_name + '-ivf2-u8')) - preprocessor.store_metadata(os.path.join(NARY_ADSAMPLING_DATA, dataset_name + '-ivf2-u8-matrix')) + preprocessor.store_metadata(os.path.join(PDX_ADSAMPLING_DATA, dataset_name + '-ivf2-u8-matrix')) if __name__ == "__main__": diff --git a/benchmarks/python_scripts/setup_bond.py b/benchmarks/python_scripts/setup_bond.py deleted file mode 100644 index 534a2fb..0000000 --- a/benchmarks/python_scripts/setup_bond.py +++ /dev/null @@ -1,91 +0,0 @@ -import faiss -from pdxearch.index_base import BaseIndexPDXIVF, BaseIndexPDXFlat -from pdxearch.constants import PDXConstants -from setup_utils import * -from setup_settings import * -from sklearn import preprocessing - - -def generate_bond_ivf(dataset_name: str, normalize=True): - base_idx = BaseIndexPDXIVF(DIMENSIONALITIES[dataset_name], 'l2sq') - index_path = os.path.join(CORE_INDEXES_FAISS, get_core_index_filename(dataset_name)) - # Reads the core index created by faiss to generate the PDX index - base_idx.core_index.read_index(index_path) - data = read_hdf5_train_data(dataset_name) - if normalize: - print('Normalizing') - data = preprocessing.normalize(data, axis=1, norm='l2') - print('Saving...') - # PDX - base_idx._to_pdx(data, _type='pdx', use_original_centroids=True, bond=True) - base_idx._persist(os.path.join(PDX_DATA, dataset_name + '-ivf')) - - - -def generate_bond_flat(dataset_name: str, normalize=True): - base_idx = BaseIndexPDXFlat(DIMENSIONALITIES[dataset_name], 'l2sq') - print('Reading train data') - data = read_hdf5_train_data(dataset_name) - if normalize: - print('Normalizing') - data = preprocessing.normalize(data, axis=1, norm='l2') - print('Saving') - # PDX FLAT - base_idx._to_pdx(data, size_partition=PDXConstants.PDXEARCH_VECTOR_SIZE, _type='pdx', bond=True) - base_idx._persist(os.path.join(PDX_DATA, dataset_name + '-flat')) - - - - -if __name__ == "__main__": - generate_bond_ivf('coco-nomic-768-normalized', normalize=True) - generate_bond_ivf('simplewiki-openai-3072-normalized', normalize=True) - generate_bond_ivf('imagenet-align-640-normalized', normalize=True) - generate_bond_ivf('imagenet-clip-512-normalized', normalize=True) - generate_bond_ivf('laion-clip-512-normalized', normalize=True) - generate_bond_ivf('codesearchnet-jina-768-cosine', normalize=True) - generate_bond_ivf('yi-128-ip', normalize=True) - generate_bond_ivf('landmark-dino-768-cosine', normalize=True) - generate_bond_ivf('landmark-nomic-768-normalized', normalize=True) - generate_bond_ivf('arxiv-nomic-768-normalized', normalize=True) - generate_bond_ivf('ccnews-nomic-768-normalized', normalize=True) - generate_bond_ivf('celeba-resnet-2048-cosine', normalize=True) - generate_bond_ivf('llama-128-ip', normalize=True) - generate_bond_ivf('yandex-200-cosine', normalize=True) - generate_bond_ivf('word2vec-300', normalize=True) - generate_bond_ivf('sift-128-euclidean', normalize=True) - generate_bond_ivf('openai-1536-angular', normalize=True) - generate_bond_ivf('msong-420', normalize=True) - generate_bond_ivf('instructorxl-arxiv-768', normalize=True) - generate_bond_ivf('contriever-768', normalize=True) - generate_bond_ivf('gist-960-euclidean', normalize=True) - generate_bond_ivf('yahoo-minilm-384-normalized', normalize=True) - generate_bond_ivf('gooaq-distilroberta-768-normalized', normalize=True) - generate_bond_ivf('agnews-mxbai-1024-euclidean', normalize=True) - generate_bond_ivf('glove-200-angular', normalize=True) - - generate_bond_flat('coco-nomic-768-normalized', normalize=True) - generate_bond_flat('simplewiki-openai-3072-normalized', normalize=True) - generate_bond_flat('imagenet-align-640-normalized', normalize=True) - generate_bond_flat('imagenet-clip-512-normalized', normalize=True) - generate_bond_flat('laion-clip-512-normalized', normalize=True) - generate_bond_flat('codesearchnet-jina-768-cosine', normalize=True) - generate_bond_flat('yi-128-ip', normalize=True) - generate_bond_flat('landmark-dino-768-cosine', normalize=True) - generate_bond_flat('landmark-nomic-768-normalized', normalize=True) - generate_bond_flat('arxiv-nomic-768-normalized', normalize=True) - generate_bond_flat('ccnews-nomic-768-normalized', normalize=True) - generate_bond_flat('celeba-resnet-2048-cosine', normalize=True) - generate_bond_flat('llama-128-ip', normalize=True) - generate_bond_flat('yandex-200-cosine', normalize=True) - generate_bond_flat('word2vec-300', normalize=True) - generate_bond_flat('sift-128-euclidean', normalize=True) - generate_bond_flat('openai-1536-angular', normalize=True) - generate_bond_flat('msong-420', normalize=True) - generate_bond_flat('instructorxl-arxiv-768', normalize=True) - generate_bond_flat('contriever-768', normalize=True) - generate_bond_flat('gist-960-euclidean', normalize=True) - generate_bond_flat('yahoo-minilm-384-normalized', normalize=True) - generate_bond_flat('gooaq-distilroberta-768-normalized', normalize=True) - generate_bond_flat('agnews-mxbai-1024-euclidean', normalize=True) - generate_bond_flat('glove-200-angular', normalize=True) diff --git a/benchmarks/python_scripts/setup_data.py b/benchmarks/python_scripts/setup_data.py index c1aeb56..62a2aef 100644 --- a/benchmarks/python_scripts/setup_data.py +++ b/benchmarks/python_scripts/setup_data.py @@ -3,7 +3,6 @@ from setup_settings import RAW_DATA, DATA_DIRECTORY, DATASETS from setup_ground_truth import generate_ground_truth from setup_adsampling import generate_adsampling_ivf, generate_adsampling_ivf_global8, generate_adsampling_ivf2_global8, generate_adsampling_ivf2 -from setup_bond import generate_bond_ivf, generate_bond_flat from setup_core_index import generate_core_ivf, generate_core_ivf2 from setup_test_data import generate_test_data @@ -12,16 +11,15 @@ GENERATE_IVF = True # Creates IVF indexes with FAISS KNN = [100] ALGORITHMS = [ # Choose the pruning algorithms for which indexes are going to be created - 'adsampling', - # 'bond' + 'adsampling' ] DATASETS_TO_USE = [ - 'openai-1536-angular', + # 'openai-1536-angular', 'agnews-mxbai-1024-euclidean', - 'instructorxl-arxiv-768', - 'simplewiki-openai-3072-normalized', - 'msong-420', - 'llama-128-ip', + # 'instructorxl-arxiv-768', + # 'simplewiki-openai-3072-normalized', + # 'msong-420', + # 'llama-128-ip', ] if __name__ == "__main__": if DOWNLOAD: @@ -59,10 +57,3 @@ generate_adsampling_ivf_global8(dataset) generate_adsampling_ivf2_global8(dataset) - # Generate BOND Data - if 'bond' in ALGORITHMS: - print('==== Generating BOND IVF [PDX]') - generate_bond_ivf(dataset) - print('==== Generating BOND Flat (for exact-search)') - generate_bond_flat(dataset) - diff --git a/benchmarks/python_scripts/setup_settings.py b/benchmarks/python_scripts/setup_settings.py index 3305ea3..3d547d2 100644 --- a/benchmarks/python_scripts/setup_settings.py +++ b/benchmarks/python_scripts/setup_settings.py @@ -26,9 +26,6 @@ QUERIES_DATA = os.path.join(SOURCE_DIR, DATA_DIRECTORY, "queries") -NARY_DATA = os.path.join(SOURCE_DIR, DATA_DIRECTORY, "nary") -NARY_ADSAMPLING_DATA = os.path.join(SOURCE_DIR, DATA_DIRECTORY, "adsampling_nary") - PDX_DATA = os.path.join(SOURCE_DIR, DATA_DIRECTORY, "pdx") PDX_ADSAMPLING_DATA = os.path.join(SOURCE_DIR, DATA_DIRECTORY, "adsampling_pdx") @@ -36,7 +33,6 @@ directories = [ RAW_DATA, GROUND_TRUTH_DATA, PURESCAN_DATA, - NARY_DATA, NARY_ADSAMPLING_DATA, PDX_DATA, PDX_ADSAMPLING_DATA, CORE_INDEXES, CORE_INDEXES_FAISS, CORE_INDEXES_FAISS_U8, CORE_INDEXES_FAISS_L0, CORE_INDEXES_LORANN, QUERIES_DATA, diff --git a/benchmarks/python_scripts/setup_utils.py b/benchmarks/python_scripts/setup_utils.py index d273b08..ce16029 100644 --- a/benchmarks/python_scripts/setup_utils.py +++ b/benchmarks/python_scripts/setup_utils.py @@ -56,5 +56,3 @@ def get_delta_d(ndim): if __name__ == '__main__': if not os.path.exists(RAW_DATA): os.makedirs(RAW_DATA) - if not os.path.exists(NARY_DATA): - os.makedirs(NARY_DATA) diff --git a/benchmarks/results/DEFAULT/IVF_PDX_ADSAMPLING.csv b/benchmarks/results/DEFAULT/IVF_PDX_ADSAMPLING.csv new file mode 100644 index 0000000..8223d00 --- /dev/null +++ b/benchmarks/results/DEFAULT/IVF_PDX_ADSAMPLING.csv @@ -0,0 +1,11 @@ +dataset,algorithm,avg,max,min,recall,ivf_nprobe,epsilon,knn,n_queries,selectivity,num_measure_runs,avg_all,max_all,min_all +agnews-mxbai-1024-euclidean,adsampling,3.11399,3.38392,2.85396,0.00011,128,0,100,1000,0.2,1,3.119,3.874,1.909 +agnews-mxbai-1024-euclidean,adsampling,3.09117,3.53542,2.66817,5e-05,128,0,100,1000,0.2,1,3.093,3.613,2.619 +openai-1536-angular,adsampling,3.81455,6.88221,0.752791,8e-05,128,0,100,1000,0.2,1,3.815,6.882,0.7528 +agnews-mxbai-1024-euclidean,adsampling,15.5035,18.6757,13.0275,0.00025,24,0,100,1000,0.2,1,16.49,24.83,11.87 +agnews-mxbai-1024-euclidean,adsampling,8.4187,9.17887,7.65458,0.00033,2,0,100,1000,0.2,1,8.442,16.65,7.623 +agnews-mxbai-1024-euclidean,adsampling,14.7612,17.0919,12.5011,0.00025,24,0,100,1000,0.2,1,15.17,24.26,11.24 +agnews-mxbai-1024-euclidean,adsampling,10.1151,10.8596,9.44171,0.00048,24,0,100,1000,0.2,1,10.13,12.23,9.356 +agnews-mxbai-1024-euclidean,adsampling,10.3856,11.3599,9.40304,0.00048,24,0,100,1000,0.2,1,10.41,20.79,9.403 +agnews-mxbai-1024-euclidean,adsampling,15.6141,18.7602,12.4789,0.00011,24,0,100,1000,0.2,1,15.59,25.86,11.52 +agnews-mxbai-1024-euclidean,adsampling,10.5254,13.5434,8.07146,0.901379,24,0,100,1000,0.2,1,11.02,20.96,8.071 diff --git a/benchmarks/results/DEFAULT/U8_IVF_PDX_ADSAMPLING.csv b/benchmarks/results/DEFAULT/U8_IVF_PDX_ADSAMPLING.csv new file mode 100644 index 0000000..0cde565 --- /dev/null +++ b/benchmarks/results/DEFAULT/U8_IVF_PDX_ADSAMPLING.csv @@ -0,0 +1,10 @@ +dataset,algorithm,avg,max,min,recall,ivf_nprobe,epsilon,knn,n_queries,selectivity,num_measure_runs,avg_all,max_all,min_all +agnews-mxbai-1024-euclidean,adsampling,1.36104,1.64892,0.888584,0.00016,128,0,100,1000,0.2,1,1.281,1.84,0.4758 +agnews-mxbai-1024-euclidean,adsampling,11.2748,12.7267,10.0541,7e-05,24,0,100,1000,0.2,1,11.28,15.72,8.713 +agnews-mxbai-1024-euclidean,adsampling,11.4124,12.8628,9.75858,0.0001,24,0,100,1000,0.2,1,11.24,23.5,8.256 +agnews-mxbai-1024-euclidean,adsampling,8.12569,8.74596,7.52208,3e-05,2,0,100,1000,0.2,1,8.163,18.04,7.395 +agnews-mxbai-1024-euclidean,adsampling,8.69309,9.80162,7.64971,0.899809,24,0,100,1000,0.2,1,8.769,44.12,7.65 +agnews-mxbai-1024-euclidean,adsampling,0.342781,0.413125,0.267625,0.899809,24,0,100,1000,0.2,1,0.3429,0.4226,0.2427 +agnews-mxbai-1024-euclidean,adsampling,0.330006,0.405208,0.263084,0.899809,24,0,100,1000,0.2,1,0.3306,0.4435,0.2631 +agnews-mxbai-1024-euclidean,adsampling,0.328877,0.40475,0.258958,0.899809,24,0,100,1000,0.2,1,0.3291,0.451,0.2446 +agnews-mxbai-1024-euclidean,adsampling,0.355976,0.459792,0.269,0.899809,24,0,100,1000,0.2,1,0.3576,0.5143,0.269 diff --git a/examples/README.md b/examples/README.md index 42c056a..a1206e7 100644 --- a/examples/README.md +++ b/examples/README.md @@ -16,5 +16,4 @@ Our examples look for `.hdf5` files in `/benchmarks/datasets/downloaded`. These - `pdx_ivf.py`: Pruned search + ADSampling with a **vanilla IVF index** (built with FAISS). The recall is controlled with the `nprobe` parameter. - `pdx_ivf_exhaustive.py`: Pruned search with ADSampling with a IVF index (built with FAISS). This example explore all the clusters (therefore, it is an exhaustive search). This lets the pruning strategy shine and get **up to 13x speedup**. - `pdx_noindex.py`: Pruned search with ADSampling on the entire collection (no index). This produces nearly exact results. -- `pdx_noindex_bond.py`: Pruned search with BOND on the entire collection (no index). This produces exact results and does not need a preprocessing of the data. - `pdx_persist.py`: Example to store the PDX index and the metadata of ADSampling in a file to use later. diff --git a/examples/pdx_noindex_bond.py b/examples/pdx_noindex_bond.py deleted file mode 100644 index e91f380..0000000 --- a/examples/pdx_noindex_bond.py +++ /dev/null @@ -1,54 +0,0 @@ -import numpy as np -import faiss -import os -from examples_utils import TicToc, read_hdf5_data -from pdxearch.index_factory import IndexPDXBONDFlat - -""" -PDXearch (pruned search) + BOND on the entire collection (no index) -This produces exact results, and does not need to transform the vectors -Download the .hdf5 data here: https://drive.google.com/drive/folders/1f76UCrU52N2wToGMFg9ir1MY8ZocrN34?usp=sharing -""" -if __name__ == "__main__": - dataset_name = 'msong-420.hdf5' - num_dimensions = 420 - knn = 100 - print(f'Running example: PDXearch + BOND (no index)\n- D={num_dimensions}\n- k={knn}\n- dataset={dataset_name}') - train, queries = read_hdf5_data(os.path.join('./benchmarks/datasets/downloaded', dataset_name)) - queries = queries[:100] - - index = IndexPDXBONDFlat(ndim=num_dimensions) - print('PDXifying Collection') - index.preprocess(train) - index.add(train) - - print(f'{len(queries)} queries with PDX') - times = [] - clock = TicToc() - results = [] - for i in range(len(queries)): - q = np.ascontiguousarray(queries[i]) - clock.tic() - index.search(q, knn) - times.append(clock.toc()) - print('PDX med. time:', np.median(np.array(times))) - # To check results of first query - results = index.search(queries[0], knn) - print(results) - - print(f'{len(queries)} queries with FAISS') - # We need to normalize the queries - index.preprocess(queries, inplace=True) - times = [] - clock = TicToc() - results = [] - faiss_index = faiss.IndexFlatL2(num_dimensions) - faiss_index.add(train) - for i in range(len(queries)): - q = np.ascontiguousarray(np.array([queries[i]])) - clock.tic() - faiss_index.search(q, k=knn) - times.append(clock.toc()) - print('FAISS med. time:', np.median(np.array(times))) - # To check results of first query - print(faiss_index.search(np.array([queries[0]]), k=knn)) diff --git a/examples/pdx_simple.py b/examples/pdx_simple.py index a9ec785..4498689 100644 --- a/examples/pdx_simple.py +++ b/examples/pdx_simple.py @@ -3,7 +3,7 @@ import sklearn.datasets from sklearn.model_selection import train_test_split from examples_utils import TicToc -from pdxearch.index_factory import IndexPDXADSamplingIVFFlat, IndexPDXBONDIVFFlat +from pdxearch.index_factory import IndexPDXADSamplingIVFFlat """ PDXearch (pruned search) + ADSampling with an IVF index (built with FAISS) diff --git a/include/lib/lib.hpp b/include/lib/lib.hpp index cd97959..7983349 100644 --- a/include/lib/lib.hpp +++ b/include/lib/lib.hpp @@ -11,7 +11,6 @@ #include "index_base/pdx_ivf.hpp" #include "index_base/pdx_ivf2.hpp" #include "pdxearch.hpp" -#include "pruners/bond.hpp" #include "pruners/adsampling.hpp" // TODO: the python wrapper and the core API should not be interleaved @@ -305,179 +304,6 @@ class IndexADSamplingIVFFlat { }; -class IndexBONDIVFFlat { - using KNNCandidate = KNNCandidate; - using Index = IndexPDXIVF; - using Pruner = BondPruner; - using Searcher = PDXearch, L2, BondPruner>; - -public: - Index index = Index(); - std::unique_ptr searcher = nullptr; - - void Load(const py::bytes& data){ - py::buffer_info info(py::buffer(data).request()); - auto data_ = static_cast(info.ptr); - index.Load(data_); -#if false && defined(__AVX512FP16__) - // In Intel architectures with low bandwidth at L3/DRAM, the DISTANCE_TO_MEANS criteria performs better - pruner = Pruner(index.num_dimensions); - searcher = std::make_unique(index, pruner, 0, DISTANCE_TO_MEANS); -#else - Pruner pruner = Pruner(index.num_dimensions); - searcher = std::make_unique(index, pruner, 0, DIMENSION_ZONES); -#endif - } - - void Restore(const std::string &path){ - index.Restore(path); -#if false && defined(__AVX512FP16__) - // In Intel architectures with low bandwidth at L3/DRAM, the DISTANCE_TO_MEANS criteria performs better - pruner = Pruner(index.num_dimensions); - searcher = std::make_unique(index, pruner, 0, DISTANCE_TO_MEANS); -#else - Pruner pruner = Pruner(index.num_dimensions); - searcher = std::make_unique(index, pruner, 0, DIMENSION_ZONES); -#endif - } - - std::vector Search(float *q, uint32_t k) const { - return searcher->Search(q, k); - } - - // Serialize return value - std::pair, py::array_t> - _py_Search(const py::array_t& q, uint32_t k, uint32_t n_probe) const { - auto buf = q.request(); // Get buffer info - if (buf.ndim != 1) { - throw std::runtime_error("Input should be a 1-D NumPy array"); - } - auto query = static_cast(buf.ptr); - searcher->SetNProbe(n_probe); - std::vector results = searcher->Search(query, k); - size_t n = results.size(); - py::array_t ids(n); - py::array_t distances(n); - auto ids_ptr = ids.mutable_unchecked<1>(); - auto distances_ptr = distances.mutable_unchecked<1>(); - for (size_t i = 0; i < n; ++i) { - ids_ptr(i) = results[i].index; - distances_ptr(i) = results[i].distance; - } - return {ids, distances}; - } - - std::pair, py::array_t> - _py_FilteredSearch( - const py::array_t& q, uint32_t k, uint32_t n_probe, - const py::array_t& n_passing_tuples, const py::array_t& selection_vector - ) const { - auto buf = q.request(); // Get buffer info - if (buf.ndim != 1) { - throw std::runtime_error("Input should be a 1-D NumPy array"); - } - auto query = static_cast(buf.ptr); - auto n_passing_tuples_p = static_cast(n_passing_tuples.request().ptr); - auto selection_vector_p = static_cast(selection_vector.request().ptr); - - PredicateEvaluator pe = PredicateEvaluator(searcher->pdx_data.num_clusters); - pe.LoadSelectionVector(n_passing_tuples_p, selection_vector_p); - - searcher->SetNProbe(n_probe); - std::vector results = searcher->FilteredSearch(query, k, pe); - size_t n = results.size(); - py::array_t ids(n); - py::array_t distances(n); - auto ids_ptr = ids.mutable_unchecked<1>(); - auto distances_ptr = distances.mutable_unchecked<1>(); - for (size_t i = 0; i < n; ++i) { - ids_ptr(i) = results[i].index; - distances_ptr(i) = results[i].distance; - } - return {ids, distances}; - } - -}; - -class IndexBONDFlat { - using KNNCandidate = KNNCandidate; - using Index = IndexPDXIVF; - using Pruner = BondPruner; - using Searcher = PDXearch, L2, BondPruner>; -public: - Index index = Index(); - std::unique_ptr searcher = nullptr; - - void Load(const py::bytes& data){ - py::buffer_info info(py::buffer(data).request()); - auto data_ = static_cast(info.ptr); - index.Load(data_); - Pruner pruner = Pruner(index.num_dimensions); - searcher = std::make_unique(index, pruner, 0, DISTANCE_TO_MEANS); - } - - void Restore(const std::string &path){ - index.Restore(path); - Pruner pruner = Pruner(index.num_dimensions); - searcher = std::make_unique(index, pruner, 0, DISTANCE_TO_MEANS); - } - - std::vector Search(float *q, uint32_t k) const { - return searcher->Search(q, k); - } - - // Serialize return value - std::pair, py::array_t> - _py_Search(const py::array_t& q, uint32_t k) const { - auto buf = q.request(); // Get buffer info - if (buf.ndim != 1) { - throw std::runtime_error("Input should be a 1-D NumPy array"); - } - auto query = static_cast(buf.ptr); - std::vector results = searcher->Search(query, k); - size_t n = results.size(); - py::array_t ids(n); - py::array_t distances(n); - auto ids_ptr = ids.mutable_unchecked<1>(); - auto distances_ptr = distances.mutable_unchecked<1>(); - for (size_t i = 0; i < n; ++i) { - ids_ptr(i) = results[i].index; - distances_ptr(i) = results[i].distance; - } - return {ids, distances}; - } - - std::pair, py::array_t> - _py_FilteredSearch( - const py::array_t& q, uint32_t k, - const py::array_t& n_passing_tuples, const py::array_t& selection_vector - ) const { - auto buf = q.request(); // Get buffer info - if (buf.ndim != 1) { - throw std::runtime_error("Input should be a 1-D NumPy array"); - } - auto query = static_cast(buf.ptr); - auto n_passing_tuples_p = static_cast(n_passing_tuples.request().ptr); - auto selection_vector_p = static_cast(selection_vector.request().ptr); - - PredicateEvaluator pe = PredicateEvaluator(searcher->pdx_data.num_clusters); - pe.LoadSelectionVector(n_passing_tuples_p, selection_vector_p); - - std::vector results = searcher->FilteredSearch(query, k, pe); - size_t n = results.size(); - py::array_t ids(n); - py::array_t distances(n); - auto ids_ptr = ids.mutable_unchecked<1>(); - auto distances_ptr = distances.mutable_unchecked<1>(); - for (size_t i = 0; i < n; ++i) { - ids_ptr(i) = results[i].index; - distances_ptr(i) = results[i].distance; - } - return {ids, distances}; - } - -}; - class IndexADSamplingFlat { using KNNCandidate = KNNCandidate; @@ -570,56 +396,6 @@ class IndexADSamplingFlat { }; -class IndexPDXFlat { - using KNNCandidate = KNNCandidate; - using Index = IndexPDXIVF; - using Pruner = BondPruner; - using Searcher = PDXearch, L2, BondPruner>; - -public: - Index index = Index(); - std::unique_ptr searcher = nullptr; - - void Load(const py::bytes& data){ - py::buffer_info info(py::buffer(data).request()); - auto data_ = static_cast(info.ptr); - index.Load(data_); - Pruner pruner = Pruner(index.num_dimensions); - searcher = std::make_unique(index, pruner, 0, SEQUENTIAL); - } - - void Restore(const std::string &path){ - index.Restore(path); - Pruner pruner = Pruner(index.num_dimensions); - searcher = std::make_unique(index, pruner, 0, SEQUENTIAL); - } - - std::vector Search(float *q, uint32_t k) const { - return searcher->LinearScan(q, k); - } - - // Serialize return value - std::pair, py::array_t> - _py_Search(const py::array_t& q, uint32_t k) const { - auto buf = q.request(); // Get buffer info - if (buf.ndim != 1) { - throw std::runtime_error("Input should be a 1-D NumPy array"); - } - auto query = static_cast(buf.ptr); - std::vector results = searcher->Search(query, k); - size_t n = results.size(); - py::array_t ids(n); - py::array_t distances(n); - auto ids_ptr = ids.mutable_unchecked<1>(); - auto distances_ptr = distances.mutable_unchecked<1>(); - for (size_t i = 0; i < n; ++i) { - ids_ptr(i) = results[i].index; - distances_ptr(i) = results[i].distance; - } - return {ids, distances}; - } -}; - } // namespace PDX #endif // PDX_LIB \ No newline at end of file diff --git a/include/pdxearch.hpp b/include/pdxearch.hpp index bbafd1a..f3b4d1d 100644 --- a/include/pdxearch.hpp +++ b/include/pdxearch.hpp @@ -13,7 +13,6 @@ #include "index_base/pdx_ivf.hpp" #include "index_base/pdx_ivf2.hpp" #include "pruners/adsampling.hpp" -#include "pruners/bond.hpp" namespace PDX { @@ -59,10 +58,6 @@ class PDXearch { cluster_offsets[i] = total_embeddings; total_embeddings += pdx_data.clusters[i].num_embeddings; } - if constexpr(std::is_same_v>) { - pdx_data.num_horizontal_dimensions = 0; - pdx_data.num_vertical_dimensions = pdx_data.num_dimensions; - } quantizer.SetD(pdx_data.num_dimensions); } diff --git a/include/pruners/bond.hpp b/include/pruners/bond.hpp deleted file mode 100644 index 8c9d0fc..0000000 --- a/include/pruners/bond.hpp +++ /dev/null @@ -1,42 +0,0 @@ -#ifndef PDX_PXD_BOND_SEARCH_HPP -#define PDX_PXD_BOND_SEARCH_HPP - -#include - -namespace PDX { - -/****************************************************************** - * BOND Pruner - ******************************************************************/ -template -class BondPruner { - using DISTANCES_TYPE = DistanceType_t; - using QUANTIZED_VECTOR_TYPE = QuantizedVectorType_t; - using DATA_TYPE = DataType_t; - using CLUSTER_TYPE = Cluster; - using KNNCandidate_t = KNNCandidate; - using VectorComparator_t = VectorComparator; -public: - uint32_t num_dimensions; - - BondPruner(uint32_t num_dimensions) : num_dimensions(num_dimensions) {}; - - // TODO: Do not copy - void PreprocessQuery(float *raw_query, float *query) { - memcpy((void *) query, (void *) raw_query, num_dimensions * sizeof(QUANTIZED_VECTOR_TYPE)); - } - - template - DistanceType_t GetPruningThreshold( - uint32_t k, - std::priority_queue, std::vector>, VectorComparator> &heap, - const uint32_t current_dimension_idx - ) { - return heap.size() == k ? heap.top().distance : std::numeric_limits>::max(); - } - -}; - -} // namespace PDX - -#endif //PDX_PXD_BOND_SEARCH_HPP diff --git a/include/utils/benchmark_utils.hpp b/include/utils/benchmark_utils.hpp index f93247a..d46a893 100644 --- a/include/utils/benchmark_utils.hpp +++ b/include/utils/benchmark_utils.hpp @@ -37,9 +37,7 @@ struct PhasesRuntime { class BenchmarkUtils { public: inline static std::string PDX_DATA = std::string{CMAKE_SOURCE_DIR} + "/benchmarks/datasets/pdx/"; - inline static std::string NARY_DATA = std::string{CMAKE_SOURCE_DIR} + "/benchmarks/datasets/nary/"; inline static std::string PDX_ADSAMPLING_DATA = std::string{CMAKE_SOURCE_DIR} + "/benchmarks/datasets/adsampling_pdx/"; - inline static std::string NARY_ADSAMPLING_DATA = std::string{CMAKE_SOURCE_DIR} + "/benchmarks/datasets/adsampling_nary/"; inline static std::string GROUND_TRUTH_DATA = std::string{CMAKE_SOURCE_DIR} + "/benchmarks/datasets/ground_truth/"; inline static std::string FILTERED_GROUND_TRUTH_DATA = std::string{CMAKE_SOURCE_DIR} + "/benchmarks/datasets/ground_truth_filtered/"; inline static std::string PURESCAN_DATA = std::string{CMAKE_SOURCE_DIR} + "/benchmarks/datasets/purescan/"; diff --git a/python/lib.cpp b/python/lib.cpp index dbf5199..a7a7d82 100644 --- a/python/lib.cpp +++ b/python/lib.cpp @@ -61,20 +61,6 @@ PYBIND11_MODULE(compiled, m) { .def("search", &PDX::IndexADSamplingIVFFlat::_py_Search, py::arg("q"), py::arg("k"), py::arg("n_probe")) .def("filtered_search", &PDX::IndexADSamplingIVFFlat::_py_FilteredSearch, py::arg("q"), py::arg("k"), py::arg("n_probe"), py::arg("n_passing_tuples"), py::arg("selection_vector")); - py::class_(m, "IndexBONDIVFFlat") - .def(py::init<>()) - .def("restore", &PDX::IndexBONDIVFFlat::Restore, py::arg("path")) - .def("load", &PDX::IndexBONDIVFFlat::Load, py::arg("data")) - .def("search", &PDX::IndexBONDIVFFlat::_py_Search, py::arg("q"), py::arg("k"), py::arg("n_probe")) - .def("filtered_search", &PDX::IndexBONDIVFFlat::_py_FilteredSearch, py::arg("q"), py::arg("k"), py::arg("n_probe"), py::arg("n_passing_tuples"), py::arg("selection_vector")); - - py::class_(m, "IndexBONDFlat") - .def(py::init<>()) - .def("restore", &PDX::IndexBONDFlat::Restore, py::arg("path")) - .def("load", &PDX::IndexBONDFlat::Load, py::arg("data")) - .def("search", &PDX::IndexBONDFlat::_py_Search, py::arg("q"), py::arg("k")) - .def("filtered_search", &PDX::IndexBONDFlat::_py_FilteredSearch, py::arg("q"), py::arg("k"), py::arg("n_passing_tuples"), py::arg("selection_vector")); - py::class_(m, "IndexADSamplingFlat") .def(py::init<>()) .def("restore", &PDX::IndexADSamplingFlat::Restore, py::arg("path"), py::arg("matrix_path")) @@ -83,10 +69,5 @@ PYBIND11_MODULE(compiled, m) { .def("search", &PDX::IndexADSamplingFlat::_py_Search, py::arg("q"), py::arg("k")) .def("filtered_search", &PDX::IndexADSamplingFlat::_py_FilteredSearch, py::arg("q"), py::arg("k"), py::arg("n_passing_tuples"), py::arg("selection_vector")); - py::class_(m, "IndexPDXFlat") - .def(py::init<>()) - .def("restore", &PDX::IndexPDXFlat::Restore, py::arg("path")) - .def("load", &PDX::IndexPDXFlat::Load, py::arg("data")) - .def("search", &PDX::IndexPDXFlat::_py_Search, py::arg("q"), py::arg("k")); } \ No newline at end of file diff --git a/python/pdxearch/constants.py b/python/pdxearch/constants.py index 48b679e..33a0812 100644 --- a/python/pdxearch/constants.py +++ b/python/pdxearch/constants.py @@ -14,7 +14,7 @@ class PDXConstants: SUPPORTED_METRICS = [ "l2sq" ] - HAS_FFTW = ctypes.util.find_library("fftw3f") is not None + HAS_FFTW = False # ctypes.util.find_library("fftw3f") is not None class PDXDistanceMetrics(Enum): diff --git a/python/pdxearch/index_factory.py b/python/pdxearch/index_factory.py index c06ad2a..ce70675 100644 --- a/python/pdxearch/index_factory.py +++ b/python/pdxearch/index_factory.py @@ -8,8 +8,6 @@ ) from pdxearch.compiled import ( IndexADSamplingIVFFlat as _IndexPDXADSamplingIVFFlat, - IndexBONDIVFFlat as _IndexPDXBONDIVFFlat, - IndexBONDFlat as _IndexBONDFlat, IndexADSamplingFlat as _IndexADSamplingFlat, IndexPDXFlat as _IndexPDXFlat, IndexADSamplingIVF2SQ8 as _IndexADSamplingIVF2SQ8, @@ -187,92 +185,6 @@ def filtered_search(self, q: np.ndarray, knn: int, nprobe: int = 16): def set_pruning_confidence(self, confidence: float): self.pdx_index.set_pruning_confidence(confidence) -class IndexPDXBONDIVFFlat(BaseIndexPDXIVF): - def __init__( - self, - *, ndim: int = 128, - metric: str = "l2sq", - nbuckets: int = 256, - normalize: bool = True - ) -> None: - super().__init__(ndim, metric, nbuckets, normalize) - self.preprocessor = Preprocessor() - self.pdx_index = _IndexPDXBONDIVFFlat() - self.pe = PredicateEvaluator() - - def preprocess(self, data, inplace: bool = True): - return self.preprocessor.preprocess(data, inplace=inplace, normalize=self.normalize) - - def add_persist(self, data, path: str): - self._add(data) - self._to_pdx(data, _type='pdx', use_original_centroids=True, bond=True) - self._persist(path) - - def persist(self, path: str): # TODO: Rename - self._persist(path) - - def add(self, data): - self._add(data) - self._to_pdx(data, _type='pdx', use_original_centroids=True, bond=True) - self.pdx_index.load(self.materialized_index) - - def restore(self, path: str, matrix_path: str): - self.pdx_index.restore(path, matrix_path) - - def search(self, q: np.ndarray, knn: int, nprobe: int = 16): - return self.pdx_index.search(q, knn, nprobe) - - def evaluate_predicate(self, passing_tuples_ids): - self.pe.evaluate_predicate(passing_tuples_ids, self.core_index.labels) - - def filtered_search(self, q: np.ndarray, knn: int, nprobe: int = 16): - if len(self.pe.n_passing_tuples) == 0 or len(self.pe.selection_vector) == 0: - raise ValueError("Call .evaluate_predicate([]) first to generate the selection_vector of your predicate") - return self.pdx_index.filtered_search(q, knn, nprobe, self.pe.n_passing_tuples, self.pe.selection_vector) - - -class IndexPDXBONDFlat(BaseIndexPDXFlat): - def __init__( - self, *, - ndim: int = 128, - metric: str = "l2sq", - normalize: bool = True - ) -> None: - super().__init__(ndim=ndim, metric=metric, normalize=normalize) - self.preprocessor = Preprocessor() - self.pdx_index = _IndexBONDFlat() - self.block_sizes = PDXConstants.PDXEARCH_VECTOR_SIZE - self.pe = PredicateEvaluator() - - def preprocess(self, data, inplace: bool = True): - return self.preprocessor.preprocess(data, inplace=inplace, normalize=self.normalize) - - def add_persist(self, data, path: str): - self._to_pdx(data, self.block_sizes, bond=True) - self._persist(path) - - def persist(self, path: str): # TODO: Rename - self._persist(path) - - def add(self, data): - self._to_pdx(data, self.block_sizes, bond=True) - self.pdx_index.load(self.materialized_index) - - def restore(self, path: str): - self.pdx_index.restore(path) - - def search(self, q: np.ndarray, knn: int): - return self.pdx_index.search(q, knn) - - def evaluate_predicate(self, passing_tuples_ids): - self.pe.evaluate_predicate(passing_tuples_ids, [x.indices for x in self.partitions]) - - def filtered_search(self, q: np.ndarray, knn: int, nprobe: int = 16): - if len(self.pe.n_passing_tuples) == 0 or len(self.pe.selection_vector) == 0: - raise ValueError("Call .evaluate_predicate([]) first to generate the selection_vector of your predicate") - return self.pdx_index.filtered_search(q, knn, self.pe.n_passing_tuples, self.pe.selection_vector) - - class IndexPDXADSamplingFlat(BaseIndexPDXFlat): def __init__(self, *, ndim: int = 128, metric: str = "l2sq", normalize: bool = True) -> None: super().__init__(ndim=ndim, metric=metric, normalize=normalize) @@ -314,30 +226,3 @@ def filtered_search(self, q: np.ndarray, knn: int, nprobe: int = 16): raise ValueError("Call .evaluate_predicate([]) first to generate the selection_vector of your predicate") return self.pdx_index.filtered_search(q, knn, self.pe.n_passing_tuples, self.pe.selection_vector) - -class IndexPDXFlat(BaseIndexPDXFlat): - def __init__(self, *, ndim: int = 128, metric: str = "l2sq", normalize: bool = True) -> None: - super().__init__(ndim, metric, normalize) - self.pdx_index = _IndexPDXFlat() - self.preprocessor = Preprocessor() - self.block_sizes = PDXConstants.PDX_VECTOR_SIZE - - def add_persist(self, data, path: str): - self._to_pdx(data, self.block_sizes, bond=True) - self._persist(path) - - def preprocess(self, data, inplace: bool = True): - return self.preprocessor.preprocess(data, inplace=inplace, normalize=self.normalize) - - def persist(self, path: str): # TODO: Rename - self._persist(path) - - def add(self, data): - self._to_pdx(data, self.block_sizes, bond=True) - self.pdx_index.load(self.materialized_index) - - def restore(self, path: str): - self.pdx_index.restore(path) - - def search(self, q: np.ndarray, knn: int): - return self.pdx_index.search(q, knn) \ No newline at end of file From b240a8301e99cafcc0ccafbf01acbc8df59d1327 Mon Sep 17 00:00:00 2001 From: lkuffo Date: Mon, 23 Feb 2026 22:04:10 +0100 Subject: [PATCH 02/39] Working refactor --- CMakeLists.txt | 79 +- benchmarks/CMakeLists.txt | 21 +- .../bench_adsampling/pdx_ivf_adsampling.cpp | 7 +- .../results/DEFAULT/IVF_PDX_ADSAMPLING.csv | 25 + include/clustering.hpp | 79 + include/common.hpp | 299 ++-- include/db_mock/predicate_evaluator.hpp | 64 +- include/distance_computers/avx2_computers.hpp | 360 ++--- .../distance_computers/avx512_computers.hpp | 500 ++---- include/distance_computers/base_computers.hpp | 64 +- include/distance_computers/neon_computers.hpp | 345 ++--- .../distance_computers/scalar_computers.hpp | 215 ++- include/index_base/pdx_ivf.hpp | 178 ++- include/pdxearch.hpp | 1374 ++++++----------- include/pruners/adsampling.hpp | 155 +- include/quantizers/global.h | 72 - include/quantizers/scalar.hpp | 83 + include/utils/benchmark_utils.hpp | 4 +- include/utils/file_reader.hpp | 4 +- include/utils/matrix.h | 111 -- include/utils/utils.hpp | 157 ++ include/wrapper.hpp | 237 +++ 22 files changed, 2100 insertions(+), 2333 deletions(-) create mode 100644 include/clustering.hpp delete mode 100644 include/quantizers/global.h create mode 100644 include/quantizers/scalar.hpp delete mode 100644 include/utils/matrix.h create mode 100644 include/utils/utils.hpp create mode 100644 include/wrapper.hpp diff --git a/CMakeLists.txt b/CMakeLists.txt index 2b3fd0b..43ae90e 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1,13 +1,54 @@ cmake_minimum_required(VERSION 3.26) -set(CMAKE_CXX_STANDARD 17) project(PDX) +# Default to Release build if not specified +if(NOT CMAKE_BUILD_TYPE) + set(CMAKE_BUILD_TYPE Release CACHE STRING "Build type (default: Release)" FORCE) +endif() +message(STATUS "Build type: ${CMAKE_BUILD_TYPE}") + +set(CMAKE_CXX_STANDARD 17) +set(CMAKE_CXX_STANDARD_REQUIRED ON) +set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wall") +set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} -O3 -march=native") +set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_CURRENT_SOURCE_DIR}/extern/findFFTW") + add_compile_options(-fPIC) include(FetchContent) include(CheckCXXCompilerFlag) include(CMakePrintHelpers) include(CTest) -# include(ExternalProject) + +# find_package(OpenMP REQUIRED) + +list(PREPEND CMAKE_PREFIX_PATH /usr/local) + +set(MKL_INTERFACE_FULL "intel_lp64") +find_package(MKL CONFIG QUIET) +if (MKL_FOUND) + message(STATUS "MKL library found") + message(STATUS "MKL targets: ${MKL_IMPORTED_TARGETS}") + get_target_property(mkl_includes MKL::MKL INTERFACE_INCLUDE_DIRECTORIES) + message(STATUS "MKL includes: ${mkl_includes}") + add_definitions(-DEIGEN_USE_MKL_ALL) + set(MKL_COMMON_LIBS MKL::MKL OpenMP::OpenMP_CXX m dl) + set(BLAS_LINK_LIBRARIES "") +else() + set(MKL_COMMON_LIBS "") + message(STATUS "MKL not found. Trying to find a BLAS implementation") + + # On macOS, prefer Apple's Accelerate framework over other BLAS implementations + if(APPLE) + set(BLA_VENDOR Apple) + message(STATUS "macOS detected: prioritizing Apple Accelerate framework") + endif() + + find_package(BLAS REQUIRED) + message(STATUS "BLAS library found: ${BLAS_LIBRARIES}") + add_definitions(-DEIGEN_USE_BLAS) + # set(BLAS_LINK_LIBRARIES ${BLAS_LIBRARIES} OpenMP::OpenMP_CXX) + set(BLAS_LINK_LIBRARIES ${BLAS_LIBRARIES}) +endif() set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "extern/findFFTW") #find_package(FFTW QUIET) @@ -17,41 +58,9 @@ set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "extern/findFFTW") # include_directories(${FFTW_INCLUDE_DIRS}) #else() # message(WARNING "FFTW (+float capabilities) not found, we recommend you install it") -# TODO: Perhaps downloading? -# set(FFTW_INSTALL_DIR ${CMAKE_BINARY_DIR}/fftw-install) -# ExternalProject_Add(fftw -# URL http://www.fftw.org/fftw-3.3.10.tar.gz -# PREFIX fftw-src -# CONFIGURE_COMMAND ./configure --prefix=${FFTW_INSTALL_DIR} --enable-float -# BUILD_COMMAND make -j4 -# INSTALL_COMMAND make install -# ) -# set(FFTW_INCLUDE_DIRS ${FFTW_INSTALL_DIR}/include) -# set(FFTW_FLOAT_LIB ${FFTW_INSTALL_DIR}/lib/libfftw3f.so) -# set(FFTW_FOUND TRUE) -# add_definitions(-DHAS_FFTW) #endif() -# Installing FFTW https://www.fftw.org/fftw3_doc/Installation-on-Unix.html --------------------------------------------- -# wget https://www.fftw.org/fftw-3.3.10.tar.gz -# tar -xvzf fftw-3.3.10.tar.gz -# cd fftw-3.3.10 -# ./configure --enable-float --enable-shared # --enabled-shared is required for ctypes.util.find_library("fftw3f") -# sudo make -# sudo make install -# ldconfig - -# CMAKE_SOURCE_DIR: ---------------------------------------------------------------------------------------------------- add_compile_definitions(CMAKE_SOURCE_DIR="${CMAKE_SOURCE_DIR}") -# Gtest: --------------------------------------------------------------------------------------------------------------- -#include(FetchContent) -#FetchContent_Declare( -# googletest -# URL https://github.com/google/googletest/archive/03597a01ee50ed33e9dfd640b249b4be3799d395.zip -#) -#set(gtest_force_shared_crt ON CACHE BOOL "" FORCE) -#FetchContent_MakeAvailable(googletest) - -include_directories(include extern) +include_directories(include extern/Eigen) add_subdirectory(benchmarks) diff --git a/benchmarks/CMakeLists.txt b/benchmarks/CMakeLists.txt index df5bd36..f89e09b 100644 --- a/benchmarks/CMakeLists.txt +++ b/benchmarks/CMakeLists.txt @@ -1,3 +1,10 @@ +set(BENCH_COMMON_LIBS ${MKL_COMMON_LIBS} ${BLAS_LINK_LIBRARIES}) + +if (FFTW_FOUND) + message(STATUS "Linking FFTW: ${FFTW_FLOAT_LIB} ${FFTW_FLOAT_OPENMP_LIB}") + list(APPEND BENCH_COMMON_LIBS ${FFTW_FLOAT_LIB} ${FFTW_FLOAT_OPENMP_LIB}) +endif() + add_executable(BenchmarkPDXADSampling bench_adsampling/pdx_ivf_adsampling.cpp) add_executable(BenchmarkIVF2ADSampling bench_adsampling/pdx_ivf2_adsampling.cpp) add_executable(BenchmarkU8PDXADSampling bench_adsampling/pdx_ivf_adsampling_u8.cpp) @@ -5,11 +12,9 @@ add_executable(BenchmarkU8IVF2ADSampling bench_adsampling/pdx_ivf2_adsampling_u8 add_executable(FilteredBenchmarkU8IVF2ADSampling bench_adsampling/pdx_ivf2_adsampling_u8_filtered.cpp) add_executable(FilteredBenchmarkPDXADSampling bench_adsampling/pdx_ivf_adsampling_filtered.cpp) -if (FFTW_FOUND) - target_link_libraries(BenchmarkPDXADSampling ${FFTW_FLOAT_LIB}) - target_link_libraries(FilteredBenchmarkPDXADSampling ${FFTW_FLOAT_LIB}) - target_link_libraries(BenchmarkIVF2ADSampling ${FFTW_FLOAT_LIB}) - target_link_libraries(BenchmarkU8PDXADSampling ${FFTW_FLOAT_LIB}) - target_link_libraries(BenchmarkU8IVF2ADSampling ${FFTW_FLOAT_LIB}) - target_link_libraries(FilteredBenchmarkU8IVF2ADSampling ${FFTW_FLOAT_LIB}) -endif() +target_link_libraries(BenchmarkPDXADSampling ${BENCH_COMMON_LIBS}) +target_link_libraries(FilteredBenchmarkPDXADSampling ${BENCH_COMMON_LIBS}) +target_link_libraries(BenchmarkIVF2ADSampling ${BENCH_COMMON_LIBS}) +target_link_libraries(BenchmarkU8PDXADSampling ${BENCH_COMMON_LIBS}) +target_link_libraries(BenchmarkU8IVF2ADSampling ${BENCH_COMMON_LIBS}) +target_link_libraries(FilteredBenchmarkU8IVF2ADSampling ${BENCH_COMMON_LIBS}) diff --git a/benchmarks/bench_adsampling/pdx_ivf_adsampling.cpp b/benchmarks/bench_adsampling/pdx_ivf_adsampling.cpp index 0b71da8..1476dab 100644 --- a/benchmarks/bench_adsampling/pdx_ivf_adsampling.cpp +++ b/benchmarks/bench_adsampling/pdx_ivf_adsampling.cpp @@ -30,12 +30,9 @@ int main(int argc, char *argv[]) { uint8_t KNN = BenchmarkUtils::KNN; float SELECTIVITY_THRESHOLD = BenchmarkUtils::SELECTIVITY_THRESHOLD; - float EPSILON0 = BenchmarkUtils::EPSILON0; size_t NUM_QUERIES; size_t NUM_MEASURE_RUNS = BenchmarkUtils::NUM_MEASURE_RUNS; - PDX::DimensionsOrder DIMENSION_ORDER = PDX::SEQUENTIAL; - std::string RESULTS_PATH; RESULTS_PATH = BENCHMARK_UTILS.RESULTS_DIR_PATH + "IVF_PDX_ADSAMPLING.csv"; @@ -58,8 +55,8 @@ int main(int argc, char *argv[]) { auto *int_ground_truth = reinterpret_cast(ground_truth.get()); query += 1; // skip number of embeddings - PDX::ADSamplingPruner pruner = PDX::ADSamplingPruner(pdx_data.num_dimensions, EPSILON0, _matrix); - PDX::PDXearch searcher = PDX::PDXearch(pdx_data, pruner, 1, DIMENSION_ORDER); + PDX::ADSamplingPruner pruner = PDX::ADSamplingPruner(pdx_data.num_dimensions, _matrix); + PDX::PDXearch searcher = PDX::PDXearch(pdx_data, pruner); std::vector nprobes_to_use; if (arg_ivf_nprobe > 0) { diff --git a/benchmarks/results/DEFAULT/IVF_PDX_ADSAMPLING.csv b/benchmarks/results/DEFAULT/IVF_PDX_ADSAMPLING.csv index 8223d00..825379d 100644 --- a/benchmarks/results/DEFAULT/IVF_PDX_ADSAMPLING.csv +++ b/benchmarks/results/DEFAULT/IVF_PDX_ADSAMPLING.csv @@ -9,3 +9,28 @@ agnews-mxbai-1024-euclidean,adsampling,10.1151,10.8596,9.44171,0.00048,24,0,100, agnews-mxbai-1024-euclidean,adsampling,10.3856,11.3599,9.40304,0.00048,24,0,100,1000,0.2,1,10.41,20.79,9.403 agnews-mxbai-1024-euclidean,adsampling,15.6141,18.7602,12.4789,0.00011,24,0,100,1000,0.2,1,15.59,25.86,11.52 agnews-mxbai-1024-euclidean,adsampling,10.5254,13.5434,8.07146,0.901379,24,0,100,1000,0.2,1,11.02,20.96,8.071 +agnews-mxbai-1024-euclidean,adsampling,0.710086,0.88425,0.548042,0.00016,24,0,100,1000,0.2,1,0.7117,0.923,0.4099 +agnews-mxbai-1024-euclidean,adsampling,0.656416,0.818208,0.495958,4e-05,24,0,100,1000,0.2,1,0.6583,0.8995,0.4075 +agnews-mxbai-1024-euclidean,adsampling,0.704976,0.954042,0.498292,4e-05,24,0,100,1000,0.2,1,0.7809,20.69,0.4983 +agnews-mxbai-1024-euclidean,adsampling,0.653908,0.805375,0.511375,4e-05,24,0,100,1000,0.2,1,0.6544,0.8671,0.4163 +agnews-mxbai-1024-euclidean,adsampling,0.225787,0.251625,0.2005,5e-05,1,0,100,1000,0.2,1,0.2266,0.286,0.1971 +agnews-mxbai-1024-euclidean,adsampling,0.246352,0.285417,0.210209,5e-05,2,0,100,1000,0.2,1,0.247,0.3009,0.2102 +agnews-mxbai-1024-euclidean,adsampling,0.763889,0.936667,0.596459,5e-05,30,0,100,1000,0.2,1,0.7669,1.076,0.4444 +agnews-mxbai-1024-euclidean,adsampling,0.766282,0.955792,0.582541,5e-05,30,0,100,1000,0.2,1,0.7667,1.015,0.4387 +agnews-mxbai-1024-euclidean,adsampling,0.76148,0.935375,0.592834,5e-05,30,0,100,1000,0.2,1,0.7617,1.013,0.4459 +agnews-mxbai-1024-euclidean,adsampling,0.687247,0.885083,0.50875,4e-05,24,0,100,1000,0.2,1,0.6898,1.033,0.4188 +agnews-mxbai-1024-euclidean,adsampling,0.694679,0.866541,0.528208,4e-05,24,0,100,1000,0.2,1,0.696,0.929,0.4118 +agnews-mxbai-1024-euclidean,adsampling,0.656322,0.802375,0.51225,4e-05,24,0,100,1000,0.2,1,0.657,0.8572,0.3941 +agnews-mxbai-1024-euclidean,adsampling,0.663246,0.829417,0.500125,4e-05,24,0,100,1000,0.2,1,0.6658,1.003,0.3982 +agnews-mxbai-1024-euclidean,adsampling,0.700727,0.878583,0.540458,4e-05,24,0,100,1000,0.2,1,0.7018,1.037,0.3897 +agnews-mxbai-1024-euclidean,adsampling,0.666279,0.839375,0.522041,4e-05,24,0,100,1000,0.2,1,0.6681,0.8985,0.4005 +agnews-mxbai-1024-euclidean,adsampling,62.9019,88.1363,37.2853,6e-05,3509,0,100,1000,0.2,1,69.14,1025,20.29 +agnews-mxbai-1024-euclidean,adsampling,0.696483,0.874458,0.538959,4e-05,24,0,100,1000,0.2,1,0.697,0.9259,0.423 +agnews-mxbai-1024-euclidean,adsampling,0.718798,0.938709,0.500083,4e-05,24,0,100,1000,0.2,1,0.7218,1.808,0.4432 +agnews-mxbai-1024-euclidean,adsampling,38.3509,46.3931,30.3877,6e-05,2048,0,100,1000,0.2,1,39.09,348.1,13.31 +agnews-mxbai-1024-euclidean,adsampling,29.8333,35.2115,24.3209,6e-05,1536,0,100,1000,0.2,1,32.19,689.9,10.72 +agnews-mxbai-1024-euclidean,adsampling,61.3956,82.0705,41.6479,6e-05,3509,0,100,1000,0.2,1,63.81,1016,19.79 +agnews-mxbai-1024-euclidean,adsampling,0.659602,0.809416,0.510125,4e-05,24,0,100,1000,0.2,1,0.66,0.8753,0.3866 +agnews-mxbai-1024-euclidean,adsampling,0.691954,0.834292,0.547458,4e-05,24,0,100,1000,0.2,1,0.6931,0.9468,0.4371 +agnews-mxbai-1024-euclidean,adsampling,0.53561,0.789667,0.325125,0.90166,24,0,100,1000,0.2,1,0.5368,0.9136,0.3251 +agnews-mxbai-1024-euclidean,adsampling,0.469188,0.662875,0.263625,0.90166,24,0,100,1000,0.2,1,0.4706,0.7823,0.2636 diff --git a/include/clustering.hpp b/include/clustering.hpp new file mode 100644 index 0000000..707cff7 --- /dev/null +++ b/include/clustering.hpp @@ -0,0 +1,79 @@ +#pragma once + +#include +#include "common.hpp" +#include "superkmeans/hierarchical_superkmeans.h" + +namespace PDX { + +struct KMeansResult { + // Row-major buffer of all centroids (num_clusters * num_dimensions). + std::vector centroids; + + // Mapping from a centroid to its embeddings. + // + // The embeddings are represented as indices into the original `embeddings` array. The `embeddings` array was passed + // as a parameter to the `ComputeKMeans` function. + // + // `assignments[0] -> [1, 3]` means that the 2nd and 4th embeddings in the `embeddings` array belong to the 0th + // cluster/centroid. + std::vector> assignments; + + static constexpr size_t MIN_EMBEDDINGS_TO_SAMPLE = 30720; + + explicit KMeansResult(uint32_t num_clusters) : assignments(num_clusters) { + } +}; + +// Compute centroids (clusters) and centroid-to-embedding assignments using SuperKMeans. +[[nodiscard]] inline KMeansResult ComputeKMeans(const float *const embeddings, const uint64_t num_embeddings, + const uint32_t num_dimensions, const uint32_t num_clusters, + const PDX::DistanceMetric distance_metric, const uint32_t seed) { + D_ASSERT(num_embeddings >= 1); + D_ASSERT(num_dimensions >= 1); + D_ASSERT(num_clusters >= 1); + + auto result = KMeansResult(num_clusters); + + if (num_clusters == 1) { + // Copy the first embedding as the centroid + result.centroids = std::vector(embeddings, embeddings + num_dimensions); + // Assign all embeddings to the first cluster + for (uint64_t vec_id = 0; vec_id < num_embeddings; vec_id++) { + result.assignments[0].emplace_back(vec_id); + } + return result; + } + + // Compute centroids + skmeans::HierarchicalSuperKMeansConfig config; + if (num_embeddings < KMeansResult::MIN_EMBEDDINGS_TO_SAMPLE) { + config.sampling_fraction = 1.0f; + } else { + config.sampling_fraction = 0.3f; + } + config.angular = distance_metric == PDX::DistanceMetric::COSINE || distance_metric == PDX::DistanceMetric::IP; + config.data_already_rotated = true; + config.iters_mesoclustering = 3; + config.iters_fineclustering = 5; + config.iters_refinement = 0; + config.suppress_warnings = true; + config.seed = seed; + + auto kmeans = skmeans::HierarchicalSuperKMeans(num_clusters, num_dimensions, config); + result.centroids = kmeans.Train(embeddings, num_embeddings); + + // Extract assignments + // SuperKMeans returns assignment from vec_id (not row_id) to centroid_idx + std::vector assignments = + kmeans.Assign(embeddings, result.centroids.data(), num_embeddings, num_clusters); + // Convert into assignment from centroid_idx to vec_id (not row_id) + result.assignments.resize(num_clusters); + for (uint64_t vec_id = 0; vec_id < num_embeddings; vec_id++) { + result.assignments[assignments[vec_id]].emplace_back(vec_id); + } + + return result; +}; + +} // namespace PDX diff --git a/include/common.hpp b/include/common.hpp index ed6d638..3dfcc51 100644 --- a/include/common.hpp +++ b/include/common.hpp @@ -1,115 +1,204 @@ -#ifndef PDX_COMMON_HPP -#define PDX_COMMON_HPP +#pragma once -#include #include #include - +#include +#include +#include +#include +#include + + +#ifndef PDX_RESTRICT +#if defined(__GNUC__) || defined(__clang__) +#define PDX_RESTRICT __restrict__ +#elif defined(_MSC_VER) +#define PDX_RESTRICT __restrict +#elif defined(__INTEL_COMPILER) +#define PDX_RESTRICT __restrict__ +#else +#define PDX_RESTRICT +#endif +#endif + +#if defined(__GNUC__) || defined(__clang__) +#define PDX_LIKELY(x) __builtin_expect(!!(x), 1) +#define PDX_UNLIKELY(x) __builtin_expect(!!(x), 0) +#else +#define PDX_LIKELY(x) (x) +#define PDX_UNLIKELY(x) (x) +#endif namespace PDX { - static inline float PROPORTION_VERTICAL_DIM = 0.75; - static inline size_t D_THRESHOLD_FOR_DCT_ROTATION = 512; - static constexpr size_t PDX_VECTOR_SIZE = 64; - - template - static constexpr uint32_t AlignValue(T n) { - return ((n + (val - 1)) / val) * val; - } - - enum DimensionsOrder { - SEQUENTIAL, - DISTANCE_TO_MEANS, - DECREASING, - DISTANCE_TO_MEANS_IMPROVED, - DECREASING_IMPROVED, - DIMENSION_ZONES - }; - - enum DistanceFunction { - L2, - IP, - L1, - NEGATIVE_L2 // Only the negative term of L2 (-2*q[i]*d[i]) - }; - - enum Quantization { - F32, - U8, - // TODO: - F16, - BF, - U6, - U4, - ASYMMETRIC_U8, - ASYMMETRIC_LEP_U8 - }; - - // TODO: Do the same for indexes? - template - struct DistanceType { - using type = uint32_t; // default for U8, U6, U4 - }; - template<> - struct DistanceType { - using type = float; - }; - template - using DistanceType_t = typename DistanceType::type; - - // TODO: Do the same for indexes? - template - struct DataType { - using type = uint8_t; // default for U8, U6, U4 - }; - template<> - struct DataType { - using type = float; - }; - template - using DataType_t = typename DataType::type; - - - template - struct QuantizedVectorType { - using type = uint8_t; // default for U8, U6, U4 - }; - template<> - struct QuantizedVectorType { - using type = float; - }; - template - using QuantizedVectorType_t = typename QuantizedVectorType::type; - - - template - struct KNNCandidate { - uint32_t index; - float distance; - }; - - template - struct VectorComparator { - bool operator() (const KNNCandidate& a, const KNNCandidate& b) { - return a.distance < b.distance; - } - }; - - template - struct Cluster { // default for U8, U6, U4 - uint32_t num_embeddings{}; - uint32_t *indices = nullptr; - uint8_t *data = nullptr; - }; - - template<> - struct Cluster { - uint32_t num_embeddings{}; - uint32_t *indices = nullptr; - float *data = nullptr; - }; +static constexpr float PROPORTION_HORIZONTAL_DIM = 0.75f; +static constexpr size_t D_THRESHOLD_FOR_DCT_ROTATION = 512; +static constexpr size_t PDX_MAX_DIMS = 16384; +static constexpr size_t H_DIM_SIZE = 64; +static constexpr size_t U8_INTERLEAVE_SIZE = 4; +static constexpr uint32_t DIMENSIONS_FETCHING_SIZES[20] = {16, 16, 32, 32, 32, 32, 64, 64, 64, 64, + 128, 128, 128, 128, 256, 256, 512, 1024, 2048, 16384}; + +static constexpr bool AllFetchingSizesMultipleOfU8InterleaveSize() { + for (auto s : DIMENSIONS_FETCHING_SIZES) { + if (s % U8_INTERLEAVE_SIZE != 0) { + return false; + } + } + return true; +} +static_assert(AllFetchingSizesMultipleOfU8InterleaveSize(), + "All DIMENSIONS_FETCHING_SIZES must be multiples of U8_INTERLEAVE_SIZE"); + +// Epsilon0 parameter of ADSampling (Reference: https://dl.acm.org/doi/abs/10.1145/3589282) +static constexpr float ADSAMPLING_PRUNING_AGGRESIVENESS = 1.5f; + +template +static constexpr uint32_t AlignValue(T n) { + return ((n + (val - 1)) / val) * val; +} + +enum class DistanceMetric { L2SQ, COSINE, IP }; + +enum Quantization { F32, U8, F16, BF }; + +// TODO: Do the same for indexes? +template +struct DistanceType { + using type = uint32_t; +}; +template <> +struct DistanceType { + using type = float; +}; +template +using pdx_distance_t = typename DistanceType::type; + +// TODO: Do the same for indexes? +template +struct DataType { + using type = uint8_t; // U8 +}; +template <> +struct DataType { + using type = float; +}; +template +using pdx_data_t = typename DataType::type; + +template +struct QuantizedVectorType { + using type = uint8_t; // U8 +}; +template <> +struct QuantizedVectorType { + using type = float; +}; +template +using pdx_quantized_embedding_t = typename QuantizedVectorType::type; + +using eigen_matrix_t = Eigen::Matrix; + +struct KNNCandidate { + uint32_t index; + float distance; +}; + +struct VectorComparator { + bool operator()(const KNNCandidate &a, const KNNCandidate &b) { + return a.distance < b.distance; + } +}; + +template +struct Cluster { + using data_t = pdx_data_t; + + Cluster(uint32_t num_embeddings, uint32_t num_dimensions) + : num_embeddings(num_embeddings), indices(new uint32_t[num_embeddings]), + data(new data_t[static_cast(num_embeddings) * num_dimensions]) { + } + ~Cluster() { + delete[] data; + delete[] indices; + } + uint32_t num_embeddings {}; + uint32_t *indices = nullptr; + data_t *data = nullptr; }; -#endif //PDX_COMMON_HPP +using Heap = std::priority_queue, VectorComparator>; + +struct PDXDimensionSplit { + const uint32_t horizontal_dimensions; + const uint32_t vertical_dimensions; +}; + +[[nodiscard]] static inline constexpr PDXDimensionSplit GetPDXDimensionSplit(const uint32_t num_dimensions) { + auto local_proportion_horizontal_dim = PROPORTION_HORIZONTAL_DIM; + if (num_dimensions <= 128) { + local_proportion_horizontal_dim = 0.25; + } + auto horizontal_d = static_cast(static_cast(num_dimensions) * local_proportion_horizontal_dim); + auto vertical_d = static_cast(num_dimensions - horizontal_d); + if (horizontal_d % H_DIM_SIZE > 0) { + horizontal_d = ((horizontal_d + H_DIM_SIZE / 2) / H_DIM_SIZE) * H_DIM_SIZE; + vertical_d = num_dimensions - horizontal_d; + } + if (!vertical_d) { + horizontal_d = H_DIM_SIZE; + vertical_d = num_dimensions - horizontal_d; + } + if (num_dimensions <= H_DIM_SIZE) { + horizontal_d = 0; + vertical_d = num_dimensions; + } + + assert(horizontal_d + vertical_d == num_dimensions); + + return {horizontal_d, vertical_d}; +}; + +static_assert(GetPDXDimensionSplit(4).horizontal_dimensions == 0); +static_assert(GetPDXDimensionSplit(4).vertical_dimensions == 4); + +static_assert(GetPDXDimensionSplit(33).horizontal_dimensions == 0); +static_assert(GetPDXDimensionSplit(33).vertical_dimensions == 33); + +static_assert(GetPDXDimensionSplit(64).horizontal_dimensions == 0); +static_assert(GetPDXDimensionSplit(64).vertical_dimensions == 64); + +static_assert(GetPDXDimensionSplit(65).horizontal_dimensions == 0); +static_assert(GetPDXDimensionSplit(65).vertical_dimensions == 65); + +static_assert(GetPDXDimensionSplit(100).horizontal_dimensions == 0); +static_assert(GetPDXDimensionSplit(100).vertical_dimensions == 100); + +static_assert(GetPDXDimensionSplit(127).horizontal_dimensions == 0); +static_assert(GetPDXDimensionSplit(127).vertical_dimensions == 127); + +static_assert(GetPDXDimensionSplit(128).horizontal_dimensions == 64); +static_assert(GetPDXDimensionSplit(128).vertical_dimensions == 64); + +static_assert(GetPDXDimensionSplit(256).horizontal_dimensions == 192); +static_assert(GetPDXDimensionSplit(256).vertical_dimensions == 64); + +static_assert(GetPDXDimensionSplit(1024).horizontal_dimensions == 768); +static_assert(GetPDXDimensionSplit(1024).vertical_dimensions == 256); + +static_assert(GetPDXDimensionSplit(1028).horizontal_dimensions == 768); +static_assert(GetPDXDimensionSplit(1028).vertical_dimensions == 260); + +[[nodiscard]] inline constexpr uint32_t ComputeNumberOfClusters(const uint32_t num_embeddings) { + if (num_embeddings < 500000) { + return std::ceil(2 * std::sqrt(num_embeddings)); + } else if (num_embeddings < 2500000) { + return std::ceil(4 * std::sqrt(num_embeddings)); + } else { + return std::ceil(8 * std::sqrt(num_embeddings)); + } +} + +} // namespace PDX diff --git a/include/db_mock/predicate_evaluator.hpp b/include/db_mock/predicate_evaluator.hpp index e8f99f5..119660a 100644 --- a/include/db_mock/predicate_evaluator.hpp +++ b/include/db_mock/predicate_evaluator.hpp @@ -1,61 +1,29 @@ -#ifndef PDX_PREDICATE_EVALUATOR_H -#define PDX_PREDICATE_EVALUATOR_H +#pragma once #include #include #include -#include -#include "common.hpp" -#include "utils/file_reader.hpp" namespace PDX { class PredicateEvaluator { public: - - std::unique_ptr file_buffer; - - uint32_t * n_passing_tuples = nullptr; - uint8_t * selection_vector = nullptr; - size_t passing_tuples = 0; - size_t n_clusters; - - PredicateEvaluator(size_t n_clusters): n_clusters(n_clusters){}; - - ~PredicateEvaluator() = default; - - void LoadSelectionVectorFromFile(const std::string &filename) { - file_buffer = MmapFile(filename); - LoadSelectionVector(file_buffer.get()); - } - - void LoadSelectionVector(char *input) { - char *next_value = input; - n_passing_tuples = (uint32_t *) next_value; - passing_tuples = 0; - for (size_t i = 0; i < n_clusters; i++) { - passing_tuples += n_passing_tuples[i]; - } - next_value += sizeof(uint32_t) * n_clusters; - selection_vector = (uint8_t *) next_value; - } - - void LoadSelectionVector(uint32_t *n_passing_tuples_p, uint8_t *selection_vector_p) { - n_passing_tuples = n_passing_tuples_p; - passing_tuples = 0; - for (size_t i = 0; i < n_clusters; i++) { - passing_tuples += n_passing_tuples[i]; - } - selection_vector = selection_vector_p; - } - - std::pair GetSelectionVector(const size_t cluster_id, const size_t cluster_offset) const { - return { selection_vector + cluster_offset, n_passing_tuples[cluster_id]}; - } - + // Number of tuples per cluster that passed the predicate. + std::unique_ptr n_passing_tuples; + // Contiguous array of selection vectors for each cluster. + // [(cluster 1): 0, 1, 1, (cluster 2): 1, 1, 1, 1]. + std::unique_ptr selection_vector; + size_t n_clusters; + + explicit PredicateEvaluator(size_t n_clusters, size_t total_num_embeddings) : n_clusters(n_clusters) { + n_passing_tuples = std::make_unique(n_clusters); + selection_vector = std::make_unique(total_num_embeddings); + }; + + std::pair GetSelectionVector(const size_t cluster_id, const size_t cluster_offset) const { + return {&selection_vector[cluster_offset], n_passing_tuples[cluster_id]}; + } }; }; // namespace PDX - -#endif //PDX_PREDICATE_EVALUATOR_H diff --git a/include/distance_computers/avx2_computers.hpp b/include/distance_computers/avx2_computers.hpp index 4740278..366bea9 100644 --- a/include/distance_computers/avx2_computers.hpp +++ b/include/distance_computers/avx2_computers.hpp @@ -1,212 +1,180 @@ -#ifndef PDX_AVX2_COMPUTERS_HPP -#define PDX_AVX2_COMPUTERS_HPP +#pragma once #include -#include #include #include "common.hpp" #include "distance_computers/scalar_computers.hpp" namespace PDX { -template -class SIMDComputer { -}; - -template<> -class SIMDComputer { - -}; - +template +class SIMDComputer {}; -template<> -class SIMDComputer { +template <> +class SIMDComputer { public: - using DISTANCE_TYPE = DistanceType_t; - using QUERY_TYPE = QuantizedVectorType_t; - using DATA_TYPE = DataType_t; - using scalar_computer = ScalarComputer; - - alignas(64) static DISTANCE_TYPE pruning_distances_tmp[4096]; - - // Defer to the scalar kernel - template - static void VerticalPruning( - const QUERY_TYPE *__restrict query, - const DATA_TYPE *__restrict data, - size_t n_vectors, - size_t total_vectors, - size_t start_dimension, - size_t end_dimension, - DISTANCE_TYPE *distances_p, - const uint32_t *pruning_positions = nullptr, - const uint32_t *indices_dimensions = nullptr, - const int32_t *dim_clip_value = nullptr - ) { - size_t dimensions_jump_factor = total_vectors; - for (size_t dimension_idx = start_dimension; dimension_idx < end_dimension; ++dimension_idx) { - uint32_t true_dimension_idx = dimension_idx; - if constexpr (USE_DIMENSIONS_REORDER) { - true_dimension_idx = indices_dimensions[dimension_idx]; - } - size_t offset_to_dimension_start = true_dimension_idx * dimensions_jump_factor; - for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { - auto true_vector_idx = vector_idx; - if constexpr (SKIP_PRUNED) { - true_vector_idx = pruning_positions[vector_idx]; - } - float to_multiply = query[true_dimension_idx] - data[offset_to_dimension_start + true_vector_idx]; - distances_p[true_vector_idx] += to_multiply * to_multiply; - } - } - } - - // Defer to the scalar kernel - static void Vertical( - const QUERY_TYPE *__restrict query, - const DATA_TYPE *__restrict data, - size_t start_dimension, - size_t end_dimension, - DISTANCE_TYPE *distances_p - ) { - for (size_t dim_idx = start_dimension; dim_idx < end_dimension; dim_idx++) { - size_t dimension_idx = dim_idx; - size_t offset_to_dimension_start = dimension_idx * PDX_VECTOR_SIZE; - for (size_t vector_idx = 0; vector_idx < PDX_VECTOR_SIZE; ++vector_idx) { - float to_multiply = query[dimension_idx] - data[offset_to_dimension_start + vector_idx]; - distances_p[vector_idx] += to_multiply * to_multiply; - } - } - } - - static DISTANCE_TYPE Horizontal( - const QUERY_TYPE *__restrict vector1, - const DATA_TYPE *__restrict vector2, - size_t num_dimensions - ) { - __m256 d2_vec = _mm256_setzero_ps(); - size_t i = 0; - for (; i + 8 <= num_dimensions; i += 8) { - __m256 a_vec = _mm256_loadu_ps(vector1 + i); - __m256 b_vec = _mm256_loadu_ps(vector2 + i); - __m256 d_vec = _mm256_sub_ps(a_vec, b_vec); - d2_vec = _mm256_fmadd_ps(d_vec, d_vec, d2_vec); - } - - // _simsimd_reduce_f32x8_haswell - // Convert the lower and higher 128-bit lanes of the input vector to double precision - __m128 low_f32 = _mm256_castps256_ps128(d2_vec); - __m128 high_f32 = _mm256_extractf128_ps(d2_vec, 1); - - // Convert single-precision (float) vectors to double-precision (double) vectors - __m256d low_f64 = _mm256_cvtps_pd(low_f32); - __m256d high_f64 = _mm256_cvtps_pd(high_f32); - - // Perform the addition in double-precision - __m256d sum = _mm256_add_pd(low_f64, high_f64); - - // Reduce the double-precision vector to a scalar - // Horizontal add the first and second double-precision values, and third and fourth - __m128d sum_low = _mm256_castpd256_pd128(sum); - __m128d sum_high = _mm256_extractf128_pd(sum, 1); - __m128d sum128 = _mm_add_pd(sum_low, sum_high); - - // Horizontal add again to accumulate all four values into one - sum128 = _mm_hadd_pd(sum128, sum128); - - // Convert the final sum to a scalar double-precision value and return - double d2 = _mm_cvtsd_f64(sum128); - - for (; i < num_dimensions; ++i) { - float d = vector1[i] - vector2[i]; - d2 += d * d; - } - - return static_cast(d2); - }; - + using distance_t = pdx_distance_t; + using query_t = pdx_quantized_embedding_t; + using data_t = pdx_data_t; + using scalar_computer = ScalarComputer; + + template + static void Vertical(const query_t *PDX_RESTRICT query, const data_t *PDX_RESTRICT data, size_t n_vectors, + size_t total_vectors, size_t start_dimension, size_t end_dimension, distance_t *distances_p, + const uint32_t *pruning_positions = nullptr) { + size_t dimensions_jump_factor = total_vectors; + for (size_t dimension_idx = start_dimension; dimension_idx < end_dimension; ++dimension_idx) { + size_t offset_to_dimension_start = dimension_idx * dimensions_jump_factor; + for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { + auto true_vector_idx = vector_idx; + if constexpr (SKIP_PRUNED) { + true_vector_idx = pruning_positions[vector_idx]; + } + distance_t to_multiply = query[dimension_idx] - data[offset_to_dimension_start + true_vector_idx]; + distances_p[true_vector_idx] += to_multiply * to_multiply; + } + } + } + + static distance_t Horizontal(const query_t *PDX_RESTRICT vector1, const data_t *PDX_RESTRICT vector2, + size_t num_dimensions) { + __m256 d2_vec = _mm256_setzero_ps(); + size_t i = 0; + for (; i + 8 <= num_dimensions; i += 8) { + __m256 a_vec = _mm256_loadu_ps(vector1 + i); + __m256 b_vec = _mm256_loadu_ps(vector2 + i); + __m256 d_vec = _mm256_sub_ps(a_vec, b_vec); + d2_vec = _mm256_fmadd_ps(d_vec, d_vec, d2_vec); + } + + // _simsimd_reduce_f32x8_haswell + // Convert the lower and higher 128-bit lanes of the input vector to double precision + __m128 low_f32 = _mm256_castps256_ps128(d2_vec); + __m128 high_f32 = _mm256_extractf128_ps(d2_vec, 1); + + // Convert single-precision (float) vectors to double-precision (double) vectors + __m256d low_f64 = _mm256_cvtps_pd(low_f32); + __m256d high_f64 = _mm256_cvtps_pd(high_f32); + + // Perform the addition in double-precision + __m256d sum = _mm256_add_pd(low_f64, high_f64); + + // Reduce the double-precision vector to a scalar + // Horizontal add the first and second double-precision values, and third and fourth + __m128d sum_low = _mm256_castpd256_pd128(sum); + __m128d sum_high = _mm256_extractf128_pd(sum, 1); + __m128d sum128 = _mm_add_pd(sum_low, sum_high); + + // Horizontal add again to accumulate all four values into one + sum128 = _mm_hadd_pd(sum128, sum128); + + // Convert the final sum to a scalar double-precision value and return + double d2 = _mm_cvtsd_f64(sum128); + + for (; i < num_dimensions; ++i) { + distance_t d = vector1[i] - vector2[i]; + d2 += d * d; + } + + return static_cast(d2); + }; }; template <> -class SIMDComputer{ +class SIMDComputer { public: - using DISTANCE_TYPE = DistanceType_t; - using QUERY_TYPE = QuantizedVectorType_t; - using DATA_TYPE = DataType_t; - - // Defer to the scalar kernel - template - static void VerticalPruning( - const QUERY_TYPE *__restrict query, - const DATA_TYPE *__restrict data, - size_t n_vectors, - size_t total_vectors, - size_t start_dimension, - size_t end_dimension, - DISTANCE_TYPE * distances_p, - const uint32_t * pruning_positions = nullptr, - const uint32_t * indices_dimensions = nullptr, - const int32_t * dim_clip_value = nullptr - ){ - // TODO - } - - // Defer to the scalar kernel - static void Vertical( - const QUERY_TYPE *__restrict query, - const DATA_TYPE *__restrict data, - size_t start_dimension, - size_t end_dimension, - DISTANCE_TYPE * distances_p - ){ - // TODO - } - - static DISTANCE_TYPE Horizontal( - const QUERY_TYPE *__restrict vector1, - const DATA_TYPE *__restrict vector2, - size_t num_dimensions - ){ - __m256 d2_vec = _mm256_setzero_ps(); - size_t i = 0; - for (; i + 8 <= num_dimensions; i += 8) { - __m256 a_vec = _mm256_loadu_ps(vector1 + i); - __m256 b_vec = _mm256_loadu_ps(vector2 + i); - d2_vec = _mm256_fmadd_ps(a_vec, b_vec, d2_vec); - } - - // _simsimd_reduce_f32x8_haswell - // Convert the lower and higher 128-bit lanes of the input vector to double precision - __m128 low_f32 = _mm256_castps256_ps128(d2_vec); - __m128 high_f32 = _mm256_extractf128_ps(d2_vec, 1); - - // Convert single-precision (float) vectors to double-precision (double) vectors - __m256d low_f64 = _mm256_cvtps_pd(low_f32); - __m256d high_f64 = _mm256_cvtps_pd(high_f32); - - // Perform the addition in double-precision - __m256d sum = _mm256_add_pd(low_f64, high_f64); - - // Reduce the double-precision vector to a scalar - // Horizontal add the first and second double-precision values, and third and fourth - __m128d sum_low = _mm256_castpd256_pd128(sum); - __m128d sum_high = _mm256_extractf128_pd(sum, 1); - __m128d sum128 = _mm_add_pd(sum_low, sum_high); - - // Horizontal add again to accumulate all four values into one - sum128 = _mm_hadd_pd(sum128, sum128); - - // Convert the final sum to a scalar double-precision value and return - double d2 = _mm_cvtsd_f64(sum128); - - for (; i < num_dimensions; ++i) { - d2 += vector1[i] * vector2[i]; - } - return static_cast(d2); - }; - + using distance_t = pdx_distance_t; + using query_t = pdx_quantized_embedding_t; + using data_t = pdx_data_t; + + template + static void Vertical(const query_t *PDX_RESTRICT query, const data_t *PDX_RESTRICT data, size_t n_vectors, + size_t total_vectors, size_t start_dimension, size_t end_dimension, distance_t *distances_p, + const uint32_t *pruning_positions = nullptr) { + auto *query_grouped = reinterpret_cast(query); + size_t dim_idx = start_dimension; + for (; dim_idx + 4 <= end_dimension; dim_idx += 4) { + uint32_t dimension_idx = dim_idx; + size_t offset_to_dimension_start = dimension_idx * total_vectors; + size_t i = 0; + if constexpr (!SKIP_PRUNED) { + uint32_t query_value = query_grouped[dimension_idx / 4]; + __m256i vec1_u8 = _mm256_set1_epi32(query_value); + __m256i zeros = _mm256_setzero_si256(); + for (; i + 8 <= n_vectors; i += 8) { + // Load 8 accumulated distances and 32 bytes of data (4 dims x 8 vectors). + __m256i res = _mm256_loadu_si256(reinterpret_cast(&distances_p[i])); + __m256i vec2_u8 = + _mm256_loadu_si256(reinterpret_cast(&data[offset_to_dimension_start + i * 4])); + __m256i diff = + _mm256_or_si256(_mm256_subs_epu8(vec1_u8, vec2_u8), _mm256_subs_epu8(vec2_u8, vec1_u8)); + // Zero-extend bytes to 16-bit, square, and horizontal-add to 32-bit. + __m256i lo16 = _mm256_unpacklo_epi8(diff, zeros); + __m256i hi16 = _mm256_unpackhi_epi8(diff, zeros); + __m256i sq_lo = _mm256_madd_epi16(lo16, lo16); + __m256i sq_hi = _mm256_madd_epi16(hi16, hi16); + // hadd combines partial sums: [v0, v1, v2, v3, v4, v5, v6, v7]. + __m256i sums = _mm256_hadd_epi32(sq_lo, sq_hi); + _mm256_storeu_si256(reinterpret_cast<__m256i *>(&distances_p[i]), _mm256_add_epi32(res, sums)); + } + } + // Scalar tail (vectors). + for (; i < n_vectors; ++i) { + size_t vector_idx = i; + if constexpr (SKIP_PRUNED) { + vector_idx = pruning_positions[vector_idx]; + } + int da = query[dimension_idx] - data[offset_to_dimension_start + (vector_idx * 4)]; + int db = query[dimension_idx + 1] - data[offset_to_dimension_start + (vector_idx * 4) + 1]; + int dc = query[dimension_idx + 2] - data[offset_to_dimension_start + (vector_idx * 4) + 2]; + int dd = query[dimension_idx + 3] - data[offset_to_dimension_start + (vector_idx * 4) + 3]; + distances_p[vector_idx] += (da * da) + (db * db) + (dc * dc) + (dd * dd); + } + } + if (dim_idx < end_dimension) { + auto remaining = static_cast(end_dimension - dim_idx); + size_t offset = dim_idx * total_vectors; + for (size_t i = 0; i < n_vectors; ++i) { + size_t vector_idx = i; + if constexpr (SKIP_PRUNED) { + vector_idx = pruning_positions[vector_idx]; + } + for (uint32_t k = 0; k < remaining; ++k) { + int diff = query[dim_idx + k] - data[offset + vector_idx * remaining + k]; + distances_p[vector_idx] += diff * diff; + } + } + } + } + + static distance_t Horizontal(const query_t *PDX_RESTRICT vector1, const data_t *PDX_RESTRICT vector2, + size_t num_dimensions) { + __m256i d2_vec = _mm256_setzero_si256(); + __m256i zeros = _mm256_setzero_si256(); + size_t i = 0; + for (; i + 32 <= num_dimensions; i += 32) { + __m256i a_vec = _mm256_loadu_si256(reinterpret_cast(vector1 + i)); + __m256i b_vec = _mm256_loadu_si256(reinterpret_cast(vector2 + i)); + __m256i diff = _mm256_or_si256(_mm256_subs_epu8(a_vec, b_vec), _mm256_subs_epu8(b_vec, a_vec)); + __m256i lo16 = _mm256_unpacklo_epi8(diff, zeros); + __m256i hi16 = _mm256_unpackhi_epi8(diff, zeros); + d2_vec = _mm256_add_epi32(d2_vec, _mm256_madd_epi16(lo16, lo16)); + d2_vec = _mm256_add_epi32(d2_vec, _mm256_madd_epi16(hi16, hi16)); + } + // Reduce 8 x i32 to scalar (simsimd_reduce_i32x8_haswell) + __m128i lo = _mm256_castsi256_si128(d2_vec); + __m128i hi = _mm256_extracti128_si256(d2_vec, 1); + __m128i sum128 = _mm_add_epi32(lo, hi); + sum128 = _mm_hadd_epi32(sum128, sum128); + sum128 = _mm_hadd_epi32(sum128, sum128); + distance_t distance = _mm_cvtsi128_si32(sum128); + // Scalar tail. + for (; i < num_dimensions; ++i) { + int n = static_cast(vector1[i]) - static_cast(vector2[i]); + distance += n * n; + } + return distance; + }; }; -} - -#endif //PDX_AVX2_COMPUTERS_HPP +} // namespace PDX diff --git a/include/distance_computers/avx512_computers.hpp b/include/distance_computers/avx512_computers.hpp index 391a1cc..45c05f1 100644 --- a/include/distance_computers/avx512_computers.hpp +++ b/include/distance_computers/avx512_computers.hpp @@ -1,358 +1,178 @@ -#ifndef PDX_AVX512_COMPUTERS_HPP -#define PDX_AVX512_COMPUTERS_HPP +#pragma once #include -#include #include #include #include "common.hpp" -#include "distance_computers/scalar_computers.hpp" namespace PDX { -template +template class SIMDComputer {}; template <> -class SIMDComputer{ +class SIMDComputer { public: - using DISTANCE_TYPE = DistanceType_t; - using QUERY_TYPE = QuantizedVectorType_t; - using DATA_TYPE = DataType_t; - - template - static void VerticalPruning( - const QUERY_TYPE *__restrict query, - const DATA_TYPE *__restrict data, - size_t n_vectors, - size_t total_vectors, - size_t start_dimension, - size_t end_dimension, - DISTANCE_TYPE * distances_p, - const uint32_t * pruning_positions = nullptr, - const uint32_t * indices_dimensions = nullptr, - const int32_t * dim_clip_value = nullptr, - const float * scaling_factors = nullptr - ){ - __m512i res; - __m512i vec2_u8; - __m512i vec1_u8; - __m512i diff_u8; - __m256i y_res; - __m256i y_vec2_u8; - __m256i y_vec1_u8; - __m256i y_diff_u8; - uint32_t * query_grouped = (uint32_t *)query; - for (size_t dim_idx = start_dimension; dim_idx < end_dimension; dim_idx+=4) { - uint32_t dimension_idx = dim_idx; - if constexpr (USE_DIMENSIONS_REORDER){ - dimension_idx = indices_dimensions[dim_idx]; - } - size_t offset_to_dimension_start = dimension_idx * total_vectors; - size_t i = 0; - if constexpr (!SKIP_PRUNED){ - // To load the query efficiently I will load it as uint32_t (4 bytes packed in 1 word) - uint32_t query_value = query_grouped[dimension_idx / 4]; - // And then broadcast it to the register - vec1_u8 = _mm512_set1_epi32(query_value); - for (; i + 16 <= n_vectors; i+=16) { - // Read 64 bytes of data (64 values) with 4 dimensions of 16 vectors - res = _mm512_load_si512(&distances_p[i]); - vec2_u8 = _mm512_loadu_si512(&data[offset_to_dimension_start + i * 4]); // This 4 is because everytime I read 4 dimensions - diff_u8 = _mm512_or_si512(_mm512_subs_epu8(vec1_u8, vec2_u8), _mm512_subs_epu8(vec2_u8, vec1_u8)); - _mm512_store_epi32(&distances_p[i], _mm512_dpbusds_epi32(res, diff_u8, diff_u8)); - } - y_vec1_u8 = _mm256_set1_epi32(query_value); - for (; i + 8 <= n_vectors; i+=8) { - // Read 32 bytes of data (32 values) with 4 dimensions of 8 vectors - y_res = _mm256_load_epi32(&distances_p[i]); - y_vec2_u8 = _mm256_loadu_epi8(&data[offset_to_dimension_start + i * 4]); // This 4 is because everytime I read 4 dimensions - y_diff_u8 = _mm256_or_si256(_mm256_subs_epu8(y_vec1_u8, y_vec2_u8), _mm256_subs_epu8(y_vec2_u8, y_vec1_u8)); - _mm256_store_epi32(&distances_p[i], _mm256_dpbusds_epi32(y_res, y_diff_u8, y_diff_u8)); - } - } - // rest - for (; i < n_vectors; ++i) { - size_t vector_idx = i; - if constexpr (SKIP_PRUNED){ - vector_idx = pruning_positions[vector_idx]; - } - int to_multiply_a = query[dimension_idx] - data[offset_to_dimension_start + (vector_idx * 4)]; - int to_multiply_b = query[dimension_idx + 1] - data[offset_to_dimension_start + (vector_idx * 4) + 1]; - int to_multiply_c = query[dimension_idx + 2] - data[offset_to_dimension_start + (vector_idx * 4) + 2]; - int to_multiply_d = query[dimension_idx + 3] - data[offset_to_dimension_start + (vector_idx * 4) + 3]; - distances_p[vector_idx] += (to_multiply_a * to_multiply_a) + - (to_multiply_b * to_multiply_b) + - (to_multiply_c * to_multiply_c) + - (to_multiply_d * to_multiply_d); - } - } - } - - static void Vertical( - const QUERY_TYPE *__restrict query, - const DATA_TYPE *__restrict data, - size_t start_dimension, - size_t end_dimension, - DISTANCE_TYPE * distances_p, - const float * scaling_factors = nullptr - ){ - __m512i res[4]; - const uint32_t * query_grouped = (uint32_t *)query; - // Load 64 initial values - for (size_t i = 0; i < 4; ++i) { - res[i] = _mm512_load_si512(&distances_p[i * 16]); - } - // Compute L2 - for (size_t dim_idx = start_dimension; dim_idx < end_dimension; dim_idx+=4) { - const uint32_t dimension_idx = dim_idx; - // To load the query efficiently I will load it as uint32_t (4 bytes packed in 1 word) - const uint32_t query_value = query_grouped[dimension_idx / 4]; - // And then broadcast it to the register - const __m512i vec1_u8 = _mm512_set1_epi32(query_value); - const size_t offset_to_dimension_start = dimension_idx * PDX_VECTOR_SIZE; - for (int i = 0; i < 4; ++i) { // total: 64 vectors (4 iterations of 16 vectors) * 4 dimensions each (at 1 byte per value = 2048-bits) - // Read 64 bytes of data (64 values) with 4 dimensions of 16 vectors - const __m512i vec2_u8 = _mm512_loadu_si512(&data[offset_to_dimension_start + i * 64]); - const __m512i diff_u8 = _mm512_or_si512(_mm512_subs_epu8(vec1_u8, vec2_u8), _mm512_subs_epu8(vec2_u8, vec1_u8)); - // I can use this asymmetric dot product as my values are actually 7-bit - // Hence, the [sign] properties of the second operand is ignored - // As results will never be negative, it can be stored on res[i] without issues - // and it saturates to MAX_INT - res[i] = _mm512_dpbusds_epi32(res[i], diff_u8, diff_u8); - } - } - // Store results back - for (int i = 0; i < 4; ++i) { - _mm512_store_epi32(&distances_p[i * 16], res[i]); - } - } - - static DISTANCE_TYPE Horizontal( - const QUERY_TYPE *__restrict vector1, - const DATA_TYPE *__restrict vector2, - size_t num_dimensions, - const float * scaling_factors = nullptr - ){ - __m512i d2_i32_vec = _mm512_setzero_si512(); - __m512i a_u8_vec, b_u8_vec; - -simsimd_l2sq_u8_ice_cycle: - if (num_dimensions < 64) { - const __mmask64 mask = (__mmask64)_bzhi_u64(0xFFFFFFFFFFFFFFFF, num_dimensions); - a_u8_vec = _mm512_maskz_loadu_epi8(mask, vector1); - b_u8_vec = _mm512_maskz_loadu_epi8(mask, vector2); - num_dimensions = 0; - } - else { - a_u8_vec = _mm512_loadu_si512(vector1); - b_u8_vec = _mm512_loadu_si512(vector2); - vector1 += 64, vector2 += 64, num_dimensions -= 64; - } - - // Substracting unsigned vectors in AVX-512 is done by saturating subtraction: - __m512i d_u8_vec = _mm512_or_si512(_mm512_subs_epu8(a_u8_vec, b_u8_vec), _mm512_subs_epu8(b_u8_vec, a_u8_vec)); - - // Multiply and accumulate at `int8` level which are actually uint7, accumulate at `int32` level: - d2_i32_vec = _mm512_dpbusds_epi32(d2_i32_vec, d_u8_vec, d_u8_vec); - if (num_dimensions) goto simsimd_l2sq_u8_ice_cycle; - return _mm512_reduce_add_epi32(d2_i32_vec); - }; + using distance_t = pdx_distance_t; + using query_t = pdx_quantized_embedding_t; + using data_t = pdx_data_t; + + template + static void Vertical(const query_t *PDX_RESTRICT query, const data_t *PDX_RESTRICT data, size_t n_vectors, + size_t total_vectors, size_t start_dimension, size_t end_dimension, distance_t *distances_p, + const uint32_t *pruning_positions = nullptr) { + size_t dimensions_jump_factor = total_vectors; + for (size_t dimension_idx = start_dimension; dimension_idx < end_dimension; ++dimension_idx) { + size_t offset_to_dimension_start = dimension_idx * dimensions_jump_factor; + for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { + auto true_vector_idx = vector_idx; + if constexpr (SKIP_PRUNED) { + true_vector_idx = pruning_positions[vector_idx]; + } + distance_t to_multiply = query[dimension_idx] - data[offset_to_dimension_start + true_vector_idx]; + distances_p[true_vector_idx] += to_multiply * to_multiply; + } + } + } + + static distance_t Horizontal(const query_t *PDX_RESTRICT vector1, const data_t *PDX_RESTRICT vector2, + size_t num_dimensions) { + __m512 d2_vec = _mm512_setzero(); + __m512 a_vec, b_vec; + simsimd_l2sq_f32_skylake_cycle: + if (num_dimensions < 16) { + __mmask16 mask = static_cast<__mmask16>(_bzhi_u32(0xFFFFFFFF, num_dimensions)); + a_vec = _mm512_maskz_loadu_ps(mask, vector1); + b_vec = _mm512_maskz_loadu_ps(mask, vector2); + num_dimensions = 0; + } else { + a_vec = _mm512_loadu_ps(vector1); + b_vec = _mm512_loadu_ps(vector2); + vector1 += 16, vector2 += 16, num_dimensions -= 16; + } + __m512 d_vec = _mm512_sub_ps(a_vec, b_vec); + d2_vec = _mm512_fmadd_ps(d_vec, d_vec, d2_vec); + if (num_dimensions) { + goto simsimd_l2sq_f32_skylake_cycle; + } + + // _simsimd_reduce_f32x16_skylake + __m512 x = _mm512_add_ps(d2_vec, _mm512_shuffle_f32x4(d2_vec, d2_vec, _MM_SHUFFLE(0, 0, 3, 2))); + __m128 r = _mm512_castps512_ps128(_mm512_add_ps(x, _mm512_shuffle_f32x4(x, x, _MM_SHUFFLE(0, 0, 0, 1)))); + r = _mm_hadd_ps(r, r); + return _mm_cvtss_f32(_mm_hadd_ps(r, r)); + }; }; - template <> -class SIMDComputer{ +class SIMDComputer { public: - using DISTANCE_TYPE = DistanceType_t; - using QUERY_TYPE = QuantizedVectorType_t; - using DATA_TYPE = DataType_t; - using scalar_computer = ScalarComputer; - - alignas(64) static DISTANCE_TYPE pruning_distances_tmp[4096]; - - static void GatherDistances( - size_t n_vectors, - DISTANCE_TYPE * distances_p, - const uint32_t * pruning_positions - ){ - for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { - auto true_vector_idx = pruning_positions[vector_idx]; - pruning_distances_tmp[vector_idx] = distances_p[true_vector_idx]; - } - } - - template - static void GatherBasedKernel( - const QUERY_TYPE *__restrict query, - const DATA_TYPE *__restrict data, - size_t n_vectors, - size_t total_vectors, - size_t start_dimension, - size_t end_dimension, - DISTANCE_TYPE * distances_p, - const uint32_t * pruning_positions = nullptr, - const uint32_t * indices_dimensions = nullptr, - const int32_t * dim_clip_value = nullptr, - const float * scaling_factors = nullptr - ){ - GatherDistances(n_vectors, distances_p, pruning_positions); - __m512 data_vec, d_vec, cur_dist_vec; - __m256 data_vec_m256, d_vec_m256, cur_dist_vec_m256; - // Then we move data to be sequential - size_t dimensions_jump_factor = total_vectors; - for (size_t dimension_idx = start_dimension; dimension_idx < end_dimension; ++dimension_idx) { - uint32_t true_dimension_idx = dimension_idx; - if constexpr (USE_DIMENSIONS_REORDER) { - true_dimension_idx = indices_dimensions[dimension_idx]; - } - __m512 query_vec; - query_vec = _mm512_set1_ps(query[true_dimension_idx]); -// if constexpr (L_ALPHA == IP){ -// query_vec = _mm512_set1_ps(-2 * query[true_dimension_idx]); -// } - size_t offset_to_dimension_start = true_dimension_idx * dimensions_jump_factor; - const float * tmp_data = data + offset_to_dimension_start; - // Now we do the sequential distance calculation loop which would use SIMD - // Up to 16 - size_t i = 0; - for (; i + 16 < n_vectors; i+=16) { - cur_dist_vec = _mm512_load_ps(&pruning_distances_tmp[i]); - data_vec = _mm512_i32gather_ps( - _mm512_load_epi32(&pruning_positions[i]), - tmp_data, sizeof(DISTANCE_TYPE) - ); - d_vec = _mm512_sub_ps(data_vec, query_vec); - cur_dist_vec = _mm512_fmadd_ps(d_vec, d_vec, cur_dist_vec); - _mm512_store_ps(&pruning_distances_tmp[i], cur_dist_vec); - } - __m256 query_vec_m256; - query_vec_m256 = _mm256_set1_ps(query[true_dimension_idx]); -// if constexpr (L_ALPHA == IP){ -// query_vec_m256 = _mm256_set1_ps(-2 * query[true_dimension_idx]); -// } - // Up to 8 - for (; i + 8 < n_vectors; i+=8) { - cur_dist_vec_m256 = _mm256_load_ps(&pruning_distances_tmp[i]); - data_vec_m256 = _mm256_i32gather_ps( - tmp_data, _mm256_load_epi32(&pruning_positions[i]), - sizeof(DISTANCE_TYPE) - ); - d_vec_m256 = _mm256_sub_ps(data_vec_m256, query_vec_m256); - cur_dist_vec_m256 = _mm256_fmadd_ps(d_vec_m256, d_vec_m256, cur_dist_vec_m256); - _mm256_store_ps(&pruning_distances_tmp[i], cur_dist_vec_m256); - } - // Tail - for (; i < n_vectors; i++){ - float to_multiply = query[true_dimension_idx] - tmp_data[pruning_positions[i]]; - pruning_distances_tmp[i] += to_multiply * to_multiply; - } - } - // We now move distances back - for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { - auto true_vector_idx = pruning_positions[vector_idx]; - distances_p[true_vector_idx] = pruning_distances_tmp[vector_idx]; - } - } - - // Defer to the scalar kernel - template - static void VerticalPruning( - const QUERY_TYPE *__restrict query, - const DATA_TYPE *__restrict data, - size_t n_vectors, - size_t total_vectors, - size_t start_dimension, - size_t end_dimension, - DISTANCE_TYPE * distances_p, - const uint32_t * pruning_positions = nullptr, - const uint32_t * indices_dimensions = nullptr, - const int32_t * dim_clip_value = nullptr, - const float * scaling_factors = nullptr - ){ - // SIMD is less efficient when looping on the array of not-yet pruned vectors - // A way to improve the performance by ~20% is using a GATHER intrinsic. However this only works on Intel microarchs. - // In AMD (Zen 4, Zen 3) using a GATHER is shooting ourselves in the foot (~80 uops) - // __AVX512FP16__ macro let us detect Intel architectures (from Sapphire Rapids onwards) -#if false && defined(__AVX512FP16__) - if (n_vectors >= 8) { - GatherBasedKernel( - query, data, n_vectors, total_vectors, start_dimension, end_dimension, - distances_p, pruning_positions, indices_dimensions - ); - return; - } -#endif - size_t dimensions_jump_factor = total_vectors; - for (size_t dimension_idx = start_dimension; dimension_idx < end_dimension; ++dimension_idx) { - uint32_t true_dimension_idx = dimension_idx; - if constexpr (USE_DIMENSIONS_REORDER){ - true_dimension_idx = indices_dimensions[dimension_idx]; - } - size_t offset_to_dimension_start = true_dimension_idx * dimensions_jump_factor; - for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { - auto true_vector_idx = vector_idx; - if constexpr(SKIP_PRUNED){ - true_vector_idx = pruning_positions[vector_idx]; - } - float to_multiply = query[true_dimension_idx] - data[offset_to_dimension_start + true_vector_idx]; - distances_p[true_vector_idx] += to_multiply * to_multiply; - } - } - } - - // Defer to the scalar kernel - static void Vertical( - const QUERY_TYPE *__restrict query, - const DATA_TYPE *__restrict data, - size_t start_dimension, - size_t end_dimension, - DISTANCE_TYPE * distances_p, - const float * scaling_factors = nullptr - ){ - for (size_t dim_idx = start_dimension; dim_idx < end_dimension; dim_idx++) { - size_t dimension_idx = dim_idx; - size_t offset_to_dimension_start = dimension_idx * PDX_VECTOR_SIZE; - for (size_t vector_idx = 0; vector_idx < PDX_VECTOR_SIZE; ++vector_idx) { - float to_multiply = query[dimension_idx] - data[offset_to_dimension_start + vector_idx]; - distances_p[vector_idx] += to_multiply * to_multiply; - } - } - } - - static DISTANCE_TYPE Horizontal( - const QUERY_TYPE *__restrict vector1, - const DATA_TYPE *__restrict vector2, - size_t num_dimensions, - const float * scaling_factors = nullptr - ){ - __m512 d2_vec = _mm512_setzero(); - __m512 a_vec, b_vec; - simsimd_l2sq_f32_skylake_cycle: - if (num_dimensions < 16) { - __mmask16 mask = (__mmask16)_bzhi_u32(0xFFFFFFFF, num_dimensions); - a_vec = _mm512_maskz_loadu_ps(mask, vector1); - b_vec = _mm512_maskz_loadu_ps(mask, vector2); - num_dimensions = 0; - } else { - a_vec = _mm512_loadu_ps(vector1); - b_vec = _mm512_loadu_ps(vector2); - vector1 += 16, vector2 += 16, num_dimensions -= 16; - } - __m512 d_vec = _mm512_sub_ps(a_vec, b_vec); - d2_vec = _mm512_fmadd_ps(d_vec, d_vec, d2_vec); - if (num_dimensions) - goto simsimd_l2sq_f32_skylake_cycle; - - // _simsimd_reduce_f32x16_skylake - __m512 x = _mm512_add_ps(d2_vec, _mm512_shuffle_f32x4(d2_vec, d2_vec, _MM_SHUFFLE(0, 0, 3, 2))); - __m128 r = _mm512_castps512_ps128(_mm512_add_ps(x, _mm512_shuffle_f32x4(x, x, _MM_SHUFFLE(0, 0, 0, 1)))); - r = _mm_hadd_ps(r, r); - return _mm_cvtss_f32(_mm_hadd_ps(r, r)); - }; + using distance_t = pdx_distance_t; + using query_t = pdx_quantized_embedding_t; + using data_t = pdx_data_t; + + template + static void Vertical(const query_t *PDX_RESTRICT query, const data_t *PDX_RESTRICT data, size_t n_vectors, + size_t total_vectors, size_t start_dimension, size_t end_dimension, distance_t *distances_p, + const uint32_t *pruning_positions = nullptr) { + __m512i res; + __m512i vec2_u8; + __m512i vec1_u8; + __m512i diff_u8; + __m256i y_res; + __m256i y_vec2_u8; + __m256i y_vec1_u8; + __m256i y_diff_u8; + uint32_t *query_grouped = reinterpret_cast(query); + size_t dim_idx = start_dimension; + for (; dim_idx + 4 <= end_dimension; dim_idx += 4) { + uint32_t dimension_idx = dim_idx; + size_t offset_to_dimension_start = dimension_idx * total_vectors; + size_t i = 0; + if constexpr (!SKIP_PRUNED) { + // To load the query efficiently I will load it as uint32_t (4 bytes packed in 1 word) + uint32_t query_value = query_grouped[dimension_idx / 4]; + // And then broadcast it to the register + vec1_u8 = _mm512_set1_epi32(query_value); + for (; i + 16 <= n_vectors; i += 16) { + // Read 64 bytes of data (64 values) with 4 dimensions of 16 vectors + res = _mm512_load_si512(&distances_p[i]); + vec2_u8 = _mm512_loadu_si512( + &data[offset_to_dimension_start + i * 4]); // This 4 is because everytime I read 4 dimensions + diff_u8 = _mm512_or_si512(_mm512_subs_epu8(vec1_u8, vec2_u8), _mm512_subs_epu8(vec2_u8, vec1_u8)); + // We can use this asymmetric dot product as our values are mostly 7-bit + // Hence, the [sign] properties of the second operand are ignored + // As results will never be negative, it can be stored on distances_p[i] without issues + // and it saturates to MAX_INT + _mm512_store_epi32(&distances_p[i], _mm512_dpbusds_epi32(res, diff_u8, diff_u8)); + } + y_vec1_u8 = _mm256_set1_epi32(query_value); + for (; i + 8 <= n_vectors; i += 8) { + // Read 32 bytes of data (32 values) with 4 dimensions of 8 vectors + y_res = _mm256_load_epi32(&distances_p[i]); + y_vec2_u8 = _mm256_loadu_epi8( + &data[offset_to_dimension_start + i * 4]); // This 4 is because everytime I read 4 dimensions + y_diff_u8 = + _mm256_or_si256(_mm256_subs_epu8(y_vec1_u8, y_vec2_u8), _mm256_subs_epu8(y_vec2_u8, y_vec1_u8)); + _mm256_store_epi32(&distances_p[i], _mm256_dpbusds_epi32(y_res, y_diff_u8, y_diff_u8)); + } + } + // Scalar tail (vectors). + for (; i < n_vectors; ++i) { + size_t vector_idx = i; + if constexpr (SKIP_PRUNED) { + vector_idx = pruning_positions[vector_idx]; + } + int to_multiply_a = query[dimension_idx] - data[offset_to_dimension_start + (vector_idx * 4)]; + int to_multiply_b = query[dimension_idx + 1] - data[offset_to_dimension_start + (vector_idx * 4) + 1]; + int to_multiply_c = query[dimension_idx + 2] - data[offset_to_dimension_start + (vector_idx * 4) + 2]; + int to_multiply_d = query[dimension_idx + 3] - data[offset_to_dimension_start + (vector_idx * 4) + 3]; + distances_p[vector_idx] += (to_multiply_a * to_multiply_a) + (to_multiply_b * to_multiply_b) + + (to_multiply_c * to_multiply_c) + (to_multiply_d * to_multiply_d); + } + } + if (dim_idx < end_dimension) { + auto remaining = static_cast(end_dimension - dim_idx); + size_t offset = dim_idx * total_vectors; + for (size_t i = 0; i < n_vectors; ++i) { + size_t vector_idx = i; + if constexpr (SKIP_PRUNED) { + vector_idx = pruning_positions[vector_idx]; + } + for (uint32_t k = 0; k < remaining; ++k) { + int diff = query[dim_idx + k] - data[offset + vector_idx * remaining + k]; + distances_p[vector_idx] += diff * diff; + } + } + } + } + + static distance_t Horizontal(const query_t *PDX_RESTRICT vector1, const data_t *PDX_RESTRICT vector2, + size_t num_dimensions) { + __m512i d2_i32_vec = _mm512_setzero_si512(); + __m512i a_u8_vec, b_u8_vec; + + simsimd_l2sq_u8_ice_cycle: + if (num_dimensions < 64) { + const __mmask64 mask = static_cast<__mmask64>(_bzhi_u64(0xFFFFFFFFFFFFFFFF, num_dimensions)); + a_u8_vec = _mm512_maskz_loadu_epi8(mask, vector1); + b_u8_vec = _mm512_maskz_loadu_epi8(mask, vector2); + num_dimensions = 0; + } else { + a_u8_vec = _mm512_loadu_si512(vector1); + b_u8_vec = _mm512_loadu_si512(vector2); + vector1 += 64, vector2 += 64, num_dimensions -= 64; + } + + // Substracting unsigned vectors in AVX-512 is done by saturating subtraction: + __m512i d_u8_vec = _mm512_or_si512(_mm512_subs_epu8(a_u8_vec, b_u8_vec), _mm512_subs_epu8(b_u8_vec, a_u8_vec)); + + // Multiply and accumulate at `int8` level which are actually uint7, accumulate at `int32` level: + d2_i32_vec = _mm512_dpbusds_epi32(d2_i32_vec, d_u8_vec, d_u8_vec); + if (num_dimensions) + goto simsimd_l2sq_u8_ice_cycle; + return _mm512_reduce_add_epi32(d2_i32_vec); + }; }; -} - - -#endif //PDX_AVX512_COMPUTERS_HPP +} // namespace PDX diff --git a/include/distance_computers/base_computers.hpp b/include/distance_computers/base_computers.hpp index f3be17e..b9f5216 100644 --- a/include/distance_computers/base_computers.hpp +++ b/include/distance_computers/base_computers.hpp @@ -1,59 +1,59 @@ #pragma once -#ifndef PDX_BASE_COMPUTERS_HPP -#define PDX_BASE_COMPUTERS_HPP -#include -#include -#include #include "common.hpp" #ifdef __ARM_NEON -#include "neon_computers.hpp" +#include "distance_computers/neon_computers.hpp" #endif #if defined(__AVX2__) && !defined(__AVX512F__) -#include "avx2_computers.hpp" +#include "distance_computers/avx2_computers.hpp" #endif #ifdef __AVX512F__ -#include "avx512_computers.hpp" +#include "distance_computers/avx512_computers.hpp" +#endif + +// Fallback to scalar computer. +#if !defined(__ARM_NEON) && !defined(__AVX2__) && !defined(__AVX512F__) +#include "distance_computers/scalar_computers.hpp" #endif // TODO: Support SVE namespace PDX { -template +template class DistanceComputer {}; -template<> -class DistanceComputer { - using computer = SIMDComputer; -public: - constexpr static auto VerticalReorderedPruning = computer::VerticalPruning; - constexpr static auto VerticalPruning = computer::VerticalPruning; - constexpr static auto VerticalReordered = computer::VerticalPruning; - constexpr static auto Vertical = computer::VerticalPruning; - - constexpr static auto VerticalBlock = computer::Vertical; - constexpr static auto Horizontal = computer::Horizontal; -}; +template <> +class DistanceComputer { +#if !defined(__ARM_NEON) && !defined(__AVX2__) && !defined(__AVX512F__) + using computer = ScalarComputer; +#else + using computer = SIMDComputer; +#endif -template<> -class DistanceComputer { - using computer = SIMDComputer; public: - constexpr static auto VerticalReorderedPruning = computer::VerticalPruning; - constexpr static auto VerticalPruning = computer::VerticalPruning; - constexpr static auto VerticalReordered = computer::VerticalPruning; - constexpr static auto Vertical = computer::VerticalPruning; + constexpr static auto VerticalPruning = computer::Vertical; + constexpr static auto Vertical = computer::Vertical; - constexpr static auto VerticalBlock = computer::Vertical; - constexpr static auto Horizontal = computer::Horizontal; + constexpr static auto Horizontal = computer::Horizontal; }; +template <> +class DistanceComputer { +#if !defined(__ARM_NEON) && !defined(__AVX2__) && !defined(__AVX512F__) + using computer = ScalarComputer; +#else + using computer = SIMDComputer; +#endif -}; // namespace PDX +public: + constexpr static auto VerticalPruning = computer::Vertical; + constexpr static auto Vertical = computer::Vertical; + constexpr static auto Horizontal = computer::Horizontal; +}; -#endif //PDX_BASE_COMPUTERS_HPP \ No newline at end of file +} // namespace PDX diff --git a/include/distance_computers/neon_computers.hpp b/include/distance_computers/neon_computers.hpp index fc0f256..7814718 100644 --- a/include/distance_computers/neon_computers.hpp +++ b/include/distance_computers/neon_computers.hpp @@ -1,231 +1,156 @@ #pragma once -#ifndef PDX_NEON_COMPUTERS_HPP -#define PDX_NEON_COMPUTERS_HPP #include -#include #include "arm_neon.h" -#include -#include #include "common.hpp" namespace PDX { -template +template class SIMDComputer {}; template <> -class SIMDComputer{ +class SIMDComputer { public: - using DISTANCE_TYPE = DistanceType_t; - using QUERY_TYPE = QuantizedVectorType_t; - using DATA_TYPE = DataType_t; - - template - static void VerticalPruning( - const QUERY_TYPE *__restrict query, - const DATA_TYPE *__restrict data, - size_t n_vectors, - size_t total_vectors, - size_t start_dimension, - size_t end_dimension, - DISTANCE_TYPE * distances_p, - const uint32_t * pruning_positions = nullptr, - const uint32_t * indices_dimensions = nullptr, - const int32_t * dim_clip_value = nullptr, - const float * scaling_factors = nullptr - ){ - // TODO: Handle tail in dimension length, for now im not going to worry on that as all the datasets are divisible by 4 - for (size_t dim_idx = start_dimension; dim_idx < end_dimension; dim_idx+=4) { - uint32_t dimension_idx = dim_idx; - if constexpr (USE_DIMENSIONS_REORDER){ - dimension_idx = indices_dimensions[dim_idx]; - } - uint8x8_t vals = vld1_u8(&query[dimension_idx]); - size_t offset_to_dimension_start = dimension_idx * total_vectors; - size_t i = 0; - if constexpr (!SKIP_PRUNED){ - const uint8x16_t idx = {0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3}; - const uint8x16_t vec1_u8 = vqtbl1q_u8(vcombine_u8(vals, vals), idx); - for (; i + 4 <= n_vectors; i+=4) { - // Read 16 bytes of data (16 values) with 4 dimensions of 4 vectors - uint32x4_t res = vld1q_u32(&distances_p[i]); - uint8x16_t vec2_u8 = vld1q_u8(&data[offset_to_dimension_start + i * 4]); // This 4 is because everytime I read 4 dimensions - uint8x16_t diff_u8 = vabdq_u8(vec1_u8, vec2_u8); - vst1q_u32(&distances_p[i], vdotq_u32(res, diff_u8, diff_u8)); - } - } - for (; i < n_vectors; ++i) { - size_t vector_idx = i; - if constexpr (SKIP_PRUNED){ - vector_idx = pruning_positions[vector_idx]; - } - // L2 - int to_multiply_a = query[dimension_idx] - data[offset_to_dimension_start + (vector_idx * 4)]; - int to_multiply_b = query[dimension_idx + 1] - data[offset_to_dimension_start + (vector_idx * 4) + 1]; - int to_multiply_c = query[dimension_idx + 2] - data[offset_to_dimension_start + (vector_idx * 4) + 2]; - int to_multiply_d = query[dimension_idx + 3] - data[offset_to_dimension_start + (vector_idx * 4) + 3]; - distances_p[vector_idx] += (to_multiply_a * to_multiply_a) + - (to_multiply_b * to_multiply_b) + - (to_multiply_c * to_multiply_c) + - (to_multiply_d * to_multiply_d); - - } - } - } - - static void Vertical( - const QUERY_TYPE *__restrict query, - const DATA_TYPE *__restrict data, - size_t start_dimension, - size_t end_dimension, - DISTANCE_TYPE * distances_p, - const float * scaling_factors = nullptr - ){ - uint32x4_t res[16]; - // Load initial values - for (size_t i = 0; i < 16; ++i) { - res[i] = vdupq_n_u32(0); - } - // Compute L2 - for (size_t dim_idx = start_dimension; dim_idx < end_dimension; dim_idx+=4) { - uint32_t dimension_idx = dim_idx; - uint8x8_t vals = vld1_u8(&query[dimension_idx]); - uint8x16_t idx = {0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3}; - uint8x16_t vec1_u8 = vqtbl1q_u8(vcombine_u8(vals, vals), idx); - size_t offset_to_dimension_start = dimension_idx * PDX_VECTOR_SIZE; - for (int i = 0; i < 16; ++i) { // total: 64 vectors * 4 dimensions each (at 1 byte per value = 2048-bits) - // Read 16 bytes of data (16 values) with 4 dimensions of 4 vectors - uint8x16_t vec2_u8 = vld1q_u8(&data[offset_to_dimension_start + i * 16]); - uint8x16_t diff_u8 = vabdq_u8(vec1_u8, vec2_u8); - res[i] = vdotq_u32(res[i], diff_u8, diff_u8); - } - } - // Store results back - for (int i = 0; i < 16; ++i) { - vst1q_u32(&distances_p[i * 4], res[i]); - } - } - - static DISTANCE_TYPE Horizontal( - const QUERY_TYPE *__restrict vector1, - const DATA_TYPE *__restrict vector2, - size_t num_dimensions, - const float * scaling_factors = nullptr - ){ - uint32x4_t sum_vec = vdupq_n_u32(0); - size_t i = 0; - for (; i + 16 <= num_dimensions; i += 16) { - uint8x16_t a_vec = vld1q_u8(vector1 + i); - uint8x16_t b_vec = vld1q_u8(vector2 + i); - uint8x16_t d_vec = vabdq_u8(a_vec, b_vec); - sum_vec = vdotq_u32(sum_vec, d_vec, d_vec); - } - DISTANCE_TYPE distance = vaddvq_u32(sum_vec); - for (; i < num_dimensions; ++i) { - int n = (int)vector1[i] - vector2[i]; - distance += n * n; - } - return distance; - }; -}; - - -template <> -class SIMDComputer{ -public: - using DISTANCE_TYPE = DistanceType_t; - using QUERY_TYPE = QuantizedVectorType_t; - using DATA_TYPE = DataType_t; - - // Defer to the scalar kernel - template - static void VerticalPruning( - const QUERY_TYPE *__restrict query, - const DATA_TYPE *__restrict data, - size_t n_vectors, - size_t total_vectors, - size_t start_dimension, - size_t end_dimension, - DISTANCE_TYPE * distances_p, - const uint32_t * pruning_positions, - const uint32_t * indices_dimensions, - const int32_t * dim_clip_value, - const float * scaling_factors - ){ - size_t dimensions_jump_factor = total_vectors; - for (size_t dimension_idx = start_dimension; dimension_idx < end_dimension; ++dimension_idx) { - uint32_t true_dimension_idx = dimension_idx; - if constexpr (USE_DIMENSIONS_REORDER){ - true_dimension_idx = indices_dimensions[dimension_idx]; - } - size_t offset_to_dimension_start = true_dimension_idx * dimensions_jump_factor; - for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { - auto true_vector_idx = vector_idx; - if constexpr(SKIP_PRUNED){ - true_vector_idx = pruning_positions[vector_idx]; - } - float to_multiply = query[true_dimension_idx] - data[offset_to_dimension_start + true_vector_idx]; - distances_p[true_vector_idx] += to_multiply * to_multiply; - } - } - } - - // Defer to the scalar kernel - static void Vertical( - const QUERY_TYPE *__restrict query, - const DATA_TYPE *__restrict data, - size_t start_dimension, - size_t end_dimension, - DISTANCE_TYPE * distances_p, - const float * scaling_factors - ){ - for (size_t dim_idx = start_dimension; dim_idx < end_dimension; dim_idx++) { - size_t dimension_idx = dim_idx; - size_t offset_to_dimension_start = dimension_idx * PDX_VECTOR_SIZE; - for (size_t vector_idx = 0; vector_idx < PDX_VECTOR_SIZE; ++vector_idx) { - float to_multiply = query[dimension_idx] - data[offset_to_dimension_start + vector_idx]; - distances_p[vector_idx] += to_multiply * to_multiply; - } - } - } - - static DISTANCE_TYPE Horizontal( - const QUERY_TYPE *__restrict vector1, - const DATA_TYPE *__restrict vector2, - size_t num_dimensions, - const float * scaling_factors = nullptr - ){ + using distance_t = pdx_distance_t; + using query_t = pdx_quantized_embedding_t; + using data_t = pdx_data_t; + + template + static void Vertical(const query_t *PDX_RESTRICT query, const data_t *PDX_RESTRICT data, size_t n_vectors, + size_t total_vectors, size_t start_dimension, size_t end_dimension, distance_t *distances_p, + const uint32_t *pruning_positions = nullptr) { + size_t dimensions_jump_factor = total_vectors; + for (size_t dimension_idx = start_dimension; dimension_idx < end_dimension; ++dimension_idx) { + size_t offset_to_dimension_start = dimension_idx * dimensions_jump_factor; + for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { + auto true_vector_idx = vector_idx; + if constexpr (SKIP_PRUNED) { + true_vector_idx = pruning_positions[vector_idx]; + } + distance_t to_multiply = query[dimension_idx] - data[offset_to_dimension_start + true_vector_idx]; + distances_p[true_vector_idx] += to_multiply * to_multiply; + } + } + } + + static distance_t Horizontal(const query_t *PDX_RESTRICT vector1, const data_t *PDX_RESTRICT vector2, + size_t num_dimensions) { #if defined(__APPLE__) - float distance = 0.0; + distance_t distance = 0.0; #pragma clang loop vectorize(enable) - for (size_t i = 0; i < num_dimensions; ++i) { - float diff = vector1[i] - vector2[i]; - distance += diff * diff; - } - return distance; + for (size_t i = 0; i < num_dimensions; ++i) { + distance_t diff = vector1[i] - vector2[i]; + distance += diff * diff; + } + return distance; #else - float32x4_t sum_vec = vdupq_n_f32(0); - size_t i = 0; - for (; i + 4 <= num_dimensions; i += 4) { - float32x4_t a_vec = vld1q_f32(vector1 + i); - float32x4_t b_vec = vld1q_f32(vector2 + i); - float32x4_t diff_vec = vsubq_f32(a_vec, b_vec); - sum_vec = vfmaq_f32(sum_vec, diff_vec, diff_vec); - } - DISTANCE_TYPE distance = vaddvq_f32(sum_vec); - for (; i < num_dimensions; ++i) { - float diff = vector1[i] - vector2[i]; - distance += diff * diff; - } - return distance; + float32x4_t sum_vec = vdupq_n_f32(0); + size_t i = 0; + for (; i + 4 <= num_dimensions; i += 4) { + float32x4_t a_vec = vld1q_f32(vector1 + i); + float32x4_t b_vec = vld1q_f32(vector2 + i); + float32x4_t diff_vec = vsubq_f32(a_vec, b_vec); + sum_vec = vfmaq_f32(sum_vec, diff_vec, diff_vec); + } + distance_t distance = vaddvq_f32(sum_vec); + for (; i < num_dimensions; ++i) { + distance_t diff = vector1[i] - vector2[i]; + distance += diff * diff; + } + return distance; #endif - }; + }; }; +// Equivalent of vdotq_u32(acc, a, a) for squared accumulation. +static inline uint32x4_t squared_dot_accumulate(uint32x4_t acc, uint8x16_t a) { +#ifdef __ARM_FEATURE_DOTPROD + return vdotq_u32(acc, a, a); +#else + uint16x8_t sq_lo = vmull_u8(vget_low_u8(a), vget_low_u8(a)); + uint16x8_t sq_hi = vmull_u8(vget_high_u8(a), vget_high_u8(a)); + uint32x4_t partial_lo = vpaddlq_u16(sq_lo); + uint32x4_t partial_hi = vpaddlq_u16(sq_hi); + return vaddq_u32(acc, vpaddq_u32(partial_lo, partial_hi)); +#endif +} +template <> +class SIMDComputer { +public: + using distance_t = pdx_distance_t; + using query_t = pdx_quantized_embedding_t; + using data_t = pdx_data_t; + + template + static void Vertical(const query_t *PDX_RESTRICT query, const data_t *PDX_RESTRICT data, size_t n_vectors, + size_t total_vectors, size_t start_dimension, size_t end_dimension, distance_t *distances_p, + const uint32_t *pruning_positions = nullptr) { + size_t dim_idx = start_dimension; + for (; dim_idx + 4 <= end_dimension; dim_idx += 4) { + uint32_t dimension_idx = dim_idx; + uint8x8_t vals = vld1_u8(&query[dimension_idx]); + size_t offset_to_dimension_start = dimension_idx * total_vectors; + size_t i = 0; + if constexpr (!SKIP_PRUNED) { + const uint8x16_t idx = {0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3}; + const uint8x16_t vec1_u8 = vqtbl1q_u8(vcombine_u8(vals, vals), idx); + for (; i + 4 <= n_vectors; i += 4) { + // Read 16 bytes of data (16 values) with 4 dimensions of 4 vectors + uint32x4_t res = vld1q_u32(&distances_p[i]); + uint8x16_t vec2_u8 = vld1q_u8( + &data[offset_to_dimension_start + i * 4]); // This 4 is because everytime I read 4 dimensions + uint8x16_t diff_u8 = vabdq_u8(vec1_u8, vec2_u8); + vst1q_u32(&distances_p[i], squared_dot_accumulate(res, diff_u8)); + } + } + for (; i < n_vectors; ++i) { + size_t vector_idx = i; + if constexpr (SKIP_PRUNED) { + vector_idx = pruning_positions[vector_idx]; + } + int to_multiply_a = query[dimension_idx] - data[offset_to_dimension_start + (vector_idx * 4)]; + int to_multiply_b = query[dimension_idx + 1] - data[offset_to_dimension_start + (vector_idx * 4) + 1]; + int to_multiply_c = query[dimension_idx + 2] - data[offset_to_dimension_start + (vector_idx * 4) + 2]; + int to_multiply_d = query[dimension_idx + 3] - data[offset_to_dimension_start + (vector_idx * 4) + 3]; + distances_p[vector_idx] += (to_multiply_a * to_multiply_a) + (to_multiply_b * to_multiply_b) + + (to_multiply_c * to_multiply_c) + (to_multiply_d * to_multiply_d); + } + } + if (dim_idx < end_dimension) { + auto remaining = static_cast(end_dimension - dim_idx); + size_t offset = dim_idx * total_vectors; + for (size_t i = 0; i < n_vectors; ++i) { + size_t vector_idx = i; + if constexpr (SKIP_PRUNED) { + vector_idx = pruning_positions[vector_idx]; + } + for (uint32_t k = 0; k < remaining; ++k) { + int diff = query[dim_idx + k] - data[offset + vector_idx * remaining + k]; + distances_p[vector_idx] += diff * diff; + } + } + } + } + + static distance_t Horizontal(const query_t *PDX_RESTRICT vector1, const data_t *PDX_RESTRICT vector2, + size_t num_dimensions) { + uint32x4_t sum_vec = vdupq_n_u32(0); + size_t i = 0; + for (; i + 16 <= num_dimensions; i += 16) { + uint8x16_t a_vec = vld1q_u8(vector1 + i); + uint8x16_t b_vec = vld1q_u8(vector2 + i); + uint8x16_t d_vec = vabdq_u8(a_vec, b_vec); + sum_vec = squared_dot_accumulate(sum_vec, d_vec); + } + distance_t distance = vaddvq_u32(sum_vec); + for (; i < num_dimensions; ++i) { + int n = static_cast(vector1[i]) - vector2[i]; + distance += n * n; + } + return distance; + }; +}; } // namespace PDX - - -#endif //PDX_NEON_COMPUTERS_HPP diff --git a/include/distance_computers/scalar_computers.hpp b/include/distance_computers/scalar_computers.hpp index 28d4ffe..4f739a2 100644 --- a/include/distance_computers/scalar_computers.hpp +++ b/include/distance_computers/scalar_computers.hpp @@ -1,145 +1,102 @@ -#ifndef PDX_SCALAR_COMPUTERS_HPP -#define PDX_SCALAR_COMPUTERS_HPP +#pragma once #include -#include -#include -#include #include "common.hpp" namespace PDX { -template +template class ScalarComputer {}; template <> -class ScalarComputer{}; - - -template <> -class ScalarComputer{ +class ScalarComputer { public: - using DISTANCE_TYPE = DistanceType_t; - using QUERY_TYPE = QuantizedVectorType_t; - using DATA_TYPE = DataType_t; - - // Defer to the scalar kernel - template - static void VerticalPruning( - const QUERY_TYPE *__restrict query, - const DATA_TYPE *__restrict data, - size_t n_vectors, - size_t total_vectors, - size_t start_dimension, - size_t end_dimension, - DISTANCE_TYPE * distances_p, - const uint32_t * pruning_positions = nullptr, - const uint32_t * indices_dimensions = nullptr, - const int32_t * dim_clip_value = nullptr - ){ - size_t dimensions_jump_factor = total_vectors; - for (size_t dimension_idx = start_dimension; dimension_idx < end_dimension; ++dimension_idx) { - uint32_t true_dimension_idx = dimension_idx; - if constexpr (USE_DIMENSIONS_REORDER){ - true_dimension_idx = indices_dimensions[dimension_idx]; - } - size_t offset_to_dimension_start = true_dimension_idx * dimensions_jump_factor; - for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { - auto true_vector_idx = vector_idx; - if constexpr(SKIP_PRUNED){ - true_vector_idx = pruning_positions[vector_idx]; - } - DISTANCE_TYPE to_multiply = query[true_dimension_idx] - data[offset_to_dimension_start + true_vector_idx]; - distances_p[true_vector_idx] += to_multiply * to_multiply; - } - } - } - - // Defer to the scalar kernel - static void Vertical( - const QUERY_TYPE *__restrict query, - const DATA_TYPE *__restrict data, - size_t start_dimension, - size_t end_dimension, - DISTANCE_TYPE * distances_p - ){ - for (size_t dim_idx = start_dimension; dim_idx < end_dimension; dim_idx++) { - size_t dimension_idx = dim_idx; - size_t offset_to_dimension_start = dimension_idx * PDX_VECTOR_SIZE; - for (size_t vector_idx = 0; vector_idx < PDX_VECTOR_SIZE; ++vector_idx) { - DISTANCE_TYPE to_multiply = query[dimension_idx] - data[offset_to_dimension_start + vector_idx]; - distances_p[vector_idx] += to_multiply * to_multiply; -// if constexpr (L_ALPHA == IP){ -// distances_p[vector_idx] -= 2 * query[dimension_idx] * data[offset_to_dimension_start + vector_idx]; -// } - } - } - } - - static DISTANCE_TYPE Horizontal( - const QUERY_TYPE *__restrict vector1, - const DATA_TYPE *__restrict vector2, - size_t num_dimensions - ){ - DISTANCE_TYPE distance = 0.0; - for (size_t dimension_idx = 0; dimension_idx < num_dimensions; ++dimension_idx) { - DISTANCE_TYPE to_multiply = vector1[dimension_idx] - vector2[dimension_idx]; - distance += to_multiply * to_multiply; - } - return distance; - }; - + using distance_t = pdx_distance_t; + using query_t = pdx_quantized_embedding_t; + using data_t = pdx_data_t; + + template + static void Vertical(const query_t *PDX_RESTRICT query, const data_t *PDX_RESTRICT data, size_t n_vectors, + size_t total_vectors, size_t start_dimension, size_t end_dimension, distance_t *distances_p, + const uint32_t *pruning_positions = nullptr) { + size_t dimensions_jump_factor = total_vectors; + for (size_t dimension_idx = start_dimension; dimension_idx < end_dimension; ++dimension_idx) { + size_t offset_to_dimension_start = dimension_idx * dimensions_jump_factor; + for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { + auto true_vector_idx = vector_idx; + if constexpr (SKIP_PRUNED) { + true_vector_idx = pruning_positions[vector_idx]; + } + distance_t to_multiply = query[dimension_idx] - data[offset_to_dimension_start + true_vector_idx]; + distances_p[true_vector_idx] += to_multiply * to_multiply; + } + } + } + + static distance_t Horizontal(const query_t *PDX_RESTRICT vector1, const data_t *PDX_RESTRICT vector2, + size_t num_dimensions) { + distance_t distance = 0.0; +#pragma clang loop vectorize(enable) + for (size_t dimension_idx = 0; dimension_idx < num_dimensions; ++dimension_idx) { + distance_t to_multiply = vector1[dimension_idx] - vector2[dimension_idx]; + distance += to_multiply * to_multiply; + } + return distance; + }; }; template <> -class ScalarComputer{ +class ScalarComputer { public: - using DISTANCE_TYPE = DistanceType_t; - using QUERY_TYPE = QuantizedVectorType_t; - using DATA_TYPE = DataType_t; - - // Defer to the scalar kernel - template - static void VerticalPruning( - const QUERY_TYPE *__restrict query, - const DATA_TYPE *__restrict data, - size_t n_vectors, - size_t total_vectors, - size_t start_dimension, - size_t end_dimension, - DISTANCE_TYPE * distances_p, - const uint32_t * pruning_positions = nullptr, - const uint32_t * indices_dimensions = nullptr, - const int32_t * dim_clip_value = nullptr - ){ - // TODO - } - - // Defer to the scalar kernel - static void Vertical( - const QUERY_TYPE *__restrict query, - const DATA_TYPE *__restrict data, - size_t start_dimension, - size_t end_dimension, - DISTANCE_TYPE * distances_p - ){ - // TODO - } - - static DISTANCE_TYPE Horizontal( - const QUERY_TYPE *__restrict vector1, - const DATA_TYPE *__restrict vector2, - size_t num_dimensions - ){ - DISTANCE_TYPE distance = 0.0; - for (size_t dimension_idx = 0; dimension_idx < num_dimensions; ++dimension_idx) { - distance += vector1[dimension_idx] * vector2[dimension_idx]; - } - return distance; - }; - + using distance_t = pdx_distance_t; + using query_t = pdx_quantized_embedding_t; + using data_t = pdx_data_t; + + template + static void Vertical(const query_t *PDX_RESTRICT query, const data_t *PDX_RESTRICT data, size_t n_vectors, + size_t total_vectors, size_t start_dimension, size_t end_dimension, distance_t *distances_p, + const uint32_t *pruning_positions = nullptr) { + size_t dim_idx = start_dimension; + for (; dim_idx + 4 <= end_dimension; dim_idx += 4) { + uint32_t dimension_idx = dim_idx; + size_t offset_to_dimension_start = dimension_idx * total_vectors; + for (size_t i = 0; i < n_vectors; ++i) { + size_t vector_idx = i; + if constexpr (SKIP_PRUNED) { + vector_idx = pruning_positions[vector_idx]; + } + int da = query[dimension_idx] - data[offset_to_dimension_start + (vector_idx * 4)]; + int db = query[dimension_idx + 1] - data[offset_to_dimension_start + (vector_idx * 4) + 1]; + int dc = query[dimension_idx + 2] - data[offset_to_dimension_start + (vector_idx * 4) + 2]; + int dd = query[dimension_idx + 3] - data[offset_to_dimension_start + (vector_idx * 4) + 3]; + distances_p[vector_idx] += (da * da) + (db * db) + (dc * dc) + (dd * dd); + } + } + if (dim_idx < end_dimension) { + auto remaining = static_cast(end_dimension - dim_idx); + size_t offset = dim_idx * total_vectors; + for (size_t i = 0; i < n_vectors; ++i) { + size_t vector_idx = i; + if constexpr (SKIP_PRUNED) { + vector_idx = pruning_positions[vector_idx]; + } + for (uint32_t k = 0; k < remaining; ++k) { + int diff = query[dim_idx + k] - data[offset + vector_idx * remaining + k]; + distances_p[vector_idx] += diff * diff; + } + } + } + } + + static distance_t Horizontal(const query_t *PDX_RESTRICT vector1, const data_t *PDX_RESTRICT vector2, + size_t num_dimensions) { + distance_t distance = 0; + for (size_t i = 0; i < num_dimensions; ++i) { + int diff = static_cast(vector1[i]) - static_cast(vector2[i]); + distance += diff * diff; + } + return distance; + }; }; -} - -#endif //PDX_SCALAR_COMPUTERS_HPP +} // namespace PDX diff --git a/include/index_base/pdx_ivf.hpp b/include/index_base/pdx_ivf.hpp index 2ee8eec..ebe9c12 100644 --- a/include/index_base/pdx_ivf.hpp +++ b/include/index_base/pdx_ivf.hpp @@ -1,17 +1,9 @@ -#ifndef PDX_IVF_HPP -#define PDX_IVF_HPP +#pragma once #include #include #include -#include #include -#include -#include -#include -#include -#include -#include "utils/file_reader.hpp" #include "common.hpp" namespace PDX { @@ -26,21 +18,39 @@ class IndexPDXIVF{}; template <> class IndexPDXIVF { public: - - using CLUSTER_TYPE = Cluster; + using cluster_t = Cluster; std::unique_ptr file_buffer; - uint32_t num_dimensions{}; - uint32_t num_clusters{}; - uint32_t num_horizontal_dimensions{}; - uint32_t num_vertical_dimensions{}; - std::vector clusters; - float *means{}; - bool is_ivf{}; - bool is_normalized{}; - float *centroids{}; - float *centroids_pdx{}; + // const uint32_t num_dimensions {}; + // const uint64_t total_num_embeddings {}; + // const uint32_t num_clusters {}; + // const uint32_t num_vertical_dimensions {}; + // const uint32_t num_horizontal_dimensions {}; + // std::vector clusters; + // const bool is_normalized {}; + // std::vector centroids; + + uint32_t num_dimensions {}; + uint64_t total_num_embeddings {}; + uint32_t num_clusters {}; + uint32_t num_vertical_dimensions {}; + uint32_t num_horizontal_dimensions {}; + std::vector clusters; + bool is_normalized {}; + std::vector centroids; + + IndexPDXIVF() = default; + + ~IndexPDXIVF() = default; + + IndexPDXIVF(uint32_t num_dimensions, uint64_t total_num_embeddings, uint32_t num_clusters, bool is_normalized) + : num_dimensions(num_dimensions), total_num_embeddings(total_num_embeddings), num_clusters(num_clusters), + num_vertical_dimensions(GetPDXDimensionSplit(num_dimensions).vertical_dimensions), + num_horizontal_dimensions(GetPDXDimensionSplit(num_dimensions).horizontal_dimensions), + is_normalized(is_normalized) { + clusters.reserve(num_clusters); + } void Restore(const std::string &filename) { file_buffer = MmapFile(filename); @@ -58,53 +68,76 @@ class IndexPDXIVF { next_value += sizeof(uint32_t); auto *nums_embeddings = (uint32_t *) next_value; next_value += num_clusters * sizeof(uint32_t); - clusters.resize(num_clusters); + clusters.reserve(num_clusters); for (size_t i = 0; i < num_clusters; ++i) { - CLUSTER_TYPE &cluster = clusters[i]; - cluster.num_embeddings = nums_embeddings[i]; - cluster.data = (float *) next_value; - next_value += sizeof(float) * cluster.num_embeddings * num_dimensions; + clusters.emplace_back(nums_embeddings[i], num_dimensions); + memcpy(clusters[i].data, next_value, sizeof(float) * clusters[i].num_embeddings * num_dimensions); + next_value += sizeof(float) * clusters[i].num_embeddings * num_dimensions; } for (size_t i = 0; i < num_clusters; ++i) { - CLUSTER_TYPE &cluster = clusters[i]; - cluster.indices = (uint32_t *) next_value; - next_value += sizeof(uint32_t) * cluster.num_embeddings; + memcpy(clusters[i].indices, next_value, sizeof(uint32_t) * clusters[i].num_embeddings); + next_value += sizeof(uint32_t) * clusters[i].num_embeddings; } - means = (float *) next_value; - next_value += sizeof(float) * num_dimensions; + next_value += sizeof(float) * num_dimensions; // Skipping means (not used) + is_normalized = ((char *) next_value)[0]; next_value += sizeof(char); - is_ivf = ((char *) next_value)[0]; - next_value += sizeof(char); - if (is_ivf) { - centroids = (float *) next_value; - next_value += sizeof(float) * num_clusters * num_dimensions; - centroids_pdx = (float *) next_value; - } + next_value += sizeof(char); // Skipping is_ivf field (not used) + + centroids.resize(num_clusters * num_dimensions); + memcpy(centroids.data(), (float *) next_value, sizeof(float) * num_clusters * num_dimensions); } }; template <> class IndexPDXIVF { public: - - using CLUSTER_TYPE = Cluster; - + using cluster_t = Cluster; std::unique_ptr file_buffer; - uint32_t num_dimensions{}; - uint32_t num_clusters{}; - uint32_t num_horizontal_dimensions{}; - uint32_t num_vertical_dimensions{}; - std::vector> clusters; - float *means{}; - bool is_ivf{}; - bool is_normalized{}; - float *centroids{}; - float *centroids_pdx{}; - - float for_base {}; - float scale_factor {}; + // const uint32_t num_dimensions {}; + // const uint64_t total_num_embeddings {}; + // const uint32_t num_clusters {}; + // const uint32_t num_vertical_dimensions {}; + // const uint32_t num_horizontal_dimensions {}; + // std::vector clusters; + // const bool is_normalized {}; + // std::vector centroids; + + // const float quantization_scale = 1.0f; + // const float quantization_scale_squared = 1.0f; + // const float inverse_quantization_scale_squared = 1.0f; + // const float quantization_base = 0.0f; + + uint32_t num_dimensions {}; + uint64_t total_num_embeddings {}; + uint32_t num_clusters {}; + uint32_t num_vertical_dimensions {}; + uint32_t num_horizontal_dimensions {}; + std::vector clusters; + bool is_normalized {}; + std::vector centroids; + + float quantization_scale = 1.0f; + float quantization_scale_squared = 1.0f; + float inverse_quantization_scale_squared = 1.0f; + float quantization_base = 0.0f; + + IndexPDXIVF(uint32_t num_dimensions, uint64_t total_num_embeddings, uint32_t num_clusters, bool is_normalized, + float quantization_scale, float quantization_base) + : num_dimensions(num_dimensions), total_num_embeddings(total_num_embeddings), num_clusters(num_clusters), + num_vertical_dimensions(GetPDXDimensionSplit(num_dimensions).vertical_dimensions), + num_horizontal_dimensions(GetPDXDimensionSplit(num_dimensions).horizontal_dimensions), + is_normalized(is_normalized), quantization_scale(quantization_scale), + quantization_scale_squared(quantization_scale * quantization_scale), + inverse_quantization_scale_squared(1.0f / (quantization_scale * quantization_scale)), + quantization_base(quantization_base) { + clusters.reserve(num_clusters); + } + + IndexPDXIVF() = default; + + ~IndexPDXIVF() = default; void Restore(const std::string &filename) { file_buffer = MmapFile(filename); @@ -122,37 +155,32 @@ class IndexPDXIVF { next_value += sizeof(uint32_t); auto *nums_embeddings = (uint32_t *) next_value; next_value += num_clusters * sizeof(uint32_t); - clusters.resize(num_clusters); + clusters.reserve(num_clusters); for (size_t i = 0; i < num_clusters; ++i) { - CLUSTER_TYPE &cluster = clusters[i]; - cluster.num_embeddings = nums_embeddings[i]; - cluster.data = (uint8_t *) next_value; - next_value += sizeof(uint8_t) * cluster.num_embeddings * num_dimensions; + clusters.emplace_back(nums_embeddings[i], num_dimensions); + memcpy(clusters[i].data, next_value, sizeof(uint8_t) * clusters[i].num_embeddings * num_dimensions); + next_value += sizeof(uint8_t) * clusters[i].num_embeddings * num_dimensions; } for (size_t i = 0; i < num_clusters; ++i) { - CLUSTER_TYPE &cluster = clusters[i]; - cluster.indices = (uint32_t *) next_value; - next_value += sizeof(uint32_t) * cluster.num_embeddings; + memcpy(clusters[i].indices, next_value, sizeof(uint32_t) * clusters[i].num_embeddings); + next_value += sizeof(uint32_t) * clusters[i].num_embeddings; } - // means = (float *) next_value; - // next_value += sizeof(float) * num_dimensions; is_normalized = ((char *) next_value)[0]; next_value += sizeof(char); - is_ivf = ((char *) next_value)[0]; - next_value += sizeof(char); - if (is_ivf) { - centroids = (float *) next_value; - next_value += sizeof(float) * num_clusters * num_dimensions; - centroids_pdx = (float *) next_value; - } + next_value += sizeof(char); // Skipping is_ivf field (not used) - for_base = ((float *) next_value)[0]; + centroids.resize(num_clusters * num_dimensions); + memcpy(centroids.data(), (float *) next_value, sizeof(float) * num_clusters * num_dimensions); + next_value += sizeof(float) * num_clusters * num_dimensions; + + quantization_base = ((float *) next_value)[0]; next_value += sizeof(float); - scale_factor = ((float *) next_value)[0]; + quantization_scale = ((float *) next_value)[0]; next_value += sizeof(float); + + quantization_scale_squared = quantization_scale * quantization_scale; + inverse_quantization_scale_squared = 1.0f / (quantization_scale * quantization_scale); } }; } // namespace PDX - -#endif //PDX_IVF_HPP diff --git a/include/pdxearch.hpp b/include/pdxearch.hpp index f3b4d1d..4f9a113 100644 --- a/include/pdxearch.hpp +++ b/include/pdxearch.hpp @@ -1,168 +1,113 @@ -#ifndef PDX_PDXEARCH_HPP -#define PDX_PDXEARCH_HPP +#pragma once +#include #include #include #include -#include +#include +#include #include "common.hpp" -#include "db_mock/predicate_evaluator.hpp" #include "utils/tictoc.hpp" +#include "db_mock/predicate_evaluator.hpp" #include "distance_computers/base_computers.hpp" -#include "quantizers/global.h" +#include "quantizers/scalar.hpp" #include "index_base/pdx_ivf.hpp" -#include "index_base/pdx_ivf2.hpp" #include "pruners/adsampling.hpp" namespace PDX { -/****************************************************************** - * PDXearch - * Implements our algorithm for vertical pruning - ******************************************************************/ -template< - Quantization q=F32, - class Index=IndexPDXIVF, - class Quantizer=Global8Quantizer, - DistanceFunction alpha=L2, - class Pruner=ADSamplingPruner -> +template , class Quantizer = ScalarQuantizer, + DistanceMetric alpha = DistanceMetric::L2SQ, class Pruner = ADSamplingPruner> class PDXearch { public: - using DISTANCES_TYPE = DistanceType_t; - using QUANTIZED_VECTOR_TYPE = QuantizedVectorType_t; - using DATA_TYPE = DataType_t; - using INDEX_TYPE = Index; - using CLUSTER_TYPE = Cluster; - using KNNCandidate_t = KNNCandidate; - using VectorComparator_t = VectorComparator; - - Quantizer quantizer; - Pruner pruner; - INDEX_TYPE &pdx_data; - uint32_t current_dimension_idx {0}; - - PDXearch( - INDEX_TYPE &data_index, - Pruner &pruner, - int position_prune_distance, - DimensionsOrder dimension_order - ) : pdx_data(data_index), - pruner(pruner), - is_positional_pruning(position_prune_distance), - dimension_order(dimension_order){ - indices_dimensions.resize(pdx_data.num_dimensions); - clusters_indices.resize(pdx_data.num_clusters); - cluster_offsets.resize(pdx_data.num_clusters); - for (size_t i = 0; i < pdx_data.num_clusters; ++i){ - cluster_offsets[i] = total_embeddings; - total_embeddings += pdx_data.clusters[i].num_embeddings; - } - quantizer.SetD(pdx_data.num_dimensions); - } - - void SetNProbe(size_t nprobe){ - ivf_nprobe = nprobe; - } + using distance_t = pdx_distance_t; + using data_t = pdx_data_t; + using quantized_embedding_t = pdx_quantized_embedding_t; + using index_t = Index; + using cluster_t = Cluster; + using distance_computer_t = DistanceComputer; + + Quantizer quantizer; + Pruner &pruner; + index_t &pdx_data; + + PDXearch(index_t &data_index, Pruner &pruner) + : quantizer(data_index.num_dimensions), pruner(pruner), pdx_data(data_index), + cluster_offsets(new size_t[data_index.num_clusters]), + cluster_indices_in_access_order(new uint32_t[data_index.num_clusters]), + quantized_query_buf(new quantized_embedding_t[data_index.num_dimensions]) { + for (size_t i = 0; i < data_index.num_clusters; ++i) { + cluster_offsets[i] = total_embeddings; + total_embeddings += data_index.clusters[i].num_embeddings; + max_cluster_size = std::max(max_cluster_size, static_cast(data_index.clusters[i].num_embeddings)); + } + } + + void SetNProbe(size_t nprobe) { + ivf_nprobe = nprobe; + } TicToc end_to_end_clock = TicToc(); - void ResetClocks(){ end_to_end_clock.Reset(); } + std::unique_ptr cluster_offsets; + protected: float selectivity_threshold = 0.80; size_t ivf_nprobe = 0; - int is_positional_pruning = false; - size_t current_cluster = 0; - - DimensionsOrder dimension_order = SEQUENTIAL; - // Evaluating the pruning threshold is so fast that we can allow smaller fetching sizes - // to avoid more data access. Super useful in architectures with low bandwidth at L3/DRAM like Intel SPR - static constexpr uint32_t DIMENSIONS_FETCHING_SIZES[24] = { - 4, 4, 8, 8, 8, 16, 16, 32, 32, 32, 32, - 64, 64, 64, 64, 128, 128, 128, 128, 256, - 256, 512, 1024, 2048 - }; - - size_t H_DIM_SIZE = 64; - size_t cur_subgrouping_size_idx {0}; size_t total_embeddings {0}; + size_t max_cluster_size {0}; - std::vector indices_dimensions; - std::vector clusters_indices; - std::vector clusters_indices_l0; - std::vector cluster_offsets; - - size_t n_vectors_not_pruned = 0; - - DISTANCES_TYPE pruning_threshold = std::numeric_limits::max(); - DistanceType_t pruning_threshold_l0 = std::numeric_limits>::max(); + // Prioritized list of indices of the clusters to probe. E.g., [0, 2, 1]. + std::unique_ptr cluster_indices_in_access_order; - // For pruning we do not use tight loops of 64. We know that tight loops bring benefits - // to the distance kernels (40% faster), however doing so + PRUNING in the tight block of 64 - // slightly reduces the performance of PDXearch. We are still investigating why. - static constexpr uint16_t PDX_VECTOR_SIZE = 64; - alignas(64) inline static DISTANCES_TYPE distances[PDX_VECTOR_SIZE]; // Used in full scans (no pruning) - alignas(64) inline static DISTANCES_TYPE pruning_distances[10240]; // TODO: Use dynamic arrays. Buckets with more than 10k vectors (rare) overflow - alignas(64) inline static uint32_t pruning_positions[10240]; - std::priority_queue, std::vector>, VectorComparator> best_k{}; + // Indexes into the cluster_indices_in_access_order list. This offset is + // incremented by 1 after probing a cluster. + uint64_t cluster_indices_in_access_order_offset {0}; - alignas(64) inline static DistanceType_t centroids_distances[PDX_VECTOR_SIZE]; - alignas(64) inline static DistanceType_t pruning_distances_l0[10240]; - alignas(64) inline static uint32_t pruning_positions_l0[10240]; - std::priority_queue, std::vector>, VectorComparator> best_k_centroids{}; + // Start: State for the current filtered search. + uint32_t k = 0; + quantized_embedding_t *prepared_query = nullptr; + // Predicate evaluator for this rowgroup. + std::unique_ptr predicate_evaluator; - void ResetDistancesScalar(size_t n_vectors){ - memset((void*) distances, 0, n_vectors * sizeof(DISTANCES_TYPE)); - } - - template - void ResetPruningDistances( - size_t n_vectors, - DistanceType_t *pruning_distances - ){ - memset((void*) pruning_distances, 0, n_vectors * sizeof(DistanceType_t)); - } + // Per-search query buffers, filled by QuantizeEmbedding in InitializeSearch (U8 path). + // Used by Search/FilteredSearch and passed to Warmup/Prune. + std::unique_ptr quantized_query_buf; - template - void ResetDistancesVectorized( - DistanceType_t *distances - ){ - memset((void*) distances, 0, PDX_VECTOR_SIZE * sizeof(DistanceType_t)); + void ResetPruningDistances(size_t n_vectors, distance_t *pruning_distances) { + std::fill(pruning_distances, pruning_distances + n_vectors, distance_t {0}); } - // The pruning threshold by default is the top of the heap - template - void GetPruningThreshold( - uint32_t k, std::priority_queue, std::vector>, VectorComparator> &heap, - DistanceType_t &pruning_threshold - ){ - pruning_threshold = pruner.template GetPruningThreshold(k, heap, current_dimension_idx); - }; - - template - void EvaluatePruningPredicateScalar( - uint32_t &n_pruned, - size_t n_vectors, - DistanceType_t *pruning_distances, - const DistanceType_t pruning_threshold - ) { - for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { - n_pruned += pruning_distances[vector_idx] >= pruning_threshold; + // The pruning threshold by default is the top of the heap + void GetPruningThreshold(uint32_t k, + std::priority_queue, VectorComparator> &heap, + distance_t &pruning_threshold, uint32_t current_dimension_idx) { + const float float_threshold = pruner.GetPruningThreshold(k, heap, current_dimension_idx); + if constexpr (Q == U8) { + // We need to avoid undefined behaviour when overflow happens + const float scaled = float_threshold * pdx_data.quantization_scale_squared; + pruning_threshold = scaled >= static_cast(std::numeric_limits::max()) + ? std::numeric_limits::max() + : static_cast(scaled); + } else { + pruning_threshold = float_threshold; } }; - template - void EvaluatePruningPredicateOnPositionsArray( - size_t n_vectors, - size_t &n_vectors_not_pruned, - uint32_t * pruning_positions, - DistanceType_t pruning_threshold, - DistanceType_t * pruning_distances - ){ + void EvaluatePruningPredicateScalar(uint32_t &n_pruned, size_t n_vectors, distance_t *pruning_distances, + const distance_t pruning_threshold) { + for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { + n_pruned += pruning_distances[vector_idx] >= pruning_threshold; + } + }; + + void EvaluatePruningPredicateOnPositionsArray(size_t n_vectors, size_t &n_vectors_not_pruned, + uint32_t *pruning_positions, distance_t pruning_threshold, + distance_t *pruning_distances) { n_vectors_not_pruned = 0; for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { pruning_positions[n_vectors_not_pruned] = pruning_positions[vector_idx]; @@ -170,789 +115,434 @@ class PDXearch { } }; - template - void EvaluatePruningPredicateVectorized( - uint32_t &n_pruned, - DistanceType_t pruning_threshold, - DistanceType_t * pruning_distances - ) { - for (size_t vector_idx = 0; vector_idx < PDX_VECTOR_SIZE; ++vector_idx) { - n_pruned += pruning_distances[vector_idx] >= pruning_threshold; - } - }; - - template - void InitPositionsArray( - size_t n_vectors, - size_t &n_vectors_not_pruned, - uint32_t * pruning_positions, - DistanceType_t pruning_threshold, - DistanceType_t * pruning_distances - ){ - n_vectors_not_pruned = 0; - for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { - pruning_positions[n_vectors_not_pruned] = vector_idx; - n_vectors_not_pruned += pruning_distances[vector_idx] < pruning_threshold; - } - }; - - template - void InitPositionsArrayFromSelectionVector( - size_t n_vectors, - size_t &n_vectors_not_pruned, - uint32_t * pruning_positions, - DistanceType_t pruning_threshold, - DistanceType_t * pruning_distances, - const uint8_t * selection_vector - ) { - n_vectors_not_pruned = 0; - for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { - pruning_positions[n_vectors_not_pruned] = vector_idx; - n_vectors_not_pruned += selection_vector[vector_idx] == 1; - } - }; - - template - void MaskDistancesWithSelectionVector( - size_t n_vectors, - size_t &n_vectors_not_pruned, - uint32_t * pruning_positions, - DistanceType_t pruning_threshold, - DistanceType_t * pruning_distances, - const uint8_t * selection_vector - ) { - for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { - if (selection_vector[vector_idx] == 0) { - // Why max()/2? To prevent overflow if distances are still added to these - pruning_distances[vector_idx] = std::numeric_limits>::max() / 2; - } - } - }; - - void GetDimensionsAccessOrder(const float *__restrict query, const float *__restrict means) { - std::iota(indices_dimensions.begin(), indices_dimensions.end(), 0); - if (dimension_order == DISTANCE_TO_MEANS) { - std::sort(indices_dimensions.begin(), indices_dimensions.end(), - [&query, &means](size_t i1, size_t i2) { - return std::abs(query[i1] - means[i1]) > std::abs(query[i2] - means[i2]); - }); - } else if (dimension_order == DISTANCE_TO_MEANS_IMPROVED) { - // Improves Cache performance - auto const top_perc = static_cast(std::floor(indices_dimensions.size() / 4)); - std::partial_sort(indices_dimensions.begin(), indices_dimensions.begin() + top_perc, indices_dimensions.end(), - [&query, &means](size_t i1, size_t i2) { - return std::abs(query[i1] - means[i1]) > std::abs(query[i2] - means[i2]); - }); - // By taking the top 25% dimensions and sorting them ascendingly by index - std::sort(indices_dimensions.begin(), indices_dimensions.begin() + top_perc); - // Then sorting the rest of the dimensions ascendingly by index - std::sort(indices_dimensions.begin() + top_perc, indices_dimensions.end()); - } else if (dimension_order == DIMENSION_ZONES){ - uint16_t dimensions = pdx_data.num_dimensions; - size_t estimated_embeddings_per_vg = total_embeddings / pdx_data.num_clusters; - size_t n_dimensions_per_zone = 8192 / estimated_embeddings_per_vg; - size_t total_zones = dimensions / n_dimensions_per_zone; - std::vector> zones; - std::vector zone_ranking; - std::vector zones_indexes; - zones.resize(total_zones); - zones_indexes.resize(total_zones); - zone_ranking.resize(total_zones); - std::iota(zones_indexes.begin(), zones_indexes.end(), 0); - for (size_t i = 0; i < total_zones; i++){ - uint16_t start = i * n_dimensions_per_zone; - uint16_t end = i * n_dimensions_per_zone + n_dimensions_per_zone; - if (end > dimensions - 1 ){ - end = dimensions - 1; - } - //end = std::min(end, (uint16_t) dimensions - 1); - zones[i] = std::pair(start, end); - zone_ranking[i] = 0.0; - } - for (size_t i = 0; i < total_zones; i++){ - uint16_t zone_start = zones[i].first; - uint16_t zone_end = zones[i].second; - for (size_t d = zone_start; d < zone_end; d++){ - zone_ranking[i] += std::abs(query[d] - means[d]); - } - // Normalizing - zone_ranking[i] = zone_ranking[i] / (1.0 * (zone_end - zone_start)); - } - auto const top_perc = static_cast(std::ceil(zones.size() / 8)); - - std::partial_sort(zones_indexes.begin(), zones_indexes.begin() + top_perc, zones_indexes.end(), - [&zone_ranking](size_t i1, size_t i2) { - return zone_ranking[i1] > zone_ranking[i2]; - }); - // We also prioritize them - std::sort(zones_indexes.begin(), zones_indexes.begin() + top_perc); - // The rest we resort to access them sequentially by zone - std::sort(zones_indexes.begin() + top_perc, zones_indexes.end()); - size_t offset_tmp = 0; - for (size_t i = 0; i < total_zones; i++){ - std::pair priority_zone = zones[zones_indexes[i]]; - for (size_t d = priority_zone.first; d < priority_zone.second; d++){ - indices_dimensions[offset_tmp] = d; - offset_tmp += 1; - } - } - } - } - - static void GetClustersAccessOrderIVF(const float *__restrict query, const INDEX_TYPE &data, size_t nprobe, std::vector &clusters_indices) { - std::vector distances_to_centroids; - distances_to_centroids.resize(data.num_clusters); - for (size_t cluster_idx = 0; cluster_idx < data.num_clusters; cluster_idx++) { - distances_to_centroids[cluster_idx] = - DistanceComputer::Horizontal(query, - data.centroids + - cluster_idx * - data.num_dimensions, - data.num_dimensions, - nullptr); - } - clusters_indices.resize(data.num_clusters); - std::iota(clusters_indices.begin(), clusters_indices.end(), 0); - std::partial_sort(clusters_indices.begin(), clusters_indices.begin() + nprobe, clusters_indices.end(), - [&distances_to_centroids](size_t i1, size_t i2) { - return distances_to_centroids[i1] < distances_to_centroids[i2]; - } - ); - } - - // On the first bucket, we do a full scan (we do not prune vectors) - template - void Start( - const QuantizedVectorType_t *__restrict query, - const DataType_t * data, - const size_t n_vectors, - uint32_t k, - const uint32_t * vector_indices, - uint32_t * pruning_positions, - DistanceType_t * pruning_distances, - std::priority_queue, std::vector>, VectorComparator> &heap - ) { - ResetPruningDistances(n_vectors, pruning_distances); - DistanceComputer::Vertical( - query, data, n_vectors, n_vectors, 0, pdx_data.num_vertical_dimensions, - pruning_distances, pruning_positions, indices_dimensions.data(), quantizer.dim_clip_value, - nullptr); - for (size_t horizontal_dimension = 0; horizontal_dimension < pdx_data.num_horizontal_dimensions; horizontal_dimension+=H_DIM_SIZE) { - for (size_t vector_idx = 0; vector_idx < n_vectors; vector_idx++) { - size_t data_pos = (pdx_data.num_vertical_dimensions * n_vectors) + - (horizontal_dimension * n_vectors) + - (vector_idx * H_DIM_SIZE); - pruning_distances[vector_idx] += DistanceComputer::Horizontal( - query + pdx_data.num_vertical_dimensions + horizontal_dimension, - data + data_pos, - H_DIM_SIZE, - nullptr - ); - } - } - size_t max_possible_k = std::min((size_t) k - heap.size(), n_vectors); // Note: Start() should not be called if heap.size() >= k - std::vector indices_sorted; - indices_sorted.resize(n_vectors); - std::iota(indices_sorted.begin(), indices_sorted.end(), 0); - std::partial_sort(indices_sorted.begin(), indices_sorted.begin() + max_possible_k, indices_sorted.end(), - [pruning_distances](size_t i1, size_t i2) { - return pruning_distances[i1] < pruning_distances[i2]; - }); - // insert first k results into the heap - for (size_t idx = 0; idx < max_possible_k; ++idx) { - auto embedding = KNNCandidate{}; - size_t index = indices_sorted[idx]; - embedding.index = vector_indices[index]; - embedding.distance = pruning_distances[index]; - heap.push(embedding); - } - } - - // On the first bucket, we do a full scan (we do not prune vectors) - template - void FilteredStart( - const QuantizedVectorType_t *__restrict query, - const DataType_t * data, - const size_t n_vectors, - uint32_t k, - const uint32_t * vector_indices, - uint32_t * pruning_positions, - DistanceType_t * pruning_distances, - std::priority_queue, std::vector>, VectorComparator> &heap, - uint8_t * selection_vector, - uint32_t passing_tuples - ) { - ResetPruningDistances(n_vectors, pruning_distances); - n_vectors_not_pruned = 0; - float selection_percentage = (passing_tuples / (float) n_vectors); - InitPositionsArrayFromSelectionVector( - n_vectors, n_vectors_not_pruned, pruning_positions, pruning_threshold, pruning_distances, selection_vector - ); - // Always start with horizontal block, regardless of selectivity - for (size_t horizontal_dimension = 0; horizontal_dimension < pdx_data.num_horizontal_dimensions; horizontal_dimension+=H_DIM_SIZE) { - size_t offset_data = (pdx_data.num_vertical_dimensions * n_vectors) + - (horizontal_dimension * n_vectors); - for (size_t vector_idx = 0; vector_idx < n_vectors_not_pruned; vector_idx++) { - size_t v_idx = pruning_positions[vector_idx]; - size_t data_pos = offset_data + (v_idx * H_DIM_SIZE); - pruning_distances[v_idx] += DistanceComputer::Horizontal( - query + pdx_data.num_vertical_dimensions + horizontal_dimension, - data + data_pos, - H_DIM_SIZE, - nullptr - ); - } - } - if (selection_percentage > 0.20) { // TODO: 0.20 comes from the `selectivity_threshold` - // It is then faster to do the full scan (thanks to SIMD) - DistanceComputer::Vertical( - query, data, n_vectors, n_vectors, 0, pdx_data.num_vertical_dimensions, - pruning_distances, pruning_positions, indices_dimensions.data(), quantizer.dim_clip_value, - nullptr); - } else { - // We access individual values - DistanceComputer::VerticalPruning( - query, data, n_vectors_not_pruned, - n_vectors, 0, pdx_data.num_vertical_dimensions, pruning_distances, - pruning_positions, indices_dimensions.data(), quantizer.dim_clip_value, - nullptr); - } - // TODO: Everything down from here is a bottleneck when selection % is ultra low - size_t max_possible_k = std::min((size_t) k - heap.size(), (size_t) passing_tuples); - MaskDistancesWithSelectionVector( - n_vectors, n_vectors_not_pruned, pruning_positions, pruning_threshold, pruning_distances, selection_vector - ); - std::vector indices_sorted; - indices_sorted.resize(n_vectors); - std::iota(indices_sorted.begin(), indices_sorted.end(), 0); - std::partial_sort(indices_sorted.begin(), indices_sorted.begin() + max_possible_k, indices_sorted.end(), - [pruning_distances](size_t i1, size_t i2) { - return pruning_distances[i1] < pruning_distances[i2]; - }); - // insert first k results into the heap - for (size_t idx = 0; idx < max_possible_k; ++idx) { - auto embedding = KNNCandidate{}; - size_t index = indices_sorted[idx]; - embedding.index = vector_indices[index]; - embedding.distance = pruning_distances[index]; - heap.push(embedding); - } - } - - // On the warmup phase, we keep scanning dimensions until the amount of not-yet pruned vectors is low - template - void Warmup( - const QuantizedVectorType_t *__restrict query, - const DataType_t *__restrict data, - const size_t n_vectors, - uint32_t k, - float tuples_threshold, - uint32_t * pruning_positions, - DistanceType_t * pruning_distances, - DistanceType_t &pruning_threshold, - std::priority_queue, std::vector>, VectorComparator> &heap, - uint32_t passing_tuples = 0, - uint8_t * selection_vector = nullptr - ) { - current_dimension_idx = 0; - cur_subgrouping_size_idx = 0; - size_t tuples_needed_to_exit = std::ceil(1.0 * tuples_threshold * n_vectors); - ResetPruningDistances(n_vectors, pruning_distances); - uint32_t n_tuples_to_prune = 0; - if constexpr (FILTERED) { - float selection_percentage = (passing_tuples / (float) n_vectors); - MaskDistancesWithSelectionVector( - n_vectors, n_vectors_not_pruned, pruning_positions, pruning_threshold, pruning_distances, selection_vector - ); - if (selection_percentage < 0.20) { - // Go directly to the PRUNE phase for direct tuples access in the Horizontal block - return; - } - } - if (!is_positional_pruning) GetPruningThreshold(k, heap, pruning_threshold); - while ( - 1.0 * n_tuples_to_prune < tuples_needed_to_exit && - current_dimension_idx < pdx_data.num_vertical_dimensions) { - size_t last_dimension_to_fetch = std::min(current_dimension_idx + DIMENSIONS_FETCHING_SIZES[cur_subgrouping_size_idx], - pdx_data.num_vertical_dimensions); - if (dimension_order == SEQUENTIAL){ - DistanceComputer::Vertical(query, data, n_vectors, n_vectors, current_dimension_idx, - last_dimension_to_fetch, pruning_distances, - pruning_positions, indices_dimensions.data(), quantizer.dim_clip_value, - nullptr); - } else { - DistanceComputer::VerticalReordered(query, data, n_vectors, n_vectors, current_dimension_idx, - last_dimension_to_fetch, pruning_distances, - pruning_positions, indices_dimensions.data(), quantizer.dim_clip_value, - nullptr); - } - current_dimension_idx = last_dimension_to_fetch; - cur_subgrouping_size_idx += 1; - if (is_positional_pruning) GetPruningThreshold(k, heap, pruning_threshold); - n_tuples_to_prune = 0; - EvaluatePruningPredicateScalar(n_tuples_to_prune, n_vectors, pruning_distances, pruning_threshold); - } - } - - // We scan only the not-yet pruned vectors - template - void Prune( - const QuantizedVectorType_t *__restrict query, - const DataType_t *__restrict data, - const size_t n_vectors, - uint32_t k, - uint32_t * pruning_positions, - DistanceType_t *pruning_distances, - DistanceType_t &pruning_threshold, - std::priority_queue, std::vector>, VectorComparator> &heap - ) { - GetPruningThreshold(k, heap, pruning_threshold); - InitPositionsArray(n_vectors, n_vectors_not_pruned, pruning_positions, pruning_threshold, pruning_distances); - size_t cur_n_vectors_not_pruned = 0; - size_t current_vertical_dimension = current_dimension_idx; - size_t current_horizontal_dimension = 0; - while ( - pdx_data.num_horizontal_dimensions && - n_vectors_not_pruned && - current_horizontal_dimension < pdx_data.num_horizontal_dimensions - ) { - cur_n_vectors_not_pruned = n_vectors_not_pruned; - size_t offset_data = (pdx_data.num_vertical_dimensions * n_vectors) + - (current_horizontal_dimension * n_vectors); - for (size_t vector_idx = 0; vector_idx < n_vectors_not_pruned; vector_idx++) { - size_t v_idx = pruning_positions[vector_idx]; - size_t data_pos = offset_data + (v_idx * H_DIM_SIZE); - __builtin_prefetch(data + data_pos, 0, 3); - } - size_t offset_query = pdx_data.num_vertical_dimensions + current_horizontal_dimension; - for (size_t vector_idx = 0; vector_idx < n_vectors_not_pruned; vector_idx++) { - size_t v_idx = pruning_positions[vector_idx]; - size_t data_pos = offset_data + (v_idx * H_DIM_SIZE); - pruning_distances[v_idx] += DistanceComputer::Horizontal( - query + offset_query, - data + data_pos, - H_DIM_SIZE, - nullptr - ); - } - // end of clipping - current_horizontal_dimension += H_DIM_SIZE; - current_dimension_idx += H_DIM_SIZE; - if (is_positional_pruning) GetPruningThreshold(k, heap, pruning_threshold); - assert(current_dimension_idx == current_vertical_dimension + current_horizontal_dimension); - EvaluatePruningPredicateOnPositionsArray(cur_n_vectors_not_pruned, n_vectors_not_pruned, pruning_positions, pruning_threshold, pruning_distances); - } - // GO THROUGH THE REST IN THE VERTICAL - while ( - n_vectors_not_pruned && - current_vertical_dimension < pdx_data.num_vertical_dimensions - ) { - cur_n_vectors_not_pruned = n_vectors_not_pruned; - size_t last_dimension_to_test_idx = std::min(current_vertical_dimension + H_DIM_SIZE, - (size_t)pdx_data.num_vertical_dimensions); - if (dimension_order == SEQUENTIAL){ - DistanceComputer::VerticalPruning( - query, data, cur_n_vectors_not_pruned, - n_vectors, current_vertical_dimension, - last_dimension_to_test_idx, pruning_distances, - pruning_positions, indices_dimensions.data(), quantizer.dim_clip_value, - nullptr); - } else { - DistanceComputer::VerticalReorderedPruning( - query, data, cur_n_vectors_not_pruned, - n_vectors, current_vertical_dimension, - last_dimension_to_test_idx, pruning_distances, - pruning_positions, indices_dimensions.data(), quantizer.dim_clip_value, - nullptr); - } - current_dimension_idx = std::min(current_dimension_idx+H_DIM_SIZE, (size_t)pdx_data.num_dimensions); - current_vertical_dimension = std::min((uint32_t)(current_vertical_dimension+H_DIM_SIZE), pdx_data.num_vertical_dimensions); - assert(current_dimension_idx == current_vertical_dimension + current_horizontal_dimension); - if (is_positional_pruning) GetPruningThreshold(k, heap, pruning_threshold); - EvaluatePruningPredicateOnPositionsArray(cur_n_vectors_not_pruned, n_vectors_not_pruned, pruning_positions, pruning_threshold, pruning_distances); - if (current_dimension_idx == pdx_data.num_dimensions) break; - } - } - - // TODO: Manage the heap elsewhere - template - void MergeIntoHeap( - const uint32_t * vector_indices, - size_t n_vectors, - uint32_t k, - const uint32_t * pruning_positions, - DistanceType_t *pruning_distances, - DistanceType_t *distances, - std::priority_queue, std::vector>, VectorComparator> &heap - ) { - for (size_t position_idx = 0; position_idx < n_vectors; ++position_idx) { - size_t index = position_idx; - //DISTANCES_TYPE current_distance; - float current_distance; - if constexpr (IS_PRUNING){ - index = pruning_positions[position_idx]; - current_distance = pruning_distances[index]; - } else { - current_distance = distances[index]; - } - if (heap.size() < k || current_distance < heap.top().distance) { - KNNCandidate embedding{}; - embedding.distance = current_distance; - embedding.index = vector_indices[index]; - if (heap.size() >= k) { - heap.pop(); - } - heap.push(embedding); - } - } - } - - std::vector BuildResultSet(uint32_t k){ - size_t result_set_size = std::min(best_k.size(), (size_t) k); - std::vector result; - result.resize(result_set_size); - // We return distances in the original domain (do we need it?) - float inverse_scale_factor; - if constexpr (q == U8) { - inverse_scale_factor = 1.0f / pdx_data.scale_factor; - inverse_scale_factor = inverse_scale_factor * inverse_scale_factor; - } - for (int i = result_set_size - 1; i >= 0; --i) { - const KNNCandidate_t& embedding = best_k.top(); - if constexpr (q == U8) { - result[i].distance = embedding.distance * inverse_scale_factor; - } else if constexpr (q == F32){ - result[i].distance = embedding.distance; - } - result[i].index = embedding.index; - best_k.pop(); - } - return result; - } - - void BuildResultSetCentroids(uint32_t k){ - for (int i = k - 1; i >= 0; --i) { - const KNNCandidate& embedding = best_k_centroids.top(); - clusters_indices[i] = embedding.index; - best_k_centroids.pop(); - } - } - - // We store centroids using PDX in tight blocks of 64 - void GetClustersAccessOrderIVFPDX(const float *__restrict query) { - best_k_centroids = std::priority_queue, std::vector>, VectorComparator>{}; - clusters_indices.resize(pdx_data.num_clusters); - std::iota(clusters_indices.begin(), clusters_indices.end(), 0); - float * tmp_centroids_pdx = pdx_data.centroids_pdx; - uint32_t * tmp_cluster_indices = clusters_indices.data(); - size_t SKIPPING_SIZE = PDX_VECTOR_SIZE * pdx_data.num_dimensions; - size_t remainder_block_size = pdx_data.num_clusters % PDX_VECTOR_SIZE; - size_t full_blocks = std::floor(1.0 * pdx_data.num_clusters / PDX_VECTOR_SIZE); - for (size_t centroid_idx = 0; centroid_idx < full_blocks; ++centroid_idx) { - // TODO: Use another distances array for the centroids so I can use ResetDistancesVectorized() instead of memset - memset((void*) distances, 0, PDX_VECTOR_SIZE * sizeof(float)); - DistanceComputer::VerticalBlock(query, tmp_centroids_pdx, 0, pdx_data.num_dimensions, distances, nullptr); - MergeIntoHeap(tmp_cluster_indices, PDX_VECTOR_SIZE, ivf_nprobe, pruning_positions, pruning_distances, distances, best_k_centroids); - tmp_cluster_indices += PDX_VECTOR_SIZE; - tmp_centroids_pdx += SKIPPING_SIZE; - } - if (remainder_block_size){ - memset((void*) distances, 0, PDX_VECTOR_SIZE * sizeof(float)); - DistanceComputer::Vertical(query, tmp_centroids_pdx, remainder_block_size, remainder_block_size, 0, pdx_data.num_dimensions, distances, nullptr, nullptr, nullptr, nullptr); - MergeIntoHeap(tmp_cluster_indices, remainder_block_size, ivf_nprobe, pruning_positions, pruning_distances, distances, best_k_centroids); - } - for (size_t i = 0; i < ivf_nprobe; ++i){ - const KNNCandidate & c = best_k_centroids.top(); - clusters_indices[ivf_nprobe - i - 1] = c.index; // I need to inverse the allocation - best_k_centroids.pop(); - } - memset((void*) distances, 0, PDX_VECTOR_SIZE * sizeof(float)); - } - - // We store centroids using PDX in tight blocks of 64 - // TODO: Always assumes multiple of 64 - void GetL0ClustersAccessOrderPDX(const float *__restrict query) { - best_k_centroids = std::priority_queue, std::vector>, VectorComparator> {}; - clusters_indices_l0.resize(pdx_data.num_clusters_l0); - std::iota(clusters_indices_l0.begin(), clusters_indices_l0.end(), 0); - float * tmp_centroids_pdx = pdx_data.centroids_pdx; - uint32_t * tmp_cluster_indices = clusters_indices_l0.data(); - size_t SKIPPING_SIZE = PDX_VECTOR_SIZE * pdx_data.num_dimensions; - size_t full_blocks = std::floor(1.0 * pdx_data.num_clusters_l0 / PDX_VECTOR_SIZE); - for (size_t centroid_idx = 0; centroid_idx < full_blocks; ++centroid_idx) { - memset((void*) centroids_distances, 0, PDX_VECTOR_SIZE * sizeof(float)); - DistanceComputer::VerticalBlock(query, tmp_centroids_pdx, 0, pdx_data.num_dimensions, centroids_distances, nullptr); - tmp_cluster_indices += PDX_VECTOR_SIZE; - tmp_centroids_pdx += SKIPPING_SIZE; - } - std::vector indices_sorted; - indices_sorted.resize(pdx_data.num_clusters_l0); - std::iota(indices_sorted.begin(), indices_sorted.end(), 0); - std::partial_sort(indices_sorted.begin(), indices_sorted.begin() + 64, indices_sorted.end(), - [](size_t i1, size_t i2) { - return centroids_distances[i1] < centroids_distances[i2]; - }); - // Sort the distance of the first N centroids to determine access order - for (size_t idx = 0; idx < pdx_data.num_clusters_l0; ++idx) { - clusters_indices_l0[idx] = indices_sorted[idx]; - } - } - - void GetL1ClustersAccessOrderPDX( - const float *__restrict query, - size_t n_buckets, - bool safe_to_prune_space = true - ){ - size_t clusters_to_visit = pdx_data.num_clusters_l0; - if ((n_buckets < pdx_data.num_clusters / 2) && safe_to_prune_space) { - // We prune half of the super-clusters only if the user wants to - // visit less than half of the available clusters - clusters_to_visit = pdx_data.num_clusters_l0 / 2; - } - current_dimension_idx = 0; - for (size_t cluster_idx = 0; cluster_idx < clusters_to_visit; ++cluster_idx) { - current_cluster = clusters_indices_l0[cluster_idx]; - Cluster& cluster = pdx_data.clusters_l0[current_cluster]; - if (best_k_centroids.size() < n_buckets) { - // The heap may not be filled with the first super-cluster - // if the number of clusters probed is high or if the - // number of vectors per super-cluster is small. - Start( - query, cluster.data, cluster.num_embeddings, n_buckets, cluster.indices, - pruning_positions_l0, pruning_distances_l0, best_k_centroids - ); - continue; - } - Warmup(query, cluster.data, cluster.num_embeddings, n_buckets, selectivity_threshold, pruning_positions_l0, pruning_distances_l0, pruning_threshold_l0, best_k_centroids); - Prune(query, cluster.data, cluster.num_embeddings, n_buckets, pruning_positions_l0, pruning_distances_l0, pruning_threshold_l0, best_k_centroids); - if (n_vectors_not_pruned){ - MergeIntoHeap(cluster.indices, n_vectors_not_pruned, n_buckets, pruning_positions_l0, pruning_distances_l0, nullptr, best_k_centroids); - } - } - // Rare case in which half of the space is not enough to fill the best_k_centroids heap - if (best_k_centroids.size() < n_buckets) { - // From 32 to 64 (in the default case) - for (size_t cluster_idx = clusters_to_visit; cluster_idx < pdx_data.num_clusters_l0; ++cluster_idx) { - current_cluster = clusters_indices_l0[cluster_idx]; - Cluster& cluster = pdx_data.clusters_l0[current_cluster]; - Start( - query, cluster.data, cluster.num_embeddings, n_buckets, cluster.indices, - pruning_positions_l0, pruning_distances_l0, best_k_centroids - ); - if (best_k_centroids.size() == n_buckets) { - break; - } - } - } - BuildResultSetCentroids(n_buckets); - } + template + void InitPositionsArray(size_t n_vectors, size_t &n_vectors_not_pruned, uint32_t *pruning_positions, + distance_t pruning_threshold, distance_t *pruning_distances, + const uint8_t *selection_vector = nullptr) { + n_vectors_not_pruned = 0; + if constexpr (IS_FILTERED) { + for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { + pruning_positions[n_vectors_not_pruned] = vector_idx; + n_vectors_not_pruned += + (pruning_distances[vector_idx] < pruning_threshold) && (selection_vector[vector_idx] == 1); + } + } else { + for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { + pruning_positions[n_vectors_not_pruned] = vector_idx; + n_vectors_not_pruned += pruning_distances[vector_idx] < pruning_threshold; + } + } + }; + + void InitPositionsArrayFromSelectionVector(size_t n_vectors, size_t &n_vectors_not_pruned, + uint32_t *pruning_positions, const uint8_t *selection_vector) { + n_vectors_not_pruned = 0; + for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { + pruning_positions[n_vectors_not_pruned] = vector_idx; + n_vectors_not_pruned += selection_vector[vector_idx] == 1; + } + }; + + void MaskDistancesWithSelectionVector(size_t n_vectors, distance_t *pruning_distances, + const uint8_t *selection_vector) { + for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { + if (selection_vector[vector_idx] == 0) { + // Why max()/2? To prevent overflow if distances are still added to these + pruning_distances[vector_idx] = std::numeric_limits::max() / 2; + } + } + }; + + static void GetClustersAccessOrderIVF(const float *PDX_RESTRICT query, const index_t &data, size_t nprobe, + uint32_t *clusters_indices) { + std::unique_ptr distances_to_centroids(new float[data.num_clusters]); + for (size_t cluster_idx = 0; cluster_idx < data.num_clusters; cluster_idx++) { + distances_to_centroids[cluster_idx] = DistanceComputer::Horizontal( + query, data.centroids.data() + cluster_idx * data.num_dimensions, data.num_dimensions); + } + std::iota(clusters_indices, clusters_indices + data.num_clusters, static_cast(0)); + if (nprobe >= data.num_clusters) { + std::sort(clusters_indices, clusters_indices + data.num_clusters, + [&distances_to_centroids](size_t i1, size_t i2) { + return distances_to_centroids[i1] < distances_to_centroids[i2]; + }); + } else { + std::partial_sort(clusters_indices, clusters_indices + static_cast(nprobe), + clusters_indices + data.num_clusters, [&distances_to_centroids](size_t i1, size_t i2) { + return distances_to_centroids[i1] < distances_to_centroids[i2]; + }); + } + } + + // On the first bucket, we do a full scan (we do not prune vectors) + void Start(const quantized_embedding_t *PDX_RESTRICT query, const data_t *data, const size_t n_vectors, uint32_t k, + const uint32_t *vector_indices, uint32_t *pruning_positions, distance_t *pruning_distances, + std::priority_queue, VectorComparator> &heap) { + ResetPruningDistances(n_vectors, pruning_distances); + distance_computer_t::Vertical(query, data, n_vectors, n_vectors, 0, pdx_data.num_vertical_dimensions, + pruning_distances, pruning_positions); + for (size_t horizontal_dimension = 0; horizontal_dimension < pdx_data.num_horizontal_dimensions; + horizontal_dimension += H_DIM_SIZE) { + for (size_t vector_idx = 0; vector_idx < n_vectors; vector_idx++) { + size_t data_pos = (pdx_data.num_vertical_dimensions * n_vectors) + (horizontal_dimension * n_vectors) + + (vector_idx * H_DIM_SIZE); + pruning_distances[vector_idx] += distance_computer_t::Horizontal( + query + pdx_data.num_vertical_dimensions + horizontal_dimension, data + data_pos, H_DIM_SIZE); + } + } + size_t max_possible_k = std::min(static_cast(k) - heap.size(), + n_vectors); // Note: Start() should not be called if heap.size() >= k + std::unique_ptr indices_sorted(new size_t[n_vectors]); + std::iota(indices_sorted.get(), indices_sorted.get() + n_vectors, static_cast(0)); + std::partial_sort(indices_sorted.get(), indices_sorted.get() + static_cast(max_possible_k), + indices_sorted.get() + n_vectors, [pruning_distances](size_t i1, size_t i2) { + return pruning_distances[i1] < pruning_distances[i2]; + }); + // insert first k results into the heap + for (size_t idx = 0; idx < max_possible_k; ++idx) { + auto embedding = KNNCandidate {}; + size_t index = indices_sorted[idx]; + embedding.index = vector_indices[index]; + embedding.distance = static_cast(pruning_distances[index]); + if constexpr (Q == U8) { + embedding.distance *= pdx_data.inverse_quantization_scale_squared; + } + heap.push(embedding); + } + } + + // On the first bucket, we do a full scan (we do not prune vectors) + void FilteredStart(const quantized_embedding_t *PDX_RESTRICT query, const data_t *data, const size_t n_vectors, + uint32_t k, const uint32_t *vector_indices, uint32_t *pruning_positions, + distance_t *pruning_distances, + std::priority_queue, VectorComparator> &heap, + uint8_t *selection_vector, uint32_t passing_tuples) { + ResetPruningDistances(n_vectors, pruning_distances); + size_t n_vectors_not_pruned = 0; + float selection_percentage = (static_cast(passing_tuples) / static_cast(n_vectors)); + InitPositionsArrayFromSelectionVector(n_vectors, n_vectors_not_pruned, pruning_positions, selection_vector); + // Always start with horizontal block, regardless of selectivity + for (size_t horizontal_dimension = 0; horizontal_dimension < pdx_data.num_horizontal_dimensions; + horizontal_dimension += H_DIM_SIZE) { + size_t offset_data = (pdx_data.num_vertical_dimensions * n_vectors) + (horizontal_dimension * n_vectors); + for (size_t vector_idx = 0; vector_idx < n_vectors_not_pruned; vector_idx++) { + size_t v_idx = pruning_positions[vector_idx]; + size_t data_pos = offset_data + (v_idx * H_DIM_SIZE); + pruning_distances[v_idx] += distance_computer_t::Horizontal( + query + pdx_data.num_vertical_dimensions + horizontal_dimension, data + data_pos, H_DIM_SIZE); + } + } + if (selection_percentage > (1 - selectivity_threshold)) { + // It is then faster to do the full scan (thanks to SIMD) + distance_computer_t::Vertical(query, data, n_vectors, n_vectors, 0, pdx_data.num_vertical_dimensions, + pruning_distances, pruning_positions); + } else { + // We access individual values + distance_computer_t::VerticalPruning(query, data, n_vectors_not_pruned, n_vectors, 0, + pdx_data.num_vertical_dimensions, pruning_distances, + pruning_positions); + } + // TODO: Everything down from here is a bottleneck when selection % is ultra low + size_t max_possible_k = std::min(static_cast(k) - heap.size(), static_cast(passing_tuples)); + MaskDistancesWithSelectionVector(n_vectors, pruning_distances, selection_vector); + std::unique_ptr indices_sorted(new size_t[n_vectors]); + std::iota(indices_sorted.get(), indices_sorted.get() + n_vectors, static_cast(0)); + std::partial_sort(indices_sorted.get(), indices_sorted.get() + static_cast(max_possible_k), + indices_sorted.get() + n_vectors, [pruning_distances](size_t i1, size_t i2) { + return pruning_distances[i1] < pruning_distances[i2]; + }); + // insert first k results into the heap + for (size_t idx = 0; idx < max_possible_k; ++idx) { + auto embedding = KNNCandidate {}; + size_t index = indices_sorted[idx]; + embedding.index = vector_indices[index]; + embedding.distance = static_cast(pruning_distances[index]); + if constexpr (Q == U8) { + embedding.distance *= pdx_data.inverse_quantization_scale_squared; + } + heap.push(embedding); + } + } + + // On the warmup phase, we keep scanning dimensions until the amount of not-yet pruned vectors is low + template + void Warmup(const quantized_embedding_t *PDX_RESTRICT query, const data_t *PDX_RESTRICT data, + const size_t n_vectors, uint32_t k, float tuples_threshold, uint32_t *pruning_positions, + distance_t *pruning_distances, distance_t &pruning_threshold, + std::priority_queue, VectorComparator> &heap, + uint32_t ¤t_dimension_idx, size_t &n_vectors_not_pruned, uint32_t passing_tuples = 0, + uint8_t *selection_vector = nullptr) { + current_dimension_idx = 0; + size_t cur_subgrouping_size_idx = 0; + size_t tuples_needed_to_exit = static_cast(std::ceil(tuples_threshold * static_cast(n_vectors))); + ResetPruningDistances(n_vectors, pruning_distances); + uint32_t n_tuples_to_prune = 0; + if constexpr (FILTERED) { + float selection_percentage = (static_cast(passing_tuples) / static_cast(n_vectors)); + MaskDistancesWithSelectionVector(n_vectors, pruning_distances, selection_vector); + if (selection_percentage < (1 - tuples_threshold)) { + // Go directly to the PRUNE phase for direct tuples access in the Horizontal block + return; + } + } + GetPruningThreshold(k, heap, pruning_threshold, current_dimension_idx); + while (n_tuples_to_prune < tuples_needed_to_exit && current_dimension_idx < pdx_data.num_vertical_dimensions) { + size_t last_dimension_to_fetch = + std::min(current_dimension_idx + DIMENSIONS_FETCHING_SIZES[cur_subgrouping_size_idx], + pdx_data.num_vertical_dimensions); + distance_computer_t::Vertical(query, data, n_vectors, n_vectors, current_dimension_idx, + last_dimension_to_fetch, pruning_distances, pruning_positions); + current_dimension_idx = last_dimension_to_fetch; + cur_subgrouping_size_idx += 1; + GetPruningThreshold(k, heap, pruning_threshold, current_dimension_idx); + n_tuples_to_prune = 0; + EvaluatePruningPredicateScalar(n_tuples_to_prune, n_vectors, pruning_distances, pruning_threshold); + } + } + + // We scan only the not-yet pruned vectors + template + void Prune(const quantized_embedding_t *PDX_RESTRICT query, const data_t *PDX_RESTRICT data, const size_t n_vectors, + uint32_t k, uint32_t *pruning_positions, distance_t *pruning_distances, distance_t &pruning_threshold, + std::priority_queue, VectorComparator> &heap, + uint32_t ¤t_dimension_idx, size_t &n_vectors_not_pruned, + const uint8_t *selection_vector = nullptr) { + GetPruningThreshold(k, heap, pruning_threshold, current_dimension_idx); + InitPositionsArray(n_vectors, n_vectors_not_pruned, pruning_positions, pruning_threshold, + pruning_distances, selection_vector); + size_t cur_n_vectors_not_pruned = 0; + size_t current_vertical_dimension = current_dimension_idx; + size_t current_horizontal_dimension = 0; + while (pdx_data.num_horizontal_dimensions && n_vectors_not_pruned && + current_horizontal_dimension < pdx_data.num_horizontal_dimensions) { + cur_n_vectors_not_pruned = n_vectors_not_pruned; + size_t offset_data = + (pdx_data.num_vertical_dimensions * n_vectors) + (current_horizontal_dimension * n_vectors); + for (size_t vector_idx = 0; vector_idx < n_vectors_not_pruned; vector_idx++) { + size_t v_idx = pruning_positions[vector_idx]; + size_t data_pos = offset_data + (v_idx * H_DIM_SIZE); + __builtin_prefetch(data + data_pos, 0, 3); + } + size_t offset_query = pdx_data.num_vertical_dimensions + current_horizontal_dimension; + for (size_t vector_idx = 0; vector_idx < n_vectors_not_pruned; vector_idx++) { + size_t v_idx = pruning_positions[vector_idx]; + size_t data_pos = offset_data + (v_idx * H_DIM_SIZE); + pruning_distances[v_idx] += + distance_computer_t::Horizontal(query + offset_query, data + data_pos, H_DIM_SIZE); + } + // end of clipping + current_horizontal_dimension += H_DIM_SIZE; + current_dimension_idx += H_DIM_SIZE; + GetPruningThreshold(k, heap, pruning_threshold, current_dimension_idx); + assert(current_dimension_idx == current_vertical_dimension + current_horizontal_dimension); + EvaluatePruningPredicateOnPositionsArray(cur_n_vectors_not_pruned, n_vectors_not_pruned, pruning_positions, + pruning_threshold, pruning_distances); + } + // GO THROUGH THE REST IN THE VERTICAL + while (n_vectors_not_pruned && current_vertical_dimension < pdx_data.num_vertical_dimensions) { + cur_n_vectors_not_pruned = n_vectors_not_pruned; + size_t last_dimension_to_test_idx = std::min(current_vertical_dimension + H_DIM_SIZE, + static_cast(pdx_data.num_vertical_dimensions)); + distance_computer_t::VerticalPruning(query, data, cur_n_vectors_not_pruned, n_vectors, + current_vertical_dimension, last_dimension_to_test_idx, + pruning_distances, pruning_positions); + current_dimension_idx = + std::min(current_dimension_idx + H_DIM_SIZE, static_cast(pdx_data.num_dimensions)); + current_vertical_dimension = std::min(static_cast(current_vertical_dimension + H_DIM_SIZE), + pdx_data.num_vertical_dimensions); + assert(current_dimension_idx == current_vertical_dimension + current_horizontal_dimension); + GetPruningThreshold(k, heap, pruning_threshold, current_dimension_idx); + EvaluatePruningPredicateOnPositionsArray(cur_n_vectors_not_pruned, n_vectors_not_pruned, pruning_positions, + pruning_threshold, pruning_distances); + if (current_dimension_idx == pdx_data.num_dimensions) { + break; + } + } + } + + void MergeIntoHeap(const uint32_t *vector_indices, const size_t n_vectors, const uint32_t k, + const uint32_t *pruning_positions, const distance_t *pruning_distances, + std::priority_queue, VectorComparator> &heap) { + for (size_t position_idx = 0; position_idx < n_vectors; ++position_idx) { + const size_t index = pruning_positions[position_idx]; + float current_distance = static_cast(pruning_distances[index]); + if constexpr (Q == U8) { + current_distance *= pdx_data.inverse_quantization_scale_squared; + } + if (heap.size() < k || current_distance < heap.top().distance) { + KNNCandidate embedding {}; + embedding.distance = current_distance; + embedding.index = vector_indices[index]; + if (heap.size() >= k) { + heap.pop(); + } + heap.push(embedding); + } + } + } + + [[nodiscard]] static std::vector + BuildResultSetFromHeap(uint32_t k, + std::priority_queue, VectorComparator> &heap) { + // Pop the initialization element from the heap, as it can't be part of the result. + if (!heap.empty() && heap.top().distance == std::numeric_limits::max()) { + heap.pop(); + } + + size_t result_set_size = std::min(heap.size(), static_cast(k)); + std::vector result; + result.resize(result_set_size); + for (size_t i = result_set_size; i > 0; --i) { + result[i - 1] = heap.top(); + heap.pop(); + } + return result; + } void GetClustersAccessOrderRandom() { - std::iota(clusters_indices.begin(), clusters_indices.end(), 0); + std::iota(cluster_indices_in_access_order.get(), cluster_indices_in_access_order.get() + pdx_data.num_clusters, 0); } public: /****************************************************************** * Search methods ******************************************************************/ - std::vector Search(float *__restrict raw_query, uint32_t k) { + std::vector Search(const float *PDX_RESTRICT const raw_query, const uint32_t k) { #ifdef BENCHMARK_TIME this->ResetClocks(); this->end_to_end_clock.Tic(); #endif - best_k = std::priority_queue, VectorComparator_t>{}; - alignas(64) float query[pdx_data.num_dimensions]; - if (!pdx_data.is_normalized) { - pruner.PreprocessQuery(raw_query, query); - } else { - alignas(64) float normalized_query[pdx_data.num_dimensions]; - quantizer.NormalizeQuery(raw_query, normalized_query); - pruner.PreprocessQuery(normalized_query, query); - } - GetDimensionsAccessOrder(query, pdx_data.means); - size_t clusters_to_visit = (ivf_nprobe == 0 || ivf_nprobe > pdx_data.num_clusters) - ? pdx_data.num_clusters - : ivf_nprobe; - if constexpr (std::is_same_v>) { - // Multilevel access - GetL0ClustersAccessOrderPDX(query); - GetL1ClustersAccessOrderPDX(query, clusters_to_visit); - } else { - if (pdx_data.is_ivf) { - // TODO: Incorporate this to U8 PDX (no IVF2) - // GetClustersAccessOrderIVFPDX(query); - GetClustersAccessOrderIVF(query, pdx_data, clusters_to_visit, clusters_indices); - } else { - // If there is no index, we just access the clusters in order - GetClustersAccessOrderRandom(); - } - } - // PDXearch core - current_dimension_idx = 0; - QUANTIZED_VECTOR_TYPE *prepared_query; - if constexpr (q == U8) { - quantizer.PrepareQuery(query, pdx_data.for_base, pdx_data.scale_factor); - prepared_query = quantizer.quantized_query; - } else { - prepared_query = query; - } - for (size_t cluster_idx = 0; cluster_idx < clusters_to_visit; ++cluster_idx) { - current_cluster = clusters_indices[cluster_idx]; - CLUSTER_TYPE& cluster = pdx_data.clusters[current_cluster]; - if (best_k.size() < k) { - // We cannot prune until we fill the heap - Start( - prepared_query, cluster.data, cluster.num_embeddings, k, cluster.indices, - pruning_positions, pruning_distances, best_k - ); - continue; - } - Warmup(prepared_query, cluster.data, cluster.num_embeddings, k, selectivity_threshold, pruning_positions, pruning_distances, pruning_threshold, best_k); - Prune(prepared_query, cluster.data, cluster.num_embeddings, k, pruning_positions, pruning_distances, pruning_threshold, best_k); - if (n_vectors_not_pruned){ - MergeIntoHeap(cluster.indices, n_vectors_not_pruned, k, pruning_positions, pruning_distances, nullptr, best_k); - } - } - std::vector result = BuildResultSet(k); + Heap local_heap {}; + std::unique_ptr query(new float[pdx_data.num_dimensions]); + if (!pdx_data.is_normalized) { + pruner.PreprocessQuery(raw_query, query.get()); + } else { + std::unique_ptr normalized_query(new float[pdx_data.num_dimensions]); + quantizer.NormalizeQuery(raw_query, normalized_query.get()); + pruner.PreprocessQuery(normalized_query.get(), query.get()); + } + size_t clusters_to_visit = + (ivf_nprobe == 0 || ivf_nprobe > pdx_data.num_clusters) ? pdx_data.num_clusters : ivf_nprobe; + std::unique_ptr local_cluster_order(new uint32_t[pdx_data.num_clusters]); + GetClustersAccessOrderIVF(query.get(), pdx_data, clusters_to_visit, local_cluster_order.get()); + // PDXearch core + std::unique_ptr local_quantized_query( + new quantized_embedding_t[pdx_data.num_dimensions]); + quantized_embedding_t *local_prepared_query; + if constexpr (Q == U8) { + quantizer.QuantizeEmbedding(query.get(), pdx_data.quantization_base, pdx_data.quantization_scale, + local_quantized_query.get()); + local_prepared_query = local_quantized_query.get(); + } else { + local_prepared_query = query.get(); + } + + std::unique_ptr pruning_distances(new distance_t[max_cluster_size]); + std::unique_ptr pruning_positions(new uint32_t[max_cluster_size]); + + for (size_t cluster_idx = 0; cluster_idx < clusters_to_visit; ++cluster_idx) { + distance_t pruning_threshold = std::numeric_limits::max(); + uint32_t current_dimension_idx = 0; + size_t n_vectors_not_pruned = 0; + + const size_t current_cluster_idx = local_cluster_order[cluster_idx]; + cluster_t &cluster = pdx_data.clusters[current_cluster_idx]; + if (cluster.num_embeddings == 0) { + continue; + } + if (local_heap.size() < k) { + // We cannot prune until we fill the heap + Start(local_prepared_query, cluster.data, cluster.num_embeddings, k, cluster.indices, + pruning_positions.get(), pruning_distances.get(), local_heap); + continue; + } + Warmup(local_prepared_query, cluster.data, cluster.num_embeddings, k, selectivity_threshold, + pruning_positions.get(), pruning_distances.get(), pruning_threshold, local_heap, + current_dimension_idx, n_vectors_not_pruned); + Prune(local_prepared_query, cluster.data, cluster.num_embeddings, k, pruning_positions.get(), + pruning_distances.get(), pruning_threshold, local_heap, current_dimension_idx, n_vectors_not_pruned); + if (n_vectors_not_pruned) { + MergeIntoHeap(cluster.indices, n_vectors_not_pruned, k, pruning_positions.get(), + pruning_distances.get(), local_heap); + } + } + std::vector result = BuildResultSetFromHeap(k, local_heap); #ifdef BENCHMARK_TIME this->end_to_end_clock.Toc(); #endif return result; } - std::vector FilteredSearch(float *__restrict raw_query, uint32_t k, const PredicateEvaluator& predicate_evaluator) { + std::vector FilteredSearch(const float *PDX_RESTRICT const raw_query, const uint32_t k, + const PredicateEvaluator &predicate_evaluator) { #ifdef BENCHMARK_TIME this->ResetClocks(); this->end_to_end_clock.Tic(); #endif - best_k = std::priority_queue, VectorComparator_t>{}; - alignas(64) float query[pdx_data.num_dimensions]; - if (!pdx_data.is_normalized) { - pruner.PreprocessQuery(raw_query, query); - } else { - alignas(64) float normalized_query[pdx_data.num_dimensions]; - quantizer.NormalizeQuery(raw_query, normalized_query); - pruner.PreprocessQuery(normalized_query, query); - } - GetDimensionsAccessOrder(query, pdx_data.means); - size_t clusters_to_visit = (ivf_nprobe == 0 || ivf_nprobe > pdx_data.num_clusters) - ? pdx_data.num_clusters - : ivf_nprobe; - if constexpr (std::is_same_v>) { - // Multilevel access - GetL0ClustersAccessOrderPDX(query); - GetL1ClustersAccessOrderPDX(query, clusters_to_visit, false); - } else { - if (pdx_data.is_ivf) { - // GetClustersAccessOrderIVFPDX(query); - GetClustersAccessOrderIVF(query, pdx_data, clusters_to_visit, clusters_indices); - } else { - // If there is no index, we just access the clusters in order - GetClustersAccessOrderRandom(); - } - } - // PDXearch core - current_dimension_idx = 0; - QUANTIZED_VECTOR_TYPE *prepared_query; - if constexpr (q == U8) { - quantizer.PrepareQuery(query, pdx_data.for_base, pdx_data.scale_factor); - prepared_query = quantizer.quantized_query; - } else { - prepared_query = query; - } - for (size_t cluster_idx = 0; cluster_idx < clusters_to_visit; ++cluster_idx) { - current_cluster = clusters_indices[cluster_idx]; - auto [selection_vector, passing_tuples] = predicate_evaluator.GetSelectionVector(current_cluster, cluster_offsets[current_cluster]); - if (passing_tuples == 0) { - continue; - } - CLUSTER_TYPE& cluster = pdx_data.clusters[current_cluster]; - if (best_k.size() < k) { - // We cannot prune until we fill the heap - FilteredStart( - prepared_query, cluster.data, cluster.num_embeddings, k, cluster.indices, - pruning_positions, pruning_distances, best_k, selection_vector, passing_tuples - ); - continue; - } - Warmup(prepared_query, cluster.data, cluster.num_embeddings, k, selectivity_threshold, pruning_positions, pruning_distances, pruning_threshold, best_k, passing_tuples, selection_vector); - Prune(prepared_query, cluster.data, cluster.num_embeddings, k, pruning_positions, pruning_distances, pruning_threshold, best_k); - if (n_vectors_not_pruned){ - MergeIntoHeap(cluster.indices, n_vectors_not_pruned, k, pruning_positions, pruning_distances, nullptr, best_k); - } - } - std::vector result = BuildResultSet(k); -#ifdef BENCHMARK_TIME - this->end_to_end_clock.Toc(); -#endif - return result; - } - - - template = 0> - std::vector LinearScan(float *__restrict raw_query, uint32_t k) { -#ifdef BENCHMARK_TIME - this->ResetClocks(); - this->end_to_end_clock.Tic(); -#endif - std::vector dummy_for_bases(4096, 0); - std::vector dummy_scale_factors(4096, 1); - alignas(64) float query[pdx_data.num_dimensions]; - if (!pdx_data.is_normalized) { - } else { - quantizer.NormalizeQuery(raw_query, query); - } - quantizer.PrepareQuery(query, dummy_for_bases.data(), dummy_scale_factors); - best_k = std::priority_queue, VectorComparator_t>{}; - size_t clusters_to_visit = pdx_data.num_clusters; - GetClustersAccessOrderRandom(); - for (size_t cluster_idx = 0; cluster_idx < clusters_to_visit; ++cluster_idx) { - current_cluster = clusters_indices[cluster_idx]; - CLUSTER_TYPE& cluster = pdx_data.clusters[current_cluster]; - if (cluster.num_embeddings == PDX_VECTOR_SIZE){ - ResetDistancesVectorized(distances); - DistanceComputer::VerticalBlock(query, cluster.data, 0, pdx_data.num_dimensions, distances, nullptr); - MergeIntoHeap(cluster.indices, PDX_VECTOR_SIZE, k, nullptr, nullptr, distances, best_k); - } else if (cluster.num_embeddings < PDX_VECTOR_SIZE) { - ResetDistancesVectorized(distances); - DistanceComputer::Vertical(query, cluster.data, cluster.num_embeddings, cluster.num_embeddings, 0, pdx_data.num_dimensions, distances, - nullptr, nullptr, nullptr, nullptr); - MergeIntoHeap(cluster.indices, cluster.num_embeddings, k, nullptr, nullptr, distances, best_k); - } - } - std::vector result = BuildResultSet(k); -#ifdef BENCHMARK_TIME - this->end_to_end_clock.Toc(); -#endif - return result; - } - - // Full Linear Scans that do not prune vectors - template = 0> - std::vector LinearScan(float *__restrict raw_query, uint32_t k) { -#ifdef BENCHMARK_TIME - this->ResetClocks(); - this->end_to_end_clock.Tic(); -#endif - float query[pdx_data.num_dimensions]; - pruner.PreprocessQuery(raw_query, query); - best_k = std::priority_queue, VectorComparator_t>{}; - size_t clusters_to_visit = pdx_data.num_clusters; - GetClustersAccessOrderRandom(); - for (size_t cluster_idx = 0; cluster_idx < clusters_to_visit; ++cluster_idx) { - current_cluster = clusters_indices[cluster_idx]; - CLUSTER_TYPE& cluster = pdx_data.clusters[current_cluster]; - if (cluster.num_embeddings == PDX_VECTOR_SIZE){ - ResetDistancesVectorized(distances); - DistanceComputer::VerticalBlock(query, cluster.data, 0, pdx_data.num_dimensions, distances, nullptr); - MergeIntoHeap(cluster.indices, PDX_VECTOR_SIZE, k, nullptr, nullptr, distances, best_k); - } else if (cluster.num_embeddings < PDX_VECTOR_SIZE) { - ResetDistancesVectorized(distances); - DistanceComputer::Vertical(query, cluster.data, cluster.num_embeddings, cluster.num_embeddings, 0, pdx_data.num_dimensions, distances, nullptr, nullptr, nullptr, nullptr); - MergeIntoHeap(cluster.indices, cluster.num_embeddings, k, nullptr, nullptr, distances, best_k); - } - } - std::vector result = BuildResultSet(k); + Heap local_heap {}; + std::unique_ptr query(new float[pdx_data.num_dimensions]); + if (!pdx_data.is_normalized) { + pruner.PreprocessQuery(raw_query, query.get()); + } else { + std::unique_ptr normalized_query(new float[pdx_data.num_dimensions]); + quantizer.NormalizeQuery(raw_query, normalized_query.get()); + pruner.PreprocessQuery(normalized_query.get(), query.get()); + } + + size_t clusters_to_visit = + (ivf_nprobe == 0 || ivf_nprobe > pdx_data.num_clusters) ? pdx_data.num_clusters : ivf_nprobe; + + std::unique_ptr local_cluster_order(new uint32_t[pdx_data.num_clusters]); + GetClustersAccessOrderIVF(query.get(), pdx_data, clusters_to_visit, local_cluster_order.get()); + // PDXearch core + std::unique_ptr local_quantized_query( + new quantized_embedding_t[pdx_data.num_dimensions]); + quantized_embedding_t *local_prepared_query; + if constexpr (Q == U8) { + quantizer.QuantizeEmbedding(query.get(), pdx_data.quantization_base, pdx_data.quantization_scale, + local_quantized_query.get()); + local_prepared_query = local_quantized_query.get(); + } else { + local_prepared_query = query.get(); + } + + std::unique_ptr pruning_distances(new distance_t[max_cluster_size]); + std::unique_ptr pruning_positions(new uint32_t[max_cluster_size]); + + for (size_t cluster_idx = 0; cluster_idx < clusters_to_visit; ++cluster_idx) { + distance_t pruning_threshold = std::numeric_limits::max(); + uint32_t current_dimension_idx = 0; + size_t n_vectors_not_pruned = 0; + + const size_t current_cluster_idx = local_cluster_order[cluster_idx]; + auto [selection_vector, passing_tuples] = + predicate_evaluator.GetSelectionVector(current_cluster_idx, cluster_offsets[current_cluster_idx]); + if (passing_tuples == 0) { + continue; + } + cluster_t &cluster = pdx_data.clusters[current_cluster_idx]; + if (cluster.num_embeddings == 0) { + continue; + } + if (local_heap.size() < k) { + // We cannot prune until we fill the heap + FilteredStart(local_prepared_query, cluster.data, cluster.num_embeddings, k, cluster.indices, + pruning_positions.get(), pruning_distances.get(), local_heap, selection_vector, + passing_tuples); + continue; + } + Warmup(local_prepared_query, cluster.data, cluster.num_embeddings, k, selectivity_threshold, + pruning_positions.get(), pruning_distances.get(), pruning_threshold, local_heap, + current_dimension_idx, n_vectors_not_pruned, passing_tuples, selection_vector); + Prune(local_prepared_query, cluster.data, cluster.num_embeddings, k, pruning_positions.get(), + pruning_distances.get(), pruning_threshold, local_heap, current_dimension_idx, + n_vectors_not_pruned, selection_vector); + if (n_vectors_not_pruned) { + MergeIntoHeap(cluster.indices, n_vectors_not_pruned, k, pruning_positions.get(), + pruning_distances.get(), local_heap); + } + } + std::vector result = BuildResultSetFromHeap(k, local_heap); #ifdef BENCHMARK_TIME this->end_to_end_clock.Toc(); #endif @@ -962,5 +552,3 @@ class PDXearch { }; } // namespace PDX - -#endif //PDX_PDXEARCH_HPP diff --git a/include/pruners/adsampling.hpp b/include/pruners/adsampling.hpp index 0e1fe04..dc7a67a 100644 --- a/include/pruners/adsampling.hpp +++ b/include/pruners/adsampling.hpp @@ -1,92 +1,101 @@ -#ifndef PDX_ADSAMPLING_U8_HPP -#define PDX_ADSAMPLING_U8_HPP +#pragma once -#include +#include #include -#include +#include "common.hpp" + #ifdef HAS_FFTW - #include +#include #endif namespace PDX { -static std::vector ratios{}; - /****************************************************************** * ADSampling pruner ******************************************************************/ -template +template class ADSamplingPruner { - using DISTANCES_TYPE = DistanceType_t; - using KNNCandidate_t = KNNCandidate; - using VectorComparator_t = VectorComparator; + using matrix_t = eigen_matrix_t; public: + const uint32_t num_dimensions; - uint32_t num_dimensions; - - ADSamplingPruner(uint32_t num_dimensions, float epsilon0, float * matrix_p) - : num_dimensions(num_dimensions), epsilon0(epsilon0){ - ratios.resize(num_dimensions); - for (size_t i = 0; i < num_dimensions; ++i) { - ratios[i] = GetRatio(i); - } + ADSamplingPruner(const uint32_t num_dimensions, const float *matrix_p) : num_dimensions(num_dimensions) { + ratios.resize(num_dimensions); + for (size_t i = 0; i < num_dimensions; ++i) { + ratios[i] = GetRatio(i); + } #ifdef HAS_FFTW - if (num_dimensions >= D_THRESHOLD_FOR_DCT_ROTATION) { - matrix = Eigen::Map>(matrix_p, 1, num_dimensions); - } else { - matrix = Eigen::Map>(matrix_p, num_dimensions, num_dimensions); - } + if (num_dimensions >= D_THRESHOLD_FOR_DCT_ROTATION) { + matrix = Eigen::Map(matrix_p, 1, num_dimensions); + } else { + matrix = Eigen::Map(matrix_p, num_dimensions, num_dimensions); + } #else - matrix = Eigen::Map>(matrix_p, num_dimensions, num_dimensions); + matrix = Eigen::Map(matrix_p, num_dimensions, num_dimensions); #endif - } - - void SetEpsilon0(float epsilon0) { - ADSamplingPruner::epsilon0 = epsilon0; - for (size_t i = 0; i < num_dimensions; ++i) { - ratios[i] = GetRatio(i); - } - } - - void SetMatrix(const Eigen::MatrixXf &matrix) { - ADSamplingPruner::matrix = matrix; - } - - template - DistanceType_t GetPruningThreshold( - uint32_t k, - std::priority_queue, std::vector>, VectorComparator> &heap, - const uint32_t current_dimension_idx - ) { - float ratio = current_dimension_idx == num_dimensions ? 1 : ratios[current_dimension_idx]; - //return std::numeric_limits>::max(); - return heap.top().distance * ratio; - } - - void PreprocessQuery(float *raw_query, float * query) { - Multiply(raw_query, query, num_dimensions); - } + } + + void SetPruningAggresiveness(const float pruning_aggressiveness) { + ADSamplingPruner::pruning_aggressiveness = pruning_aggressiveness; + for (size_t i = 0; i < num_dimensions; ++i) { + ratios[i] = GetRatio(i); + } + } + + void SetMatrix(const Eigen::MatrixXf &matrix) { + ADSamplingPruner::matrix = matrix; + } + + float GetPruningThreshold(uint32_t, + std::priority_queue, VectorComparator> &heap, + const uint32_t current_dimension_idx) const { + float ratio = current_dimension_idx == num_dimensions ? 1 : ratios[current_dimension_idx]; + return heap.top().distance * ratio; + } + + void PreprocessQuery(const float *PDX_RESTRICT const raw_query_embedding, + float *PDX_RESTRICT const output_query_embedding) const { + PreprocessEmbeddings(raw_query_embedding, output_query_embedding, 1); + } + + void PreprocessEmbeddings(const float *PDX_RESTRICT const input_embeddings, + float *PDX_RESTRICT const output_embeddings, const size_t num_embeddings) const { + Rotate(input_embeddings, output_embeddings, num_embeddings); + } private: - float epsilon0 = 2.1; - Eigen::Matrix matrix; - - float GetRatio(const size_t &visited_dimensions) { - if (visited_dimensions == 0) { - return 1; - } - if (visited_dimensions == (int) num_dimensions) { - return 1.0; - } - return 1.0 * visited_dimensions / ((int) num_dimensions) * - (1.0 + epsilon0 / std::sqrt(visited_dimensions)) * (1.0 + epsilon0 / std::sqrt(visited_dimensions)); - } - - void Multiply(float *raw_query, float *query, uint32_t num_dimensions) { + float pruning_aggressiveness = ADSAMPLING_PRUNING_AGGRESIVENESS; + matrix_t matrix; + std::vector ratios; + + float GetRatio(const size_t &visited_dimensions) const { + if (visited_dimensions == 0) { + return 1; + } + if (visited_dimensions == num_dimensions) { + return 1.0; + } + return static_cast(visited_dimensions) / num_dimensions * + (1.0 + pruning_aggressiveness / std::sqrt(visited_dimensions)) * + (1.0 + pruning_aggressiveness / std::sqrt(visited_dimensions)); + } + + /** + * @brief Rotates embeddings using the rotation matrix. + * + * Transforms embeddings to a rotated space where dimensions contribute more equally + * to the total distance, enabling effective early termination. + * + * @param embeddings Input embeddings (row-major, n × num_dimensions) + * @param out_buffer Output buffer for rotated embeddings (n × num_dimensions) + * @param n Number of embeddings to rotate + */ + void Rotate(const float *PDX_RESTRICT const embeddings, float *PDX_RESTRICT const out_buffer, + const size_t n) const { +#ifdef HAS_FFTW Eigen::Map query_matrix(raw_query, num_dimensions); Eigen::Map output(query, num_dimensions); -#ifdef HAS_FFTW if (num_dimensions >= D_THRESHOLD_FOR_DCT_ROTATION) { Eigen::RowVectorXf first_row = matrix.row(0); Eigen::RowVectorXf pre_output = query_matrix.array() * first_row.array(); @@ -99,11 +108,17 @@ class ADSamplingPruner { return; } #endif - output.noalias() = query_matrix * matrix; + const char trans_a = 'N'; + const char trans_b = 'N'; + const float alpha = 1.0f; + const float beta = 0.0f; + int dim = static_cast(num_dimensions); + int n_blas = static_cast(n); + sgemm_(&trans_a, &trans_b, &dim, &n_blas, &dim, &alpha, matrix.data(), &dim, embeddings, &dim, &beta, + out_buffer, &dim); } }; } // namespace PDX -#endif // PDX_ADSAMPLING_U8_HPP diff --git a/include/quantizers/global.h b/include/quantizers/global.h deleted file mode 100644 index 0336488..0000000 --- a/include/quantizers/global.h +++ /dev/null @@ -1,72 +0,0 @@ -#ifndef PDX_QUANTIZERS_H -#define PDX_QUANTIZERS_H - -#include -#include -#include -#include "common.hpp" - -namespace PDX { - -class Quantizer { - -public: - virtual ~Quantizer() = default; - -public: - void NormalizeQuery(const float * src, float * out) { - float sum = 0.0f; - for (size_t i = 0; i < num_dimensions; ++i) { - sum += src[i] * src[i]; - } - float norm = std::sqrt(sum); - for (size_t i = 0; i < num_dimensions; ++i) { - out[i] = src[i] / norm; - } - } - size_t num_dimensions = 0; - - void SetD(size_t d){ - num_dimensions = d; - } - - virtual void ScaleQuery(const float * src, int32_t * dst) {}; -}; - -template -class Global8Quantizer : public Quantizer { -public: - using QUANTIZED_QUERY_TYPE = QuantizedVectorType_t; - alignas(64) inline static int32_t dim_clip_value[4096]; - alignas(64) inline static QUANTIZED_QUERY_TYPE quantized_query[4096]; - - Global8Quantizer(){ - if constexpr (q == Quantization::U8){ - MAX_VALUE = 255; - } - } - - uint8_t MAX_VALUE; - - void PrepareQuery( - const float *query, - const float for_base, - const float scale_factor - ){ - for (size_t i = 0; i < num_dimensions; ++i){ - // Scale factor is global in symmetric kernel - int rounded = std::round((query[i] - for_base) * scale_factor); - dim_clip_value[i] = rounded; - if (rounded > MAX_VALUE || rounded < 0) { - quantized_query[i] = 0; - }else { - quantized_query[i] = static_cast(rounded); - } - } - }; - -}; - -}; // namespace PDX - -#endif //PDX_QUANTIZERS_H diff --git a/include/quantizers/scalar.hpp b/include/quantizers/scalar.hpp new file mode 100644 index 0000000..6e338ea --- /dev/null +++ b/include/quantizers/scalar.hpp @@ -0,0 +1,83 @@ +#pragma once + +#include +#include +#include +#include +#include "common.hpp" + +namespace PDX { + +struct ScalarQuantizationParams { + float quantization_base; + float quantization_scale; +}; + +class Quantizer { + +public: + explicit Quantizer(size_t num_dimensions) : num_dimensions(num_dimensions) { + } + virtual ~Quantizer() = default; + +public: + void NormalizeQuery(const float *src, float *out) const { + float sum = 0.0f; + for (size_t i = 0; i < num_dimensions; ++i) { + sum += src[i] * src[i]; + } + + if (sum == 0.0f) { + return; + } + + float norm = std::sqrt(sum); + for (size_t i = 0; i < num_dimensions; ++i) { + out[i] = src[i] / norm; + } + } + const size_t num_dimensions; +}; + +template +class ScalarQuantizer : public Quantizer { +public: + using quantized_embedding_t = pdx_quantized_embedding_t; + + explicit ScalarQuantizer(size_t num_dimensions) : Quantizer(num_dimensions) { + } + +#ifdef __AVX512F__ + // We rely on _mm512_dpbusds_epi32 that has asymmetric operands + static constexpr uint8_t MAX_VALUE = 127; +#else + static constexpr uint8_t MAX_VALUE = 255; +#endif + + static ScalarQuantizationParams ComputeQuantizationParams(const float *embeddings, const size_t total_elements) { + float global_min = std::numeric_limits::max(); + float global_max = std::numeric_limits::lowest(); + for (size_t i = 0; i < total_elements; ++i) { + global_min = std::min(global_min, embeddings[i]); + global_max = std::max(global_max, embeddings[i]); + } + const float range = global_max - global_min; + return {global_min, (range > 0) ? static_cast(MAX_VALUE) / range : 1.0f}; + } + + void QuantizeEmbedding(const float *embedding, const float quantization_base, const float quantization_scale, + quantized_embedding_t *output_quantized_embedding) { + for (size_t i = 0; i < num_dimensions; ++i) { + const int rounded = static_cast(std::round((embedding[i] - quantization_base) * quantization_scale)); + if (PDX_UNLIKELY(rounded > MAX_VALUE)) { + output_quantized_embedding[i] = MAX_VALUE; + } else if (PDX_UNLIKELY(rounded < 0)) { + output_quantized_embedding[i] = 0; + } else { + output_quantized_embedding[i] = static_cast(rounded); + } + } + } +}; + +} // namespace PDX diff --git a/include/utils/benchmark_utils.hpp b/include/utils/benchmark_utils.hpp index d46a893..60157a3 100644 --- a/include/utils/benchmark_utils.hpp +++ b/include/utils/benchmark_utils.hpp @@ -14,6 +14,8 @@ #include #include #include +#include +#include #include #include "../common.hpp" @@ -129,7 +131,7 @@ class BenchmarkUtils { inline static uint8_t GROUND_TRUTH_MAX_K = 100; // To properly skip on the ground truth file (do not change) template - static void VerifyResult(float &recalls, const std::vector> &result, size_t knn, + static void VerifyResult(float &recalls, const std::vector &result, size_t knn, const uint32_t *int_ground_truth, size_t n_query) { std::unordered_set seen; for (const auto& val : result) { diff --git a/include/utils/file_reader.hpp b/include/utils/file_reader.hpp index 666ed06..d7e8ef5 100644 --- a/include/utils/file_reader.hpp +++ b/include/utils/file_reader.hpp @@ -16,9 +16,7 @@ #include #endif -/****************************************************************** - * File reader - ******************************************************************/ + inline std::unique_ptr MmapFile(const std::string& filename) { struct stat file_stats {}; int fd = ::open(filename.c_str(), O_RDONLY); diff --git a/include/utils/matrix.h b/include/utils/matrix.h deleted file mode 100644 index 66c1c41..0000000 --- a/include/utils/matrix.h +++ /dev/null @@ -1,111 +0,0 @@ -#pragma once -#ifndef MATRIX_HPP_ -#define MATRIX_HPP_ -#include -#include -#include -#include -#include -#include -#include -#include - -/****************************************************************** - * Eigen Matrix wrapper for ADSampling data transformations - ******************************************************************/ -template -class Matrix { -private: - -public: - T* data; - size_t n; - size_t d; - - Matrix(); // Default - Matrix(char * data_file_path); // IO - Matrix(size_t n, size_t d); // Matrix of size n * d - - // Deconstruction - // ~Matrix(){ delete [] data;} - - Matrix & operator = (const Matrix &X){ - delete [] data; - n = X.n; - d = X.d; - data = new T [n*d]; - memcpy(data, X.data, sizeof(T) * n * d); - return *this; - } - - Matrix & operator = (const float *query) { - delete [] data; - data = new T [n*d]; - memcpy(data, query, sizeof(T) * n * d); - return *this; - } - - void mul(const Matrix &A, Matrix &result) const; - float dist(size_t a, const Matrix &B, size_t b) const; -}; - -template -Matrix::Matrix(){ - n = 0; - d = 0; - data = NULL; -} - -template -Matrix::Matrix(char *data_file_path){ - n = 0; - d = 0; - data = NULL; - printf("%s\n",data_file_path); - std::ifstream in(data_file_path, std::ios::binary); - if (!in.is_open()) { - std::cout << "open file error" << std::endl; - exit(-1); - } - in.read((char*)&d, 4); - - std::cerr << "Dimensionality - " << d < -Matrix::Matrix(size_t _n,size_t _d){ - n = _n; - d = _d; - data = new T [n * d]; -} - -void eigen_mul(const Eigen::MatrixXf &A, const Eigen::MatrixXf &B, Eigen::MatrixXf &C){ - C = A * B; -} - -template -Matrix mul(const Matrix &A, const Matrix &B){ -} - -template -float Matrix::dist(size_t a, const Matrix &B, size_t b)const{ - float dist = 0; - for(size_t i=0;i +#include +#include +#include +#include +#include +#include "common.hpp" +#include "index_base/pdx_ivf.hpp" +#include "quantizers/scalar.hpp" +#include "pruners/adsampling.hpp" + + +namespace PDX { + +// Generate a rotation matrix suitable for PDXearch's ADSampling pruning algorithm. +[[nodiscard]] inline unique_ptr GenerateRandomRotationMatrix(const size_t num_dimensions, const int32_t seed) { + auto rotation_matrix = make_uniq_array(num_dimensions * num_dimensions); + + std::mt19937 gen(seed); + std::normal_distribution normal_dist; + + Eigen::MatrixXf random_matrix { + Eigen::MatrixXf::Zero(static_cast(num_dimensions), static_cast(num_dimensions))}; + for (size_t i = 0; i < num_dimensions; ++i) { + for (size_t j = 0; j < num_dimensions; ++j) { + random_matrix(static_cast(i), static_cast(j)) = normal_dist(gen); + } + } + + const Eigen::HouseholderQR qr {random_matrix}; + const Eigen::MatrixXf transformation_matrix {qr.householderQ()}; + + for (size_t i = 0; i < num_dimensions; ++i) { + for (size_t j = 0; j < num_dimensions; ++j) { + rotation_matrix[i * num_dimensions + j] = + transformation_matrix(static_cast(i), static_cast(j)); + } + } + + return rotation_matrix; +} + +// Store the embeddings into this cluster's preallocated buffers in the transposed PDX layout. +// +// See the README of the following for a description of the PDX layout: +// https://github.com/cwida/pdx +template +inline void StoreClusterEmbeddings(typename PDX::IndexPDXIVF::cluster_t &cluster, const PDX::IndexPDXIVF &index, + const T *embeddings, const size_t num_embeddings); + +template <> +inline void +StoreClusterEmbeddings(PDX::IndexPDXIVF::cluster_t &cluster, + const PDX::IndexPDXIVF &index, + const float *const embeddings, const size_t num_embeddings) { + // Store the cluster's data using the transposed PDX layout for float32 as described in: + // https://github.com/cwida/pdx?tab=readme-ov-file#the-data-layout + using matrix_t = PDX::eigen_matrix_t; + using h_matrix_t = Eigen::Matrix; + + const auto vertical_d = index.num_vertical_dimensions; + const auto horizontal_d = index.num_horizontal_dimensions; + + Eigen::Map in(embeddings, num_embeddings, index.num_dimensions); + + // Vertical block: transpose the first vertical_d columns into dimension-major order. + Eigen::Map out(cluster.data, vertical_d, num_embeddings); + out.noalias() = in.leftCols(vertical_d).transpose(); + + // Horizontal blocks: copy H_DIM_SIZE columns at a time, keeping each embedding's values contiguous. + float *horizontal_out = cluster.data + num_embeddings * vertical_d; + for (size_t j = 0; j < horizontal_d; j += PDX::H_DIM_SIZE) { + Eigen::Map out_h(horizontal_out, num_embeddings, PDX::H_DIM_SIZE); + out_h.noalias() = in.block(0, vertical_d + j, num_embeddings, PDX::H_DIM_SIZE); + horizontal_out += num_embeddings * PDX::H_DIM_SIZE; + } +} + +template <> +inline void +StoreClusterEmbeddings(PDX::IndexPDXIVF::cluster_t &cluster, + const PDX::IndexPDXIVF &index, + const uint8_t *const embeddings, const size_t num_embeddings) { + // Store the cluster's data using the transposed PDX layout for U8. + // The vertical block uses interleaving: for each group of U8_INTERLEAVE_SIZE consecutive dimensions, + // each vector's values are stored contiguously. This enables NEON vdotq_u32 / AVX2 processing. + using u8_matrix_t = Eigen::Matrix; + using u8_v_matrix_t = Eigen::Matrix; + using u8_h_matrix_t = Eigen::Matrix; + + const auto vertical_d = index.num_vertical_dimensions; + const auto horizontal_d = index.num_horizontal_dimensions; + + Eigen::Map in(embeddings, num_embeddings, index.num_dimensions); + + // Vertical dimensions (interleaved): copy U8_INTERLEAVE_SIZE columns at a time into row-major blocks. + size_t dim = 0; + for (; dim + PDX::U8_INTERLEAVE_SIZE <= vertical_d; dim += PDX::U8_INTERLEAVE_SIZE) { + Eigen::Map out_v(cluster.data + dim * num_embeddings, num_embeddings, PDX::U8_INTERLEAVE_SIZE); + out_v.noalias() = in.block(0, dim, num_embeddings, PDX::U8_INTERLEAVE_SIZE); + } + // Compact tail for remaining vertical dimensions (< U8_INTERLEAVE_SIZE). + if (dim < vertical_d) { + auto remaining = static_cast(vertical_d - dim); + Eigen::Map out_v(cluster.data + dim * num_embeddings, num_embeddings, remaining); + out_v.noalias() = in.block(0, dim, num_embeddings, remaining); + } + + // Horizontal blocks: copy H_DIM_SIZE columns at a time, keeping each embedding's values contiguous. + uint8_t *horizontal_out = cluster.data + num_embeddings * vertical_d; + for (size_t j = 0; j < horizontal_d; j += PDX::H_DIM_SIZE) { + Eigen::Map out_h(horizontal_out, num_embeddings, PDX::H_DIM_SIZE); + out_h.noalias() = in.block(0, vertical_d + j, num_embeddings, PDX::H_DIM_SIZE); + horizontal_out += num_embeddings * PDX::H_DIM_SIZE; + } +} + +class EmbeddingPreprocessor { +private: + // For rotation matrix multiplication. + PDX::ADSamplingPruner pruner; + // For normalization. + PDX::Quantizer quantizer; + const size_t num_dimensions; + +public: + explicit EmbeddingPreprocessor(const size_t num_dimensions, const float *const rotation_matrix) + : pruner(num_dimensions, rotation_matrix), quantizer(num_dimensions), num_dimensions(num_dimensions) { + } + + // Warning: modifies the input_embedding. + void PreprocessEmbedding(float *const input_embedding, float *const output_embedding, const bool normalize) const { + // In-place normalization. + if (normalize) { + quantizer.NormalizeQuery(input_embedding, input_embedding); + } + pruner.PreprocessQuery(input_embedding, output_embedding); + } + + // Warning: modifies the input_embeddings. + void PreprocessEmbeddings(float *const input_embeddings, float *const output_embeddings, + const size_t num_embeddings, const bool normalize) const { + // In-place normalization. + if (normalize) { + for (size_t i = 0; i < num_embeddings; i++) { + quantizer.NormalizeQuery(input_embeddings + i * num_dimensions, input_embeddings + i * num_dimensions); + } + } + pruner.PreprocessEmbeddings(input_embeddings, output_embeddings, num_embeddings); + } +}; + +[[nodiscard]] inline constexpr bool DistanceMetricRequiresNormalization(const PDX::DistanceMetric distance_metric) { + return distance_metric == PDX::DistanceMetric::COSINE || distance_metric == PDX::DistanceMetric::IP; +} + +} // namespace PDX \ No newline at end of file diff --git a/include/wrapper.hpp b/include/wrapper.hpp new file mode 100644 index 0000000..40d3882 --- /dev/null +++ b/include/wrapper.hpp @@ -0,0 +1,237 @@ +#pragma once + +#include +#include +#include + +#include "common.hpp" +#include "db_mock/predicate_evaluator.hpp" +#include "index_base/pdx_ivf.hpp" +#include "pruners/adsampling.hpp" +#include "pdxearch.hpp" +#include "clustering.hpp" + +namespace PDX { + +class PDXearchWrapper { +public: + static constexpr PDX::DistanceMetric DEFAULT_DISTANCE_METRIC = PDX::DistanceMetric::L2SQ; + static constexpr PDX::Quantization DEFAULT_QUANTIZATION = PDX::Quantization::U8; + static constexpr int32_t DEFAULT_N_PROBE = 24; + +private: + const uint32_t num_dimensions; + // Whether the embeddings (both the query embeddings and those stored in the + // index) are normalized. The PDXearch kernel only implements Euclidian + // distance (L2SQ). To compute the cosine and inner product distances we + // normalize and then use L2SQ. + const bool is_normalized; + + // The fields below are index options that can be set during index creation + const PDX::DistanceMetric distance_metric; + const PDX::Quantization quantization; + const uint32_t n_probe; + const int32_t seed; + +protected: + const unique_ptr rotation_matrix; + +public: + PDXearchWrapper(PDX::Quantization quantization, PDX::DistanceMetric distance_metric, uint32_t num_dimensions, + uint32_t n_probe, int32_t seed) + : num_dimensions(num_dimensions), is_normalized(DistanceMetricRequiresNormalization(distance_metric)), + distance_metric(distance_metric), quantization(quantization), n_probe(n_probe), seed(seed), + rotation_matrix(GenerateRandomRotationMatrix(num_dimensions, seed)) { + } + virtual ~PDXearchWrapper() = default; + + void Insert(row_t row_id, const float *embedding) { + throw NotImplementedException("PDXearchWrapper::Insert() not implemented"); + } + void Delete(row_t row_id) { + throw NotImplementedException("PDXearchWrapper::Delete() not implemented"); + } + + uint64_t GetInMemorySize() const { + throw NotImplementedException("PDXearchWrapper::GetInMemorySize() not implemented"); + } + + uint32_t GetNumDimensions() const { + return num_dimensions; + } + PDX::DistanceMetric GetDistanceMetric() const { + return distance_metric; + } + PDX::Quantization GetQuantization() const { + return quantization; + } + bool IsNormalized() const { + return is_normalized; + } + uint32_t GetNProbe() const { + return n_probe; + } + int32_t GetSeed() const { + return seed; + } + float *GetRotationMatrix() const { + return rotation_matrix.get(); + } +}; + +struct RowIdClusterMapping { + uint32_t cluster_id; + uint32_t index_in_cluster; +}; + +// The PDXearchWrapper for the global implementation +template +class PDXearchWrapperGlobal : public PDXearchWrapper { +public: + using embedding_storage_t = PDX::pdx_data_t; + +private: + uint32_t num_clusters {}; + uint64_t total_num_embeddings {}; + std::vector row_id_cluster_mapping; + + std::unique_ptr> index; + std::unique_ptr> pruner; + std::unique_ptr> searcher; + +public: + PDXearchWrapperGlobal(PDX::DistanceMetric distance_metric, uint32_t num_dimensions, uint32_t n_probe, int32_t seed, + size_t estimated_cardinality) + : PDXearchWrapper(Q, distance_metric, num_dimensions, n_probe, seed), + num_clusters(ComputeNumberOfClusters(estimated_cardinality)), total_num_embeddings(estimated_cardinality), + row_id_cluster_mapping(estimated_cardinality), + pruner(make_uniq>(num_dimensions, rotation_matrix.get())) { + // Additional constraints on the number of dimensions are enforced in `pdxearch_index_plan.cpp`. + D_ASSERT(num_dimensions > 0); + D_ASSERT(estimated_cardinality > 0); + D_ASSERT(num_clusters > 0); + } + + // Initialize the wrapper's state. This is called once. + void SetUpIndex(const row_t *const row_ids, const float *const embeddings, const size_t num_embeddings) { + D_ASSERT(num_embeddings == total_num_embeddings); + + const auto num_dimensions = GetNumDimensions(); + + float quantization_base = 0.0f; + float quantization_scale = 1.0f; + if constexpr (Q == PDX::U8) { + const auto params = PDX::ScalarQuantizer::ComputeQuantizationParams( + embeddings, static_cast(num_embeddings) * num_dimensions); + quantization_base = params.quantization_base; + quantization_scale = params.quantization_scale; + index = make_uniq>(num_dimensions, num_embeddings, num_clusters, IsNormalized(), + quantization_scale, quantization_base); + } else { + index = make_uniq>(num_dimensions, num_embeddings, num_clusters, IsNormalized()); + } + + // Compute K-means centroids and embedding-to-centroid assignment (always on float embeddings). + KMeansResult kmeans_result = + ComputeKMeans(embeddings, num_embeddings, num_dimensions, num_clusters, GetDistanceMetric(), GetSeed()); + + // Store centroids. + index->centroids = std::move(kmeans_result.centroids); + + // Row-major buffer that the current cluster's embeddings are "gathered" into. This buffer is the source for + // StoreClusterEmbeddings, the result of which is persistently stored in the index. The buffer is reused across + // clusters. For F32: buffer is float. For U8: buffer is uint8_t (quantized). + size_t max_cluster_size = 0; + for (size_t i = 0; i < num_clusters; i++) { + max_cluster_size = std::max(max_cluster_size, kmeans_result.assignments[i].size()); + } + auto tmp_cluster_embeddings = + std::make_unique(static_cast(max_cluster_size * num_dimensions)); + + // Set up the IVF clusters' metadata and store the embeddings. + for (size_t cluster_idx = 0; cluster_idx < num_clusters; cluster_idx++) { + const auto cluster_size = kmeans_result.assignments[cluster_idx].size(); + auto &cluster = index->clusters.emplace_back(cluster_size, num_dimensions); + + for (size_t position_in_cluster = 0; position_in_cluster < cluster_size; position_in_cluster++) { + const auto embedding_idx = kmeans_result.assignments[cluster_idx][position_in_cluster]; + const row_t row_id = row_ids[embedding_idx]; + + D_ASSERT(row_id < total_num_embeddings); + (row_id_cluster_mapping)[row_id] = {static_cast(cluster_idx), + static_cast(position_in_cluster)}; + cluster.indices[position_in_cluster] = row_id; + + if constexpr (Q == PDX::U8) { + PDX::ScalarQuantizer quantizer(num_dimensions); + quantizer.QuantizeEmbedding(embeddings + (embedding_idx * num_dimensions), quantization_base, + quantization_scale, + tmp_cluster_embeddings.get() + (position_in_cluster * num_dimensions)); + } else { + memcpy(tmp_cluster_embeddings.get() + (position_in_cluster * num_dimensions), + embeddings + (embedding_idx * num_dimensions), num_dimensions * sizeof(float)); + } + } + + StoreClusterEmbeddings(cluster, *index, tmp_cluster_embeddings.get(), cluster_size); + } + + // Note: the searcher depends on a fully initialized index in its constructor. + searcher = make_uniq>(*index, *pruner); + } + + std::unique_ptr> Search(const float *const query_embedding, const size_t limit, + const uint32_t n_probe) const { + searcher->SetNProbe(n_probe); + const std::vector results = searcher->Search(query_embedding, limit); + std::unique_ptr> row_ids = make_uniq>(results.size()); + for (size_t i = 0; i < results.size(); i++) { + (*row_ids)[i] = results[i].index; + } + return row_ids; + } + + PDX::PredicateEvaluator CreatePredicateEvaluator(std::vector> &row_id_vectors) const { + auto predicate_evaluator = PDX::PredicateEvaluator(num_clusters, total_num_embeddings); + + for (auto &[row_id_vector, vector_size] : row_id_vectors) { + row_id_vector.Flatten(vector_size); + const auto row_id_data = FlatVector::GetData(row_id_vector); + const auto &validity = FlatVector::Validity(row_id_vector); + + for (size_t i = 0; i < vector_size; i++) { + if (validity.RowIsValid(i)) { + const auto &[cluster_id, index_in_cluster] = (row_id_cluster_mapping)[row_id_data[i]]; + predicate_evaluator.n_passing_tuples[cluster_id]++; + predicate_evaluator.selection_vector[searcher->cluster_offsets[cluster_id] + index_in_cluster] = 1; + } + } + } + + return predicate_evaluator; + } + + std::unique_ptr> FilteredSearch(const float *const query_embedding, const size_t limit, + std::vector> &row_id_vectors, + const uint32_t n_probe) const { + const PDX::PredicateEvaluator predicate_evaluator = CreatePredicateEvaluator(row_id_vectors); + + searcher->SetNProbe(n_probe); + std::vector results = + searcher->FilteredSearch(query_embedding, limit, predicate_evaluator); + std::unique_ptr> row_ids = make_uniq>(results.size()); + for (size_t i = 0; i < results.size(); i++) { + (*row_ids)[i] = results[i].index; + } + return row_ids; + } + + size_t GetNumClusters() const { + return num_clusters; + } +}; + +using PDXearchWrapperGlobalF32 = PDXearchWrapperGlobal; +using PDXearchWrapperGlobalU8 = PDXearchWrapperGlobal; + +} // namespace PDX From e4fb3adadc57f118d16f2a25e9e76f794b2bcf22 Mon Sep 17 00:00:00 2001 From: lkuffo Date: Mon, 23 Feb 2026 22:07:20 +0100 Subject: [PATCH 03/39] U8 works --- benchmarks/bench_adsampling/pdx_ivf_adsampling_u8.cpp | 7 ++----- benchmarks/results/DEFAULT/U8_IVF_PDX_ADSAMPLING.csv | 4 ++++ 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/benchmarks/bench_adsampling/pdx_ivf_adsampling_u8.cpp b/benchmarks/bench_adsampling/pdx_ivf_adsampling_u8.cpp index 2c2cd24..6c89a64 100644 --- a/benchmarks/bench_adsampling/pdx_ivf_adsampling_u8.cpp +++ b/benchmarks/bench_adsampling/pdx_ivf_adsampling_u8.cpp @@ -29,12 +29,9 @@ int main(int argc, char *argv[]) { const bool VERIFY_RESULTS = BenchmarkUtils::VERIFY_RESULTS; uint8_t KNN = BenchmarkUtils::KNN; - float EPSILON0 = BenchmarkUtils::EPSILON0; size_t NUM_QUERIES; size_t NUM_MEASURE_RUNS = BenchmarkUtils::NUM_MEASURE_RUNS; - PDX::DimensionsOrder DIMENSION_ORDER = PDX::SEQUENTIAL; - std::string RESULTS_PATH; RESULTS_PATH = BENCHMARK_UTILS.RESULTS_DIR_PATH + "U8_IVF_PDX_ADSAMPLING.csv"; @@ -56,8 +53,8 @@ int main(int argc, char *argv[]) { auto *int_ground_truth = reinterpret_cast(ground_truth.get()); query += 1; // skip number of embeddings - PDX::ADSamplingPruner pruner = PDX::ADSamplingPruner(pdx_data.num_dimensions, EPSILON0, _matrix); - PDX::PDXearch searcher = PDX::PDXearch(pdx_data, pruner, 1, DIMENSION_ORDER); + PDX::ADSamplingPruner pruner = PDX::ADSamplingPruner(pdx_data.num_dimensions, _matrix); + PDX::PDXearch searcher = PDX::PDXearch(pdx_data, pruner); std::vector nprobes_to_use; if (arg_ivf_nprobe > 0) { diff --git a/benchmarks/results/DEFAULT/U8_IVF_PDX_ADSAMPLING.csv b/benchmarks/results/DEFAULT/U8_IVF_PDX_ADSAMPLING.csv index 0cde565..46c47c5 100644 --- a/benchmarks/results/DEFAULT/U8_IVF_PDX_ADSAMPLING.csv +++ b/benchmarks/results/DEFAULT/U8_IVF_PDX_ADSAMPLING.csv @@ -8,3 +8,7 @@ agnews-mxbai-1024-euclidean,adsampling,0.342781,0.413125,0.267625,0.899809,24,0, agnews-mxbai-1024-euclidean,adsampling,0.330006,0.405208,0.263084,0.899809,24,0,100,1000,0.2,1,0.3306,0.4435,0.2631 agnews-mxbai-1024-euclidean,adsampling,0.328877,0.40475,0.258958,0.899809,24,0,100,1000,0.2,1,0.3291,0.451,0.2446 agnews-mxbai-1024-euclidean,adsampling,0.355976,0.459792,0.269,0.899809,24,0,100,1000,0.2,1,0.3576,0.5143,0.269 +agnews-mxbai-1024-euclidean,adsampling,0.307076,0.375792,0.239333,0.900079,24,0,100,1000,0.2,1,0.308,0.4208,0.232 +agnews-mxbai-1024-euclidean,adsampling,0.307517,0.387834,0.232584,0.900079,24,0,100,1000,0.2,1,0.3091,0.4511,0.2326 +agnews-mxbai-1024-euclidean,adsampling,0.306141,0.379916,0.232542,0.900079,24,0,100,1000,0.2,1,0.3072,0.4393,0.2266 +agnews-mxbai-1024-euclidean,adsampling,0.308194,0.38125,0.239584,0.900079,24,0,100,1000,0.2,1,0.3096,0.4782,0.2396 From 540af93992b0c3e46c878b46c7daf1892668eac2 Mon Sep 17 00:00:00 2001 From: lkuffo Date: Tue, 24 Feb 2026 00:34:57 +0100 Subject: [PATCH 04/39] IVF2 working --- .../bench_adsampling/pdx_ivf2_adsampling.cpp | 32 +-- .../pdx_ivf2_adsampling_u8.cpp | 31 +-- .../pdx_ivf2_adsampling_u8_filtered.cpp | 1 - .../bench_adsampling/pdx_ivf_adsampling.cpp | 27 +- .../pdx_ivf_adsampling_filtered.cpp | 2 +- .../pdx_ivf_adsampling_u8.cpp | 26 +- .../results/DEFAULT/IVF2_PDX_ADSAMPLING.csv | 27 ++ .../results/DEFAULT/IVF_PDX_ADSAMPLING.csv | 6 + .../DEFAULT/U8_IVF2_PDX_ADSAMPLING.csv | 6 + .../results/DEFAULT/U8_IVF_PDX_ADSAMPLING.csv | 8 + include/index_base/pdx_ivf2.hpp | 199 --------------- .../pdx_ivf.hpp => ivf_wrapper.hpp} | 102 +++++++- include/lib/lib.hpp | 3 +- include/pdx_index.hpp | 152 +++++++++++ include/pdxearch.hpp | 32 +-- include/pruners/adsampling.hpp | 4 - include/utils/utils.hpp | 4 +- include/wrapper.hpp | 237 ------------------ python/pdxearch/index_base.py | 2 +- 19 files changed, 363 insertions(+), 538 deletions(-) create mode 100644 benchmarks/results/DEFAULT/IVF2_PDX_ADSAMPLING.csv create mode 100644 benchmarks/results/DEFAULT/U8_IVF2_PDX_ADSAMPLING.csv delete mode 100644 include/index_base/pdx_ivf2.hpp rename include/{index_base/pdx_ivf.hpp => ivf_wrapper.hpp} (67%) create mode 100644 include/pdx_index.hpp delete mode 100644 include/wrapper.hpp diff --git a/benchmarks/bench_adsampling/pdx_ivf2_adsampling.cpp b/benchmarks/bench_adsampling/pdx_ivf2_adsampling.cpp index 00ff70b..629ce2b 100644 --- a/benchmarks/bench_adsampling/pdx_ivf2_adsampling.cpp +++ b/benchmarks/bench_adsampling/pdx_ivf2_adsampling.cpp @@ -9,9 +9,7 @@ #include #include #include "utils/file_reader.hpp" -#include "index_base/pdx_ivf2.hpp" -#include "pruners/adsampling.hpp" -#include "pdxearch.hpp" +#include "pdx_index.hpp" #include "utils/benchmark_utils.hpp" int main(int argc, char *argv[]) { @@ -23,18 +21,15 @@ int main(int argc, char *argv[]) { if (argc > 2){ arg_ivf_nprobe = atoi(argv[2]); } - std::cout << "==> PDX IVF ADSampling\n"; + std::cout << "==> PDX IVF2 ADSampling\n"; std::string ALGORITHM = "adsampling"; const bool VERIFY_RESULTS = BenchmarkUtils::VERIFY_RESULTS; uint8_t KNN = BenchmarkUtils::KNN; - float EPSILON0 = BenchmarkUtils::EPSILON0; size_t NUM_QUERIES; size_t NUM_MEASURE_RUNS = BenchmarkUtils::NUM_MEASURE_RUNS; - PDX::DimensionsOrder DIMENSION_ORDER = PDX::SEQUENTIAL; - std::string RESULTS_PATH; RESULTS_PATH = BENCHMARK_UTILS.RESULTS_DIR_PATH + "IVF2_PDX_ADSAMPLING.csv"; @@ -43,11 +38,9 @@ int main(int argc, char *argv[]) { if (arg_dataset.size() > 0 && arg_dataset != dataset){ continue; } - PDX::IndexPDXIVF2 pdx_data = PDX::IndexPDXIVF2(); - pdx_data.Restore(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf2"); - - std::unique_ptr _matrix_ptr = MmapFile(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf2-matrix"); - auto *_matrix = reinterpret_cast(_matrix_ptr.get()); + PDX::PDXTreeIndexF32 pdx_index; + pdx_index.Restore(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf2", + BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf2-matrix"); std::unique_ptr query_ptr = MmapFile(BenchmarkUtils::QUERIES_DATA + dataset); auto *query = reinterpret_cast(query_ptr.get()); @@ -57,9 +50,6 @@ int main(int argc, char *argv[]) { auto *int_ground_truth = reinterpret_cast(ground_truth.get()); query += 1; // skip number of embeddings - PDX::ADSamplingPruner pruner = PDX::ADSamplingPruner(pdx_data.num_dimensions, EPSILON0, _matrix); - PDX::PDXearch searcher = PDX::PDXearch(pdx_data, pruner, 1, DIMENSION_ORDER); - std::vector nprobes_to_use; if (arg_ivf_nprobe > 0) { nprobes_to_use = {arg_ivf_nprobe}; @@ -68,7 +58,7 @@ int main(int argc, char *argv[]) { } for (size_t ivf_nprobe : nprobes_to_use) { - if (pdx_data.num_clusters < ivf_nprobe){ + if (pdx_index.GetNumClusters() < ivf_nprobe){ continue; } if (arg_ivf_nprobe > 0 && ivf_nprobe != arg_ivf_nprobe){ @@ -76,20 +66,20 @@ int main(int argc, char *argv[]) { } std::vector runtimes; runtimes.resize(NUM_MEASURE_RUNS * NUM_QUERIES); - searcher.SetNProbe(ivf_nprobe); + pdx_index.SetNProbe(ivf_nprobe); float recalls = 0; if (VERIFY_RESULTS) { for (size_t l = 0; l < NUM_QUERIES; ++l) { - auto result = searcher.Search(query + l * pdx_data.num_dimensions, KNN); + auto result = pdx_index.Search(query + l * pdx_index.GetNumDimensions(), KNN); BenchmarkUtils::VerifyResult(recalls, result, KNN, int_ground_truth, l); } } for (size_t j = 0; j < NUM_MEASURE_RUNS; ++j) { for (size_t l = 0; l < NUM_QUERIES; ++l) { - searcher.Search(query + l * pdx_data.num_dimensions, KNN); + pdx_index.Search(query + l * pdx_index.GetNumDimensions(), KNN); runtimes[j + l * NUM_MEASURE_RUNS] = { - searcher.end_to_end_clock.accum_time + pdx_index.GetSearcher().end_to_end_clock.accum_time }; } } @@ -108,4 +98,4 @@ int main(int argc, char *argv[]) { } } return 0; -} \ No newline at end of file +} diff --git a/benchmarks/bench_adsampling/pdx_ivf2_adsampling_u8.cpp b/benchmarks/bench_adsampling/pdx_ivf2_adsampling_u8.cpp index d90d7e9..7caf5f8 100644 --- a/benchmarks/bench_adsampling/pdx_ivf2_adsampling_u8.cpp +++ b/benchmarks/bench_adsampling/pdx_ivf2_adsampling_u8.cpp @@ -9,9 +9,7 @@ #include #include #include "utils/file_reader.hpp" -#include "index_base/pdx_ivf2.hpp" -#include "pruners/adsampling.hpp" -#include "pdxearch.hpp" +#include "pdx_index.hpp" #include "utils/benchmark_utils.hpp" int main(int argc, char *argv[]) { @@ -23,18 +21,15 @@ int main(int argc, char *argv[]) { if (argc > 2){ arg_ivf_nprobe = atoi(argv[2]); } - std::cout << "==> PDX IVF ADSampling\n"; + std::cout << "==> PDX IVF2 ADSampling U8\n"; std::string ALGORITHM = "adsampling"; const bool VERIFY_RESULTS = BenchmarkUtils::VERIFY_RESULTS; uint8_t KNN = BenchmarkUtils::KNN; - float EPSILON0 = BenchmarkUtils::EPSILON0; size_t NUM_QUERIES; size_t NUM_MEASURE_RUNS = BenchmarkUtils::NUM_MEASURE_RUNS; - PDX::DimensionsOrder DIMENSION_ORDER = PDX::SEQUENTIAL; - std::string RESULTS_PATH; RESULTS_PATH = BENCHMARK_UTILS.RESULTS_DIR_PATH + "U8_IVF2_PDX_ADSAMPLING.csv"; @@ -43,10 +38,9 @@ int main(int argc, char *argv[]) { if (arg_dataset.size() > 0 && arg_dataset != dataset){ continue; } - PDX::IndexPDXIVF2 pdx_data = PDX::IndexPDXIVF2(); - pdx_data.Restore(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf2-u8"); - std::unique_ptr _matrix_ptr = MmapFile(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf2-u8-matrix"); - auto *_matrix = reinterpret_cast(_matrix_ptr.get()); + PDX::PDXTreeIndexU8 pdx_index; + pdx_index.Restore(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf2-u8", + BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf2-u8-matrix"); std::unique_ptr query_ptr = MmapFile(BenchmarkUtils::QUERIES_DATA + dataset); auto *query = reinterpret_cast(query_ptr.get()); @@ -56,9 +50,6 @@ int main(int argc, char *argv[]) { auto *int_ground_truth = reinterpret_cast(ground_truth.get()); query += 1; // skip number of embeddings - PDX::ADSamplingPruner pruner = PDX::ADSamplingPruner(pdx_data.num_dimensions, EPSILON0, _matrix); - PDX::PDXearch searcher = PDX::PDXearch>(pdx_data, pruner, 1, DIMENSION_ORDER); - std::vector nprobes_to_use; if (arg_ivf_nprobe > 0) { nprobes_to_use = {arg_ivf_nprobe}; @@ -67,7 +58,7 @@ int main(int argc, char *argv[]) { } for (size_t ivf_nprobe : nprobes_to_use) { - if (pdx_data.num_clusters < ivf_nprobe){ + if (pdx_index.GetNumClusters() < ivf_nprobe){ continue; } if (arg_ivf_nprobe > 0 && ivf_nprobe != arg_ivf_nprobe){ @@ -75,20 +66,20 @@ int main(int argc, char *argv[]) { } std::vector runtimes; runtimes.resize(NUM_MEASURE_RUNS * NUM_QUERIES); - searcher.SetNProbe(ivf_nprobe); + pdx_index.SetNProbe(ivf_nprobe); float recalls = 0; if (VERIFY_RESULTS) { for (size_t l = 0; l < NUM_QUERIES; ++l) { - auto result = searcher.Search(query + l * pdx_data.num_dimensions, KNN); + auto result = pdx_index.Search(query + l * pdx_index.GetNumDimensions(), KNN); BenchmarkUtils::VerifyResult(recalls, result, KNN, int_ground_truth, l); } } for (size_t j = 0; j < NUM_MEASURE_RUNS; ++j) { for (size_t l = 0; l < NUM_QUERIES; ++l) { - searcher.Search(query + l * pdx_data.num_dimensions, KNN); + pdx_index.Search(query + l * pdx_index.GetNumDimensions(), KNN); runtimes[j + l * NUM_MEASURE_RUNS] = { - searcher.end_to_end_clock.accum_time + pdx_index.GetSearcher().end_to_end_clock.accum_time }; } } @@ -107,4 +98,4 @@ int main(int argc, char *argv[]) { } } return 0; -} \ No newline at end of file +} diff --git a/benchmarks/bench_adsampling/pdx_ivf2_adsampling_u8_filtered.cpp b/benchmarks/bench_adsampling/pdx_ivf2_adsampling_u8_filtered.cpp index e65d24e..22220d5 100644 --- a/benchmarks/bench_adsampling/pdx_ivf2_adsampling_u8_filtered.cpp +++ b/benchmarks/bench_adsampling/pdx_ivf2_adsampling_u8_filtered.cpp @@ -9,7 +9,6 @@ #include #include #include "utils/file_reader.hpp" -#include "index_base/pdx_ivf2.hpp" #include "pruners/adsampling.hpp" #include "pdxearch.hpp" #include "db_mock/predicate_evaluator.hpp" diff --git a/benchmarks/bench_adsampling/pdx_ivf_adsampling.cpp b/benchmarks/bench_adsampling/pdx_ivf_adsampling.cpp index 1476dab..125f81c 100644 --- a/benchmarks/bench_adsampling/pdx_ivf_adsampling.cpp +++ b/benchmarks/bench_adsampling/pdx_ivf_adsampling.cpp @@ -9,9 +9,7 @@ #include #include #include "utils/file_reader.hpp" -#include "index_base/pdx_ivf.hpp" -#include "pdxearch.hpp" -#include "pruners/adsampling.hpp" +#include "pdx_index.hpp" #include "utils/benchmark_utils.hpp" int main(int argc, char *argv[]) { @@ -41,11 +39,9 @@ int main(int argc, char *argv[]) { if (arg_dataset.size() > 0 && arg_dataset != dataset){ continue; } - PDX::IndexPDXIVF pdx_data = PDX::IndexPDXIVF(); - pdx_data.Restore(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf"); - - std::unique_ptr _matrix_ptr = MmapFile(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-matrix"); - auto *_matrix = reinterpret_cast(_matrix_ptr.get()); + PDX::PDXIndexF32 pdx_index; + pdx_index.Restore(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf", + BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-matrix"); std::unique_ptr query_ptr = MmapFile(BenchmarkUtils::QUERIES_DATA + dataset); auto *query = reinterpret_cast(query_ptr.get()); @@ -55,9 +51,6 @@ int main(int argc, char *argv[]) { auto *int_ground_truth = reinterpret_cast(ground_truth.get()); query += 1; // skip number of embeddings - PDX::ADSamplingPruner pruner = PDX::ADSamplingPruner(pdx_data.num_dimensions, _matrix); - PDX::PDXearch searcher = PDX::PDXearch(pdx_data, pruner); - std::vector nprobes_to_use; if (arg_ivf_nprobe > 0) { nprobes_to_use = {arg_ivf_nprobe}; @@ -66,7 +59,7 @@ int main(int argc, char *argv[]) { } for (size_t ivf_nprobe : nprobes_to_use) { - if (pdx_data.num_clusters < ivf_nprobe){ + if (pdx_index.GetNumClusters() < ivf_nprobe){ continue; } if (arg_ivf_nprobe > 0 && ivf_nprobe != arg_ivf_nprobe){ @@ -74,20 +67,20 @@ int main(int argc, char *argv[]) { } std::vector runtimes; runtimes.resize(NUM_MEASURE_RUNS * NUM_QUERIES); - searcher.SetNProbe(ivf_nprobe); + pdx_index.SetNProbe(ivf_nprobe); float recalls = 0; if (VERIFY_RESULTS) { for (size_t l = 0; l < NUM_QUERIES; ++l) { - auto result = searcher.Search(query + l * pdx_data.num_dimensions, KNN); + auto result = pdx_index.Search(query + l * pdx_index.GetNumDimensions(), KNN); BenchmarkUtils::VerifyResult(recalls, result, KNN, int_ground_truth, l); } } for (size_t j = 0; j < NUM_MEASURE_RUNS; ++j) { for (size_t l = 0; l < NUM_QUERIES; ++l) { - searcher.Search(query + l * pdx_data.num_dimensions, KNN); + pdx_index.Search(query + l * pdx_index.GetNumDimensions(), KNN); runtimes[j + l * NUM_MEASURE_RUNS] = { - searcher.end_to_end_clock.accum_time + pdx_index.GetSearcher().end_to_end_clock.accum_time }; } } @@ -106,4 +99,4 @@ int main(int argc, char *argv[]) { } } return 0; -} \ No newline at end of file +} diff --git a/benchmarks/bench_adsampling/pdx_ivf_adsampling_filtered.cpp b/benchmarks/bench_adsampling/pdx_ivf_adsampling_filtered.cpp index 1ea749b..a276680 100644 --- a/benchmarks/bench_adsampling/pdx_ivf_adsampling_filtered.cpp +++ b/benchmarks/bench_adsampling/pdx_ivf_adsampling_filtered.cpp @@ -9,7 +9,7 @@ #include #include #include "utils/file_reader.hpp" -#include "index_base/pdx_ivf.hpp" +#include "ivf_wrapper.hpp" #include "pdxearch.hpp" #include "pruners/adsampling.hpp" #include "utils/benchmark_utils.hpp" diff --git a/benchmarks/bench_adsampling/pdx_ivf_adsampling_u8.cpp b/benchmarks/bench_adsampling/pdx_ivf_adsampling_u8.cpp index 6c89a64..955e86a 100644 --- a/benchmarks/bench_adsampling/pdx_ivf_adsampling_u8.cpp +++ b/benchmarks/bench_adsampling/pdx_ivf_adsampling_u8.cpp @@ -9,9 +9,7 @@ #include #include #include "utils/file_reader.hpp" -#include "index_base/pdx_ivf.hpp" -#include "pdxearch.hpp" -#include "pruners/adsampling.hpp" +#include "pdx_index.hpp" #include "utils/benchmark_utils.hpp" int main(int argc, char *argv[]) { @@ -40,10 +38,9 @@ int main(int argc, char *argv[]) { if (arg_dataset.size() > 0 && arg_dataset != dataset){ continue; } - PDX::IndexPDXIVF pdx_data = PDX::IndexPDXIVF(); - pdx_data.Restore(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf-u8"); - std::unique_ptr _matrix_ptr = MmapFile(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf-u8-matrix"); - auto *_matrix = reinterpret_cast(_matrix_ptr.get()); + PDX::PDXIndexU8 pdx_index; + pdx_index.Restore(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf-u8", + BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf-u8-matrix"); std::unique_ptr query_ptr = MmapFile(BenchmarkUtils::QUERIES_DATA + dataset); auto *query = reinterpret_cast(query_ptr.get()); @@ -53,9 +50,6 @@ int main(int argc, char *argv[]) { auto *int_ground_truth = reinterpret_cast(ground_truth.get()); query += 1; // skip number of embeddings - PDX::ADSamplingPruner pruner = PDX::ADSamplingPruner(pdx_data.num_dimensions, _matrix); - PDX::PDXearch searcher = PDX::PDXearch(pdx_data, pruner); - std::vector nprobes_to_use; if (arg_ivf_nprobe > 0) { nprobes_to_use = {arg_ivf_nprobe}; @@ -64,7 +58,7 @@ int main(int argc, char *argv[]) { } for (size_t ivf_nprobe : nprobes_to_use) { - if (pdx_data.num_clusters < ivf_nprobe){ + if (pdx_index.GetNumClusters() < ivf_nprobe){ continue; } if (arg_ivf_nprobe > 0 && ivf_nprobe != arg_ivf_nprobe){ @@ -72,20 +66,20 @@ int main(int argc, char *argv[]) { } std::vector runtimes; runtimes.resize(NUM_MEASURE_RUNS * NUM_QUERIES); - searcher.SetNProbe(ivf_nprobe); + pdx_index.SetNProbe(ivf_nprobe); float recalls = 0; if (VERIFY_RESULTS) { for (size_t l = 0; l < NUM_QUERIES; ++l) { - auto result = searcher.Search(query + l * pdx_data.num_dimensions, KNN); + auto result = pdx_index.Search(query + l * pdx_index.GetNumDimensions(), KNN); BenchmarkUtils::VerifyResult(recalls, result, KNN, int_ground_truth, l); } } for (size_t j = 0; j < NUM_MEASURE_RUNS; ++j) { for (size_t l = 0; l < NUM_QUERIES; ++l) { - searcher.Search(query + l * pdx_data.num_dimensions, KNN); + pdx_index.Search(query + l * pdx_index.GetNumDimensions(), KNN); runtimes[j + l * NUM_MEASURE_RUNS] = { - searcher.end_to_end_clock.accum_time + pdx_index.GetSearcher().end_to_end_clock.accum_time }; } } @@ -104,4 +98,4 @@ int main(int argc, char *argv[]) { } } return 0; -} \ No newline at end of file +} diff --git a/benchmarks/results/DEFAULT/IVF2_PDX_ADSAMPLING.csv b/benchmarks/results/DEFAULT/IVF2_PDX_ADSAMPLING.csv new file mode 100644 index 0000000..ed2a103 --- /dev/null +++ b/benchmarks/results/DEFAULT/IVF2_PDX_ADSAMPLING.csv @@ -0,0 +1,27 @@ +dataset,algorithm,avg,max,min,recall,ivf_nprobe,epsilon,knn,n_queries,selectivity,num_measure_runs,avg_all,max_all,min_all +agnews-mxbai-1024-euclidean,adsampling,0.309509,0.56625,0.082167,0.4631,24,0,100,1000,0.2,1,0.4775,40.72,0.08217 +agnews-mxbai-1024-euclidean,adsampling,0.304629,0.529125,0.086,0.4631,24,0,100,1000,0.2,1,0.3052,0.6262,0.086 +agnews-mxbai-1024-euclidean,adsampling,0.26844,0.483958,0.077708,0.90164,24,0,100,1000,0.2,1,0.2707,0.6815,0.07771 +agnews-mxbai-1024-euclidean,adsampling,0.267786,0.483917,0.052833,0.90164,24,0,100,1000,0.2,1,0.2688,0.5469,0.05283 +agnews-mxbai-1024-euclidean,adsampling,0.268511,0.492792,0.066833,0.90164,24,0,100,1000,0.2,1,0.2693,0.5525,0.06683 +agnews-mxbai-1024-euclidean,adsampling,0.2092,0.365042,0.062417,0.862759,15,0,100,1000,0.2,1,0.2105,0.4066,0.06242 +agnews-mxbai-1024-euclidean,adsampling,0.204621,0.355292,0.052667,0.862759,15,0,100,1000,0.2,1,0.2057,0.4081,0.05267 +agnews-mxbai-1024-euclidean,adsampling,0.241776,0.432417,0.050416,0.8881,20,0,100,1000,0.2,1,0.2431,0.5125,0.05042 +agnews-mxbai-1024-euclidean,adsampling,0.241244,0.43525,0.059458,0.8881,20,0,100,1000,0.2,1,0.2419,0.4667,0.05946 +agnews-mxbai-1024-euclidean,adsampling,0.267327,0.488375,0.064875,0.90164,24,0,100,1000,0.2,1,0.2684,0.5773,0.06488 +agnews-mxbai-1024-euclidean,adsampling,0.300162,0.520792,0.088916,0.4631,24,0,100,1000,0.2,1,0.3007,0.5517,0.08892 +agnews-mxbai-1024-euclidean,adsampling,0.291026,0.506541,0.087417,0.4631,24,0,100,1000,0.2,1,0.2913,0.5564,0.08742 +agnews-mxbai-1024-euclidean,adsampling,0.288451,0.511458,0.084459,0.4631,24,0,100,1000,0.2,1,0.2887,0.536,0.08446 +agnews-mxbai-1024-euclidean,adsampling,0.296167,0.521292,0.08725,0.4631,24,0,100,1000,0.2,1,0.2972,0.5788,0.08725 +agnews-mxbai-1024-euclidean,adsampling,0.290358,0.498792,0.082791,0.4631,24,0,100,1000,0.2,1,0.2915,0.6732,0.08279 +agnews-mxbai-1024-euclidean,adsampling,0.266681,0.485666,0.06025,0.69214,24,0,100,1000,0.2,1,0.2672,0.5287,0.06025 +agnews-mxbai-1024-euclidean,adsampling,0.260031,0.473042,0.073833,0.9012,24,0,100,1000,0.2,1,0.2619,0.5677,0.07383 +agnews-mxbai-1024-euclidean,adsampling,0.26105,0.486,0.069542,0.9012,24,0,100,1000,0.2,1,0.2618,0.5406,0.06954 +agnews-mxbai-1024-euclidean,adsampling,0.258132,0.470458,0.052417,0.9016,24,0,100,1000,0.2,1,0.2594,0.5215,0.05242 +agnews-mxbai-1024-euclidean,adsampling,0.270587,0.498083,0.065625,0.9016,24,0,100,1000,0.2,1,0.2732,0.6475,0.06563 +agnews-mxbai-1024-euclidean,adsampling,0.25982,0.482083,0.052916,0.9016,24,0,100,1000,0.2,1,0.2617,0.6257,0.05292 +agnews-mxbai-1024-euclidean,adsampling,0.269162,0.493708,0.0605,0.9016,24,0,100,1000,0.2,1,0.2706,0.5822,0.0605 +agnews-mxbai-1024-euclidean,adsampling,0.257207,0.477,0.05175,0.9012,24,0,100,1000,0.2,1,0.2583,0.5552,0.05175 +agnews-mxbai-1024-euclidean,adsampling,0.260366,0.4805,0.059459,0.9012,24,0,100,1000,0.2,1,0.2618,0.5382,0.05946 +agnews-mxbai-1024-euclidean,adsampling,0.269808,0.495583,0.058375,0.9012,24,0,100,1000,0.2,1,0.2715,0.5795,0.05838 +agnews-mxbai-1024-euclidean,adsampling,0.295702,0.560791,0.0575,0.9012,24,0,100,1000,0.2,1,0.3006,0.8067,0.0575 diff --git a/benchmarks/results/DEFAULT/IVF_PDX_ADSAMPLING.csv b/benchmarks/results/DEFAULT/IVF_PDX_ADSAMPLING.csv index 825379d..64c8546 100644 --- a/benchmarks/results/DEFAULT/IVF_PDX_ADSAMPLING.csv +++ b/benchmarks/results/DEFAULT/IVF_PDX_ADSAMPLING.csv @@ -34,3 +34,9 @@ agnews-mxbai-1024-euclidean,adsampling,0.659602,0.809416,0.510125,4e-05,24,0,100 agnews-mxbai-1024-euclidean,adsampling,0.691954,0.834292,0.547458,4e-05,24,0,100,1000,0.2,1,0.6931,0.9468,0.4371 agnews-mxbai-1024-euclidean,adsampling,0.53561,0.789667,0.325125,0.90166,24,0,100,1000,0.2,1,0.5368,0.9136,0.3251 agnews-mxbai-1024-euclidean,adsampling,0.469188,0.662875,0.263625,0.90166,24,0,100,1000,0.2,1,0.4706,0.7823,0.2636 +agnews-mxbai-1024-euclidean,adsampling,0.464634,0.6715,0.276666,0.90166,24,0,100,1000,0.2,1,0.4664,0.7472,0.2767 +agnews-mxbai-1024-euclidean,adsampling,0.467765,0.682792,0.265416,0.90166,24,0,100,1000,0.2,1,0.4707,0.808,0.2654 +agnews-mxbai-1024-euclidean,adsampling,0.465191,0.681792,0.254916,0.90166,24,0,100,1000,0.2,1,0.4668,0.7656,0.2549 +agnews-mxbai-1024-euclidean,adsampling,0.911099,1.58692,0.377125,0.975328,128,0,100,1000,0.2,1,0.9179,2.734,0.3771 +agnews-mxbai-1024-euclidean,adsampling,0.470744,0.672417,0.277833,0.90166,24,0,100,1000,0.2,1,0.4713,0.7826,0.2778 +agnews-mxbai-1024-euclidean,adsampling,0.474207,0.691959,0.276166,0.90166,24,0,100,1000,0.2,1,0.4755,0.793,0.2762 diff --git a/benchmarks/results/DEFAULT/U8_IVF2_PDX_ADSAMPLING.csv b/benchmarks/results/DEFAULT/U8_IVF2_PDX_ADSAMPLING.csv new file mode 100644 index 0000000..fc7a76d --- /dev/null +++ b/benchmarks/results/DEFAULT/U8_IVF2_PDX_ADSAMPLING.csv @@ -0,0 +1,6 @@ +dataset,algorithm,avg,max,min,recall,ivf_nprobe,epsilon,knn,n_queries,selectivity,num_measure_runs,avg_all,max_all,min_all +agnews-mxbai-1024-euclidean,adsampling,0.106526,0.170417,0.041375,0.900339,24,0,100,1000,0.2,1,0.1068,0.2076,0.03904 +agnews-mxbai-1024-euclidean,adsampling,0.104055,0.168458,0.040791,0.900339,24,0,100,1000,0.2,1,0.1044,0.194,0.04079 +agnews-mxbai-1024-euclidean,adsampling,0.109186,0.177083,0.047167,0.900339,24,0,100,1000,0.2,1,0.1111,0.2971,0.03217 +agnews-mxbai-1024-euclidean,adsampling,0.106314,0.167,0.043042,0.900339,24,0,100,1000,0.2,1,0.1069,0.1936,0.039 +agnews-mxbai-1024-euclidean,adsampling,0.104194,0.162291,0.043625,0.900339,24,0,100,1000,0.2,1,0.1045,0.1879,0.04362 diff --git a/benchmarks/results/DEFAULT/U8_IVF_PDX_ADSAMPLING.csv b/benchmarks/results/DEFAULT/U8_IVF_PDX_ADSAMPLING.csv index 46c47c5..0945d27 100644 --- a/benchmarks/results/DEFAULT/U8_IVF_PDX_ADSAMPLING.csv +++ b/benchmarks/results/DEFAULT/U8_IVF_PDX_ADSAMPLING.csv @@ -12,3 +12,11 @@ agnews-mxbai-1024-euclidean,adsampling,0.307076,0.375792,0.239333,0.900079,24,0, agnews-mxbai-1024-euclidean,adsampling,0.307517,0.387834,0.232584,0.900079,24,0,100,1000,0.2,1,0.3091,0.4511,0.2326 agnews-mxbai-1024-euclidean,adsampling,0.306141,0.379916,0.232542,0.900079,24,0,100,1000,0.2,1,0.3072,0.4393,0.2266 agnews-mxbai-1024-euclidean,adsampling,0.308194,0.38125,0.239584,0.900079,24,0,100,1000,0.2,1,0.3096,0.4782,0.2396 +agnews-mxbai-1024-euclidean,adsampling,0.306373,0.375375,0.240625,0.900079,24,0,100,1000,0.2,1,0.3068,0.3949,0.233 +agnews-mxbai-1024-euclidean,adsampling,0.354936,0.550334,0.246792,0.900079,24,0,100,1000,0.2,1,0.3644,0.8134,0.2468 +agnews-mxbai-1024-euclidean,adsampling,0.309817,0.391125,0.235583,0.900079,24,0,100,1000,0.2,1,0.3119,0.6435,0.2227 +agnews-mxbai-1024-euclidean,adsampling,0.305317,0.382083,0.24075,0.900079,24,0,100,1000,0.2,1,0.3072,0.4726,0.2407 +agnews-mxbai-1024-euclidean,adsampling,0.305049,0.377208,0.235583,0.900079,24,0,100,1000,0.2,1,0.3058,0.4353,0.2356 +agnews-mxbai-1024-euclidean,adsampling,0.328986,0.43875,0.239375,0.900079,24,0,100,1000,0.2,1,0.342,5.541,0.2394 +agnews-mxbai-1024-euclidean,adsampling,0.503654,0.744292,0.307042,0.971026,128,0,100,1000,0.2,1,0.5037,0.7443,0.307 +agnews-mxbai-1024-euclidean,adsampling,0.315617,0.397708,0.240667,0.900079,24,0,100,1000,0.2,1,0.3184,0.5377,0.2407 diff --git a/include/index_base/pdx_ivf2.hpp b/include/index_base/pdx_ivf2.hpp deleted file mode 100644 index d183b1e..0000000 --- a/include/index_base/pdx_ivf2.hpp +++ /dev/null @@ -1,199 +0,0 @@ -#ifndef PDX_IVF2_HPP -#define PDX_IVF2_HPP - -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include "utils/file_reader.hpp" -#include "common.hpp" - -namespace PDX { - - -/****************************************************************** - * Very rudimentary memory to IVF2 index reader - ******************************************************************/ -template -class IndexPDXIVF2{}; - -template <> -class IndexPDXIVF2 { -public: - - using CLUSTER_TYPE = Cluster; - using CLUSTER_TYPE_L0 = Cluster; - - std::unique_ptr file_buffer; - - uint32_t num_dimensions{}; - uint32_t num_clusters{}; - uint32_t num_horizontal_dimensions{}; - uint32_t num_vertical_dimensions{}; - - std::vector clusters; - uint32_t num_clusters_l0 {}; - std::vector> clusters_l0; - - float *means{}; - bool is_ivf{}; - bool is_normalized{}; - float *centroids{}; - float *centroids_pdx{}; - - void Restore(const std::string &filename) { - file_buffer = MmapFile(filename); - Load(file_buffer.get()); - } - - void Load(char *input) { - char *next_value = input; - num_dimensions = ((uint32_t *) input)[0]; - num_vertical_dimensions = ((uint32_t *) input)[1]; - num_horizontal_dimensions = ((uint32_t *) input)[2]; - - - next_value += sizeof(uint32_t) * 3; - num_clusters = ((uint32_t *) next_value)[0]; - next_value += sizeof(uint32_t); - - num_clusters_l0 = ((uint32_t *) next_value)[0]; - next_value += sizeof(uint32_t); - - // L0 load - auto *nums_embeddings_l0 = (uint32_t *) next_value; - next_value += num_clusters_l0 * sizeof(uint32_t); - - clusters_l0.resize(num_clusters_l0); - - for (size_t i = 0; i < num_clusters_l0; ++i) { - CLUSTER_TYPE_L0 &cluster_l0 = clusters_l0[i]; - cluster_l0.num_embeddings = nums_embeddings_l0[i]; - cluster_l0.data = (float *) next_value; - next_value += sizeof(float) * cluster_l0.num_embeddings * num_dimensions; - } - for (size_t i = 0; i < num_clusters_l0; ++i) { - CLUSTER_TYPE_L0 &cluster_l0 = clusters_l0[i]; - cluster_l0.indices = (uint32_t *) next_value; - next_value += sizeof(uint32_t) * cluster_l0.num_embeddings; - } - - auto *nums_embeddings = (uint32_t *) next_value; - next_value += num_clusters * sizeof(uint32_t); - clusters.resize(num_clusters); - for (size_t i = 0; i < num_clusters; ++i) { - CLUSTER_TYPE &cluster = clusters[i]; - cluster.num_embeddings = nums_embeddings[i]; - cluster.data = (float *) next_value; - next_value += sizeof(float) * cluster.num_embeddings * num_dimensions; - } - for (size_t i = 0; i < num_clusters; ++i) { - CLUSTER_TYPE &cluster = clusters[i]; - cluster.indices = (uint32_t *) next_value; - next_value += sizeof(uint32_t) * cluster.num_embeddings; - } - is_normalized = ((char *) next_value)[0]; - next_value += sizeof(char); - - centroids_pdx = (float *) next_value; - } -}; - -template <> -class IndexPDXIVF2 { -public: - - using CLUSTER_TYPE = Cluster; - using CLUSTER_TYPE_L0 = Cluster; - - std::unique_ptr file_buffer; - - uint32_t num_dimensions {}; - uint32_t num_clusters {}; - uint32_t num_horizontal_dimensions {}; - uint32_t num_vertical_dimensions {}; - - std::vector> clusters; - - uint32_t num_clusters_l0 {}; - std::vector> clusters_l0; - - float *means{}; - bool is_normalized{}; - float *centroids{}; - float *centroids_pdx{}; - - float for_base{}; - float scale_factor{}; - - void Restore(const std::string &filename) { - file_buffer = MmapFile(filename); - Load(file_buffer.get()); - } - - void Load(char *input) { - char *next_value = input; - num_dimensions = ((uint32_t *) input)[0]; - num_vertical_dimensions = ((uint32_t *) input)[1]; - num_horizontal_dimensions = ((uint32_t *) input)[2]; - - next_value += sizeof(uint32_t) * 3; - num_clusters = ((uint32_t *) next_value)[0]; - next_value += sizeof(uint32_t); - num_clusters_l0 = ((uint32_t *) next_value)[0]; - next_value += sizeof(uint32_t); - - // L0 load - auto *nums_embeddings_l0 = (uint32_t *) next_value; - next_value += num_clusters_l0 * sizeof(uint32_t); - - clusters_l0.resize(num_clusters_l0); - - for (size_t i = 0; i < num_clusters_l0; ++i) { - CLUSTER_TYPE_L0 &cluster_l0 = clusters_l0[i]; - cluster_l0.num_embeddings = nums_embeddings_l0[i]; - cluster_l0.data = (float *) next_value; - next_value += sizeof(float) * cluster_l0.num_embeddings * num_dimensions; - } - for (size_t i = 0; i < num_clusters_l0; ++i) { - CLUSTER_TYPE_L0 &cluster_l0 = clusters_l0[i]; - cluster_l0.indices = (uint32_t *) next_value; - next_value += sizeof(uint32_t) * cluster_l0.num_embeddings; - } - - // L1 load - auto *nums_embeddings = (uint32_t *) next_value; - next_value += num_clusters * sizeof(uint32_t); - clusters.resize(num_clusters); - for (size_t i = 0; i < num_clusters; ++i) { - CLUSTER_TYPE &cluster = clusters[i]; - cluster.num_embeddings = nums_embeddings[i]; - cluster.data = (uint8_t *) next_value; - next_value += sizeof(uint8_t) * cluster.num_embeddings * num_dimensions; - } - for (size_t i = 0; i < num_clusters; ++i) { - CLUSTER_TYPE &cluster = clusters[i]; - cluster.indices = (uint32_t *) next_value; - next_value += sizeof(uint32_t) * cluster.num_embeddings; - } - is_normalized = ((char *) next_value)[0]; - next_value += sizeof(char); - - centroids_pdx = (float *) next_value; - next_value += sizeof(float) * num_clusters_l0 * num_dimensions; - - for_base = ((float *) next_value)[0]; - next_value += sizeof(float); - scale_factor = ((float *) next_value)[0]; - } -}; - -} // namespace PDX - -#endif //PDX_IVF2_HPP diff --git a/include/index_base/pdx_ivf.hpp b/include/ivf_wrapper.hpp similarity index 67% rename from include/index_base/pdx_ivf.hpp rename to include/ivf_wrapper.hpp index ebe9c12..5ec2811 100644 --- a/include/index_base/pdx_ivf.hpp +++ b/include/ivf_wrapper.hpp @@ -1,6 +1,7 @@ #pragma once #include +#include #include #include #include @@ -9,9 +10,6 @@ namespace PDX { -/****************************************************************** - * Very rudimentary memory to IVF index reader - ******************************************************************/ template class IndexPDXIVF{}; @@ -183,4 +181,102 @@ class IndexPDXIVF { } }; +template +class IndexPDXIVF2 : public IndexPDXIVF { +public: + using data_t = pdx_data_t; + + IndexPDXIVF l0; // Meso clusters + + IndexPDXIVF2() = default; + + void Restore(const std::string &filename) { + this->file_buffer = MmapFile(filename); + Load(this->file_buffer.get()); + } + + void Load(char *input) { + char *next_value = input; + + // Header + uint32_t dims = ((uint32_t *) input)[0]; + uint32_t v_dims = ((uint32_t *) input)[1]; + uint32_t h_dims = ((uint32_t *) input)[2]; + next_value += sizeof(uint32_t) * 3; + + uint32_t n_clusters_l1 = ((uint32_t *) next_value)[0]; + next_value += sizeof(uint32_t); + uint32_t n_clusters_l0 = ((uint32_t *) next_value)[0]; + next_value += sizeof(uint32_t); + + // === L0 (meso-clusters, always F32) === + l0.num_dimensions = dims; + l0.num_vertical_dimensions = v_dims; + l0.num_horizontal_dimensions = h_dims; + l0.num_clusters = n_clusters_l0; + + auto *nums_embeddings_l0 = (uint32_t *) next_value; + next_value += n_clusters_l0 * sizeof(uint32_t); + + l0.clusters.reserve(n_clusters_l0); + for (size_t i = 0; i < n_clusters_l0; ++i) { + l0.clusters.emplace_back(nums_embeddings_l0[i], dims); + memcpy(l0.clusters[i].data, next_value, + sizeof(float) * l0.clusters[i].num_embeddings * dims); + next_value += sizeof(float) * l0.clusters[i].num_embeddings * dims; + } + for (size_t i = 0; i < n_clusters_l0; ++i) { + memcpy(l0.clusters[i].indices, next_value, + sizeof(uint32_t) * l0.clusters[i].num_embeddings); + next_value += sizeof(uint32_t) * l0.clusters[i].num_embeddings; + } + + // === L1 (data clusters, inherited fields) === + this->num_dimensions = dims; + this->num_vertical_dimensions = v_dims; + this->num_horizontal_dimensions = h_dims; + this->num_clusters = n_clusters_l1; + + auto *nums_embeddings_l1 = (uint32_t *) next_value; + next_value += n_clusters_l1 * sizeof(uint32_t); + + this->clusters.reserve(n_clusters_l1); + for (size_t i = 0; i < n_clusters_l1; ++i) { + this->clusters.emplace_back(nums_embeddings_l1[i], dims); + memcpy(this->clusters[i].data, next_value, + sizeof(data_t) * this->clusters[i].num_embeddings * dims); + next_value += sizeof(data_t) * this->clusters[i].num_embeddings * dims; + } + for (size_t i = 0; i < n_clusters_l1; ++i) { + memcpy(this->clusters[i].indices, next_value, + sizeof(uint32_t) * this->clusters[i].num_embeddings); + next_value += sizeof(uint32_t) * this->clusters[i].num_embeddings; + } + + // === Shared metadata === + bool normalized = ((char *) next_value)[0]; + this->is_normalized = normalized; + l0.is_normalized = normalized; + next_value += sizeof(char); + + // === L0 centroids (centroids_pdx from file) === + l0.centroids.resize(n_clusters_l0 * dims); + memcpy(l0.centroids.data(), (float *) next_value, + sizeof(float) * n_clusters_l0 * dims); + next_value += sizeof(float) * n_clusters_l0 * dims; + + // === U8 quantization params === + if constexpr (Q == U8) { + this->quantization_base = ((float *) next_value)[0]; + next_value += sizeof(float); + this->quantization_scale = ((float *) next_value)[0]; + next_value += sizeof(float); + this->quantization_scale_squared = + this->quantization_scale * this->quantization_scale; + this->inverse_quantization_scale_squared = + 1.0f / this->quantization_scale_squared; + } + } +}; + } // namespace PDX diff --git a/include/lib/lib.hpp b/include/lib/lib.hpp index 7983349..aa4a1bd 100644 --- a/include/lib/lib.hpp +++ b/include/lib/lib.hpp @@ -8,8 +8,7 @@ #include #include #include "utils/file_reader.hpp" -#include "index_base/pdx_ivf.hpp" -#include "index_base/pdx_ivf2.hpp" +#include "ivf_wrapper.hpp" #include "pdxearch.hpp" #include "pruners/adsampling.hpp" diff --git a/include/pdx_index.hpp b/include/pdx_index.hpp new file mode 100644 index 0000000..68775f5 --- /dev/null +++ b/include/pdx_index.hpp @@ -0,0 +1,152 @@ +#pragma once + +#include +#include +#include +#include + +#include "common.hpp" +#include "ivf_wrapper.hpp" +#include "pruners/adsampling.hpp" +#include "pdxearch.hpp" +#include "utils/file_reader.hpp" + +namespace PDX { + +template +class PDXIndex { +public: + using embedding_storage_t = PDX::pdx_data_t; + +private: + std::unique_ptr matrix_buffer; + + PDX::IndexPDXIVF index; + std::unique_ptr pruner; + std::unique_ptr> searcher; + +public: + PDXIndex() = default; + + void Restore(const std::string &index_path, const std::string &matrix_path) { + index.Restore(index_path); + + matrix_buffer = MmapFile(matrix_path); + auto *matrix = reinterpret_cast(matrix_buffer.get()); + + pruner = std::make_unique(index.num_dimensions, matrix); + searcher = std::make_unique>(index, *pruner); + } + + std::vector Search(const float *query_embedding, size_t knn) const { + return searcher->Search(query_embedding, knn); + } + + void SetNProbe(uint32_t n_probe) const { + searcher->SetNProbe(n_probe); + } + + const PDX::PDXearch &GetSearcher() const { + return *searcher; + } + + uint32_t GetNumDimensions() const { + return index.num_dimensions; + } + + uint32_t GetNumClusters() const { + return index.num_clusters; + } + + // === CreateIndex path (DuckDB integration) — commented out for now === + // Requires: D_ASSERT, make_uniq, row_t, Vector, FlatVector, NotImplementedException, + // clustering.hpp, db_mock/predicate_evaluator.hpp + // + // void CreateIndex(const row_t *const row_ids, const float *const embeddings, const size_t num_embeddings); + // std::unique_ptr> Search(const float *query_embedding, size_t limit, uint32_t n_probe) const; + // PDX::PredicateEvaluator CreatePredicateEvaluator(...) const; + // std::unique_ptr> FilteredSearch(...) const; +}; + +template +class PDXTreeIndex { +public: + using embedding_storage_t = PDX::pdx_data_t; + +private: + std::unique_ptr matrix_buffer; + + PDX::IndexPDXIVF2 index; + std::unique_ptr pruner; + std::unique_ptr> searcher; + std::unique_ptr> top_level_searcher; + +public: + PDXTreeIndex() = default; + + void Restore(const std::string &index_path, const std::string &matrix_path) { + index.Restore(index_path); + + matrix_buffer = MmapFile(matrix_path); + auto *matrix = reinterpret_cast(matrix_buffer.get()); + + pruner = std::make_unique(index.num_dimensions, matrix); + searcher = std::make_unique>(index, *pruner); + top_level_searcher = std::make_unique>(index.l0, *pruner); + } + + std::vector Search(const float *query_embedding, size_t knn) const { + auto n_probe_top_level = GetTopLevelNumClusters(); + // if (searcher->GetNProbe() < GetNumClusters() / 2){ + // // We confidently prune half of the meso-clusters only if the user wants to + // // visit less than half of the available clusters + // n_probe_top_level /= 2; + // } + top_level_searcher->SetNProbe(n_probe_top_level); + auto top_level_results = top_level_searcher->Search(query_embedding, searcher->GetNProbe()); + + std::vector top_level_indexes(top_level_results.size()); + for (size_t i = 0; i < top_level_results.size(); i++) { + top_level_indexes[i] = top_level_results[i].index; + } + searcher->SetClusterAccessOrder(top_level_indexes); + + return searcher->Search(query_embedding, knn); + } + + void SetNProbe(uint32_t n_probe) const { + searcher->SetNProbe(n_probe); + } + + const PDX::PDXearch &GetSearcher() const { + return *searcher; + } + + uint32_t GetNumDimensions() const { + return index.num_dimensions; + } + + uint32_t GetNumClusters() const { + return index.num_clusters; + } + + uint32_t GetTopLevelNumClusters() const { + return index.l0.num_clusters; + } + + // === CreateIndex path (DuckDB integration) — commented out for now === + // Requires: D_ASSERT, make_uniq, row_t, Vector, FlatVector, NotImplementedException, + // clustering.hpp, db_mock/predicate_evaluator.hpp + // + // void CreateIndex(const row_t *const row_ids, const float *const embeddings, const size_t num_embeddings); + // std::unique_ptr> Search(const float *query_embedding, size_t limit, uint32_t n_probe) const; + // PDX::PredicateEvaluator CreatePredicateEvaluator(...) const; + // std::unique_ptr> FilteredSearch(...) const; +}; + +using PDXIndexF32 = PDXIndex; +using PDXIndexU8 = PDXIndex; +using PDXTreeIndexF32 = PDXTreeIndex; +using PDXTreeIndexU8 = PDXTreeIndex; + +} // namespace PDX diff --git a/include/pdxearch.hpp b/include/pdxearch.hpp index 4f9a113..c34d2f0 100644 --- a/include/pdxearch.hpp +++ b/include/pdxearch.hpp @@ -11,13 +11,13 @@ #include "db_mock/predicate_evaluator.hpp" #include "distance_computers/base_computers.hpp" #include "quantizers/scalar.hpp" -#include "index_base/pdx_ivf.hpp" +#include "ivf_wrapper.hpp" #include "pruners/adsampling.hpp" namespace PDX { template , class Quantizer = ScalarQuantizer, - DistanceMetric alpha = DistanceMetric::L2SQ, class Pruner = ADSamplingPruner> + DistanceMetric alpha = DistanceMetric::L2SQ, class Pruner = ADSamplingPruner> class PDXearch { public: using distance_t = pdx_distance_t; @@ -33,9 +33,7 @@ class PDXearch { PDXearch(index_t &data_index, Pruner &pruner) : quantizer(data_index.num_dimensions), pruner(pruner), pdx_data(data_index), - cluster_offsets(new size_t[data_index.num_clusters]), - cluster_indices_in_access_order(new uint32_t[data_index.num_clusters]), - quantized_query_buf(new quantized_embedding_t[data_index.num_dimensions]) { + cluster_offsets(new size_t[data_index.num_clusters]) { for (size_t i = 0; i < data_index.num_clusters; ++i) { cluster_offsets[i] = total_embeddings; total_embeddings += data_index.clusters[i].num_embeddings; @@ -47,6 +45,15 @@ class PDXearch { ivf_nprobe = nprobe; } + size_t GetNProbe() const { + return ivf_nprobe; + } + + void SetClusterAccessOrder(const std::vector &cluster_indexes) { + cluster_indices_in_access_order = std::make_unique(cluster_indexes.size()); + std::copy(cluster_indexes.begin(), cluster_indexes.end(), cluster_indices_in_access_order.get()); + } + TicToc end_to_end_clock = TicToc(); void ResetClocks(){ end_to_end_clock.Reset(); @@ -64,20 +71,12 @@ class PDXearch { // Prioritized list of indices of the clusters to probe. E.g., [0, 2, 1]. std::unique_ptr cluster_indices_in_access_order; - // Indexes into the cluster_indices_in_access_order list. This offset is - // incremented by 1 after probing a cluster. - uint64_t cluster_indices_in_access_order_offset {0}; - // Start: State for the current filtered search. uint32_t k = 0; quantized_embedding_t *prepared_query = nullptr; // Predicate evaluator for this rowgroup. std::unique_ptr predicate_evaluator; - // Per-search query buffers, filled by QuantizeEmbedding in InitializeSearch (U8 path). - // Used by Search/FilteredSearch and passed to Warmup/Prune. - std::unique_ptr quantized_query_buf; - void ResetPruningDistances(size_t n_vectors, distance_t *pruning_distances) { std::fill(pruning_distances, pruning_distances + n_vectors, distance_t {0}); } @@ -424,7 +423,12 @@ class PDXearch { size_t clusters_to_visit = (ivf_nprobe == 0 || ivf_nprobe > pdx_data.num_clusters) ? pdx_data.num_clusters : ivf_nprobe; std::unique_ptr local_cluster_order(new uint32_t[pdx_data.num_clusters]); - GetClustersAccessOrderIVF(query.get(), pdx_data, clusters_to_visit, local_cluster_order.get()); + if (cluster_indices_in_access_order) { + std::copy(cluster_indices_in_access_order.get(), + cluster_indices_in_access_order.get() + clusters_to_visit, local_cluster_order.get()); + } else { + GetClustersAccessOrderIVF(query.get(), pdx_data, clusters_to_visit, local_cluster_order.get()); + } // PDXearch core std::unique_ptr local_quantized_query( new quantized_embedding_t[pdx_data.num_dimensions]); diff --git a/include/pruners/adsampling.hpp b/include/pruners/adsampling.hpp index dc7a67a..be8f088 100644 --- a/include/pruners/adsampling.hpp +++ b/include/pruners/adsampling.hpp @@ -10,10 +10,6 @@ namespace PDX { -/****************************************************************** - * ADSampling pruner - ******************************************************************/ -template class ADSamplingPruner { using matrix_t = eigen_matrix_t; diff --git a/include/utils/utils.hpp b/include/utils/utils.hpp index cf8d0fa..4703eaa 100644 --- a/include/utils/utils.hpp +++ b/include/utils/utils.hpp @@ -5,7 +5,7 @@ #include #include #include "common.hpp" -#include "index_base/pdx_ivf.hpp" +#include "ivf_wrapper.hpp" #include "quantizers/scalar.hpp" #include "pruners/adsampling.hpp" @@ -118,7 +118,7 @@ StoreClusterEmbeddings(PDX::IndexPDXIVF pruner; + PDX::ADSamplingPruner pruner; // For normalization. PDX::Quantizer quantizer; const size_t num_dimensions; diff --git a/include/wrapper.hpp b/include/wrapper.hpp deleted file mode 100644 index 40d3882..0000000 --- a/include/wrapper.hpp +++ /dev/null @@ -1,237 +0,0 @@ -#pragma once - -#include -#include -#include - -#include "common.hpp" -#include "db_mock/predicate_evaluator.hpp" -#include "index_base/pdx_ivf.hpp" -#include "pruners/adsampling.hpp" -#include "pdxearch.hpp" -#include "clustering.hpp" - -namespace PDX { - -class PDXearchWrapper { -public: - static constexpr PDX::DistanceMetric DEFAULT_DISTANCE_METRIC = PDX::DistanceMetric::L2SQ; - static constexpr PDX::Quantization DEFAULT_QUANTIZATION = PDX::Quantization::U8; - static constexpr int32_t DEFAULT_N_PROBE = 24; - -private: - const uint32_t num_dimensions; - // Whether the embeddings (both the query embeddings and those stored in the - // index) are normalized. The PDXearch kernel only implements Euclidian - // distance (L2SQ). To compute the cosine and inner product distances we - // normalize and then use L2SQ. - const bool is_normalized; - - // The fields below are index options that can be set during index creation - const PDX::DistanceMetric distance_metric; - const PDX::Quantization quantization; - const uint32_t n_probe; - const int32_t seed; - -protected: - const unique_ptr rotation_matrix; - -public: - PDXearchWrapper(PDX::Quantization quantization, PDX::DistanceMetric distance_metric, uint32_t num_dimensions, - uint32_t n_probe, int32_t seed) - : num_dimensions(num_dimensions), is_normalized(DistanceMetricRequiresNormalization(distance_metric)), - distance_metric(distance_metric), quantization(quantization), n_probe(n_probe), seed(seed), - rotation_matrix(GenerateRandomRotationMatrix(num_dimensions, seed)) { - } - virtual ~PDXearchWrapper() = default; - - void Insert(row_t row_id, const float *embedding) { - throw NotImplementedException("PDXearchWrapper::Insert() not implemented"); - } - void Delete(row_t row_id) { - throw NotImplementedException("PDXearchWrapper::Delete() not implemented"); - } - - uint64_t GetInMemorySize() const { - throw NotImplementedException("PDXearchWrapper::GetInMemorySize() not implemented"); - } - - uint32_t GetNumDimensions() const { - return num_dimensions; - } - PDX::DistanceMetric GetDistanceMetric() const { - return distance_metric; - } - PDX::Quantization GetQuantization() const { - return quantization; - } - bool IsNormalized() const { - return is_normalized; - } - uint32_t GetNProbe() const { - return n_probe; - } - int32_t GetSeed() const { - return seed; - } - float *GetRotationMatrix() const { - return rotation_matrix.get(); - } -}; - -struct RowIdClusterMapping { - uint32_t cluster_id; - uint32_t index_in_cluster; -}; - -// The PDXearchWrapper for the global implementation -template -class PDXearchWrapperGlobal : public PDXearchWrapper { -public: - using embedding_storage_t = PDX::pdx_data_t; - -private: - uint32_t num_clusters {}; - uint64_t total_num_embeddings {}; - std::vector row_id_cluster_mapping; - - std::unique_ptr> index; - std::unique_ptr> pruner; - std::unique_ptr> searcher; - -public: - PDXearchWrapperGlobal(PDX::DistanceMetric distance_metric, uint32_t num_dimensions, uint32_t n_probe, int32_t seed, - size_t estimated_cardinality) - : PDXearchWrapper(Q, distance_metric, num_dimensions, n_probe, seed), - num_clusters(ComputeNumberOfClusters(estimated_cardinality)), total_num_embeddings(estimated_cardinality), - row_id_cluster_mapping(estimated_cardinality), - pruner(make_uniq>(num_dimensions, rotation_matrix.get())) { - // Additional constraints on the number of dimensions are enforced in `pdxearch_index_plan.cpp`. - D_ASSERT(num_dimensions > 0); - D_ASSERT(estimated_cardinality > 0); - D_ASSERT(num_clusters > 0); - } - - // Initialize the wrapper's state. This is called once. - void SetUpIndex(const row_t *const row_ids, const float *const embeddings, const size_t num_embeddings) { - D_ASSERT(num_embeddings == total_num_embeddings); - - const auto num_dimensions = GetNumDimensions(); - - float quantization_base = 0.0f; - float quantization_scale = 1.0f; - if constexpr (Q == PDX::U8) { - const auto params = PDX::ScalarQuantizer::ComputeQuantizationParams( - embeddings, static_cast(num_embeddings) * num_dimensions); - quantization_base = params.quantization_base; - quantization_scale = params.quantization_scale; - index = make_uniq>(num_dimensions, num_embeddings, num_clusters, IsNormalized(), - quantization_scale, quantization_base); - } else { - index = make_uniq>(num_dimensions, num_embeddings, num_clusters, IsNormalized()); - } - - // Compute K-means centroids and embedding-to-centroid assignment (always on float embeddings). - KMeansResult kmeans_result = - ComputeKMeans(embeddings, num_embeddings, num_dimensions, num_clusters, GetDistanceMetric(), GetSeed()); - - // Store centroids. - index->centroids = std::move(kmeans_result.centroids); - - // Row-major buffer that the current cluster's embeddings are "gathered" into. This buffer is the source for - // StoreClusterEmbeddings, the result of which is persistently stored in the index. The buffer is reused across - // clusters. For F32: buffer is float. For U8: buffer is uint8_t (quantized). - size_t max_cluster_size = 0; - for (size_t i = 0; i < num_clusters; i++) { - max_cluster_size = std::max(max_cluster_size, kmeans_result.assignments[i].size()); - } - auto tmp_cluster_embeddings = - std::make_unique(static_cast(max_cluster_size * num_dimensions)); - - // Set up the IVF clusters' metadata and store the embeddings. - for (size_t cluster_idx = 0; cluster_idx < num_clusters; cluster_idx++) { - const auto cluster_size = kmeans_result.assignments[cluster_idx].size(); - auto &cluster = index->clusters.emplace_back(cluster_size, num_dimensions); - - for (size_t position_in_cluster = 0; position_in_cluster < cluster_size; position_in_cluster++) { - const auto embedding_idx = kmeans_result.assignments[cluster_idx][position_in_cluster]; - const row_t row_id = row_ids[embedding_idx]; - - D_ASSERT(row_id < total_num_embeddings); - (row_id_cluster_mapping)[row_id] = {static_cast(cluster_idx), - static_cast(position_in_cluster)}; - cluster.indices[position_in_cluster] = row_id; - - if constexpr (Q == PDX::U8) { - PDX::ScalarQuantizer quantizer(num_dimensions); - quantizer.QuantizeEmbedding(embeddings + (embedding_idx * num_dimensions), quantization_base, - quantization_scale, - tmp_cluster_embeddings.get() + (position_in_cluster * num_dimensions)); - } else { - memcpy(tmp_cluster_embeddings.get() + (position_in_cluster * num_dimensions), - embeddings + (embedding_idx * num_dimensions), num_dimensions * sizeof(float)); - } - } - - StoreClusterEmbeddings(cluster, *index, tmp_cluster_embeddings.get(), cluster_size); - } - - // Note: the searcher depends on a fully initialized index in its constructor. - searcher = make_uniq>(*index, *pruner); - } - - std::unique_ptr> Search(const float *const query_embedding, const size_t limit, - const uint32_t n_probe) const { - searcher->SetNProbe(n_probe); - const std::vector results = searcher->Search(query_embedding, limit); - std::unique_ptr> row_ids = make_uniq>(results.size()); - for (size_t i = 0; i < results.size(); i++) { - (*row_ids)[i] = results[i].index; - } - return row_ids; - } - - PDX::PredicateEvaluator CreatePredicateEvaluator(std::vector> &row_id_vectors) const { - auto predicate_evaluator = PDX::PredicateEvaluator(num_clusters, total_num_embeddings); - - for (auto &[row_id_vector, vector_size] : row_id_vectors) { - row_id_vector.Flatten(vector_size); - const auto row_id_data = FlatVector::GetData(row_id_vector); - const auto &validity = FlatVector::Validity(row_id_vector); - - for (size_t i = 0; i < vector_size; i++) { - if (validity.RowIsValid(i)) { - const auto &[cluster_id, index_in_cluster] = (row_id_cluster_mapping)[row_id_data[i]]; - predicate_evaluator.n_passing_tuples[cluster_id]++; - predicate_evaluator.selection_vector[searcher->cluster_offsets[cluster_id] + index_in_cluster] = 1; - } - } - } - - return predicate_evaluator; - } - - std::unique_ptr> FilteredSearch(const float *const query_embedding, const size_t limit, - std::vector> &row_id_vectors, - const uint32_t n_probe) const { - const PDX::PredicateEvaluator predicate_evaluator = CreatePredicateEvaluator(row_id_vectors); - - searcher->SetNProbe(n_probe); - std::vector results = - searcher->FilteredSearch(query_embedding, limit, predicate_evaluator); - std::unique_ptr> row_ids = make_uniq>(results.size()); - for (size_t i = 0; i < results.size(); i++) { - (*row_ids)[i] = results[i].index; - } - return row_ids; - } - - size_t GetNumClusters() const { - return num_clusters; - } -}; - -using PDXearchWrapperGlobalF32 = PDXearchWrapperGlobal; -using PDXearchWrapperGlobalU8 = PDXearchWrapperGlobal; - -} // namespace PDX diff --git a/python/pdxearch/index_base.py b/python/pdxearch/index_base.py index 1520dd5..17c39d9 100644 --- a/python/pdxearch/index_base.py +++ b/python/pdxearch/index_base.py @@ -176,7 +176,7 @@ def _materialize_index(self, _type='pdx', **kwargs): data.extend(self.normalize.to_bytes(1, sys.byteorder)) # TODO: Support other multiples of 64 if len(self.centroids_l0) == PDXConstants.PDX_CENTROIDS_VECTOR_SIZE: - data.extend(self.centroids_l0.tobytes("F")) # PDX + data.extend(self.centroids_l0.tobytes("C")) # PDX else: data.extend(self.centroids_l0.tobytes("C")) # Nary format if (_type == 'pdx-v4-h') and kwargs.get('quantize', False): From 32f420f4d8578a6280626e60733204f633431737 Mon Sep 17 00:00:00 2001 From: lkuffo Date: Tue, 24 Feb 2026 00:41:04 +0100 Subject: [PATCH 05/39] IVF2 working --- .../results/DEFAULT/IVF2_PDX_ADSAMPLING.csv | 1 + include/ivf_wrapper.hpp | 195 ++++++------------ include/utils/benchmark_utils.hpp | 5 +- include/utils/file_reader.hpp | 4 +- include/utils/tictoc.hpp | 9 +- include/utils/utils.hpp | 2 + 6 files changed, 66 insertions(+), 150 deletions(-) diff --git a/benchmarks/results/DEFAULT/IVF2_PDX_ADSAMPLING.csv b/benchmarks/results/DEFAULT/IVF2_PDX_ADSAMPLING.csv index ed2a103..8e6e117 100644 --- a/benchmarks/results/DEFAULT/IVF2_PDX_ADSAMPLING.csv +++ b/benchmarks/results/DEFAULT/IVF2_PDX_ADSAMPLING.csv @@ -25,3 +25,4 @@ agnews-mxbai-1024-euclidean,adsampling,0.257207,0.477,0.05175,0.9012,24,0,100,10 agnews-mxbai-1024-euclidean,adsampling,0.260366,0.4805,0.059459,0.9012,24,0,100,1000,0.2,1,0.2618,0.5382,0.05946 agnews-mxbai-1024-euclidean,adsampling,0.269808,0.495583,0.058375,0.9012,24,0,100,1000,0.2,1,0.2715,0.5795,0.05838 agnews-mxbai-1024-euclidean,adsampling,0.295702,0.560791,0.0575,0.9012,24,0,100,1000,0.2,1,0.3006,0.8067,0.0575 +agnews-mxbai-1024-euclidean,adsampling,0.253945,0.469292,0.059667,0.9016,24,0,100,1000,0.2,1,0.2547,0.5349,0.05967 diff --git a/include/ivf_wrapper.hpp b/include/ivf_wrapper.hpp index 5ec2811..3d4a4e6 100644 --- a/include/ivf_wrapper.hpp +++ b/include/ivf_wrapper.hpp @@ -9,25 +9,13 @@ namespace PDX { - -template -class IndexPDXIVF{}; - -template <> -class IndexPDXIVF { +template +class IndexPDXIVF { public: - using cluster_t = Cluster; - - std::unique_ptr file_buffer; + using cluster_t = Cluster; + using data_t = pdx_data_t; - // const uint32_t num_dimensions {}; - // const uint64_t total_num_embeddings {}; - // const uint32_t num_clusters {}; - // const uint32_t num_vertical_dimensions {}; - // const uint32_t num_horizontal_dimensions {}; - // std::vector clusters; - // const bool is_normalized {}; - // std::vector centroids; + std::unique_ptr file_buffer; uint32_t num_dimensions {}; uint64_t total_num_embeddings {}; @@ -38,9 +26,14 @@ class IndexPDXIVF { bool is_normalized {}; std::vector centroids; - IndexPDXIVF() = default; + // U8-specific quantization parameters + float quantization_scale = 1.0f; + float quantization_scale_squared = 1.0f; + float inverse_quantization_scale_squared = 1.0f; + float quantization_base = 0.0f; - ~IndexPDXIVF() = default; + IndexPDXIVF() = default; + ~IndexPDXIVF() = default; IndexPDXIVF(uint32_t num_dimensions, uint64_t total_num_embeddings, uint32_t num_clusters, bool is_normalized) : num_dimensions(num_dimensions), total_num_embeddings(total_num_embeddings), num_clusters(num_clusters), @@ -50,77 +43,6 @@ class IndexPDXIVF { clusters.reserve(num_clusters); } - void Restore(const std::string &filename) { - file_buffer = MmapFile(filename); - Load(file_buffer.get()); - } - - void Load(char *input) { - char *next_value = input; - num_dimensions = ((uint32_t *) input)[0]; - num_vertical_dimensions = ((uint32_t *) input)[1]; - num_horizontal_dimensions = ((uint32_t *) input)[2]; - - next_value += sizeof(uint32_t) * 3; - num_clusters = ((uint32_t *) next_value)[0]; - next_value += sizeof(uint32_t); - auto *nums_embeddings = (uint32_t *) next_value; - next_value += num_clusters * sizeof(uint32_t); - clusters.reserve(num_clusters); - for (size_t i = 0; i < num_clusters; ++i) { - clusters.emplace_back(nums_embeddings[i], num_dimensions); - memcpy(clusters[i].data, next_value, sizeof(float) * clusters[i].num_embeddings * num_dimensions); - next_value += sizeof(float) * clusters[i].num_embeddings * num_dimensions; - } - for (size_t i = 0; i < num_clusters; ++i) { - memcpy(clusters[i].indices, next_value, sizeof(uint32_t) * clusters[i].num_embeddings); - next_value += sizeof(uint32_t) * clusters[i].num_embeddings; - } - next_value += sizeof(float) * num_dimensions; // Skipping means (not used) - - is_normalized = ((char *) next_value)[0]; - next_value += sizeof(char); - next_value += sizeof(char); // Skipping is_ivf field (not used) - - centroids.resize(num_clusters * num_dimensions); - memcpy(centroids.data(), (float *) next_value, sizeof(float) * num_clusters * num_dimensions); - } -}; - -template <> -class IndexPDXIVF { -public: - using cluster_t = Cluster; - std::unique_ptr file_buffer; - - // const uint32_t num_dimensions {}; - // const uint64_t total_num_embeddings {}; - // const uint32_t num_clusters {}; - // const uint32_t num_vertical_dimensions {}; - // const uint32_t num_horizontal_dimensions {}; - // std::vector clusters; - // const bool is_normalized {}; - // std::vector centroids; - - // const float quantization_scale = 1.0f; - // const float quantization_scale_squared = 1.0f; - // const float inverse_quantization_scale_squared = 1.0f; - // const float quantization_base = 0.0f; - - uint32_t num_dimensions {}; - uint64_t total_num_embeddings {}; - uint32_t num_clusters {}; - uint32_t num_vertical_dimensions {}; - uint32_t num_horizontal_dimensions {}; - std::vector clusters; - bool is_normalized {}; - std::vector centroids; - - float quantization_scale = 1.0f; - float quantization_scale_squared = 1.0f; - float inverse_quantization_scale_squared = 1.0f; - float quantization_base = 0.0f; - IndexPDXIVF(uint32_t num_dimensions, uint64_t total_num_embeddings, uint32_t num_clusters, bool is_normalized, float quantization_scale, float quantization_base) : num_dimensions(num_dimensions), total_num_embeddings(total_num_embeddings), num_clusters(num_clusters), @@ -133,52 +55,53 @@ class IndexPDXIVF { clusters.reserve(num_clusters); } - IndexPDXIVF() = default; - - ~IndexPDXIVF() = default; - - void Restore(const std::string &filename) { - file_buffer = MmapFile(filename); - Load(file_buffer.get()); - } - - void Load(char *input) { - char *next_value = input; - num_dimensions = ((uint32_t *) input)[0]; - num_vertical_dimensions = ((uint32_t *) input)[1]; - num_horizontal_dimensions = ((uint32_t *) input)[2]; - - next_value += sizeof(uint32_t) * 3; - num_clusters = ((uint32_t *) next_value)[0]; - next_value += sizeof(uint32_t); - auto *nums_embeddings = (uint32_t *) next_value; - next_value += num_clusters * sizeof(uint32_t); - clusters.reserve(num_clusters); - for (size_t i = 0; i < num_clusters; ++i) { - clusters.emplace_back(nums_embeddings[i], num_dimensions); - memcpy(clusters[i].data, next_value, sizeof(uint8_t) * clusters[i].num_embeddings * num_dimensions); - next_value += sizeof(uint8_t) * clusters[i].num_embeddings * num_dimensions; - } - for (size_t i = 0; i < num_clusters; ++i) { - memcpy(clusters[i].indices, next_value, sizeof(uint32_t) * clusters[i].num_embeddings); - next_value += sizeof(uint32_t) * clusters[i].num_embeddings; - } - is_normalized = ((char *) next_value)[0]; - next_value += sizeof(char); - next_value += sizeof(char); // Skipping is_ivf field (not used) - - centroids.resize(num_clusters * num_dimensions); - memcpy(centroids.data(), (float *) next_value, sizeof(float) * num_clusters * num_dimensions); - next_value += sizeof(float) * num_clusters * num_dimensions; - - quantization_base = ((float *) next_value)[0]; - next_value += sizeof(float); - quantization_scale = ((float *) next_value)[0]; - next_value += sizeof(float); - - quantization_scale_squared = quantization_scale * quantization_scale; - inverse_quantization_scale_squared = 1.0f / (quantization_scale * quantization_scale); - } + void Restore(const std::string &filename) { + file_buffer = MmapFile(filename); + Load(file_buffer.get()); + } + + void Load(char *input) { + char *next_value = input; + num_dimensions = ((uint32_t *) input)[0]; + num_vertical_dimensions = ((uint32_t *) input)[1]; + num_horizontal_dimensions = ((uint32_t *) input)[2]; + + next_value += sizeof(uint32_t) * 3; + num_clusters = ((uint32_t *) next_value)[0]; + next_value += sizeof(uint32_t); + auto *nums_embeddings = (uint32_t *) next_value; + next_value += num_clusters * sizeof(uint32_t); + clusters.reserve(num_clusters); + for (size_t i = 0; i < num_clusters; ++i) { + clusters.emplace_back(nums_embeddings[i], num_dimensions); + memcpy(clusters[i].data, next_value, sizeof(data_t) * clusters[i].num_embeddings * num_dimensions); + next_value += sizeof(data_t) * clusters[i].num_embeddings * num_dimensions; + } + for (size_t i = 0; i < num_clusters; ++i) { + memcpy(clusters[i].indices, next_value, sizeof(uint32_t) * clusters[i].num_embeddings); + next_value += sizeof(uint32_t) * clusters[i].num_embeddings; + } + if constexpr (Q == F32) { + next_value += sizeof(float) * num_dimensions; // Skipping means (not used) + } + + is_normalized = ((char *) next_value)[0]; + next_value += sizeof(char); + next_value += sizeof(char); // Skipping is_ivf field (not used) + + centroids.resize(num_clusters * num_dimensions); + memcpy(centroids.data(), (float *) next_value, sizeof(float) * num_clusters * num_dimensions); + next_value += sizeof(float) * num_clusters * num_dimensions; + + if constexpr (Q == U8) { + quantization_base = ((float *) next_value)[0]; + next_value += sizeof(float); + quantization_scale = ((float *) next_value)[0]; + next_value += sizeof(float); + quantization_scale_squared = quantization_scale * quantization_scale; + inverse_quantization_scale_squared = 1.0f / quantization_scale_squared; + } + } }; template diff --git a/include/utils/benchmark_utils.hpp b/include/utils/benchmark_utils.hpp index 60157a3..50d6f7b 100644 --- a/include/utils/benchmark_utils.hpp +++ b/include/utils/benchmark_utils.hpp @@ -1,5 +1,4 @@ -#ifndef PDX_BENCHMARK_UTILS_HPP -#define PDX_BENCHMARK_UTILS_HPP +#pragma once #include #include @@ -238,5 +237,3 @@ class BenchmarkUtils { }; BenchmarkUtils BENCHMARK_UTILS; - -#endif //PDX_BENCHMARK_UTILS_HPP diff --git a/include/utils/file_reader.hpp b/include/utils/file_reader.hpp index d7e8ef5..cf5714f 100644 --- a/include/utils/file_reader.hpp +++ b/include/utils/file_reader.hpp @@ -1,5 +1,4 @@ -#ifndef PDX_UTILS_HPP -#define PDX_UTILS_HPP +#pragma once #include #include @@ -32,4 +31,3 @@ inline std::unique_ptr MmapFile(const std::string& filename) { return data; } -#endif //PDX_UTILS_HPP diff --git a/include/utils/tictoc.hpp b/include/utils/tictoc.hpp index 528e005..0551a6f 100644 --- a/include/utils/tictoc.hpp +++ b/include/utils/tictoc.hpp @@ -1,5 +1,4 @@ -#ifndef PDX_TICTOC_HPP -#define PDX_TICTOC_HPP +#pragma once #include @@ -17,9 +16,7 @@ #include #include -/****************************************************************** - * Clock to benchmark algorithms runtime - ******************************************************************/ + class TicToc { public: size_t accum_time = 0; @@ -40,5 +37,3 @@ class TicToc { end - start).count(); } }; - -#endif // PDX_TICTOC_HPP \ No newline at end of file diff --git a/include/utils/utils.hpp b/include/utils/utils.hpp index 4703eaa..6e54e12 100644 --- a/include/utils/utils.hpp +++ b/include/utils/utils.hpp @@ -1,3 +1,5 @@ +#pragma once + #include #include #include From 2c975cdb8ae5c1ecd57c982e9b0cf2c8d57acce1 Mon Sep 17 00:00:00 2001 From: lkuffo Date: Tue, 24 Feb 2026 00:45:31 +0100 Subject: [PATCH 06/39] Adding SuperKMeans --- .gitmodules | 3 +++ extern/SuperKMeans | 1 + 2 files changed, 4 insertions(+) create mode 160000 extern/SuperKMeans diff --git a/.gitmodules b/.gitmodules index 9b9ae47..4f1e28d 100644 --- a/.gitmodules +++ b/.gitmodules @@ -8,3 +8,6 @@ [submodule "extern/findFFTW"] path = extern/findFFTW url = https://github.com/egpbos/findfftw.git +[submodule "extern/SuperKMeans"] + path = extern/SuperKMeans + url = https://github.com/lkuffo/SuperKMeans diff --git a/extern/SuperKMeans b/extern/SuperKMeans new file mode 160000 index 0000000..9fe7d1b --- /dev/null +++ b/extern/SuperKMeans @@ -0,0 +1 @@ +Subproject commit 9fe7d1bbd490f58cedec273374f7af0704a74742 From 7cfc6aa61df9e22ee3fd48f855b3656acf14b06a Mon Sep 17 00:00:00 2001 From: lkuffo Date: Tue, 24 Feb 2026 09:48:44 +0100 Subject: [PATCH 07/39] Build Index done --- .gitignore | 1 + CMakeLists.txt | 7 +- benchmarks/BenchmarkEndToEnd | Bin 0 -> 395232 bytes benchmarks/CMakeLists.txt | 2 + .../bench_adsampling/pdx_end_to_end.cpp | 161 ++++++++++++++++++ .../DEFAULT/END_TO_END_PDX_ADSAMPLING.csv | 27 +++ include/clustering.hpp | 30 ++-- include/ivf_wrapper.hpp | 3 + include/pdx_index.hpp | 122 +++++++++++-- include/pdxearch.hpp | 2 +- include/pruners/adsampling.hpp | 19 +++ include/utils/benchmark_utils.hpp | 80 +++++++++ include/utils/file_reader.hpp | 2 +- include/utils/tictoc.hpp | 4 + include/utils/utils.hpp | 4 +- 15 files changed, 426 insertions(+), 38 deletions(-) create mode 100755 benchmarks/BenchmarkEndToEnd create mode 100644 benchmarks/bench_adsampling/pdx_end_to_end.cpp create mode 100644 benchmarks/results/DEFAULT/END_TO_END_PDX_ADSAMPLING.csv diff --git a/.gitignore b/.gitignore index 7ff288c..cfaf054 100644 --- a/.gitignore +++ b/.gitignore @@ -64,6 +64,7 @@ pdxearch.egg-info /benchmarks/datasets/adsampling_pdx /benchmarks/datasets/downloaded +/benchmarks/datasets/raw /benchmarks/datasets/pdx /benchmarks/datasets/purescan /benchmarks/datasets/queries diff --git a/CMakeLists.txt b/CMakeLists.txt index 43ae90e..16aefa7 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -19,7 +19,9 @@ include(CheckCXXCompilerFlag) include(CMakePrintHelpers) include(CTest) -# find_package(OpenMP REQUIRED) +include_directories(${CMAKE_CURRENT_SOURCE_DIR}/extern/SuperKMeans/include) + +find_package(OpenMP REQUIRED) list(PREPEND CMAKE_PREFIX_PATH /usr/local) @@ -46,8 +48,7 @@ else() find_package(BLAS REQUIRED) message(STATUS "BLAS library found: ${BLAS_LIBRARIES}") 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zzti!ZDf;f&^RE5t#xcppiK99Hg313m lvMXY@_;Hi3$B(I))uhepJqK1EkNhR^=i7tLe?<$z{{s(cQ@8*C literal 0 HcmV?d00001 diff --git a/benchmarks/CMakeLists.txt b/benchmarks/CMakeLists.txt index f89e09b..4d3f242 100644 --- a/benchmarks/CMakeLists.txt +++ b/benchmarks/CMakeLists.txt @@ -11,6 +11,7 @@ add_executable(BenchmarkU8PDXADSampling bench_adsampling/pdx_ivf_adsampling_u8.c add_executable(BenchmarkU8IVF2ADSampling bench_adsampling/pdx_ivf2_adsampling_u8.cpp) add_executable(FilteredBenchmarkU8IVF2ADSampling bench_adsampling/pdx_ivf2_adsampling_u8_filtered.cpp) add_executable(FilteredBenchmarkPDXADSampling bench_adsampling/pdx_ivf_adsampling_filtered.cpp) +add_executable(BenchmarkEndToEnd bench_adsampling/pdx_end_to_end.cpp) target_link_libraries(BenchmarkPDXADSampling ${BENCH_COMMON_LIBS}) target_link_libraries(FilteredBenchmarkPDXADSampling ${BENCH_COMMON_LIBS}) @@ -18,3 +19,4 @@ target_link_libraries(BenchmarkIVF2ADSampling ${BENCH_COMMON_LIBS}) target_link_libraries(BenchmarkU8PDXADSampling ${BENCH_COMMON_LIBS}) target_link_libraries(BenchmarkU8IVF2ADSampling ${BENCH_COMMON_LIBS}) target_link_libraries(FilteredBenchmarkU8IVF2ADSampling ${BENCH_COMMON_LIBS}) +target_link_libraries(BenchmarkEndToEnd ${BENCH_COMMON_LIBS}) diff --git a/benchmarks/bench_adsampling/pdx_end_to_end.cpp b/benchmarks/bench_adsampling/pdx_end_to_end.cpp new file mode 100644 index 0000000..2371431 --- /dev/null +++ b/benchmarks/bench_adsampling/pdx_end_to_end.cpp @@ -0,0 +1,161 @@ +#ifndef BENCHMARK_TIME +#define BENCHMARK_TIME = true +#endif + +#ifndef PDX_USE_EXPLICIT_SIMD +#define PDX_USE_EXPLICIT_SIMD = true +#endif + +#include +#include +#include +#include +#include +#include + +#include "pdx_index.hpp" +#include "utils/benchmark_utils.hpp" +#include "utils/file_reader.hpp" + +int main(int argc, char *argv[]) { + if (argc < 2) { + std::cerr << "Usage: " << argv[0] << " [ivf_nprobe]\n"; + std::cerr << "Available datasets:"; + for (const auto &[name, _] : RAW_DATASET_PARAMS) { + std::cerr << " " << name; + } + std::cerr << "\n"; + return 1; + } + std::string dataset = argv[1]; + size_t arg_ivf_nprobe = (argc > 2) ? std::atoi(argv[2]) : 0; + + auto it = RAW_DATASET_PARAMS.find(dataset); + if (it == RAW_DATASET_PARAMS.end()) { + std::cerr << "Unknown dataset: " << dataset << "\n"; + return 1; + } + const auto &info = it->second; + const size_t n = info.num_embeddings; + const size_t d = info.num_dimensions; + const size_t n_queries = info.num_queries; + + std::cout << "==> PDX End-to-End (Build + Search)\n"; + std::cout << "Dataset: " << dataset << " (n=" << n << ", d=" << d << ")\n"; + + std::string ALGORITHM = "end_to_end_adsampling"; + uint8_t KNN = BenchmarkUtils::KNN; + size_t NUM_MEASURE_RUNS = BenchmarkUtils::NUM_MEASURE_RUNS; + std::string RESULTS_PATH = BENCHMARK_UTILS.RESULTS_DIR_PATH + "END_TO_END_PDX_ADSAMPLING.csv"; + + // Read data + std::string data_path = RAW_DATA_DIR + "/data_" + dataset + ".bin"; + std::string query_path = RAW_DATA_DIR + "/data_" + dataset + "_test.bin"; + + std::vector data(n * d); + { + std::ifstream file(data_path, std::ios::binary); + if (!file) { std::cerr << "Failed to open " << data_path << "\n"; return 1; } + file.read(reinterpret_cast(data.data()), n * d * sizeof(float)); + } + + std::vector queries(n_queries * d); + { + std::ifstream file(query_path, std::ios::binary); + if (!file) { std::cerr << "Failed to open " << query_path << "\n"; return 1; } + file.read(reinterpret_cast(queries.data()), n_queries * d * sizeof(float)); + } + + PDX::PDXIndexConfig index_config { + .num_dimensions = static_cast(d), + .distance_metric = info.distance_metric, + .seed = 42, + .normalize = true, + .sampling_fraction = 1.0f + }; + + std::cout << "Building index (num_clusters=auto)...\n"; + auto build_start = std::chrono::high_resolution_clock::now(); + PDX::PDXIndexF32 pdx_index(index_config); + pdx_index.BuildIndex(data.data(), n); + auto build_end = std::chrono::high_resolution_clock::now(); + double build_ms = std::chrono::duration(build_end - build_start).count(); + std::cout << "Build time: " << build_ms << " ms\n"; + std::cout << "Clusters: " << pdx_index.GetNumClusters() << "\n"; + + // Load ground truth + bool use_skmeans_gt = false; + std::unordered_map> gt_map; + std::unique_ptr gt_buffer; + uint32_t *int_ground_truth = nullptr; + + if (use_skmeans_gt) { + std::string gt_path = GROUND_TRUTH_JSON_DIR + "/" + dataset + ".json"; + gt_map = ParseGroundTruthJson(gt_path); + if (gt_map.empty()) { + std::cerr << "No ground truth found at " << gt_path << "\n"; + return 1; + } + std::cout << "Ground truth loaded (json): " << gt_map.size() << " queries\n"; + } else { + std::string gt_path = BenchmarkUtils::GROUND_TRUTH_DATA + info.pdx_dataset_name + "_100_norm"; + gt_buffer = MmapFile(gt_path); + int_ground_truth = reinterpret_cast(gt_buffer.get()); + std::cout << "Ground truth loaded (pdx binary): " << gt_path << "\n"; + } + + // Search + recall + runtime at various nprobe values + std::vector nprobes_to_use; + if (arg_ivf_nprobe > 0) { + nprobes_to_use = {arg_ivf_nprobe}; + } else { + nprobes_to_use.assign(std::begin(BenchmarkUtils::IVF_PROBES), std::end(BenchmarkUtils::IVF_PROBES)); + } + + for (size_t ivf_nprobe : nprobes_to_use) { + if (pdx_index.GetNumClusters() < ivf_nprobe) continue; + + pdx_index.SetNProbe(ivf_nprobe); + + // Recall pass + float recalls = 0; + if (use_skmeans_gt) { + for (size_t l = 0; l < n_queries; ++l) { + auto result = pdx_index.Search(queries.data() + l * d, KNN); + if (gt_map.count(static_cast(l))) { + recalls += ComputeRecallFromJson(result, gt_map.at(static_cast(l)), KNN); + } + } + } else { + for (size_t l = 0; l < n_queries; ++l) { + auto result = pdx_index.Search(queries.data() + l * d, KNN); + BenchmarkUtils::VerifyResult(recalls, result, KNN, int_ground_truth, l); + } + } + + // Runtime pass + std::vector runtimes; + runtimes.resize(NUM_MEASURE_RUNS * n_queries); + for (size_t j = 0; j < NUM_MEASURE_RUNS; ++j) { + for (size_t l = 0; l < n_queries; ++l) { + pdx_index.Search(queries.data() + l * d, KNN); + runtimes[j + l * NUM_MEASURE_RUNS] = { + pdx_index.GetSearcher().end_to_end_clock.accum_time + }; + } + } + + BenchmarkMetadata results_metadata = { + dataset, + ALGORITHM, + NUM_MEASURE_RUNS, + n_queries, + ivf_nprobe, + KNN, + recalls, + }; + BenchmarkUtils::SaveResults(runtimes, RESULTS_PATH, results_metadata); + } + + return 0; +} diff --git a/benchmarks/results/DEFAULT/END_TO_END_PDX_ADSAMPLING.csv b/benchmarks/results/DEFAULT/END_TO_END_PDX_ADSAMPLING.csv new file mode 100644 index 0000000..84cd67a --- /dev/null +++ b/benchmarks/results/DEFAULT/END_TO_END_PDX_ADSAMPLING.csv @@ -0,0 +1,27 @@ +dataset,algorithm,avg,max,min,recall,ivf_nprobe,epsilon,knn,n_queries,selectivity,num_measure_runs,avg_all,max_all,min_all +mxbai,end_to_end_adsampling,0.480148,0.697209,0.255625,0.868438,24,0,100,1000,0,1,0.4814,0.7349,0.2556 +mxbai,end_to_end_adsampling,0.527772,0.796375,0.295167,0.892249,32,0,100,1000,0,1,0.5286,0.8305,0.2952 +mxbai,end_to_end_adsampling,0.913976,1.56483,0.35875,0.964088,128,0,100,1000,0,1,0.9194,2.635,0.3588 +mxbai,end_to_end_adsampling,0.493388,0.725292,0.269208,0.868438,24,0,100,1000,0,1,0.494,0.832,0.2692 +mxbai,end_to_end_adsampling,0.474547,0.68475,0.291291,0.870669,24,0,100,1000,0,1,0.4793,1.569,0.2913 +mxbai,end_to_end_adsampling,0.481953,0.708334,0.272125,0.887039,24,0,100,1000,0,1,0.4825,0.8234,0.2721 +mxbai,end_to_end_adsampling,0.484814,0.710042,0.280208,0.887039,24,0,100,1000,0,1,0.4848,0.71,0.2802 +mxbai,end_to_end_adsampling,0.481816,0.696583,0.259833,0.879489,24,0,100,1000,0,1,0.482,0.7113,0.2598 +mxbai,end_to_end_adsampling,0.486383,0.696084,0.267916,0.892209,24,0,100,1000,0,1,0.4869,0.7387,0.2679 +mxbai,end_to_end_adsampling,0.49279,0.743167,0.264375,0.892209,24,0,100,1000,0,1,0.4934,0.7885,0.2644 +mxbai,end_to_end_adsampling,0.48762,0.715334,0.278792,0.892169,24,0,100,1000,0,1,0.4884,0.7435,0.2788 +mxbai,end_to_end_adsampling,0.475671,0.69125,0.279458,0.879049,24,0,100,1000,0,1,0.4757,0.6913,0.2795 +mxbai,end_to_end_adsampling,0.47454,0.686166,0.281791,0.879049,24,0,100,1000,0,1,0.4748,0.6983,0.2818 +mxbai,end_to_end_adsampling,0.48248,0.706,0.258458,0.870639,24,0,100,1000,0,1,0.4889,1.008,0.2585 +mxbai,end_to_end_adsampling,0.460918,0.66475,0.265292,0.870639,24,0,100,1000,0,1,0.4609,0.6647,0.2653 +mxbai,end_to_end_adsampling,0.461712,0.657834,0.268666,0.870639,24,0,100,1000,0,1,0.4617,0.6578,0.2687 +mxbai,end_to_end_adsampling,0.476161,0.699,0.266209,0.892169,24,0,100,1000,0,1,0.4762,0.699,0.2662 +mxbai,end_to_end_adsampling,0.476871,0.707917,0.26475,0.892169,24,0,100,1000,0,1,0.4769,0.7079,0.2647 +mxbai,end_to_end_adsampling,0.464998,0.685542,0.270417,0.78731,24,0,100,1000,0,1,0.4652,0.7045,0.2704 +mxbai,end_to_end_adsampling,0.462425,0.675875,0.267959,0.894148,24,0,100,1000,0,1,0.4627,0.6963,0.268 +mxbai,end_to_end_adsampling,0.483872,0.709916,0.266709,0.892599,24,0,100,1000,0,1,0.4846,0.7426,0.2667 +mxbai,end_to_end_adsampling,0.471211,0.696875,0.266709,0.892599,24,0,100,1000,0,1,0.4717,0.7232,0.2667 +mxbai,end_to_end_adsampling,0.466941,0.683917,0.264916,0.894148,24,0,100,1000,0,1,0.4679,0.7028,0.2649 +mxbai,end_to_end_adsampling,0.472717,0.693667,0.268167,0.900299,24,0,100,1000,0,1,0.473,0.727,0.2682 +mxbai,end_to_end_adsampling,0.480276,0.709958,0.284875,0.89764,24,0,100,1000,0,1,0.4808,0.7421,0.2849 +mxbai,end_to_end_adsampling,0.48062,0.699125,0.2595,0.900299,24,0,100,1000,0,1,0.4812,0.7641,0.2595 diff --git a/include/clustering.hpp b/include/clustering.hpp index 707cff7..61916f6 100644 --- a/include/clustering.hpp +++ b/include/clustering.hpp @@ -28,45 +28,45 @@ struct KMeansResult { // Compute centroids (clusters) and centroid-to-embedding assignments using SuperKMeans. [[nodiscard]] inline KMeansResult ComputeKMeans(const float *const embeddings, const uint64_t num_embeddings, const uint32_t num_dimensions, const uint32_t num_clusters, - const PDX::DistanceMetric distance_metric, const uint32_t seed) { - D_ASSERT(num_embeddings >= 1); - D_ASSERT(num_dimensions >= 1); - D_ASSERT(num_clusters >= 1); + const PDX::DistanceMetric distance_metric, const uint32_t seed, + const bool normalize = false, const float sampling_fraction = 0.0f, + const uint32_t kmeans_iters = 10) { + assert(num_embeddings >= 1); + assert(num_dimensions >= 1); + assert(num_clusters >= 1); auto result = KMeansResult(num_clusters); if (num_clusters == 1) { - // Copy the first embedding as the centroid result.centroids = std::vector(embeddings, embeddings + num_dimensions); - // Assign all embeddings to the first cluster for (uint64_t vec_id = 0; vec_id < num_embeddings; vec_id++) { result.assignments[0].emplace_back(vec_id); } return result; } - // Compute centroids - skmeans::HierarchicalSuperKMeansConfig config; - if (num_embeddings < KMeansResult::MIN_EMBEDDINGS_TO_SAMPLE) { + skmeans::SuperKMeansConfig config; + if (sampling_fraction > 0.0f) { + config.sampling_fraction = sampling_fraction; + } else if (num_embeddings < KMeansResult::MIN_EMBEDDINGS_TO_SAMPLE) { config.sampling_fraction = 1.0f; } else { config.sampling_fraction = 0.3f; } - config.angular = distance_metric == PDX::DistanceMetric::COSINE || distance_metric == PDX::DistanceMetric::IP; + config.angular = normalize || distance_metric == PDX::DistanceMetric::COSINE || distance_metric == PDX::DistanceMetric::IP; config.data_already_rotated = true; - config.iters_mesoclustering = 3; - config.iters_fineclustering = 5; - config.iters_refinement = 0; + config.iters = kmeans_iters; config.suppress_warnings = true; config.seed = seed; - auto kmeans = skmeans::HierarchicalSuperKMeans(num_clusters, num_dimensions, config); + //auto kmeans = skmeans::HierarchicalSuperKMeans(num_clusters, num_dimensions, config); + auto kmeans = skmeans::SuperKMeans(num_clusters, num_dimensions, config); result.centroids = kmeans.Train(embeddings, num_embeddings); // Extract assignments // SuperKMeans returns assignment from vec_id (not row_id) to centroid_idx std::vector assignments = - kmeans.Assign(embeddings, result.centroids.data(), num_embeddings, num_clusters); + kmeans.FastAssign(embeddings, result.centroids.data(), num_embeddings, num_clusters); // Convert into assignment from centroid_idx to vec_id (not row_id) result.assignments.resize(num_clusters); for (uint64_t vec_id = 0; vec_id < num_embeddings; vec_id++) { diff --git a/include/ivf_wrapper.hpp b/include/ivf_wrapper.hpp index 3d4a4e6..8e13b23 100644 --- a/include/ivf_wrapper.hpp +++ b/include/ivf_wrapper.hpp @@ -6,6 +6,7 @@ #include #include #include "common.hpp" +#include "utils/file_reader.hpp" namespace PDX { @@ -34,6 +35,8 @@ class IndexPDXIVF { IndexPDXIVF() = default; ~IndexPDXIVF() = default; + IndexPDXIVF(IndexPDXIVF &&) = default; + IndexPDXIVF &operator=(IndexPDXIVF &&) = default; IndexPDXIVF(uint32_t num_dimensions, uint64_t total_num_embeddings, uint32_t num_clusters, bool is_normalized) : num_dimensions(num_dimensions), total_num_embeddings(total_num_embeddings), num_clusters(num_clusters), diff --git a/include/pdx_index.hpp b/include/pdx_index.hpp index 68775f5..ddc2b9a 100644 --- a/include/pdx_index.hpp +++ b/include/pdx_index.hpp @@ -2,7 +2,9 @@ #include #include +#include #include +#include #include #include "common.hpp" @@ -10,9 +12,22 @@ #include "pruners/adsampling.hpp" #include "pdxearch.hpp" #include "utils/file_reader.hpp" +#include "clustering.hpp" +#include "utils/utils.hpp" +#include "quantizers/scalar.hpp" namespace PDX { +struct PDXIndexConfig { + uint32_t num_dimensions; + DistanceMetric distance_metric = DistanceMetric::L2SQ; + uint32_t seed = 42; + uint32_t num_clusters = 0; // 0 = auto-compute from num_embeddings + bool normalize = false; + float sampling_fraction = 0.0f; // 0 = auto (1.0 if small dataset, 0.3 otherwise) + uint32_t kmeans_iters = 10; +}; + template class PDXIndex { public: @@ -21,6 +36,7 @@ class PDXIndex { private: std::unique_ptr matrix_buffer; + PDXIndexConfig config {}; PDX::IndexPDXIVF index; std::unique_ptr pruner; std::unique_ptr> searcher; @@ -28,6 +44,10 @@ class PDXIndex { public: PDXIndex() = default; + explicit PDXIndex(PDXIndexConfig config) : config(config) { + pruner = std::make_unique(config.num_dimensions, config.seed); + } + void Restore(const std::string &index_path, const std::string &matrix_path) { index.Restore(index_path); @@ -58,14 +78,92 @@ class PDXIndex { return index.num_clusters; } - // === CreateIndex path (DuckDB integration) — commented out for now === - // Requires: D_ASSERT, make_uniq, row_t, Vector, FlatVector, NotImplementedException, - // clustering.hpp, db_mock/predicate_evaluator.hpp - // - // void CreateIndex(const row_t *const row_ids, const float *const embeddings, const size_t num_embeddings); - // std::unique_ptr> Search(const float *query_embedding, size_t limit, uint32_t n_probe) const; - // PDX::PredicateEvaluator CreatePredicateEvaluator(...) const; - // std::unique_ptr> FilteredSearch(...) const; + void BuildIndex(const float *const embeddings, const size_t num_embeddings) { + std::vector row_ids(num_embeddings); + std::iota(row_ids.begin(), row_ids.end(), 0); + BuildIndex(row_ids.data(), embeddings, num_embeddings); + } + + void BuildIndex(const size_t *const row_ids, const float *const embeddings, const size_t num_embeddings) { + const auto num_dimensions = config.num_dimensions; + auto num_clusters = config.num_clusters; + if (num_clusters == 0) { + num_clusters = ComputeNumberOfClusters(num_embeddings); + } + const bool normalize = config.normalize || DistanceMetricRequiresNormalization(config.distance_metric); + + assert(num_dimensions > 0); + assert(num_embeddings > 0); + assert(pruner); + + // Preprocess: normalize (if needed) + rotate. + const size_t total_floats = num_embeddings * num_dimensions; + std::unique_ptr normalized; + const float *rotation_input = embeddings; + if (normalize) { + normalized.reset(new float[total_floats]); + std::memcpy(normalized.get(), embeddings, total_floats * sizeof(float)); + PDX::Quantizer quantizer(num_dimensions); + for (size_t i = 0; i < num_embeddings; i++) { + quantizer.NormalizeQuery(normalized.get() + i * num_dimensions, + normalized.get() + i * num_dimensions); + } + rotation_input = normalized.get(); + } + std::unique_ptr preprocessed(new float[total_floats]); + pruner->PreprocessEmbeddings(rotation_input, preprocessed.get(), num_embeddings); + + float quantization_base = 0.0f; + float quantization_scale = 1.0f; + if constexpr (Q == PDX::U8) { + const auto params = PDX::ScalarQuantizer::ComputeQuantizationParams( + preprocessed.get(), static_cast(num_embeddings) * num_dimensions); + quantization_base = params.quantization_base; + quantization_scale = params.quantization_scale; + index = PDX::IndexPDXIVF(num_dimensions, num_embeddings, num_clusters, + normalize, quantization_scale, quantization_base); + } else { + index = PDX::IndexPDXIVF(num_dimensions, num_embeddings, num_clusters, normalize); + } + + KMeansResult kmeans_result = ComputeKMeans(preprocessed.get(), num_embeddings, num_dimensions, + num_clusters, config.distance_metric, config.seed, + config.normalize, config.sampling_fraction, config.kmeans_iters); + index.centroids = std::move(kmeans_result.centroids); + + size_t max_cluster_size = 0; + for (size_t i = 0; i < num_clusters; i++) { + max_cluster_size = std::max(max_cluster_size, kmeans_result.assignments[i].size()); + } + std::unique_ptr tmp_cluster_embeddings( + new embedding_storage_t[static_cast(max_cluster_size) * num_dimensions]); + + for (size_t cluster_idx = 0; cluster_idx < num_clusters; cluster_idx++) { + const auto cluster_size = kmeans_result.assignments[cluster_idx].size(); + auto &cluster = index.clusters.emplace_back(cluster_size, num_dimensions); + + for (size_t position_in_cluster = 0; position_in_cluster < cluster_size; position_in_cluster++) { + const auto embedding_idx = kmeans_result.assignments[cluster_idx][position_in_cluster]; + cluster.indices[position_in_cluster] = row_ids[embedding_idx]; + + if constexpr (Q == PDX::U8) { + PDX::ScalarQuantizer quantizer(num_dimensions); + quantizer.QuantizeEmbedding(preprocessed.get() + (embedding_idx * num_dimensions), + quantization_base, quantization_scale, + tmp_cluster_embeddings.get() + (position_in_cluster * num_dimensions)); + } else { + std::memcpy(tmp_cluster_embeddings.get() + (position_in_cluster * num_dimensions), + preprocessed.get() + (embedding_idx * num_dimensions), + num_dimensions * sizeof(float)); + } + } + StoreClusterEmbeddings(cluster, index, tmp_cluster_embeddings.get(), + cluster_size); + } + + searcher = std::make_unique>(index, *pruner); + } + }; template @@ -134,14 +232,6 @@ class PDXTreeIndex { return index.l0.num_clusters; } - // === CreateIndex path (DuckDB integration) — commented out for now === - // Requires: D_ASSERT, make_uniq, row_t, Vector, FlatVector, NotImplementedException, - // clustering.hpp, db_mock/predicate_evaluator.hpp - // - // void CreateIndex(const row_t *const row_ids, const float *const embeddings, const size_t num_embeddings); - // std::unique_ptr> Search(const float *query_embedding, size_t limit, uint32_t n_probe) const; - // PDX::PredicateEvaluator CreatePredicateEvaluator(...) const; - // std::unique_ptr> FilteredSearch(...) const; }; using PDXIndexF32 = PDXIndex; diff --git a/include/pdxearch.hpp b/include/pdxearch.hpp index c34d2f0..ff28249 100644 --- a/include/pdxearch.hpp +++ b/include/pdxearch.hpp @@ -50,7 +50,7 @@ class PDXearch { } void SetClusterAccessOrder(const std::vector &cluster_indexes) { - cluster_indices_in_access_order = std::make_unique(cluster_indexes.size()); + cluster_indices_in_access_order.reset(new uint32_t[cluster_indexes.size()]); std::copy(cluster_indexes.begin(), cluster_indexes.end(), cluster_indices_in_access_order.get()); } diff --git a/include/pruners/adsampling.hpp b/include/pruners/adsampling.hpp index be8f088..f0eb531 100644 --- a/include/pruners/adsampling.hpp +++ b/include/pruners/adsampling.hpp @@ -2,6 +2,7 @@ #include #include +#include #include "common.hpp" #ifdef HAS_FFTW @@ -16,6 +17,24 @@ class ADSamplingPruner { public: const uint32_t num_dimensions; + ADSamplingPruner(const uint32_t num_dimensions, const int32_t seed) : num_dimensions(num_dimensions) { + ratios.resize(num_dimensions); + for (size_t i = 0; i < num_dimensions; ++i) { + ratios[i] = GetRatio(i); + } + std::mt19937 gen(seed); + std::normal_distribution normal_dist; + Eigen::MatrixXf random_matrix = Eigen::MatrixXf::Zero( + static_cast(num_dimensions), static_cast(num_dimensions)); + for (size_t i = 0; i < num_dimensions; ++i) { + for (size_t j = 0; j < num_dimensions; ++j) { + random_matrix(static_cast(i), static_cast(j)) = normal_dist(gen); + } + } + const Eigen::HouseholderQR qr(random_matrix); + matrix = qr.householderQ(); + } + ADSamplingPruner(const uint32_t num_dimensions, const float *matrix_p) : num_dimensions(num_dimensions) { ratios.resize(num_dimensions); for (size_t i = 0; i < num_dimensions; ++i) { diff --git a/include/utils/benchmark_utils.hpp b/include/utils/benchmark_utils.hpp index 50d6f7b..a4ee0f1 100644 --- a/include/utils/benchmark_utils.hpp +++ b/include/utils/benchmark_utils.hpp @@ -12,13 +12,43 @@ #include #include #include +#include #include #include #include #include #include "../common.hpp" +#include "utils/file_reader.hpp" +// Raw binary data paths (SuperKMeans convention: data_.bin / data__test.bin) +inline std::string RAW_DATA_DIR = std::string{CMAKE_SOURCE_DIR} + "/../../SuperKMeans/benchmarks/data"; +inline std::string GROUND_TRUTH_JSON_DIR = std::string{CMAKE_SOURCE_DIR} + "/../../SuperKMeans/benchmarks/ground_truth"; + +struct RawDatasetInfo { + size_t num_embeddings; + size_t num_dimensions; + size_t num_queries; + PDX::DistanceMetric distance_metric; + std::string pdx_dataset_name; // Name used in PDX ground truth / query files +}; + +inline const std::unordered_map RAW_DATASET_PARAMS = { + {"sift", {1000000, 128, 1000, PDX::DistanceMetric::L2SQ, "sift-128-euclidean"}}, + {"yi", {187843, 128, 1000, PDX::DistanceMetric::IP, "yi-128-ip"}}, + {"llama", {256921, 128, 1000, PDX::DistanceMetric::IP, "llama-128-ip"}}, + {"glove200", {1183514, 200, 1000, PDX::DistanceMetric::COSINE, "glove-200-angular"}}, + {"yandex", {1000000, 200, 1000, PDX::DistanceMetric::COSINE, "yandex-200-cosine"}}, + {"yahoo", {677305, 384, 1000, PDX::DistanceMetric::COSINE, "yahoo-minilm-384-normalized"}}, + {"clip", {1281167, 512, 1000, PDX::DistanceMetric::L2SQ, "imagenet-clip-512-normalized"}}, + {"contriever", {990000, 768, 1000, PDX::DistanceMetric::L2SQ, "contriever-768"}}, + {"gist", {1000000, 960, 1000, PDX::DistanceMetric::L2SQ, "gist-960-euclidean"}}, + {"mxbai", {769382, 1024, 1000, PDX::DistanceMetric::L2SQ, "agnews-mxbai-1024-euclidean"}}, + {"openai", {999000, 1536, 1000, PDX::DistanceMetric::L2SQ, "openai-1536-angular"}}, + {"arxiv", {2253000, 768, 1000, PDX::DistanceMetric::L2SQ, "arxiv-nomic-768-normalized"}}, + {"wiki", {260372, 3072, 1000, PDX::DistanceMetric::L2SQ, "simplewiki-openai-3072-normalized"}}, +}; + struct BenchmarkMetadata { std::string dataset; std::string algorithm; @@ -237,3 +267,53 @@ class BenchmarkUtils { }; BenchmarkUtils BENCHMARK_UTILS; + +inline std::unordered_map> ParseGroundTruthJson(const std::string &filename) { + std::unordered_map> gt_map; + std::ifstream file(filename); + if (!file.is_open()) return gt_map; + + std::string line; + std::getline(file, line); + + size_t pos = 0; + while ((pos = line.find("\"", pos)) != std::string::npos) { + size_t key_start = pos + 1; + size_t key_end = line.find("\"", key_start); + if (key_end == std::string::npos) break; + + int query_idx = std::stoi(line.substr(key_start, key_end - key_start)); + + size_t arr_start = line.find("[", key_end); + size_t arr_end = line.find("]", arr_start); + if (arr_start == std::string::npos || arr_end == std::string::npos) break; + + std::string arr_str = line.substr(arr_start + 1, arr_end - arr_start - 1); + std::vector ids; + std::istringstream iss(arr_str); + std::string token; + while (std::getline(iss, token, ',')) { + token.erase(0, token.find_first_not_of(" \t")); + token.erase(token.find_last_not_of(" \t") + 1); + if (!token.empty()) ids.push_back(std::stoi(token)); + } + gt_map[query_idx] = ids; + pos = arr_end + 1; + } + return gt_map; +} + +inline float ComputeRecallFromJson(const std::vector &result, + const std::vector >_ids, size_t knn) { + size_t hits = 0; + size_t gt_count = std::min(knn, gt_ids.size()); + for (size_t i = 0; i < result.size(); i++) { + for (size_t j = 0; j < gt_count; j++) { + if (result[i].index == static_cast(gt_ids[j])) { + hits++; + break; + } + } + } + return static_cast(hits) / static_cast(gt_count); +} diff --git a/include/utils/file_reader.hpp b/include/utils/file_reader.hpp index cf5714f..a6320ee 100644 --- a/include/utils/file_reader.hpp +++ b/include/utils/file_reader.hpp @@ -24,7 +24,7 @@ inline std::unique_ptr MmapFile(const std::string& filename) { fstat(fd, &file_stats); size_t file_size = file_stats.st_size; - auto data = std::make_unique(file_size); + std::unique_ptr data(new char[file_size]); std::ifstream input(filename, std::ios::binary); input.read(data.get(), file_size); diff --git a/include/utils/tictoc.hpp b/include/utils/tictoc.hpp index 0551a6f..a6f203a 100644 --- a/include/utils/tictoc.hpp +++ b/include/utils/tictoc.hpp @@ -36,4 +36,8 @@ class TicToc { accum_time += std::chrono::duration_cast( end - start).count(); } + + double GetMilliseconds() const { + return static_cast(accum_time) / 1e6; + } }; diff --git a/include/utils/utils.hpp b/include/utils/utils.hpp index 6e54e12..1f978e7 100644 --- a/include/utils/utils.hpp +++ b/include/utils/utils.hpp @@ -15,8 +15,8 @@ namespace PDX { // Generate a rotation matrix suitable for PDXearch's ADSampling pruning algorithm. -[[nodiscard]] inline unique_ptr GenerateRandomRotationMatrix(const size_t num_dimensions, const int32_t seed) { - auto rotation_matrix = make_uniq_array(num_dimensions * num_dimensions); +[[nodiscard]] inline std::unique_ptr GenerateRandomRotationMatrix(const size_t num_dimensions, const int32_t seed) { + auto rotation_matrix = std::make_unique(num_dimensions * num_dimensions); std::mt19937 gen(seed); std::normal_distribution normal_dist; From 4a7f91e24246f154f0873f6b079404825750f27b Mon Sep 17 00:00:00 2001 From: lkuffo Date: Tue, 24 Feb 2026 10:18:32 +0100 Subject: [PATCH 08/39] End to end benchmark that creates indexes from scratch --- .gitignore | 3 +- benchmarks/BenchmarkEndToEnd | Bin 395232 -> 436608 bytes .../bench_adsampling/pdx_end_to_end.cpp | 142 ++++++++++-------- .../DEFAULT/END_TO_END_PDX_ADSAMPLING.csv | 9 ++ include/ivf_wrapper.hpp | 11 ++ include/pdx_index.hpp | 128 ++++++++++++++++ 6 files changed, 233 insertions(+), 60 deletions(-) diff --git a/.gitignore b/.gitignore index cfaf054..baadf33 100644 --- a/.gitignore +++ b/.gitignore @@ -98,4 +98,5 @@ cmake_install.cmake /benchmarks/BenchmarkPDXLinearScan /benchmarks/BenchmarkU* /benchmarks/G4* -/benchmarks/BenchmarkU8* \ No newline at end of file +/benchmarks/BenchmarkU8* +/benchmarks/BenchmarkEndToEnd \ No newline at end of file diff --git a/benchmarks/BenchmarkEndToEnd b/benchmarks/BenchmarkEndToEnd index 4c93f002d327aaa59bf60fc295d3822c353c8b23..0f14b637cc8309831be84eafc0526f8bc541cac5 100755 GIT binary patch delta 89487 zcmd4430#%M_dov3T$T$WE?gF`%BB*cp`xOq;stZb3Jr0|6`>5N%sq4AqLks1alPo2 z8ltAct&9Y{xht00W~gO*;_@+EG8GrDiueCM&+}Zo5Y_kd`F#K1*Uw(JXXebAGc)I$ zIdkUBJP+k>1Z1Aj=PYh=z(uO{?|^A_tfu<_7t>Gue6&xyNDQSq6$g29XV%-NMVbQd 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zU0eO!(7WTqPWMM_uld}zyw{|ZcKcVoKPsKM*JsS@FO&8-p4jlT)~HRpqlVTSHL6eQ z7#EY1Ci8n9teFw=px%b$E~NFCAzuumm%T|V7}$MPyM7Tf69>NDH|5TS>o@C8H|UYv zd*X&G!p&xLn?B7OyE~$Z*OzsD&n#E2jNH)JP^zaJT-fpS!`?*L^3IaC34MqqDl-Z2Bx}&a39Lc6U}--*0)jX3-Rd^Zu`c8@ne) zFWoe<{;o;?Y7u_jT)k44U)fzW5RHe~ODmv;Y7A diff --git a/benchmarks/bench_adsampling/pdx_end_to_end.cpp b/benchmarks/bench_adsampling/pdx_end_to_end.cpp index 2371431..b199331 100644 --- a/benchmarks/bench_adsampling/pdx_end_to_end.cpp +++ b/benchmarks/bench_adsampling/pdx_end_to_end.cpp @@ -17,55 +17,16 @@ #include "utils/benchmark_utils.hpp" #include "utils/file_reader.hpp" -int main(int argc, char *argv[]) { - if (argc < 2) { - std::cerr << "Usage: " << argv[0] << " [ivf_nprobe]\n"; - std::cerr << "Available datasets:"; - for (const auto &[name, _] : RAW_DATASET_PARAMS) { - std::cerr << " " << name; - } - std::cerr << "\n"; - return 1; - } - std::string dataset = argv[1]; - size_t arg_ivf_nprobe = (argc > 2) ? std::atoi(argv[2]) : 0; - - auto it = RAW_DATASET_PARAMS.find(dataset); - if (it == RAW_DATASET_PARAMS.end()) { - std::cerr << "Unknown dataset: " << dataset << "\n"; - return 1; - } - const auto &info = it->second; - const size_t n = info.num_embeddings; +template +void RunBenchmark(const RawDatasetInfo &info, const std::string &dataset, const std::string &algorithm, + const float *data, const float *queries, const std::vector &nprobes_to_use) { const size_t d = info.num_dimensions; + const size_t n = info.num_embeddings; const size_t n_queries = info.num_queries; - - std::cout << "==> PDX End-to-End (Build + Search)\n"; - std::cout << "Dataset: " << dataset << " (n=" << n << ", d=" << d << ")\n"; - - std::string ALGORITHM = "end_to_end_adsampling"; uint8_t KNN = BenchmarkUtils::KNN; size_t NUM_MEASURE_RUNS = BenchmarkUtils::NUM_MEASURE_RUNS; std::string RESULTS_PATH = BENCHMARK_UTILS.RESULTS_DIR_PATH + "END_TO_END_PDX_ADSAMPLING.csv"; - // Read data - std::string data_path = RAW_DATA_DIR + "/data_" + dataset + ".bin"; - std::string query_path = RAW_DATA_DIR + "/data_" + dataset + "_test.bin"; - - std::vector data(n * d); - { - std::ifstream file(data_path, std::ios::binary); - if (!file) { std::cerr << "Failed to open " << data_path << "\n"; return 1; } - file.read(reinterpret_cast(data.data()), n * d * sizeof(float)); - } - - std::vector queries(n_queries * d); - { - std::ifstream file(query_path, std::ios::binary); - if (!file) { std::cerr << "Failed to open " << query_path << "\n"; return 1; } - file.read(reinterpret_cast(queries.data()), n_queries * d * sizeof(float)); - } - PDX::PDXIndexConfig index_config { .num_dimensions = static_cast(d), .distance_metric = info.distance_metric, @@ -76,8 +37,8 @@ int main(int argc, char *argv[]) { std::cout << "Building index (num_clusters=auto)...\n"; auto build_start = std::chrono::high_resolution_clock::now(); - PDX::PDXIndexF32 pdx_index(index_config); - pdx_index.BuildIndex(data.data(), n); + IndexT pdx_index(index_config); + pdx_index.BuildIndex(data, n); auto build_end = std::chrono::high_resolution_clock::now(); double build_ms = std::chrono::duration(build_end - build_start).count(); std::cout << "Build time: " << build_ms << " ms\n"; @@ -94,7 +55,7 @@ int main(int argc, char *argv[]) { gt_map = ParseGroundTruthJson(gt_path); if (gt_map.empty()) { std::cerr << "No ground truth found at " << gt_path << "\n"; - return 1; + return; } std::cout << "Ground truth loaded (json): " << gt_map.size() << " queries\n"; } else { @@ -104,14 +65,6 @@ int main(int argc, char *argv[]) { std::cout << "Ground truth loaded (pdx binary): " << gt_path << "\n"; } - // Search + recall + runtime at various nprobe values - std::vector nprobes_to_use; - if (arg_ivf_nprobe > 0) { - nprobes_to_use = {arg_ivf_nprobe}; - } else { - nprobes_to_use.assign(std::begin(BenchmarkUtils::IVF_PROBES), std::end(BenchmarkUtils::IVF_PROBES)); - } - for (size_t ivf_nprobe : nprobes_to_use) { if (pdx_index.GetNumClusters() < ivf_nprobe) continue; @@ -121,15 +74,15 @@ int main(int argc, char *argv[]) { float recalls = 0; if (use_skmeans_gt) { for (size_t l = 0; l < n_queries; ++l) { - auto result = pdx_index.Search(queries.data() + l * d, KNN); + auto result = pdx_index.Search(queries + l * d, KNN); if (gt_map.count(static_cast(l))) { recalls += ComputeRecallFromJson(result, gt_map.at(static_cast(l)), KNN); } } } else { for (size_t l = 0; l < n_queries; ++l) { - auto result = pdx_index.Search(queries.data() + l * d, KNN); - BenchmarkUtils::VerifyResult(recalls, result, KNN, int_ground_truth, l); + auto result = pdx_index.Search(queries + l * d, KNN); + BenchmarkUtils::VerifyResult(recalls, result, KNN, int_ground_truth, l); } } @@ -138,7 +91,7 @@ int main(int argc, char *argv[]) { runtimes.resize(NUM_MEASURE_RUNS * n_queries); for (size_t j = 0; j < NUM_MEASURE_RUNS; ++j) { for (size_t l = 0; l < n_queries; ++l) { - pdx_index.Search(queries.data() + l * d, KNN); + pdx_index.Search(queries + l * d, KNN); runtimes[j + l * NUM_MEASURE_RUNS] = { pdx_index.GetSearcher().end_to_end_clock.accum_time }; @@ -147,7 +100,7 @@ int main(int argc, char *argv[]) { BenchmarkMetadata results_metadata = { dataset, - ALGORITHM, + algorithm, NUM_MEASURE_RUNS, n_queries, ivf_nprobe, @@ -156,6 +109,77 @@ int main(int argc, char *argv[]) { }; BenchmarkUtils::SaveResults(runtimes, RESULTS_PATH, results_metadata); } +} + +int main(int argc, char *argv[]) { + if (argc < 3) { + std::cerr << "Usage: " << argv[0] << " [ivf_nprobe]\n"; + std::cerr << "Index types: pdx_f32, pdx_u8, pdx_tree_f32, pdx_tree_u8\n"; + std::cerr << "Available datasets:"; + for (const auto &[name, _] : RAW_DATASET_PARAMS) { + std::cerr << " " << name; + } + std::cerr << "\n"; + return 1; + } + std::string dataset = argv[1]; + std::string index_type = argv[2]; + size_t arg_ivf_nprobe = (argc > 3) ? std::atoi(argv[3]) : 0; + + auto it = RAW_DATASET_PARAMS.find(dataset); + if (it == RAW_DATASET_PARAMS.end()) { + std::cerr << "Unknown dataset: " << dataset << "\n"; + return 1; + } + const auto &info = it->second; + const size_t n = info.num_embeddings; + const size_t d = info.num_dimensions; + const size_t n_queries = info.num_queries; + + std::cout << "==> PDX End-to-End (Build + Search)\n"; + std::cout << "Dataset: " << dataset << " (n=" << n << ", d=" << d << ")\n"; + std::cout << "Index type: " << index_type << "\n"; + + // Read data + std::string data_path = RAW_DATA_DIR + "/data_" + dataset + ".bin"; + std::string query_path = RAW_DATA_DIR + "/data_" + dataset + "_test.bin"; + + std::vector data(n * d); + { + std::ifstream file(data_path, std::ios::binary); + if (!file) { std::cerr << "Failed to open " << data_path << "\n"; return 1; } + file.read(reinterpret_cast(data.data()), n * d * sizeof(float)); + } + + std::vector queries(n_queries * d); + { + std::ifstream file(query_path, std::ios::binary); + if (!file) { std::cerr << "Failed to open " << query_path << "\n"; return 1; } + file.read(reinterpret_cast(queries.data()), n_queries * d * sizeof(float)); + } + + std::vector nprobes_to_use; + if (arg_ivf_nprobe > 0) { + nprobes_to_use = {arg_ivf_nprobe}; + } else { + nprobes_to_use.assign(std::begin(BenchmarkUtils::IVF_PROBES), std::end(BenchmarkUtils::IVF_PROBES)); + } + + std::string algorithm = "end_to_end_" + index_type; + + if (index_type == "pdx_f32") { + RunBenchmark(info, dataset, algorithm, data.data(), queries.data(), nprobes_to_use); + } else if (index_type == "pdx_u8") { + RunBenchmark(info, dataset, algorithm, data.data(), queries.data(), nprobes_to_use); + } else if (index_type == "pdx_tree_f32") { + RunBenchmark(info, dataset, algorithm, data.data(), queries.data(), nprobes_to_use); + } else if (index_type == "pdx_tree_u8") { + RunBenchmark(info, dataset, algorithm, data.data(), queries.data(), nprobes_to_use); + } else { + std::cerr << "Unknown index type: " << index_type << "\n"; + std::cerr << "Valid types: pdx_f32, pdx_u8, pdx_tree_f32, pdx_tree_u8\n"; + return 1; + } return 0; } diff --git a/benchmarks/results/DEFAULT/END_TO_END_PDX_ADSAMPLING.csv b/benchmarks/results/DEFAULT/END_TO_END_PDX_ADSAMPLING.csv index 84cd67a..99d6929 100644 --- a/benchmarks/results/DEFAULT/END_TO_END_PDX_ADSAMPLING.csv +++ b/benchmarks/results/DEFAULT/END_TO_END_PDX_ADSAMPLING.csv @@ -25,3 +25,12 @@ mxbai,end_to_end_adsampling,0.466941,0.683917,0.264916,0.894148,24,0,100,1000,0, mxbai,end_to_end_adsampling,0.472717,0.693667,0.268167,0.900299,24,0,100,1000,0,1,0.473,0.727,0.2682 mxbai,end_to_end_adsampling,0.480276,0.709958,0.284875,0.89764,24,0,100,1000,0,1,0.4808,0.7421,0.2849 mxbai,end_to_end_adsampling,0.48062,0.699125,0.2595,0.900299,24,0,100,1000,0,1,0.4812,0.7641,0.2595 +mxbai,end_to_end_adsampling,0.474468,0.697833,0.269833,0.900299,24,0,100,1000,0,1,0.475,0.7324,0.2698 +mxbai,end_to_end_adsampling,0.306642,0.371042,0.245875,0.898968,24,0,100,1000,0,1,0.307,0.4139,0.2371 +mxbai,end_to_end_adsampling,0.308307,0.371958,0.244833,0.901748,25,0,100,1000,0,1,0.3086,0.4083,0.2386 +mxbai,end_to_end_adsampling,0.263858,0.491958,0.0675,0.902799,25,0,100,1000,0,1,0.2639,0.492,0.0675 +mxbai,end_to_end_adsampling,0.102027,0.163792,0.038375,0.901368,25,0,100,1000,0,1,0.1023,0.1836,0.03837 +mxbai,end_to_end_pdx_f32,0.474313,0.703625,0.271291,0.903179,25,0,100,1000,0,1,0.4746,0.7174,0.2713 +mxbai,end_to_end_pdx_u8,0.320404,0.392583,0.248917,0.901748,25,0,100,1000,0,1,0.3209,0.4425,0.2452 +mxbai,end_to_end_pdx_tree_f32,0.270043,0.496916,0.057458,0.902799,25,0,100,1000,0,1,0.2703,0.5101,0.05746 +mxbai,end_to_end_pdx_tree_u8,0.106266,0.175334,0.046542,0.901368,25,0,100,1000,0,1,0.1065,0.1891,0.03388 diff --git a/include/ivf_wrapper.hpp b/include/ivf_wrapper.hpp index 8e13b23..b53ec49 100644 --- a/include/ivf_wrapper.hpp +++ b/include/ivf_wrapper.hpp @@ -115,6 +115,17 @@ class IndexPDXIVF2 : public IndexPDXIVF { IndexPDXIVF l0; // Meso clusters IndexPDXIVF2() = default; + ~IndexPDXIVF2() = default; + IndexPDXIVF2(IndexPDXIVF2 &&) = default; + IndexPDXIVF2 &operator=(IndexPDXIVF2 &&) = default; + + IndexPDXIVF2(uint32_t num_dimensions, uint64_t total_num_embeddings, uint32_t num_clusters, bool is_normalized) + : IndexPDXIVF(num_dimensions, total_num_embeddings, num_clusters, is_normalized) {} + + IndexPDXIVF2(uint32_t num_dimensions, uint64_t total_num_embeddings, uint32_t num_clusters, bool is_normalized, + float quantization_scale, float quantization_base) + : IndexPDXIVF(num_dimensions, total_num_embeddings, num_clusters, is_normalized, + quantization_scale, quantization_base) {} void Restore(const std::string &filename) { this->file_buffer = MmapFile(filename); diff --git a/include/pdx_index.hpp b/include/pdx_index.hpp index ddc2b9a..d86a9d7 100644 --- a/include/pdx_index.hpp +++ b/include/pdx_index.hpp @@ -23,6 +23,7 @@ struct PDXIndexConfig { DistanceMetric distance_metric = DistanceMetric::L2SQ; uint32_t seed = 42; uint32_t num_clusters = 0; // 0 = auto-compute from num_embeddings + uint32_t num_meso_clusters = 0; bool normalize = false; float sampling_fraction = 0.0f; // 0 = auto (1.0 if small dataset, 0.3 otherwise) uint32_t kmeans_iters = 10; @@ -174,6 +175,7 @@ class PDXTreeIndex { private: std::unique_ptr matrix_buffer; + PDXIndexConfig config {}; PDX::IndexPDXIVF2 index; std::unique_ptr pruner; std::unique_ptr> searcher; @@ -182,6 +184,10 @@ class PDXTreeIndex { public: PDXTreeIndex() = default; + explicit PDXTreeIndex(PDXIndexConfig config) : config(config) { + pruner = std::make_unique(config.num_dimensions, config.seed); + } + void Restore(const std::string &index_path, const std::string &matrix_path) { index.Restore(index_path); @@ -212,6 +218,128 @@ class PDXTreeIndex { return searcher->Search(query_embedding, knn); } + void BuildIndex(const float *const embeddings, const size_t num_embeddings) { + std::vector row_ids(num_embeddings); + std::iota(row_ids.begin(), row_ids.end(), 0); + BuildIndex(row_ids.data(), embeddings, num_embeddings); + } + + void BuildIndex(const size_t *const row_ids, const float *const embeddings, const size_t num_embeddings) { + const auto num_dimensions = config.num_dimensions; + auto num_clusters = config.num_clusters; + if (num_clusters == 0) { + num_clusters = ComputeNumberOfClusters(num_embeddings); + } + const bool normalize = config.normalize || DistanceMetricRequiresNormalization(config.distance_metric); + + assert(num_dimensions > 0); + assert(num_embeddings > 0); + assert(pruner); + + // Preprocess: normalize (if needed) + rotate. + const size_t total_floats = num_embeddings * num_dimensions; + std::unique_ptr normalized; + const float *rotation_input = embeddings; + if (normalize) { + normalized.reset(new float[total_floats]); + std::memcpy(normalized.get(), embeddings, total_floats * sizeof(float)); + PDX::Quantizer quantizer(num_dimensions); + for (size_t i = 0; i < num_embeddings; i++) { + quantizer.NormalizeQuery(normalized.get() + i * num_dimensions, + normalized.get() + i * num_dimensions); + } + rotation_input = normalized.get(); + } + std::unique_ptr preprocessed(new float[total_floats]); + pruner->PreprocessEmbeddings(rotation_input, preprocessed.get(), num_embeddings); + + float quantization_base = 0.0f; + float quantization_scale = 1.0f; + if constexpr (Q == PDX::U8) { + const auto params = PDX::ScalarQuantizer::ComputeQuantizationParams( + preprocessed.get(), static_cast(num_embeddings) * num_dimensions); + quantization_base = params.quantization_base; + quantization_scale = params.quantization_scale; + index = PDX::IndexPDXIVF2(num_dimensions, num_embeddings, num_clusters, + normalize, quantization_scale, quantization_base); + } else { + index = PDX::IndexPDXIVF2(num_dimensions, num_embeddings, num_clusters, normalize); + } + + KMeansResult kmeans_result = ComputeKMeans(preprocessed.get(), num_embeddings, num_dimensions, + num_clusters, config.distance_metric, config.seed, + config.normalize, config.sampling_fraction, config.kmeans_iters); + index.centroids = std::move(kmeans_result.centroids); + + size_t max_cluster_size = 0; + for (size_t i = 0; i < num_clusters; i++) { + max_cluster_size = std::max(max_cluster_size, kmeans_result.assignments[i].size()); + } + std::unique_ptr tmp_cluster_embeddings( + new embedding_storage_t[static_cast(max_cluster_size) * num_dimensions]); + + for (size_t cluster_idx = 0; cluster_idx < num_clusters; cluster_idx++) { + const auto cluster_size = kmeans_result.assignments[cluster_idx].size(); + auto &cluster = index.clusters.emplace_back(cluster_size, num_dimensions); + + for (size_t position_in_cluster = 0; position_in_cluster < cluster_size; position_in_cluster++) { + const auto embedding_idx = kmeans_result.assignments[cluster_idx][position_in_cluster]; + cluster.indices[position_in_cluster] = row_ids[embedding_idx]; + + if constexpr (Q == PDX::U8) { + PDX::ScalarQuantizer quantizer(num_dimensions); + quantizer.QuantizeEmbedding(preprocessed.get() + (embedding_idx * num_dimensions), + quantization_base, quantization_scale, + tmp_cluster_embeddings.get() + (position_in_cluster * num_dimensions)); + } else { + std::memcpy(tmp_cluster_embeddings.get() + (position_in_cluster * num_dimensions), + preprocessed.get() + (embedding_idx * num_dimensions), + num_dimensions * sizeof(float)); + } + } + StoreClusterEmbeddings(cluster, index, tmp_cluster_embeddings.get(), + cluster_size); + } + searcher = std::make_unique>(index, *pruner); + + // L0 index + auto l0_num_clusters = config.num_meso_clusters; + if (l0_num_clusters == 0){ + l0_num_clusters = static_cast(std::sqrt(num_clusters)); + } + + index.l0 = PDX::IndexPDXIVF(num_dimensions, num_clusters, l0_num_clusters, normalize); + KMeansResult l0_kmeans_result = ComputeKMeans(index.centroids.data(), num_clusters, num_dimensions, + l0_num_clusters, config.distance_metric, config.seed, + config.normalize, 1.0f, 10); // No sampling for l0 + index.l0.centroids = std::move(l0_kmeans_result.centroids); + + size_t l0_max_cluster_size = 0; + for (size_t i = 0; i < l0_num_clusters; i++) { + l0_max_cluster_size = std::max(l0_max_cluster_size, l0_kmeans_result.assignments[i].size()); + } + std::unique_ptr l0_tmp_cluster_embeddings( + new float[static_cast(l0_max_cluster_size) * num_dimensions]); + + for (size_t cluster_idx = 0; cluster_idx < l0_num_clusters; cluster_idx++) { + const auto cluster_size = l0_kmeans_result.assignments[cluster_idx].size(); + auto &cluster = index.l0.clusters.emplace_back(cluster_size, num_dimensions); + + for (size_t position_in_cluster = 0; position_in_cluster < cluster_size; position_in_cluster++) { + const auto embedding_idx = l0_kmeans_result.assignments[cluster_idx][position_in_cluster]; + cluster.indices[position_in_cluster] = embedding_idx; + + std::memcpy(l0_tmp_cluster_embeddings.get() + (position_in_cluster * num_dimensions), + index.centroids.data() + (embedding_idx * num_dimensions), + num_dimensions * sizeof(float)); + } + StoreClusterEmbeddings(cluster, index.l0, l0_tmp_cluster_embeddings.get(), + cluster_size); + } + + top_level_searcher = std::make_unique>(index.l0, *pruner); + } + void SetNProbe(uint32_t n_probe) const { searcher->SetNProbe(n_probe); } From d8b11f7a7cb9d9f8b1d4adedaba79a031af0f26b Mon Sep 17 00:00:00 2001 From: lkuffo Date: Tue, 24 Feb 2026 10:41:42 +0100 Subject: [PATCH 09/39] Refactor structure --- benchmarks/CMakeLists.txt | 2 + .../bench_adsampling/pdx_end_to_end.cpp | 6 +- .../bench_adsampling/pdx_ivf2_adsampling.cpp | 6 +- .../pdx_ivf2_adsampling_u8.cpp | 6 +- .../pdx_ivf2_adsampling_u8_filtered.cpp | 10 +- .../bench_adsampling/pdx_ivf_adsampling.cpp | 6 +- .../pdx_ivf_adsampling_filtered.cpp | 10 +- .../pdx_ivf_adsampling_u8.cpp | 6 +- .../utils => benchmarks}/benchmark_utils.hpp | 4 +- .../DEFAULT/END_TO_END_PDX_ADSAMPLING.csv | 1 + include/{ => pdx}/clustering.hpp | 2 +- include/{ => pdx}/common.hpp | 4 + .../{ => pdx}/db_mock/predicate_evaluator.hpp | 0 .../distance_computers/avx2_computers.hpp | 4 +- .../distance_computers/avx512_computers.hpp | 2 +- .../distance_computers/base_computers.hpp | 10 +- .../distance_computers/neon_computers.hpp | 2 +- .../distance_computers/scalar_computers.hpp | 2 +- include/{pdx_index.hpp => pdx/index.hpp} | 45 ++++- include/{ => pdx}/ivf_wrapper.hpp | 4 +- include/pdx/layout.hpp | 73 ++++++++ include/{ => pdx}/lib/lib.hpp | 8 +- include/{ => pdx}/pruners/adsampling.hpp | 2 +- include/{ => pdx}/quantizers/scalar.hpp | 2 +- include/{pdxearch.hpp => pdx/searcher.hpp} | 14 +- include/{utils/tictoc.hpp => pdx/utils.hpp} | 27 ++- include/utils/file_reader.hpp | 33 ---- include/utils/utils.hpp | 159 ------------------ python/lib.cpp | 2 +- 29 files changed, 191 insertions(+), 261 deletions(-) rename {include/utils => benchmarks}/benchmark_utils.hpp (99%) rename include/{ => pdx}/clustering.hpp (99%) rename include/{ => pdx}/common.hpp (96%) rename include/{ => pdx}/db_mock/predicate_evaluator.hpp (100%) rename include/{ => pdx}/distance_computers/avx2_computers.hpp (98%) rename include/{ => pdx}/distance_computers/avx512_computers.hpp (99%) rename include/{ => pdx}/distance_computers/base_computers.hpp (84%) rename include/{ => pdx}/distance_computers/neon_computers.hpp (99%) rename include/{ => pdx}/distance_computers/scalar_computers.hpp (99%) rename include/{pdx_index.hpp => pdx/index.hpp} (91%) rename include/{ => pdx}/ivf_wrapper.hpp (99%) create mode 100644 include/pdx/layout.hpp rename include/{ => pdx}/lib/lib.hpp (99%) rename include/{ => pdx}/pruners/adsampling.hpp (99%) rename include/{ => pdx}/quantizers/scalar.hpp (98%) rename include/{pdxearch.hpp => pdx/searcher.hpp} (99%) rename include/{utils/tictoc.hpp => pdx/utils.hpp} (62%) delete mode 100644 include/utils/file_reader.hpp delete mode 100644 include/utils/utils.hpp diff --git a/benchmarks/CMakeLists.txt b/benchmarks/CMakeLists.txt index 4d3f242..26933b8 100644 --- a/benchmarks/CMakeLists.txt +++ b/benchmarks/CMakeLists.txt @@ -1,3 +1,5 @@ +include_directories(${CMAKE_CURRENT_SOURCE_DIR}) + set(BENCH_COMMON_LIBS ${MKL_COMMON_LIBS} ${BLAS_LINK_LIBRARIES}) if (FFTW_FOUND) diff --git a/benchmarks/bench_adsampling/pdx_end_to_end.cpp b/benchmarks/bench_adsampling/pdx_end_to_end.cpp index b199331..b7d6793 100644 --- a/benchmarks/bench_adsampling/pdx_end_to_end.cpp +++ b/benchmarks/bench_adsampling/pdx_end_to_end.cpp @@ -13,9 +13,9 @@ #include #include -#include "pdx_index.hpp" -#include "utils/benchmark_utils.hpp" -#include "utils/file_reader.hpp" +#include "pdx/index.hpp" +#include "benchmark_utils.hpp" +#include "pdx/utils.hpp" template void RunBenchmark(const RawDatasetInfo &info, const std::string &dataset, const std::string &algorithm, diff --git a/benchmarks/bench_adsampling/pdx_ivf2_adsampling.cpp b/benchmarks/bench_adsampling/pdx_ivf2_adsampling.cpp index 629ce2b..1a20d8c 100644 --- a/benchmarks/bench_adsampling/pdx_ivf2_adsampling.cpp +++ b/benchmarks/bench_adsampling/pdx_ivf2_adsampling.cpp @@ -8,9 +8,9 @@ #include #include -#include "utils/file_reader.hpp" -#include "pdx_index.hpp" -#include "utils/benchmark_utils.hpp" +#include "pdx/utils.hpp" +#include "pdx/index.hpp" +#include "benchmark_utils.hpp" int main(int argc, char *argv[]) { std::string arg_dataset; diff --git a/benchmarks/bench_adsampling/pdx_ivf2_adsampling_u8.cpp b/benchmarks/bench_adsampling/pdx_ivf2_adsampling_u8.cpp index 7caf5f8..8aaf741 100644 --- a/benchmarks/bench_adsampling/pdx_ivf2_adsampling_u8.cpp +++ b/benchmarks/bench_adsampling/pdx_ivf2_adsampling_u8.cpp @@ -8,9 +8,9 @@ #include #include -#include "utils/file_reader.hpp" -#include "pdx_index.hpp" -#include "utils/benchmark_utils.hpp" +#include "pdx/utils.hpp" +#include "pdx/index.hpp" +#include "benchmark_utils.hpp" int main(int argc, char *argv[]) { std::string arg_dataset; diff --git a/benchmarks/bench_adsampling/pdx_ivf2_adsampling_u8_filtered.cpp b/benchmarks/bench_adsampling/pdx_ivf2_adsampling_u8_filtered.cpp index 22220d5..77b32c1 100644 --- a/benchmarks/bench_adsampling/pdx_ivf2_adsampling_u8_filtered.cpp +++ b/benchmarks/bench_adsampling/pdx_ivf2_adsampling_u8_filtered.cpp @@ -8,11 +8,11 @@ #include #include -#include "utils/file_reader.hpp" -#include "pruners/adsampling.hpp" -#include "pdxearch.hpp" -#include "db_mock/predicate_evaluator.hpp" -#include "utils/benchmark_utils.hpp" +#include "pdx/utils.hpp" +#include "pdx/pruners/adsampling.hpp" +#include "pdx/searcher.hpp" +#include "pdx/db_mock/predicate_evaluator.hpp" +#include "benchmark_utils.hpp" int main(int argc, char *argv[]) { std::string arg_dataset; diff --git a/benchmarks/bench_adsampling/pdx_ivf_adsampling.cpp b/benchmarks/bench_adsampling/pdx_ivf_adsampling.cpp index 125f81c..e7dc9d5 100644 --- a/benchmarks/bench_adsampling/pdx_ivf_adsampling.cpp +++ b/benchmarks/bench_adsampling/pdx_ivf_adsampling.cpp @@ -8,9 +8,9 @@ #include #include -#include "utils/file_reader.hpp" -#include "pdx_index.hpp" -#include "utils/benchmark_utils.hpp" +#include "pdx/utils.hpp" +#include "pdx/index.hpp" +#include "benchmark_utils.hpp" int main(int argc, char *argv[]) { std::string arg_dataset; diff --git a/benchmarks/bench_adsampling/pdx_ivf_adsampling_filtered.cpp b/benchmarks/bench_adsampling/pdx_ivf_adsampling_filtered.cpp index a276680..fafb1ce 100644 --- a/benchmarks/bench_adsampling/pdx_ivf_adsampling_filtered.cpp +++ b/benchmarks/bench_adsampling/pdx_ivf_adsampling_filtered.cpp @@ -8,11 +8,11 @@ #include #include -#include "utils/file_reader.hpp" -#include "ivf_wrapper.hpp" -#include "pdxearch.hpp" -#include "pruners/adsampling.hpp" -#include "utils/benchmark_utils.hpp" +#include "pdx/utils.hpp" +#include "pdx/ivf_wrapper.hpp" +#include "pdx/searcher.hpp" +#include "pdx/pruners/adsampling.hpp" +#include "benchmark_utils.hpp" int main(int argc, char *argv[]) { std::string arg_dataset; diff --git a/benchmarks/bench_adsampling/pdx_ivf_adsampling_u8.cpp b/benchmarks/bench_adsampling/pdx_ivf_adsampling_u8.cpp index 955e86a..dcdca0d 100644 --- a/benchmarks/bench_adsampling/pdx_ivf_adsampling_u8.cpp +++ b/benchmarks/bench_adsampling/pdx_ivf_adsampling_u8.cpp @@ -8,9 +8,9 @@ #include #include -#include "utils/file_reader.hpp" -#include "pdx_index.hpp" -#include "utils/benchmark_utils.hpp" +#include "pdx/utils.hpp" +#include "pdx/index.hpp" +#include "benchmark_utils.hpp" int main(int argc, char *argv[]) { std::string arg_dataset; diff --git a/include/utils/benchmark_utils.hpp b/benchmarks/benchmark_utils.hpp similarity index 99% rename from include/utils/benchmark_utils.hpp rename to benchmarks/benchmark_utils.hpp index a4ee0f1..8be0723 100644 --- a/include/utils/benchmark_utils.hpp +++ b/benchmarks/benchmark_utils.hpp @@ -17,8 +17,8 @@ #include #include #include -#include "../common.hpp" -#include "utils/file_reader.hpp" +#include "pdx/common.hpp" +#include "pdx/utils.hpp" // Raw binary data paths (SuperKMeans convention: data_.bin / data__test.bin) diff --git a/benchmarks/results/DEFAULT/END_TO_END_PDX_ADSAMPLING.csv b/benchmarks/results/DEFAULT/END_TO_END_PDX_ADSAMPLING.csv index 99d6929..566443a 100644 --- a/benchmarks/results/DEFAULT/END_TO_END_PDX_ADSAMPLING.csv +++ b/benchmarks/results/DEFAULT/END_TO_END_PDX_ADSAMPLING.csv @@ -34,3 +34,4 @@ mxbai,end_to_end_pdx_f32,0.474313,0.703625,0.271291,0.903179,25,0,100,1000,0,1,0 mxbai,end_to_end_pdx_u8,0.320404,0.392583,0.248917,0.901748,25,0,100,1000,0,1,0.3209,0.4425,0.2452 mxbai,end_to_end_pdx_tree_f32,0.270043,0.496916,0.057458,0.902799,25,0,100,1000,0,1,0.2703,0.5101,0.05746 mxbai,end_to_end_pdx_tree_u8,0.106266,0.175334,0.046542,0.901368,25,0,100,1000,0,1,0.1065,0.1891,0.03388 +mxbai,end_to_end_pdx_tree_f32,0.263403,0.494292,0.054875,0.902799,25,0,100,1000,0,1,0.2637,0.5128,0.05488 diff --git a/include/clustering.hpp b/include/pdx/clustering.hpp similarity index 99% rename from include/clustering.hpp rename to include/pdx/clustering.hpp index 61916f6..6c74f43 100644 --- a/include/clustering.hpp +++ b/include/pdx/clustering.hpp @@ -1,7 +1,7 @@ #pragma once #include -#include "common.hpp" +#include "pdx/common.hpp" #include "superkmeans/hierarchical_superkmeans.h" namespace PDX { diff --git a/include/common.hpp b/include/pdx/common.hpp similarity index 96% rename from include/common.hpp rename to include/pdx/common.hpp index 3dfcc51..1a36d29 100644 --- a/include/common.hpp +++ b/include/pdx/common.hpp @@ -201,4 +201,8 @@ static_assert(GetPDXDimensionSplit(1028).vertical_dimensions == 260); } } +[[nodiscard]] inline constexpr bool DistanceMetricRequiresNormalization(const PDX::DistanceMetric distance_metric) { + return distance_metric == PDX::DistanceMetric::COSINE || distance_metric == PDX::DistanceMetric::IP; +} + } // namespace PDX diff --git a/include/db_mock/predicate_evaluator.hpp b/include/pdx/db_mock/predicate_evaluator.hpp similarity index 100% rename from include/db_mock/predicate_evaluator.hpp rename to include/pdx/db_mock/predicate_evaluator.hpp diff --git a/include/distance_computers/avx2_computers.hpp b/include/pdx/distance_computers/avx2_computers.hpp similarity index 98% rename from include/distance_computers/avx2_computers.hpp rename to include/pdx/distance_computers/avx2_computers.hpp index 366bea9..53c9bc3 100644 --- a/include/distance_computers/avx2_computers.hpp +++ b/include/pdx/distance_computers/avx2_computers.hpp @@ -2,8 +2,8 @@ #include #include -#include "common.hpp" -#include "distance_computers/scalar_computers.hpp" +#include "pdx/common.hpp" +#include "pdx/distance_computers/scalar_computers.hpp" namespace PDX { diff --git a/include/distance_computers/avx512_computers.hpp b/include/pdx/distance_computers/avx512_computers.hpp similarity index 99% rename from include/distance_computers/avx512_computers.hpp rename to include/pdx/distance_computers/avx512_computers.hpp index 45c05f1..62a9c29 100644 --- a/include/distance_computers/avx512_computers.hpp +++ b/include/pdx/distance_computers/avx512_computers.hpp @@ -3,7 +3,7 @@ #include #include #include -#include "common.hpp" +#include "pdx/common.hpp" namespace PDX { diff --git a/include/distance_computers/base_computers.hpp b/include/pdx/distance_computers/base_computers.hpp similarity index 84% rename from include/distance_computers/base_computers.hpp rename to include/pdx/distance_computers/base_computers.hpp index b9f5216..b934dcf 100644 --- a/include/distance_computers/base_computers.hpp +++ b/include/pdx/distance_computers/base_computers.hpp @@ -1,22 +1,22 @@ #pragma once -#include "common.hpp" +#include "pdx/common.hpp" #ifdef __ARM_NEON -#include "distance_computers/neon_computers.hpp" +#include "pdx/distance_computers/neon_computers.hpp" #endif #if defined(__AVX2__) && !defined(__AVX512F__) -#include "distance_computers/avx2_computers.hpp" +#include "pdx/distance_computers/avx2_computers.hpp" #endif #ifdef __AVX512F__ -#include "distance_computers/avx512_computers.hpp" +#include "pdx/distance_computers/avx512_computers.hpp" #endif // Fallback to scalar computer. #if !defined(__ARM_NEON) && !defined(__AVX2__) && !defined(__AVX512F__) -#include "distance_computers/scalar_computers.hpp" +#include "pdx/distance_computers/scalar_computers.hpp" #endif // TODO: Support SVE diff --git a/include/distance_computers/neon_computers.hpp b/include/pdx/distance_computers/neon_computers.hpp similarity index 99% rename from include/distance_computers/neon_computers.hpp rename to include/pdx/distance_computers/neon_computers.hpp index 7814718..cb5c725 100644 --- a/include/distance_computers/neon_computers.hpp +++ b/include/pdx/distance_computers/neon_computers.hpp @@ -2,7 +2,7 @@ #include #include "arm_neon.h" -#include "common.hpp" +#include "pdx/common.hpp" namespace PDX { diff --git a/include/distance_computers/scalar_computers.hpp b/include/pdx/distance_computers/scalar_computers.hpp similarity index 99% rename from include/distance_computers/scalar_computers.hpp rename to include/pdx/distance_computers/scalar_computers.hpp index 4f739a2..371c3bd 100644 --- a/include/distance_computers/scalar_computers.hpp +++ b/include/pdx/distance_computers/scalar_computers.hpp @@ -1,7 +1,7 @@ #pragma once #include -#include "common.hpp" +#include "pdx/common.hpp" namespace PDX { diff --git a/include/pdx_index.hpp b/include/pdx/index.hpp similarity index 91% rename from include/pdx_index.hpp rename to include/pdx/index.hpp index d86a9d7..dd523d2 100644 --- a/include/pdx_index.hpp +++ b/include/pdx/index.hpp @@ -7,14 +7,14 @@ #include #include -#include "common.hpp" -#include "ivf_wrapper.hpp" -#include "pruners/adsampling.hpp" -#include "pdxearch.hpp" -#include "utils/file_reader.hpp" -#include "clustering.hpp" -#include "utils/utils.hpp" -#include "quantizers/scalar.hpp" +#include "pdx/common.hpp" +#include "pdx/ivf_wrapper.hpp" +#include "pdx/pruners/adsampling.hpp" +#include "pdx/searcher.hpp" +#include "pdx/clustering.hpp" +#include "pdx/layout.hpp" +#include "pdx/utils.hpp" +#include "pdx/quantizers/scalar.hpp" namespace PDX { @@ -367,4 +367,33 @@ using PDXIndexU8 = PDXIndex; using PDXTreeIndexF32 = PDXTreeIndex; using PDXTreeIndexU8 = PDXTreeIndex; +class EmbeddingPreprocessor { +private: + PDX::ADSamplingPruner pruner; + PDX::Quantizer quantizer; + const size_t num_dimensions; + +public: + explicit EmbeddingPreprocessor(const size_t num_dimensions, const float *const rotation_matrix) + : pruner(num_dimensions, rotation_matrix), quantizer(num_dimensions), num_dimensions(num_dimensions) { + } + + void PreprocessEmbedding(float *const input_embedding, float *const output_embedding, const bool normalize) const { + if (normalize) { + quantizer.NormalizeQuery(input_embedding, input_embedding); + } + pruner.PreprocessQuery(input_embedding, output_embedding); + } + + void PreprocessEmbeddings(float *const input_embeddings, float *const output_embeddings, + const size_t num_embeddings, const bool normalize) const { + if (normalize) { + for (size_t i = 0; i < num_embeddings; i++) { + quantizer.NormalizeQuery(input_embeddings + i * num_dimensions, input_embeddings + i * num_dimensions); + } + } + pruner.PreprocessEmbeddings(input_embeddings, output_embeddings, num_embeddings); + } +}; + } // namespace PDX diff --git a/include/ivf_wrapper.hpp b/include/pdx/ivf_wrapper.hpp similarity index 99% rename from include/ivf_wrapper.hpp rename to include/pdx/ivf_wrapper.hpp index b53ec49..c4eca4e 100644 --- a/include/ivf_wrapper.hpp +++ b/include/pdx/ivf_wrapper.hpp @@ -5,8 +5,8 @@ #include #include #include -#include "common.hpp" -#include "utils/file_reader.hpp" +#include "pdx/common.hpp" +#include "pdx/utils.hpp" namespace PDX { diff --git a/include/pdx/layout.hpp b/include/pdx/layout.hpp new file mode 100644 index 0000000..9fe01a3 --- /dev/null +++ b/include/pdx/layout.hpp @@ -0,0 +1,73 @@ +#pragma once + +#include "pdx/common.hpp" +#include "pdx/ivf_wrapper.hpp" + +namespace PDX { + +// Store the embeddings into this cluster's preallocated buffers in the transposed PDX layout. +// +// See the README of the following for a description of the PDX layout: +// https://github.com/cwida/pdx +template +inline void StoreClusterEmbeddings(typename PDX::IndexPDXIVF::cluster_t &cluster, const PDX::IndexPDXIVF &index, + const T *embeddings, const size_t num_embeddings); + +template <> +inline void +StoreClusterEmbeddings(PDX::IndexPDXIVF::cluster_t &cluster, + const PDX::IndexPDXIVF &index, + const float *const embeddings, const size_t num_embeddings) { + using matrix_t = PDX::eigen_matrix_t; + using h_matrix_t = Eigen::Matrix; + + const auto vertical_d = index.num_vertical_dimensions; + const auto horizontal_d = index.num_horizontal_dimensions; + + Eigen::Map in(embeddings, num_embeddings, index.num_dimensions); + + Eigen::Map out(cluster.data, vertical_d, num_embeddings); + out.noalias() = in.leftCols(vertical_d).transpose(); + + float *horizontal_out = cluster.data + num_embeddings * vertical_d; + for (size_t j = 0; j < horizontal_d; j += PDX::H_DIM_SIZE) { + Eigen::Map out_h(horizontal_out, num_embeddings, PDX::H_DIM_SIZE); + out_h.noalias() = in.block(0, vertical_d + j, num_embeddings, PDX::H_DIM_SIZE); + horizontal_out += num_embeddings * PDX::H_DIM_SIZE; + } +} + +template <> +inline void +StoreClusterEmbeddings(PDX::IndexPDXIVF::cluster_t &cluster, + const PDX::IndexPDXIVF &index, + const uint8_t *const embeddings, const size_t num_embeddings) { + using u8_matrix_t = Eigen::Matrix; + using u8_v_matrix_t = Eigen::Matrix; + using u8_h_matrix_t = Eigen::Matrix; + + const auto vertical_d = index.num_vertical_dimensions; + const auto horizontal_d = index.num_horizontal_dimensions; + + Eigen::Map in(embeddings, num_embeddings, index.num_dimensions); + + size_t dim = 0; + for (; dim + PDX::U8_INTERLEAVE_SIZE <= vertical_d; dim += PDX::U8_INTERLEAVE_SIZE) { + Eigen::Map out_v(cluster.data + dim * num_embeddings, num_embeddings, PDX::U8_INTERLEAVE_SIZE); + out_v.noalias() = in.block(0, dim, num_embeddings, PDX::U8_INTERLEAVE_SIZE); + } + if (dim < vertical_d) { + auto remaining = static_cast(vertical_d - dim); + Eigen::Map out_v(cluster.data + dim * num_embeddings, num_embeddings, remaining); + out_v.noalias() = in.block(0, dim, num_embeddings, remaining); + } + + uint8_t *horizontal_out = cluster.data + num_embeddings * vertical_d; + for (size_t j = 0; j < horizontal_d; j += PDX::H_DIM_SIZE) { + Eigen::Map out_h(horizontal_out, num_embeddings, PDX::H_DIM_SIZE); + out_h.noalias() = in.block(0, vertical_d + j, num_embeddings, PDX::H_DIM_SIZE); + horizontal_out += num_embeddings * PDX::H_DIM_SIZE; + } +} + +} // namespace PDX diff --git a/include/lib/lib.hpp b/include/pdx/lib/lib.hpp similarity index 99% rename from include/lib/lib.hpp rename to include/pdx/lib/lib.hpp index aa4a1bd..5b16553 100644 --- a/include/lib/lib.hpp +++ b/include/pdx/lib/lib.hpp @@ -7,10 +7,10 @@ #include #include #include -#include "utils/file_reader.hpp" -#include "ivf_wrapper.hpp" -#include "pdxearch.hpp" -#include "pruners/adsampling.hpp" +#include "pdx/utils.hpp" +#include "pdx/ivf_wrapper.hpp" +#include "pdx/searcher.hpp" +#include "pdx/pruners/adsampling.hpp" // TODO: the python wrapper and the core API should not be interleaved namespace py = pybind11; diff --git a/include/pruners/adsampling.hpp b/include/pdx/pruners/adsampling.hpp similarity index 99% rename from include/pruners/adsampling.hpp rename to include/pdx/pruners/adsampling.hpp index f0eb531..7132510 100644 --- a/include/pruners/adsampling.hpp +++ b/include/pdx/pruners/adsampling.hpp @@ -3,7 +3,7 @@ #include #include #include -#include "common.hpp" +#include "pdx/common.hpp" #ifdef HAS_FFTW #include diff --git a/include/quantizers/scalar.hpp b/include/pdx/quantizers/scalar.hpp similarity index 98% rename from include/quantizers/scalar.hpp rename to include/pdx/quantizers/scalar.hpp index 6e338ea..10f1c8c 100644 --- a/include/quantizers/scalar.hpp +++ b/include/pdx/quantizers/scalar.hpp @@ -4,7 +4,7 @@ #include #include #include -#include "common.hpp" +#include "pdx/common.hpp" namespace PDX { diff --git a/include/pdxearch.hpp b/include/pdx/searcher.hpp similarity index 99% rename from include/pdxearch.hpp rename to include/pdx/searcher.hpp index ff28249..3d2fb3e 100644 --- a/include/pdxearch.hpp +++ b/include/pdx/searcher.hpp @@ -6,13 +6,13 @@ #include #include #include -#include "common.hpp" -#include "utils/tictoc.hpp" -#include "db_mock/predicate_evaluator.hpp" -#include "distance_computers/base_computers.hpp" -#include "quantizers/scalar.hpp" -#include "ivf_wrapper.hpp" -#include "pruners/adsampling.hpp" +#include "pdx/common.hpp" +#include "pdx/utils.hpp" +#include "pdx/db_mock/predicate_evaluator.hpp" +#include "pdx/distance_computers/base_computers.hpp" +#include "pdx/quantizers/scalar.hpp" +#include "pdx/ivf_wrapper.hpp" +#include "pdx/pruners/adsampling.hpp" namespace PDX { diff --git a/include/utils/tictoc.hpp b/include/pdx/utils.hpp similarity index 62% rename from include/utils/tictoc.hpp rename to include/pdx/utils.hpp index a6f203a..d6a367c 100644 --- a/include/utils/tictoc.hpp +++ b/include/pdx/utils.hpp @@ -1,21 +1,19 @@ #pragma once - #include +#include #include #include -#include #include #include #include #include #include -#include -#include #include -#include -#include +#ifdef linux +#include +#endif class TicToc { public: @@ -36,8 +34,23 @@ class TicToc { accum_time += std::chrono::duration_cast( end - start).count(); } - + double GetMilliseconds() const { return static_cast(accum_time) / 1e6; } }; + +inline std::unique_ptr MmapFile(const std::string& filename) { + struct stat file_stats {}; + int fd = ::open(filename.c_str(), O_RDONLY); + if (fd == -1) throw std::runtime_error("Failed to open file"); + + fstat(fd, &file_stats); + size_t file_size = file_stats.st_size; + + std::unique_ptr data(new char[file_size]); + std::ifstream input(filename, std::ios::binary); + input.read(data.get(), file_size); + + return data; +} diff --git a/include/utils/file_reader.hpp b/include/utils/file_reader.hpp deleted file mode 100644 index a6320ee..0000000 --- a/include/utils/file_reader.hpp +++ /dev/null @@ -1,33 +0,0 @@ -#pragma once - -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -#ifdef linux -#include -#endif - - -inline std::unique_ptr MmapFile(const std::string& filename) { - struct stat file_stats {}; - int fd = ::open(filename.c_str(), O_RDONLY); - if (fd == -1) throw std::runtime_error("Failed to open file"); - - fstat(fd, &file_stats); - size_t file_size = file_stats.st_size; - - std::unique_ptr data(new char[file_size]); - std::ifstream input(filename, std::ios::binary); - input.read(data.get(), file_size); - - return data; -} - diff --git a/include/utils/utils.hpp b/include/utils/utils.hpp deleted file mode 100644 index 1f978e7..0000000 --- a/include/utils/utils.hpp +++ /dev/null @@ -1,159 +0,0 @@ -#pragma once - -#include -#include -#include -#include -#include -#include -#include "common.hpp" -#include "ivf_wrapper.hpp" -#include "quantizers/scalar.hpp" -#include "pruners/adsampling.hpp" - - -namespace PDX { - -// Generate a rotation matrix suitable for PDXearch's ADSampling pruning algorithm. -[[nodiscard]] inline std::unique_ptr GenerateRandomRotationMatrix(const size_t num_dimensions, const int32_t seed) { - auto rotation_matrix = std::make_unique(num_dimensions * num_dimensions); - - std::mt19937 gen(seed); - std::normal_distribution normal_dist; - - Eigen::MatrixXf random_matrix { - Eigen::MatrixXf::Zero(static_cast(num_dimensions), static_cast(num_dimensions))}; - for (size_t i = 0; i < num_dimensions; ++i) { - for (size_t j = 0; j < num_dimensions; ++j) { - random_matrix(static_cast(i), static_cast(j)) = normal_dist(gen); - } - } - - const Eigen::HouseholderQR qr {random_matrix}; - const Eigen::MatrixXf transformation_matrix {qr.householderQ()}; - - for (size_t i = 0; i < num_dimensions; ++i) { - for (size_t j = 0; j < num_dimensions; ++j) { - rotation_matrix[i * num_dimensions + j] = - transformation_matrix(static_cast(i), static_cast(j)); - } - } - - return rotation_matrix; -} - -// Store the embeddings into this cluster's preallocated buffers in the transposed PDX layout. -// -// See the README of the following for a description of the PDX layout: -// https://github.com/cwida/pdx -template -inline void StoreClusterEmbeddings(typename PDX::IndexPDXIVF::cluster_t &cluster, const PDX::IndexPDXIVF &index, - const T *embeddings, const size_t num_embeddings); - -template <> -inline void -StoreClusterEmbeddings(PDX::IndexPDXIVF::cluster_t &cluster, - const PDX::IndexPDXIVF &index, - const float *const embeddings, const size_t num_embeddings) { - // Store the cluster's data using the transposed PDX layout for float32 as described in: - // https://github.com/cwida/pdx?tab=readme-ov-file#the-data-layout - using matrix_t = PDX::eigen_matrix_t; - using h_matrix_t = Eigen::Matrix; - - const auto vertical_d = index.num_vertical_dimensions; - const auto horizontal_d = index.num_horizontal_dimensions; - - Eigen::Map in(embeddings, num_embeddings, index.num_dimensions); - - // Vertical block: transpose the first vertical_d columns into dimension-major order. - Eigen::Map out(cluster.data, vertical_d, num_embeddings); - out.noalias() = in.leftCols(vertical_d).transpose(); - - // Horizontal blocks: copy H_DIM_SIZE columns at a time, keeping each embedding's values contiguous. - float *horizontal_out = cluster.data + num_embeddings * vertical_d; - for (size_t j = 0; j < horizontal_d; j += PDX::H_DIM_SIZE) { - Eigen::Map out_h(horizontal_out, num_embeddings, PDX::H_DIM_SIZE); - out_h.noalias() = in.block(0, vertical_d + j, num_embeddings, PDX::H_DIM_SIZE); - horizontal_out += num_embeddings * PDX::H_DIM_SIZE; - } -} - -template <> -inline void -StoreClusterEmbeddings(PDX::IndexPDXIVF::cluster_t &cluster, - const PDX::IndexPDXIVF &index, - const uint8_t *const embeddings, const size_t num_embeddings) { - // Store the cluster's data using the transposed PDX layout for U8. - // The vertical block uses interleaving: for each group of U8_INTERLEAVE_SIZE consecutive dimensions, - // each vector's values are stored contiguously. This enables NEON vdotq_u32 / AVX2 processing. - using u8_matrix_t = Eigen::Matrix; - using u8_v_matrix_t = Eigen::Matrix; - using u8_h_matrix_t = Eigen::Matrix; - - const auto vertical_d = index.num_vertical_dimensions; - const auto horizontal_d = index.num_horizontal_dimensions; - - Eigen::Map in(embeddings, num_embeddings, index.num_dimensions); - - // Vertical dimensions (interleaved): copy U8_INTERLEAVE_SIZE columns at a time into row-major blocks. - size_t dim = 0; - for (; dim + PDX::U8_INTERLEAVE_SIZE <= vertical_d; dim += PDX::U8_INTERLEAVE_SIZE) { - Eigen::Map out_v(cluster.data + dim * num_embeddings, num_embeddings, PDX::U8_INTERLEAVE_SIZE); - out_v.noalias() = in.block(0, dim, num_embeddings, PDX::U8_INTERLEAVE_SIZE); - } - // Compact tail for remaining vertical dimensions (< U8_INTERLEAVE_SIZE). - if (dim < vertical_d) { - auto remaining = static_cast(vertical_d - dim); - Eigen::Map out_v(cluster.data + dim * num_embeddings, num_embeddings, remaining); - out_v.noalias() = in.block(0, dim, num_embeddings, remaining); - } - - // Horizontal blocks: copy H_DIM_SIZE columns at a time, keeping each embedding's values contiguous. - uint8_t *horizontal_out = cluster.data + num_embeddings * vertical_d; - for (size_t j = 0; j < horizontal_d; j += PDX::H_DIM_SIZE) { - Eigen::Map out_h(horizontal_out, num_embeddings, PDX::H_DIM_SIZE); - out_h.noalias() = in.block(0, vertical_d + j, num_embeddings, PDX::H_DIM_SIZE); - horizontal_out += num_embeddings * PDX::H_DIM_SIZE; - } -} - -class EmbeddingPreprocessor { -private: - // For rotation matrix multiplication. - PDX::ADSamplingPruner pruner; - // For normalization. - PDX::Quantizer quantizer; - const size_t num_dimensions; - -public: - explicit EmbeddingPreprocessor(const size_t num_dimensions, const float *const rotation_matrix) - : pruner(num_dimensions, rotation_matrix), quantizer(num_dimensions), num_dimensions(num_dimensions) { - } - - // Warning: modifies the input_embedding. - void PreprocessEmbedding(float *const input_embedding, float *const output_embedding, const bool normalize) const { - // In-place normalization. - if (normalize) { - quantizer.NormalizeQuery(input_embedding, input_embedding); - } - pruner.PreprocessQuery(input_embedding, output_embedding); - } - - // Warning: modifies the input_embeddings. - void PreprocessEmbeddings(float *const input_embeddings, float *const output_embeddings, - const size_t num_embeddings, const bool normalize) const { - // In-place normalization. - if (normalize) { - for (size_t i = 0; i < num_embeddings; i++) { - quantizer.NormalizeQuery(input_embeddings + i * num_dimensions, input_embeddings + i * num_dimensions); - } - } - pruner.PreprocessEmbeddings(input_embeddings, output_embeddings, num_embeddings); - } -}; - -[[nodiscard]] inline constexpr bool DistanceMetricRequiresNormalization(const PDX::DistanceMetric distance_metric) { - return distance_metric == PDX::DistanceMetric::COSINE || distance_metric == PDX::DistanceMetric::IP; -} - -} // namespace PDX \ No newline at end of file diff --git a/python/lib.cpp b/python/lib.cpp index a7a7d82..b661a9d 100644 --- a/python/lib.cpp +++ b/python/lib.cpp @@ -1,5 +1,5 @@ #include -#include "lib/lib.hpp" +#include "pdx/lib/lib.hpp" namespace py = pybind11; From 52580a157e7e737c2d0646675615aae21ecb1092 Mon Sep 17 00:00:00 2001 From: lkuffo Date: Tue, 24 Feb 2026 12:11:33 +0100 Subject: [PATCH 10/39] Serialize and deserialize --- benchmarks/BenchmarkEndToEnd | Bin 436608 -> 439344 bytes benchmarks/BenchmarkSerialization | Bin 0 -> 437608 bytes benchmarks/CMakeLists.txt | 22 ++- .../bench_adsampling/pdx_end_to_end.cpp | 14 +- benchmarks/bench_adsampling/pdx_ivf.cpp | 113 ++++++++++++ .../bench_adsampling/pdx_ivf2_adsampling.cpp | 11 +- .../pdx_ivf2_adsampling_u8.cpp | 11 +- .../bench_adsampling/pdx_ivf_adsampling.cpp | 11 +- .../pdx_ivf_adsampling_u8.cpp | 11 +- .../bench_adsampling/pdx_serialization.cpp | 123 +++++++++++++ benchmarks/benchmark_utils.hpp | 24 +++ .../DEFAULT/END_TO_END_PDX_ADSAMPLING.csv | 1 + .../results/DEFAULT/IVF_PDX_ADSAMPLING.csv | 2 + include/pdx/common.hpp | 2 + include/pdx/index.hpp | 164 ++++++++++++++---- include/pdx/ivf_wrapper.hpp | 99 +++++++++-- include/pdx/pruners/adsampling.hpp | 2 + include/pdx/searcher.hpp | 19 -- include/pdx/utils.hpp | 26 --- 19 files changed, 519 insertions(+), 136 deletions(-) create mode 100755 benchmarks/BenchmarkSerialization create mode 100644 benchmarks/bench_adsampling/pdx_ivf.cpp create mode 100644 benchmarks/bench_adsampling/pdx_serialization.cpp diff --git a/benchmarks/BenchmarkEndToEnd b/benchmarks/BenchmarkEndToEnd index 0f14b637cc8309831be84eafc0526f8bc541cac5..49a2ddced2a7816c6248e11cc61aa93a0ce16560 100755 GIT binary patch delta 95945 zcmd443tZI2`aeE1yWnyWkUOiw0)>K#iil!f7A!L@E8WUHaHa-jKTcKV z@V-%&X&Tt9V^i9wY%{f1PI60B@0nU|$9Sz2rG=Vn6_)U^OSIe-!CLOvCCVhxH!66w z2JWeYDXkT1K%s$eX}MjfkN7w6EhXHrfLACh46XTn%DaXRQ5ElND4T?aLTQ8AYK5P% 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z>xYg$-)ZE1)?*WgJ^8q2V)dFoZ{7O(@~2J?=<>mHr>C`}GBFzZ>88zV#g+KKAAn*?-L_&678op6%E7UT^BCKGp9rue|7%3l^sB zbTpqG*Y33z(%RqF_inKK+S`XL`{B_cO;^4Cxy9Y$nid;>Et #include #include @@ -89,11 +81,15 @@ void RunBenchmark(const RawDatasetInfo &info, const std::string &dataset, const // Runtime pass std::vector runtimes; runtimes.resize(NUM_MEASURE_RUNS * n_queries); + TicToc clock; for (size_t j = 0; j < NUM_MEASURE_RUNS; ++j) { for (size_t l = 0; l < n_queries; ++l) { + clock.Reset(); + clock.Tic(); pdx_index.Search(queries + l * d, KNN); + clock.Toc(); runtimes[j + l * NUM_MEASURE_RUNS] = { - pdx_index.GetSearcher().end_to_end_clock.accum_time + clock.accum_time }; } } diff --git a/benchmarks/bench_adsampling/pdx_ivf.cpp b/benchmarks/bench_adsampling/pdx_ivf.cpp new file mode 100644 index 0000000..9253ece --- /dev/null +++ b/benchmarks/bench_adsampling/pdx_ivf.cpp @@ -0,0 +1,113 @@ +#include +#include +#include "pdx/utils.hpp" +#include "pdx/index.hpp" +#include "benchmark_utils.hpp" + +int main(int argc, char *argv[]) { + if (argc < 2) { + std::cerr << "Usage: " << argv[0] << " [dataset] [nprobe]\n"; + std::cerr << "Index types: pdx_f32, pdx_u8, pdx_tree_f32, pdx_tree_u8\n"; + std::cerr << "Available datasets:"; + for (const auto &[name, _] : RAW_DATASET_PARAMS) { + std::cerr << " " << name; + } + std::cerr << "\n"; + return 1; + } + + std::string index_type = argv[1]; + std::string arg_dataset; + size_t arg_ivf_nprobe = 0; + if (argc > 2) { + arg_dataset = argv[2]; + } + if (argc > 3) { + arg_ivf_nprobe = atoi(argv[3]); + } + + std::cout << "==> PDX IVF ADSampling (" << index_type << ")\n"; + + std::string ALGORITHM = "adsampling"; + const bool VERIFY_RESULTS = BenchmarkUtils::VERIFY_RESULTS; + + uint8_t KNN = BenchmarkUtils::KNN; + size_t NUM_QUERIES; + size_t NUM_MEASURE_RUNS = BenchmarkUtils::NUM_MEASURE_RUNS; + + // Build results file name from index type (e.g., "pdx_f32" -> "PDX_F32_ADSAMPLING.csv") + std::string index_type_upper = index_type; + for (auto &c : index_type_upper) c = toupper(c); + std::string RESULTS_PATH = BENCHMARK_UTILS.RESULTS_DIR_PATH + index_type_upper + "_ADSAMPLING.csv"; + + for (const auto &[dataset, info] : RAW_DATASET_PARAMS) { + if (arg_dataset.size() > 0 && arg_dataset != dataset) { + continue; + } + + std::string index_path = BenchmarkUtils::PDX_DATA + dataset + "-" + index_type; + std::cout << "Loading " << index_path << "...\n"; + auto pdx_index = PDX::LoadPDXIndex(index_path); + + std::unique_ptr query_ptr = MmapFile(BenchmarkUtils::QUERIES_DATA + info.pdx_dataset_name); + auto *query = reinterpret_cast(query_ptr.get()); + + NUM_QUERIES = info.num_queries; + std::unique_ptr ground_truth = MmapFile( + BenchmarkUtils::GROUND_TRUTH_DATA + info.pdx_dataset_name + "_100_norm"); + auto *int_ground_truth = reinterpret_cast(ground_truth.get()); + query += 1; // skip number of embeddings + + std::vector nprobes_to_use; + if (arg_ivf_nprobe > 0) { + nprobes_to_use = {arg_ivf_nprobe}; + } else { + nprobes_to_use.assign(std::begin(BenchmarkUtils::IVF_PROBES), std::end(BenchmarkUtils::IVF_PROBES)); + } + + for (size_t ivf_nprobe : nprobes_to_use) { + if (pdx_index->GetNumClusters() < ivf_nprobe) { + continue; + } + if (arg_ivf_nprobe > 0 && ivf_nprobe != arg_ivf_nprobe) { + continue; + } + std::vector runtimes; + runtimes.resize(NUM_MEASURE_RUNS * NUM_QUERIES); + pdx_index->SetNProbe(ivf_nprobe); + + float recalls = 0; + if (VERIFY_RESULTS) { + for (size_t l = 0; l < NUM_QUERIES; ++l) { + auto result = pdx_index->Search(query + l * pdx_index->GetNumDimensions(), KNN); + BenchmarkUtils::VerifyResult(recalls, result, KNN, int_ground_truth, l); + } + } + TicToc clock; + for (size_t j = 0; j < NUM_MEASURE_RUNS; ++j) { + for (size_t l = 0; l < NUM_QUERIES; ++l) { + clock.Reset(); + clock.Tic(); + pdx_index->Search(query + l * pdx_index->GetNumDimensions(), KNN); + clock.Toc(); + runtimes[j + l * NUM_MEASURE_RUNS] = { + clock.accum_time + }; + } + } + float real_selectivity = 1 - BenchmarkUtils::SELECTIVITY_THRESHOLD; + BenchmarkMetadata results_metadata = { + dataset, + ALGORITHM, + NUM_MEASURE_RUNS, + NUM_QUERIES, + ivf_nprobe, + KNN, + recalls, + real_selectivity + }; + BenchmarkUtils::SaveResults(runtimes, RESULTS_PATH, results_metadata); + } + } + return 0; +} diff --git a/benchmarks/bench_adsampling/pdx_ivf2_adsampling.cpp b/benchmarks/bench_adsampling/pdx_ivf2_adsampling.cpp index 1a20d8c..a369350 100644 --- a/benchmarks/bench_adsampling/pdx_ivf2_adsampling.cpp +++ b/benchmarks/bench_adsampling/pdx_ivf2_adsampling.cpp @@ -34,19 +34,18 @@ int main(int argc, char *argv[]) { RESULTS_PATH = BENCHMARK_UTILS.RESULTS_DIR_PATH + "IVF2_PDX_ADSAMPLING.csv"; - for (const auto & dataset : BenchmarkUtils::DATASETS) { + for (const auto & [dataset, info] : RAW_DATASET_PARAMS) { if (arg_dataset.size() > 0 && arg_dataset != dataset){ continue; } PDX::PDXTreeIndexF32 pdx_index; - pdx_index.Restore(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf2", - BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf2-matrix"); + pdx_index.Restore(BenchmarkUtils::PDX_DATA + dataset + "-pdx_tree_f32"); - std::unique_ptr query_ptr = MmapFile(BenchmarkUtils::QUERIES_DATA + dataset); + std::unique_ptr query_ptr = MmapFile(BenchmarkUtils::QUERIES_DATA + info.pdx_dataset_name); auto *query = reinterpret_cast(query_ptr.get()); - NUM_QUERIES = 1000; - std::unique_ptr ground_truth = MmapFile(BenchmarkUtils::GROUND_TRUTH_DATA + dataset + "_100_norm"); + NUM_QUERIES = info.num_queries; + std::unique_ptr ground_truth = MmapFile(BenchmarkUtils::GROUND_TRUTH_DATA + info.pdx_dataset_name + "_100_norm"); auto *int_ground_truth = reinterpret_cast(ground_truth.get()); query += 1; // skip number of embeddings diff --git a/benchmarks/bench_adsampling/pdx_ivf2_adsampling_u8.cpp b/benchmarks/bench_adsampling/pdx_ivf2_adsampling_u8.cpp index 8aaf741..3399652 100644 --- a/benchmarks/bench_adsampling/pdx_ivf2_adsampling_u8.cpp +++ b/benchmarks/bench_adsampling/pdx_ivf2_adsampling_u8.cpp @@ -34,19 +34,18 @@ int main(int argc, char *argv[]) { RESULTS_PATH = BENCHMARK_UTILS.RESULTS_DIR_PATH + "U8_IVF2_PDX_ADSAMPLING.csv"; - for (const auto & dataset : BenchmarkUtils::DATASETS) { + for (const auto & [dataset, info] : RAW_DATASET_PARAMS) { if (arg_dataset.size() > 0 && arg_dataset != dataset){ continue; } PDX::PDXTreeIndexU8 pdx_index; - pdx_index.Restore(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf2-u8", - BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf2-u8-matrix"); + pdx_index.Restore(BenchmarkUtils::PDX_DATA + dataset + "-pdx_tree_u8"); - std::unique_ptr query_ptr = MmapFile(BenchmarkUtils::QUERIES_DATA + dataset); + std::unique_ptr query_ptr = MmapFile(BenchmarkUtils::QUERIES_DATA + info.pdx_dataset_name); auto *query = reinterpret_cast(query_ptr.get()); - NUM_QUERIES = 1000; + NUM_QUERIES = info.num_queries; - std::unique_ptr ground_truth = MmapFile(BenchmarkUtils::GROUND_TRUTH_DATA + dataset + "_100_norm"); + std::unique_ptr ground_truth = MmapFile(BenchmarkUtils::GROUND_TRUTH_DATA + info.pdx_dataset_name + "_100_norm"); auto *int_ground_truth = reinterpret_cast(ground_truth.get()); query += 1; // skip number of embeddings diff --git a/benchmarks/bench_adsampling/pdx_ivf_adsampling.cpp b/benchmarks/bench_adsampling/pdx_ivf_adsampling.cpp index e7dc9d5..f1bd673 100644 --- a/benchmarks/bench_adsampling/pdx_ivf_adsampling.cpp +++ b/benchmarks/bench_adsampling/pdx_ivf_adsampling.cpp @@ -35,19 +35,18 @@ int main(int argc, char *argv[]) { RESULTS_PATH = BENCHMARK_UTILS.RESULTS_DIR_PATH + "IVF_PDX_ADSAMPLING.csv"; - for (const auto & dataset : BenchmarkUtils::DATASETS) { + for (const auto & [dataset, info] : RAW_DATASET_PARAMS) { if (arg_dataset.size() > 0 && arg_dataset != dataset){ continue; } PDX::PDXIndexF32 pdx_index; - pdx_index.Restore(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf", - BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-matrix"); + pdx_index.Restore(BenchmarkUtils::PDX_DATA + dataset + "-pdx_f32"); - std::unique_ptr query_ptr = MmapFile(BenchmarkUtils::QUERIES_DATA + dataset); + std::unique_ptr query_ptr = MmapFile(BenchmarkUtils::QUERIES_DATA + info.pdx_dataset_name); auto *query = reinterpret_cast(query_ptr.get()); - NUM_QUERIES = 1000; - std::unique_ptr ground_truth = MmapFile(BenchmarkUtils::GROUND_TRUTH_DATA + dataset + "_100_norm"); + NUM_QUERIES = info.num_queries; + std::unique_ptr ground_truth = MmapFile(BenchmarkUtils::GROUND_TRUTH_DATA + info.pdx_dataset_name + "_100_norm"); auto *int_ground_truth = reinterpret_cast(ground_truth.get()); query += 1; // skip number of embeddings diff --git a/benchmarks/bench_adsampling/pdx_ivf_adsampling_u8.cpp b/benchmarks/bench_adsampling/pdx_ivf_adsampling_u8.cpp index dcdca0d..2d5366c 100644 --- a/benchmarks/bench_adsampling/pdx_ivf_adsampling_u8.cpp +++ b/benchmarks/bench_adsampling/pdx_ivf_adsampling_u8.cpp @@ -34,19 +34,18 @@ int main(int argc, char *argv[]) { RESULTS_PATH = BENCHMARK_UTILS.RESULTS_DIR_PATH + "U8_IVF_PDX_ADSAMPLING.csv"; - for (const auto & dataset : BenchmarkUtils::DATASETS) { + for (const auto & [dataset, info] : RAW_DATASET_PARAMS) { if (arg_dataset.size() > 0 && arg_dataset != dataset){ continue; } PDX::PDXIndexU8 pdx_index; - pdx_index.Restore(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf-u8", - BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf-u8-matrix"); + pdx_index.Restore(BenchmarkUtils::PDX_DATA + dataset + "-pdx_u8"); - std::unique_ptr query_ptr = MmapFile(BenchmarkUtils::QUERIES_DATA + dataset); + std::unique_ptr query_ptr = MmapFile(BenchmarkUtils::QUERIES_DATA + info.pdx_dataset_name); auto *query = reinterpret_cast(query_ptr.get()); - NUM_QUERIES = 1000; - std::unique_ptr ground_truth = MmapFile(BenchmarkUtils::GROUND_TRUTH_DATA + dataset + "_100_norm"); + NUM_QUERIES = info.num_queries; + std::unique_ptr ground_truth = MmapFile(BenchmarkUtils::GROUND_TRUTH_DATA + info.pdx_dataset_name + "_100_norm"); auto *int_ground_truth = reinterpret_cast(ground_truth.get()); query += 1; // skip number of embeddings diff --git a/benchmarks/bench_adsampling/pdx_serialization.cpp b/benchmarks/bench_adsampling/pdx_serialization.cpp new file mode 100644 index 0000000..8487998 --- /dev/null +++ b/benchmarks/bench_adsampling/pdx_serialization.cpp @@ -0,0 +1,123 @@ +#include +#include +#include +#include +#include +#include + +#include "pdx/index.hpp" +#include "benchmark_utils.hpp" + +template +void BuildAndSave(const RawDatasetInfo &info, const std::string &dataset, const std::string &index_type, + const float *data) { + const size_t d = info.num_dimensions; + const size_t n = info.num_embeddings; + + PDX::PDXIndexConfig index_config { + .num_dimensions = static_cast(d), + .distance_metric = info.distance_metric, + .seed = 42, + .normalize = true, + .sampling_fraction = 1.0f + }; + + std::cout << "Building " << index_type << " index...\n"; + auto build_start = std::chrono::high_resolution_clock::now(); + IndexT pdx_index(index_config); + pdx_index.BuildIndex(data, n); + auto build_end = std::chrono::high_resolution_clock::now(); + double build_ms = std::chrono::duration(build_end - build_start).count(); + std::cout << "Build time: " << build_ms << " ms\n"; + std::cout << "Clusters: " << pdx_index.GetNumClusters() << "\n"; + + std::string save_path = BenchmarkUtils::PDX_DATA + dataset + "-" + index_type; + std::cout << "Saving to " << save_path << "...\n"; + pdx_index.Save(save_path); + std::cout << "Done.\n"; +} + +int main(int argc, char *argv[]) { + if (argc < 3) { + std::cerr << "Usage: " << argv[0] << " \n"; + std::cerr << "Index types: pdx_f32, pdx_u8, pdx_tree_f32, pdx_tree_u8\n"; + std::cerr << "Available datasets:"; + for (const auto &[name, _] : RAW_DATASET_PARAMS) { + std::cerr << " " << name; + } + std::cerr << "\n"; + return 1; + } + std::string dataset = argv[1]; + std::string index_type = argv[2]; + + auto it = RAW_DATASET_PARAMS.find(dataset); + if (it == RAW_DATASET_PARAMS.end()) { + std::cerr << "Unknown dataset: " << dataset << "\n"; + return 1; + } + const auto &info = it->second; + const size_t n = info.num_embeddings; + const size_t d = info.num_dimensions; + + std::cout << "==> PDX Serialization\n"; + std::cout << "Dataset: " << dataset << " (" << info.pdx_dataset_name << ", n=" << n << ", d=" << d << ")\n"; + std::cout << "Index type: " << index_type << "\n"; + + // Read raw data + std::string data_path = RAW_DATA_DIR + "/data_" + dataset + ".bin"; + std::vector data(n * d); + { + std::ifstream file(data_path, std::ios::binary); + if (!file) { std::cerr << "Failed to open " << data_path << "\n"; return 1; } + file.read(reinterpret_cast(data.data()), n * d * sizeof(float)); + } + + // Ensure output directory exists + std::filesystem::create_directories(BenchmarkUtils::PDX_DATA); + + std::string save_path = BenchmarkUtils::PDX_DATA + dataset + "-" + index_type; + + if (index_type == "pdx_f32") { + BuildAndSave(info, dataset, index_type, data.data()); + } else if (index_type == "pdx_u8") { + BuildAndSave(info, dataset, index_type, data.data()); + } else if (index_type == "pdx_tree_f32") { + BuildAndSave(info, dataset, index_type, data.data()); + } else if (index_type == "pdx_tree_u8") { + BuildAndSave(info, dataset, index_type, data.data()); + } else { + std::cerr << "Unknown index type: " << index_type << "\n"; + std::cerr << "Valid types: pdx_f32, pdx_u8, pdx_tree_f32, pdx_tree_u8\n"; + return 1; + } + + // Verify: load back without knowing the type and run queries + std::cout << "\n==> Verification: Loading index from " << save_path << "...\n"; + auto loaded_index = PDX::LoadPDXIndex(save_path); + + // Load queries + std::unique_ptr query_ptr = MmapFile(BenchmarkUtils::QUERIES_DATA + info.pdx_dataset_name); + auto *queries = reinterpret_cast(query_ptr.get()); + queries += 1; // skip header + + // Load ground truth + std::unique_ptr gt_buffer = MmapFile( + BenchmarkUtils::GROUND_TRUTH_DATA + info.pdx_dataset_name + "_100_norm"); + auto *int_ground_truth = reinterpret_cast(gt_buffer.get()); + + const size_t n_queries = info.num_queries; + const uint8_t KNN = BenchmarkUtils::KNN; + + loaded_index->SetNProbe(25); + float recalls = 0; + for (size_t l = 0; l < n_queries; ++l) { + auto result = loaded_index->Search(queries + l * d, KNN); + BenchmarkUtils::VerifyResult(recalls, result, KNN, int_ground_truth, l); + } + float avg_recall = recalls / n_queries; + std::cout << "Recall@" << +KNN << " (nprobe=25): " << avg_recall << "\n"; + + std::cout << "Verification complete.\n"; + return 0; +} diff --git a/benchmarks/benchmark_utils.hpp b/benchmarks/benchmark_utils.hpp index 8be0723..242c147 100644 --- a/benchmarks/benchmark_utils.hpp +++ b/benchmarks/benchmark_utils.hpp @@ -20,6 +20,30 @@ #include "pdx/common.hpp" #include "pdx/utils.hpp" +class TicToc { +public: + size_t accum_time = 0; + std::chrono::high_resolution_clock::time_point start = std::chrono::high_resolution_clock::now(); + + void Reset() { + accum_time = 0; + start = std::chrono::high_resolution_clock::now(); + } + + inline void Tic() { + start = std::chrono::high_resolution_clock::now(); + } + + inline void Toc() { + auto end = std::chrono::high_resolution_clock::now(); + accum_time += std::chrono::duration_cast( + end - start).count(); + } + + double GetMilliseconds() const { + return static_cast(accum_time) / 1e6; + } +}; // Raw binary data paths (SuperKMeans convention: data_.bin / data__test.bin) inline std::string RAW_DATA_DIR = std::string{CMAKE_SOURCE_DIR} + "/../../SuperKMeans/benchmarks/data"; diff --git a/benchmarks/results/DEFAULT/END_TO_END_PDX_ADSAMPLING.csv b/benchmarks/results/DEFAULT/END_TO_END_PDX_ADSAMPLING.csv index 566443a..6b6a873 100644 --- a/benchmarks/results/DEFAULT/END_TO_END_PDX_ADSAMPLING.csv +++ b/benchmarks/results/DEFAULT/END_TO_END_PDX_ADSAMPLING.csv @@ -35,3 +35,4 @@ mxbai,end_to_end_pdx_u8,0.320404,0.392583,0.248917,0.901748,25,0,100,1000,0,1,0. mxbai,end_to_end_pdx_tree_f32,0.270043,0.496916,0.057458,0.902799,25,0,100,1000,0,1,0.2703,0.5101,0.05746 mxbai,end_to_end_pdx_tree_u8,0.106266,0.175334,0.046542,0.901368,25,0,100,1000,0,1,0.1065,0.1891,0.03388 mxbai,end_to_end_pdx_tree_f32,0.263403,0.494292,0.054875,0.902799,25,0,100,1000,0,1,0.2637,0.5128,0.05488 +mxbai,end_to_end_pdx_f32,0.484354,0.693959,0.290042,0.903179,25,0,100,1000,0,1,0.4848,0.7457,0.29 diff --git a/benchmarks/results/DEFAULT/IVF_PDX_ADSAMPLING.csv b/benchmarks/results/DEFAULT/IVF_PDX_ADSAMPLING.csv index 64c8546..9f36a8c 100644 --- a/benchmarks/results/DEFAULT/IVF_PDX_ADSAMPLING.csv +++ b/benchmarks/results/DEFAULT/IVF_PDX_ADSAMPLING.csv @@ -40,3 +40,5 @@ agnews-mxbai-1024-euclidean,adsampling,0.465191,0.681792,0.254916,0.90166,24,0,1 agnews-mxbai-1024-euclidean,adsampling,0.911099,1.58692,0.377125,0.975328,128,0,100,1000,0.2,1,0.9179,2.734,0.3771 agnews-mxbai-1024-euclidean,adsampling,0.470744,0.672417,0.277833,0.90166,24,0,100,1000,0.2,1,0.4713,0.7826,0.2778 agnews-mxbai-1024-euclidean,adsampling,0.474207,0.691959,0.276166,0.90166,24,0,100,1000,0.2,1,0.4755,0.793,0.2762 +mxbai,adsampling,0.466896,0.6805,0.264625,0.903179,25,0,100,1000,0.2,1,0.4674,0.7081,0.2646 +mxbai,adsampling,0.466858,0.6985,0.2595,0.903179,25,0,100,1000,0.2,1,0.4679,0.8007,0.2595 diff --git a/include/pdx/common.hpp b/include/pdx/common.hpp index 1a36d29..81eb414 100644 --- a/include/pdx/common.hpp +++ b/include/pdx/common.hpp @@ -62,6 +62,8 @@ enum class DistanceMetric { L2SQ, COSINE, IP }; enum Quantization { F32, U8, F16, BF }; +enum class PDXIndexType : uint8_t { PDX_F32 = 0, PDX_U8 = 1, PDX_TREE_F32 = 2, PDX_TREE_U8 = 3 }; + // TODO: Do the same for indexes? template struct DistanceType { diff --git a/include/pdx/index.hpp b/include/pdx/index.hpp index dd523d2..c011e53 100644 --- a/include/pdx/index.hpp +++ b/include/pdx/index.hpp @@ -3,8 +3,10 @@ #include #include #include +#include #include #include +#include #include #include "pdx/common.hpp" @@ -23,25 +25,39 @@ struct PDXIndexConfig { DistanceMetric distance_metric = DistanceMetric::L2SQ; uint32_t seed = 42; uint32_t num_clusters = 0; // 0 = auto-compute from num_embeddings - uint32_t num_meso_clusters = 0; + uint32_t num_meso_clusters = 0; bool normalize = false; float sampling_fraction = 0.0f; // 0 = auto (1.0 if small dataset, 0.3 otherwise) uint32_t kmeans_iters = 10; }; +class IPDXIndex { +public: + virtual ~IPDXIndex() = default; + virtual std::vector Search(const float *query_embedding, size_t knn) const = 0; + virtual void SetNProbe(uint32_t n_probe) const = 0; + virtual void Save(const std::string &path) const = 0; + virtual void Restore(const std::string &path) = 0; + virtual uint32_t GetNumDimensions() const = 0; + virtual uint32_t GetNumClusters() const = 0; +}; + template -class PDXIndex { +class PDXIndex : public IPDXIndex { public: using embedding_storage_t = PDX::pdx_data_t; private: - std::unique_ptr matrix_buffer; - PDXIndexConfig config {}; PDX::IndexPDXIVF index; std::unique_ptr pruner; std::unique_ptr> searcher; + static constexpr PDXIndexType GetIndexType() { + if constexpr (Q == F32) return PDXIndexType::PDX_F32; + else return PDXIndexType::PDX_U8; + } + public: PDXIndex() = default; @@ -49,21 +65,54 @@ class PDXIndex { pruner = std::make_unique(config.num_dimensions, config.seed); } - void Restore(const std::string &index_path, const std::string &matrix_path) { - index.Restore(index_path); + void Save(const std::string &path) const override { + std::ofstream out(path, std::ios::binary); + + // Index type flag + uint8_t type_flag = static_cast(GetIndexType()); + out.write(reinterpret_cast(&type_flag), sizeof(uint8_t)); + + // Rotation matrix + const auto &matrix = pruner->GetMatrix(); + uint32_t matrix_rows = static_cast(matrix.rows()); + uint32_t matrix_cols = static_cast(matrix.cols()); + out.write(reinterpret_cast(&matrix_rows), sizeof(uint32_t)); + out.write(reinterpret_cast(&matrix_cols), sizeof(uint32_t)); + out.write(reinterpret_cast(matrix.data()), + sizeof(float) * matrix_rows * matrix_cols); + + // IVF data + index.Save(out); + } + + void Restore(const std::string &path) override { + auto buffer = MmapFile(path); + char *ptr = buffer.get(); + + // Index type flag + ptr += sizeof(uint8_t); + + // Rotation matrix + uint32_t matrix_rows = *reinterpret_cast(ptr); + ptr += sizeof(uint32_t); + uint32_t matrix_cols = *reinterpret_cast(ptr); + ptr += sizeof(uint32_t); + auto *matrix_data = reinterpret_cast(ptr); + ptr += sizeof(float) * matrix_rows * matrix_cols; - matrix_buffer = MmapFile(matrix_path); - auto *matrix = reinterpret_cast(matrix_buffer.get()); + // Load IVF data + index.Load(ptr); - pruner = std::make_unique(index.num_dimensions, matrix); + // Create pruner and searcher + pruner = std::make_unique(index.num_dimensions, matrix_data); searcher = std::make_unique>(index, *pruner); } - std::vector Search(const float *query_embedding, size_t knn) const { + std::vector Search(const float *query_embedding, size_t knn) const override { return searcher->Search(query_embedding, knn); } - void SetNProbe(uint32_t n_probe) const { + void SetNProbe(uint32_t n_probe) const override { searcher->SetNProbe(n_probe); } @@ -71,11 +120,11 @@ class PDXIndex { return *searcher; } - uint32_t GetNumDimensions() const { + uint32_t GetNumDimensions() const override { return index.num_dimensions; } - uint32_t GetNumClusters() const { + uint32_t GetNumClusters() const override { return index.num_clusters; } @@ -168,19 +217,22 @@ class PDXIndex { }; template -class PDXTreeIndex { +class PDXTreeIndex : public IPDXIndex { public: using embedding_storage_t = PDX::pdx_data_t; private: - std::unique_ptr matrix_buffer; - PDXIndexConfig config {}; PDX::IndexPDXIVF2 index; std::unique_ptr pruner; std::unique_ptr> searcher; std::unique_ptr> top_level_searcher; + static constexpr PDXIndexType GetIndexType() { + if constexpr (Q == F32) return PDXIndexType::PDX_TREE_F32; + else return PDXIndexType::PDX_TREE_U8; + } + public: PDXTreeIndex() = default; @@ -188,24 +240,52 @@ class PDXTreeIndex { pruner = std::make_unique(config.num_dimensions, config.seed); } - void Restore(const std::string &index_path, const std::string &matrix_path) { - index.Restore(index_path); + void Save(const std::string &path) const override { + std::ofstream out(path, std::ios::binary); + + // Index type flag + uint8_t type_flag = static_cast(GetIndexType()); + out.write(reinterpret_cast(&type_flag), sizeof(uint8_t)); + + // Rotation matrix + const auto &matrix = pruner->GetMatrix(); + uint32_t matrix_rows = static_cast(matrix.rows()); + uint32_t matrix_cols = static_cast(matrix.cols()); + out.write(reinterpret_cast(&matrix_rows), sizeof(uint32_t)); + out.write(reinterpret_cast(&matrix_cols), sizeof(uint32_t)); + out.write(reinterpret_cast(matrix.data()), + sizeof(float) * matrix_rows * matrix_cols); + + // IVF2 data + index.Save(out); + } + + void Restore(const std::string &path) override { + auto buffer = MmapFile(path); + char *ptr = buffer.get(); + + // Index type flag + ptr += sizeof(uint8_t); - matrix_buffer = MmapFile(matrix_path); - auto *matrix = reinterpret_cast(matrix_buffer.get()); + // Rotation matrix + uint32_t matrix_rows = *reinterpret_cast(ptr); + ptr += sizeof(uint32_t); + uint32_t matrix_cols = *reinterpret_cast(ptr); + ptr += sizeof(uint32_t); + auto *matrix_data = reinterpret_cast(ptr); + ptr += sizeof(float) * matrix_rows * matrix_cols; - pruner = std::make_unique(index.num_dimensions, matrix); + // Load IVF2 data + index.Load(ptr); + + // Create pruner and searchers + pruner = std::make_unique(index.num_dimensions, matrix_data); searcher = std::make_unique>(index, *pruner); top_level_searcher = std::make_unique>(index.l0, *pruner); } - std::vector Search(const float *query_embedding, size_t knn) const { + std::vector Search(const float *query_embedding, size_t knn) const override { auto n_probe_top_level = GetTopLevelNumClusters(); - // if (searcher->GetNProbe() < GetNumClusters() / 2){ - // // We confidently prune half of the meso-clusters only if the user wants to - // // visit less than half of the available clusters - // n_probe_top_level /= 2; - // } top_level_searcher->SetNProbe(n_probe_top_level); auto top_level_results = top_level_searcher->Search(query_embedding, searcher->GetNProbe()); @@ -340,7 +420,7 @@ class PDXTreeIndex { top_level_searcher = std::make_unique>(index.l0, *pruner); } - void SetNProbe(uint32_t n_probe) const { + void SetNProbe(uint32_t n_probe) const override { searcher->SetNProbe(n_probe); } @@ -348,11 +428,11 @@ class PDXTreeIndex { return *searcher; } - uint32_t GetNumDimensions() const { + uint32_t GetNumDimensions() const override { return index.num_dimensions; } - uint32_t GetNumClusters() const { + uint32_t GetNumClusters() const override { return index.num_clusters; } @@ -367,6 +447,30 @@ using PDXIndexU8 = PDXIndex; using PDXTreeIndexF32 = PDXTreeIndex; using PDXTreeIndexU8 = PDXTreeIndex; +inline std::unique_ptr LoadPDXIndex(const std::string &path) { + auto buffer = MmapFile(path); + auto type = static_cast(buffer.get()[0]); + std::unique_ptr idx; + switch (type) { + case PDXIndexType::PDX_F32: + idx = std::make_unique(); + break; + case PDXIndexType::PDX_U8: + idx = std::make_unique(); + break; + case PDXIndexType::PDX_TREE_F32: + idx = std::make_unique(); + break; + case PDXIndexType::PDX_TREE_U8: + idx = std::make_unique(); + break; + default: + throw std::runtime_error("Unknown PDX index type: " + std::to_string(static_cast(type))); + } + idx->Restore(path); + return idx; +} + class EmbeddingPreprocessor { private: PDX::ADSamplingPruner pruner; diff --git a/include/pdx/ivf_wrapper.hpp b/include/pdx/ivf_wrapper.hpp index c4eca4e..1ecb751 100644 --- a/include/pdx/ivf_wrapper.hpp +++ b/include/pdx/ivf_wrapper.hpp @@ -3,6 +3,7 @@ #include #include #include +#include #include #include #include "pdx/common.hpp" @@ -16,8 +17,6 @@ class IndexPDXIVF { using cluster_t = Cluster; using data_t = pdx_data_t; - std::unique_ptr file_buffer; - uint32_t num_dimensions {}; uint64_t total_num_embeddings {}; uint32_t num_clusters {}; @@ -58,11 +57,6 @@ class IndexPDXIVF { clusters.reserve(num_clusters); } - void Restore(const std::string &filename) { - file_buffer = MmapFile(filename); - Load(file_buffer.get()); - } - void Load(char *input) { char *next_value = input; num_dimensions = ((uint32_t *) input)[0]; @@ -84,13 +78,9 @@ class IndexPDXIVF { memcpy(clusters[i].indices, next_value, sizeof(uint32_t) * clusters[i].num_embeddings); next_value += sizeof(uint32_t) * clusters[i].num_embeddings; } - if constexpr (Q == F32) { - next_value += sizeof(float) * num_dimensions; // Skipping means (not used) - } is_normalized = ((char *) next_value)[0]; next_value += sizeof(char); - next_value += sizeof(char); // Skipping is_ivf field (not used) centroids.resize(num_clusters * num_dimensions); memcpy(centroids.data(), (float *) next_value, sizeof(float) * num_clusters * num_dimensions); @@ -105,6 +95,36 @@ class IndexPDXIVF { inverse_quantization_scale_squared = 1.0f / quantization_scale_squared; } } + + void Save(std::ostream &out) const { + out.write(reinterpret_cast(&num_dimensions), sizeof(uint32_t)); + out.write(reinterpret_cast(&num_vertical_dimensions), sizeof(uint32_t)); + out.write(reinterpret_cast(&num_horizontal_dimensions), sizeof(uint32_t)); + out.write(reinterpret_cast(&num_clusters), sizeof(uint32_t)); + + for (size_t i = 0; i < num_clusters; ++i) { + out.write(reinterpret_cast(&clusters[i].num_embeddings), sizeof(uint32_t)); + } + for (size_t i = 0; i < num_clusters; ++i) { + out.write(reinterpret_cast(clusters[i].data), + sizeof(data_t) * clusters[i].num_embeddings * num_dimensions); + } + for (size_t i = 0; i < num_clusters; ++i) { + out.write(reinterpret_cast(clusters[i].indices), + sizeof(uint32_t) * clusters[i].num_embeddings); + } + + char norm = is_normalized; + out.write(&norm, sizeof(char)); + + out.write(reinterpret_cast(centroids.data()), + sizeof(float) * num_clusters * num_dimensions); + + if constexpr (Q == U8) { + out.write(reinterpret_cast(&quantization_base), sizeof(float)); + out.write(reinterpret_cast(&quantization_scale), sizeof(float)); + } + } }; template @@ -127,11 +147,6 @@ class IndexPDXIVF2 : public IndexPDXIVF { : IndexPDXIVF(num_dimensions, total_num_embeddings, num_clusters, is_normalized, quantization_scale, quantization_base) {} - void Restore(const std::string &filename) { - this->file_buffer = MmapFile(filename); - Load(this->file_buffer.get()); - } - void Load(char *input) { char *next_value = input; @@ -214,6 +229,58 @@ class IndexPDXIVF2 : public IndexPDXIVF { 1.0f / this->quantization_scale_squared; } } + + void Save(std::ostream &out) const { + // Header: dimensions (shared between L0 and L1) + out.write(reinterpret_cast(&this->num_dimensions), sizeof(uint32_t)); + out.write(reinterpret_cast(&this->num_vertical_dimensions), sizeof(uint32_t)); + out.write(reinterpret_cast(&this->num_horizontal_dimensions), sizeof(uint32_t)); + + // Number of clusters: L1 then L0 + out.write(reinterpret_cast(&this->num_clusters), sizeof(uint32_t)); + uint32_t n_clusters_l0 = l0.num_clusters; + out.write(reinterpret_cast(&n_clusters_l0), sizeof(uint32_t)); + + // === L0 (meso-clusters, always F32) === + for (size_t i = 0; i < n_clusters_l0; ++i) { + out.write(reinterpret_cast(&l0.clusters[i].num_embeddings), sizeof(uint32_t)); + } + for (size_t i = 0; i < n_clusters_l0; ++i) { + out.write(reinterpret_cast(l0.clusters[i].data), + sizeof(float) * l0.clusters[i].num_embeddings * this->num_dimensions); + } + for (size_t i = 0; i < n_clusters_l0; ++i) { + out.write(reinterpret_cast(l0.clusters[i].indices), + sizeof(uint32_t) * l0.clusters[i].num_embeddings); + } + + // === L1 (data clusters) === + for (size_t i = 0; i < this->num_clusters; ++i) { + out.write(reinterpret_cast(&this->clusters[i].num_embeddings), sizeof(uint32_t)); + } + for (size_t i = 0; i < this->num_clusters; ++i) { + out.write(reinterpret_cast(this->clusters[i].data), + sizeof(data_t) * this->clusters[i].num_embeddings * this->num_dimensions); + } + for (size_t i = 0; i < this->num_clusters; ++i) { + out.write(reinterpret_cast(this->clusters[i].indices), + sizeof(uint32_t) * this->clusters[i].num_embeddings); + } + + // === Shared metadata === + char norm = this->is_normalized; + out.write(&norm, sizeof(char)); + + // L0 centroids + out.write(reinterpret_cast(l0.centroids.data()), + sizeof(float) * n_clusters_l0 * this->num_dimensions); + + // === U8 quantization params === + if constexpr (Q == U8) { + out.write(reinterpret_cast(&this->quantization_base), sizeof(float)); + out.write(reinterpret_cast(&this->quantization_scale), sizeof(float)); + } + } }; } // namespace PDX diff --git a/include/pdx/pruners/adsampling.hpp b/include/pdx/pruners/adsampling.hpp index 7132510..0f31c98 100644 --- a/include/pdx/pruners/adsampling.hpp +++ b/include/pdx/pruners/adsampling.hpp @@ -62,6 +62,8 @@ class ADSamplingPruner { ADSamplingPruner::matrix = matrix; } + const matrix_t& GetMatrix() const { return matrix; } + float GetPruningThreshold(uint32_t, std::priority_queue, VectorComparator> &heap, const uint32_t current_dimension_idx) const { diff --git a/include/pdx/searcher.hpp b/include/pdx/searcher.hpp index 3d2fb3e..64a39a4 100644 --- a/include/pdx/searcher.hpp +++ b/include/pdx/searcher.hpp @@ -54,11 +54,6 @@ class PDXearch { std::copy(cluster_indexes.begin(), cluster_indexes.end(), cluster_indices_in_access_order.get()); } - TicToc end_to_end_clock = TicToc(); - void ResetClocks(){ - end_to_end_clock.Reset(); - } - std::unique_ptr cluster_offsets; protected: @@ -407,10 +402,6 @@ class PDXearch { * Search methods ******************************************************************/ std::vector Search(const float *PDX_RESTRICT const raw_query, const uint32_t k) { -#ifdef BENCHMARK_TIME - this->ResetClocks(); - this->end_to_end_clock.Tic(); -#endif Heap local_heap {}; std::unique_ptr query(new float[pdx_data.num_dimensions]); if (!pdx_data.is_normalized) { @@ -471,18 +462,11 @@ class PDXearch { } } std::vector result = BuildResultSetFromHeap(k, local_heap); -#ifdef BENCHMARK_TIME - this->end_to_end_clock.Toc(); -#endif return result; } std::vector FilteredSearch(const float *PDX_RESTRICT const raw_query, const uint32_t k, const PredicateEvaluator &predicate_evaluator) { -#ifdef BENCHMARK_TIME - this->ResetClocks(); - this->end_to_end_clock.Tic(); -#endif Heap local_heap {}; std::unique_ptr query(new float[pdx_data.num_dimensions]); if (!pdx_data.is_normalized) { @@ -547,9 +531,6 @@ class PDXearch { } } std::vector result = BuildResultSetFromHeap(k, local_heap); -#ifdef BENCHMARK_TIME - this->end_to_end_clock.Toc(); -#endif return result; } diff --git a/include/pdx/utils.hpp b/include/pdx/utils.hpp index d6a367c..3987046 100644 --- a/include/pdx/utils.hpp +++ b/include/pdx/utils.hpp @@ -9,37 +9,11 @@ #include #include #include -#include #ifdef linux #include #endif -class TicToc { -public: - size_t accum_time = 0; - std::chrono::high_resolution_clock::time_point start = std::chrono::high_resolution_clock::now(); - - void Reset() { - accum_time = 0; - start = std::chrono::high_resolution_clock::now(); - } - - inline void Tic() { - start = std::chrono::high_resolution_clock::now(); - } - - inline void Toc() { - auto end = std::chrono::high_resolution_clock::now(); - accum_time += std::chrono::duration_cast( - end - start).count(); - } - - double GetMilliseconds() const { - return static_cast(accum_time) / 1e6; - } -}; - inline std::unique_ptr MmapFile(const std::string& filename) { struct stat file_stats {}; int fd = ::open(filename.c_str(), O_RDONLY); From 3087114c32694011b2fece6ddfe727693c321058 Mon Sep 17 00:00:00 2001 From: lkuffo Date: Tue, 24 Feb 2026 12:12:03 +0100 Subject: [PATCH 11/39] Removing old benchmarks --- .../bench_adsampling/pdx_ivf2_adsampling.cpp | 100 ----------------- .../pdx_ivf2_adsampling_u8.cpp | 100 ----------------- .../bench_adsampling/pdx_ivf_adsampling.cpp | 101 ------------------ .../pdx_ivf_adsampling_u8.cpp | 100 ----------------- 4 files changed, 401 deletions(-) delete mode 100644 benchmarks/bench_adsampling/pdx_ivf2_adsampling.cpp delete mode 100644 benchmarks/bench_adsampling/pdx_ivf2_adsampling_u8.cpp delete mode 100644 benchmarks/bench_adsampling/pdx_ivf_adsampling.cpp delete mode 100644 benchmarks/bench_adsampling/pdx_ivf_adsampling_u8.cpp diff --git a/benchmarks/bench_adsampling/pdx_ivf2_adsampling.cpp b/benchmarks/bench_adsampling/pdx_ivf2_adsampling.cpp deleted file mode 100644 index a369350..0000000 --- a/benchmarks/bench_adsampling/pdx_ivf2_adsampling.cpp +++ /dev/null @@ -1,100 +0,0 @@ -#ifndef BENCHMARK_TIME -#define BENCHMARK_TIME = true -#endif - -#ifndef PDX_USE_EXPLICIT_SIMD -#define PDX_USE_EXPLICIT_SIMD = true -#endif - -#include -#include -#include "pdx/utils.hpp" -#include "pdx/index.hpp" -#include "benchmark_utils.hpp" - -int main(int argc, char *argv[]) { - std::string arg_dataset; - size_t arg_ivf_nprobe = 0; - if (argc > 1){ - arg_dataset = argv[1]; - } - if (argc > 2){ - arg_ivf_nprobe = atoi(argv[2]); - } - std::cout << "==> PDX IVF2 ADSampling\n"; - - std::string ALGORITHM = "adsampling"; - const bool VERIFY_RESULTS = BenchmarkUtils::VERIFY_RESULTS; - - uint8_t KNN = BenchmarkUtils::KNN; - size_t NUM_QUERIES; - size_t NUM_MEASURE_RUNS = BenchmarkUtils::NUM_MEASURE_RUNS; - - std::string RESULTS_PATH; - RESULTS_PATH = BENCHMARK_UTILS.RESULTS_DIR_PATH + "IVF2_PDX_ADSAMPLING.csv"; - - - for (const auto & [dataset, info] : RAW_DATASET_PARAMS) { - if (arg_dataset.size() > 0 && arg_dataset != dataset){ - continue; - } - PDX::PDXTreeIndexF32 pdx_index; - pdx_index.Restore(BenchmarkUtils::PDX_DATA + dataset + "-pdx_tree_f32"); - - std::unique_ptr query_ptr = MmapFile(BenchmarkUtils::QUERIES_DATA + info.pdx_dataset_name); - auto *query = reinterpret_cast(query_ptr.get()); - - NUM_QUERIES = info.num_queries; - std::unique_ptr ground_truth = MmapFile(BenchmarkUtils::GROUND_TRUTH_DATA + info.pdx_dataset_name + "_100_norm"); - auto *int_ground_truth = reinterpret_cast(ground_truth.get()); - query += 1; // skip number of embeddings - - std::vector nprobes_to_use; - if (arg_ivf_nprobe > 0) { - nprobes_to_use = {arg_ivf_nprobe}; - } else { - nprobes_to_use.assign(std::begin(BenchmarkUtils::IVF_PROBES), std::end(BenchmarkUtils::IVF_PROBES)); - } - - for (size_t ivf_nprobe : nprobes_to_use) { - if (pdx_index.GetNumClusters() < ivf_nprobe){ - continue; - } - if (arg_ivf_nprobe > 0 && ivf_nprobe != arg_ivf_nprobe){ - continue; - } - std::vector runtimes; - runtimes.resize(NUM_MEASURE_RUNS * NUM_QUERIES); - pdx_index.SetNProbe(ivf_nprobe); - - float recalls = 0; - if (VERIFY_RESULTS) { - for (size_t l = 0; l < NUM_QUERIES; ++l) { - auto result = pdx_index.Search(query + l * pdx_index.GetNumDimensions(), KNN); - BenchmarkUtils::VerifyResult(recalls, result, KNN, int_ground_truth, l); - } - } - for (size_t j = 0; j < NUM_MEASURE_RUNS; ++j) { - for (size_t l = 0; l < NUM_QUERIES; ++l) { - pdx_index.Search(query + l * pdx_index.GetNumDimensions(), KNN); - runtimes[j + l * NUM_MEASURE_RUNS] = { - pdx_index.GetSearcher().end_to_end_clock.accum_time - }; - } - } - float real_selectivity = 1 - BenchmarkUtils::SELECTIVITY_THRESHOLD; - BenchmarkMetadata results_metadata = { - dataset, - ALGORITHM, - NUM_MEASURE_RUNS, - NUM_QUERIES, - ivf_nprobe, - KNN, - recalls, - real_selectivity - }; - BenchmarkUtils::SaveResults(runtimes, RESULTS_PATH, results_metadata); - } - } - return 0; -} diff --git a/benchmarks/bench_adsampling/pdx_ivf2_adsampling_u8.cpp b/benchmarks/bench_adsampling/pdx_ivf2_adsampling_u8.cpp deleted file mode 100644 index 3399652..0000000 --- a/benchmarks/bench_adsampling/pdx_ivf2_adsampling_u8.cpp +++ /dev/null @@ -1,100 +0,0 @@ -#ifndef BENCHMARK_TIME -#define BENCHMARK_TIME = true -#endif - -#ifndef PDX_USE_EXPLICIT_SIMD -#define PDX_USE_EXPLICIT_SIMD = true -#endif - -#include -#include -#include "pdx/utils.hpp" -#include "pdx/index.hpp" -#include "benchmark_utils.hpp" - -int main(int argc, char *argv[]) { - std::string arg_dataset; - size_t arg_ivf_nprobe = 0; - if (argc > 1){ - arg_dataset = argv[1]; - } - if (argc > 2){ - arg_ivf_nprobe = atoi(argv[2]); - } - std::cout << "==> PDX IVF2 ADSampling U8\n"; - - std::string ALGORITHM = "adsampling"; - const bool VERIFY_RESULTS = BenchmarkUtils::VERIFY_RESULTS; - - uint8_t KNN = BenchmarkUtils::KNN; - size_t NUM_QUERIES; - size_t NUM_MEASURE_RUNS = BenchmarkUtils::NUM_MEASURE_RUNS; - - std::string RESULTS_PATH; - RESULTS_PATH = BENCHMARK_UTILS.RESULTS_DIR_PATH + "U8_IVF2_PDX_ADSAMPLING.csv"; - - - for (const auto & [dataset, info] : RAW_DATASET_PARAMS) { - if (arg_dataset.size() > 0 && arg_dataset != dataset){ - continue; - } - PDX::PDXTreeIndexU8 pdx_index; - pdx_index.Restore(BenchmarkUtils::PDX_DATA + dataset + "-pdx_tree_u8"); - - std::unique_ptr query_ptr = MmapFile(BenchmarkUtils::QUERIES_DATA + info.pdx_dataset_name); - auto *query = reinterpret_cast(query_ptr.get()); - NUM_QUERIES = info.num_queries; - - std::unique_ptr ground_truth = MmapFile(BenchmarkUtils::GROUND_TRUTH_DATA + info.pdx_dataset_name + "_100_norm"); - auto *int_ground_truth = reinterpret_cast(ground_truth.get()); - query += 1; // skip number of embeddings - - std::vector nprobes_to_use; - if (arg_ivf_nprobe > 0) { - nprobes_to_use = {arg_ivf_nprobe}; - } else { - nprobes_to_use.assign(std::begin(BenchmarkUtils::IVF_PROBES), std::end(BenchmarkUtils::IVF_PROBES)); - } - - for (size_t ivf_nprobe : nprobes_to_use) { - if (pdx_index.GetNumClusters() < ivf_nprobe){ - continue; - } - if (arg_ivf_nprobe > 0 && ivf_nprobe != arg_ivf_nprobe){ - continue; - } - std::vector runtimes; - runtimes.resize(NUM_MEASURE_RUNS * NUM_QUERIES); - pdx_index.SetNProbe(ivf_nprobe); - - float recalls = 0; - if (VERIFY_RESULTS) { - for (size_t l = 0; l < NUM_QUERIES; ++l) { - auto result = pdx_index.Search(query + l * pdx_index.GetNumDimensions(), KNN); - BenchmarkUtils::VerifyResult(recalls, result, KNN, int_ground_truth, l); - } - } - for (size_t j = 0; j < NUM_MEASURE_RUNS; ++j) { - for (size_t l = 0; l < NUM_QUERIES; ++l) { - pdx_index.Search(query + l * pdx_index.GetNumDimensions(), KNN); - runtimes[j + l * NUM_MEASURE_RUNS] = { - pdx_index.GetSearcher().end_to_end_clock.accum_time - }; - } - } - float real_selectivity = 1 - BenchmarkUtils::SELECTIVITY_THRESHOLD; - BenchmarkMetadata results_metadata = { - dataset, - ALGORITHM, - NUM_MEASURE_RUNS, - NUM_QUERIES, - ivf_nprobe, - KNN, - recalls, - real_selectivity - }; - BenchmarkUtils::SaveResults(runtimes, RESULTS_PATH, results_metadata); - } - } - return 0; -} diff --git a/benchmarks/bench_adsampling/pdx_ivf_adsampling.cpp b/benchmarks/bench_adsampling/pdx_ivf_adsampling.cpp deleted file mode 100644 index f1bd673..0000000 --- a/benchmarks/bench_adsampling/pdx_ivf_adsampling.cpp +++ /dev/null @@ -1,101 +0,0 @@ -#ifndef BENCHMARK_TIME -#define BENCHMARK_TIME = true -#endif - -#ifndef PDX_USE_EXPLICIT_SIMD -#define PDX_USE_EXPLICIT_SIMD = true -#endif - -#include -#include -#include "pdx/utils.hpp" -#include "pdx/index.hpp" -#include "benchmark_utils.hpp" - -int main(int argc, char *argv[]) { - std::string arg_dataset; - size_t arg_ivf_nprobe = 0; - if (argc > 1){ - arg_dataset = argv[1]; - } - if (argc > 2){ - arg_ivf_nprobe = atoi(argv[2]); - } - std::cout << "==> PDX IVF ADSampling\n"; - - std::string ALGORITHM = "adsampling"; - const bool VERIFY_RESULTS = BenchmarkUtils::VERIFY_RESULTS; - - uint8_t KNN = BenchmarkUtils::KNN; - float SELECTIVITY_THRESHOLD = BenchmarkUtils::SELECTIVITY_THRESHOLD; - size_t NUM_QUERIES; - size_t NUM_MEASURE_RUNS = BenchmarkUtils::NUM_MEASURE_RUNS; - - std::string RESULTS_PATH; - RESULTS_PATH = BENCHMARK_UTILS.RESULTS_DIR_PATH + "IVF_PDX_ADSAMPLING.csv"; - - - for (const auto & [dataset, info] : RAW_DATASET_PARAMS) { - if (arg_dataset.size() > 0 && arg_dataset != dataset){ - continue; - } - PDX::PDXIndexF32 pdx_index; - pdx_index.Restore(BenchmarkUtils::PDX_DATA + dataset + "-pdx_f32"); - - std::unique_ptr query_ptr = MmapFile(BenchmarkUtils::QUERIES_DATA + info.pdx_dataset_name); - auto *query = reinterpret_cast(query_ptr.get()); - - NUM_QUERIES = info.num_queries; - std::unique_ptr ground_truth = MmapFile(BenchmarkUtils::GROUND_TRUTH_DATA + info.pdx_dataset_name + "_100_norm"); - auto *int_ground_truth = reinterpret_cast(ground_truth.get()); - query += 1; // skip number of embeddings - - std::vector nprobes_to_use; - if (arg_ivf_nprobe > 0) { - nprobes_to_use = {arg_ivf_nprobe}; - } else { - nprobes_to_use.assign(std::begin(BenchmarkUtils::IVF_PROBES), std::end(BenchmarkUtils::IVF_PROBES)); - } - - for (size_t ivf_nprobe : nprobes_to_use) { - if (pdx_index.GetNumClusters() < ivf_nprobe){ - continue; - } - if (arg_ivf_nprobe > 0 && ivf_nprobe != arg_ivf_nprobe){ - continue; - } - std::vector runtimes; - runtimes.resize(NUM_MEASURE_RUNS * NUM_QUERIES); - pdx_index.SetNProbe(ivf_nprobe); - - float recalls = 0; - if (VERIFY_RESULTS) { - for (size_t l = 0; l < NUM_QUERIES; ++l) { - auto result = pdx_index.Search(query + l * pdx_index.GetNumDimensions(), KNN); - BenchmarkUtils::VerifyResult(recalls, result, KNN, int_ground_truth, l); - } - } - for (size_t j = 0; j < NUM_MEASURE_RUNS; ++j) { - for (size_t l = 0; l < NUM_QUERIES; ++l) { - pdx_index.Search(query + l * pdx_index.GetNumDimensions(), KNN); - runtimes[j + l * NUM_MEASURE_RUNS] = { - pdx_index.GetSearcher().end_to_end_clock.accum_time - }; - } - } - float real_selectivity = 1 - SELECTIVITY_THRESHOLD; - BenchmarkMetadata results_metadata = { - dataset, - ALGORITHM, - NUM_MEASURE_RUNS, - NUM_QUERIES, - ivf_nprobe, - KNN, - recalls, - real_selectivity - }; - BenchmarkUtils::SaveResults(runtimes, RESULTS_PATH, results_metadata); - } - } - return 0; -} diff --git a/benchmarks/bench_adsampling/pdx_ivf_adsampling_u8.cpp b/benchmarks/bench_adsampling/pdx_ivf_adsampling_u8.cpp deleted file mode 100644 index 2d5366c..0000000 --- a/benchmarks/bench_adsampling/pdx_ivf_adsampling_u8.cpp +++ /dev/null @@ -1,100 +0,0 @@ -#ifndef BENCHMARK_TIME -#define BENCHMARK_TIME = true -#endif - -#ifndef PDX_USE_EXPLICIT_SIMD -#define PDX_USE_EXPLICIT_SIMD = true -#endif - -#include -#include -#include "pdx/utils.hpp" -#include "pdx/index.hpp" -#include "benchmark_utils.hpp" - -int main(int argc, char *argv[]) { - std::string arg_dataset; - size_t arg_ivf_nprobe = 0; - if (argc > 1){ - arg_dataset = argv[1]; - } - if (argc > 2){ - arg_ivf_nprobe = atoi(argv[2]); - } - std::cout << "==> PDX IVF ADSampling\n"; - - std::string ALGORITHM = "adsampling"; - const bool VERIFY_RESULTS = BenchmarkUtils::VERIFY_RESULTS; - - uint8_t KNN = BenchmarkUtils::KNN; - size_t NUM_QUERIES; - size_t NUM_MEASURE_RUNS = BenchmarkUtils::NUM_MEASURE_RUNS; - - std::string RESULTS_PATH; - RESULTS_PATH = BENCHMARK_UTILS.RESULTS_DIR_PATH + "U8_IVF_PDX_ADSAMPLING.csv"; - - - for (const auto & [dataset, info] : RAW_DATASET_PARAMS) { - if (arg_dataset.size() > 0 && arg_dataset != dataset){ - continue; - } - PDX::PDXIndexU8 pdx_index; - pdx_index.Restore(BenchmarkUtils::PDX_DATA + dataset + "-pdx_u8"); - - std::unique_ptr query_ptr = MmapFile(BenchmarkUtils::QUERIES_DATA + info.pdx_dataset_name); - auto *query = reinterpret_cast(query_ptr.get()); - - NUM_QUERIES = info.num_queries; - std::unique_ptr ground_truth = MmapFile(BenchmarkUtils::GROUND_TRUTH_DATA + info.pdx_dataset_name + "_100_norm"); - auto *int_ground_truth = reinterpret_cast(ground_truth.get()); - query += 1; // skip number of embeddings - - std::vector nprobes_to_use; - if (arg_ivf_nprobe > 0) { - nprobes_to_use = {arg_ivf_nprobe}; - } else { - nprobes_to_use.assign(std::begin(BenchmarkUtils::IVF_PROBES), std::end(BenchmarkUtils::IVF_PROBES)); - } - - for (size_t ivf_nprobe : nprobes_to_use) { - if (pdx_index.GetNumClusters() < ivf_nprobe){ - continue; - } - if (arg_ivf_nprobe > 0 && ivf_nprobe != arg_ivf_nprobe){ - continue; - } - std::vector runtimes; - runtimes.resize(NUM_MEASURE_RUNS * NUM_QUERIES); - pdx_index.SetNProbe(ivf_nprobe); - - float recalls = 0; - if (VERIFY_RESULTS) { - for (size_t l = 0; l < NUM_QUERIES; ++l) { - auto result = pdx_index.Search(query + l * pdx_index.GetNumDimensions(), KNN); - BenchmarkUtils::VerifyResult(recalls, result, KNN, int_ground_truth, l); - } - } - for (size_t j = 0; j < NUM_MEASURE_RUNS; ++j) { - for (size_t l = 0; l < NUM_QUERIES; ++l) { - pdx_index.Search(query + l * pdx_index.GetNumDimensions(), KNN); - runtimes[j + l * NUM_MEASURE_RUNS] = { - pdx_index.GetSearcher().end_to_end_clock.accum_time - }; - } - } - float real_selectivity = 1 - BenchmarkUtils::SELECTIVITY_THRESHOLD; - BenchmarkMetadata results_metadata = { - dataset, - ALGORITHM, - NUM_MEASURE_RUNS, - NUM_QUERIES, - ivf_nprobe, - KNN, - recalls, - real_selectivity - }; - BenchmarkUtils::SaveResults(runtimes, RESULTS_PATH, results_metadata); - } - } - return 0; -} From cb79831dde50d4a43ca05b10613cac4a4a663aa2 Mon Sep 17 00:00:00 2001 From: lkuffo Date: Tue, 24 Feb 2026 13:27:00 +0100 Subject: [PATCH 12/39] Refactoring bindings --- CMakeLists.txt | 42 ++ benchmarks/BenchmarkEndToEnd | Bin 439344 -> 439344 bytes benchmarks/BenchmarkPDXIVF | Bin 0 -> 375392 bytes benchmarks/python_scripts/setup_adsampling.py | 223 ++------ benchmarks/python_scripts/setup_utils.py | 18 + .../DEFAULT/END_TO_END_PDX_ADSAMPLING.csv | 8 + .../DEFAULT/PDX_TREE_U8_ADSAMPLING.csv | 3 + examples/pdx_2l_ivf.py | 51 +- examples/pdx_2l_ivf_8bit.py | 65 +-- examples/pdx_ivf.py | 53 +- include/pdx/index.hpp | 9 +- include/pdx/lib/lib.hpp | 478 ++++-------------- pyproject.toml | 43 +- python/lib.cpp | 91 +--- python/pdxearch/__init__.py | 9 +- python/pdxearch/constants.py | 22 - python/pdxearch/index_base.py | 440 ---------------- python/pdxearch/index_core.py | 199 -------- python/pdxearch/index_factory.py | 389 +++++++------- python/pdxearch/preprocessors.py | 65 --- setup.py | 133 ----- 21 files changed, 517 insertions(+), 1824 deletions(-) create mode 100755 benchmarks/BenchmarkPDXIVF create mode 100644 benchmarks/results/DEFAULT/PDX_TREE_U8_ADSAMPLING.csv delete mode 100644 python/pdxearch/constants.py delete mode 100644 python/pdxearch/index_base.py delete mode 100644 python/pdxearch/index_core.py delete mode 100644 python/pdxearch/preprocessors.py delete mode 100644 setup.py diff --git a/CMakeLists.txt b/CMakeLists.txt index 16aefa7..a621b10 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -65,3 +65,45 @@ add_compile_definitions(CMAKE_SOURCE_DIR="${CMAKE_SOURCE_DIR}") include_directories(include extern/Eigen) add_subdirectory(benchmarks) + +# Python bindings: only build if pybind11 is available +find_package(Python COMPONENTS Interpreter Development.Module QUIET) +set(PYBIND11_FINDPYTHON ON) +find_package(pybind11 CONFIG QUIET) + +if(Python_FOUND AND pybind11_FOUND) + message(STATUS "Python bindings enabled") + message(STATUS "Python executable: ${Python_EXECUTABLE}") + message(STATUS "pybind11 found: ${pybind11_VERSION}") + + pybind11_add_module(compiled + python/lib.cpp + ) + + target_include_directories(compiled PRIVATE + ${CMAKE_CURRENT_SOURCE_DIR}/include + ${CMAKE_CURRENT_SOURCE_DIR}/extern/Eigen + ${CMAKE_CURRENT_SOURCE_DIR}/extern/SuperKMeans/include + ) + + if(MKL_FOUND) + target_link_libraries(compiled PRIVATE ${MKL_COMMON_LIBS}) + else() + target_link_libraries(compiled PRIVATE ${BLAS_LINK_LIBRARIES}) + endif() + + target_compile_options(compiled PRIVATE + -Wall + -Wno-unknown-pragmas + $<$:-O3 -march=native> + ) + + target_compile_features(compiled PRIVATE cxx_std_17) + + install(TARGETS compiled + LIBRARY DESTINATION pdxearch + COMPONENT python + ) +else() + message(STATUS "Python bindings disabled (pybind11 not found)") +endif() diff --git a/benchmarks/BenchmarkEndToEnd b/benchmarks/BenchmarkEndToEnd index 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100644 --- a/benchmarks/python_scripts/setup_adsampling.py +++ b/benchmarks/python_scripts/setup_adsampling.py @@ -1,191 +1,52 @@ -import faiss from setup_utils import * from setup_settings import * -from pdxearch.index_base import BaseIndexPDXIVF, BaseIndexPDXIVF2 -from pdxearch.preprocessors import ADSampling -from sklearn import preprocessing -from sklearn.preprocessing import MinMaxScaler +from pdxearch import IndexPDXIVF, IndexPDXIVFSQ8, IndexPDXIVF2, IndexPDXIVF2SQ8 +INDEX_CLASSES = { + "pdx_f32": IndexPDXIVF, + "pdx_u8": IndexPDXIVFSQ8, + "pdx_tree_f32": IndexPDXIVF2, + "pdx_tree_u8": IndexPDXIVF2SQ8, +} -def generate_adsampling_ivf(dataset_name: str, _type='pdx', normalize=True): - print(dataset_name) - base_idx = BaseIndexPDXIVF(DIMENSIONALITIES[dataset_name], 'l2sq') - # Core index IVF must exist - index_path = os.path.join(CORE_INDEXES_FAISS, get_core_index_filename(dataset_name, norm=normalize)) - # Reads the core index created by faiss to generate the PDX index - base_idx.core_index.read_index(index_path) - data = read_hdf5_train_data(dataset_name) - if normalize: - print('Normalizing') - data = preprocessing.normalize(data, axis=1, norm='l2') - preprocessor = ADSampling(DIMENSIONALITIES[dataset_name]) - preprocessor.preprocess(data, inplace=True) - print('Saving...') - # PDX - base_idx._to_pdx(data, _type='pdx', centroids_preprocessor=preprocessor, use_original_centroids=True) - base_idx._persist(os.path.join(PDX_ADSAMPLING_DATA, dataset_name + '-ivf')) - # Store metadata needed by ADSampling - preprocessor.store_metadata(os.path.join(PDX_ADSAMPLING_DATA, dataset_name + '-matrix')) +def generate_index(dataset_abbrev: str, index_type: str, normalize=True): + hdf5_name, dims = DATASET_INFO[dataset_abbrev] + print(f'{dataset_abbrev} -> {hdf5_name} ({index_type})') + data = read_hdf5_train_data(hdf5_name) -def generate_adsampling_ivf_global8(dataset_name: str, normalize=True): - base_idx = BaseIndexPDXIVF(DIMENSIONALITIES[dataset_name], 'l2sq') - # Core index IVF must exist - index_path = os.path.join(CORE_INDEXES_FAISS, get_core_index_filename(dataset_name, norm=normalize)) - # Reads the core index created by faiss to generate the PDX index - base_idx.core_index.read_index(index_path) - print('Reading train data') - data = read_hdf5_train_data(dataset_name) - preprocessor = ADSampling(DIMENSIONALITIES[dataset_name]) - preprocessor.preprocess(data, inplace=True, normalize=True) - print('Saving') - # PDX FLAT BLOCKIFIED - base_idx._to_pdx(data, _type='pdx-v4-h', quantize=True, centroids_preprocessor=preprocessor, use_original_centroids=True) - base_idx._persist(os.path.join(PDX_ADSAMPLING_DATA, dataset_name + '-ivf-u8')) + cls = INDEX_CLASSES[index_type] + index = cls(num_dimensions=dims, normalize=normalize) + print('Building index...') + index.build(data) + print(f'Index built: {index.num_clusters} clusters') - preprocessor.store_metadata(os.path.join(PDX_ADSAMPLING_DATA, dataset_name + '-ivf-u8-matrix')) - -def generate_adsampling_ivf2(dataset_name: str, normalize=True): - base_idx = BaseIndexPDXIVF2(DIMENSIONALITIES[dataset_name], 'l2sq') - # Core index IVF must exist - index_path = os.path.join(CORE_INDEXES_FAISS, get_core_index_filename(dataset_name, norm=normalize)) - index_path_l0 = os.path.join(CORE_INDEXES_FAISS_L0, get_core_index_filename(dataset_name, norm=normalize)) - # Reads the core index created by faiss to generate the PDX index - base_idx.core_index.read_index(index_path, index_path_l0) - data = read_hdf5_train_data(dataset_name) - preprocessor = ADSampling(DIMENSIONALITIES[dataset_name]) - preprocessor.preprocess(data, inplace=True) - print('Saving...') - # PDX - base_idx._to_pdx(data, _type='pdx', centroids_preprocessor=preprocessor, use_original_centroids=True) - base_idx._persist(os.path.join(PDX_ADSAMPLING_DATA, dataset_name + '-ivf2')) - - # Store metadata needed by ADSampling - preprocessor.store_metadata(os.path.join(PDX_ADSAMPLING_DATA, dataset_name + '-ivf2-matrix')) - -def generate_adsampling_ivf2_global8(dataset_name: str, normalize=True): - base_idx = BaseIndexPDXIVF2(DIMENSIONALITIES[dataset_name], 'l2sq') - # Core index IVF must exist - index_path = os.path.join(CORE_INDEXES_FAISS, get_core_index_filename(dataset_name, norm=normalize)) - index_path_l0 = os.path.join(CORE_INDEXES_FAISS_L0, get_core_index_filename(dataset_name, norm=normalize)) - # Reads the core index created by faiss to generate the PDX index - print('Reading index') - base_idx.core_index.read_index(index_path, index_path_l0) - # base_idx.core_index.index = faiss.read_index(index_path) - # base_idx.core_index.index_l0 = faiss.read_index(index_path_l0) - print('Reading train data') - data = read_hdf5_train_data(dataset_name) - preprocessor = ADSampling(DIMENSIONALITIES[dataset_name]) - preprocessor.preprocess(data, inplace=True, normalize=True) - print('Saving') - # PDX FLAT BLOCKIFIED - base_idx._to_pdx(data, _type='pdx-v4-h', quantize=True, centroids_preprocessor=preprocessor, use_original_centroids=True) - base_idx._persist(os.path.join(PDX_ADSAMPLING_DATA, dataset_name + '-ivf2-u8')) - - preprocessor.store_metadata(os.path.join(PDX_ADSAMPLING_DATA, dataset_name + '-ivf2-u8-matrix')) + save_path = os.path.join(PDX_ADSAMPLING_DATA, dataset_abbrev + '-' + index_type) + print(f'Saving to {save_path}') + index.save(save_path) + print('Done.') if __name__ == "__main__": - # generate_adsampling_ivf('coco-nomic-768-normalized', normalize=True) - # generate_adsampling_ivf('simplewiki-openai-3072-normalized', normalize=True) - # generate_adsampling_ivf('imagenet-align-640-normalized', normalize=True) - # generate_adsampling_ivf('imagenet-clip-512-normalized', normalize=True) - # generate_adsampling_ivf('laion-clip-512-normalized', normalize=True) - # generate_adsampling_ivf('codesearchnet-jina-768-cosine', normalize=True) - # generate_adsampling_ivf('yi-128-ip', normalize=True) - # generate_adsampling_ivf('landmark-dino-768-cosine', normalize=True) - # generate_adsampling_ivf('landmark-nomic-768-normalized', normalize=True) - # generate_adsampling_ivf('arxiv-nomic-768-normalized', normalize=True) - # generate_adsampling_ivf('ccnews-nomic-768-normalized', normalize=True) - # generate_adsampling_ivf('celeba-resnet-2048-cosine', normalize=True) - # generate_adsampling_ivf('llama-128-ip', normalize=True) - # generate_adsampling_ivf('yandex-200-cosine', normalize=True) - # generate_adsampling_ivf('word2vec-300', normalize=True) - # generate_adsampling_ivf('sift-128-euclidean', normalize=True) - # generate_adsampling_ivf('openai-1536-angular', normalize=True) - # generate_adsampling_ivf('msong-420', normalize=True) - # generate_adsampling_ivf('instructorxl-arxiv-768', normalize=True) - # generate_adsampling_ivf('contriever-768', normalize=True) - # generate_adsampling_ivf('gist-960-euclidean', normalize=True) - # generate_adsampling_ivf('yahoo-minilm-384-normalized', normalize=True) - # generate_adsampling_ivf('gooaq-distilroberta-768-normalized', normalize=True) - # generate_adsampling_ivf('agnews-mxbai-1024-euclidean', normalize=True) - # generate_adsampling_ivf('glove-200-angular', normalize=True) - - # generate_adsampling_ivf_global8('coco-nomic-768-normalized', normalize=True) - # generate_adsampling_ivf_global8('simplewiki-openai-3072-normalized', normalize=True) - # generate_adsampling_ivf_global8('imagenet-align-640-normalized', normalize=True) - # generate_adsampling_ivf_global8('imagenet-clip-512-normalized', normalize=True) - # generate_adsampling_ivf_global8('laion-clip-512-normalized', normalize=True) - # generate_adsampling_ivf_global8('codesearchnet-jina-768-cosine', normalize=True) - # generate_adsampling_ivf_global8('yi-128-ip', normalize=True) - # generate_adsampling_ivf_global8('landmark-dino-768-cosine', normalize=True) - # generate_adsampling_ivf_global8('landmark-nomic-768-normalized', normalize=True) - # generate_adsampling_ivf_global8('arxiv-nomic-768-normalized', normalize=True) - # generate_adsampling_ivf_global8('ccnews-nomic-768-normalized', normalize=True) - # generate_adsampling_ivf_global8('celeba-resnet-2048-cosine', normalize=True) - # generate_adsampling_ivf_global8('llama-128-ip', normalize=True) - # generate_adsampling_ivf_global8('yandex-200-cosine', normalize=True) - # generate_adsampling_ivf_global8('word2vec-300', normalize=True) - # generate_adsampling_ivf_global8('sift-128-euclidean', normalize=True) - # generate_adsampling_ivf_global8('openai-1536-angular', normalize=True) - # generate_adsampling_ivf_global8('msong-420', normalize=True) - # generate_adsampling_ivf_global8('instructorxl-arxiv-768', normalize=True) - # generate_adsampling_ivf_global8('contriever-768', normalize=True) - # generate_adsampling_ivf_global8('gist-960-euclidean', normalize=True) - # generate_adsampling_ivf_global8('yahoo-minilm-384-normalized', normalize=True) - # generate_adsampling_ivf_global8('gooaq-distilroberta-768-normalized', normalize=True) - # generate_adsampling_ivf_global8('agnews-mxbai-1024-euclidean', normalize=True) - # generate_adsampling_ivf_global8('glove-200-angular', normalize=True) - - # generate_adsampling_ivf2('coco-nomic-768-normalized', normalize=True) - # generate_adsampling_ivf2('simplewiki-openai-3072-normalized', normalize=True) - # generate_adsampling_ivf2('imagenet-align-640-normalized', normalize=True) - # generate_adsampling_ivf2('imagenet-clip-512-normalized', normalize=True) - # generate_adsampling_ivf2('laion-clip-512-normalized', normalize=True) - # generate_adsampling_ivf2('codesearchnet-jina-768-cosine', normalize=True) - # generate_adsampling_ivf2('yi-128-ip', normalize=True) - # generate_adsampling_ivf2('landmark-dino-768-cosine', normalize=True) - # generate_adsampling_ivf2('landmark-nomic-768-normalized', normalize=True) - # generate_adsampling_ivf2('arxiv-nomic-768-normalized', normalize=True) - # generate_adsampling_ivf2('ccnews-nomic-768-normalized', normalize=True) - # generate_adsampling_ivf2('celeba-resnet-2048-cosine', normalize=True) - # generate_adsampling_ivf2('llama-128-ip', normalize=True) - # generate_adsampling_ivf2('yandex-200-cosine', normalize=True) - # generate_adsampling_ivf2('word2vec-300', normalize=True) - # generate_adsampling_ivf2('sift-128-euclidean', normalize=True) - # generate_adsampling_ivf2('openai-1536-angular', normalize=True) - # generate_adsampling_ivf2('msong-420', normalize=True) - # generate_adsampling_ivf2('instructorxl-arxiv-768', normalize=True) - # generate_adsampling_ivf2('contriever-768', normalize=True) - # generate_adsampling_ivf2('gist-960-euclidean', normalize=True) - # generate_adsampling_ivf2('yahoo-minilm-384-normalized', normalize=True) - # generate_adsampling_ivf2('gooaq-distilroberta-768-normalized', normalize=True) - # generate_adsampling_ivf2('agnews-mxbai-1024-euclidean', normalize=True) - # generate_adsampling_ivf2('glove-200-angular', normalize=True) - # - # generate_adsampling_ivf2_global8('coco-nomic-768-normalized', normalize=True) - # generate_adsampling_ivf2_global8('simplewiki-openai-3072-normalized', normalize=True) - # generate_adsampling_ivf2_global8('imagenet-align-640-normalized', normalize=True) - # generate_adsampling_ivf2_global8('imagenet-clip-512-normalized', normalize=True) - # generate_adsampling_ivf2_global8('laion-clip-512-normalized', normalize=True) - # generate_adsampling_ivf2_global8('codesearchnet-jina-768-cosine', normalize=True) - # generate_adsampling_ivf2_global8('yi-128-ip', normalize=True) - # generate_adsampling_ivf2_global8('landmark-dino-768-cosine', normalize=True) - # generate_adsampling_ivf2_global8('landmark-nomic-768-normalized', normalize=True) - # generate_adsampling_ivf2_global8('arxiv-nomic-768-normalized', normalize=True) - generate_adsampling_ivf2_global8('ccnews-nomic-768-normalized', normalize=True) - # generate_adsampling_ivf2_global8('celeba-resnet-2048-cosine', normalize=True) - # generate_adsampling_ivf2_global8('llama-128-ip', normalize=True) - # generate_adsampling_ivf2_global8('yandex-200-cosine', normalize=True) - # generate_adsampling_ivf2_global8('word2vec-300', normalize=True) - # generate_adsampling_ivf2_global8('sift-128-euclidean', normalize=True) - # generate_adsampling_ivf2_global8('openai-1536-angular', normalize=True) - # generate_adsampling_ivf2_global8('msong-420', normalize=True) - # generate_adsampling_ivf2_global8('instructorxl-arxiv-768', normalize=True) - # generate_adsampling_ivf2_global8('contriever-768', normalize=True) - # generate_adsampling_ivf2_global8('gist-960-euclidean', normalize=True) - # generate_adsampling_ivf2_global8('yahoo-minilm-384-normalized', normalize=True) - # generate_adsampling_ivf2_global8('gooaq-distilroberta-768-normalized', normalize=True) - # generate_adsampling_ivf2_global8('agnews-mxbai-1024-euclidean', normalize=True) - # generate_adsampling_ivf2_global8('glove-200-angular', normalize=True) \ No newline at end of file + # generate_index('sift', 'pdx_f32', normalize=True) + # generate_index('sift', 'pdx_u8', normalize=True) + # generate_index('sift', 'pdx_tree_f32', normalize=True) + # generate_index('sift', 'pdx_tree_u8', normalize=True) + + # generate_index('mxbai', 'pdx_f32', normalize=True) + # generate_index('mxbai', 'pdx_u8', normalize=True) + # generate_index('mxbai', 'pdx_tree_f32', normalize=True) + # generate_index('mxbai', 'pdx_tree_u8', normalize=True) + + # generate_index('gist', 'pdx_f32', normalize=True) + # generate_index('openai', 'pdx_f32', normalize=True) + # generate_index('arxiv', 'pdx_f32', normalize=True) + # generate_index('wiki', 'pdx_f32', normalize=True) + # generate_index('contriever', 'pdx_f32', normalize=True) + # generate_index('clip', 'pdx_f32', normalize=True) + # generate_index('yahoo', 'pdx_f32', normalize=True) + # generate_index('yandex', 'pdx_f32', normalize=True) + # generate_index('glove200', 'pdx_f32', normalize=True) + # generate_index('yi', 'pdx_f32', normalize=True) + # generate_index('llama', 'pdx_f32', normalize=True) + pass diff --git a/benchmarks/python_scripts/setup_utils.py b/benchmarks/python_scripts/setup_utils.py index ce16029..f8d05b1 100644 --- a/benchmarks/python_scripts/setup_utils.py +++ b/benchmarks/python_scripts/setup_utils.py @@ -2,6 +2,24 @@ import h5py from setup_settings import * +# Abbreviated name -> (hdf5_dataset_name, num_dimensions) +# Matches RAW_DATASET_PARAMS in benchmark_utils.hpp +DATASET_INFO = { + "sift": ("sift-128-euclidean", 128), + "yi": ("yi-128-ip", 128), + "llama": ("llama-128-ip", 128), + "glove200": ("glove-200-angular", 200), + "yandex": ("yandex-200-cosine", 200), + "yahoo": ("yahoo-minilm-384-normalized", 384), + "clip": ("imagenet-clip-512-normalized", 512), + "contriever": ("contriever-768", 768), + "gist": ("gist-960-euclidean", 960), + "mxbai": ("agnews-mxbai-1024-euclidean", 1024), + "openai": ("openai-1536-angular", 1536), + "arxiv": ("arxiv-nomic-768-normalized", 768), + "wiki": ("simplewiki-openai-3072-normalized", 3072), +} + def read_hdf5_train_data(dataset): hdf5_file_name = os.path.join(RAW_DATA, dataset + ".hdf5") diff --git a/benchmarks/results/DEFAULT/END_TO_END_PDX_ADSAMPLING.csv b/benchmarks/results/DEFAULT/END_TO_END_PDX_ADSAMPLING.csv index 6b6a873..9bac49b 100644 --- a/benchmarks/results/DEFAULT/END_TO_END_PDX_ADSAMPLING.csv +++ b/benchmarks/results/DEFAULT/END_TO_END_PDX_ADSAMPLING.csv @@ -36,3 +36,11 @@ mxbai,end_to_end_pdx_tree_f32,0.270043,0.496916,0.057458,0.902799,25,0,100,1000, mxbai,end_to_end_pdx_tree_u8,0.106266,0.175334,0.046542,0.901368,25,0,100,1000,0,1,0.1065,0.1891,0.03388 mxbai,end_to_end_pdx_tree_f32,0.263403,0.494292,0.054875,0.902799,25,0,100,1000,0,1,0.2637,0.5128,0.05488 mxbai,end_to_end_pdx_f32,0.484354,0.693959,0.290042,0.903179,25,0,100,1000,0,1,0.4848,0.7457,0.29 +mxbai,end_to_end_pdx_f32,0.474917,0.7145,0.272833,0.903179,25,0,100,1000,0,1,0.4749,0.7145,0.2728 +mxbai,end_to_end_pdx_tree_u8,0.194591,0.29325,0.110125,0.901368,25,0,100,1000,0,1,0.1955,0.3175,0.1101 +mxbai,end_to_end_pdx_tree_u8,0.192249,0.287917,0.10725,0.901368,25,0,100,1000,0,1,0.1933,0.3307,0.1072 +mxbai,end_to_end_pdx_tree_u8,0.190416,0.28475,0.110708,0.901368,25,0,100,1000,0,1,0.1912,0.3083,0.1107 +mxbai,end_to_end_pdx_f32,0.472606,0.692875,0.263875,0.903179,25,0,100,1000,0,1,0.4731,0.7357,0.2639 +mxbai,end_to_end_pdx_u8,0.310213,0.38425,0.236792,0.901748,25,0,100,1000,0,1,0.3112,0.5605,0.2368 +mxbai,end_to_end_pdx_tree_u8,0.191827,0.28925,0.114,0.901368,25,0,100,1000,0,1,0.1924,0.3234,0.114 +mxbai,end_to_end_pdx_tree_u8,0.169494,0.246584,0.088584,0.901288,25,0,100,1000,0,1,0.1697,0.2606,0.08858 diff --git a/benchmarks/results/DEFAULT/PDX_TREE_U8_ADSAMPLING.csv b/benchmarks/results/DEFAULT/PDX_TREE_U8_ADSAMPLING.csv new file mode 100644 index 0000000..f824ea2 --- /dev/null +++ b/benchmarks/results/DEFAULT/PDX_TREE_U8_ADSAMPLING.csv @@ -0,0 +1,3 @@ +dataset,algorithm,avg,max,min,recall,ivf_nprobe,epsilon,knn,n_queries,selectivity,num_measure_runs,avg_all,max_all,min_all +mxbai,adsampling,0.212453,0.318375,0.131292,0.901368,25,0,100,1000,0.2,1,0.2135,0.3588,0.1313 +mxbai,adsampling,0.199531,0.293875,0.12325,0.901368,25,0,100,1000,0.2,1,0.2001,0.3107,0.1232 diff --git a/examples/pdx_2l_ivf.py b/examples/pdx_2l_ivf.py index 701567c..55dbec3 100644 --- a/examples/pdx_2l_ivf.py +++ b/examples/pdx_2l_ivf.py @@ -1,60 +1,39 @@ -import math import os import numpy as np from examples_utils import TicToc, read_hdf5_data -from pdxearch.index_factory import IndexPDXIVF2 +from pdxearch import IndexPDXIVF2 + np.random.seed(42) """ -PDXearch (pruned search) + ADSampling with a Two-Level IVF index (built with FAISS) -Recall is controled with nprobe parameter +PDXearch with a two-level IVF index (F32). +Recall is controlled with nprobe parameter. Download the .hdf5 data here: https://drive.google.com/drive/folders/1f76UCrU52N2wToGMFg9ir1MY8ZocrN34?usp=sharing """ if __name__ == "__main__": dataset_name = 'agnews-mxbai-1024-euclidean.hdf5' num_dimensions = 1024 - nprobe = 64 + nprobe = 25 knn = 100 - print(f'Running example: PDXearch + ADSampling (Two-Level IVF Flat)\n- D={num_dimensions}\n- k={knn}\n- nprobe={nprobe}\n- dataset={dataset_name}') + print(f'Running example: PDXearch Two-Level IVF (F32)\n- D={num_dimensions}\n- k={knn}\n- nprobe={nprobe}\n- dataset={dataset_name}') train, queries = read_hdf5_data(os.path.join('./benchmarks/datasets/downloaded', dataset_name)) - nbuckets = 4 * math.ceil(math.sqrt(len(train))) - index = IndexPDXIVF2(ndim=num_dimensions, nbuckets=nbuckets, normalize=True) - print('Preprocessing') - index.preprocess(train) - print('Training') - training_points = nbuckets * 50 - rng = np.random.default_rng() - training_sample_idxs = rng.choice(len(train), size=training_points, replace=False) - training_sample_idxs.sort() - index.train(train[training_sample_idxs]) - print('PDXifying') - index.add(train) + index = IndexPDXIVF2(num_dimensions=num_dimensions, normalize=True) + print('Building index...') + index.build(train) + print(f'Index built: {index.num_clusters} clusters') + print(f'{len(queries)} queries with PDX') times = [] clock = TicToc() - results = [] for i in range(len(queries)): q = np.ascontiguousarray(queries[i]) clock.tic() index.search(q, knn, nprobe=nprobe) times.append(clock.toc()) print('PDX med. time:', np.median(np.array(times))) - # To check results of first query - results = index.search(np.ascontiguousarray(queries[0]), knn, nprobe=nprobe) - print(results) - print(f'{len(queries)} queries with FAISS') - times = [] - clock = TicToc() - results = [] - queries = index.preprocess(queries, inplace=False) - index.core_index.index.nprobe = nprobe - for i in range(len(queries)): - q = np.ascontiguousarray(np.array([queries[i]])) - clock.tic() - index.core_index.index.search(q, k=knn) - times.append(clock.toc()) - print('FAISS med. time:', np.median(np.array(times))) - # To check results of first query - print(index.core_index.index.search(np.array([queries[0]]), k=knn)) + # Show results of first query + ids, dists = index.search(np.ascontiguousarray(queries[0]), knn, nprobe=nprobe) + print('First query results (ids):', ids[:10]) + print('First query results (dists):', dists[:10]) diff --git a/examples/pdx_2l_ivf_8bit.py b/examples/pdx_2l_ivf_8bit.py index f938d39..10fdbb1 100644 --- a/examples/pdx_2l_ivf_8bit.py +++ b/examples/pdx_2l_ivf_8bit.py @@ -1,73 +1,38 @@ -import math import os import numpy as np from examples_utils import TicToc, read_hdf5_data -from pdxearch.index_factory import IndexPDXIVF2SQ8 +from pdxearch import IndexPDXIVF2SQ8 + np.random.seed(42) """ -PDXearch (pruned search) + ADSampling with a Two-Level IVF index (built with FAISS) + 8-bit Scalar Quantization -Recall is controled with nprobe parameter +PDXearch with a two-level IVF index + 8-bit scalar quantization (U8). +Recall is controlled with nprobe parameter. Download the .hdf5 data here: https://drive.google.com/drive/folders/1f76UCrU52N2wToGMFg9ir1MY8ZocrN34?usp=sharing """ if __name__ == "__main__": dataset_name = 'agnews-mxbai-1024-euclidean.hdf5' num_dimensions = 1024 - nprobe = 128 + nprobe = 25 knn = 100 - print(f'Running example: PDXearch + ADSampling (Two-Level IVF SQ8)\n- D={num_dimensions}\n- k={knn}\n- nprobe={nprobe}\n- dataset={dataset_name}') + print(f'Running example: PDXearch Two-Level IVF (SQ8)\n- D={num_dimensions}\n- k={knn}\n- nprobe={nprobe}\n- dataset={dataset_name}') train, queries = read_hdf5_data(os.path.join('./benchmarks/datasets/downloaded', dataset_name)) - nbuckets = 4 * math.ceil(math.sqrt(len(train))) - index = IndexPDXIVF2SQ8(ndim=num_dimensions, nbuckets=nbuckets, normalize=True) - print('Preprocessing') - index.preprocess(train) - print('Training') - training_points = nbuckets * 50 - rng = np.random.default_rng() - training_sample_idxs = rng.choice(len(train), size=training_points, replace=False) - training_sample_idxs.sort() - index.train(train[training_sample_idxs]) - print('PDXifying') - index.add(train) + index = IndexPDXIVF2SQ8(num_dimensions=num_dimensions, normalize=True) + print('Building index...') + index.build(train) + print(f'Index built: {index.num_clusters} clusters') + print(f'{len(queries)} queries with PDX') times = [] clock = TicToc() - results = [] for i in range(len(queries)): clock.tic() index.search(queries[i], knn, nprobe=nprobe) times.append(clock.toc()) print('PDX med. time:', np.median(np.array(times))) - # To check results of first query - results = index.search(np.ascontiguousarray(queries[0]), knn, nprobe=nprobe) - print(results) - - print(f'{len(queries)} queries with FAISS F32') - times = [] - clock = TicToc() - results = [] - queries = index.preprocess(queries, inplace=False) - index.core_index.index.nprobe = nprobe - for i in range(len(queries)): - q = np.ascontiguousarray(np.array([queries[i]])) - clock.tic() - index.core_index.index.search(q, k=knn) - times.append(clock.toc()) - print('FAISS med. time:', np.median(np.array(times))) - # To check results of first query - print(index.core_index.index.search(np.array([queries[0]]), k=knn)) - # Scalar Quantization in FAISS is EXTREMELY slow in ARM due to lack of SIMD - # print('Training FAISS SQ8') - # f_index = faiss.IndexScalarQuantizer(num_dimensions, faiss.ScalarQuantizer.QT_8bit) - # f_index.train(train[training_sample_idxs]) - # f_index.add(train) - # f_index.nprobe = nprobe - # print(f'{len(queries)} queries with FAISS U8') - # for i in range(len(queries)): - # q = np.ascontiguousarray(np.array([queries[i]])) - # clock.tic() - # f_index.search(q, k=knn) - # times.append(clock.toc()) - # print('FAISS med. time:', np.median(np.array(times))) + # Show results of first query + ids, dists = index.search(np.ascontiguousarray(queries[0]), knn, nprobe=nprobe) + print('First query results (ids):', ids[:10]) + print('First query results (dists):', dists[:10]) diff --git a/examples/pdx_ivf.py b/examples/pdx_ivf.py index 3d8d63d..8f205d6 100644 --- a/examples/pdx_ivf.py +++ b/examples/pdx_ivf.py @@ -1,62 +1,39 @@ -import math import os import numpy as np from examples_utils import TicToc, read_hdf5_data -from pdxearch.index_factory import IndexPDXADSamplingIVFFlat +from pdxearch import IndexPDXIVF + np.random.seed(42) """ -PDXearch (pruned search) + ADSampling with an IVF index (built with FAISS) -Recall is controled with nprobe parameter +PDXearch with a single-level IVF index (F32). +Recall is controlled with nprobe parameter. Download the .hdf5 data here: https://drive.google.com/drive/folders/1f76UCrU52N2wToGMFg9ir1MY8ZocrN34?usp=sharing """ if __name__ == "__main__": dataset_name = 'agnews-mxbai-1024-euclidean.hdf5' num_dimensions = 1024 - nprobe = 64 + nprobe = 25 knn = 100 - print(f'Running example: PDXearch + ADSampling (IVFFlat)\n- D={num_dimensions}\n- k={knn}\n- nprobe={nprobe}\n- dataset={dataset_name}') + print(f'Running example: PDXearch IVF (F32)\n- D={num_dimensions}\n- k={knn}\n- nprobe={nprobe}\n- dataset={dataset_name}') train, queries = read_hdf5_data(os.path.join('./benchmarks/datasets/downloaded', dataset_name)) - nbuckets = 1 * math.ceil(math.sqrt(len(train))) - index = IndexPDXADSamplingIVFFlat(ndim=num_dimensions, nbuckets=nbuckets, normalize=True) - print('Preprocessing') - index.preprocess(train) - print('Training') - training_points = nbuckets * 50 - rng = np.random.default_rng() - training_sample_idxs = rng.choice(len(train), size=training_points, replace=False) - training_sample_idxs.sort() - index.train(train[training_sample_idxs]) - print('PDXifying') - index.add(train) + index = IndexPDXIVF(num_dimensions=num_dimensions, normalize=True) + print('Building index...') + index.build(train) + print(f'Index built: {index.num_clusters} clusters') + print(f'{len(queries)} queries with PDX') times = [] clock = TicToc() - results = [] for i in range(len(queries)): q = np.ascontiguousarray(queries[i]) clock.tic() index.search(q, knn, nprobe=nprobe) times.append(clock.toc()) print('PDX med. time:', np.median(np.array(times))) - # To check results of first query - results = index.search(np.ascontiguousarray(queries[0]), knn, nprobe=nprobe) - print(results) - - print(f'{len(queries)} queries with FAISS') - times = [] - clock = TicToc() - results = [] - queries = index.preprocess(queries, inplace=False) - index.core_index.index.nprobe = nprobe - for i in range(len(queries)): - q = np.ascontiguousarray(np.array([queries[i]])) - clock.tic() - index.core_index.index.search(q, k=knn) - times.append(clock.toc()) - print('FAISS med. time:', np.median(np.array(times))) - # To check results of first query - print(index.core_index.index.search(np.array([queries[0]]), k=knn)) - + # Show results of first query + ids, dists = index.search(np.ascontiguousarray(queries[0]), knn, nprobe=nprobe) + print('First query results (ids):', ids[:10]) + print('First query results (dists):', dists[:10]) diff --git a/include/pdx/index.hpp b/include/pdx/index.hpp index c011e53..423bc5b 100644 --- a/include/pdx/index.hpp +++ b/include/pdx/index.hpp @@ -35,6 +35,7 @@ class IPDXIndex { public: virtual ~IPDXIndex() = default; virtual std::vector Search(const float *query_embedding, size_t knn) const = 0; + virtual void BuildIndex(const float *embeddings, size_t num_embeddings) = 0; virtual void SetNProbe(uint32_t n_probe) const = 0; virtual void Save(const std::string &path) const = 0; virtual void Restore(const std::string &path) = 0; @@ -128,7 +129,7 @@ class PDXIndex : public IPDXIndex { return index.num_clusters; } - void BuildIndex(const float *const embeddings, const size_t num_embeddings) { + void BuildIndex(const float *const embeddings, const size_t num_embeddings) override { std::vector row_ids(num_embeddings); std::iota(row_ids.begin(), row_ids.end(), 0); BuildIndex(row_ids.data(), embeddings, num_embeddings); @@ -286,6 +287,10 @@ class PDXTreeIndex : public IPDXIndex { std::vector Search(const float *query_embedding, size_t knn) const override { auto n_probe_top_level = GetTopLevelNumClusters(); + // We confidently prune half of the search space + if (searcher->GetNProbe() < GetNumClusters() / 2) { + n_probe_top_level /= 2; + } top_level_searcher->SetNProbe(n_probe_top_level); auto top_level_results = top_level_searcher->Search(query_embedding, searcher->GetNProbe()); @@ -298,7 +303,7 @@ class PDXTreeIndex : public IPDXIndex { return searcher->Search(query_embedding, knn); } - void BuildIndex(const float *const embeddings, const size_t num_embeddings) { + void BuildIndex(const float *const embeddings, const size_t num_embeddings) override { std::vector row_ids(num_embeddings); std::iota(row_ids.begin(), row_ids.end(), 0); BuildIndex(row_ids.data(), embeddings, num_embeddings); diff --git a/include/pdx/lib/lib.hpp b/include/pdx/lib/lib.hpp index 5b16553..246c9a7 100644 --- a/include/pdx/lib/lib.hpp +++ b/include/pdx/lib/lib.hpp @@ -1,400 +1,112 @@ -#ifndef PDX_LIB -#define PDX_LIB +#pragma once #include -#include #include #include -#include #include -#include "pdx/utils.hpp" -#include "pdx/ivf_wrapper.hpp" -#include "pdx/searcher.hpp" -#include "pdx/pruners/adsampling.hpp" +#include +#include + +#include "pdx/index.hpp" -// TODO: the python wrapper and the core API should not be interleaved namespace py = pybind11; -/****************************************************************** - * Very rudimentary wrappers for python bindings - * Probably a lot of room for improvement (TODO) - ******************************************************************/ namespace PDX { -class IndexADSamplingIVF2SQ8 { - - using KNNCandidate = KNNCandidate; - using Index = IndexPDXIVF2; - using Pruner = ADSamplingPruner; - using Searcher = PDXearch; - -public: - Index index = Index(); - std::unique_ptr searcher = nullptr; - std::unique_ptr transformation_matrix = nullptr; - constexpr static float epsilon0 = 1.5; - - void Load(const py::bytes& data, const py::array_t& _matrix){ - py::buffer_info info(py::buffer(data).request()); - auto data_ = static_cast(info.ptr); - index.Load(data_); - - auto matrix_buf = _matrix.request(); - auto matrix_ptr = static_cast(matrix_buf.ptr); - Pruner pruner = Pruner(index.num_dimensions, epsilon0, matrix_ptr); - searcher = std::make_unique(index, pruner, 1, SEQUENTIAL); - } - - void Restore(const std::string &path, const std::string &matrix_path){ - index.Restore(path); - transformation_matrix = MmapFile(matrix_path); - auto *_matrix = reinterpret_cast(transformation_matrix.get()); - Pruner pruner = Pruner(index.num_dimensions, epsilon0, _matrix); - searcher = std::make_unique(index, pruner, 1, SEQUENTIAL); - } - - void SetPruningConfidence(float confidence) const { - searcher->pruner.SetEpsilon0(confidence); - } - - std::vector Search(float *q, uint32_t k) const { - return searcher->Search(q, k); - } - - // Serialize return value - std::pair, py::array_t> - _py_Search(const py::array_t& q, uint32_t k, uint32_t n_probe) const { - auto buf = q.request(); // Get buffer info - if (buf.ndim != 1) { - throw std::runtime_error("Input should be a 1-D NumPy array"); - } - auto query = static_cast(buf.ptr); - searcher->SetNProbe(n_probe); - std::vector results = searcher->Search(query, k); - size_t n = results.size(); - py::array_t ids(n); - py::array_t distances(n); - auto ids_ptr = ids.mutable_unchecked<1>(); - auto distances_ptr = distances.mutable_unchecked<1>(); - for (size_t i = 0; i < n; ++i) { - ids_ptr(i) = results[i].index; - distances_ptr(i) = results[i].distance; - } - return {ids, distances}; - } - - std::pair, py::array_t> - _py_FilteredSearch( - const py::array_t& q, uint32_t k, uint32_t n_probe, - const py::array_t& n_passing_tuples, const py::array_t& selection_vector - ) const { - auto buf = q.request(); // Get buffer info - if (buf.ndim != 1) { - throw std::runtime_error("Input should be a 1-D NumPy array"); - } - auto query = static_cast(buf.ptr); - auto n_passing_tuples_p = static_cast(n_passing_tuples.request().ptr); - auto selection_vector_p = static_cast(selection_vector.request().ptr); - - PredicateEvaluator pe = PredicateEvaluator(searcher->pdx_data.num_clusters); - pe.LoadSelectionVector(n_passing_tuples_p, selection_vector_p); - - searcher->SetNProbe(n_probe); - std::vector results = searcher->FilteredSearch(query, k, pe); - size_t n = results.size(); - py::array_t ids(n); - py::array_t distances(n); - auto ids_ptr = ids.mutable_unchecked<1>(); - auto distances_ptr = distances.mutable_unchecked<1>(); - for (size_t i = 0; i < n; ++i) { - ids_ptr(i) = results[i].index; - distances_ptr(i) = results[i].distance; - } - return {ids, distances}; - } - -}; - -class IndexADSamplingIVF2Flat { - - using KNNCandidate = KNNCandidate; - using Index = IndexPDXIVF2; - using Pruner = ADSamplingPruner; - using Searcher = PDXearch; - -public: - Index index = Index(); - std::unique_ptr searcher = nullptr; - std::unique_ptr transformation_matrix = nullptr; - constexpr static float epsilon0 = 1.5; - - void Load(const py::bytes& data, const py::array_t& _matrix){ - py::buffer_info info(py::buffer(data).request()); - auto data_ = static_cast(info.ptr); - index.Load(data_); - - auto matrix_buf = _matrix.request(); - auto matrix_ptr = static_cast(matrix_buf.ptr); - Pruner pruner = Pruner(index.num_dimensions, epsilon0, matrix_ptr); - searcher = std::make_unique(index, pruner, 1, SEQUENTIAL); - } - - void Restore(const std::string &path, const std::string &matrix_path){ - index.Restore(path); - transformation_matrix = MmapFile(matrix_path); - auto *_matrix = reinterpret_cast(transformation_matrix.get()); - Pruner pruner = Pruner(index.num_dimensions, epsilon0, _matrix); - searcher = std::make_unique(index, pruner, 1, SEQUENTIAL); - } - - void SetPruningConfidence(float confidence) const { - searcher->pruner.SetEpsilon0(confidence); - } - - std::vector Search(float *q, uint32_t k) const { - return searcher->Search(q, k); - } - - // Serialize return value - std::pair, py::array_t> - _py_Search(const py::array_t& q, uint32_t k, uint32_t n_probe) const { - auto buf = q.request(); // Get buffer info - if (buf.ndim != 1) { - throw std::runtime_error("Input should be a 1-D NumPy array"); - } - auto query = static_cast(buf.ptr); - searcher->SetNProbe(n_probe); - std::vector results = searcher->Search(query, k); - size_t n = results.size(); - py::array_t ids(n); - py::array_t distances(n); - auto ids_ptr = ids.mutable_unchecked<1>(); - auto distances_ptr = distances.mutable_unchecked<1>(); - for (size_t i = 0; i < n; ++i) { - ids_ptr(i) = results[i].index; - distances_ptr(i) = results[i].distance; - } - return {ids, distances}; - } - - std::pair, py::array_t> - _py_FilteredSearch( - const py::array_t& q, uint32_t k, uint32_t n_probe, - const py::array_t& n_passing_tuples, const py::array_t& selection_vector - ) const { - auto buf = q.request(); // Get buffer info - if (buf.ndim != 1) { - throw std::runtime_error("Input should be a 1-D NumPy array"); - } - auto query = static_cast(buf.ptr); - auto n_passing_tuples_p = static_cast(n_passing_tuples.request().ptr); - auto selection_vector_p = static_cast(selection_vector.request().ptr); - - PredicateEvaluator pe = PredicateEvaluator(searcher->pdx_data.num_clusters); - pe.LoadSelectionVector(n_passing_tuples_p, selection_vector_p); +inline DistanceMetric ToDistanceMetric(uint8_t metric) { + switch (metric) { + case 0: return DistanceMetric::L2SQ; + case 1: return DistanceMetric::COSINE; + case 2: return DistanceMetric::IP; + default: throw std::runtime_error("Unknown distance metric: " + std::to_string(metric)); + } +} - searcher->SetNProbe(n_probe); - std::vector results = searcher->FilteredSearch(query, k, pe); - size_t n = results.size(); - py::array_t ids(n); - py::array_t distances(n); - auto ids_ptr = ids.mutable_unchecked<1>(); - auto distances_ptr = distances.mutable_unchecked<1>(); - for (size_t i = 0; i < n; ++i) { - ids_ptr(i) = results[i].index; - distances_ptr(i) = results[i].distance; - } - return {ids, distances}; - } +class PyPDXIndex { + std::unique_ptr index; -}; + PyPDXIndex() = default; -class IndexADSamplingIVFFlat { - - using KNNCandidate = KNNCandidate; - using Index = IndexPDXIVF; - using Pruner = ADSamplingPruner; - using Searcher = PDXearch; - public: - Index index = Index(); - std::unique_ptr searcher = nullptr; - std::unique_ptr transformation_matrix = nullptr; - constexpr static float epsilon0 = 1.5; - - void Load(const py::bytes& data, const py::array_t& _matrix){ - py::buffer_info info(py::buffer(data).request()); - auto data_ = static_cast(info.ptr); - index.Load(data_); - - auto matrix_buf = _matrix.request(); - auto matrix_ptr = static_cast(matrix_buf.ptr); - Pruner pruner = Pruner(index.num_dimensions, epsilon0, matrix_ptr); - searcher = std::make_unique(index, pruner, 1, SEQUENTIAL); - } - - void Restore(const std::string &path, const std::string &matrix_path){ - index.Restore(path); - transformation_matrix = MmapFile(matrix_path); - auto *_matrix = reinterpret_cast(transformation_matrix.get()); - Pruner pruner = Pruner(index.num_dimensions, epsilon0, _matrix); - searcher = std::make_unique(index, pruner, 1, SEQUENTIAL); - } - - void SetPruningConfidence(float confidence) const { - searcher->pruner.SetEpsilon0(confidence); - } - - std::vector Search(float *q, uint32_t k) const { - return searcher->Search(q, k); - } - - // Serialize return value - std::pair, py::array_t> - _py_Search(const py::array_t& q, uint32_t k, uint32_t n_probe) const { - auto buf = q.request(); // Get buffer info - if (buf.ndim != 1) { - throw std::runtime_error("Input should be a 1-D NumPy array"); - } - auto query = static_cast(buf.ptr); - searcher->SetNProbe(n_probe); - std::vector results = searcher->Search(query, k); - size_t n = results.size(); - py::array_t ids(n); - py::array_t distances(n); - auto ids_ptr = ids.mutable_unchecked<1>(); - auto distances_ptr = distances.mutable_unchecked<1>(); - for (size_t i = 0; i < n; ++i) { - ids_ptr(i) = results[i].index; - distances_ptr(i) = results[i].distance; - } - return {ids, distances}; - } - - std::pair, py::array_t> - _py_FilteredSearch( - const py::array_t& q, uint32_t k, uint32_t n_probe, - const py::array_t& n_passing_tuples, const py::array_t& selection_vector - ) const { - auto buf = q.request(); // Get buffer info - if (buf.ndim != 1) { - throw std::runtime_error("Input should be a 1-D NumPy array"); - } - auto query = static_cast(buf.ptr); - auto n_passing_tuples_p = static_cast(n_passing_tuples.request().ptr); - auto selection_vector_p = static_cast(selection_vector.request().ptr); - - PredicateEvaluator pe = PredicateEvaluator(searcher->pdx_data.num_clusters); - pe.LoadSelectionVector(n_passing_tuples_p, selection_vector_p); - - searcher->SetNProbe(n_probe); - std::vector results = searcher->FilteredSearch(query, k, pe); - size_t n = results.size(); - py::array_t ids(n); - py::array_t distances(n); - auto ids_ptr = ids.mutable_unchecked<1>(); - auto distances_ptr = distances.mutable_unchecked<1>(); - for (size_t i = 0; i < n; ++i) { - ids_ptr(i) = results[i].index; - distances_ptr(i) = results[i].distance; - } - return {ids, distances}; - } - -}; - -class IndexADSamplingFlat { - - using KNNCandidate = KNNCandidate; - using Index = IndexPDXIVF; - using Pruner = ADSamplingPruner; - using Searcher = PDXearch; - -public: - Index index = Index(); - std::unique_ptr searcher = nullptr; - std::unique_ptr transformation_matrix = nullptr; - constexpr static float epsilon0 = 1.5; - - void Load(const py::bytes& data, const py::array_t& _matrix){ - py::buffer_info info(py::buffer(data).request()); - auto data_ = static_cast(info.ptr); - index.Load(data_); - - auto matrix_buf = _matrix.request(); - auto matrix_ptr = static_cast(matrix_buf.ptr); - Pruner pruner = Pruner(index.num_dimensions, epsilon0, matrix_ptr); - searcher = std::make_unique(index, pruner, 1, SEQUENTIAL); - } - - void Restore(const std::string &path, const std::string &matrix_path){ - index.Restore(path); - transformation_matrix = MmapFile(matrix_path); - auto *_matrix = reinterpret_cast(transformation_matrix.get()); - Pruner pruner = Pruner(index.num_dimensions, epsilon0, _matrix); - searcher = std::make_unique(index, pruner, 1, SEQUENTIAL); - } - - void SetPruningConfidence(float confidence) const { - searcher->pruner.SetEpsilon0(confidence); - } - - std::vector Search(float *q, uint32_t k) const { - return searcher->Search(q, k); - } - - // Serialize return value - std::pair, py::array_t> - _py_Search(const py::array_t& q, uint32_t k) const { - auto buf = q.request(); // Get buffer info - if (buf.ndim != 1) { - throw std::runtime_error("Input should be a 1-D NumPy array"); - } - auto query = static_cast(buf.ptr); - std::vector results = searcher->Search(query, k); - size_t n = results.size(); - py::array_t ids(n); - py::array_t distances(n); - auto ids_ptr = ids.mutable_unchecked<1>(); - auto distances_ptr = distances.mutable_unchecked<1>(); - for (size_t i = 0; i < n; ++i) { - ids_ptr(i) = results[i].index; - distances_ptr(i) = results[i].distance; - } - return {ids, distances}; - } - - std::pair, py::array_t> - _py_FilteredSearch( - const py::array_t& q, uint32_t k, - const py::array_t& n_passing_tuples, const py::array_t& selection_vector - ) const { - auto buf = q.request(); // Get buffer info - if (buf.ndim != 1) { - throw std::runtime_error("Input should be a 1-D NumPy array"); - } - auto query = static_cast(buf.ptr); - auto n_passing_tuples_p = static_cast(n_passing_tuples.request().ptr); - auto selection_vector_p = static_cast(selection_vector.request().ptr); - - PredicateEvaluator pe = PredicateEvaluator(searcher->pdx_data.num_clusters); - pe.LoadSelectionVector(n_passing_tuples_p, selection_vector_p); - - std::vector results = searcher->FilteredSearch(query, k, pe); - size_t n = results.size(); - py::array_t ids(n); - py::array_t distances(n); - auto ids_ptr = ids.mutable_unchecked<1>(); - auto distances_ptr = distances.mutable_unchecked<1>(); - for (size_t i = 0; i < n; ++i) { - ids_ptr(i) = results[i].index; - distances_ptr(i) = results[i].distance; - } - return {ids, distances}; - } - + PyPDXIndex(const std::string &index_type, uint32_t num_dimensions, + uint8_t distance_metric, uint32_t seed, uint32_t num_clusters, + uint32_t num_meso_clusters, bool normalize, + float sampling_fraction, uint32_t kmeans_iters) { + PDXIndexConfig config { + .num_dimensions = num_dimensions, + .distance_metric = ToDistanceMetric(distance_metric), + .seed = seed, + .num_clusters = num_clusters, + .num_meso_clusters = num_meso_clusters, + .normalize = normalize, + .sampling_fraction = sampling_fraction, + .kmeans_iters = kmeans_iters, + }; + if (index_type == "pdx_f32") { + index = std::make_unique(config); + } else if (index_type == "pdx_u8") { + index = std::make_unique(config); + } else if (index_type == "pdx_tree_f32") { + index = std::make_unique(config); + } else if (index_type == "pdx_tree_u8") { + index = std::make_unique(config); + } else { + throw std::runtime_error("Unknown index type: " + index_type + + ". Valid types: pdx_f32, pdx_u8, pdx_tree_f32, pdx_tree_u8"); + } + } + + static PyPDXIndex LoadFromFile(const std::string &path) { + PyPDXIndex self; + self.index = PDX::LoadPDXIndex(path); + return self; + } + + void BuildIndex(const py::array_t &data) { + auto buf = data.request(); + if (buf.ndim != 2) { + throw std::runtime_error("data must be a 2D numpy array (n_embeddings x dimensions)"); + } + auto *ptr = static_cast(buf.ptr); + size_t n = static_cast(buf.shape[0]); + index->BuildIndex(ptr, n); + } + + std::pair, py::array_t> + Search(const py::array_t &query, uint32_t knn) const { + auto buf = query.request(); + if (buf.ndim != 1) { + throw std::runtime_error("query must be a 1D numpy array"); + } + auto *ptr = static_cast(buf.ptr); + auto results = index->Search(ptr, knn); + size_t n = results.size(); + py::array_t ids(n); + py::array_t distances(n); + auto ids_ptr = ids.mutable_unchecked<1>(); + auto distances_ptr = distances.mutable_unchecked<1>(); + for (size_t i = 0; i < n; ++i) { + ids_ptr(i) = results[i].index; + distances_ptr(i) = results[i].distance; + } + return {ids, distances}; + } + + void SetNProbe(uint32_t n) const { + index->SetNProbe(n); + } + + void Save(const std::string &path) const { + index->Save(path); + } + + uint32_t GetNumDimensions() const { + return index->GetNumDimensions(); + } + + uint32_t GetNumClusters() const { + return index->GetNumClusters(); + } }; } // namespace PDX - -#endif // PDX_LIB \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index a8e5f29..c6b5a68 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,10 +1,39 @@ [build-system] -requires = [ - "setuptools>=42", - "wheel", - "cmake>=3.22", - "pybind11", +requires = ["scikit-build-core>=0.3.3", "pybind11>=2.10.0"] +build-backend = "scikit_build_core.build" + +[project] +name = "pdxearch" +version = "0.4" +description = "Faster similarity search with PDX: A transposed data layout for vectors" +readme = "README.md" +requires-python = ">=3.8" +authors = [ + {name = "CWI", email = "lxkr@cwi.nl"} +] +license = {text = "Apache-2.0"} +classifiers = [ + "Development Status :: 3 - Alpha", + "Natural Language :: English", + "Intended Audience :: Developers", + "Intended Audience :: Information Technology", + "License :: OSI Approved :: Apache Software License", + "Programming Language :: C++", + "Programming Language :: Python :: 3 :: Only", + "Operating System :: MacOS", + "Operating System :: Unix", + "Topic :: Database :: Database Engines/Servers", + "Topic :: Scientific/Engineering :: Artificial Intelligence", +] +dependencies = [ "numpy", - "scipy" ] -build-backend = "setuptools.build_meta" \ No newline at end of file + +[project.urls] +Homepage = "https://github.com/cwida/PDX" +Repository = "https://github.com/cwida/PDX" + +[tool.scikit-build] +cmake.source-dir = "." +wheel.packages = ["python/pdxearch"] +cmake.build-type = "Release" diff --git a/python/lib.cpp b/python/lib.cpp index b661a9d..95c6ee1 100644 --- a/python/lib.cpp +++ b/python/lib.cpp @@ -3,71 +3,28 @@ namespace py = pybind11; -/****************************************************************** - * Wrapper for Python bindings - * TODO: Implement quantized classes - ******************************************************************/ PYBIND11_MODULE(compiled, m) { - - m.doc() = "A library to do vertical pruned vector similarity search"; - - py::class_>(m, "Cluster") - .def(py::init<>()) - .def_readwrite("num_embeddings", &PDX::Cluster::num_embeddings) - .def_readwrite("indices", &PDX::Cluster::indices) - .def_readwrite("data", &PDX::Cluster::data); - - py::class_>(m, "IndexPDXIVFFlat") - .def_readwrite("num_dimensions", &PDX::IndexPDXIVF::num_dimensions) - .def_readwrite("num_clusters", &PDX::IndexPDXIVF::num_clusters) - .def_readwrite("clusters", &PDX::IndexPDXIVF::clusters) - .def_readwrite("means", &PDX::IndexPDXIVF::means) - .def_readwrite("is_ivf", &PDX::IndexPDXIVF::is_ivf) - .def_readwrite("centroids", &PDX::IndexPDXIVF::centroids) - .def_readwrite("centroids_pdx", &PDX::IndexPDXIVF::centroids_pdx) - .def("restore", &PDX::IndexPDXIVF::Restore); - - py::class_>(m, "KNNCandidate") - .def(py::init<>()) - .def_readwrite("index", &PDX::KNNCandidate::index) - .def_readwrite("distance", &PDX::KNNCandidate::distance); - - py::class_>(m, "KNNCandidateSQ8") - .def(py::init<>()) - .def_readwrite("index", &PDX::KNNCandidate::index) - .def_readwrite("distance", &PDX::KNNCandidate::distance); - - py::class_(m, "IndexADSamplingIVF2SQ8") - .def(py::init<>()) - .def("restore", &PDX::IndexADSamplingIVF2SQ8::Restore, py::arg("path"), py::arg("matrix_path")) - .def("load", &PDX::IndexADSamplingIVF2SQ8::Load, py::arg("data"), py::arg("matrix")) - .def("set_pruning_confidence", &PDX::IndexADSamplingIVF2SQ8::SetPruningConfidence, py::arg("confidence")) - .def("search", &PDX::IndexADSamplingIVF2SQ8::_py_Search, py::arg("q"), py::arg("k"), py::arg("n_probe")) - .def("filtered_search", &PDX::IndexADSamplingIVF2SQ8::_py_FilteredSearch, py::arg("q"), py::arg("k"), py::arg("n_probe"), py::arg("n_passing_tuples"), py::arg("selection_vector")); - - py::class_(m, "IndexADSamplingIVF2Flat") - .def(py::init<>()) - .def("restore", &PDX::IndexADSamplingIVF2Flat::Restore, py::arg("path"), py::arg("matrix_path")) - .def("load", &PDX::IndexADSamplingIVF2Flat::Load, py::arg("data"), py::arg("matrix")) - .def("set_pruning_confidence", &PDX::IndexADSamplingIVF2Flat::SetPruningConfidence, py::arg("confidence")) - .def("search", &PDX::IndexADSamplingIVF2Flat::_py_Search, py::arg("q"), py::arg("k"), py::arg("n_probe")) - .def("filtered_search", &PDX::IndexADSamplingIVF2Flat::_py_FilteredSearch, py::arg("q"), py::arg("k"), py::arg("n_probe"), py::arg("n_passing_tuples"), py::arg("selection_vector")); - - py::class_(m, "IndexADSamplingIVFFlat") - .def(py::init<>()) - .def("restore", &PDX::IndexADSamplingIVFFlat::Restore, py::arg("path"), py::arg("matrix_path")) - .def("load", &PDX::IndexADSamplingIVFFlat::Load, py::arg("data"), py::arg("matrix")) - .def("set_pruning_confidence", &PDX::IndexADSamplingIVFFlat::SetPruningConfidence, py::arg("confidence")) - .def("search", &PDX::IndexADSamplingIVFFlat::_py_Search, py::arg("q"), py::arg("k"), py::arg("n_probe")) - .def("filtered_search", &PDX::IndexADSamplingIVFFlat::_py_FilteredSearch, py::arg("q"), py::arg("k"), py::arg("n_probe"), py::arg("n_passing_tuples"), py::arg("selection_vector")); - - py::class_(m, "IndexADSamplingFlat") - .def(py::init<>()) - .def("restore", &PDX::IndexADSamplingFlat::Restore, py::arg("path"), py::arg("matrix_path")) - .def("load", &PDX::IndexADSamplingFlat::Load, py::arg("data"), py::arg("matrix")) - .def("set_pruning_confidence", &PDX::IndexADSamplingFlat::SetPruningConfidence, py::arg("confidence")) - .def("search", &PDX::IndexADSamplingFlat::_py_Search, py::arg("q"), py::arg("k")) - .def("filtered_search", &PDX::IndexADSamplingFlat::_py_FilteredSearch, py::arg("q"), py::arg("k"), py::arg("n_passing_tuples"), py::arg("selection_vector")); - - -} \ No newline at end of file + m.doc() = "PDXearch: Faster similarity search with a transposed data layout for vectors"; + + py::class_(m, "PDXIndex") + .def(py::init(), + py::arg("index_type"), + py::arg("num_dimensions"), + py::arg("distance_metric") = 0, + py::arg("seed") = 42, + py::arg("num_clusters") = 0, + py::arg("num_meso_clusters") = 0, + py::arg("normalize") = false, + py::arg("sampling_fraction") = 0.0f, + py::arg("kmeans_iters") = 10) + .def("build_index", &PDX::PyPDXIndex::BuildIndex, py::arg("data")) + .def("search", &PDX::PyPDXIndex::Search, py::arg("query"), py::arg("knn")) + .def("set_nprobe", &PDX::PyPDXIndex::SetNProbe, py::arg("nprobe")) + .def("save", &PDX::PyPDXIndex::Save, py::arg("path")) + .def("get_num_dimensions", &PDX::PyPDXIndex::GetNumDimensions) + .def("get_num_clusters", &PDX::PyPDXIndex::GetNumClusters); + + m.def("load_index", &PDX::PyPDXIndex::LoadFromFile, py::arg("path"), + "Load a PDX index from a single file (auto-detects type)."); +} diff --git a/python/pdxearch/__init__.py b/python/pdxearch/__init__.py index f514c84..2083617 100644 --- a/python/pdxearch/__init__.py +++ b/python/pdxearch/__init__.py @@ -1,2 +1,7 @@ -import numpy as np -np.random.seed(42) +from pdxearch.index_factory import ( + IndexPDXIVF, + IndexPDXIVFSQ8, + IndexPDXIVF2, + IndexPDXIVF2SQ8, + load_index, +) diff --git a/python/pdxearch/constants.py b/python/pdxearch/constants.py deleted file mode 100644 index 33a0812..0000000 --- a/python/pdxearch/constants.py +++ /dev/null @@ -1,22 +0,0 @@ -from enum import Enum -import ctypes.util - -# Some constants -class PDXConstants: - PDX_VECTOR_SIZE = 64 - PDX_CENTROIDS_VECTOR_SIZE = 64 - D_THRESHOLD_FOR_DCT_ROTATION = 512 - HORIZONTAL_DIMENSIONS_GROUPING = 64 - X4_GROUPING = 4 - U8_MAX = 255 # TODO: Fix for Intel can only by max of 127 (there are mathematical workarounds) - VERTICAL_PROPORTION_DIM = 0.75 - PDXEARCH_VECTOR_SIZE = 10240 # Probably ADSampling can do less and find benefits - SUPPORTED_METRICS = [ - "l2sq" - ] - HAS_FFTW = False # ctypes.util.find_library("fftw3f") is not None - - -class PDXDistanceMetrics(Enum): - l2sq = 1 - diff --git a/python/pdxearch/index_base.py b/python/pdxearch/index_base.py deleted file mode 100644 index 17c39d9..0000000 --- a/python/pdxearch/index_base.py +++ /dev/null @@ -1,440 +0,0 @@ -import numpy as np -import sys -import math -from typing import List -from pdxearch.index_core import IVF, IVF2 -from pdxearch.constants import PDXConstants - -class Partition: - def __init__(self): - self.num_embeddings = 0 - self.indices = np.array([]) - self.blocks = [] - -class BaseIndexPDXIVF2: - def __init__( - self, - ndim: int, - metric: str, - nbuckets: int = 16, - nbuckets_l0: int = 2, - normalize: bool = True - ): - if ndim < 128: - raise Exception('`ndim` must be >= 128') - if ndim % 4 != 0: - raise Exception('`ndim` must be multiple of 4') - self.ndim = ndim - self.normalize = normalize - self.dtype = np.float32 - self.nbuckets = nbuckets - self.nbuckets_l0 = nbuckets_l0 - self.for_base = 0.0 - self.scale_factor = 0.0 - - self.core_index = IVF2(ndim, metric, nbuckets, nbuckets_l0) - - self.means: np.array = None - self.centroids: np.array = np.array([], dtype=np.float32) - self.partitions: List[Partition] = [] - self.num_partitions: int = 0 - - self.means_l0: np.array = None - self.centroids_l0: np.array = np.array([], dtype=np.float32) - self.partitions_l0: List[Partition] = [] - self.num_partitions_l0: int = 0 - - self.materialized_index = None - - def train(self, data: np.array, **kwargs): - self.core_index.train(data, **kwargs) - - def _add(self, data: np.array, **kwargs): - self.core_index.add(data, **kwargs) - - def _train_add_l0(self, **kwargs): - self.core_index.train_add_l0(**kwargs) - - # Separate the data in the PDX blocks - # TODO: Most probably this can be much more efficient - def _to_pdx(self, data: np.array, _type='pdx', centroids_preprocessor=None, use_original_centroids=False, **kwargs): - use_sq = kwargs.get('quantize', False) - self.partitions = [] - self.partitions_l0 = [] - self.means = data.mean(axis=0, dtype=np.float32) - self.centroids = np.array(self.core_index.centroids, dtype=np.float32) - self.centroids_l0 = np.array(self.core_index.centroids_l0, dtype=np.float32) - if use_original_centroids: - if centroids_preprocessor is not None: - centroids_preprocessor.preprocess(self.centroids, inplace=True) - centroids_preprocessor.preprocess(self.centroids_l0, inplace=True) - for list_id in range(self.core_index.nbuckets_l0): - list_ids = self.core_index.labels_l0[list_id] - num_list_embeddings = len(list_ids) - partition = Partition() - partition.num_embeddings = num_list_embeddings - partition.indices = np.zeros((partition.num_embeddings,), dtype=np.uint32) - for embedding_index in range(partition.num_embeddings): - partition.indices[embedding_index] = list_ids[embedding_index] - partition.blocks.append(self.centroids[partition.indices, :]) - self.partitions_l0.append(partition) - self.num_partitions_l0 = len(self.partitions_l0) - if use_sq: - data_max = data.max() - data_min = data.min() - data_range = data_max - data_min - data = data - data_min - global_scale_factor = float(PDXConstants.U8_MAX) / data_range - self.scale_factor = global_scale_factor - self.for_base = data_min - - for list_id in range(self.core_index.nbuckets): - list_ids = self.core_index.labels[list_id] - num_list_embeddings = len(list_ids) - partition = Partition() - partition.num_embeddings = num_list_embeddings - partition.indices = np.zeros((partition.num_embeddings,), dtype=np.uint32) - for embedding_index in range(partition.num_embeddings): - partition.indices[embedding_index] = list_ids[embedding_index] - if (_type == 'pdx-v4-h') and use_sq: - # TODO: Move outside (?) - pre_data = data[partition.indices, :] - pre_data = pre_data * global_scale_factor - pre_data = pre_data.round(decimals=0).astype(dtype=np.int32) - for_data = pre_data - for_data = for_data.astype(dtype=np.uint8) # Always using np.uint8 - partition.blocks.append(for_data) - else: - partition.blocks.append(data[partition.indices, :]) - self.partitions.append(partition) - self.num_partitions = len(self.partitions) - self._materialize_index(_type, **kwargs) - - # Materialize the index with the given layout - # TODO: Most probably this can be much more efficient - def _materialize_index(self, _type='pdx', **kwargs): - data = bytearray() - data.extend(np.int32(self.ndim).tobytes("C")) - - h_dims = int(self.ndim * PDXConstants.VERTICAL_PROPORTION_DIM) - v_dims = self.ndim - h_dims - if h_dims % PDXConstants.HORIZONTAL_DIMENSIONS_GROUPING != 0: - h_dims = math.floor((h_dims / PDXConstants.HORIZONTAL_DIMENSIONS_GROUPING) + 0.5) * PDXConstants.HORIZONTAL_DIMENSIONS_GROUPING - v_dims = self.ndim - h_dims - h_dims_block = kwargs.get('h_dims_block', PDXConstants.HORIZONTAL_DIMENSIONS_GROUPING) - if v_dims == 0: - h_dims = PDXConstants.HORIZONTAL_DIMENSIONS_GROUPING - v_dims = self.ndim - h_dims - - data.extend(np.int32(v_dims).tobytes("C")) - data.extend(np.int32(h_dims).tobytes("C")) - - data.extend(np.int32(self.num_partitions).tobytes("C")) - data.extend(np.int32(self.num_partitions_l0).tobytes("C")) - # L0 - for i in range(self.num_partitions_l0): - data.extend(np.int32(self.partitions_l0[i].num_embeddings).tobytes("C")) - for i in range(self.num_partitions_l0): - for p in range(len(self.partitions_l0[i].blocks)): - vertical_block = self.partitions_l0[i].blocks[p][:, :v_dims] - rows, _ = vertical_block.shape - data.extend(vertical_block.tobytes("F")) # PDX vertical block - pdx_h_block = self.partitions_l0[i].blocks[p][:, v_dims:].reshape(rows, -1, h_dims_block).transpose(1, 0, 2).reshape(-1) - data.extend(pdx_h_block.tobytes("C")) # PDX horizontal block to improve sequential access - for i in range(self.num_partitions_l0): - data.extend(self.partitions_l0[i].indices.tobytes("C")) - - # L1 - for i in range(self.num_partitions): - data.extend(np.int32(self.partitions[i].num_embeddings).tobytes("C")) - for i in range(self.num_partitions): - for p in range(len(self.partitions[i].blocks)): - assert h_dims % PDXConstants.X4_GROUPING == 0 - assert v_dims % PDXConstants.X4_GROUPING == 0 - assert h_dims + v_dims == self.ndim - assert v_dims != 0 - assert h_dims != 0 - assert h_dims % PDXConstants.HORIZONTAL_DIMENSIONS_GROUPING == 0 - if _type == 'pdx': - vertical_block = self.partitions[i].blocks[p][:, :v_dims] - rows, _ = vertical_block.shape - data.extend(vertical_block.tobytes("F")) # PDX vertical block - pdx_h_block = self.partitions[i].blocks[p][:, v_dims:].reshape(rows, -1, h_dims_block).transpose(1, 0, 2).reshape(-1) - data.extend(pdx_h_block.tobytes("C")) # PDX horizontal block to improve sequential access - elif _type == 'pdx-v4-h': - tmp_block = self.partitions[i].blocks[p][:, :v_dims] - rows, _ = tmp_block.shape - pdx_4_block = tmp_block.reshape(rows, -1, 4).transpose(1, 0, 2).reshape(-1) - assert h_dims % h_dims_block == 0 - data.extend(pdx_4_block.tobytes("C")) - # Horizontal block (rest) - pdx_h_block = self.partitions[i].blocks[p][:, v_dims:].reshape(rows, -1, h_dims_block).transpose(1, 0, 2).reshape(-1) - data.extend(pdx_h_block.tobytes("C")) - - for i in range(self.num_partitions): - data.extend(self.partitions[i].indices.tobytes("C")) - data.extend(self.normalize.to_bytes(1, sys.byteorder)) - # TODO: Support other multiples of 64 - if len(self.centroids_l0) == PDXConstants.PDX_CENTROIDS_VECTOR_SIZE: - data.extend(self.centroids_l0.tobytes("C")) # PDX - else: - data.extend(self.centroids_l0.tobytes("C")) # Nary format - if (_type == 'pdx-v4-h') and kwargs.get('quantize', False): - data.extend(np.float32(self.for_base).tobytes("C")) - data.extend(np.float32(self.scale_factor).tobytes("C")) - self.materialized_index = bytes(data) - - def _persist(self, path: str): - if self.materialized_index is None: - raise Exception('The index have not been created') - with open(path, "wb") as file: - file.write(self.materialized_index) - -class BaseIndexPDXIVF: - def __init__( - self, - ndim: int, - metric: str, - nbuckets: int = 16, - normalize: bool = True - ): - if ndim < 128: - raise Exception('`ndim` must be >= 128') - if ndim % 4 != 0: - raise Exception('`ndim` must be multiple of 4') - self.ndim = ndim - self.normalize = normalize - self.dtype = np.float32 - self.nbuckets = nbuckets - self.core_index = IVF(ndim, metric, nbuckets) - self.means: np.array = None - self.centroids: np.array = np.array([], dtype=np.float32) - self.partitions: List[Partition] = [] - self.num_partitions: int = 0 - self.materialized_index = None - self.for_base = 0.0 - self.scale_factor = 0.0 - - def train(self, data: np.array, **kwargs): - self.core_index.train(data, **kwargs) - - def _add(self, data: np.array, **kwargs): - self.core_index.add(data, **kwargs) - - # Separate the data in the PDX blocks - # TODO: Most probably this can be much more efficient - def _to_pdx(self, data: np.array, _type='pdx', centroids_preprocessor=None, use_original_centroids=False, **kwargs): - use_sq = kwargs.get('quantize', False) - self.partitions = [] - self.means = data.mean(axis=0, dtype=np.float32) - self.centroids = np.array(self.core_index.centroids, dtype=np.float32) - if use_original_centroids and centroids_preprocessor is not None: - centroids_preprocessor.preprocess(self.centroids, inplace=True) - if use_sq: - data_max = data.max() - data_min = data.min() - data_range = data_max - data_min - data = data - data_min - self.for_base = data_min - global_scale_factor = float(PDXConstants.U8_MAX) / data_range - for list_id in range(self.core_index.nbuckets): - list_ids = self.core_index.labels[list_id] - num_list_embeddings = len(list_ids) - partition = Partition() - partition.num_embeddings = num_list_embeddings - partition.indices = np.zeros((partition.num_embeddings,), dtype=np.uint32) - for embedding_index in range(partition.num_embeddings): - partition.indices[embedding_index] = list_ids[embedding_index] - if (_type == 'pdx-v4-h') and use_sq: - # TODO: Get out - pre_data = data[partition.indices, :] - pre_data = pre_data * global_scale_factor - pre_data = pre_data.round(decimals=0).astype(dtype=np.int32) - for_data = pre_data - self.scale_factor = global_scale_factor - for_data = for_data.astype(dtype=np.uint8) # Always using np.uint8 - partition.blocks.append(for_data) - else: - partition.blocks.append(data[partition.indices, :]) - if not use_original_centroids: - partition_centroids = np.mean(data[partition.indices, :], axis=0, dtype=np.float32) - self.centroids = np.append(self.centroids, partition_centroids) - self.partitions.append(partition) - self.num_partitions = len(self.partitions) - self._materialize_index(_type, **kwargs) - - # Materialize the index with the given layout - # TODO: Most probably this can be much more efficient - def _materialize_index(self, _type='pdx', **kwargs): - data = bytearray() - data.extend(np.int32(self.ndim).tobytes("C")) - - h_dims = int(self.ndim * PDXConstants.VERTICAL_PROPORTION_DIM) - v_dims = self.ndim - h_dims - if h_dims % PDXConstants.HORIZONTAL_DIMENSIONS_GROUPING != 0: - h_dims = math.floor((h_dims / PDXConstants.HORIZONTAL_DIMENSIONS_GROUPING) + 0.5) * PDXConstants.HORIZONTAL_DIMENSIONS_GROUPING - v_dims = self.ndim - h_dims - h_dims_block = kwargs.get('h_dims_block', PDXConstants.HORIZONTAL_DIMENSIONS_GROUPING) - if v_dims == 0: - h_dims = PDXConstants.HORIZONTAL_DIMENSIONS_GROUPING - v_dims = self.ndim - h_dims - data.extend(np.int32(v_dims).tobytes("C")) - data.extend(np.int32(h_dims).tobytes("C")) - - data.extend(np.int32(self.num_partitions).tobytes("C")) - - for i in range(self.num_partitions): - data.extend(np.int32(self.partitions[i].num_embeddings).tobytes("C")) - for i in range(self.num_partitions): - for p in range(len(self.partitions[i].blocks)): - assert h_dims % PDXConstants.X4_GROUPING == 0 - assert v_dims % PDXConstants.X4_GROUPING == 0 - assert h_dims + v_dims == self.ndim - assert v_dims != 0 - assert h_dims != 0 - assert h_dims % PDXConstants.HORIZONTAL_DIMENSIONS_GROUPING == 0 - if _type == 'pdx': - if kwargs.get('bond', False): - data.extend(self.partitions[i].blocks[p].tobytes("F")) - else: - vertical_block = self.partitions[i].blocks[p][:, :v_dims] - rows, _ = vertical_block.shape - data.extend(vertical_block.tobytes("F")) # PDX vertical block - pdx_h_block = self.partitions[i].blocks[p][:, v_dims:].reshape(rows, -1, h_dims_block).transpose(1, 0, 2).reshape(-1) - data.extend(pdx_h_block.tobytes("C")) # PDX horizontal block to improve sequential access - elif _type == 'pdx-v4-h': - tmp_block = self.partitions[i].blocks[p][:, :v_dims] - rows, _ = tmp_block.shape - pdx_4_block = tmp_block.reshape(rows, -1, 4).transpose(1, 0, 2).reshape(-1) - assert h_dims % h_dims_block == 0 - # Vertical Dimensions - data.extend(pdx_4_block.tobytes("C")) - # Horizontal block (rest) - pdx_h_block = self.partitions[i].blocks[p][:, v_dims:].reshape(rows, -1, h_dims_block).transpose(1, 0, 2).reshape(-1) - data.extend(pdx_h_block.tobytes("C")) - for i in range(self.num_partitions): - data.extend(self.partitions[i].indices.tobytes("C")) - if _type == 'pdx': - data.extend(self.means.tobytes("C")) - is_ivf = True - data.extend(self.normalize.to_bytes(1, sys.byteorder)) - data.extend(is_ivf.to_bytes(1, sys.byteorder)) - # Since centroids not many, we store them twice to have a dual layout - data.extend(self.centroids.tobytes("C")) # Nary format - if _type == 'pdx': - # PDX format - centroids_written = 0 - reshaped_centroids = np.reshape(self.centroids, (self.num_partitions, self.ndim)) - while centroids_written != self.num_partitions: - if centroids_written + PDXConstants.PDX_CENTROIDS_VECTOR_SIZE > self.num_partitions: - data.extend(reshaped_centroids[centroids_written:, :].tobytes("F")) - centroids_written = self.num_partitions - else: - data.extend( - reshaped_centroids[ - centroids_written: centroids_written + PDXConstants.PDX_CENTROIDS_VECTOR_SIZE, : - ].tobytes("F") - ) - centroids_written += PDXConstants.PDX_CENTROIDS_VECTOR_SIZE - if (_type == 'pdx-v4-h') and kwargs.get('quantize', False): - data.extend(np.float32(self.for_base).tobytes("C")) - data.extend(np.float32(self.scale_factor).tobytes("C")) - self.materialized_index = bytes(data) - - def _persist(self, path: str): - if self.materialized_index is None: - raise Exception('The index have not been created') - with open(path, "wb") as file: - file.write(self.materialized_index) - - -class BaseIndexPDXFlat: - def __init__( - self, - ndim: int, - metric: str, - normalize: bool = True, - ): - if ndim < 128: - raise Exception('`ndim` must be >= 128') - if ndim % 4 != 0: - raise Exception('`ndim` must be multiple of 4') - self.ndim = ndim - self.dtype = np.float32 - self.normalize = normalize - self.means: np.array = None - self.partitions: List[Partition] = [] - self.num_partitions: int = 0 - self.materialized_index = None - - # Separate the data in the PDX blocks - # TODO: Most probably this can be much more efficient - def _to_pdx(self, data: np.array, size_partition: int, _type='pdx', **kwargs): - self.partitions = [] - self.means = data.mean(axis=0, dtype=np.float32) - num_embeddings = len(data) - num_partitions = math.ceil(num_embeddings / size_partition) - indices = np.arange(0, num_embeddings, dtype=np.uint32) - - if kwargs.get('randomize', True): - np.random.shuffle(indices) - shuffle_index = 0 - for partition_index in range(num_partitions): - partition = Partition() - partition.num_embeddings = min(num_embeddings - shuffle_index, size_partition) - partition.indices = indices[shuffle_index: shuffle_index + partition.num_embeddings] - # Each partition is one block - partition.blocks.append(data[partition.indices, :]) - shuffle_index += partition.num_embeddings - self.partitions.append(partition) - self.num_partitions = len(self.partitions) - self._materialize_index(_type, **kwargs) - - # Materialize the index with the given layout - # TODO: Most probably this can be much more efficient - def _materialize_index(self, _type='pdx', **kwargs): - data = bytearray() - data.extend(np.int32(self.ndim).tobytes("C")) - - h_dims = int(self.ndim * PDXConstants.VERTICAL_PROPORTION_DIM) - v_dims = self.ndim - h_dims - if h_dims % PDXConstants.HORIZONTAL_DIMENSIONS_GROUPING != 0: - h_dims = math.floor((h_dims / PDXConstants.HORIZONTAL_DIMENSIONS_GROUPING) + 0.5) * PDXConstants.HORIZONTAL_DIMENSIONS_GROUPING - v_dims = self.ndim - h_dims - h_dims_block = kwargs.get('h_dims_block', PDXConstants.HORIZONTAL_DIMENSIONS_GROUPING) - if v_dims == 0: - h_dims = PDXConstants.HORIZONTAL_DIMENSIONS_GROUPING - v_dims = self.ndim - h_dims - data.extend(np.int32(v_dims).tobytes("C")) - data.extend(np.int32(h_dims).tobytes("C")) - - data.extend(np.int32(self.num_partitions).tobytes("C")) - - for i in range(self.num_partitions): - data.extend(np.int32(self.partitions[i].num_embeddings).tobytes("C")) - for i in range(self.num_partitions): - for p in range(len(self.partitions[i].blocks)): - if _type == 'pdx': - # In Bond we use fully decomposed - if kwargs.get('bond', False): - data.extend(self.partitions[i].blocks[p].tobytes("F")) - else: # In ADSampling we use a hybrid layout - vertical_block = self.partitions[i].blocks[p][:, :v_dims] - rows, _ = vertical_block.shape - data.extend(vertical_block.tobytes("F")) # PDX vertical block - pdx_h_block = self.partitions[i].blocks[p][:, v_dims:].reshape(rows, -1, h_dims_block).transpose(1, 0, 2).reshape(-1) - data.extend(pdx_h_block.tobytes("C")) # PDX horizontal block to improve sequential access - for i in range(self.num_partitions): - data.extend(self.partitions[i].indices.tobytes("C")) - data.extend(self.means.tobytes("C")) - is_ivf = False - data.extend(self.normalize.to_bytes(1, sys.byteorder)) - data.extend(is_ivf.to_bytes(1, sys.byteorder)) - self.materialized_index = bytes(data) - - def _persist(self, path: str): - if self.materialized_index is None: - raise Exception('The index have not been created') - with open(path, "wb") as file: - file.write(self.materialized_index) - diff --git a/python/pdxearch/index_core.py b/python/pdxearch/index_core.py deleted file mode 100644 index 8f7d6eb..0000000 --- a/python/pdxearch/index_core.py +++ /dev/null @@ -1,199 +0,0 @@ -import numpy as np -import struct - -from multiprocessing import cpu_count - -# -# Wrapper of FAISS FlatIVF index -# TODO: Support more distance metrics -# TODO: Replace FAISS implementation with a propietary one -# - -class PartitionsUtils: - @staticmethod - def write_centroids_and_labels(centroids, labels, path): - data = bytearray() - num_centroids = len(centroids) - data.extend(np.int32(num_centroids).tobytes("C")) - data.extend(centroids.tobytes("C")) - for l in labels: - labels_n = len(l) - data.extend(np.int32(labels_n).tobytes("C")) - data.extend(l.tobytes("C")) - with open(path + '.bin', "wb") as file: - file.write(bytes(data)) - - @staticmethod - def read_centroids_and_labels(ndim, f): - # Read number of centroids - num_centroids_bytes = f.read(4) - num_centroids = struct.unpack(' None: - IVF.faiss.omp_set_num_threads(cpu_count()) - if metric not in ["l2sq"]: - raise Exception("Distance metric not supported yet") - self.ndim = ndim - self.labels = [] - self.nbuckets = nbuckets - self.metric = metric - self.centroids = np.array([], dtype=np.float32) - - match self.metric: - case "l2sq": - quantizer = IVF.faiss.IndexFlatL2(int(self.ndim)) - case _: - quantizer = IVF.faiss.IndexFlatL2(int(self.ndim)) - self.index: IVF.faiss.IndexIVFFlat = IVF.faiss.IndexIVFFlat(quantizer, int(self.ndim), int(self.nbuckets)) - - - def train(self, data, **kwargs): - self.index.train(data, **kwargs) - - def add(self, data, **kwargs): - self.index.add(data, **kwargs) - self.centroids = self.index.quantizer.reconstruct_n(0, self.index.nlist) - self.fill_labels() - - def read_index(self, path): - with open(path + '.bin', 'rb') as f: - self.centroids, self.labels = self.read_centroids_and_labels(self.ndim, f) - self.nbuckets = len(self.labels) - # print('L1 buckets: ', self.nbuckets) - - def fill_labels(self): - for i in range(self.nbuckets): - _, cur_labels = self.get_inverted_list_metadata(i) - self.labels.append(cur_labels) - - def get_inverted_list_size(self, list_id): - return self.index.invlists.list_size(list_id) - - def get_inverted_list_ids(self, list_id): - return self.index.invlists.get_ids(list_id) - - def get_inverted_list_metadata(self, list_id): - num_list_embeddings = self.get_inverted_list_size(list_id) - list_ids = IVF.faiss.rev_swig_ptr(self.get_inverted_list_ids(list_id), num_list_embeddings) - return num_list_embeddings, list_ids - - def persist_core(self, path: str): - self.write_centroids_and_labels(self.centroids, self.labels, path) - - -class IVF2(PartitionsUtils): - import faiss - def __init__( - self, - ndim: int = 0, - metric: str = "l2sq", - nbuckets: int = 4, - nbuckets_l0: int = 32 - ) -> None: - IVF2.faiss.omp_set_num_threads(cpu_count()) - if metric not in ["l2sq"]: - raise Exception("Distance metric not supported yet") - self.ndim = ndim - self.nbuckets = nbuckets - self.nbuckets_l0 = nbuckets_l0 - self.metric = metric - self.labels = [] - self.labels_l0 = [] - self.centroids = np.array([], dtype=np.float32) - self.centroids_l0 = np.array([], dtype=np.float32) - - match self.metric: - case "l2sq": - quantizer = IVF2.faiss.IndexFlatL2(int(self.ndim)) - quantizer_l0 = IVF2.faiss.IndexFlatL2(int(self.ndim)) - case _: - quantizer = IVF2.faiss.IndexFlatL2(int(self.ndim)) - quantizer_l0 = IVF2.faiss.IndexFlatL2(int(self.ndim)) - self.index: IVF2.faiss.IndexIVFFlat = IVF2.faiss.IndexIVFFlat(quantizer, int(self.ndim), int(self.nbuckets)) - self.index_l0: IVF2.faiss.IndexIVFFlat = IVF2.faiss.IndexIVFFlat(quantizer_l0, int(self.ndim), int(self.nbuckets_l0)) - - def train(self, data, **kwargs): - self.index.train(data, **kwargs) - - def add(self, data, **kwargs): - self.index.add(data, **kwargs) - self.fill_labels(level=1) - - def read_index(self, path, path_l0): - with open(path + '.bin', 'rb') as f: - self.centroids, self.labels = self.read_centroids_and_labels(self.ndim, f) - self.nbuckets = len(self.labels) - - with open(path_l0 + '.bin', 'rb') as f: - self.centroids_l0, self.labels_l0 = self.read_centroids_and_labels(self.ndim, f) - self.nbuckets_l0 = len(self.labels_l0) - - # print('L0 buckets: ', self.nbuckets_l0) - # print('L1 buckets: ', self.nbuckets) - - def fill_labels(self, level=1): - if level == 1: - for i in range(self.nbuckets): - _, cur_labels = self.get_inverted_list_metadata(i, level=1) - self.labels.append(cur_labels) - else: - for i in range(self.nbuckets_l0): - _, cur_labels = self.get_inverted_list_metadata(i, level=0) - self.labels_l0.append(cur_labels) - - def train_add_l0(self, **kwargs): - self.centroids = self.index.quantizer.reconstruct_n(0, self.index.nlist) - # print(len(self.centroids), self.nbuckets_l0) - self.index_l0.train(self.centroids, **kwargs) - self.index_l0.add(self.centroids, **kwargs) - self.fill_labels(level=0) - self.centroids_l0 = self.index_l0.quantizer.reconstruct_n(0, self.index_l0.nlist) - - def get_inverted_list_size(self, list_id, level=1): - if level == 1: - return self.index.invlists.list_size(list_id) - else: - return self.index_l0.invlists.list_size(list_id) - - def get_inverted_list_ids(self, list_id, level=1): - if level == 1: - return self.index.invlists.get_ids(list_id) - else: - return self.index_l0.invlists.get_ids(list_id) - - def get_inverted_list_metadata(self, list_id, level=1): - num_list_embeddings = self.get_inverted_list_size(list_id, level) - list_ids = IVF2.faiss.rev_swig_ptr(self.get_inverted_list_ids(list_id, level), num_list_embeddings) - return num_list_embeddings, list_ids - - def persist_core(self, path: str, path_l0: str): - self.write_centroids_and_labels(self.centroids, self.labels, path) - self.write_centroids_and_labels(self.centroids_l0, self.labels_l0, path_l0) - IVF2.faiss.write_index(self.index, path) - -class HNSW: - pass diff --git a/python/pdxearch/index_factory.py b/python/pdxearch/index_factory.py index ce70675..6d83385 100644 --- a/python/pdxearch/index_factory.py +++ b/python/pdxearch/index_factory.py @@ -1,228 +1,219 @@ import numpy as np -from pdxearch.index_base import ( - BaseIndexPDXIVF, BaseIndexPDXIVF2, BaseIndexPDXFlat -) -from pdxearch.preprocessors import ( - ADSampling, Preprocessor -) -from pdxearch.compiled import ( - IndexADSamplingIVFFlat as _IndexPDXADSamplingIVFFlat, - IndexADSamplingFlat as _IndexADSamplingFlat, - IndexPDXFlat as _IndexPDXFlat, - IndexADSamplingIVF2SQ8 as _IndexADSamplingIVF2SQ8, - IndexADSamplingIVF2Flat as _IndexADSamplingIVF2Flat -) -from pdxearch.constants import PDXConstants - -from pdxearch.predicate_evaluator import PredicateEvaluator - -# -# Python wrappers of the C++ lib -# - -class IndexPDXIVF2(BaseIndexPDXIVF2): +from pdxearch.compiled import PDXIndex as _PDXIndex, load_index as _load_index +# from pdxearch.predicate_evaluator import PredicateEvaluator + +METRIC_MAP = {"l2sq": 0, "cosine": 1, "ip": 2} + + +class IndexPDXIVF: + """Single-level IVF index (F32).""" + def __init__( - self, - *, - ndim: int = 128, - metric: str = "l2sq", - nbuckets: int = 256, - nbuckets_l0: int = 64, - normalize: bool = True + self, + *, + num_dimensions: int, + distance_metric: str = "l2sq", + normalize: bool = True, + seed: int = 42, + num_clusters: int = 0, + sampling_fraction: float = 0.0, + kmeans_iters: int = 10, ) -> None: - super().__init__(ndim, metric, nbuckets, nbuckets_l0, normalize) - self.preprocessor = ADSampling(ndim) - self.pdx_index = _IndexADSamplingIVF2Flat() - self.pe = PredicateEvaluator() - - def preprocess(self, data, inplace: bool = True): - return self.preprocessor.preprocess(data, inplace=inplace, normalize=self.normalize) + self._index = _PDXIndex( + "pdx_f32", num_dimensions, METRIC_MAP[distance_metric], + seed, num_clusters, 0, normalize, sampling_fraction, kmeans_iters, + ) + # self.pe = PredicateEvaluator() - # Used in Python API (TODO) - def add_persist(self, data, path: str, matrix_path: str): - self._add(data) - self._train_add_l0() - self._to_pdx(data, _type='pdx', use_original_centroids=True) - self._persist(path) - self.preprocessor.store_metadata(matrix_path) + def build(self, data: np.ndarray) -> None: + self._index.build_index(np.ascontiguousarray(data, dtype=np.float32)) - # Used in Python API (TODO) - def persist(self, path: str, matrix_path: str): # TODO: Rename - self._persist(path) - self.preprocessor.store_metadata(matrix_path) + def search(self, query: np.ndarray, knn: int, nprobe: int = 16): + self._index.set_nprobe(nprobe) + return self._index.search(np.ascontiguousarray(query, dtype=np.float32), knn) - def add(self, data): - self._add(data) - self._train_add_l0() - self._to_pdx(data, _type='pdx', use_original_centroids=True) - self.pdx_index.load(self.materialized_index, self.preprocessor.transformation_matrix) + def save(self, path: str) -> None: + self._index.save(path) - # Used in Python API (TODO) - def restore(self, path: str, matrix_path: str): - self.pdx_index.restore(path, matrix_path) + # def evaluate_predicate(self, passing_tuples_ids): + # self.pe.evaluate_predicate(passing_tuples_ids, self._index.get_labels()) - def search(self, q: np.ndarray, knn: int, nprobe: int = 16): - return self.pdx_index.search(q, knn, nprobe) + # def filtered_search(self, query: np.ndarray, knn: int, nprobe: int = 16): + # if len(self.pe.n_passing_tuples) == 0 or len(self.pe.selection_vector) == 0: + # raise ValueError("Call .evaluate_predicate([]) first") + # self._index.set_nprobe(nprobe) + # return self._index.filtered_search( + # np.ascontiguousarray(query, dtype=np.float32), knn, + # self.pe.n_passing_tuples, self.pe.selection_vector, + # ) - def evaluate_predicate(self, passing_tuples_ids): - self.pe.evaluate_predicate(passing_tuples_ids, self.core_index.labels) + @property + def num_dimensions(self) -> int: + return self._index.get_num_dimensions() - def filtered_search(self, q: np.ndarray, knn: int, nprobe: int = 16): - if len(self.pe.n_passing_tuples) == 0 or len(self.pe.selection_vector) == 0: - raise ValueError("Call .evaluate_predicate([]) first to generate the selection_vector of your predicate") - return self.pdx_index.filtered_search(q, knn, nprobe, self.pe.n_passing_tuples, self.pe.selection_vector) + @property + def num_clusters(self) -> int: + return self._index.get_num_clusters() - def set_pruning_confidence(self, confidence: float): - self.pdx_index.set_pruning_confidence(confidence) +class IndexPDXIVFSQ8: + """Single-level IVF index (U8 scalar quantization).""" -class IndexPDXIVF2SQ8(BaseIndexPDXIVF2): def __init__( - self, - *, - ndim: int = 128, - metric: str = "l2sq", - nbuckets: int = 256, - nbuckets_l0: int = 64, - normalize: bool = True + self, + *, + num_dimensions: int, + distance_metric: str = "l2sq", + normalize: bool = True, + seed: int = 42, + num_clusters: int = 0, + sampling_fraction: float = 0.0, + kmeans_iters: int = 10, ) -> None: - super().__init__(ndim, metric, nbuckets, nbuckets_l0, normalize) - self.preprocessor = ADSampling(ndim) - self.pdx_index = _IndexADSamplingIVF2SQ8() - self.pe = PredicateEvaluator() + self._index = _PDXIndex( + "pdx_u8", num_dimensions, METRIC_MAP[distance_metric], + seed, num_clusters, 0, normalize, sampling_fraction, kmeans_iters, + ) + # self.pe = PredicateEvaluator() - def preprocess(self, data, inplace: bool = True): - return self.preprocessor.preprocess(data, inplace=inplace, normalize=self.normalize) + def build(self, data: np.ndarray) -> None: + self._index.build_index(np.ascontiguousarray(data, dtype=np.float32)) - # Used in Python API (TODO) - def add_persist(self, data, path: str, matrix_path: str): - self._add(data) - self._train_add_l0() - self._to_pdx(data, _type='pdx-v4-h', quantize=True, use_original_centroids=True) - self._persist(path) - self.preprocessor.store_metadata(matrix_path) + def search(self, query: np.ndarray, knn: int, nprobe: int = 16): + self._index.set_nprobe(nprobe) + return self._index.search(np.ascontiguousarray(query, dtype=np.float32), knn) - # Used in Python API (TODO) - def persist(self, path: str, matrix_path: str): # TODO: Rename - self._persist(path) - self.preprocessor.store_metadata(matrix_path) + def save(self, path: str) -> None: + self._index.save(path) - def add(self, data): - self._add(data) - self._train_add_l0() - self._to_pdx(data, _type='pdx-v4-h', quantize=True, use_original_centroids=True) - self.pdx_index.load(self.materialized_index, self.preprocessor.transformation_matrix) + # def evaluate_predicate(self, passing_tuples_ids): + # self.pe.evaluate_predicate(passing_tuples_ids, self._index.get_labels()) - # Used in Python API (TODO) - def restore(self, path: str, matrix_path: str): - self.pdx_index.restore(path, matrix_path) + # def filtered_search(self, query: np.ndarray, knn: int, nprobe: int = 16): + # if len(self.pe.n_passing_tuples) == 0 or len(self.pe.selection_vector) == 0: + # raise ValueError("Call .evaluate_predicate([]) first") + # self._index.set_nprobe(nprobe) + # return self._index.filtered_search( + # np.ascontiguousarray(query, dtype=np.float32), knn, + # self.pe.n_passing_tuples, self.pe.selection_vector, + # ) - def search(self, q: np.ndarray, knn: int, nprobe: int = 16): - return self.pdx_index.search(q, knn, nprobe) + @property + def num_dimensions(self) -> int: + return self._index.get_num_dimensions() - def evaluate_predicate(self, passing_tuples_ids): - self.pe.evaluate_predicate(passing_tuples_ids, self.core_index.labels) + @property + def num_clusters(self) -> int: + return self._index.get_num_clusters() - def filtered_search(self, q: np.ndarray, knn: int, nprobe: int = 16): - if len(self.pe.n_passing_tuples) == 0 or len(self.pe.selection_vector) == 0: - raise ValueError("Call .evaluate_predicate([]) first to generate the selection_vector of your predicate") - return self.pdx_index.filtered_search(q, knn, nprobe, self.pe.n_passing_tuples, self.pe.selection_vector) - def set_pruning_confidence(self, confidence: float): - self.pdx_index.set_pruning_confidence(confidence) +class IndexPDXIVF2: + """Two-level IVF index (F32).""" - -class IndexPDXADSamplingIVFFlat(BaseIndexPDXIVF): def __init__( - self, - *, ndim: int = 128, - metric: str = "l2sq", - nbuckets: int = 256, - normalize: bool = True + self, + *, + num_dimensions: int, + distance_metric: str = "l2sq", + normalize: bool = True, + seed: int = 42, + num_clusters: int = 0, + num_meso_clusters: int = 0, + sampling_fraction: float = 0.0, + kmeans_iters: int = 10, ) -> None: - super().__init__(ndim, metric, nbuckets, normalize) - self.preprocessor = ADSampling(ndim) - self.pdx_index = _IndexPDXADSamplingIVFFlat() - self.pe = PredicateEvaluator() - - def preprocess(self, data, inplace: bool = True): - return self.preprocessor.preprocess(data, inplace=inplace, normalize=self.normalize) - - def add_persist(self, data, path: str, matrix_path: str): - self._add(data) - # I don't need to pass the centroid preprocessor here, as the centroids are already rotated - # Because the training was done on the transformed vectors - self._to_pdx(data, _type='pdx', use_original_centroids=True) - self._persist(path) - self.preprocessor.store_metadata(matrix_path) - - def persist(self, path: str, matrix_path: str): # TODO: Rename - self._persist(path) - self.preprocessor.store_metadata(matrix_path) - - def add(self, data): - self._add(data) - # I don't need to pass the centroid preprocessor here, as the centroids are already rotated - # Because the training was done on the transformed vectors - self._to_pdx(data, _type='pdx', use_original_centroids=True) - self.pdx_index.load(self.materialized_index, self.preprocessor.transformation_matrix) - - def restore(self, path: str, matrix_path: str): - self.pdx_index.restore(path, matrix_path) - - def search(self, q: np.ndarray, knn: int, nprobe: int = 16): - return self.pdx_index.search(q, knn, nprobe) - - def evaluate_predicate(self, passing_tuples_ids): - self.pe.evaluate_predicate(passing_tuples_ids, self.core_index.labels) - - def filtered_search(self, q: np.ndarray, knn: int, nprobe: int = 16): - if len(self.pe.n_passing_tuples) == 0 or len(self.pe.selection_vector) == 0: - raise ValueError("Call .evaluate_predicate([]) first to generate the selection_vector of your predicate") - return self.pdx_index.filtered_search(q, knn, nprobe, self.pe.n_passing_tuples, self.pe.selection_vector) - - def set_pruning_confidence(self, confidence: float): - self.pdx_index.set_pruning_confidence(confidence) - -class IndexPDXADSamplingFlat(BaseIndexPDXFlat): - def __init__(self, *, ndim: int = 128, metric: str = "l2sq", normalize: bool = True) -> None: - super().__init__(ndim=ndim, metric=metric, normalize=normalize) - self.preprocessor = ADSampling(ndim) - self.pdx_index = _IndexADSamplingFlat() - self.block_sizes = PDXConstants.PDXEARCH_VECTOR_SIZE - self.pe = PredicateEvaluator() - - def preprocess(self, data: np.array, inplace: bool = True): - return self.preprocessor.preprocess(data, inplace=inplace, normalize=self.normalize) - - def add_persist(self, data: np.array, path: str, matrix_path: str): - self._to_pdx(data, self.block_sizes) - self._persist(path) - self.preprocessor.store_metadata(matrix_path) - - def persist(self, path: str, matrix_path: str): # TODO: Rename - self._persist(path) - self.preprocessor.store_metadata(matrix_path) - - def add(self, data): - self._to_pdx(data, self.block_sizes) - self.pdx_index.load(self.materialized_index, self.preprocessor.transformation_matrix) - - def restore(self, path: str, matrix_path: str): - self.pdx_index.restore(path, matrix_path) - - def search(self, q: np.ndarray, knn: int): - return self.pdx_index.search(q, knn) - - def set_pruning_confidence(self, confidence: float): - self.pdx_index.set_pruning_confidence(confidence) - - def evaluate_predicate(self, passing_tuples_ids): - self.pe.evaluate_predicate(passing_tuples_ids, [x.indices for x in self.partitions]) - - def filtered_search(self, q: np.ndarray, knn: int, nprobe: int = 16): - if len(self.pe.n_passing_tuples) == 0 or len(self.pe.selection_vector) == 0: - raise ValueError("Call .evaluate_predicate([]) first to generate the selection_vector of your predicate") - return self.pdx_index.filtered_search(q, knn, self.pe.n_passing_tuples, self.pe.selection_vector) + self._index = _PDXIndex( + "pdx_tree_f32", num_dimensions, METRIC_MAP[distance_metric], + seed, num_clusters, num_meso_clusters, normalize, + sampling_fraction, kmeans_iters, + ) + # self.pe = PredicateEvaluator() + + def build(self, data: np.ndarray) -> None: + self._index.build_index(np.ascontiguousarray(data, dtype=np.float32)) + + def search(self, query: np.ndarray, knn: int, nprobe: int = 16): + self._index.set_nprobe(nprobe) + return self._index.search(np.ascontiguousarray(query, dtype=np.float32), knn) + + def save(self, path: str) -> None: + self._index.save(path) + # def evaluate_predicate(self, passing_tuples_ids): + # self.pe.evaluate_predicate(passing_tuples_ids, self._index.get_labels()) + + # def filtered_search(self, query: np.ndarray, knn: int, nprobe: int = 16): + # if len(self.pe.n_passing_tuples) == 0 or len(self.pe.selection_vector) == 0: + # raise ValueError("Call .evaluate_predicate([]) first") + # self._index.set_nprobe(nprobe) + # return self._index.filtered_search( + # np.ascontiguousarray(query, dtype=np.float32), knn, + # self.pe.n_passing_tuples, self.pe.selection_vector, + # ) + + @property + def num_dimensions(self) -> int: + return self._index.get_num_dimensions() + + @property + def num_clusters(self) -> int: + return self._index.get_num_clusters() + + +class IndexPDXIVF2SQ8: + """Two-level IVF index (U8 scalar quantization).""" + + def __init__( + self, + *, + num_dimensions: int, + distance_metric: str = "l2sq", + normalize: bool = True, + seed: int = 42, + num_clusters: int = 0, + num_meso_clusters: int = 0, + sampling_fraction: float = 0.0, + kmeans_iters: int = 10, + ) -> None: + self._index = _PDXIndex( + "pdx_tree_u8", num_dimensions, METRIC_MAP[distance_metric], + seed, num_clusters, num_meso_clusters, normalize, + sampling_fraction, kmeans_iters, + ) + # self.pe = PredicateEvaluator() + + def build(self, data: np.ndarray) -> None: + self._index.build_index(np.ascontiguousarray(data, dtype=np.float32)) + + def search(self, query: np.ndarray, knn: int, nprobe: int = 16): + self._index.set_nprobe(nprobe) + return self._index.search(np.ascontiguousarray(query, dtype=np.float32), knn) + + def save(self, path: str) -> None: + self._index.save(path) + + # def evaluate_predicate(self, passing_tuples_ids): + # self.pe.evaluate_predicate(passing_tuples_ids, self._index.get_labels()) + + # def filtered_search(self, query: np.ndarray, knn: int, nprobe: int = 16): + # if len(self.pe.n_passing_tuples) == 0 or len(self.pe.selection_vector) == 0: + # raise ValueError("Call .evaluate_predicate([]) first") + # self._index.set_nprobe(nprobe) + # return self._index.filtered_search( + # np.ascontiguousarray(query, dtype=np.float32), knn, + # self.pe.n_passing_tuples, self.pe.selection_vector, + # ) + + @property + def num_dimensions(self) -> int: + return self._index.get_num_dimensions() + + @property + def num_clusters(self) -> int: + return self._index.get_num_clusters() + + +def load_index(path: str): + """Load a PDX index from a single file (auto-detects type).""" + return _load_index(path) diff --git a/python/pdxearch/preprocessors.py b/python/pdxearch/preprocessors.py deleted file mode 100644 index aab51fb..0000000 --- a/python/pdxearch/preprocessors.py +++ /dev/null @@ -1,65 +0,0 @@ -import numpy as np -from scipy.fft import dct -np.random.seed(42) - -from pdxearch.constants import PDXConstants -from abc import ABC, abstractmethod - -# -# Preprocessors (ADSampling and BSA) -# These classes includes the methods for data transformation and storing the respective metadata -# - - -class Preprocessor: - def preprocess(self, data: np.array, inplace=False, normalize=True): - if inplace: - data[:] = self.normalize(data) if normalize else data - else: - return self.normalize(data) if normalize else data - - def store_metadata(self, *args): - pass - - def normalize(self, data): - norms = np.linalg.norm(data, axis=1, keepdims=True) - norms[norms == 0] = 1 - return data/norms - - -class ADSampling(Preprocessor): - def __init__( - self, - ndim - ): - self.d = ndim - if PDXConstants.HAS_FFTW and self.d >= PDXConstants.D_THRESHOLD_FOR_DCT_ROTATION: - self.transformation_matrix = np.random.choice([-1.0, 1.0], size=(ndim)).astype(np.float32) - else: - self.transformation_matrix, _ = np.linalg.qr(np.random.randn(ndim, ndim).astype(np.float32)) - - def fjlt(self, X): - n, _ = X.shape - X = X * self.transformation_matrix - X = dct(X, norm='ortho', axis=1) - return X - - def preprocess(self, data: np.array, inplace=False, normalize=True): - if PDXConstants.HAS_FFTW and self.d >= PDXConstants.D_THRESHOLD_FOR_DCT_ROTATION: - if inplace: - data[:] = self.fjlt(self.normalize(data) if normalize else data) - else: - return self.fjlt(self.normalize(data) if normalize else data) - else: - if inplace: - data[:] = np.dot( - self.normalize(data) if normalize else data, - self.transformation_matrix) - else: - return np.dot( - self.normalize(data) if normalize else data, - self.transformation_matrix) - - def store_metadata(self, path: str): - with open(path, "wb") as file: - file.write(self.transformation_matrix.tobytes("C")) diff --git a/setup.py b/setup.py deleted file mode 100644 index 89f3fc4..0000000 --- a/setup.py +++ /dev/null @@ -1,133 +0,0 @@ -import os -import sys -import ctypes.util -from setuptools import setup - -from pybind11.setup_helpers import Pybind11Extension - -# -# This setup.py was based on USearch setup.py (https://github.com/unum-cloud/usearch/blob/main/setup.py) -# - -cxx = os.environ.get("CXX") -if cxx == "" or cxx is None: - raise Exception('Set CXX variable in your environment') - -compile_args = [] -link_args = [] -macros_args = [] - -is_linux: bool = sys.platform == "linux" -is_macos: bool = sys.platform == "darwin" -is_windows: bool = sys.platform == "win32" - -has_fftw = ctypes.util.find_library("fftw3f") is not None -if has_fftw: - macros_args.append(("HAS_FFTW", "1")) - -if is_windows: - raise Exception('Windows not yet implemented') - -# TODO: in intel sapphirerapids one have to force LLVM to use 512 registers -if is_linux: - compile_args.append("-std=c++17") - compile_args.append("-O3") - compile_args.append("-march=native") - compile_args.append("-fPIC") - compile_args.append("-Wno-unknown-pragmas") - compile_args.append("-fdiagnostics-color=always") - compile_args.append("-Wl,--unresolved-symbols=ignore-in-shared-libs") - link_args.append("-static-libstdc++") - if has_fftw: - # link_args.append("-lfftw3") - link_args.append("-lfftw3f") - -if is_macos: - compile_args.append("-std=c++17") - compile_args.append("-mmacosx-version-min=10.13") - compile_args.append("-O3") - compile_args.append("-march=native") - compile_args.append("-fPIC") - compile_args.append("-arch") - compile_args.append("arm64") # TODO: Currently not supporting non-ARM Macs - compile_args.append("-Wl,") - compile_args.append("-Wno-unknown-pragmas") - if has_fftw: - # link_args.append("-lfftw3") - link_args.append("-lfftw3f") - - -ext_modules = [ - Pybind11Extension( - "pdxearch.compiled", - sources=["python/lib.cpp"], - extra_compile_args=compile_args, - extra_link_args=link_args, - define_macros=macros_args, - language="c++", - ), -] - -include_dirs = [ - "extern", - "include", - "python", -] - -# TODO: We also require FAISS, but installing from PyPI is not ideal -install_requires = [ - "setuptools>=42", - "wheel", - "cmake>=3.22", - "pybind11", - "numpy", - "scipy" -] - -# Taken from Usearch setup.py (https://github.com/unum-cloud/usearch/blob/main/setup.py) -# With Clang, `setuptools` doesn't properly use the `language="c++"` argument we pass. -# The right thing would be to pass down `-x c++` to the compiler, before specifying the source files. -# This nasty workaround overrides the `CC` environment variable with the `CXX` variable. -cc_compiler_variable = os.environ.get("CC") -cxx_compiler_variable = os.environ.get("CXX") -if cxx_compiler_variable: - os.environ["CC"] = cxx_compiler_variable - -description = "Faster similarity search with PDX: A transposed data layout for vectors" -long_description = "" - -setup( - name="pdxearch", - version="0.3", - packages=["pdxearch"], - package_dir={"pdxearch": "python/pdxearch"}, - description=description, - author="CWI", - author_email="lxkr@cwi.nl", - url="https://github.com/cwida/pdxearch", - long_description=long_description, - long_description_content_type="text/markdown", - license="Apache-2.0", - classifiers=[ - "Development Status :: 1 - Planning", - "Natural Language :: English", - "Intended Audience :: Developers", - "Intended Audience :: Information Technology", - "License :: OSI Approved :: Apache Software License", - "Programming Language :: C++", - "Programming Language :: Python :: 3 :: Only", - "Operating System :: MacOS", - "Operating System :: Unix", - # "Operating System :: Microsoft :: Windows", - "Topic :: Database :: Database Engines/Servers", - "Topic :: Scientific/Engineering :: Artificial Intelligence", - ], - # cmdclass={"build_ext": build_ext}, - include_dirs=include_dirs, - ext_modules=ext_modules, - install_requires=install_requires, -) - -# Reset the CC environment variable, that we overrode earlier. -if cxx_compiler_variable and cc_compiler_variable: - os.environ["CC"] = cc_compiler_variable From 58f5b257b6893f871a8c61779c3b931c77f12e11 Mon Sep 17 00:00:00 2001 From: lkuffo Date: Tue, 24 Feb 2026 14:21:45 +0100 Subject: [PATCH 13/39] Get used memory in bytes --- .gitignore | 3 + benchmarks/BenchmarkEndToEnd | Bin 439344 -> 438960 bytes benchmarks/BenchmarkPDXIVF | Bin 375392 -> 0 bytes benchmarks/BenchmarkSerialization | Bin 437608 -> 0 bytes benchmarks/CMakeLists.txt | 12 +- .../{bench_adsampling => }/pdx_end_to_end.cpp | 0 benchmarks/{bench_adsampling => }/pdx_ivf.cpp | 0 .../pdx_ivf2_adsampling_u8_filtered.cpp | 0 .../pdx_ivf_adsampling_filtered.cpp | 0 .../pdx_serialization.cpp | 0 benchmarks/python_scripts/setup_adsampling.py | 6 +- benchmarks/python_scripts/setup_core_index.py | 131 ------------------ .../python_scripts/setup_filtered_search.py | 2 +- .../DEFAULT/END_TO_END_PDX_ADSAMPLING.csv | 4 + examples/pdx_2l_ivf.py | 4 +- examples/pdx_2l_ivf_8bit.py | 4 +- examples/pdx_filtered.py | 4 +- include/pdx/clustering.hpp | 2 +- include/pdx/common.hpp | 19 --- include/pdx/index.hpp | 22 +-- include/pdx/ivf_wrapper.hpp | 92 +++++++++--- include/pdx/layout.hpp | 10 +- include/pdx/searcher.hpp | 2 +- python/pdxearch/__init__.py | 4 +- python/pdxearch/index_factory.py | 4 +- 25 files changed, 120 insertions(+), 205 deletions(-) delete mode 100755 benchmarks/BenchmarkPDXIVF delete mode 100755 benchmarks/BenchmarkSerialization rename benchmarks/{bench_adsampling => }/pdx_end_to_end.cpp (100%) rename benchmarks/{bench_adsampling => }/pdx_ivf.cpp (100%) rename benchmarks/{bench_adsampling => }/pdx_ivf2_adsampling_u8_filtered.cpp (100%) rename benchmarks/{bench_adsampling => }/pdx_ivf_adsampling_filtered.cpp (100%) rename benchmarks/{bench_adsampling => }/pdx_serialization.cpp (100%) delete mode 100644 benchmarks/python_scripts/setup_core_index.py diff --git a/.gitignore b/.gitignore index baadf33..e765b47 100644 --- a/.gitignore +++ b/.gitignore @@ -91,6 +91,9 @@ cmake_install.cmake /benchmarks/python_scripts/indexes 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z>xYg$-)ZE1)?*WgJ^8q2V)dFoZ{7O(@~2J?=<>mHr>C`}GBFzZ>88zV#g+KKAAn*?-L_&678op6%E7UT^BCKGp9rue|7%3l^sB zbTpqG*Y33z(%RqF_inKK+S`XL`{B_cO;^4Cxy9Y$nid;>Et= 1); assert(num_dimensions >= 1); assert(num_clusters >= 1); diff --git a/include/pdx/common.hpp b/include/pdx/common.hpp index 81eb414..c3d8797 100644 --- a/include/pdx/common.hpp +++ b/include/pdx/common.hpp @@ -112,25 +112,6 @@ struct VectorComparator { } }; -template -struct Cluster { - using data_t = pdx_data_t; - - Cluster(uint32_t num_embeddings, uint32_t num_dimensions) - : num_embeddings(num_embeddings), indices(new uint32_t[num_embeddings]), - data(new data_t[static_cast(num_embeddings) * num_dimensions]) { - } - - ~Cluster() { - delete[] data; - delete[] indices; - } - - uint32_t num_embeddings {}; - uint32_t *indices = nullptr; - data_t *data = nullptr; -}; - using Heap = std::priority_queue, VectorComparator>; struct PDXDimensionSplit { diff --git a/include/pdx/index.hpp b/include/pdx/index.hpp index 423bc5b..46a5a5c 100644 --- a/include/pdx/index.hpp +++ b/include/pdx/index.hpp @@ -50,7 +50,7 @@ class PDXIndex : public IPDXIndex { private: PDXIndexConfig config {}; - PDX::IndexPDXIVF index; + PDX::IVF index; std::unique_ptr pruner; std::unique_ptr> searcher; @@ -171,10 +171,10 @@ class PDXIndex : public IPDXIndex { preprocessed.get(), static_cast(num_embeddings) * num_dimensions); quantization_base = params.quantization_base; quantization_scale = params.quantization_scale; - index = PDX::IndexPDXIVF(num_dimensions, num_embeddings, num_clusters, - normalize, quantization_scale, quantization_base); + index = PDX::IVF(num_dimensions, num_embeddings, num_clusters, + normalize, quantization_scale, quantization_base); } else { - index = PDX::IndexPDXIVF(num_dimensions, num_embeddings, num_clusters, normalize); + index = PDX::IVF(num_dimensions, num_embeddings, num_clusters, normalize); } KMeansResult kmeans_result = ComputeKMeans(preprocessed.get(), num_embeddings, num_dimensions, @@ -224,7 +224,7 @@ class PDXTreeIndex : public IPDXIndex { private: PDXIndexConfig config {}; - PDX::IndexPDXIVF2 index; + PDX::IVFTree index; std::unique_ptr pruner; std::unique_ptr> searcher; std::unique_ptr> top_level_searcher; @@ -257,7 +257,7 @@ class PDXTreeIndex : public IPDXIndex { out.write(reinterpret_cast(matrix.data()), sizeof(float) * matrix_rows * matrix_cols); - // IVF2 data + // IVFTree data index.Save(out); } @@ -276,7 +276,7 @@ class PDXTreeIndex : public IPDXIndex { auto *matrix_data = reinterpret_cast(ptr); ptr += sizeof(float) * matrix_rows * matrix_cols; - // Load IVF2 data + // Load IVFTree data index.Load(ptr); // Create pruner and searchers @@ -345,10 +345,10 @@ class PDXTreeIndex : public IPDXIndex { preprocessed.get(), static_cast(num_embeddings) * num_dimensions); quantization_base = params.quantization_base; quantization_scale = params.quantization_scale; - index = PDX::IndexPDXIVF2(num_dimensions, num_embeddings, num_clusters, - normalize, quantization_scale, quantization_base); + index = PDX::IVFTree(num_dimensions, num_embeddings, num_clusters, + normalize, quantization_scale, quantization_base); } else { - index = PDX::IndexPDXIVF2(num_dimensions, num_embeddings, num_clusters, normalize); + index = PDX::IVFTree(num_dimensions, num_embeddings, num_clusters, normalize); } KMeansResult kmeans_result = ComputeKMeans(preprocessed.get(), num_embeddings, num_dimensions, @@ -393,7 +393,7 @@ class PDXTreeIndex : public IPDXIndex { l0_num_clusters = static_cast(std::sqrt(num_clusters)); } - index.l0 = PDX::IndexPDXIVF(num_dimensions, num_clusters, l0_num_clusters, normalize); + index.l0 = PDX::IVF(num_dimensions, num_clusters, l0_num_clusters, normalize); KMeansResult l0_kmeans_result = ComputeKMeans(index.centroids.data(), num_clusters, num_dimensions, l0_num_clusters, config.distance_metric, config.seed, config.normalize, 1.0f, 10); // No sampling for l0 diff --git a/include/pdx/ivf_wrapper.hpp b/include/pdx/ivf_wrapper.hpp index 1ecb751..22ef6f0 100644 --- a/include/pdx/ivf_wrapper.hpp +++ b/include/pdx/ivf_wrapper.hpp @@ -12,7 +12,33 @@ namespace PDX { template -class IndexPDXIVF { +struct Cluster { + using data_t = pdx_data_t; + + Cluster(uint32_t num_embeddings, uint32_t num_dimensions) + : num_embeddings(num_embeddings), num_dimensions(num_dimensions), indices(new uint32_t[num_embeddings]), + data(new data_t[static_cast(num_embeddings) * num_dimensions]) { + } + + ~Cluster() { + delete[] data; + delete[] indices; + } + + uint32_t num_embeddings {}; + const uint32_t num_dimensions {}; + uint32_t *indices = nullptr; + data_t *data = nullptr; + + size_t GetInMemorySizeInBytes() const { + return sizeof(*this) + num_embeddings * sizeof(*indices) + + num_embeddings * static_cast(num_dimensions) * sizeof(*data); + } + +}; + +template +class IVF { public: using cluster_t = Cluster; using data_t = pdx_data_t; @@ -32,12 +58,12 @@ class IndexPDXIVF { float inverse_quantization_scale_squared = 1.0f; float quantization_base = 0.0f; - IndexPDXIVF() = default; - ~IndexPDXIVF() = default; - IndexPDXIVF(IndexPDXIVF &&) = default; - IndexPDXIVF &operator=(IndexPDXIVF &&) = default; + IVF() = default; + ~IVF() = default; + IVF(IVF &&) = default; + IVF &operator=(IVF &&) = default; - IndexPDXIVF(uint32_t num_dimensions, uint64_t total_num_embeddings, uint32_t num_clusters, bool is_normalized) + IVF(uint32_t num_dimensions, uint64_t total_num_embeddings, uint32_t num_clusters, bool is_normalized) : num_dimensions(num_dimensions), total_num_embeddings(total_num_embeddings), num_clusters(num_clusters), num_vertical_dimensions(GetPDXDimensionSplit(num_dimensions).vertical_dimensions), num_horizontal_dimensions(GetPDXDimensionSplit(num_dimensions).horizontal_dimensions), @@ -45,7 +71,7 @@ class IndexPDXIVF { clusters.reserve(num_clusters); } - IndexPDXIVF(uint32_t num_dimensions, uint64_t total_num_embeddings, uint32_t num_clusters, bool is_normalized, + IVF(uint32_t num_dimensions, uint64_t total_num_embeddings, uint32_t num_clusters, bool is_normalized, float quantization_scale, float quantization_base) : num_dimensions(num_dimensions), total_num_embeddings(total_num_embeddings), num_clusters(num_clusters), num_vertical_dimensions(GetPDXDimensionSplit(num_dimensions).vertical_dimensions), @@ -125,26 +151,38 @@ class IndexPDXIVF { out.write(reinterpret_cast(&quantization_scale), sizeof(float)); } } + + size_t GetInMemorySizeInBytes() const { + size_t in_memory_size_in_bytes = 0; + in_memory_size_in_bytes += sizeof(*this); + for (const auto &cluster : clusters) { + in_memory_size_in_bytes += cluster.GetInMemorySizeInBytes(); + } + in_memory_size_in_bytes += (clusters.capacity() - clusters.size()) * sizeof(*clusters.data()); + in_memory_size_in_bytes += centroids.capacity() * sizeof(*centroids.data()); + return in_memory_size_in_bytes; + } + }; template -class IndexPDXIVF2 : public IndexPDXIVF { +class IVFTree : public IVF { public: using data_t = pdx_data_t; - IndexPDXIVF l0; // Meso clusters + IVF l0; // Meso clusters - IndexPDXIVF2() = default; - ~IndexPDXIVF2() = default; - IndexPDXIVF2(IndexPDXIVF2 &&) = default; - IndexPDXIVF2 &operator=(IndexPDXIVF2 &&) = default; + IVFTree() = default; + ~IVFTree() = default; + IVFTree(IVFTree &&) = default; + IVFTree &operator=(IVFTree &&) = default; - IndexPDXIVF2(uint32_t num_dimensions, uint64_t total_num_embeddings, uint32_t num_clusters, bool is_normalized) - : IndexPDXIVF(num_dimensions, total_num_embeddings, num_clusters, is_normalized) {} + IVFTree(uint32_t num_dimensions, uint64_t total_num_embeddings, uint32_t num_clusters, bool is_normalized) + : IVF(num_dimensions, total_num_embeddings, num_clusters, is_normalized) {} - IndexPDXIVF2(uint32_t num_dimensions, uint64_t total_num_embeddings, uint32_t num_clusters, bool is_normalized, + IVFTree(uint32_t num_dimensions, uint64_t total_num_embeddings, uint32_t num_clusters, bool is_normalized, float quantization_scale, float quantization_base) - : IndexPDXIVF(num_dimensions, total_num_embeddings, num_clusters, is_normalized, + : IVF(num_dimensions, total_num_embeddings, num_clusters, is_normalized, quantization_scale, quantization_base) {} void Load(char *input) { @@ -281,6 +319,26 @@ class IndexPDXIVF2 : public IndexPDXIVF { out.write(reinterpret_cast(&this->quantization_scale), sizeof(float)); } } + + size_t GetInMemorySizeInBytes() const { + size_t size = sizeof(*this); + + // L1 clusters (inherited from base) + for (const auto &cluster : this->clusters) { + size += cluster.GetInMemorySizeInBytes(); + } + size += (this->clusters.capacity() - this->clusters.size()) * sizeof(Cluster); + size += this->centroids.capacity() * sizeof(float); + + // L0 meso-clusters + for (const auto &cluster : l0.clusters) { + size += cluster.GetInMemorySizeInBytes(); + } + size += (l0.clusters.capacity() - l0.clusters.size()) * sizeof(Cluster); + size += l0.centroids.capacity() * sizeof(float); + + return size; + } }; } // namespace PDX diff --git a/include/pdx/layout.hpp b/include/pdx/layout.hpp index 9fe01a3..16970e3 100644 --- a/include/pdx/layout.hpp +++ b/include/pdx/layout.hpp @@ -10,13 +10,13 @@ namespace PDX { // See the README of the following for a description of the PDX layout: // https://github.com/cwida/pdx template -inline void StoreClusterEmbeddings(typename PDX::IndexPDXIVF::cluster_t &cluster, const PDX::IndexPDXIVF &index, +inline void StoreClusterEmbeddings(typename PDX::IVF::cluster_t &cluster, const PDX::IVF &index, const T *embeddings, const size_t num_embeddings); template <> inline void -StoreClusterEmbeddings(PDX::IndexPDXIVF::cluster_t &cluster, - const PDX::IndexPDXIVF &index, +StoreClusterEmbeddings(PDX::IVF::cluster_t &cluster, + const PDX::IVF &index, const float *const embeddings, const size_t num_embeddings) { using matrix_t = PDX::eigen_matrix_t; using h_matrix_t = Eigen::Matrix; @@ -39,8 +39,8 @@ StoreClusterEmbeddings(PDX::IndexPDXIVF inline void -StoreClusterEmbeddings(PDX::IndexPDXIVF::cluster_t &cluster, - const PDX::IndexPDXIVF &index, +StoreClusterEmbeddings(PDX::IVF::cluster_t &cluster, + const PDX::IVF &index, const uint8_t *const embeddings, const size_t num_embeddings) { using u8_matrix_t = Eigen::Matrix; using u8_v_matrix_t = Eigen::Matrix; diff --git a/include/pdx/searcher.hpp b/include/pdx/searcher.hpp index 64a39a4..caf511f 100644 --- a/include/pdx/searcher.hpp +++ b/include/pdx/searcher.hpp @@ -16,7 +16,7 @@ namespace PDX { -template , class Quantizer = ScalarQuantizer, +template , class Quantizer = ScalarQuantizer, DistanceMetric alpha = DistanceMetric::L2SQ, class Pruner = ADSamplingPruner> class PDXearch { public: diff --git a/python/pdxearch/__init__.py b/python/pdxearch/__init__.py index 2083617..74914b6 100644 --- a/python/pdxearch/__init__.py +++ b/python/pdxearch/__init__.py @@ -1,7 +1,7 @@ from pdxearch.index_factory import ( IndexPDXIVF, IndexPDXIVFSQ8, - IndexPDXIVF2, - IndexPDXIVF2SQ8, + IndexPDXIVFTree, + IndexPDXIVFTreeSQ8, load_index, ) diff --git a/python/pdxearch/index_factory.py b/python/pdxearch/index_factory.py index 6d83385..81c7955 100644 --- a/python/pdxearch/index_factory.py +++ b/python/pdxearch/index_factory.py @@ -108,7 +108,7 @@ def num_clusters(self) -> int: return self._index.get_num_clusters() -class IndexPDXIVF2: +class IndexPDXIVFTree: """Two-level IVF index (F32).""" def __init__( @@ -161,7 +161,7 @@ def num_clusters(self) -> int: return self._index.get_num_clusters() -class IndexPDXIVF2SQ8: +class IndexPDXIVFTreeSQ8: """Two-level IVF index (U8 scalar quantization).""" def __init__( From d2d9ad2bfb762ffc5d7e6a3b10c280522e98f49a Mon Sep 17 00:00:00 2001 From: lkuffo Date: Tue, 24 Feb 2026 14:55:04 +0100 Subject: [PATCH 14/39] Get in memory size --- benchmarks/BenchmarkEndToEnd | Bin 438960 -> 0 bytes benchmarks/pdx_end_to_end.cpp | 5 +- benchmarks/pdx_ivf.cpp | 5 +- benchmarks/pdx_serialization.cpp | 5 + benchmarks/python_scripts/ivf_faiss.py | 1 - benchmarks/python_scripts/ivf_faiss_sq8.py | 1 - .../python_scripts/playground_adsampling.py | 1 - benchmarks/python_scripts/setup_adsampling.py | 52 --------- benchmarks/python_scripts/setup_data.py | 48 +++----- .../python_scripts/setup_filtered_search.py | 1 - .../python_scripts/setup_fvecs_to_hdf5.py | 19 ---- .../python_scripts/setup_ground_truth.py | 73 ------------ benchmarks/python_scripts/setup_pdx.py | 84 ++++++++++++++ benchmarks/python_scripts/setup_settings.py | 104 ------------------ benchmarks/python_scripts/setup_test_data.py | 43 -------- benchmarks/python_scripts/setup_utils.py | 64 ++++++----- .../results/DEFAULT/PDX_F32_ADSAMPLING.csv | 3 + .../DEFAULT/PDX_TREE_F32_ADSAMPLING.csv | 3 + .../DEFAULT/PDX_TREE_U8_ADSAMPLING.csv | 3 + .../results/DEFAULT/PDX_U8_ADSAMPLING.csv | 4 + examples/pdx_2l_ivf.py | 1 + examples/pdx_2l_ivf_8bit.py | 1 + examples/pdx_ivf.py | 1 + include/pdx/index.hpp | 44 ++++++++ include/pdx/lib/lib.hpp | 4 + python/lib.cpp | 3 +- python/pdxearch/index_factory.py | 16 +++ 27 files changed, 231 insertions(+), 358 deletions(-) delete mode 100755 benchmarks/BenchmarkEndToEnd delete mode 100644 benchmarks/python_scripts/setup_adsampling.py delete mode 100644 benchmarks/python_scripts/setup_fvecs_to_hdf5.py delete mode 100644 benchmarks/python_scripts/setup_ground_truth.py create mode 100644 benchmarks/python_scripts/setup_pdx.py delete mode 100644 benchmarks/python_scripts/setup_settings.py delete mode 100644 benchmarks/python_scripts/setup_test_data.py create mode 100644 benchmarks/results/DEFAULT/PDX_F32_ADSAMPLING.csv create mode 100644 benchmarks/results/DEFAULT/PDX_TREE_F32_ADSAMPLING.csv create mode 100644 benchmarks/results/DEFAULT/PDX_U8_ADSAMPLING.csv diff --git a/benchmarks/BenchmarkEndToEnd b/benchmarks/BenchmarkEndToEnd deleted file mode 100755 index e0b0e52cd12ceeed1f1d94f9f731412979e212ab..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 438960 zcmeFa3v^V~+3>&5OfECIKp+VT7jsofpo&xwQbRLIL_>mTF|D@RCV;kqa8W>8M93sT 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"pdx/index.hpp" #include "benchmark_utils.hpp" @@ -35,6 +36,8 @@ void RunBenchmark(const RawDatasetInfo &info, const std::string &dataset, const double build_ms = std::chrono::duration(build_end - build_start).count(); std::cout << "Build time: " << build_ms << " ms\n"; std::cout << "Clusters: " << pdx_index.GetNumClusters() << "\n"; + std::cout << "Index in-memory size: " << std::fixed << std::setprecision(2) + << static_cast(pdx_index.GetInMemorySizeInBytes()) / (1024.0 * 1024.0) << " MB\n"; // Load ground truth bool use_skmeans_gt = false; diff --git a/benchmarks/pdx_ivf.cpp b/benchmarks/pdx_ivf.cpp index 9253ece..2887284 100644 --- a/benchmarks/pdx_ivf.cpp +++ b/benchmarks/pdx_ivf.cpp @@ -1,5 +1,6 @@ -#include +#include #include +#include #include "pdx/utils.hpp" #include "pdx/index.hpp" #include "benchmark_utils.hpp" @@ -48,6 +49,8 @@ int main(int argc, char *argv[]) { std::string index_path = BenchmarkUtils::PDX_DATA + dataset + "-" + index_type; std::cout << "Loading " << index_path << "...\n"; auto pdx_index = PDX::LoadPDXIndex(index_path); + std::cout << "Index in-memory size: " << std::fixed << std::setprecision(2) + << static_cast(pdx_index->GetInMemorySizeInBytes()) / (1024.0 * 1024.0) << " MB\n"; std::unique_ptr query_ptr = MmapFile(BenchmarkUtils::QUERIES_DATA + info.pdx_dataset_name); auto *query = reinterpret_cast(query_ptr.get()); diff --git a/benchmarks/pdx_serialization.cpp b/benchmarks/pdx_serialization.cpp index 8487998..e2878dc 100644 --- a/benchmarks/pdx_serialization.cpp +++ b/benchmarks/pdx_serialization.cpp @@ -1,6 +1,7 @@ #include #include #include +#include #include #include #include @@ -30,6 +31,8 @@ void BuildAndSave(const RawDatasetInfo &info, const std::string &dataset, const double build_ms = std::chrono::duration(build_end - build_start).count(); std::cout << "Build time: " << build_ms << " ms\n"; std::cout << "Clusters: " << pdx_index.GetNumClusters() << "\n"; + std::cout << "Index in-memory size: " << std::fixed << std::setprecision(2) + << static_cast(pdx_index.GetInMemorySizeInBytes()) / (1024.0 * 1024.0) << " MB\n"; std::string save_path = BenchmarkUtils::PDX_DATA + dataset + "-" + index_type; std::cout << "Saving to " << save_path << "...\n"; @@ -95,6 +98,8 @@ int main(int argc, char *argv[]) { // Verify: load back without knowing the type and run queries std::cout << "\n==> Verification: Loading index from " << save_path << "...\n"; auto loaded_index = PDX::LoadPDXIndex(save_path); + std::cout << "Loaded index in-memory size: " << std::fixed << std::setprecision(2) + << static_cast(loaded_index->GetInMemorySizeInBytes()) / (1024.0 * 1024.0) << " MB\n"; // Load queries std::unique_ptr query_ptr = MmapFile(BenchmarkUtils::QUERIES_DATA + info.pdx_dataset_name); diff --git a/benchmarks/python_scripts/ivf_faiss.py b/benchmarks/python_scripts/ivf_faiss.py index c6c279e..28cbb55 100644 --- a/benchmarks/python_scripts/ivf_faiss.py +++ b/benchmarks/python_scripts/ivf_faiss.py @@ -4,7 +4,6 @@ from sklearn import preprocessing from benchmark_utils import * from setup_utils import * -from setup_settings import * DATASETS_TO_USE = [ 'openai-1536-angular', diff --git a/benchmarks/python_scripts/ivf_faiss_sq8.py b/benchmarks/python_scripts/ivf_faiss_sq8.py index 890c237..4e51226 100644 --- a/benchmarks/python_scripts/ivf_faiss_sq8.py +++ b/benchmarks/python_scripts/ivf_faiss_sq8.py @@ -4,7 +4,6 @@ from numpy.random import default_rng from benchmark_utils import * from setup_utils import * -from setup_settings import * from sklearn import preprocessing BUILD = False diff --git a/benchmarks/python_scripts/playground_adsampling.py b/benchmarks/python_scripts/playground_adsampling.py index 776c97d..28bca43 100644 --- a/benchmarks/python_scripts/playground_adsampling.py +++ b/benchmarks/python_scripts/playground_adsampling.py @@ -1,7 +1,6 @@ import json from WrapperBruteForce import BruteForceSKLearn from setup_utils import * -from setup_settings import * from sklearn import preprocessing import math import time diff --git a/benchmarks/python_scripts/setup_adsampling.py b/benchmarks/python_scripts/setup_adsampling.py deleted file mode 100644 index 650da59..0000000 --- a/benchmarks/python_scripts/setup_adsampling.py +++ /dev/null @@ -1,52 +0,0 @@ -from setup_utils import * -from setup_settings import * -from pdxearch import IndexPDXIVF, IndexPDXIVFSQ8, IndexPDXIVFTree, IndexPDXIVFTreeSQ8 - -INDEX_CLASSES = { - "pdx_f32": IndexPDXIVF, - "pdx_u8": IndexPDXIVFSQ8, - "pdx_tree_f32": IndexPDXIVFTree, - "pdx_tree_u8": IndexPDXIVFTreeSQ8, -} - - -def generate_index(dataset_abbrev: str, index_type: str, normalize=True): - hdf5_name, dims = DATASET_INFO[dataset_abbrev] - print(f'{dataset_abbrev} -> {hdf5_name} ({index_type})') - data = read_hdf5_train_data(hdf5_name) - - cls = INDEX_CLASSES[index_type] - index = cls(num_dimensions=dims, normalize=normalize) - print('Building index...') - index.build(data) - print(f'Index built: {index.num_clusters} clusters') - - save_path = os.path.join(PDX_ADSAMPLING_DATA, dataset_abbrev + '-' + index_type) - print(f'Saving to {save_path}') - index.save(save_path) - print('Done.') - - -if __name__ == "__main__": - # generate_index('sift', 'pdx_f32', normalize=True) - # generate_index('sift', 'pdx_u8', normalize=True) - # generate_index('sift', 'pdx_tree_f32', normalize=True) - # generate_index('sift', 'pdx_tree_u8', normalize=True) - - # generate_index('mxbai', 'pdx_f32', normalize=True) - # generate_index('mxbai', 'pdx_u8', normalize=True) - # generate_index('mxbai', 'pdx_tree_f32', normalize=True) - # generate_index('mxbai', 'pdx_tree_u8', normalize=True) - - # generate_index('gist', 'pdx_f32', normalize=True) - # generate_index('openai', 'pdx_f32', normalize=True) - # generate_index('arxiv', 'pdx_f32', normalize=True) - # generate_index('wiki', 'pdx_f32', normalize=True) - # generate_index('contriever', 'pdx_f32', normalize=True) - # generate_index('clip', 'pdx_f32', normalize=True) - # generate_index('yahoo', 'pdx_f32', normalize=True) - # generate_index('yandex', 'pdx_f32', normalize=True) - # generate_index('glove200', 'pdx_f32', normalize=True) - # generate_index('yi', 'pdx_f32', normalize=True) - # generate_index('llama', 'pdx_f32', normalize=True) - pass diff --git a/benchmarks/python_scripts/setup_data.py b/benchmarks/python_scripts/setup_data.py index 62a2aef..1dcb36b 100644 --- a/benchmarks/python_scripts/setup_data.py +++ b/benchmarks/python_scripts/setup_data.py @@ -1,26 +1,20 @@ import os import zipfile -from setup_settings import RAW_DATA, DATA_DIRECTORY, DATASETS -from setup_ground_truth import generate_ground_truth -from setup_adsampling import generate_adsampling_ivf, generate_adsampling_ivf_global8, generate_adsampling_ivf2_global8, generate_adsampling_ivf2 -from setup_core_index import generate_core_ivf, generate_core_ivf2 -from setup_test_data import generate_test_data +from setup_utils import RAW_DATA, DATA_DIRECTORY, DATASET_INFO +from setup_pdx import generate_ground_truth, generate_index, generate_test_data DOWNLOAD = False # Download raw HDF5 data GENERATE_GT = False # Creates ground truth with sklearn -GENERATE_IVF = True # Creates IVF indexes with FAISS KNN = [100] -ALGORITHMS = [ # Choose the pruning algorithms for which indexes are going to be created - 'adsampling' -] +SEED = 42 DATASETS_TO_USE = [ - # 'openai-1536-angular', - 'agnews-mxbai-1024-euclidean', - # 'instructorxl-arxiv-768', - # 'simplewiki-openai-3072-normalized', - # 'msong-420', - # 'llama-128-ip', + 'sift', + 'mxbai', + 'arxiv', + 'openai', + 'wiki', ] + if __name__ == "__main__": if DOWNLOAD: import gdown @@ -34,9 +28,11 @@ zip_ref.extractall(RAW_DATA) # If you don't define some datasets we will try to use all of them - if not len(DATASETS_TO_USE): DATASETS_TO_USE = DATASETS + if not len(DATASETS_TO_USE): + DATASETS_TO_USE = list(DATASET_INFO.keys()) + for dataset in DATASETS_TO_USE: - print('\n================ PROCESSING:', dataset, '================') + print(f'\n================ PROCESSING: {dataset} ================') if GENERATE_GT: print('==== Generating ground truth...') generate_ground_truth(dataset, KNN) @@ -44,16 +40,8 @@ print('==== Saving queries in a binary format...') generate_test_data(dataset) - if GENERATE_IVF: - print('==== Creating Core ivf2 index with FAISS (this might take a while)...') - generate_core_ivf2(dataset) - - if 'adsampling' in ALGORITHMS: - print('==== Generating ADSampling...') - generate_adsampling_ivf(dataset) - generate_adsampling_ivf2(dataset) - - print('==== Generating ADSampling SQ8...') - generate_adsampling_ivf_global8(dataset) - generate_adsampling_ivf2_global8(dataset) - + print('==== Generating PDX indexes...') + generate_index(dataset, 'pdx_f32', normalize=True, seed=SEED) + generate_index(dataset, 'pdx_u8', normalize=True, seed=SEED) + generate_index(dataset, 'pdx_tree_f32', normalize=True, seed=SEED) + generate_index(dataset, 'pdx_tree_u8', normalize=True, seed=SEED) diff --git a/benchmarks/python_scripts/setup_filtered_search.py b/benchmarks/python_scripts/setup_filtered_search.py index 15dd3eb..fc8e863 100644 --- a/benchmarks/python_scripts/setup_filtered_search.py +++ b/benchmarks/python_scripts/setup_filtered_search.py @@ -4,7 +4,6 @@ import json import random from setup_utils import * -from setup_settings import * from pdxearch.index_base import BaseIndexPDXIVF, BaseIndexPDXIVFTree from sklearn import preprocessing from WrapperBruteForce import BruteForceSKLearn diff --git a/benchmarks/python_scripts/setup_fvecs_to_hdf5.py b/benchmarks/python_scripts/setup_fvecs_to_hdf5.py deleted file mode 100644 index 435f803..0000000 --- a/benchmarks/python_scripts/setup_fvecs_to_hdf5.py +++ /dev/null @@ -1,19 +0,0 @@ -from setup_utils import * - -def generate_hdf5_file(path_data: str, path_query, dataset_name): - data = read_fvecs(path_data) - queries = read_fvecs(path_query) - print('Queries', len(queries)) - print('Vectors', len(data)) - print('D=', len(data[0])) - with h5py.File(dataset_name, 'w') as f: - f.create_dataset("train", data=data) - f.create_dataset("test", data=queries) - - -if __name__ == "__main__": - generate_hdf5_file( - './benchmarks/datasets/downloaded/word2vec_base.fvecs', - './benchmarks/datasets/downloaded/word2vec_query.fvecs', - './benchmarks/datasets/downloaded/word2vec-300.hdf5' - ) \ No newline at end of file diff --git a/benchmarks/python_scripts/setup_ground_truth.py b/benchmarks/python_scripts/setup_ground_truth.py deleted file mode 100644 index 72eecf7..0000000 --- a/benchmarks/python_scripts/setup_ground_truth.py +++ /dev/null @@ -1,73 +0,0 @@ -import json -from WrapperBruteForce import BruteForceSKLearn -from setup_utils import * -from setup_settings import * -from sklearn import preprocessing - -# Generates ground truth with SKLearn -def generate_ground_truth(dataset, KNNS=(10, 100), normalize=False): - if not os.path.exists(GROUND_TRUTH_DATA): - os.makedirs(GROUND_TRUTH_DATA) - train, test = read_hdf5_data(dataset) - N_QUERIES = len(test) - # test = test[:N_QUERIES] - print('N. Queries', N_QUERIES) - - if normalize: - train = preprocessing.normalize(train, axis=1, norm='l2') - test = preprocessing.normalize(test, axis=1, norm='l2') - - algo = BruteForceSKLearn("euclidean", njobs=-1) - algo.fit(train) - for knn in KNNS: - gt_filename = get_ground_truth_filename(dataset, knn, normalize) - gt_name = os.path.join(SEMANTIC_GROUND_TRUTH_PATH, gt_filename) - gt = {} - index_data = [] - distance_data = [] - print('Querying for GT...') - dist, index = algo.query_batch(test, n=knn) - for i in range(N_QUERIES): - index_data.append(index[i]) - distance_data.append(dist[i] ** 2) - gt[i] = index[i].tolist() - with open(os.path.join(GROUND_TRUTH_DATA, gt_filename.replace('.json', '')), "wb") as file: - file.write(np.array(index_data, dtype=np.uint32).tobytes("C")) - file.write(np.array(distance_data, dtype=np.float32).tobytes("C")) - with open(gt_name, 'w') as f: - json.dump(gt, f) - - -if __name__ == "__main__": - ks = [100] - # generate_ground_truth('word2vec-300', ks, normalize=True) - - # generate_ground_truth('gooaq-distilroberta-768-normalized', ks, normalize=True) - # generate_ground_truth('agnews-mxbai-1024-euclidean', ks, normalize=True) - # generate_ground_truth('coco-nomic-768-normalized', ks, normalize=True) - # generate_ground_truth('simplewiki-openai-3072-normalized', ks, normalize=True) - - # generate_ground_truth('imagenet-align-640-normalized', ks, normalize=True) - # generate_ground_truth('yandex-200-cosine', ks, normalize=True) - # generate_ground_truth('imagenet-clip-512-normalized', ks, normalize=True) - # generate_ground_truth('laion-clip-512-normalized', ks, normalize=True) - # generate_ground_truth('codesearchnet-jina-768-cosine', ks, normalize=True) - # generate_ground_truth('yi-128-ip', ks, normalize=True) - # generate_ground_truth('landmark-dino-768-cosine', ks, normalize=True) - # generate_ground_truth('landmark-nomic-768-normalized', ks, normalize=True) - # generate_ground_truth('arxiv-nomic-768-normalized', ks, normalize=True) - # generate_ground_truth('ccnews-nomic-768-normalized', ks, normalize=True) - # generate_ground_truth('celeba-resnet-2048-cosine', ks, normalize=True) - # generate_ground_truth('llama-128-ip', ks, normalize=True) - generate_ground_truth('yahoo-minilm-384-normalized', ks, normalize=True) - - # generate_ground_truth('openai-1536-angular', ks) - # generate_ground_truth('msong-420', ks) - # generate_ground_truth('instructorxl-arxiv-768', ks) - # generate_ground_truth('sift-128-euclidean', ks) - # generate_ground_truth('gist-960-euclidean', ks) - # - # generate_ground_truth('glove-200-angular', ks) - # - # generate_ground_truth('contriever-768', ks) - diff --git a/benchmarks/python_scripts/setup_pdx.py b/benchmarks/python_scripts/setup_pdx.py new file mode 100644 index 0000000..1b8843b --- /dev/null +++ b/benchmarks/python_scripts/setup_pdx.py @@ -0,0 +1,84 @@ +import json +import sys +from setup_utils import * +from pdxearch import IndexPDXIVF, IndexPDXIVFSQ8, IndexPDXIVFTree, IndexPDXIVFTreeSQ8 +from WrapperBruteForce import BruteForceSKLearn +from sklearn import preprocessing + +INDEX_CLASSES = { + "pdx_f32": IndexPDXIVF, + "pdx_u8": IndexPDXIVFSQ8, + "pdx_tree_f32": IndexPDXIVFTree, + "pdx_tree_u8": IndexPDXIVFTreeSQ8, +} + + +def generate_ground_truth(dataset_abbrev, KNNS=(100,), normalize=True): + """Generate ground truth with SKLearn brute-force search.""" + hdf5_name, dims = DATASET_INFO[dataset_abbrev] + print(f'Generating ground truth: {dataset_abbrev} -> {hdf5_name}') + + train, test = read_hdf5_data(hdf5_name) + N_QUERIES = len(test) + print('N. Queries', N_QUERIES) + + if normalize: + train = preprocessing.normalize(train, axis=1, norm='l2') + test = preprocessing.normalize(test, axis=1, norm='l2') + + algo = BruteForceSKLearn("euclidean", njobs=-1) + algo.fit(train) + for knn in KNNS: + gt_filename = get_ground_truth_filename(hdf5_name, knn, normalize) + gt_name = os.path.join(SEMANTIC_GROUND_TRUTH_PATH, gt_filename) + gt = {} + index_data = [] + distance_data = [] + print('Querying for GT...') + dist, index = algo.query_batch(test, n=knn) + for i in range(N_QUERIES): + index_data.append(index[i]) + distance_data.append(dist[i] ** 2) + gt[i] = index[i].tolist() + with open(os.path.join(GROUND_TRUTH_DATA, gt_filename.replace('.json', '')), "wb") as file: + file.write(np.array(index_data, dtype=np.uint32).tobytes("C")) + file.write(np.array(distance_data, dtype=np.float32).tobytes("C")) + with open(gt_name, 'w') as f: + json.dump(gt, f) + + +def generate_test_data(dataset_abbrev): + """Save queries from HDF5 to binary format for C++ benchmarks.""" + hdf5_name, dims = DATASET_INFO[dataset_abbrev] + print(f'Saving test data: {dataset_abbrev} -> {hdf5_name}') + + test = read_hdf5_test_data(hdf5_name) + N_QUERIES = len(test) + with open(os.path.join(QUERIES_DATA, hdf5_name), "wb") as file: + file.write(N_QUERIES.to_bytes(4, sys.byteorder, signed=False)) + file.write(test.tobytes("C")) + + +def generate_index(dataset_abbrev: str, index_type: str, normalize=True, seed=42): + """Build and save a PDX index.""" + hdf5_name, dims = DATASET_INFO[dataset_abbrev] + print(f'{dataset_abbrev} -> {hdf5_name} ({index_type})') + data = read_hdf5_train_data(hdf5_name) + + cls = INDEX_CLASSES[index_type] + index = cls(num_dimensions=dims, normalize=normalize, seed=seed) + print('Building index...') + index.build(data) + print(f'Index built: {index.num_clusters} clusters') + + save_path = os.path.join(PDX_DATA, dataset_abbrev + '-' + index_type) + print(f'Saving to {save_path}') + index.save(save_path) + print('Done.') + + +if __name__ == "__main__": + # generate_ground_truth('mxbai', normalize=True) + # generate_test_data('mxbai') + # generate_index('mxbai', 'pdx_f32', normalize=True) + pass diff --git a/benchmarks/python_scripts/setup_settings.py b/benchmarks/python_scripts/setup_settings.py deleted file mode 100644 index 3d547d2..0000000 --- a/benchmarks/python_scripts/setup_settings.py +++ /dev/null @@ -1,104 +0,0 @@ -import os -SOURCE_DIR = os.getcwd() - -""" -Creates the directories needed to run benchmarks -""" - -# `benchmarks` directory should already exist -DATA_DIRECTORY = os.path.join("benchmarks", "datasets") -if not os.path.exists(os.path.join(SOURCE_DIR, DATA_DIRECTORY)): - os.makedirs(os.path.join(SOURCE_DIR, DATA_DIRECTORY)) - -RAW_DATA = os.path.join(SOURCE_DIR, DATA_DIRECTORY, "downloaded") -GROUND_TRUTH_DATA = os.path.join(SOURCE_DIR, DATA_DIRECTORY, "ground_truth") -FILTERED_GROUND_TRUTH_DATA = os.path.join(SOURCE_DIR, DATA_DIRECTORY, "ground_truth_filtered") -SEMANTIC_GROUND_TRUTH_PATH = os.path.join(SOURCE_DIR, "benchmarks", "gt") -SEMANTIC_FILTERED_GROUND_TRUTH_PATH = os.path.join(SOURCE_DIR, "benchmarks", "gt_filtered") - -CORE_INDEXES = os.path.join(SOURCE_DIR, "benchmarks", "core_indexes") -CORE_INDEXES_FAISS = os.path.join(CORE_INDEXES, "faiss") -CORE_INDEXES_FAISS_U8 = os.path.join(CORE_INDEXES, "faiss_sq8") -CORE_INDEXES_FAISS_L0 = os.path.join(CORE_INDEXES, "faiss_l0") -CORE_INDEXES_LORANN = os.path.join(CORE_INDEXES, "lorann") - -PURESCAN_DATA = os.path.join(SOURCE_DIR, DATA_DIRECTORY, "purescan") - -QUERIES_DATA = os.path.join(SOURCE_DIR, DATA_DIRECTORY, "queries") - -PDX_DATA = os.path.join(SOURCE_DIR, DATA_DIRECTORY, "pdx") -PDX_ADSAMPLING_DATA = os.path.join(SOURCE_DIR, DATA_DIRECTORY, "adsampling_pdx") - -FILTER_SELECTION_VECTORS = os.path.join(SOURCE_DIR, DATA_DIRECTORY, "selection_vectors") - -directories = [ - RAW_DATA, GROUND_TRUTH_DATA, PURESCAN_DATA, - PDX_DATA, PDX_ADSAMPLING_DATA, - CORE_INDEXES, CORE_INDEXES_FAISS, CORE_INDEXES_FAISS_U8, - CORE_INDEXES_FAISS_L0, CORE_INDEXES_LORANN, QUERIES_DATA, - SEMANTIC_GROUND_TRUTH_PATH, FILTER_SELECTION_VECTORS, - FILTERED_GROUND_TRUTH_DATA, SEMANTIC_FILTERED_GROUND_TRUTH_PATH -] - -for needed_directory in directories: - if not os.path.exists(needed_directory): - os.makedirs(needed_directory) - -# Datasets to set up and use -DATASETS = [ - "sift-128-euclidean", - "yi-128-ip", - "llama-128-ip", - "glove-200-angular", - "yandex-200-cosine", - "word2vec-300", - "yahoo-minilm-384-normalized", - "msong-420", - "imagenet-clip-512-normalized", - "laion-clip-512-normalized", - "imagenet-align-640-normalized", - "codesearchnet-jina-768-cosine", - "landmark-dino-768-cosine", - "landmark-nomic-768-normalized", - "arxiv-nomic-768-normalized", - "ccnews-nomic-768-normalized", - "coco-nomic-768-normalized", - "contriever-768", - "instructorxl-arxiv-768", - "gooaq-distilroberta-768-normalized", - "gist-960-euclidean", - "agnews-mxbai-1024-euclidean", - "openai-1536-angular", - "celeba-resnet-2048-cosine", - "simplewiki-openai-3072-normalized" -] - -DIMENSIONALITIES = { - 'glove-200-angular': 200, - 'sift-128-euclidean': 128, - 'trevi-4096': 4096, - 'msong-420': 420, - 'contriever-768': 768, - 'stl-9216': 9216, - 'gist-960-euclidean': 960, - 'instructorxl-arxiv-768': 768, - 'openai-1536-angular': 1536, - 'word2vec-300': 300, - 'gooaq-distilroberta-768-normalized': 768, - 'agnews-mxbai-1024-euclidean': 1024, - 'coco-nomic-768-normalized': 768, - 'simplewiki-openai-3072-normalized': 3072, - 'imagenet-align-640-normalized': 640, - 'yandex-200-cosine': 200, - 'imagenet-clip-512-normalized': 512, - 'laion-clip-512-normalized': 512, - 'codesearchnet-jina-768-cosine': 768, - 'yi-128-ip': 128, - 'landmark-dino-768-cosine': 768, - 'landmark-nomic-768-normalized': 768, - 'arxiv-nomic-768-normalized': 768, - 'ccnews-nomic-768-normalized': 768, - 'celeba-resnet-2048-cosine': 2048, - 'llama-128-ip': 128, - 'yahoo-minilm-384-normalized': 384 -} diff --git a/benchmarks/python_scripts/setup_test_data.py b/benchmarks/python_scripts/setup_test_data.py deleted file mode 100644 index 01fb3a0..0000000 --- a/benchmarks/python_scripts/setup_test_data.py +++ /dev/null @@ -1,43 +0,0 @@ -import sys -from setup_utils import * -from setup_settings import * - - -# Transforms the queries from HDF5 format to .fvecs -def generate_test_data(dataset): - if not os.path.exists(GROUND_TRUTH_DATA): - os.makedirs(GROUND_TRUTH_DATA) - test = read_hdf5_test_data(dataset) - N_QUERIES = len(test) - with open(os.path.join(QUERIES_DATA, dataset), "wb") as file: - file.write(N_QUERIES.to_bytes(4, sys.byteorder, signed=False)) - file.write(test.tobytes("C")) - - -if __name__ == "__main__": - # generate_test_data('word2vec-300') - # generate_test_data('msong-420') - # generate_test_data('instructorxl-arxiv-768') - # generate_test_data('gist-960-euclidean') - # generate_test_data('contriever-768') - # generate_test_data('gooaq-distilroberta-768-normalized') - # generate_test_data('agnews-mxbai-1024-euclidean') - # generate_test_data('coco-nomic-768-normalized') - # generate_test_data('simplewiki-openai-3072-normalized') - - generate_test_data('imagenet-align-640-normalized') - - generate_test_data('yandex-200-cosine') - generate_test_data('imagenet-clip-512-normalized') - generate_test_data('laion-clip-512-normalized') - generate_test_data('codesearchnet-jina-768-cosine') - - generate_test_data('yi-128-ip') - generate_test_data('landmark-dino-768-cosine') - generate_test_data('landmark-nomic-768-normalized') - generate_test_data('arxiv-nomic-768-normalized') - - generate_test_data('ccnews-nomic-768-normalized') - generate_test_data('celeba-resnet-2048-cosine') - generate_test_data('llama-128-ip') - generate_test_data('yahoo-minilm-384-normalized') diff --git a/benchmarks/python_scripts/setup_utils.py b/benchmarks/python_scripts/setup_utils.py index f8d05b1..89f2087 100644 --- a/benchmarks/python_scripts/setup_utils.py +++ b/benchmarks/python_scripts/setup_utils.py @@ -1,7 +1,31 @@ +import os +import sys import numpy as np import h5py -from setup_settings import * +SOURCE_DIR = os.getcwd() + +# ── Directory layout ────────────────────────────────────────────── +DATA_DIRECTORY = os.path.join("benchmarks", "datasets") +if not os.path.exists(os.path.join(SOURCE_DIR, DATA_DIRECTORY)): + os.makedirs(os.path.join(SOURCE_DIR, DATA_DIRECTORY)) + +RAW_DATA = os.path.join(SOURCE_DIR, DATA_DIRECTORY, "downloaded") +GROUND_TRUTH_DATA = os.path.join(SOURCE_DIR, DATA_DIRECTORY, "ground_truth") +FILTERED_GROUND_TRUTH_DATA = os.path.join(SOURCE_DIR, DATA_DIRECTORY, "ground_truth_filtered") +SEMANTIC_GROUND_TRUTH_PATH = os.path.join(SOURCE_DIR, "benchmarks", "gt") +SEMANTIC_FILTERED_GROUND_TRUTH_PATH = os.path.join(SOURCE_DIR, "benchmarks", "gt_filtered") + +QUERIES_DATA = os.path.join(SOURCE_DIR, DATA_DIRECTORY, "queries") +PDX_DATA = os.path.join(SOURCE_DIR, DATA_DIRECTORY, "pdx") +FILTER_SELECTION_VECTORS = os.path.join(SOURCE_DIR, DATA_DIRECTORY, "selection_vectors") + +for _d in [RAW_DATA, GROUND_TRUTH_DATA, FILTERED_GROUND_TRUTH_DATA, + SEMANTIC_GROUND_TRUTH_PATH, SEMANTIC_FILTERED_GROUND_TRUTH_PATH, + QUERIES_DATA, PDX_DATA, FILTER_SELECTION_VECTORS]: + os.makedirs(_d, exist_ok=True) + +# ── Dataset registry ────────────────────────────────────────────── # Abbreviated name -> (hdf5_dataset_name, num_dimensions) # Matches RAW_DATASET_PARAMS in benchmark_utils.hpp DATASET_INFO = { @@ -20,36 +44,28 @@ "wiki": ("simplewiki-openai-3072-normalized", 3072), } +# ── HDF5 I/O ───────────────────────────────────────────────────── -def read_hdf5_train_data(dataset): - hdf5_file_name = os.path.join(RAW_DATA, dataset + ".hdf5") +def read_hdf5_train_data(dataset_hdf5_name): + hdf5_file_name = os.path.join(RAW_DATA, dataset_hdf5_name + ".hdf5") hdf5_file = h5py.File(hdf5_file_name, "r") return np.array(hdf5_file["train"], dtype=np.float32) -def read_ivecs(filename): - a = np.fromfile(filename, dtype="int32") - d = a[0] - print(f"\t{filename} readed") - return a.reshape(-1, d + 1)[:, 1:] - - -def read_fvecs(filename): - return read_ivecs(filename).view("float32") - - -def read_hdf5_test_data(dataset): - hdf5_file_name = os.path.join(RAW_DATA, dataset + ".hdf5") +def read_hdf5_test_data(dataset_hdf5_name): + hdf5_file_name = os.path.join(RAW_DATA, dataset_hdf5_name + ".hdf5") hdf5_file = h5py.File(hdf5_file_name, "r") return np.array(hdf5_file["test"], dtype=np.float32) -def read_hdf5_data(dataset): - hdf5_file_name = os.path.join(RAW_DATA, dataset + ".hdf5") +def read_hdf5_data(dataset_hdf5_name): + hdf5_file_name = os.path.join(RAW_DATA, dataset_hdf5_name + ".hdf5") hdf5_file = h5py.File(hdf5_file_name, "r") return np.array(hdf5_file["train"], dtype=np.float32), np.array(hdf5_file["test"], dtype=np.float32) +# ── Helpers ─────────────────────────────────────────────────────── + def get_ground_truth_filename(file, k, norm=True): if norm: return f"{file}_{k}_norm.json" @@ -62,15 +78,3 @@ def get_core_index_filename(file, norm=True, balanced=False): if norm: return f"ivf_{file}_norm.index" return f"ivf_{file}.index" - - -def get_delta_d(ndim): - delta_d = 32 - if ndim < 128: - delta_d = int(ndim / 4) - return delta_d - - -if __name__ == '__main__': - if not os.path.exists(RAW_DATA): - os.makedirs(RAW_DATA) diff --git a/benchmarks/results/DEFAULT/PDX_F32_ADSAMPLING.csv b/benchmarks/results/DEFAULT/PDX_F32_ADSAMPLING.csv new file mode 100644 index 0000000..b10408a --- /dev/null +++ b/benchmarks/results/DEFAULT/PDX_F32_ADSAMPLING.csv @@ -0,0 +1,3 @@ +dataset,algorithm,avg,max,min,recall,ivf_nprobe,epsilon,knn,n_queries,selectivity,num_measure_runs,avg_all,max_all,min_all +openai,adsampling,0.915137,1.33392,0.493292,0.839519,32,0,100,1000,0.2,1,0.9182,1.812,0.4849 +arxiv,adsampling,0.524415,0.765917,0.306708,0.906279,32,0,100,1000,0.2,1,0.5256,0.8312,0.3067 diff --git a/benchmarks/results/DEFAULT/PDX_TREE_F32_ADSAMPLING.csv b/benchmarks/results/DEFAULT/PDX_TREE_F32_ADSAMPLING.csv new file mode 100644 index 0000000..ef48a72 --- /dev/null +++ b/benchmarks/results/DEFAULT/PDX_TREE_F32_ADSAMPLING.csv @@ -0,0 +1,3 @@ +dataset,algorithm,avg,max,min,recall,ivf_nprobe,epsilon,knn,n_queries,selectivity,num_measure_runs,avg_all,max_all,min_all +openai,adsampling,0.722484,1.14888,0.289916,0.836599,32,0,100,1000,0.2,1,0.7342,2.249,0.2845 +arxiv,adsampling,0.39642,0.629542,0.175917,0.904999,32,0,100,1000,0.2,1,0.3986,0.7314,0.1759 diff --git a/benchmarks/results/DEFAULT/PDX_TREE_U8_ADSAMPLING.csv b/benchmarks/results/DEFAULT/PDX_TREE_U8_ADSAMPLING.csv index f824ea2..0c3156d 100644 --- a/benchmarks/results/DEFAULT/PDX_TREE_U8_ADSAMPLING.csv +++ b/benchmarks/results/DEFAULT/PDX_TREE_U8_ADSAMPLING.csv @@ -1,3 +1,6 @@ dataset,algorithm,avg,max,min,recall,ivf_nprobe,epsilon,knn,n_queries,selectivity,num_measure_runs,avg_all,max_all,min_all mxbai,adsampling,0.212453,0.318375,0.131292,0.901368,25,0,100,1000,0.2,1,0.2135,0.3588,0.1313 mxbai,adsampling,0.199531,0.293875,0.12325,0.901368,25,0,100,1000,0.2,1,0.2001,0.3107,0.1232 +openai,adsampling,0.338263,0.462875,0.209292,0.836119,32,0,100,1000,0.2,1,0.338,0.5004,0.191 +arxiv,adsampling,0.191359,0.267833,0.115875,0.902898,32,0,100,1000,0.2,1,0.1917,0.294,0.09696 +arxiv,adsampling,0.197974,0.286083,0.111708,0.902898,32,0,100,1000,0.2,1,0.1986,0.3234,0.1024 diff --git a/benchmarks/results/DEFAULT/PDX_U8_ADSAMPLING.csv b/benchmarks/results/DEFAULT/PDX_U8_ADSAMPLING.csv new file mode 100644 index 0000000..36cf47f --- /dev/null +++ b/benchmarks/results/DEFAULT/PDX_U8_ADSAMPLING.csv @@ -0,0 +1,4 @@ +dataset,algorithm,avg,max,min,recall,ivf_nprobe,epsilon,knn,n_queries,selectivity,num_measure_runs,avg_all,max_all,min_all +openai,adsampling,0.583102,0.718916,0.450375,0.839039,32,0,100,1000,0.2,1,0.5833,0.7456,0.4233 +openai,adsampling,0.864532,1.20437,0.53175,0.926669,128,0,100,1000,0.2,1,0.8638,1.204,0.5146 +arxiv,adsampling,0.321106,0.397583,0.248,0.904168,32,0,100,1000,0.2,1,0.3215,0.4456,0.2422 diff --git a/examples/pdx_2l_ivf.py b/examples/pdx_2l_ivf.py index 73be1e9..3e13407 100644 --- a/examples/pdx_2l_ivf.py +++ b/examples/pdx_2l_ivf.py @@ -22,6 +22,7 @@ print('Building index...') index.build(train) print(f'Index built: {index.num_clusters} clusters') + print(f'Index in-memory size: {index.in_memory_size_bytes / (1024 * 1024):.2f} MB') print(f'{len(queries)} queries with PDX') times = [] diff --git a/examples/pdx_2l_ivf_8bit.py b/examples/pdx_2l_ivf_8bit.py index 5577992..7f6d899 100644 --- a/examples/pdx_2l_ivf_8bit.py +++ b/examples/pdx_2l_ivf_8bit.py @@ -22,6 +22,7 @@ print('Building index...') index.build(train) print(f'Index built: {index.num_clusters} clusters') + print(f'Index in-memory size: {index.in_memory_size_bytes / (1024 * 1024):.2f} MB') print(f'{len(queries)} queries with PDX') times = [] diff --git a/examples/pdx_ivf.py b/examples/pdx_ivf.py index 8f205d6..62fe828 100644 --- a/examples/pdx_ivf.py +++ b/examples/pdx_ivf.py @@ -22,6 +22,7 @@ print('Building index...') index.build(train) print(f'Index built: {index.num_clusters} clusters') + print(f'Index in-memory size: {index.in_memory_size_bytes / (1024 * 1024):.2f} MB') print(f'{len(queries)} queries with PDX') times = [] diff --git a/include/pdx/index.hpp b/include/pdx/index.hpp index 46a5a5c..bcdfa14 100644 --- a/include/pdx/index.hpp +++ b/include/pdx/index.hpp @@ -41,6 +41,7 @@ class IPDXIndex { virtual void Restore(const std::string &path) = 0; virtual uint32_t GetNumDimensions() const = 0; virtual uint32_t GetNumClusters() const = 0; + virtual size_t GetInMemorySizeInBytes() const = 0; }; template @@ -129,6 +130,25 @@ class PDXIndex : public IPDXIndex { return index.num_clusters; } + size_t GetInMemorySizeInBytes() const override { + size_t size = sizeof(*this); + // IVF heap allocations (sizeof(IVF) is inline in sizeof(*this)) + size += index.GetInMemorySizeInBytes() - sizeof(index); + // Pruner: rotation matrix (D x D floats) + ratios vector (D floats) + if (pruner) { + size += sizeof(*pruner); + const auto &m = pruner->GetMatrix(); + size += static_cast(m.rows()) * m.cols() * sizeof(float); + size += pruner->num_dimensions * sizeof(float); + } + // Searcher: cluster_offsets array + if (searcher) { + size += sizeof(*searcher); + size += index.num_clusters * sizeof(size_t); + } + return size; + } + void BuildIndex(const float *const embeddings, const size_t num_embeddings) override { std::vector row_ids(num_embeddings); std::iota(row_ids.begin(), row_ids.end(), 0); @@ -445,6 +465,30 @@ class PDXTreeIndex : public IPDXIndex { return index.l0.num_clusters; } + size_t GetInMemorySizeInBytes() const override { + size_t size = sizeof(*this); + // IVFTree heap allocations (L1 + L0 clusters and centroids) + size += index.GetInMemorySizeInBytes() - sizeof(index); + // Pruner: rotation matrix (D x D floats) + ratios vector (D floats) + if (pruner) { + size += sizeof(*pruner); + const auto &m = pruner->GetMatrix(); + size += static_cast(m.rows()) * m.cols() * sizeof(float); + size += pruner->num_dimensions * sizeof(float); + } + // L1 searcher: cluster_offsets array + if (searcher) { + size += sizeof(*searcher); + size += index.num_clusters * sizeof(size_t); + } + // L0 top-level searcher: cluster_offsets array + if (top_level_searcher) { + size += sizeof(*top_level_searcher); + size += index.l0.num_clusters * sizeof(size_t); + } + return size; + } + }; using PDXIndexF32 = PDXIndex; diff --git a/include/pdx/lib/lib.hpp b/include/pdx/lib/lib.hpp index 246c9a7..0803ff0 100644 --- a/include/pdx/lib/lib.hpp +++ b/include/pdx/lib/lib.hpp @@ -107,6 +107,10 @@ class PyPDXIndex { uint32_t GetNumClusters() const { return index->GetNumClusters(); } + + size_t GetInMemorySizeInBytes() const { + return index->GetInMemorySizeInBytes(); + } }; } // namespace PDX diff --git a/python/lib.cpp b/python/lib.cpp index 95c6ee1..255c008 100644 --- a/python/lib.cpp +++ b/python/lib.cpp @@ -23,7 +23,8 @@ PYBIND11_MODULE(compiled, m) { .def("set_nprobe", &PDX::PyPDXIndex::SetNProbe, py::arg("nprobe")) .def("save", &PDX::PyPDXIndex::Save, py::arg("path")) .def("get_num_dimensions", &PDX::PyPDXIndex::GetNumDimensions) - .def("get_num_clusters", &PDX::PyPDXIndex::GetNumClusters); + .def("get_num_clusters", &PDX::PyPDXIndex::GetNumClusters) + .def("get_in_memory_size_in_bytes", &PDX::PyPDXIndex::GetInMemorySizeInBytes); m.def("load_index", &PDX::PyPDXIndex::LoadFromFile, py::arg("path"), "Load a PDX index from a single file (auto-detects type)."); diff --git a/python/pdxearch/index_factory.py b/python/pdxearch/index_factory.py index 81c7955..5698d80 100644 --- a/python/pdxearch/index_factory.py +++ b/python/pdxearch/index_factory.py @@ -56,6 +56,10 @@ def num_dimensions(self) -> int: def num_clusters(self) -> int: return self._index.get_num_clusters() + @property + def in_memory_size_bytes(self) -> int: + return self._index.get_in_memory_size_in_bytes() + class IndexPDXIVFSQ8: """Single-level IVF index (U8 scalar quantization).""" @@ -107,6 +111,10 @@ def num_dimensions(self) -> int: def num_clusters(self) -> int: return self._index.get_num_clusters() + @property + def in_memory_size_bytes(self) -> int: + return self._index.get_in_memory_size_in_bytes() + class IndexPDXIVFTree: """Two-level IVF index (F32).""" @@ -160,6 +168,10 @@ def num_dimensions(self) -> int: def num_clusters(self) -> int: return self._index.get_num_clusters() + @property + def in_memory_size_bytes(self) -> int: + return self._index.get_in_memory_size_in_bytes() + class IndexPDXIVFTreeSQ8: """Two-level IVF index (U8 scalar quantization).""" @@ -213,6 +225,10 @@ def num_dimensions(self) -> int: def num_clusters(self) -> int: return self._index.get_num_clusters() + @property + def in_memory_size_bytes(self) -> int: + return self._index.get_in_memory_size_in_bytes() + def load_index(path: str): """Load a PDX index from a single file (auto-detects type).""" From 8710451a929361af00af5785849cac93b597be7e Mon Sep 17 00:00:00 2001 From: lkuffo Date: Tue, 24 Feb 2026 15:00:26 +0100 Subject: [PATCH 15/39] Format fix --- .clang-format | 32 + .clang-tidy | 22 + benchmarks/bench_kernels/kernels.cpp | 367 ++--- benchmarks/benchmark_utils.hpp | 222 +-- benchmarks/pdx_end_to_end.cpp | 71 +- benchmarks/pdx_ivf.cpp | 40 +- .../pdx_ivf2_adsampling_u8_filtered.cpp | 89 +- benchmarks/pdx_ivf_adsampling_filtered.cpp | 85 +- benchmarks/pdx_serialization.cpp | 45 +- .../results/DEFAULT/PDX_F32_ADSAMPLING.csv | 1 + .../DEFAULT/PDX_TREE_U8_ADSAMPLING.csv | 1 + include/pdx/clustering.hpp | 118 +- include/pdx/common.hpp | 130 +- include/pdx/db_mock/predicate_evaluator.hpp | 34 +- .../pdx/distance_computers/avx2_computers.hpp | 353 ++--- .../distance_computers/avx512_computers.hpp | 345 +++-- .../pdx/distance_computers/base_computers.hpp | 24 +- .../pdx/distance_computers/neon_computers.hpp | 271 ++-- .../distance_computers/scalar_computers.hpp | 183 +-- include/pdx/index.hpp | 1089 ++++++++------- include/pdx/ivf_wrapper.hpp | 704 +++++----- include/pdx/layout.hpp | 103 +- include/pdx/lib/lib.hpp | 197 +-- include/pdx/pruners/adsampling.hpp | 227 +-- include/pdx/quantizers/scalar.hpp | 115 +- include/pdx/searcher.hpp | 1222 ++++++++++------- include/pdx/utils.hpp | 5 +- python/lib.cpp | 42 +- scripts/format.sh | 62 + scripts/format_check.sh | 80 ++ scripts/tidy_check.sh | 81 ++ 31 files changed, 3678 insertions(+), 2682 deletions(-) create mode 100644 .clang-format create mode 100644 .clang-tidy create mode 100755 scripts/format.sh create mode 100755 scripts/format_check.sh create mode 100755 scripts/tidy_check.sh diff --git a/.clang-format b/.clang-format new file mode 100644 index 0000000..57091e0 --- /dev/null +++ b/.clang-format @@ -0,0 +1,32 @@ +BasedOnStyle: LLVM + +# Indentation +IndentWidth: 4 +TabWidth: 4 + +# Braces +BreakBeforeBraces: Attach +AllowShortFunctionsOnASingleLine: InlineOnly + +ColumnLimit: 100 + +# Pointer/reference alignment +PointerAlignment: Left +AlignConsecutiveAssignments: false +AlignConsecutiveDeclarations: false +AlignAfterOpenBracket: BlockIndent +AlwaysBreakTemplateDeclarations: Yes + +# Spaces +SpaceBeforeParens: ControlStatements +SpaceAfterCStyleCast: true +SpacesInParentheses: false + + +BinPackParameters: false +AllowAllParametersOfDeclarationOnNextLine: false +AlwaysBreakAfterReturnType: None +PenaltyReturnTypeOnItsOwnLine: 1024 + +BinPackArguments: false +AllowAllArgumentsOnNextLine: true \ No newline at end of file diff --git a/.clang-tidy b/.clang-tidy new file mode 100644 index 0000000..86cf168 --- /dev/null +++ b/.clang-tidy @@ -0,0 +1,22 @@ +Checks: > + -*, + bugprone-*, + clang-analyzer-*, + cppcoreguidelines-virtual-class-destructor, + modernize-pass-by-value, + modernize-use-emplace, + modernize-use-nullptr, + modernize-use-override, + modernize-use-using, + performance-*, + readability-redundant-*, + -bugprone-easily-swappable-parameters, + -performance-avoid-endl + +WarningsAsErrors: '' + +CheckOptions: + - key: bugprone-narrowing-conversions.WarnOnEquivalentBitWidth + value: false + +HeaderFilterRegex: 'include/superkmeans/.*' diff --git a/benchmarks/bench_kernels/kernels.cpp b/benchmarks/bench_kernels/kernels.cpp index 49f1855..4e59f3d 100644 --- a/benchmarks/bench_kernels/kernels.cpp +++ b/benchmarks/bench_kernels/kernels.cpp @@ -1,9 +1,9 @@ -#include -#include -#include -#include #include +#include +#include +#include #include +#include #include #if defined(__ARM_NEON) @@ -14,47 +14,46 @@ #include #endif - -template +template struct KNNCandidate { uint32_t index; float distance; }; -template<> +template <> struct KNNCandidate { uint32_t index; float distance; }; -template<> +template <> struct KNNCandidate { uint32_t index; uint32_t distance; }; -template +template struct DistanceType { using type = float; // default for f32 }; -template<> +template <> struct DistanceType { using type = uint32_t; }; -template +template using DistanceType_t = typename DistanceType::type; -template +template struct VectorComparator { - bool operator() (const KNNCandidate& a, const KNNCandidate& b) { + bool operator()(const KNNCandidate& a, const KNNCandidate& b) { return a.distance < b.distance; } }; -template +template struct VectorComparatorInverse { - bool operator() (const KNNCandidate& a, const KNNCandidate& b) { + bool operator()(const KNNCandidate& a, const KNNCandidate& b) { return a.distance > b.distance; } }; @@ -84,12 +83,7 @@ static constexpr size_t U8_N_REGISTERS_AVX = 4; // SIMD //////////////// - -inline float f32_simd_ip( - const float *first_vector, - const float *second_vector, - const size_t d -) { +inline float f32_simd_ip(const float* first_vector, const float* second_vector, const size_t d) { #if defined(__APPLE__) float distance = 0.0; #pragma clang loop vectorize(enable) @@ -115,35 +109,32 @@ inline float f32_simd_ip( __m512 a_vec, b_vec; size_t num_dimensions = d; - simsimd_ip_f32_skylake_cycle: - if (num_dimensions < 16) { - __mmask16 mask = (__mmask16)_bzhi_u32(0xFFFFFFFF, num_dimensions); - a_vec = _mm512_maskz_loadu_ps(mask, first_vector); - b_vec = _mm512_maskz_loadu_ps(mask, second_vector); - num_dimensions = 0; - } else { - a_vec = _mm512_loadu_ps(first_vector); - b_vec = _mm512_loadu_ps(second_vector); - first_vector += 16, second_vector += 16, num_dimensions -= 16; - } +simsimd_ip_f32_skylake_cycle: + if (num_dimensions < 16) { + __mmask16 mask = (__mmask16) _bzhi_u32(0xFFFFFFFF, num_dimensions); + a_vec = _mm512_maskz_loadu_ps(mask, first_vector); + b_vec = _mm512_maskz_loadu_ps(mask, second_vector); + num_dimensions = 0; + } else { + a_vec = _mm512_loadu_ps(first_vector); + b_vec = _mm512_loadu_ps(second_vector); + first_vector += 16, second_vector += 16, num_dimensions -= 16; + } d2_vec = _mm512_fmadd_ps(a_vec, b_vec, d2_vec); if (num_dimensions) goto simsimd_ip_f32_skylake_cycle; // _simsimd_reduce_f32x16_skylake __m512 x = _mm512_add_ps(d2_vec, _mm512_shuffle_f32x4(d2_vec, d2_vec, _MM_SHUFFLE(0, 0, 3, 2))); - __m128 r = _mm512_castps512_ps128(_mm512_add_ps(x, _mm512_shuffle_f32x4(x, x, _MM_SHUFFLE(0, 0, 0, 1)))); + __m128 r = + _mm512_castps512_ps128(_mm512_add_ps(x, _mm512_shuffle_f32x4(x, x, _MM_SHUFFLE(0, 0, 0, 1))) + ); r = _mm_hadd_ps(r, r); return _mm_cvtss_f32(_mm_hadd_ps(r, r)); #endif } - -inline float f32_simd_l2( - const float *first_vector, - const float *second_vector, - const size_t d -) { +inline float f32_simd_l2(const float* first_vector, const float* second_vector, const size_t d) { #if defined(__APPLE__) float distance = 0.0; #pragma clang loop vectorize(enable) @@ -173,17 +164,17 @@ inline float f32_simd_l2( __m512 a_vec, b_vec; size_t num_dimensions = d; - simsimd_l2sq_f32_skylake_cycle: - if (d < 16) { - __mmask16 mask = (__mmask16)_bzhi_u32(0xFFFFFFFF, num_dimensions); - a_vec = _mm512_maskz_loadu_ps(mask, first_vector); - b_vec = _mm512_maskz_loadu_ps(mask, second_vector); - num_dimensions = 0; - } else { - a_vec = _mm512_loadu_ps(first_vector); - b_vec = _mm512_loadu_ps(second_vector); - first_vector += 16, second_vector += 16, num_dimensions -= 16; - } +simsimd_l2sq_f32_skylake_cycle: + if (d < 16) { + __mmask16 mask = (__mmask16) _bzhi_u32(0xFFFFFFFF, num_dimensions); + a_vec = _mm512_maskz_loadu_ps(mask, first_vector); + b_vec = _mm512_maskz_loadu_ps(mask, second_vector); + num_dimensions = 0; + } else { + a_vec = _mm512_loadu_ps(first_vector); + b_vec = _mm512_loadu_ps(second_vector); + first_vector += 16, second_vector += 16, num_dimensions -= 16; + } __m512 d_vec = _mm512_sub_ps(a_vec, b_vec); d2_vec = _mm512_fmadd_ps(d_vec, d_vec, d2_vec); if (num_dimensions) @@ -191,16 +182,17 @@ inline float f32_simd_l2( // _simsimd_reduce_f32x16_skylake __m512 x = _mm512_add_ps(d2_vec, _mm512_shuffle_f32x4(d2_vec, d2_vec, _MM_SHUFFLE(0, 0, 3, 2))); - __m128 r = _mm512_castps512_ps128(_mm512_add_ps(x, _mm512_shuffle_f32x4(x, x, _MM_SHUFFLE(0, 0, 0, 1)))); + __m128 r = + _mm512_castps512_ps128(_mm512_add_ps(x, _mm512_shuffle_f32x4(x, x, _MM_SHUFFLE(0, 0, 0, 1))) + ); r = _mm_hadd_ps(r, r); return _mm_cvtss_f32(_mm_hadd_ps(r, r)); #endif } - inline uint32_t u8_simd_l2( - const uint8_t *first_vector, - const uint8_t *second_vector, + const uint8_t* first_vector, + const uint8_t* second_vector, const size_t d ) { #if defined(__ARM_NEON) @@ -223,32 +215,34 @@ inline uint32_t u8_simd_l2( __m512i a_u8_vec, b_u8_vec; size_t num_dimensions = d; - simsimd_l2sq_u8_ice_cycle: - if (num_dimensions < 64) { - const __mmask64 mask = (__mmask64)_bzhi_u64(0xFFFFFFFFFFFFFFFF, num_dimensions); - a_u8_vec = _mm512_maskz_loadu_epi8(mask, first_vector); - b_u8_vec = _mm512_maskz_loadu_epi8(mask, second_vector); - num_dimensions = 0; - } - else { - a_u8_vec = _mm512_loadu_si512(first_vector); - b_u8_vec = _mm512_loadu_si512(second_vector); - first_vector += 64, second_vector += 64, num_dimensions -= 64; - } +simsimd_l2sq_u8_ice_cycle: + if (num_dimensions < 64) { + const __mmask64 mask = (__mmask64) _bzhi_u64(0xFFFFFFFFFFFFFFFF, num_dimensions); + a_u8_vec = _mm512_maskz_loadu_epi8(mask, first_vector); + b_u8_vec = _mm512_maskz_loadu_epi8(mask, second_vector); + num_dimensions = 0; + } else { + a_u8_vec = _mm512_loadu_si512(first_vector); + b_u8_vec = _mm512_loadu_si512(second_vector); + first_vector += 64, second_vector += 64, num_dimensions -= 64; + } // Substracting unsigned vectors in AVX-512 is done by saturating subtraction: - __m512i d_u8_vec = _mm512_or_si512(_mm512_subs_epu8(a_u8_vec, b_u8_vec), _mm512_subs_epu8(b_u8_vec, a_u8_vec)); + __m512i d_u8_vec = + _mm512_or_si512(_mm512_subs_epu8(a_u8_vec, b_u8_vec), _mm512_subs_epu8(b_u8_vec, a_u8_vec)); - // Multiply and accumulate at `int8` level which are actually uint7, accumulate at `int32` level: + // Multiply and accumulate at `int8` level which are actually uint7, accumulate at `int32` + // level: d2_i32_vec = _mm512_dpbusds_epi32(d2_i32_vec, d_u8_vec, d_u8_vec); - if (num_dimensions) goto simsimd_l2sq_u8_ice_cycle; + if (num_dimensions) + goto simsimd_l2sq_u8_ice_cycle; return _mm512_reduce_add_epi32(d2_i32_vec); #endif }; inline uint32_t u8_simd_ip( - const uint8_t *first_vector, - const uint8_t *second_vector, + const uint8_t* first_vector, + const uint8_t* second_vector, const size_t d ) { #if defined(__ARM_NEON) @@ -269,22 +263,23 @@ inline uint32_t u8_simd_ip( __m512i a_u8_vec, b_u8_vec; size_t num_dimensions = d; - simsimd_l2sq_u8_ice_cycle: - if (num_dimensions < 64) { - const __mmask64 mask = (__mmask64)_bzhi_u64(0xFFFFFFFFFFFFFFFF, num_dimensions); - a_u8_vec = _mm512_maskz_loadu_epi8(mask, first_vector); - b_u8_vec = _mm512_maskz_loadu_epi8(mask, second_vector); - num_dimensions = 0; - } - else { - a_u8_vec = _mm512_loadu_si512(first_vector); - b_u8_vec = _mm512_loadu_si512(second_vector); - first_vector += 64, second_vector += 64, num_dimensions -= 64; - } +simsimd_l2sq_u8_ice_cycle: + if (num_dimensions < 64) { + const __mmask64 mask = (__mmask64) _bzhi_u64(0xFFFFFFFFFFFFFFFF, num_dimensions); + a_u8_vec = _mm512_maskz_loadu_epi8(mask, first_vector); + b_u8_vec = _mm512_maskz_loadu_epi8(mask, second_vector); + num_dimensions = 0; + } else { + a_u8_vec = _mm512_loadu_si512(first_vector); + b_u8_vec = _mm512_loadu_si512(second_vector); + first_vector += 64, second_vector += 64, num_dimensions -= 64; + } - // Multiply and accumulate at `int8` level which are actually uint7, accumulate at `int32` level: + // Multiply and accumulate at `int8` level which are actually uint7, accumulate at `int32` + // level: d2_i32_vec = _mm512_dpbusds_epi32(d2_i32_vec, a_u8_vec, b_u8_vec); - if (num_dimensions) goto simsimd_l2sq_u8_ice_cycle; + if (num_dimensions) + goto simsimd_l2sq_u8_ice_cycle; return _mm512_reduce_add_epi32(d2_i32_vec); #endif }; @@ -293,59 +288,45 @@ inline uint32_t u8_simd_ip( // PDX //////////////// - -inline void f32_pdx_ip( - const float *first_vector, - const float *second_vector, - const size_t d -) { +inline void f32_pdx_ip(const float* first_vector, const float* second_vector, const size_t d) { memset((void*) distances_f32, 0.0, F32_PDX_VECTOR_SIZE * sizeof(float)); for (size_t dim_idx = 0; dim_idx < d; dim_idx++) { const size_t dimension_idx = dim_idx; const size_t offset_to_dimension_start = dimension_idx * F32_PDX_VECTOR_SIZE; for (size_t vector_idx = 0; vector_idx < F32_PDX_VECTOR_SIZE; ++vector_idx) { - distances_f32[vector_idx] += second_vector[dimension_idx] * first_vector[offset_to_dimension_start + vector_idx]; + distances_f32[vector_idx] += + second_vector[dimension_idx] * first_vector[offset_to_dimension_start + vector_idx]; } } } -inline void f32_pdx_l1( - const float *first_vector, - const float *second_vector, - const size_t d -) { +inline void f32_pdx_l1(const float* first_vector, const float* second_vector, const size_t d) { memset((void*) distances_f32, 0.0, F32_PDX_VECTOR_SIZE * sizeof(float)); for (size_t dim_idx = 0; dim_idx < d; dim_idx++) { const size_t dimension_idx = dim_idx; const size_t offset_to_dimension_start = dimension_idx * F32_PDX_VECTOR_SIZE; for (size_t vector_idx = 0; vector_idx < F32_PDX_VECTOR_SIZE; ++vector_idx) { - float to_abs = second_vector[dimension_idx] - first_vector[offset_to_dimension_start + vector_idx]; + float to_abs = + second_vector[dimension_idx] - first_vector[offset_to_dimension_start + vector_idx]; distances_f32[vector_idx] += std::fabs(to_abs); } } } -inline void f32_pdx_l2( - const float *first_vector, - const float *second_vector, - const size_t d -) { +inline void f32_pdx_l2(const float* first_vector, const float* second_vector, const size_t d) { memset((void*) distances_f32, 0.0, F32_PDX_VECTOR_SIZE * sizeof(float)); for (size_t dim_idx = 0; dim_idx < d; dim_idx++) { const size_t dimension_idx = dim_idx; const size_t offset_to_dimension_start = dimension_idx * F32_PDX_VECTOR_SIZE; for (size_t vector_idx = 0; vector_idx < F32_PDX_VECTOR_SIZE; ++vector_idx) { - float to_multiply = second_vector[dimension_idx] - first_vector[offset_to_dimension_start + vector_idx]; + float to_multiply = + second_vector[dimension_idx] - first_vector[offset_to_dimension_start + vector_idx]; distances_f32[vector_idx] += to_multiply * to_multiply; } } } -inline void u8_pdx_l2( - const uint8_t *first_vector, - const uint8_t *second_vector, - const size_t d -) { +inline void u8_pdx_l2(const uint8_t* first_vector, const uint8_t* second_vector, const size_t d) { memset((void*) distances_u8, 0, U8_PDX_VECTOR_SIZE * sizeof(uint32_t)); #if defined(__ARM_NEON) uint32x4_t res[U8_N_REGISTERS_NEON]; @@ -355,12 +336,13 @@ inline void u8_pdx_l2( res[i] = vdupq_n_u32(0); } // Compute L2 - for (size_t dim_idx = 0; dim_idx < d; dim_idx+=4) { + for (size_t dim_idx = 0; dim_idx < d; dim_idx += 4) { const uint32_t dimension_idx = dim_idx; const uint8x8_t vals = vld1_u8(&second_vector[dimension_idx]); const uint8x16_t vec1_u8 = vqtbl1q_u8(vcombine_u8(vals, vals), idx); const size_t offset_to_dimension_start = dimension_idx * U8_PDX_VECTOR_SIZE; - for (int i = 0; i < U8_N_REGISTERS_NEON; ++i) { // total: 64 vectors * 4 dimensions each (at 1 byte per value = 2048-bits) + for (int i = 0; i < U8_N_REGISTERS_NEON; + ++i) { // total: 64 vectors * 4 dimensions each (at 1 byte per value = 2048-bits) // Read 16 bytes of data (16 values) with 4 dimensions of 4 vectors const uint8x16_t vec2_u8 = vld1q_u8(&first_vector[offset_to_dimension_start + i * 16]); const uint8x16_t diff_u8 = vabdq_u8(vec1_u8, vec2_u8); @@ -373,23 +355,28 @@ inline void u8_pdx_l2( } #elif defined(__AVX512F__) __m512i res[U8_N_REGISTERS_AVX]; - const uint32_t * query_grouped = (uint32_t *)second_vector; + const uint32_t* query_grouped = (uint32_t*) second_vector; // Load 64 initial values for (size_t i = 0; i < U8_N_REGISTERS_AVX; ++i) { res[i] = _mm512_load_si512(&distances_u8[i * 16]); } // Compute L2 - for (size_t dim_idx = 0; dim_idx < d; dim_idx+=4) { + for (size_t dim_idx = 0; dim_idx < d; dim_idx += 4) { const uint32_t dimension_idx = dim_idx; // To load the query efficiently I will load it as uint32_t (4 bytes packed in 1 word) const uint32_t query_value = query_grouped[dimension_idx / 4]; // And then broadcast it to the register const __m512i vec1_u8 = _mm512_set1_epi32(query_value); const size_t offset_to_dimension_start = dimension_idx * U8_PDX_VECTOR_SIZE; - for (int i = 0; i < U8_N_REGISTERS_AVX; ++i) { // total: 64 vectors (4 iterations of 16 vectors) * 4 dimensions each (at 1 byte per value = 2048-bits) + for (int i = 0; i < U8_N_REGISTERS_AVX; + ++i) { // total: 64 vectors (4 iterations of 16 vectors) * 4 dimensions each (at 1 byte + // per value = 2048-bits) // Read 64 bytes of data (64 values) with 4 dimensions of 16 vectors - const __m512i vec2_u8 = _mm512_loadu_si512(&first_vector[offset_to_dimension_start + i * 64]); - const __m512i diff_u8 = _mm512_or_si512(_mm512_subs_epu8(vec1_u8, vec2_u8), _mm512_subs_epu8(vec2_u8, vec1_u8)); + const __m512i vec2_u8 = + _mm512_loadu_si512(&first_vector[offset_to_dimension_start + i * 64]); + const __m512i diff_u8 = _mm512_or_si512( + _mm512_subs_epu8(vec1_u8, vec2_u8), _mm512_subs_epu8(vec2_u8, vec1_u8) + ); // I can use this asymmetric dot product as my values are actually 7-bit // Hence, the [sign] properties of the second operand is ignored // As results will never be negative, it can be stored on res[i] without issues @@ -403,11 +390,7 @@ inline void u8_pdx_l2( #endif }; -inline void u8_pdx_ip( - const uint8_t *first_vector, - const uint8_t *second_vector, - const size_t d -) { +inline void u8_pdx_ip(const uint8_t* first_vector, const uint8_t* second_vector, const size_t d) { memset((void*) distances_u8, 0, U8_PDX_VECTOR_SIZE * sizeof(uint32_t)); #if defined(__ARM_NEON) uint32x4_t res[U8_N_REGISTERS_NEON]; @@ -417,12 +400,13 @@ inline void u8_pdx_ip( res[i] = vdupq_n_u32(0); } // Compute L2 - for (size_t dim_idx = 0; dim_idx < d; dim_idx+=4) { + for (size_t dim_idx = 0; dim_idx < d; dim_idx += 4) { const uint32_t dimension_idx = dim_idx; const uint8x8_t vals = vld1_u8(&second_vector[dimension_idx]); const uint8x16_t vec1_u8 = vqtbl1q_u8(vcombine_u8(vals, vals), idx); const size_t offset_to_dimension_start = dimension_idx * U8_PDX_VECTOR_SIZE; - for (int i = 0; i < 16; ++i) { // total: 64 vectors * 4 dimensions each (at 1 byte per value = 2048-bits) + for (int i = 0; i < 16; + ++i) { // total: 64 vectors * 4 dimensions each (at 1 byte per value = 2048-bits) // Read 16 bytes of data (16 values) with 4 dimensions of 4 vectors const uint8x16_t vec2_u8 = vld1q_u8(&first_vector[offset_to_dimension_start + i * 16]); res[i] = vdotq_u32(res[i], vec2_u8, vec1_u8); @@ -434,22 +418,25 @@ inline void u8_pdx_ip( } #elif defined(__AVX512F__) __m512i res[U8_N_REGISTERS_AVX]; - const uint32_t * query_grouped = (uint32_t *)second_vector; + const uint32_t* query_grouped = (uint32_t*) second_vector; // Load 64 initial values for (size_t i = 0; i < U8_N_REGISTERS_AVX; ++i) { res[i] = _mm512_load_si512(&distances_u8[i * 16]); } // Compute L2 - for (size_t dim_idx = 0; dim_idx < d; dim_idx+=4) { + for (size_t dim_idx = 0; dim_idx < d; dim_idx += 4) { const uint32_t dimension_idx = dim_idx; // To load the query efficiently I will load it as uint32_t (4 bytes packed in 1 word) const uint32_t query_value = query_grouped[dimension_idx / 4]; // And then broadcast it to the register const __m512i vec1_u8 = _mm512_set1_epi32(query_value); const size_t offset_to_dimension_start = dimension_idx * U8_PDX_VECTOR_SIZE; - for (int i = 0; i < U8_N_REGISTERS_AVX; ++i) { // total: 64 vectors (4 iterations of 16 vectors) * 4 dimensions each (at 1 byte per value = 2048-bits) + for (int i = 0; i < U8_N_REGISTERS_AVX; + ++i) { // total: 64 vectors (4 iterations of 16 vectors) * 4 dimensions each (at 1 byte + // per value = 2048-bits) // Read 64 bytes of data (64 values) with 4 dimensions of 16 vectors - const __m512i vec2_u8 = _mm512_loadu_si512(&first_vector[offset_to_dimension_start + i * 64]); + const __m512i vec2_u8 = + _mm512_loadu_si512(&first_vector[offset_to_dimension_start + i * 64]); // I can use this asymmetric dot product as my values are actually 7-bit // Hence, the [sign] properties of the second operand is ignored // As results will never be negative, it can be stored on res[i] without issues @@ -463,15 +450,15 @@ inline void u8_pdx_ip( #endif }; -template +template std::vector> standalone_simd( - const T *first_vector, - const T *second_vector, + const T* first_vector, + const T* second_vector, const size_t d, const size_t num_queries, const size_t num_vectors, const size_t knn, - const size_t * positions = nullptr + const size_t* positions = nullptr ) { std::vector> result(knn * num_queries); std::vector> all_distances(num_vectors); @@ -484,7 +471,7 @@ std::vector> standalone_simd( data = data + (positions[j] * d); } DistanceType_t current_distance; - if constexpr (kernel == F32_SIMD_IP){ + if constexpr (kernel == F32_SIMD_IP) { current_distance = f32_simd_ip(data, query, d); } else if constexpr (kernel == F32_SIMD_L2) { current_distance = f32_simd_l2(data, query, d); @@ -501,10 +488,7 @@ std::vector> standalone_simd( } // Partial sort to get top-k - if constexpr ( - kernel == F32_SIMD_IP - || kernel == U8_SIMD_IP - ) { + if constexpr (kernel == F32_SIMD_IP || kernel == U8_SIMD_IP) { std::partial_sort( all_distances.begin(), all_distances.begin() + knn, @@ -528,10 +512,10 @@ std::vector> standalone_simd( return result; } -template +template std::vector> standalone_pdx( - const T *first_vector, - const T *second_vector, + const T* first_vector, + const T* second_vector, const size_t d, const size_t num_queries, const size_t num_vectors, @@ -544,8 +528,8 @@ std::vector> standalone_pdx( const T* data = first_vector; // Fill all_distances by direct indexing size_t global_offset = 0; - for (size_t j = 0; j < num_vectors; j+=PDX_BLOCK_SIZE) { - if constexpr (kernel == F32_PDX_IP){ + for (size_t j = 0; j < num_vectors; j += PDX_BLOCK_SIZE) { + if constexpr (kernel == F32_PDX_IP) { f32_pdx_ip(data, query, d); } else if constexpr (kernel == F32_PDX_L2) { f32_pdx_l2(data, query, d); @@ -557,7 +541,7 @@ std::vector> standalone_pdx( // TODO: Ugly (could be a bottleneck on PDX kernels) for (uint32_t z = 0; z < PDX_BLOCK_SIZE; ++z) { all_distances[global_offset].index = global_offset; - if constexpr (std::is_same_v){ + if constexpr (std::is_same_v) { all_distances[global_offset].distance = distances_f32[z]; } else if constexpr (std::is_same_v) { all_distances[global_offset].distance = distances_u8[z]; @@ -568,10 +552,7 @@ std::vector> standalone_pdx( } // Partial sort to get top-k - if constexpr ( - kernel == F32_PDX_IP - || kernel == U8_PDX_IP - ) { + if constexpr (kernel == F32_PDX_IP || kernel == U8_PDX_IP) { std::partial_sort( all_distances.begin(), all_distances.begin() + knn, @@ -598,68 +579,92 @@ std::vector> standalone_pdx( std::vector> standalone_f32( const VectorSearchKernel kernel, - const float *first_vector, - const float *second_vector, + const float* first_vector, + const float* second_vector, const size_t d, const size_t num_queries, const size_t num_vectors, const size_t knn ) { switch (kernel) { - case F32_PDX_IP: - return standalone_pdx(first_vector, second_vector, d, num_queries, num_vectors, knn); - case F32_PDX_L2: - return standalone_pdx(first_vector, second_vector, d, num_queries, num_vectors, knn); - - case F32_SIMD_IP: - return standalone_simd(first_vector, second_vector, d, num_queries, num_vectors, knn); - case F32_SIMD_L2: - return standalone_simd(first_vector, second_vector, d, num_queries, num_vectors, knn); - - default: - return standalone_pdx(first_vector, second_vector, d, num_queries, num_vectors, knn); + case F32_PDX_IP: + return standalone_pdx( + first_vector, second_vector, d, num_queries, num_vectors, knn + ); + case F32_PDX_L2: + return standalone_pdx( + first_vector, second_vector, d, num_queries, num_vectors, knn + ); + + case F32_SIMD_IP: + return standalone_simd( + first_vector, second_vector, d, num_queries, num_vectors, knn + ); + case F32_SIMD_L2: + return standalone_simd( + first_vector, second_vector, d, num_queries, num_vectors, knn + ); + + default: + return standalone_pdx( + first_vector, second_vector, d, num_queries, num_vectors, knn + ); } } std::vector> filtered_standalone_u8( const VectorSearchKernel kernel, - const uint8_t *first_vector, - const uint8_t *second_vector, + const uint8_t* first_vector, + const uint8_t* second_vector, const size_t d, const size_t num_queries, const size_t num_vectors, const size_t knn, - const size_t *positions + const size_t* positions ) { switch (kernel) { - case U8_SIMD_L2: - return standalone_simd(first_vector, second_vector, d, num_queries, num_vectors, knn, positions); - default: - return standalone_simd(first_vector, second_vector, d, num_queries, num_vectors, knn, positions); + case U8_SIMD_L2: + return standalone_simd( + first_vector, second_vector, d, num_queries, num_vectors, knn, positions + ); + default: + return standalone_simd( + first_vector, second_vector, d, num_queries, num_vectors, knn, positions + ); } } std::vector> standalone_u8( const VectorSearchKernel kernel, - const uint8_t *first_vector, - const uint8_t *second_vector, + const uint8_t* first_vector, + const uint8_t* second_vector, const size_t d, const size_t num_queries, const size_t num_vectors, const size_t knn ) { switch (kernel) { - case U8_PDX_L2: - return standalone_pdx(first_vector, second_vector, d, num_queries, num_vectors, knn); - case U8_PDX_IP: - return standalone_pdx(first_vector, second_vector, d, num_queries, num_vectors, knn); - - case U8_SIMD_L2: - return standalone_simd(first_vector, second_vector, d, num_queries, num_vectors, knn); - case U8_SIMD_IP: - return standalone_simd(first_vector, second_vector, d, num_queries, num_vectors, knn); - - default: - return standalone_pdx(first_vector, second_vector, d, num_queries, num_vectors, knn); + case U8_PDX_L2: + return standalone_pdx( + first_vector, second_vector, d, num_queries, num_vectors, knn + ); + case U8_PDX_IP: + return standalone_pdx( + first_vector, second_vector, d, num_queries, num_vectors, knn + ); + + case U8_SIMD_L2: + return standalone_simd( + first_vector, second_vector, d, num_queries, num_vectors, knn + ); + case U8_SIMD_IP: + return standalone_simd( + first_vector, second_vector, d, num_queries, num_vectors, knn + ); + + default: + return standalone_pdx( + first_vector, second_vector, d, num_queries, num_vectors, knn + ); } } diff --git a/benchmarks/benchmark_utils.hpp b/benchmarks/benchmark_utils.hpp index 242c147..0b719f8 100644 --- a/benchmarks/benchmark_utils.hpp +++ b/benchmarks/benchmark_utils.hpp @@ -1,53 +1,52 @@ #pragma once +#include "pdx/common.hpp" +#include "pdx/utils.hpp" +#include #include +#include #include +#include #include -#include +#include +#include +#include +#include #include #include #include #include #include -#include -#include -#include -#include #include #include #include -#include -#include "pdx/common.hpp" -#include "pdx/utils.hpp" class TicToc { -public: + public: size_t accum_time = 0; - std::chrono::high_resolution_clock::time_point start = std::chrono::high_resolution_clock::now(); + std::chrono::high_resolution_clock::time_point start = + std::chrono::high_resolution_clock::now(); void Reset() { accum_time = 0; start = std::chrono::high_resolution_clock::now(); } - inline void Tic() { - start = std::chrono::high_resolution_clock::now(); - } + inline void Tic() { start = std::chrono::high_resolution_clock::now(); } inline void Toc() { auto end = std::chrono::high_resolution_clock::now(); - accum_time += std::chrono::duration_cast( - end - start).count(); + accum_time += std::chrono::duration_cast(end - start).count(); } - double GetMilliseconds() const { - return static_cast(accum_time) / 1e6; - } + double GetMilliseconds() const { return static_cast(accum_time) / 1e6; } }; // Raw binary data paths (SuperKMeans convention: data_.bin / data__test.bin) -inline std::string RAW_DATA_DIR = std::string{CMAKE_SOURCE_DIR} + "/../../SuperKMeans/benchmarks/data"; -inline std::string GROUND_TRUTH_JSON_DIR = std::string{CMAKE_SOURCE_DIR} + "/../../SuperKMeans/benchmarks/ground_truth"; +inline std::string RAW_DATA_DIR = + std::string{CMAKE_SOURCE_DIR} + "/../../SuperKMeans/benchmarks/data"; +inline std::string GROUND_TRUTH_JSON_DIR = + std::string{CMAKE_SOURCE_DIR} + "/../../SuperKMeans/benchmarks/ground_truth"; struct RawDatasetInfo { size_t num_embeddings; @@ -58,19 +57,19 @@ struct RawDatasetInfo { }; inline const std::unordered_map RAW_DATASET_PARAMS = { - {"sift", {1000000, 128, 1000, PDX::DistanceMetric::L2SQ, "sift-128-euclidean"}}, - {"yi", {187843, 128, 1000, PDX::DistanceMetric::IP, "yi-128-ip"}}, - {"llama", {256921, 128, 1000, PDX::DistanceMetric::IP, "llama-128-ip"}}, - {"glove200", {1183514, 200, 1000, PDX::DistanceMetric::COSINE, "glove-200-angular"}}, - {"yandex", {1000000, 200, 1000, PDX::DistanceMetric::COSINE, "yandex-200-cosine"}}, - {"yahoo", {677305, 384, 1000, PDX::DistanceMetric::COSINE, "yahoo-minilm-384-normalized"}}, - {"clip", {1281167, 512, 1000, PDX::DistanceMetric::L2SQ, "imagenet-clip-512-normalized"}}, - {"contriever", {990000, 768, 1000, PDX::DistanceMetric::L2SQ, "contriever-768"}}, - {"gist", {1000000, 960, 1000, PDX::DistanceMetric::L2SQ, "gist-960-euclidean"}}, - {"mxbai", {769382, 1024, 1000, PDX::DistanceMetric::L2SQ, "agnews-mxbai-1024-euclidean"}}, - {"openai", {999000, 1536, 1000, PDX::DistanceMetric::L2SQ, "openai-1536-angular"}}, - {"arxiv", {2253000, 768, 1000, PDX::DistanceMetric::L2SQ, "arxiv-nomic-768-normalized"}}, - {"wiki", {260372, 3072, 1000, PDX::DistanceMetric::L2SQ, "simplewiki-openai-3072-normalized"}}, + {"sift", {1000000, 128, 1000, PDX::DistanceMetric::L2SQ, "sift-128-euclidean"}}, + {"yi", {187843, 128, 1000, PDX::DistanceMetric::IP, "yi-128-ip"}}, + {"llama", {256921, 128, 1000, PDX::DistanceMetric::IP, "llama-128-ip"}}, + {"glove200", {1183514, 200, 1000, PDX::DistanceMetric::COSINE, "glove-200-angular"}}, + {"yandex", {1000000, 200, 1000, PDX::DistanceMetric::COSINE, "yandex-200-cosine"}}, + {"yahoo", {677305, 384, 1000, PDX::DistanceMetric::COSINE, "yahoo-minilm-384-normalized"}}, + {"clip", {1281167, 512, 1000, PDX::DistanceMetric::L2SQ, "imagenet-clip-512-normalized"}}, + {"contriever", {990000, 768, 1000, PDX::DistanceMetric::L2SQ, "contriever-768"}}, + {"gist", {1000000, 960, 1000, PDX::DistanceMetric::L2SQ, "gist-960-euclidean"}}, + {"mxbai", {769382, 1024, 1000, PDX::DistanceMetric::L2SQ, "agnews-mxbai-1024-euclidean"}}, + {"openai", {999000, 1536, 1000, PDX::DistanceMetric::L2SQ, "openai-1536-angular"}}, + {"arxiv", {2253000, 768, 1000, PDX::DistanceMetric::L2SQ, "arxiv-nomic-768-normalized"}}, + {"wiki", {260372, 3072, 1000, PDX::DistanceMetric::L2SQ, "simplewiki-openai-3072-normalized"}}, }; struct BenchmarkMetadata { @@ -82,29 +81,38 @@ struct BenchmarkMetadata { size_t knn{10}; float recalls{1.0}; float selectivity_threshold{0.0}; - float epsilon {0.0}; + float epsilon{0.0}; }; struct PhasesRuntime { - size_t end_to_end {0}; + size_t end_to_end{0}; }; class BenchmarkUtils { -public: - inline static std::string PDX_DATA = std::string{CMAKE_SOURCE_DIR} + "/benchmarks/datasets/pdx/"; - inline static std::string PDX_ADSAMPLING_DATA = std::string{CMAKE_SOURCE_DIR} + "/benchmarks/datasets/adsampling_pdx/"; - inline static std::string GROUND_TRUTH_DATA = std::string{CMAKE_SOURCE_DIR} + "/benchmarks/datasets/ground_truth/"; - inline static std::string FILTERED_GROUND_TRUTH_DATA = std::string{CMAKE_SOURCE_DIR} + "/benchmarks/datasets/ground_truth_filtered/"; - inline static std::string PURESCAN_DATA = std::string{CMAKE_SOURCE_DIR} + "/benchmarks/datasets/purescan/"; - inline static std::string QUERIES_DATA = std::string{CMAKE_SOURCE_DIR} + "/benchmarks/datasets/queries/"; - inline static std::string SELECTION_VECTOR_DATA = std::string{CMAKE_SOURCE_DIR} + "/benchmarks/datasets/selection_vectors/"; + public: + inline static std::string PDX_DATA = + std::string{CMAKE_SOURCE_DIR} + "/benchmarks/datasets/pdx/"; + inline static std::string PDX_ADSAMPLING_DATA = + std::string{CMAKE_SOURCE_DIR} + "/benchmarks/datasets/adsampling_pdx/"; + inline static std::string GROUND_TRUTH_DATA = + std::string{CMAKE_SOURCE_DIR} + "/benchmarks/datasets/ground_truth/"; + inline static std::string FILTERED_GROUND_TRUTH_DATA = + std::string{CMAKE_SOURCE_DIR} + "/benchmarks/datasets/ground_truth_filtered/"; + inline static std::string PURESCAN_DATA = + std::string{CMAKE_SOURCE_DIR} + "/benchmarks/datasets/purescan/"; + inline static std::string QUERIES_DATA = + std::string{CMAKE_SOURCE_DIR} + "/benchmarks/datasets/queries/"; + inline static std::string SELECTION_VECTOR_DATA = + std::string{CMAKE_SOURCE_DIR} + "/benchmarks/datasets/selection_vectors/"; std::string CPU_ARCHITECTURE = "DEFAULT"; - std::string RESULTS_DIR_PATH = std::string{CMAKE_SOURCE_DIR} + "/benchmarks/results/" + CPU_ARCHITECTURE + "/"; + std::string RESULTS_DIR_PATH = + std::string{CMAKE_SOURCE_DIR} + "/benchmarks/results/" + CPU_ARCHITECTURE + "/"; - explicit BenchmarkUtils(){ + explicit BenchmarkUtils() { CPU_ARCHITECTURE = std::getenv("PDX_ARCH") ? std::getenv("PDX_ARCH") : "DEFAULT"; - RESULTS_DIR_PATH = std::string{CMAKE_SOURCE_DIR} + "/benchmarks/results/" + CPU_ARCHITECTURE + "/"; + RESULTS_DIR_PATH = + std::string{CMAKE_SOURCE_DIR} + "/benchmarks/results/" + CPU_ARCHITECTURE + "/"; } inline static std::string DATASETS[] = { @@ -154,46 +162,59 @@ class BenchmarkUtils { }; inline static size_t IVF_PROBES[] = { - //4000, 3980, 3967, 2048, 1024, 512, 256,224,192,160,144,128, - 2048, 1536, 1024, 512, 384, 256,224,192,160,144,128, - 112,96,80,64,56, 48, 40, - 32,28, 26,24, 22,20, 18,16, 14,12, 10,8,6,4,2, 1 + // 4000, 3980, 3967, 2048, 1024, 512, 256,224,192,160,144,128, + 2048, 1536, 1024, 512, 384, 256, 224, 192, 160, 144, 128, 112, 96, 80, 64, 56, 48, + 40, 32, 28, 26, 24, 22, 20, 18, 16, 14, 12, 10, 8, 6, 4, 2, 1 }; - inline static int POW_10[10] = { - 1, 10, 100, 1000, 10000, - 100000, 1000000, 10000000, 100000000, 1000000000 - }; + inline static int POW_10[10] = + {1, 10, 100, 1000, 10000, 100000, 1000000, 10000000, 100000000, 1000000000}; inline static size_t IVF_PROBES_PHASES[] = { - 512,256,128, 64, 32, 16, 8, 4, 2, - }; - - inline static float SELECTIVITY_THRESHOLDS[] = { - 0.005,0.01,0.05,0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5, - 0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,0.99 + 512, + 256, + 128, + 64, + 32, + 16, + 8, + 4, + 2, }; + inline static float SELECTIVITY_THRESHOLDS[] = {0.005, 0.01, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, + 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, + 0.75, 0.8, 0.85, 0.9, 0.95, 0.99}; inline static size_t NUM_MEASURE_RUNS = 1; - inline static float EPSILON0 = 1.5; //2.1; + inline static float EPSILON0 = 1.5; // 2.1; inline static float SELECTIVITY_THRESHOLD = 0.80; // more than 20% pruned to pass inline static bool VERIFY_RESULTS = true; inline static uint8_t KNN = 100; - inline static uint8_t GROUND_TRUTH_MAX_K = 100; // To properly skip on the ground truth file (do not change) - - template - static void VerifyResult(float &recalls, const std::vector &result, size_t knn, - const uint32_t *int_ground_truth, size_t n_query) { + inline static uint8_t GROUND_TRUTH_MAX_K = + 100; // To properly skip on the ground truth file (do not change) + + template + static void VerifyResult( + float& recalls, + const std::vector& result, + size_t knn, + const uint32_t* int_ground_truth, + size_t n_query + ) { std::unordered_set seen; for (const auto& val : result) { if (!seen.insert(val.index).second) { - throw std::runtime_error("Duplicates detected in the result set! This is likely a bug on PDXearch"); + throw std::runtime_error( + "Duplicates detected in the result set! This is likely a bug on PDXearch" + ); } } if (result.size() < knn) { - std::cerr << "WARNING: Result set is not complete! Set a higher `nbuckets` parameter (Only got " << result.size() << " results)" << std::endl; + std::cerr << "WARNING: Result set is not complete! Set a higher `nbuckets` parameter " + "(Only got " + << result.size() << " results)" << std::endl; } if constexpr (MEASURE_RECALL) { size_t true_positives = 0; @@ -218,9 +239,12 @@ class BenchmarkUtils { // We remove extreme outliers on both sides (Q3 + 1.5*IQR & Q1 - 1.5*IQR) static void SaveResults( - std::vector runtimes, const std::string &results_path, const BenchmarkMetadata &metadata) { + std::vector runtimes, + const std::string& results_path, + const BenchmarkMetadata& metadata + ) { bool write_header = true; - if (std::filesystem::exists(results_path)){ + if (std::filesystem::exists(results_path)) { write_header = false; } std::ofstream file{results_path, std::ios::app}; @@ -233,10 +257,9 @@ class BenchmarkUtils { auto const Q1 = runtimes.size() / 4; auto const Q2 = runtimes.size() / 2; auto const Q3 = Q1 + Q2; - std::sort(runtimes.begin(),runtimes.end(), - [](PhasesRuntime i1, PhasesRuntime i2) { - return i1.end_to_end < i2.end_to_end; - }); + std::sort(runtimes.begin(), runtimes.end(), [](PhasesRuntime i1, PhasesRuntime i2) { + return i1.end_to_end < i2.end_to_end; + }); auto const iqr = runtimes[Q3].end_to_end - runtimes[Q1].end_to_end; size_t accounted_queries = 0; for (size_t j = 0; j < metadata.num_measure_runs * metadata.num_queries; ++j) { @@ -244,10 +267,10 @@ class BenchmarkUtils { all_max_runtime = std::max(all_max_runtime, runtimes[j].end_to_end); all_sum_runtimes += runtimes[j].end_to_end; // Removing outliers - if (runtimes[j].end_to_end > runtimes[Q3].end_to_end + 1.5 * iqr){ + if (runtimes[j].end_to_end > runtimes[Q3].end_to_end + 1.5 * iqr) { continue; } - if (runtimes[j].end_to_end < runtimes[Q1].end_to_end - 1.5 * iqr){ + if (runtimes[j].end_to_end < runtimes[Q1].end_to_end - 1.5 * iqr) { continue; } min_runtime = std::min(min_runtime, runtimes[j].end_to_end); @@ -257,7 +280,8 @@ class BenchmarkUtils { } double all_min_runtime_ms = 1.0 * all_min_runtime / 1000000; double all_max_runtime_ms = 1.0 * all_max_runtime / 1000000; - double all_avg_runtime_ms = 1.0 * all_sum_runtimes / (1000000 * (metadata.num_measure_runs * metadata.num_queries)); + double all_avg_runtime_ms = + 1.0 * all_sum_runtimes / (1000000 * (metadata.num_measure_runs * metadata.num_queries)); double min_runtime_ms = 1.0 * min_runtime / 1000000; double max_runtime_ms = 1.0 * max_runtime / 1000000; double avg_runtime_ms = 1.0 * sum_runtimes / (1000000 * accounted_queries); @@ -265,7 +289,7 @@ class BenchmarkUtils { std::cout << metadata.dataset << " --------------\n"; std::cout << "n_queries: " << metadata.num_queries << "\n"; - if (metadata.ivf_nprobe > 0){ + if (metadata.ivf_nprobe > 0) { std::cout << "nprobe: " << metadata.ivf_nprobe << "\n"; } std::cout << "avg: " << std::setprecision(6) << avg_runtime_ms << "\n"; @@ -273,29 +297,31 @@ class BenchmarkUtils { std::cout << "min: " << std::setprecision(6) << min_runtime_ms << "\n"; std::cout << "rec: " << std::setprecision(6) << avg_recall << "\n"; - if (write_header){ + if (write_header) { file << "dataset,algorithm,avg,max,min,recall,ivf_nprobe,epsilon," "knn,n_queries,selectivity," - "num_measure_runs,avg_all,max_all,min_all" << "\n"; + "num_measure_runs,avg_all,max_all,min_all" + << "\n"; } - file << metadata.dataset << "," << metadata.algorithm << "," << std::setprecision(6) << avg_runtime_ms << "," << - std::setprecision(6) << max_runtime_ms << "," << std::setprecision(6) << min_runtime_ms << "," << - avg_recall << "," << metadata.ivf_nprobe << "," << metadata.epsilon << "," << +metadata.knn << "," << - metadata.num_queries << "," << std::setprecision(4) << metadata.selectivity_threshold << "," << - metadata.num_measure_runs << "," << - all_avg_runtime_ms << "," << all_max_runtime_ms << "," << all_min_runtime_ms << - "\n"; + file << metadata.dataset << "," << metadata.algorithm << "," << std::setprecision(6) + << avg_runtime_ms << "," << std::setprecision(6) << max_runtime_ms << "," + << std::setprecision(6) << min_runtime_ms << "," << avg_recall << "," + << metadata.ivf_nprobe << "," << metadata.epsilon << "," << +metadata.knn << "," + << metadata.num_queries << "," << std::setprecision(4) + << metadata.selectivity_threshold << "," << metadata.num_measure_runs << "," + << all_avg_runtime_ms << "," << all_max_runtime_ms << "," << all_min_runtime_ms + << "\n"; file.close(); } - }; BenchmarkUtils BENCHMARK_UTILS; -inline std::unordered_map> ParseGroundTruthJson(const std::string &filename) { +inline std::unordered_map> ParseGroundTruthJson(const std::string& filename) { std::unordered_map> gt_map; std::ifstream file(filename); - if (!file.is_open()) return gt_map; + if (!file.is_open()) + return gt_map; std::string line; std::getline(file, line); @@ -304,13 +330,15 @@ inline std::unordered_map> ParseGroundTruthJson(const std: while ((pos = line.find("\"", pos)) != std::string::npos) { size_t key_start = pos + 1; size_t key_end = line.find("\"", key_start); - if (key_end == std::string::npos) break; + if (key_end == std::string::npos) + break; int query_idx = std::stoi(line.substr(key_start, key_end - key_start)); size_t arr_start = line.find("[", key_end); size_t arr_end = line.find("]", arr_start); - if (arr_start == std::string::npos || arr_end == std::string::npos) break; + if (arr_start == std::string::npos || arr_end == std::string::npos) + break; std::string arr_str = line.substr(arr_start + 1, arr_end - arr_start - 1); std::vector ids; @@ -319,7 +347,8 @@ inline std::unordered_map> ParseGroundTruthJson(const std: while (std::getline(iss, token, ',')) { token.erase(0, token.find_first_not_of(" \t")); token.erase(token.find_last_not_of(" \t") + 1); - if (!token.empty()) ids.push_back(std::stoi(token)); + if (!token.empty()) + ids.push_back(std::stoi(token)); } gt_map[query_idx] = ids; pos = arr_end + 1; @@ -327,8 +356,11 @@ inline std::unordered_map> ParseGroundTruthJson(const std: return gt_map; } -inline float ComputeRecallFromJson(const std::vector &result, - const std::vector >_ids, size_t knn) { +inline float ComputeRecallFromJson( + const std::vector& result, + const std::vector& gt_ids, + size_t knn +) { size_t hits = 0; size_t gt_count = std::min(knn, gt_ids.size()); for (size_t i = 0; i < result.size(); i++) { diff --git a/benchmarks/pdx_end_to_end.cpp b/benchmarks/pdx_end_to_end.cpp index bf1678e..305c8ba 100644 --- a/benchmarks/pdx_end_to_end.cpp +++ b/benchmarks/pdx_end_to_end.cpp @@ -6,13 +6,19 @@ #include #include -#include "pdx/index.hpp" #include "benchmark_utils.hpp" +#include "pdx/index.hpp" #include "pdx/utils.hpp" template -void RunBenchmark(const RawDatasetInfo &info, const std::string &dataset, const std::string &algorithm, - const float *data, const float *queries, const std::vector &nprobes_to_use) { +void RunBenchmark( + const RawDatasetInfo& info, + const std::string& dataset, + const std::string& algorithm, + const float* data, + const float* queries, + const std::vector& nprobes_to_use +) { const size_t d = info.num_dimensions; const size_t n = info.num_embeddings; const size_t n_queries = info.num_queries; @@ -20,7 +26,7 @@ void RunBenchmark(const RawDatasetInfo &info, const std::string &dataset, const size_t NUM_MEASURE_RUNS = BenchmarkUtils::NUM_MEASURE_RUNS; std::string RESULTS_PATH = BENCHMARK_UTILS.RESULTS_DIR_PATH + "END_TO_END_PDX_ADSAMPLING.csv"; - PDX::PDXIndexConfig index_config { + PDX::PDXIndexConfig index_config{ .num_dimensions = static_cast(d), .distance_metric = info.distance_metric, .seed = 42, @@ -37,13 +43,14 @@ void RunBenchmark(const RawDatasetInfo &info, const std::string &dataset, const std::cout << "Build time: " << build_ms << " ms\n"; std::cout << "Clusters: " << pdx_index.GetNumClusters() << "\n"; std::cout << "Index in-memory size: " << std::fixed << std::setprecision(2) - << static_cast(pdx_index.GetInMemorySizeInBytes()) / (1024.0 * 1024.0) << " MB\n"; + << static_cast(pdx_index.GetInMemorySizeInBytes()) / (1024.0 * 1024.0) + << " MB\n"; // Load ground truth bool use_skmeans_gt = false; std::unordered_map> gt_map; std::unique_ptr gt_buffer; - uint32_t *int_ground_truth = nullptr; + uint32_t* int_ground_truth = nullptr; if (use_skmeans_gt) { std::string gt_path = GROUND_TRUTH_JSON_DIR + "/" + dataset + ".json"; @@ -54,14 +61,16 @@ void RunBenchmark(const RawDatasetInfo &info, const std::string &dataset, const } std::cout << "Ground truth loaded (json): " << gt_map.size() << " queries\n"; } else { - std::string gt_path = BenchmarkUtils::GROUND_TRUTH_DATA + info.pdx_dataset_name + "_100_norm"; + std::string gt_path = + BenchmarkUtils::GROUND_TRUTH_DATA + info.pdx_dataset_name + "_100_norm"; gt_buffer = MmapFile(gt_path); - int_ground_truth = reinterpret_cast(gt_buffer.get()); + int_ground_truth = reinterpret_cast(gt_buffer.get()); std::cout << "Ground truth loaded (pdx binary): " << gt_path << "\n"; } for (size_t ivf_nprobe : nprobes_to_use) { - if (pdx_index.GetNumClusters() < ivf_nprobe) continue; + if (pdx_index.GetNumClusters() < ivf_nprobe) + continue; pdx_index.SetNProbe(ivf_nprobe); @@ -91,9 +100,7 @@ void RunBenchmark(const RawDatasetInfo &info, const std::string &dataset, const clock.Tic(); pdx_index.Search(queries + l * d, KNN); clock.Toc(); - runtimes[j + l * NUM_MEASURE_RUNS] = { - clock.accum_time - }; + runtimes[j + l * NUM_MEASURE_RUNS] = {clock.accum_time}; } } @@ -110,12 +117,12 @@ void RunBenchmark(const RawDatasetInfo &info, const std::string &dataset, const } } -int main(int argc, char *argv[]) { +int main(int argc, char* argv[]) { if (argc < 3) { std::cerr << "Usage: " << argv[0] << " [ivf_nprobe]\n"; std::cerr << "Index types: pdx_f32, pdx_u8, pdx_tree_f32, pdx_tree_u8\n"; std::cerr << "Available datasets:"; - for (const auto &[name, _] : RAW_DATASET_PARAMS) { + for (const auto& [name, _] : RAW_DATASET_PARAMS) { std::cerr << " " << name; } std::cerr << "\n"; @@ -130,7 +137,7 @@ int main(int argc, char *argv[]) { std::cerr << "Unknown dataset: " << dataset << "\n"; return 1; } - const auto &info = it->second; + const auto& info = it->second; const size_t n = info.num_embeddings; const size_t d = info.num_dimensions; const size_t n_queries = info.num_queries; @@ -146,34 +153,50 @@ int main(int argc, char *argv[]) { std::vector data(n * d); { std::ifstream file(data_path, std::ios::binary); - if (!file) { std::cerr << "Failed to open " << data_path << "\n"; return 1; } - file.read(reinterpret_cast(data.data()), n * d * sizeof(float)); + if (!file) { + std::cerr << "Failed to open " << data_path << "\n"; + return 1; + } + file.read(reinterpret_cast(data.data()), n * d * sizeof(float)); } std::vector queries(n_queries * d); { std::ifstream file(query_path, std::ios::binary); - if (!file) { std::cerr << "Failed to open " << query_path << "\n"; return 1; } - file.read(reinterpret_cast(queries.data()), n_queries * d * sizeof(float)); + if (!file) { + std::cerr << "Failed to open " << query_path << "\n"; + return 1; + } + file.read(reinterpret_cast(queries.data()), n_queries * d * sizeof(float)); } std::vector nprobes_to_use; if (arg_ivf_nprobe > 0) { nprobes_to_use = {arg_ivf_nprobe}; } else { - nprobes_to_use.assign(std::begin(BenchmarkUtils::IVF_PROBES), std::end(BenchmarkUtils::IVF_PROBES)); + nprobes_to_use.assign( + std::begin(BenchmarkUtils::IVF_PROBES), std::end(BenchmarkUtils::IVF_PROBES) + ); } std::string algorithm = "end_to_end_" + index_type; if (index_type == "pdx_f32") { - RunBenchmark(info, dataset, algorithm, data.data(), queries.data(), nprobes_to_use); + RunBenchmark( + info, dataset, algorithm, data.data(), queries.data(), nprobes_to_use + ); } else if (index_type == "pdx_u8") { - RunBenchmark(info, dataset, algorithm, data.data(), queries.data(), nprobes_to_use); + RunBenchmark( + info, dataset, algorithm, data.data(), queries.data(), nprobes_to_use + ); } else if (index_type == "pdx_tree_f32") { - RunBenchmark(info, dataset, algorithm, data.data(), queries.data(), nprobes_to_use); + RunBenchmark( + info, dataset, algorithm, data.data(), queries.data(), nprobes_to_use + ); } else if (index_type == "pdx_tree_u8") { - RunBenchmark(info, dataset, algorithm, data.data(), queries.data(), nprobes_to_use); + RunBenchmark( + info, dataset, algorithm, data.data(), queries.data(), nprobes_to_use + ); } else { std::cerr << "Unknown index type: " << index_type << "\n"; std::cerr << "Valid types: pdx_f32, pdx_u8, pdx_tree_f32, pdx_tree_u8\n"; diff --git a/benchmarks/pdx_ivf.cpp b/benchmarks/pdx_ivf.cpp index 2887284..09e19e4 100644 --- a/benchmarks/pdx_ivf.cpp +++ b/benchmarks/pdx_ivf.cpp @@ -1,16 +1,16 @@ +#include "benchmark_utils.hpp" +#include "pdx/index.hpp" +#include "pdx/utils.hpp" #include #include #include -#include "pdx/utils.hpp" -#include "pdx/index.hpp" -#include "benchmark_utils.hpp" -int main(int argc, char *argv[]) { +int main(int argc, char* argv[]) { if (argc < 2) { std::cerr << "Usage: " << argv[0] << " [dataset] [nprobe]\n"; std::cerr << "Index types: pdx_f32, pdx_u8, pdx_tree_f32, pdx_tree_u8\n"; std::cerr << "Available datasets:"; - for (const auto &[name, _] : RAW_DATASET_PARAMS) { + for (const auto& [name, _] : RAW_DATASET_PARAMS) { std::cerr << " " << name; } std::cerr << "\n"; @@ -38,10 +38,12 @@ int main(int argc, char *argv[]) { // Build results file name from index type (e.g., "pdx_f32" -> "PDX_F32_ADSAMPLING.csv") std::string index_type_upper = index_type; - for (auto &c : index_type_upper) c = toupper(c); - std::string RESULTS_PATH = BENCHMARK_UTILS.RESULTS_DIR_PATH + index_type_upper + "_ADSAMPLING.csv"; + for (auto& c : index_type_upper) + c = toupper(c); + std::string RESULTS_PATH = + BENCHMARK_UTILS.RESULTS_DIR_PATH + index_type_upper + "_ADSAMPLING.csv"; - for (const auto &[dataset, info] : RAW_DATASET_PARAMS) { + for (const auto& [dataset, info] : RAW_DATASET_PARAMS) { if (arg_dataset.size() > 0 && arg_dataset != dataset) { continue; } @@ -50,22 +52,26 @@ int main(int argc, char *argv[]) { std::cout << "Loading " << index_path << "...\n"; auto pdx_index = PDX::LoadPDXIndex(index_path); std::cout << "Index in-memory size: " << std::fixed << std::setprecision(2) - << static_cast(pdx_index->GetInMemorySizeInBytes()) / (1024.0 * 1024.0) << " MB\n"; + << static_cast(pdx_index->GetInMemorySizeInBytes()) / (1024.0 * 1024.0) + << " MB\n"; - std::unique_ptr query_ptr = MmapFile(BenchmarkUtils::QUERIES_DATA + info.pdx_dataset_name); - auto *query = reinterpret_cast(query_ptr.get()); + std::unique_ptr query_ptr = + MmapFile(BenchmarkUtils::QUERIES_DATA + info.pdx_dataset_name); + auto* query = reinterpret_cast(query_ptr.get()); NUM_QUERIES = info.num_queries; - std::unique_ptr ground_truth = MmapFile( - BenchmarkUtils::GROUND_TRUTH_DATA + info.pdx_dataset_name + "_100_norm"); - auto *int_ground_truth = reinterpret_cast(ground_truth.get()); + std::unique_ptr ground_truth = + MmapFile(BenchmarkUtils::GROUND_TRUTH_DATA + info.pdx_dataset_name + "_100_norm"); + auto* int_ground_truth = reinterpret_cast(ground_truth.get()); query += 1; // skip number of embeddings std::vector nprobes_to_use; if (arg_ivf_nprobe > 0) { nprobes_to_use = {arg_ivf_nprobe}; } else { - nprobes_to_use.assign(std::begin(BenchmarkUtils::IVF_PROBES), std::end(BenchmarkUtils::IVF_PROBES)); + nprobes_to_use.assign( + std::begin(BenchmarkUtils::IVF_PROBES), std::end(BenchmarkUtils::IVF_PROBES) + ); } for (size_t ivf_nprobe : nprobes_to_use) { @@ -93,9 +99,7 @@ int main(int argc, char *argv[]) { clock.Tic(); pdx_index->Search(query + l * pdx_index->GetNumDimensions(), KNN); clock.Toc(); - runtimes[j + l * NUM_MEASURE_RUNS] = { - clock.accum_time - }; + runtimes[j + l * NUM_MEASURE_RUNS] = {clock.accum_time}; } } float real_selectivity = 1 - BenchmarkUtils::SELECTIVITY_THRESHOLD; diff --git a/benchmarks/pdx_ivf2_adsampling_u8_filtered.cpp b/benchmarks/pdx_ivf2_adsampling_u8_filtered.cpp index 77b32c1..139d1c7 100644 --- a/benchmarks/pdx_ivf2_adsampling_u8_filtered.cpp +++ b/benchmarks/pdx_ivf2_adsampling_u8_filtered.cpp @@ -6,25 +6,25 @@ #define PDX_USE_EXPLICIT_SIMD = true #endif -#include -#include -#include "pdx/utils.hpp" +#include "benchmark_utils.hpp" +#include "pdx/db_mock/predicate_evaluator.hpp" #include "pdx/pruners/adsampling.hpp" #include "pdx/searcher.hpp" -#include "pdx/db_mock/predicate_evaluator.hpp" -#include "benchmark_utils.hpp" +#include "pdx/utils.hpp" +#include +#include -int main(int argc, char *argv[]) { +int main(int argc, char* argv[]) { std::string arg_dataset; std::string arg_selectivity; size_t arg_ivf_nprobe = 0; - if (argc > 1){ + if (argc > 1) { arg_dataset = argv[1]; } - if (argc > 2){ + if (argc > 2) { arg_ivf_nprobe = atoi(argv[2]); } - if (argc > 3){ + if (argc > 3) { arg_selectivity = argv[3]; } else { arg_selectivity = "0_99"; @@ -45,40 +45,51 @@ int main(int argc, char *argv[]) { RESULTS_PATH = BENCHMARK_UTILS.RESULTS_DIR_PATH + "U8_IMI_PDX_ADSAMPLING_FILTERED.csv"; std::cout << "==> SELECTIVITY: " << arg_selectivity << std::endl; - for (const auto & dataset : BenchmarkUtils::DATASETS) { - if (arg_dataset.size() > 0 && arg_dataset != dataset){ + for (const auto& dataset : BenchmarkUtils::DATASETS) { + if (arg_dataset.size() > 0 && arg_dataset != dataset) { continue; } PDX::IndexPDXIVF2 pdx_data = PDX::IndexPDXIVF2(); pdx_data.Restore(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf2-u8"); - std::unique_ptr _matrix_ptr = MmapFile(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf2-u8-matrix"); - auto *_matrix = reinterpret_cast(_matrix_ptr.get()); + std::unique_ptr _matrix_ptr = + MmapFile(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf2-u8-matrix"); + auto* _matrix = reinterpret_cast(_matrix_ptr.get()); std::unique_ptr query_ptr = MmapFile(BenchmarkUtils::QUERIES_DATA + dataset); - auto *query = reinterpret_cast(query_ptr.get()); + auto* query = reinterpret_cast(query_ptr.get()); NUM_QUERIES = 1000; - std::unique_ptr ground_truth = MmapFile(BenchmarkUtils::FILTERED_GROUND_TRUTH_DATA + dataset + "_100_norm_" + arg_selectivity); - auto *int_ground_truth = reinterpret_cast(ground_truth.get()); + std::unique_ptr ground_truth = MmapFile( + BenchmarkUtils::FILTERED_GROUND_TRUTH_DATA + dataset + "_100_norm_" + arg_selectivity + ); + auto* int_ground_truth = reinterpret_cast(ground_truth.get()); query += 1; // skip number of embeddings - PDX::PredicateEvaluator predicate_evaluator = PDX::PredicateEvaluator(pdx_data.num_clusters); - predicate_evaluator.LoadSelectionVectorFromFile(BenchmarkUtils::SELECTION_VECTOR_DATA + dataset + "_" + arg_selectivity + ".bin"); - PDX::ADSamplingPruner pruner = PDX::ADSamplingPruner(pdx_data.num_dimensions, EPSILON0, _matrix); - PDX::PDXearch searcher = PDX::PDXearch>(pdx_data, pruner, 1, DIMENSION_ORDER); + PDX::PredicateEvaluator predicate_evaluator = + PDX::PredicateEvaluator(pdx_data.num_clusters); + predicate_evaluator.LoadSelectionVectorFromFile( + BenchmarkUtils::SELECTION_VECTOR_DATA + dataset + "_" + arg_selectivity + ".bin" + ); + PDX::ADSamplingPruner pruner = + PDX::ADSamplingPruner(pdx_data.num_dimensions, EPSILON0, _matrix); + PDX::PDXearch searcher = PDX::PDXearch>( + pdx_data, pruner, 1, DIMENSION_ORDER + ); std::vector nprobes_to_use; if (arg_ivf_nprobe > 0) { nprobes_to_use = {arg_ivf_nprobe}; } else { - nprobes_to_use.assign(std::begin(BenchmarkUtils::IVF_PROBES), std::end(BenchmarkUtils::IVF_PROBES)); + nprobes_to_use.assign( + std::begin(BenchmarkUtils::IVF_PROBES), std::end(BenchmarkUtils::IVF_PROBES) + ); } for (size_t ivf_nprobe : nprobes_to_use) { - if (pdx_data.num_clusters < ivf_nprobe){ + if (pdx_data.num_clusters < ivf_nprobe) { continue; } - if (arg_ivf_nprobe > 0 && ivf_nprobe != arg_ivf_nprobe){ + if (arg_ivf_nprobe > 0 && ivf_nprobe != arg_ivf_nprobe) { continue; } std::vector runtimes; @@ -88,28 +99,32 @@ int main(int argc, char *argv[]) { float recalls = 0; if (VERIFY_RESULTS) { for (size_t l = 0; l < NUM_QUERIES; ++l) { - auto result = searcher.FilteredSearch(query + l * pdx_data.num_dimensions, KNN, predicate_evaluator); - BenchmarkUtils::VerifyResult(recalls, result, KNN, int_ground_truth, l); + auto result = searcher.FilteredSearch( + query + l * pdx_data.num_dimensions, KNN, predicate_evaluator + ); + BenchmarkUtils::VerifyResult( + recalls, result, KNN, int_ground_truth, l + ); } } for (size_t j = 0; j < NUM_MEASURE_RUNS; ++j) { for (size_t l = 0; l < NUM_QUERIES; ++l) { - searcher.FilteredSearch(query + l * pdx_data.num_dimensions, KNN, predicate_evaluator); - runtimes[j + l * NUM_MEASURE_RUNS] = { - searcher.end_to_end_clock.accum_time - }; + searcher.FilteredSearch( + query + l * pdx_data.num_dimensions, KNN, predicate_evaluator + ); + runtimes[j + l * NUM_MEASURE_RUNS] = {searcher.end_to_end_clock.accum_time}; } } float real_selectivity = 1 - BenchmarkUtils::SELECTIVITY_THRESHOLD; BenchmarkMetadata results_metadata = { - dataset, - ALGORITHM, - NUM_MEASURE_RUNS, - NUM_QUERIES, - ivf_nprobe, - KNN, - recalls, - real_selectivity + dataset, + ALGORITHM, + NUM_MEASURE_RUNS, + NUM_QUERIES, + ivf_nprobe, + KNN, + recalls, + real_selectivity }; BenchmarkUtils::SaveResults(runtimes, RESULTS_PATH, results_metadata); } diff --git a/benchmarks/pdx_ivf_adsampling_filtered.cpp b/benchmarks/pdx_ivf_adsampling_filtered.cpp index fafb1ce..c9c2d14 100644 --- a/benchmarks/pdx_ivf_adsampling_filtered.cpp +++ b/benchmarks/pdx_ivf_adsampling_filtered.cpp @@ -6,25 +6,25 @@ #define PDX_USE_EXPLICIT_SIMD = true #endif -#include -#include -#include "pdx/utils.hpp" +#include "benchmark_utils.hpp" #include "pdx/ivf_wrapper.hpp" -#include "pdx/searcher.hpp" #include "pdx/pruners/adsampling.hpp" -#include "benchmark_utils.hpp" +#include "pdx/searcher.hpp" +#include "pdx/utils.hpp" +#include +#include -int main(int argc, char *argv[]) { +int main(int argc, char* argv[]) { std::string arg_dataset; std::string arg_selectivity; size_t arg_ivf_nprobe = 0; - if (argc > 1){ + if (argc > 1) { arg_dataset = argv[1]; } - if (argc > 2){ + if (argc > 2) { arg_ivf_nprobe = atoi(argv[2]); } - if (argc > 3){ + if (argc > 3) { arg_selectivity = argv[3]; } else { arg_selectivity = "0_99"; @@ -45,41 +45,50 @@ int main(int argc, char *argv[]) { std::string RESULTS_PATH; RESULTS_PATH = BENCHMARK_UTILS.RESULTS_DIR_PATH + "IVF_PDX_ADSAMPLING_FILTERED.csv"; std::cout << "==> SELECTIVITY: " << arg_selectivity << std::endl; - for (const auto & dataset : BenchmarkUtils::DATASETS) { - if (arg_dataset.size() > 0 && arg_dataset != dataset){ + for (const auto& dataset : BenchmarkUtils::DATASETS) { + if (arg_dataset.size() > 0 && arg_dataset != dataset) { continue; } PDX::IndexPDXIVF pdx_data = PDX::IndexPDXIVF(); pdx_data.Restore(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf"); - std::unique_ptr _matrix_ptr = MmapFile(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-matrix"); - auto *_matrix = reinterpret_cast(_matrix_ptr.get()); + std::unique_ptr _matrix_ptr = + MmapFile(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-matrix"); + auto* _matrix = reinterpret_cast(_matrix_ptr.get()); std::unique_ptr query_ptr = MmapFile(BenchmarkUtils::QUERIES_DATA + dataset); - auto *query = reinterpret_cast(query_ptr.get()); + auto* query = reinterpret_cast(query_ptr.get()); NUM_QUERIES = 1000; - std::unique_ptr ground_truth = MmapFile(BenchmarkUtils::FILTERED_GROUND_TRUTH_DATA + dataset + "_100_norm_" + arg_selectivity); - auto *int_ground_truth = reinterpret_cast(ground_truth.get()); + std::unique_ptr ground_truth = MmapFile( + BenchmarkUtils::FILTERED_GROUND_TRUTH_DATA + dataset + "_100_norm_" + arg_selectivity + ); + auto* int_ground_truth = reinterpret_cast(ground_truth.get()); query += 1; // skip number of embeddings - PDX::PredicateEvaluator predicate_evaluator = PDX::PredicateEvaluator(pdx_data.num_clusters); - predicate_evaluator.LoadSelectionVectorFromFile(BenchmarkUtils::SELECTION_VECTOR_DATA + dataset + "_" + arg_selectivity + ".bin"); - PDX::ADSamplingPruner pruner = PDX::ADSamplingPruner(pdx_data.num_dimensions, EPSILON0, _matrix); + PDX::PredicateEvaluator predicate_evaluator = + PDX::PredicateEvaluator(pdx_data.num_clusters); + predicate_evaluator.LoadSelectionVectorFromFile( + BenchmarkUtils::SELECTION_VECTOR_DATA + dataset + "_" + arg_selectivity + ".bin" + ); + PDX::ADSamplingPruner pruner = + PDX::ADSamplingPruner(pdx_data.num_dimensions, EPSILON0, _matrix); PDX::PDXearch searcher = PDX::PDXearch(pdx_data, pruner, 1, DIMENSION_ORDER); std::vector nprobes_to_use; if (arg_ivf_nprobe > 0) { nprobes_to_use = {arg_ivf_nprobe}; } else { - nprobes_to_use.assign(std::begin(BenchmarkUtils::IVF_PROBES), std::end(BenchmarkUtils::IVF_PROBES)); + nprobes_to_use.assign( + std::begin(BenchmarkUtils::IVF_PROBES), std::end(BenchmarkUtils::IVF_PROBES) + ); } for (size_t ivf_nprobe : nprobes_to_use) { - if (pdx_data.num_clusters < ivf_nprobe){ + if (pdx_data.num_clusters < ivf_nprobe) { continue; } - if (arg_ivf_nprobe > 0 && ivf_nprobe != arg_ivf_nprobe){ + if (arg_ivf_nprobe > 0 && ivf_nprobe != arg_ivf_nprobe) { continue; } std::vector runtimes; @@ -89,28 +98,32 @@ int main(int argc, char *argv[]) { float recalls = 0; if (VERIFY_RESULTS) { for (size_t l = 0; l < NUM_QUERIES; ++l) { - auto result = searcher.FilteredSearch(query + l * pdx_data.num_dimensions, KNN, predicate_evaluator); - BenchmarkUtils::VerifyResult(recalls, result, KNN, int_ground_truth, l); + auto result = searcher.FilteredSearch( + query + l * pdx_data.num_dimensions, KNN, predicate_evaluator + ); + BenchmarkUtils::VerifyResult( + recalls, result, KNN, int_ground_truth, l + ); } } for (size_t j = 0; j < NUM_MEASURE_RUNS; ++j) { for (size_t l = 0; l < NUM_QUERIES; ++l) { - searcher.FilteredSearch(query + l * pdx_data.num_dimensions, KNN, predicate_evaluator); - runtimes[j + l * NUM_MEASURE_RUNS] = { - searcher.end_to_end_clock.accum_time - }; + searcher.FilteredSearch( + query + l * pdx_data.num_dimensions, KNN, predicate_evaluator + ); + runtimes[j + l * NUM_MEASURE_RUNS] = {searcher.end_to_end_clock.accum_time}; } } float real_selectivity = 1 - SELECTIVITY_THRESHOLD; BenchmarkMetadata results_metadata = { - dataset, - ALGORITHM, - NUM_MEASURE_RUNS, - NUM_QUERIES, - ivf_nprobe, - KNN, - recalls, - real_selectivity + dataset, + ALGORITHM, + NUM_MEASURE_RUNS, + NUM_QUERIES, + ivf_nprobe, + KNN, + recalls, + real_selectivity }; BenchmarkUtils::SaveResults(runtimes, RESULTS_PATH, results_metadata); } diff --git a/benchmarks/pdx_serialization.cpp b/benchmarks/pdx_serialization.cpp index e2878dc..e83ef1e 100644 --- a/benchmarks/pdx_serialization.cpp +++ b/benchmarks/pdx_serialization.cpp @@ -6,16 +6,20 @@ #include #include -#include "pdx/index.hpp" #include "benchmark_utils.hpp" +#include "pdx/index.hpp" template -void BuildAndSave(const RawDatasetInfo &info, const std::string &dataset, const std::string &index_type, - const float *data) { +void BuildAndSave( + const RawDatasetInfo& info, + const std::string& dataset, + const std::string& index_type, + const float* data +) { const size_t d = info.num_dimensions; const size_t n = info.num_embeddings; - PDX::PDXIndexConfig index_config { + PDX::PDXIndexConfig index_config{ .num_dimensions = static_cast(d), .distance_metric = info.distance_metric, .seed = 42, @@ -32,7 +36,8 @@ void BuildAndSave(const RawDatasetInfo &info, const std::string &dataset, const std::cout << "Build time: " << build_ms << " ms\n"; std::cout << "Clusters: " << pdx_index.GetNumClusters() << "\n"; std::cout << "Index in-memory size: " << std::fixed << std::setprecision(2) - << static_cast(pdx_index.GetInMemorySizeInBytes()) / (1024.0 * 1024.0) << " MB\n"; + << static_cast(pdx_index.GetInMemorySizeInBytes()) / (1024.0 * 1024.0) + << " MB\n"; std::string save_path = BenchmarkUtils::PDX_DATA + dataset + "-" + index_type; std::cout << "Saving to " << save_path << "...\n"; @@ -40,12 +45,12 @@ void BuildAndSave(const RawDatasetInfo &info, const std::string &dataset, const std::cout << "Done.\n"; } -int main(int argc, char *argv[]) { +int main(int argc, char* argv[]) { if (argc < 3) { std::cerr << "Usage: " << argv[0] << " \n"; std::cerr << "Index types: pdx_f32, pdx_u8, pdx_tree_f32, pdx_tree_u8\n"; std::cerr << "Available datasets:"; - for (const auto &[name, _] : RAW_DATASET_PARAMS) { + for (const auto& [name, _] : RAW_DATASET_PARAMS) { std::cerr << " " << name; } std::cerr << "\n"; @@ -59,12 +64,13 @@ int main(int argc, char *argv[]) { std::cerr << "Unknown dataset: " << dataset << "\n"; return 1; } - const auto &info = it->second; + const auto& info = it->second; const size_t n = info.num_embeddings; const size_t d = info.num_dimensions; std::cout << "==> PDX Serialization\n"; - std::cout << "Dataset: " << dataset << " (" << info.pdx_dataset_name << ", n=" << n << ", d=" << d << ")\n"; + std::cout << "Dataset: " << dataset << " (" << info.pdx_dataset_name << ", n=" << n + << ", d=" << d << ")\n"; std::cout << "Index type: " << index_type << "\n"; // Read raw data @@ -72,8 +78,11 @@ int main(int argc, char *argv[]) { std::vector data(n * d); { std::ifstream file(data_path, std::ios::binary); - if (!file) { std::cerr << "Failed to open " << data_path << "\n"; return 1; } - file.read(reinterpret_cast(data.data()), n * d * sizeof(float)); + if (!file) { + std::cerr << "Failed to open " << data_path << "\n"; + return 1; + } + file.read(reinterpret_cast(data.data()), n * d * sizeof(float)); } // Ensure output directory exists @@ -99,17 +108,19 @@ int main(int argc, char *argv[]) { std::cout << "\n==> Verification: Loading index from " << save_path << "...\n"; auto loaded_index = PDX::LoadPDXIndex(save_path); std::cout << "Loaded index in-memory size: " << std::fixed << std::setprecision(2) - << static_cast(loaded_index->GetInMemorySizeInBytes()) / (1024.0 * 1024.0) << " MB\n"; + << static_cast(loaded_index->GetInMemorySizeInBytes()) / (1024.0 * 1024.0) + << " MB\n"; // Load queries - std::unique_ptr query_ptr = MmapFile(BenchmarkUtils::QUERIES_DATA + info.pdx_dataset_name); - auto *queries = reinterpret_cast(query_ptr.get()); + std::unique_ptr query_ptr = + MmapFile(BenchmarkUtils::QUERIES_DATA + info.pdx_dataset_name); + auto* queries = reinterpret_cast(query_ptr.get()); queries += 1; // skip header // Load ground truth - std::unique_ptr gt_buffer = MmapFile( - BenchmarkUtils::GROUND_TRUTH_DATA + info.pdx_dataset_name + "_100_norm"); - auto *int_ground_truth = reinterpret_cast(gt_buffer.get()); + std::unique_ptr gt_buffer = + MmapFile(BenchmarkUtils::GROUND_TRUTH_DATA + info.pdx_dataset_name + "_100_norm"); + auto* int_ground_truth = reinterpret_cast(gt_buffer.get()); const size_t n_queries = info.num_queries; const uint8_t KNN = BenchmarkUtils::KNN; diff --git a/benchmarks/results/DEFAULT/PDX_F32_ADSAMPLING.csv b/benchmarks/results/DEFAULT/PDX_F32_ADSAMPLING.csv index b10408a..44dadfc 100644 --- a/benchmarks/results/DEFAULT/PDX_F32_ADSAMPLING.csv +++ b/benchmarks/results/DEFAULT/PDX_F32_ADSAMPLING.csv @@ -1,3 +1,4 @@ dataset,algorithm,avg,max,min,recall,ivf_nprobe,epsilon,knn,n_queries,selectivity,num_measure_runs,avg_all,max_all,min_all openai,adsampling,0.915137,1.33392,0.493292,0.839519,32,0,100,1000,0.2,1,0.9182,1.812,0.4849 arxiv,adsampling,0.524415,0.765917,0.306708,0.906279,32,0,100,1000,0.2,1,0.5256,0.8312,0.3067 +arxiv,adsampling,0.520666,0.764041,0.304625,0.906279,32,0,100,1000,0.2,1,0.5223,0.8508,0.3046 diff --git a/benchmarks/results/DEFAULT/PDX_TREE_U8_ADSAMPLING.csv b/benchmarks/results/DEFAULT/PDX_TREE_U8_ADSAMPLING.csv index 0c3156d..33a780c 100644 --- a/benchmarks/results/DEFAULT/PDX_TREE_U8_ADSAMPLING.csv +++ b/benchmarks/results/DEFAULT/PDX_TREE_U8_ADSAMPLING.csv @@ -4,3 +4,4 @@ mxbai,adsampling,0.199531,0.293875,0.12325,0.901368,25,0,100,1000,0.2,1,0.2001,0 openai,adsampling,0.338263,0.462875,0.209292,0.836119,32,0,100,1000,0.2,1,0.338,0.5004,0.191 arxiv,adsampling,0.191359,0.267833,0.115875,0.902898,32,0,100,1000,0.2,1,0.1917,0.294,0.09696 arxiv,adsampling,0.197974,0.286083,0.111708,0.902898,32,0,100,1000,0.2,1,0.1986,0.3234,0.1024 +arxiv,adsampling,0.187311,0.261583,0.11575,0.902898,32,0,100,1000,0.2,1,0.1883,0.2933,0.1045 diff --git a/include/pdx/clustering.hpp b/include/pdx/clustering.hpp index 2477f1a..2bb91ce 100644 --- a/include/pdx/clustering.hpp +++ b/include/pdx/clustering.hpp @@ -1,79 +1,85 @@ #pragma once -#include #include "pdx/common.hpp" #include "superkmeans/hierarchical_superkmeans.h" +#include namespace PDX { struct KMeansResult { - // Row-major buffer of all centroids (num_clusters * num_dimensions). - std::vector centroids; + // Row-major buffer of all centroids (num_clusters * num_dimensions). + std::vector centroids; - // Mapping from a centroid to its embeddings. - // - // The embeddings are represented as indices into the original `embeddings` array. The `embeddings` array was passed - // as a parameter to the `ComputeKMeans` function. - // - // `assignments[0] -> [1, 3]` means that the 2nd and 4th embeddings in the `embeddings` array belong to the 0th - // cluster/centroid. - std::vector> assignments; + // Mapping from a centroid to its embeddings. + // + // The embeddings are represented as indices into the original `embeddings` array. The + // `embeddings` array was passed as a parameter to the `ComputeKMeans` function. + // + // `assignments[0] -> [1, 3]` means that the 2nd and 4th embeddings in the `embeddings` array + // belong to the 0th cluster/centroid. + std::vector> assignments; - static constexpr size_t MIN_EMBEDDINGS_TO_SAMPLE = 30720; + static constexpr size_t MIN_EMBEDDINGS_TO_SAMPLE = 30720; - explicit KMeansResult(uint32_t num_clusters) : assignments(num_clusters) { - } + explicit KMeansResult(uint32_t num_clusters) : assignments(num_clusters) {} }; // Compute centroids (clusters) and centroid-to-embedding assignments using SuperKMeans. -[[nodiscard]] inline KMeansResult ComputeKMeans(const float *const embeddings, const uint64_t num_embeddings, - const uint32_t num_dimensions, const uint32_t num_clusters, - const PDX::DistanceMetric distance_metric, const uint32_t seed, - const bool normalize = false, const float sampling_fraction = 0.0f, - const uint32_t kmeans_iters = 8) { - assert(num_embeddings >= 1); - assert(num_dimensions >= 1); - assert(num_clusters >= 1); +[[nodiscard]] inline KMeansResult ComputeKMeans( + const float* const embeddings, + const uint64_t num_embeddings, + const uint32_t num_dimensions, + const uint32_t num_clusters, + const PDX::DistanceMetric distance_metric, + const uint32_t seed, + const bool normalize = false, + const float sampling_fraction = 0.0f, + const uint32_t kmeans_iters = 8 +) { + assert(num_embeddings >= 1); + assert(num_dimensions >= 1); + assert(num_clusters >= 1); - auto result = KMeansResult(num_clusters); + auto result = KMeansResult(num_clusters); - if (num_clusters == 1) { - result.centroids = std::vector(embeddings, embeddings + num_dimensions); - for (uint64_t vec_id = 0; vec_id < num_embeddings; vec_id++) { - result.assignments[0].emplace_back(vec_id); - } - return result; - } + if (num_clusters == 1) { + result.centroids = std::vector(embeddings, embeddings + num_dimensions); + for (uint64_t vec_id = 0; vec_id < num_embeddings; vec_id++) { + result.assignments[0].emplace_back(vec_id); + } + return result; + } - skmeans::SuperKMeansConfig config; - if (sampling_fraction > 0.0f) { - config.sampling_fraction = sampling_fraction; - } else if (num_embeddings < KMeansResult::MIN_EMBEDDINGS_TO_SAMPLE) { - config.sampling_fraction = 1.0f; - } else { - config.sampling_fraction = 0.3f; - } - config.angular = normalize || distance_metric == PDX::DistanceMetric::COSINE || distance_metric == PDX::DistanceMetric::IP; - config.data_already_rotated = true; - config.iters = kmeans_iters; - config.suppress_warnings = true; - config.seed = seed; + skmeans::SuperKMeansConfig config; + if (sampling_fraction > 0.0f) { + config.sampling_fraction = sampling_fraction; + } else if (num_embeddings < KMeansResult::MIN_EMBEDDINGS_TO_SAMPLE) { + config.sampling_fraction = 1.0f; + } else { + config.sampling_fraction = 0.3f; + } + config.angular = normalize || distance_metric == PDX::DistanceMetric::COSINE || + distance_metric == PDX::DistanceMetric::IP; + config.data_already_rotated = true; + config.iters = kmeans_iters; + config.suppress_warnings = true; + config.seed = seed; - //auto kmeans = skmeans::HierarchicalSuperKMeans(num_clusters, num_dimensions, config); - auto kmeans = skmeans::SuperKMeans(num_clusters, num_dimensions, config); - result.centroids = kmeans.Train(embeddings, num_embeddings); + // auto kmeans = skmeans::HierarchicalSuperKMeans(num_clusters, num_dimensions, config); + auto kmeans = skmeans::SuperKMeans(num_clusters, num_dimensions, config); + result.centroids = kmeans.Train(embeddings, num_embeddings); - // Extract assignments - // SuperKMeans returns assignment from vec_id (not row_id) to centroid_idx - std::vector assignments = - kmeans.FastAssign(embeddings, result.centroids.data(), num_embeddings, num_clusters); - // Convert into assignment from centroid_idx to vec_id (not row_id) - result.assignments.resize(num_clusters); - for (uint64_t vec_id = 0; vec_id < num_embeddings; vec_id++) { - result.assignments[assignments[vec_id]].emplace_back(vec_id); - } + // Extract assignments + // SuperKMeans returns assignment from vec_id (not row_id) to centroid_idx + std::vector assignments = + kmeans.FastAssign(embeddings, result.centroids.data(), num_embeddings, num_clusters); + // Convert into assignment from centroid_idx to vec_id (not row_id) + result.assignments.resize(num_clusters); + for (uint64_t vec_id = 0; vec_id < num_embeddings; vec_id++) { + result.assignments[assignments[vec_id]].emplace_back(vec_id); + } - return result; + return result; }; } // namespace PDX diff --git a/include/pdx/common.hpp b/include/pdx/common.hpp index c3d8797..b013eb8 100644 --- a/include/pdx/common.hpp +++ b/include/pdx/common.hpp @@ -1,13 +1,12 @@ #pragma once +#include +#include #include #include #include -#include #include #include -#include - #ifndef PDX_RESTRICT #if defined(__GNUC__) || defined(__clang__) @@ -22,10 +21,10 @@ #endif #if defined(__GNUC__) || defined(__clang__) -#define PDX_LIKELY(x) __builtin_expect(!!(x), 1) +#define PDX_LIKELY(x) __builtin_expect(!!(x), 1) #define PDX_UNLIKELY(x) __builtin_expect(!!(x), 0) #else -#define PDX_LIKELY(x) (x) +#define PDX_LIKELY(x) (x) #define PDX_UNLIKELY(x) (x) #endif @@ -36,26 +35,29 @@ static constexpr size_t D_THRESHOLD_FOR_DCT_ROTATION = 512; static constexpr size_t PDX_MAX_DIMS = 16384; static constexpr size_t H_DIM_SIZE = 64; static constexpr size_t U8_INTERLEAVE_SIZE = 4; -static constexpr uint32_t DIMENSIONS_FETCHING_SIZES[20] = {16, 16, 32, 32, 32, 32, 64, 64, 64, 64, - 128, 128, 128, 128, 256, 256, 512, 1024, 2048, 16384}; +static constexpr uint32_t DIMENSIONS_FETCHING_SIZES[20] = {16, 16, 32, 32, 32, 32, 64, + 64, 64, 64, 128, 128, 128, 128, + 256, 256, 512, 1024, 2048, 16384}; static constexpr bool AllFetchingSizesMultipleOfU8InterleaveSize() { - for (auto s : DIMENSIONS_FETCHING_SIZES) { - if (s % U8_INTERLEAVE_SIZE != 0) { - return false; - } - } - return true; + for (auto s : DIMENSIONS_FETCHING_SIZES) { + if (s % U8_INTERLEAVE_SIZE != 0) { + return false; + } + } + return true; } -static_assert(AllFetchingSizesMultipleOfU8InterleaveSize(), - "All DIMENSIONS_FETCHING_SIZES must be multiples of U8_INTERLEAVE_SIZE"); +static_assert( + AllFetchingSizesMultipleOfU8InterleaveSize(), + "All DIMENSIONS_FETCHING_SIZES must be multiples of U8_INTERLEAVE_SIZE" +); // Epsilon0 parameter of ADSampling (Reference: https://dl.acm.org/doi/abs/10.1145/3589282) static constexpr float ADSAMPLING_PRUNING_AGGRESIVENESS = 1.5f; template static constexpr uint32_t AlignValue(T n) { - return ((n + (val - 1)) / val) * val; + return ((n + (val - 1)) / val) * val; } enum class DistanceMetric { L2SQ, COSINE, IP }; @@ -67,11 +69,11 @@ enum class PDXIndexType : uint8_t { PDX_F32 = 0, PDX_U8 = 1, PDX_TREE_F32 = 2, P // TODO: Do the same for indexes? template struct DistanceType { - using type = uint32_t; + using type = uint32_t; }; template <> struct DistanceType { - using type = float; + using type = float; }; template using pdx_distance_t = typename DistanceType::type; @@ -79,22 +81,22 @@ using pdx_distance_t = typename DistanceType::type; // TODO: Do the same for indexes? template struct DataType { - using type = uint8_t; // U8 + using type = uint8_t; // U8 }; template <> struct DataType { - using type = float; + using type = float; }; template using pdx_data_t = typename DataType::type; template struct QuantizedVectorType { - using type = uint8_t; // U8 + using type = uint8_t; // U8 }; template <> struct QuantizedVectorType { - using type = float; + using type = float; }; template using pdx_quantized_embedding_t = typename QuantizedVectorType::type; @@ -102,46 +104,49 @@ using pdx_quantized_embedding_t = typename QuantizedVectorType::type; using eigen_matrix_t = Eigen::Matrix; struct KNNCandidate { - uint32_t index; - float distance; + uint32_t index; + float distance; }; struct VectorComparator { - bool operator()(const KNNCandidate &a, const KNNCandidate &b) { - return a.distance < b.distance; - } + bool operator()(const KNNCandidate& a, const KNNCandidate& b) { + return a.distance < b.distance; + } }; using Heap = std::priority_queue, VectorComparator>; struct PDXDimensionSplit { - const uint32_t horizontal_dimensions; - const uint32_t vertical_dimensions; + const uint32_t horizontal_dimensions; + const uint32_t vertical_dimensions; }; -[[nodiscard]] static inline constexpr PDXDimensionSplit GetPDXDimensionSplit(const uint32_t num_dimensions) { - auto local_proportion_horizontal_dim = PROPORTION_HORIZONTAL_DIM; - if (num_dimensions <= 128) { - local_proportion_horizontal_dim = 0.25; - } - auto horizontal_d = static_cast(static_cast(num_dimensions) * local_proportion_horizontal_dim); - auto vertical_d = static_cast(num_dimensions - horizontal_d); - if (horizontal_d % H_DIM_SIZE > 0) { - horizontal_d = ((horizontal_d + H_DIM_SIZE / 2) / H_DIM_SIZE) * H_DIM_SIZE; - vertical_d = num_dimensions - horizontal_d; - } - if (!vertical_d) { - horizontal_d = H_DIM_SIZE; - vertical_d = num_dimensions - horizontal_d; - } - if (num_dimensions <= H_DIM_SIZE) { - horizontal_d = 0; - vertical_d = num_dimensions; - } - - assert(horizontal_d + vertical_d == num_dimensions); - - return {horizontal_d, vertical_d}; +[[nodiscard]] static inline constexpr PDXDimensionSplit GetPDXDimensionSplit( + const uint32_t num_dimensions +) { + auto local_proportion_horizontal_dim = PROPORTION_HORIZONTAL_DIM; + if (num_dimensions <= 128) { + local_proportion_horizontal_dim = 0.25; + } + auto horizontal_d = + static_cast(static_cast(num_dimensions) * local_proportion_horizontal_dim); + auto vertical_d = static_cast(num_dimensions - horizontal_d); + if (horizontal_d % H_DIM_SIZE > 0) { + horizontal_d = ((horizontal_d + H_DIM_SIZE / 2) / H_DIM_SIZE) * H_DIM_SIZE; + vertical_d = num_dimensions - horizontal_d; + } + if (!vertical_d) { + horizontal_d = H_DIM_SIZE; + vertical_d = num_dimensions - horizontal_d; + } + if (num_dimensions <= H_DIM_SIZE) { + horizontal_d = 0; + vertical_d = num_dimensions; + } + + assert(horizontal_d + vertical_d == num_dimensions); + + return {horizontal_d, vertical_d}; }; static_assert(GetPDXDimensionSplit(4).horizontal_dimensions == 0); @@ -175,17 +180,20 @@ static_assert(GetPDXDimensionSplit(1028).horizontal_dimensions == 768); static_assert(GetPDXDimensionSplit(1028).vertical_dimensions == 260); [[nodiscard]] inline constexpr uint32_t ComputeNumberOfClusters(const uint32_t num_embeddings) { - if (num_embeddings < 500000) { - return std::ceil(2 * std::sqrt(num_embeddings)); - } else if (num_embeddings < 2500000) { - return std::ceil(4 * std::sqrt(num_embeddings)); - } else { - return std::ceil(8 * std::sqrt(num_embeddings)); - } + if (num_embeddings < 500000) { + return std::ceil(2 * std::sqrt(num_embeddings)); + } else if (num_embeddings < 2500000) { + return std::ceil(4 * std::sqrt(num_embeddings)); + } else { + return std::ceil(8 * std::sqrt(num_embeddings)); + } } -[[nodiscard]] inline constexpr bool DistanceMetricRequiresNormalization(const PDX::DistanceMetric distance_metric) { - return distance_metric == PDX::DistanceMetric::COSINE || distance_metric == PDX::DistanceMetric::IP; +[[nodiscard]] inline constexpr bool DistanceMetricRequiresNormalization( + const PDX::DistanceMetric distance_metric +) { + return distance_metric == PDX::DistanceMetric::COSINE || + distance_metric == PDX::DistanceMetric::IP; } } // namespace PDX diff --git a/include/pdx/db_mock/predicate_evaluator.hpp b/include/pdx/db_mock/predicate_evaluator.hpp index 119660a..239ea34 100644 --- a/include/pdx/db_mock/predicate_evaluator.hpp +++ b/include/pdx/db_mock/predicate_evaluator.hpp @@ -1,29 +1,33 @@ #pragma once -#include #include #include +#include namespace PDX { class PredicateEvaluator { -public: - // Number of tuples per cluster that passed the predicate. - std::unique_ptr n_passing_tuples; - // Contiguous array of selection vectors for each cluster. - // [(cluster 1): 0, 1, 1, (cluster 2): 1, 1, 1, 1]. - std::unique_ptr selection_vector; - size_t n_clusters; + public: + // Number of tuples per cluster that passed the predicate. + std::unique_ptr n_passing_tuples; + // Contiguous array of selection vectors for each cluster. + // [(cluster 1): 0, 1, 1, (cluster 2): 1, 1, 1, 1]. + std::unique_ptr selection_vector; + size_t n_clusters; - explicit PredicateEvaluator(size_t n_clusters, size_t total_num_embeddings) : n_clusters(n_clusters) { - n_passing_tuples = std::make_unique(n_clusters); - selection_vector = std::make_unique(total_num_embeddings); - }; + explicit PredicateEvaluator(size_t n_clusters, size_t total_num_embeddings) + : n_clusters(n_clusters) { + n_passing_tuples = std::make_unique(n_clusters); + selection_vector = std::make_unique(total_num_embeddings); + }; - std::pair GetSelectionVector(const size_t cluster_id, const size_t cluster_offset) const { - return {&selection_vector[cluster_offset], n_passing_tuples[cluster_id]}; - } + std::pair GetSelectionVector( + const size_t cluster_id, + const size_t cluster_offset + ) const { + return {&selection_vector[cluster_offset], n_passing_tuples[cluster_id]}; + } }; }; // namespace PDX diff --git a/include/pdx/distance_computers/avx2_computers.hpp b/include/pdx/distance_computers/avx2_computers.hpp index 53c9bc3..382fbe2 100644 --- a/include/pdx/distance_computers/avx2_computers.hpp +++ b/include/pdx/distance_computers/avx2_computers.hpp @@ -1,9 +1,9 @@ #pragma once -#include -#include #include "pdx/common.hpp" #include "pdx/distance_computers/scalar_computers.hpp" +#include +#include namespace PDX { @@ -12,169 +12,200 @@ class SIMDComputer {}; template <> class SIMDComputer { -public: - using distance_t = pdx_distance_t; - using query_t = pdx_quantized_embedding_t; - using data_t = pdx_data_t; - using scalar_computer = ScalarComputer; - - template - static void Vertical(const query_t *PDX_RESTRICT query, const data_t *PDX_RESTRICT data, size_t n_vectors, - size_t total_vectors, size_t start_dimension, size_t end_dimension, distance_t *distances_p, - const uint32_t *pruning_positions = nullptr) { - size_t dimensions_jump_factor = total_vectors; - for (size_t dimension_idx = start_dimension; dimension_idx < end_dimension; ++dimension_idx) { - size_t offset_to_dimension_start = dimension_idx * dimensions_jump_factor; - for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { - auto true_vector_idx = vector_idx; - if constexpr (SKIP_PRUNED) { - true_vector_idx = pruning_positions[vector_idx]; - } - distance_t to_multiply = query[dimension_idx] - data[offset_to_dimension_start + true_vector_idx]; - distances_p[true_vector_idx] += to_multiply * to_multiply; - } - } - } - - static distance_t Horizontal(const query_t *PDX_RESTRICT vector1, const data_t *PDX_RESTRICT vector2, - size_t num_dimensions) { - __m256 d2_vec = _mm256_setzero_ps(); - size_t i = 0; - for (; i + 8 <= num_dimensions; i += 8) { - __m256 a_vec = _mm256_loadu_ps(vector1 + i); - __m256 b_vec = _mm256_loadu_ps(vector2 + i); - __m256 d_vec = _mm256_sub_ps(a_vec, b_vec); - d2_vec = _mm256_fmadd_ps(d_vec, d_vec, d2_vec); - } - - // _simsimd_reduce_f32x8_haswell - // Convert the lower and higher 128-bit lanes of the input vector to double precision - __m128 low_f32 = _mm256_castps256_ps128(d2_vec); - __m128 high_f32 = _mm256_extractf128_ps(d2_vec, 1); - - // Convert single-precision (float) vectors to double-precision (double) vectors - __m256d low_f64 = _mm256_cvtps_pd(low_f32); - __m256d high_f64 = _mm256_cvtps_pd(high_f32); - - // Perform the addition in double-precision - __m256d sum = _mm256_add_pd(low_f64, high_f64); - - // Reduce the double-precision vector to a scalar - // Horizontal add the first and second double-precision values, and third and fourth - __m128d sum_low = _mm256_castpd256_pd128(sum); - __m128d sum_high = _mm256_extractf128_pd(sum, 1); - __m128d sum128 = _mm_add_pd(sum_low, sum_high); - - // Horizontal add again to accumulate all four values into one - sum128 = _mm_hadd_pd(sum128, sum128); - - // Convert the final sum to a scalar double-precision value and return - double d2 = _mm_cvtsd_f64(sum128); - - for (; i < num_dimensions; ++i) { - distance_t d = vector1[i] - vector2[i]; - d2 += d * d; - } - - return static_cast(d2); - }; + public: + using distance_t = pdx_distance_t; + using query_t = pdx_quantized_embedding_t; + using data_t = pdx_data_t; + using scalar_computer = ScalarComputer; + + template + static void Vertical( + const query_t* PDX_RESTRICT query, + const data_t* PDX_RESTRICT data, + size_t n_vectors, + size_t total_vectors, + size_t start_dimension, + size_t end_dimension, + distance_t* distances_p, + const uint32_t* pruning_positions = nullptr + ) { + size_t dimensions_jump_factor = total_vectors; + for (size_t dimension_idx = start_dimension; dimension_idx < end_dimension; + ++dimension_idx) { + size_t offset_to_dimension_start = dimension_idx * dimensions_jump_factor; + for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { + auto true_vector_idx = vector_idx; + if constexpr (SKIP_PRUNED) { + true_vector_idx = pruning_positions[vector_idx]; + } + distance_t to_multiply = + query[dimension_idx] - data[offset_to_dimension_start + true_vector_idx]; + distances_p[true_vector_idx] += to_multiply * to_multiply; + } + } + } + + static distance_t Horizontal( + const query_t* PDX_RESTRICT vector1, + const data_t* PDX_RESTRICT vector2, + size_t num_dimensions + ) { + __m256 d2_vec = _mm256_setzero_ps(); + size_t i = 0; + for (; i + 8 <= num_dimensions; i += 8) { + __m256 a_vec = _mm256_loadu_ps(vector1 + i); + __m256 b_vec = _mm256_loadu_ps(vector2 + i); + __m256 d_vec = _mm256_sub_ps(a_vec, b_vec); + d2_vec = _mm256_fmadd_ps(d_vec, d_vec, d2_vec); + } + + // _simsimd_reduce_f32x8_haswell + // Convert the lower and higher 128-bit lanes of the input vector to double precision + __m128 low_f32 = _mm256_castps256_ps128(d2_vec); + __m128 high_f32 = _mm256_extractf128_ps(d2_vec, 1); + + // Convert single-precision (float) vectors to double-precision (double) vectors + __m256d low_f64 = _mm256_cvtps_pd(low_f32); + __m256d high_f64 = _mm256_cvtps_pd(high_f32); + + // Perform the addition in double-precision + __m256d sum = _mm256_add_pd(low_f64, high_f64); + + // Reduce the double-precision vector to a scalar + // Horizontal add the first and second double-precision values, and third and fourth + __m128d sum_low = _mm256_castpd256_pd128(sum); + __m128d sum_high = _mm256_extractf128_pd(sum, 1); + __m128d sum128 = _mm_add_pd(sum_low, sum_high); + + // Horizontal add again to accumulate all four values into one + sum128 = _mm_hadd_pd(sum128, sum128); + + // Convert the final sum to a scalar double-precision value and return + double d2 = _mm_cvtsd_f64(sum128); + + for (; i < num_dimensions; ++i) { + distance_t d = vector1[i] - vector2[i]; + d2 += d * d; + } + + return static_cast(d2); + }; }; template <> class SIMDComputer { -public: - using distance_t = pdx_distance_t; - using query_t = pdx_quantized_embedding_t; - using data_t = pdx_data_t; - - template - static void Vertical(const query_t *PDX_RESTRICT query, const data_t *PDX_RESTRICT data, size_t n_vectors, - size_t total_vectors, size_t start_dimension, size_t end_dimension, distance_t *distances_p, - const uint32_t *pruning_positions = nullptr) { - auto *query_grouped = reinterpret_cast(query); - size_t dim_idx = start_dimension; - for (; dim_idx + 4 <= end_dimension; dim_idx += 4) { - uint32_t dimension_idx = dim_idx; - size_t offset_to_dimension_start = dimension_idx * total_vectors; - size_t i = 0; - if constexpr (!SKIP_PRUNED) { - uint32_t query_value = query_grouped[dimension_idx / 4]; - __m256i vec1_u8 = _mm256_set1_epi32(query_value); - __m256i zeros = _mm256_setzero_si256(); - for (; i + 8 <= n_vectors; i += 8) { - // Load 8 accumulated distances and 32 bytes of data (4 dims x 8 vectors). - __m256i res = _mm256_loadu_si256(reinterpret_cast(&distances_p[i])); - __m256i vec2_u8 = - _mm256_loadu_si256(reinterpret_cast(&data[offset_to_dimension_start + i * 4])); - __m256i diff = - _mm256_or_si256(_mm256_subs_epu8(vec1_u8, vec2_u8), _mm256_subs_epu8(vec2_u8, vec1_u8)); - // Zero-extend bytes to 16-bit, square, and horizontal-add to 32-bit. - __m256i lo16 = _mm256_unpacklo_epi8(diff, zeros); - __m256i hi16 = _mm256_unpackhi_epi8(diff, zeros); - __m256i sq_lo = _mm256_madd_epi16(lo16, lo16); - __m256i sq_hi = _mm256_madd_epi16(hi16, hi16); - // hadd combines partial sums: [v0, v1, v2, v3, v4, v5, v6, v7]. - __m256i sums = _mm256_hadd_epi32(sq_lo, sq_hi); - _mm256_storeu_si256(reinterpret_cast<__m256i *>(&distances_p[i]), _mm256_add_epi32(res, sums)); - } - } - // Scalar tail (vectors). - for (; i < n_vectors; ++i) { - size_t vector_idx = i; - if constexpr (SKIP_PRUNED) { - vector_idx = pruning_positions[vector_idx]; - } - int da = query[dimension_idx] - data[offset_to_dimension_start + (vector_idx * 4)]; - int db = query[dimension_idx + 1] - data[offset_to_dimension_start + (vector_idx * 4) + 1]; - int dc = query[dimension_idx + 2] - data[offset_to_dimension_start + (vector_idx * 4) + 2]; - int dd = query[dimension_idx + 3] - data[offset_to_dimension_start + (vector_idx * 4) + 3]; - distances_p[vector_idx] += (da * da) + (db * db) + (dc * dc) + (dd * dd); - } - } - if (dim_idx < end_dimension) { - auto remaining = static_cast(end_dimension - dim_idx); - size_t offset = dim_idx * total_vectors; - for (size_t i = 0; i < n_vectors; ++i) { - size_t vector_idx = i; - if constexpr (SKIP_PRUNED) { - vector_idx = pruning_positions[vector_idx]; - } - for (uint32_t k = 0; k < remaining; ++k) { - int diff = query[dim_idx + k] - data[offset + vector_idx * remaining + k]; - distances_p[vector_idx] += diff * diff; - } - } - } - } - - static distance_t Horizontal(const query_t *PDX_RESTRICT vector1, const data_t *PDX_RESTRICT vector2, - size_t num_dimensions) { - __m256i d2_vec = _mm256_setzero_si256(); - __m256i zeros = _mm256_setzero_si256(); - size_t i = 0; - for (; i + 32 <= num_dimensions; i += 32) { - __m256i a_vec = _mm256_loadu_si256(reinterpret_cast(vector1 + i)); - __m256i b_vec = _mm256_loadu_si256(reinterpret_cast(vector2 + i)); - __m256i diff = _mm256_or_si256(_mm256_subs_epu8(a_vec, b_vec), _mm256_subs_epu8(b_vec, a_vec)); - __m256i lo16 = _mm256_unpacklo_epi8(diff, zeros); - __m256i hi16 = _mm256_unpackhi_epi8(diff, zeros); - d2_vec = _mm256_add_epi32(d2_vec, _mm256_madd_epi16(lo16, lo16)); - d2_vec = _mm256_add_epi32(d2_vec, _mm256_madd_epi16(hi16, hi16)); - } - // Reduce 8 x i32 to scalar (simsimd_reduce_i32x8_haswell) - __m128i lo = _mm256_castsi256_si128(d2_vec); - __m128i hi = _mm256_extracti128_si256(d2_vec, 1); - __m128i sum128 = _mm_add_epi32(lo, hi); - sum128 = _mm_hadd_epi32(sum128, sum128); - sum128 = _mm_hadd_epi32(sum128, sum128); - distance_t distance = _mm_cvtsi128_si32(sum128); - // Scalar tail. - for (; i < num_dimensions; ++i) { - int n = static_cast(vector1[i]) - static_cast(vector2[i]); - distance += n * n; - } - return distance; - }; + public: + using distance_t = pdx_distance_t; + using query_t = pdx_quantized_embedding_t; + using data_t = pdx_data_t; + + template + static void Vertical( + const query_t* PDX_RESTRICT query, + const data_t* PDX_RESTRICT data, + size_t n_vectors, + size_t total_vectors, + size_t start_dimension, + size_t end_dimension, + distance_t* distances_p, + const uint32_t* pruning_positions = nullptr + ) { + auto* query_grouped = reinterpret_cast(query); + size_t dim_idx = start_dimension; + for (; dim_idx + 4 <= end_dimension; dim_idx += 4) { + uint32_t dimension_idx = dim_idx; + size_t offset_to_dimension_start = dimension_idx * total_vectors; + size_t i = 0; + if constexpr (!SKIP_PRUNED) { + uint32_t query_value = query_grouped[dimension_idx / 4]; + __m256i vec1_u8 = _mm256_set1_epi32(query_value); + __m256i zeros = _mm256_setzero_si256(); + for (; i + 8 <= n_vectors; i += 8) { + // Load 8 accumulated distances and 32 bytes of data (4 dims x 8 vectors). + __m256i res = + _mm256_loadu_si256(reinterpret_cast(&distances_p[i])); + __m256i vec2_u8 = _mm256_loadu_si256( + reinterpret_cast(&data[offset_to_dimension_start + i * 4]) + ); + __m256i diff = _mm256_or_si256( + _mm256_subs_epu8(vec1_u8, vec2_u8), _mm256_subs_epu8(vec2_u8, vec1_u8) + ); + // Zero-extend bytes to 16-bit, square, and horizontal-add to 32-bit. + __m256i lo16 = _mm256_unpacklo_epi8(diff, zeros); + __m256i hi16 = _mm256_unpackhi_epi8(diff, zeros); + __m256i sq_lo = _mm256_madd_epi16(lo16, lo16); + __m256i sq_hi = _mm256_madd_epi16(hi16, hi16); + // hadd combines partial sums: [v0, v1, v2, v3, v4, v5, v6, v7]. + __m256i sums = _mm256_hadd_epi32(sq_lo, sq_hi); + _mm256_storeu_si256( + reinterpret_cast<__m256i*>(&distances_p[i]), _mm256_add_epi32(res, sums) + ); + } + } + // Scalar tail (vectors). + for (; i < n_vectors; ++i) { + size_t vector_idx = i; + if constexpr (SKIP_PRUNED) { + vector_idx = pruning_positions[vector_idx]; + } + int da = query[dimension_idx] - data[offset_to_dimension_start + (vector_idx * 4)]; + int db = query[dimension_idx + 1] - + data[offset_to_dimension_start + (vector_idx * 4) + 1]; + int dc = query[dimension_idx + 2] - + data[offset_to_dimension_start + (vector_idx * 4) + 2]; + int dd = query[dimension_idx + 3] - + data[offset_to_dimension_start + (vector_idx * 4) + 3]; + distances_p[vector_idx] += (da * da) + (db * db) + (dc * dc) + (dd * dd); + } + } + if (dim_idx < end_dimension) { + auto remaining = static_cast(end_dimension - dim_idx); + size_t offset = dim_idx * total_vectors; + for (size_t i = 0; i < n_vectors; ++i) { + size_t vector_idx = i; + if constexpr (SKIP_PRUNED) { + vector_idx = pruning_positions[vector_idx]; + } + for (uint32_t k = 0; k < remaining; ++k) { + int diff = query[dim_idx + k] - data[offset + vector_idx * remaining + k]; + distances_p[vector_idx] += diff * diff; + } + } + } + } + + static distance_t Horizontal( + const query_t* PDX_RESTRICT vector1, + const data_t* PDX_RESTRICT vector2, + size_t num_dimensions + ) { + __m256i d2_vec = _mm256_setzero_si256(); + __m256i zeros = _mm256_setzero_si256(); + size_t i = 0; + for (; i + 32 <= num_dimensions; i += 32) { + __m256i a_vec = _mm256_loadu_si256(reinterpret_cast(vector1 + i)); + __m256i b_vec = _mm256_loadu_si256(reinterpret_cast(vector2 + i)); + __m256i diff = + _mm256_or_si256(_mm256_subs_epu8(a_vec, b_vec), _mm256_subs_epu8(b_vec, a_vec)); + __m256i lo16 = _mm256_unpacklo_epi8(diff, zeros); + __m256i hi16 = _mm256_unpackhi_epi8(diff, zeros); + d2_vec = _mm256_add_epi32(d2_vec, _mm256_madd_epi16(lo16, lo16)); + d2_vec = _mm256_add_epi32(d2_vec, _mm256_madd_epi16(hi16, hi16)); + } + // Reduce 8 x i32 to scalar (simsimd_reduce_i32x8_haswell) + __m128i lo = _mm256_castsi256_si128(d2_vec); + __m128i hi = _mm256_extracti128_si256(d2_vec, 1); + __m128i sum128 = _mm_add_epi32(lo, hi); + sum128 = _mm_hadd_epi32(sum128, sum128); + sum128 = _mm_hadd_epi32(sum128, sum128); + distance_t distance = _mm_cvtsi128_si32(sum128); + // Scalar tail. + for (; i < num_dimensions; ++i) { + int n = static_cast(vector1[i]) - static_cast(vector2[i]); + distance += n * n; + } + return distance; + }; }; } // namespace PDX diff --git a/include/pdx/distance_computers/avx512_computers.hpp b/include/pdx/distance_computers/avx512_computers.hpp index 62a9c29..2fed70b 100644 --- a/include/pdx/distance_computers/avx512_computers.hpp +++ b/include/pdx/distance_computers/avx512_computers.hpp @@ -1,9 +1,9 @@ #pragma once -#include +#include "pdx/common.hpp" #include +#include #include -#include "pdx/common.hpp" namespace PDX { @@ -12,167 +12,210 @@ class SIMDComputer {}; template <> class SIMDComputer { -public: - using distance_t = pdx_distance_t; - using query_t = pdx_quantized_embedding_t; - using data_t = pdx_data_t; + public: + using distance_t = pdx_distance_t; + using query_t = pdx_quantized_embedding_t; + using data_t = pdx_data_t; - template - static void Vertical(const query_t *PDX_RESTRICT query, const data_t *PDX_RESTRICT data, size_t n_vectors, - size_t total_vectors, size_t start_dimension, size_t end_dimension, distance_t *distances_p, - const uint32_t *pruning_positions = nullptr) { - size_t dimensions_jump_factor = total_vectors; - for (size_t dimension_idx = start_dimension; dimension_idx < end_dimension; ++dimension_idx) { - size_t offset_to_dimension_start = dimension_idx * dimensions_jump_factor; - for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { - auto true_vector_idx = vector_idx; - if constexpr (SKIP_PRUNED) { - true_vector_idx = pruning_positions[vector_idx]; - } - distance_t to_multiply = query[dimension_idx] - data[offset_to_dimension_start + true_vector_idx]; - distances_p[true_vector_idx] += to_multiply * to_multiply; - } - } - } + template + static void Vertical( + const query_t* PDX_RESTRICT query, + const data_t* PDX_RESTRICT data, + size_t n_vectors, + size_t total_vectors, + size_t start_dimension, + size_t end_dimension, + distance_t* distances_p, + const uint32_t* pruning_positions = nullptr + ) { + size_t dimensions_jump_factor = total_vectors; + for (size_t dimension_idx = start_dimension; dimension_idx < end_dimension; + ++dimension_idx) { + size_t offset_to_dimension_start = dimension_idx * dimensions_jump_factor; + for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { + auto true_vector_idx = vector_idx; + if constexpr (SKIP_PRUNED) { + true_vector_idx = pruning_positions[vector_idx]; + } + distance_t to_multiply = + query[dimension_idx] - data[offset_to_dimension_start + true_vector_idx]; + distances_p[true_vector_idx] += to_multiply * to_multiply; + } + } + } - static distance_t Horizontal(const query_t *PDX_RESTRICT vector1, const data_t *PDX_RESTRICT vector2, - size_t num_dimensions) { - __m512 d2_vec = _mm512_setzero(); - __m512 a_vec, b_vec; - simsimd_l2sq_f32_skylake_cycle: - if (num_dimensions < 16) { - __mmask16 mask = static_cast<__mmask16>(_bzhi_u32(0xFFFFFFFF, num_dimensions)); - a_vec = _mm512_maskz_loadu_ps(mask, vector1); - b_vec = _mm512_maskz_loadu_ps(mask, vector2); - num_dimensions = 0; - } else { - a_vec = _mm512_loadu_ps(vector1); - b_vec = _mm512_loadu_ps(vector2); - vector1 += 16, vector2 += 16, num_dimensions -= 16; - } - __m512 d_vec = _mm512_sub_ps(a_vec, b_vec); - d2_vec = _mm512_fmadd_ps(d_vec, d_vec, d2_vec); - if (num_dimensions) { - goto simsimd_l2sq_f32_skylake_cycle; - } + static distance_t Horizontal( + const query_t* PDX_RESTRICT vector1, + const data_t* PDX_RESTRICT vector2, + size_t num_dimensions + ) { + __m512 d2_vec = _mm512_setzero(); + __m512 a_vec, b_vec; + simsimd_l2sq_f32_skylake_cycle: + if (num_dimensions < 16) { + __mmask16 mask = static_cast<__mmask16>(_bzhi_u32(0xFFFFFFFF, num_dimensions)); + a_vec = _mm512_maskz_loadu_ps(mask, vector1); + b_vec = _mm512_maskz_loadu_ps(mask, vector2); + num_dimensions = 0; + } else { + a_vec = _mm512_loadu_ps(vector1); + b_vec = _mm512_loadu_ps(vector2); + vector1 += 16, vector2 += 16, num_dimensions -= 16; + } + __m512 d_vec = _mm512_sub_ps(a_vec, b_vec); + d2_vec = _mm512_fmadd_ps(d_vec, d_vec, d2_vec); + if (num_dimensions) { + goto simsimd_l2sq_f32_skylake_cycle; + } - // _simsimd_reduce_f32x16_skylake - __m512 x = _mm512_add_ps(d2_vec, _mm512_shuffle_f32x4(d2_vec, d2_vec, _MM_SHUFFLE(0, 0, 3, 2))); - __m128 r = _mm512_castps512_ps128(_mm512_add_ps(x, _mm512_shuffle_f32x4(x, x, _MM_SHUFFLE(0, 0, 0, 1)))); - r = _mm_hadd_ps(r, r); - return _mm_cvtss_f32(_mm_hadd_ps(r, r)); - }; + // _simsimd_reduce_f32x16_skylake + __m512 x = + _mm512_add_ps(d2_vec, _mm512_shuffle_f32x4(d2_vec, d2_vec, _MM_SHUFFLE(0, 0, 3, 2))); + __m128 r = _mm512_castps512_ps128( + _mm512_add_ps(x, _mm512_shuffle_f32x4(x, x, _MM_SHUFFLE(0, 0, 0, 1))) + ); + r = _mm_hadd_ps(r, r); + return _mm_cvtss_f32(_mm_hadd_ps(r, r)); + }; }; template <> class SIMDComputer { -public: - using distance_t = pdx_distance_t; - using query_t = pdx_quantized_embedding_t; - using data_t = pdx_data_t; + public: + using distance_t = pdx_distance_t; + using query_t = pdx_quantized_embedding_t; + using data_t = pdx_data_t; - template - static void Vertical(const query_t *PDX_RESTRICT query, const data_t *PDX_RESTRICT data, size_t n_vectors, - size_t total_vectors, size_t start_dimension, size_t end_dimension, distance_t *distances_p, - const uint32_t *pruning_positions = nullptr) { - __m512i res; - __m512i vec2_u8; - __m512i vec1_u8; - __m512i diff_u8; - __m256i y_res; - __m256i y_vec2_u8; - __m256i y_vec1_u8; - __m256i y_diff_u8; - uint32_t *query_grouped = reinterpret_cast(query); - size_t dim_idx = start_dimension; - for (; dim_idx + 4 <= end_dimension; dim_idx += 4) { - uint32_t dimension_idx = dim_idx; - size_t offset_to_dimension_start = dimension_idx * total_vectors; - size_t i = 0; - if constexpr (!SKIP_PRUNED) { - // To load the query efficiently I will load it as uint32_t (4 bytes packed in 1 word) - uint32_t query_value = query_grouped[dimension_idx / 4]; - // And then broadcast it to the register - vec1_u8 = _mm512_set1_epi32(query_value); - for (; i + 16 <= n_vectors; i += 16) { - // Read 64 bytes of data (64 values) with 4 dimensions of 16 vectors - res = _mm512_load_si512(&distances_p[i]); - vec2_u8 = _mm512_loadu_si512( - &data[offset_to_dimension_start + i * 4]); // This 4 is because everytime I read 4 dimensions - diff_u8 = _mm512_or_si512(_mm512_subs_epu8(vec1_u8, vec2_u8), _mm512_subs_epu8(vec2_u8, vec1_u8)); - // We can use this asymmetric dot product as our values are mostly 7-bit - // Hence, the [sign] properties of the second operand are ignored - // As results will never be negative, it can be stored on distances_p[i] without issues - // and it saturates to MAX_INT - _mm512_store_epi32(&distances_p[i], _mm512_dpbusds_epi32(res, diff_u8, diff_u8)); - } - y_vec1_u8 = _mm256_set1_epi32(query_value); - for (; i + 8 <= n_vectors; i += 8) { - // Read 32 bytes of data (32 values) with 4 dimensions of 8 vectors - y_res = _mm256_load_epi32(&distances_p[i]); - y_vec2_u8 = _mm256_loadu_epi8( - &data[offset_to_dimension_start + i * 4]); // This 4 is because everytime I read 4 dimensions - y_diff_u8 = - _mm256_or_si256(_mm256_subs_epu8(y_vec1_u8, y_vec2_u8), _mm256_subs_epu8(y_vec2_u8, y_vec1_u8)); - _mm256_store_epi32(&distances_p[i], _mm256_dpbusds_epi32(y_res, y_diff_u8, y_diff_u8)); - } - } - // Scalar tail (vectors). - for (; i < n_vectors; ++i) { - size_t vector_idx = i; - if constexpr (SKIP_PRUNED) { - vector_idx = pruning_positions[vector_idx]; - } - int to_multiply_a = query[dimension_idx] - data[offset_to_dimension_start + (vector_idx * 4)]; - int to_multiply_b = query[dimension_idx + 1] - data[offset_to_dimension_start + (vector_idx * 4) + 1]; - int to_multiply_c = query[dimension_idx + 2] - data[offset_to_dimension_start + (vector_idx * 4) + 2]; - int to_multiply_d = query[dimension_idx + 3] - data[offset_to_dimension_start + (vector_idx * 4) + 3]; - distances_p[vector_idx] += (to_multiply_a * to_multiply_a) + (to_multiply_b * to_multiply_b) + - (to_multiply_c * to_multiply_c) + (to_multiply_d * to_multiply_d); - } - } - if (dim_idx < end_dimension) { - auto remaining = static_cast(end_dimension - dim_idx); - size_t offset = dim_idx * total_vectors; - for (size_t i = 0; i < n_vectors; ++i) { - size_t vector_idx = i; - if constexpr (SKIP_PRUNED) { - vector_idx = pruning_positions[vector_idx]; - } - for (uint32_t k = 0; k < remaining; ++k) { - int diff = query[dim_idx + k] - data[offset + vector_idx * remaining + k]; - distances_p[vector_idx] += diff * diff; - } - } - } - } + template + static void Vertical( + const query_t* PDX_RESTRICT query, + const data_t* PDX_RESTRICT data, + size_t n_vectors, + size_t total_vectors, + size_t start_dimension, + size_t end_dimension, + distance_t* distances_p, + const uint32_t* pruning_positions = nullptr + ) { + __m512i res; + __m512i vec2_u8; + __m512i vec1_u8; + __m512i diff_u8; + __m256i y_res; + __m256i y_vec2_u8; + __m256i y_vec1_u8; + __m256i y_diff_u8; + uint32_t* query_grouped = reinterpret_cast(query); + size_t dim_idx = start_dimension; + for (; dim_idx + 4 <= end_dimension; dim_idx += 4) { + uint32_t dimension_idx = dim_idx; + size_t offset_to_dimension_start = dimension_idx * total_vectors; + size_t i = 0; + if constexpr (!SKIP_PRUNED) { + // To load the query efficiently I will load it as uint32_t (4 bytes packed in 1 + // word) + uint32_t query_value = query_grouped[dimension_idx / 4]; + // And then broadcast it to the register + vec1_u8 = _mm512_set1_epi32(query_value); + for (; i + 16 <= n_vectors; i += 16) { + // Read 64 bytes of data (64 values) with 4 dimensions of 16 vectors + res = _mm512_load_si512(&distances_p[i]); + vec2_u8 = _mm512_loadu_si512(&data[offset_to_dimension_start + i * 4] + ); // This 4 is because everytime I read 4 dimensions + diff_u8 = _mm512_or_si512( + _mm512_subs_epu8(vec1_u8, vec2_u8), _mm512_subs_epu8(vec2_u8, vec1_u8) + ); + // We can use this asymmetric dot product as our values are mostly 7-bit + // Hence, the [sign] properties of the second operand are ignored + // As results will never be negative, it can be stored on distances_p[i] without + // issues and it saturates to MAX_INT + _mm512_store_epi32( + &distances_p[i], _mm512_dpbusds_epi32(res, diff_u8, diff_u8) + ); + } + y_vec1_u8 = _mm256_set1_epi32(query_value); + for (; i + 8 <= n_vectors; i += 8) { + // Read 32 bytes of data (32 values) with 4 dimensions of 8 vectors + y_res = _mm256_load_epi32(&distances_p[i]); + y_vec2_u8 = _mm256_loadu_epi8(&data[offset_to_dimension_start + i * 4] + ); // This 4 is because everytime I read 4 dimensions + y_diff_u8 = _mm256_or_si256( + _mm256_subs_epu8(y_vec1_u8, y_vec2_u8), + _mm256_subs_epu8(y_vec2_u8, y_vec1_u8) + ); + _mm256_store_epi32( + &distances_p[i], _mm256_dpbusds_epi32(y_res, y_diff_u8, y_diff_u8) + ); + } + } + // Scalar tail (vectors). + for (; i < n_vectors; ++i) { + size_t vector_idx = i; + if constexpr (SKIP_PRUNED) { + vector_idx = pruning_positions[vector_idx]; + } + int to_multiply_a = + query[dimension_idx] - data[offset_to_dimension_start + (vector_idx * 4)]; + int to_multiply_b = query[dimension_idx + 1] - + data[offset_to_dimension_start + (vector_idx * 4) + 1]; + int to_multiply_c = query[dimension_idx + 2] - + data[offset_to_dimension_start + (vector_idx * 4) + 2]; + int to_multiply_d = query[dimension_idx + 3] - + data[offset_to_dimension_start + (vector_idx * 4) + 3]; + distances_p[vector_idx] += + (to_multiply_a * to_multiply_a) + (to_multiply_b * to_multiply_b) + + (to_multiply_c * to_multiply_c) + (to_multiply_d * to_multiply_d); + } + } + if (dim_idx < end_dimension) { + auto remaining = static_cast(end_dimension - dim_idx); + size_t offset = dim_idx * total_vectors; + for (size_t i = 0; i < n_vectors; ++i) { + size_t vector_idx = i; + if constexpr (SKIP_PRUNED) { + vector_idx = pruning_positions[vector_idx]; + } + for (uint32_t k = 0; k < remaining; ++k) { + int diff = query[dim_idx + k] - data[offset + vector_idx * remaining + k]; + distances_p[vector_idx] += diff * diff; + } + } + } + } - static distance_t Horizontal(const query_t *PDX_RESTRICT vector1, const data_t *PDX_RESTRICT vector2, - size_t num_dimensions) { - __m512i d2_i32_vec = _mm512_setzero_si512(); - __m512i a_u8_vec, b_u8_vec; + static distance_t Horizontal( + const query_t* PDX_RESTRICT vector1, + const data_t* PDX_RESTRICT vector2, + size_t num_dimensions + ) { + __m512i d2_i32_vec = _mm512_setzero_si512(); + __m512i a_u8_vec, b_u8_vec; - simsimd_l2sq_u8_ice_cycle: - if (num_dimensions < 64) { - const __mmask64 mask = static_cast<__mmask64>(_bzhi_u64(0xFFFFFFFFFFFFFFFF, num_dimensions)); - a_u8_vec = _mm512_maskz_loadu_epi8(mask, vector1); - b_u8_vec = _mm512_maskz_loadu_epi8(mask, vector2); - num_dimensions = 0; - } else { - a_u8_vec = _mm512_loadu_si512(vector1); - b_u8_vec = _mm512_loadu_si512(vector2); - vector1 += 64, vector2 += 64, num_dimensions -= 64; - } + simsimd_l2sq_u8_ice_cycle: + if (num_dimensions < 64) { + const __mmask64 mask = + static_cast<__mmask64>(_bzhi_u64(0xFFFFFFFFFFFFFFFF, num_dimensions)); + a_u8_vec = _mm512_maskz_loadu_epi8(mask, vector1); + b_u8_vec = _mm512_maskz_loadu_epi8(mask, vector2); + num_dimensions = 0; + } else { + a_u8_vec = _mm512_loadu_si512(vector1); + b_u8_vec = _mm512_loadu_si512(vector2); + vector1 += 64, vector2 += 64, num_dimensions -= 64; + } - // Substracting unsigned vectors in AVX-512 is done by saturating subtraction: - __m512i d_u8_vec = _mm512_or_si512(_mm512_subs_epu8(a_u8_vec, b_u8_vec), _mm512_subs_epu8(b_u8_vec, a_u8_vec)); + // Substracting unsigned vectors in AVX-512 is done by saturating subtraction: + __m512i d_u8_vec = _mm512_or_si512( + _mm512_subs_epu8(a_u8_vec, b_u8_vec), _mm512_subs_epu8(b_u8_vec, a_u8_vec) + ); - // Multiply and accumulate at `int8` level which are actually uint7, accumulate at `int32` level: - d2_i32_vec = _mm512_dpbusds_epi32(d2_i32_vec, d_u8_vec, d_u8_vec); - if (num_dimensions) - goto simsimd_l2sq_u8_ice_cycle; - return _mm512_reduce_add_epi32(d2_i32_vec); - }; + // Multiply and accumulate at `int8` level which are actually uint7, accumulate at `int32` + // level: + d2_i32_vec = _mm512_dpbusds_epi32(d2_i32_vec, d_u8_vec, d_u8_vec); + if (num_dimensions) + goto simsimd_l2sq_u8_ice_cycle; + return _mm512_reduce_add_epi32(d2_i32_vec); + }; }; } // namespace PDX diff --git a/include/pdx/distance_computers/base_computers.hpp b/include/pdx/distance_computers/base_computers.hpp index b934dcf..4f7a2cc 100644 --- a/include/pdx/distance_computers/base_computers.hpp +++ b/include/pdx/distance_computers/base_computers.hpp @@ -29,31 +29,31 @@ class DistanceComputer {}; template <> class DistanceComputer { #if !defined(__ARM_NEON) && !defined(__AVX2__) && !defined(__AVX512F__) - using computer = ScalarComputer; + using computer = ScalarComputer; #else - using computer = SIMDComputer; + using computer = SIMDComputer; #endif -public: - constexpr static auto VerticalPruning = computer::Vertical; - constexpr static auto Vertical = computer::Vertical; + public: + constexpr static auto VerticalPruning = computer::Vertical; + constexpr static auto Vertical = computer::Vertical; - constexpr static auto Horizontal = computer::Horizontal; + constexpr static auto Horizontal = computer::Horizontal; }; template <> class DistanceComputer { #if !defined(__ARM_NEON) && !defined(__AVX2__) && !defined(__AVX512F__) - using computer = ScalarComputer; + using computer = ScalarComputer; #else - using computer = SIMDComputer; + using computer = SIMDComputer; #endif -public: - constexpr static auto VerticalPruning = computer::Vertical; - constexpr static auto Vertical = computer::Vertical; + public: + constexpr static auto VerticalPruning = computer::Vertical; + constexpr static auto Vertical = computer::Vertical; - constexpr static auto Horizontal = computer::Horizontal; + constexpr static auto Horizontal = computer::Horizontal; }; } // namespace PDX diff --git a/include/pdx/distance_computers/neon_computers.hpp b/include/pdx/distance_computers/neon_computers.hpp index cb5c725..5dffd1d 100644 --- a/include/pdx/distance_computers/neon_computers.hpp +++ b/include/pdx/distance_computers/neon_computers.hpp @@ -1,8 +1,8 @@ #pragma once -#include #include "arm_neon.h" #include "pdx/common.hpp" +#include namespace PDX { @@ -11,146 +11,173 @@ class SIMDComputer {}; template <> class SIMDComputer { -public: - using distance_t = pdx_distance_t; - using query_t = pdx_quantized_embedding_t; - using data_t = pdx_data_t; + public: + using distance_t = pdx_distance_t; + using query_t = pdx_quantized_embedding_t; + using data_t = pdx_data_t; - template - static void Vertical(const query_t *PDX_RESTRICT query, const data_t *PDX_RESTRICT data, size_t n_vectors, - size_t total_vectors, size_t start_dimension, size_t end_dimension, distance_t *distances_p, - const uint32_t *pruning_positions = nullptr) { - size_t dimensions_jump_factor = total_vectors; - for (size_t dimension_idx = start_dimension; dimension_idx < end_dimension; ++dimension_idx) { - size_t offset_to_dimension_start = dimension_idx * dimensions_jump_factor; - for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { - auto true_vector_idx = vector_idx; - if constexpr (SKIP_PRUNED) { - true_vector_idx = pruning_positions[vector_idx]; - } - distance_t to_multiply = query[dimension_idx] - data[offset_to_dimension_start + true_vector_idx]; - distances_p[true_vector_idx] += to_multiply * to_multiply; - } - } - } + template + static void Vertical( + const query_t* PDX_RESTRICT query, + const data_t* PDX_RESTRICT data, + size_t n_vectors, + size_t total_vectors, + size_t start_dimension, + size_t end_dimension, + distance_t* distances_p, + const uint32_t* pruning_positions = nullptr + ) { + size_t dimensions_jump_factor = total_vectors; + for (size_t dimension_idx = start_dimension; dimension_idx < end_dimension; + ++dimension_idx) { + size_t offset_to_dimension_start = dimension_idx * dimensions_jump_factor; + for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { + auto true_vector_idx = vector_idx; + if constexpr (SKIP_PRUNED) { + true_vector_idx = pruning_positions[vector_idx]; + } + distance_t to_multiply = + query[dimension_idx] - data[offset_to_dimension_start + true_vector_idx]; + distances_p[true_vector_idx] += to_multiply * to_multiply; + } + } + } - static distance_t Horizontal(const query_t *PDX_RESTRICT vector1, const data_t *PDX_RESTRICT vector2, - size_t num_dimensions) { + static distance_t Horizontal( + const query_t* PDX_RESTRICT vector1, + const data_t* PDX_RESTRICT vector2, + size_t num_dimensions + ) { #if defined(__APPLE__) - distance_t distance = 0.0; + distance_t distance = 0.0; #pragma clang loop vectorize(enable) - for (size_t i = 0; i < num_dimensions; ++i) { - distance_t diff = vector1[i] - vector2[i]; - distance += diff * diff; - } - return distance; + for (size_t i = 0; i < num_dimensions; ++i) { + distance_t diff = vector1[i] - vector2[i]; + distance += diff * diff; + } + return distance; #else - float32x4_t sum_vec = vdupq_n_f32(0); - size_t i = 0; - for (; i + 4 <= num_dimensions; i += 4) { - float32x4_t a_vec = vld1q_f32(vector1 + i); - float32x4_t b_vec = vld1q_f32(vector2 + i); - float32x4_t diff_vec = vsubq_f32(a_vec, b_vec); - sum_vec = vfmaq_f32(sum_vec, diff_vec, diff_vec); - } - distance_t distance = vaddvq_f32(sum_vec); - for (; i < num_dimensions; ++i) { - distance_t diff = vector1[i] - vector2[i]; - distance += diff * diff; - } - return distance; + float32x4_t sum_vec = vdupq_n_f32(0); + size_t i = 0; + for (; i + 4 <= num_dimensions; i += 4) { + float32x4_t a_vec = vld1q_f32(vector1 + i); + float32x4_t b_vec = vld1q_f32(vector2 + i); + float32x4_t diff_vec = vsubq_f32(a_vec, b_vec); + sum_vec = vfmaq_f32(sum_vec, diff_vec, diff_vec); + } + distance_t distance = vaddvq_f32(sum_vec); + for (; i < num_dimensions; ++i) { + distance_t diff = vector1[i] - vector2[i]; + distance += diff * diff; + } + return distance; #endif - }; + }; }; // Equivalent of vdotq_u32(acc, a, a) for squared accumulation. static inline uint32x4_t squared_dot_accumulate(uint32x4_t acc, uint8x16_t a) { #ifdef __ARM_FEATURE_DOTPROD - return vdotq_u32(acc, a, a); + return vdotq_u32(acc, a, a); #else - uint16x8_t sq_lo = vmull_u8(vget_low_u8(a), vget_low_u8(a)); - uint16x8_t sq_hi = vmull_u8(vget_high_u8(a), vget_high_u8(a)); - uint32x4_t partial_lo = vpaddlq_u16(sq_lo); - uint32x4_t partial_hi = vpaddlq_u16(sq_hi); - return vaddq_u32(acc, vpaddq_u32(partial_lo, partial_hi)); + uint16x8_t sq_lo = vmull_u8(vget_low_u8(a), vget_low_u8(a)); + uint16x8_t sq_hi = vmull_u8(vget_high_u8(a), vget_high_u8(a)); + uint32x4_t partial_lo = vpaddlq_u16(sq_lo); + uint32x4_t partial_hi = vpaddlq_u16(sq_hi); + return vaddq_u32(acc, vpaddq_u32(partial_lo, partial_hi)); #endif } template <> class SIMDComputer { -public: - using distance_t = pdx_distance_t; - using query_t = pdx_quantized_embedding_t; - using data_t = pdx_data_t; + public: + using distance_t = pdx_distance_t; + using query_t = pdx_quantized_embedding_t; + using data_t = pdx_data_t; - template - static void Vertical(const query_t *PDX_RESTRICT query, const data_t *PDX_RESTRICT data, size_t n_vectors, - size_t total_vectors, size_t start_dimension, size_t end_dimension, distance_t *distances_p, - const uint32_t *pruning_positions = nullptr) { - size_t dim_idx = start_dimension; - for (; dim_idx + 4 <= end_dimension; dim_idx += 4) { - uint32_t dimension_idx = dim_idx; - uint8x8_t vals = vld1_u8(&query[dimension_idx]); - size_t offset_to_dimension_start = dimension_idx * total_vectors; - size_t i = 0; - if constexpr (!SKIP_PRUNED) { - const uint8x16_t idx = {0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3}; - const uint8x16_t vec1_u8 = vqtbl1q_u8(vcombine_u8(vals, vals), idx); - for (; i + 4 <= n_vectors; i += 4) { - // Read 16 bytes of data (16 values) with 4 dimensions of 4 vectors - uint32x4_t res = vld1q_u32(&distances_p[i]); - uint8x16_t vec2_u8 = vld1q_u8( - &data[offset_to_dimension_start + i * 4]); // This 4 is because everytime I read 4 dimensions - uint8x16_t diff_u8 = vabdq_u8(vec1_u8, vec2_u8); - vst1q_u32(&distances_p[i], squared_dot_accumulate(res, diff_u8)); - } - } - for (; i < n_vectors; ++i) { - size_t vector_idx = i; - if constexpr (SKIP_PRUNED) { - vector_idx = pruning_positions[vector_idx]; - } - int to_multiply_a = query[dimension_idx] - data[offset_to_dimension_start + (vector_idx * 4)]; - int to_multiply_b = query[dimension_idx + 1] - data[offset_to_dimension_start + (vector_idx * 4) + 1]; - int to_multiply_c = query[dimension_idx + 2] - data[offset_to_dimension_start + (vector_idx * 4) + 2]; - int to_multiply_d = query[dimension_idx + 3] - data[offset_to_dimension_start + (vector_idx * 4) + 3]; - distances_p[vector_idx] += (to_multiply_a * to_multiply_a) + (to_multiply_b * to_multiply_b) + - (to_multiply_c * to_multiply_c) + (to_multiply_d * to_multiply_d); - } - } - if (dim_idx < end_dimension) { - auto remaining = static_cast(end_dimension - dim_idx); - size_t offset = dim_idx * total_vectors; - for (size_t i = 0; i < n_vectors; ++i) { - size_t vector_idx = i; - if constexpr (SKIP_PRUNED) { - vector_idx = pruning_positions[vector_idx]; - } - for (uint32_t k = 0; k < remaining; ++k) { - int diff = query[dim_idx + k] - data[offset + vector_idx * remaining + k]; - distances_p[vector_idx] += diff * diff; - } - } - } - } + template + static void Vertical( + const query_t* PDX_RESTRICT query, + const data_t* PDX_RESTRICT data, + size_t n_vectors, + size_t total_vectors, + size_t start_dimension, + size_t end_dimension, + distance_t* distances_p, + const uint32_t* pruning_positions = nullptr + ) { + size_t dim_idx = start_dimension; + for (; dim_idx + 4 <= end_dimension; dim_idx += 4) { + uint32_t dimension_idx = dim_idx; + uint8x8_t vals = vld1_u8(&query[dimension_idx]); + size_t offset_to_dimension_start = dimension_idx * total_vectors; + size_t i = 0; + if constexpr (!SKIP_PRUNED) { + const uint8x16_t idx = {0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3}; + const uint8x16_t vec1_u8 = vqtbl1q_u8(vcombine_u8(vals, vals), idx); + for (; i + 4 <= n_vectors; i += 4) { + // Read 16 bytes of data (16 values) with 4 dimensions of 4 vectors + uint32x4_t res = vld1q_u32(&distances_p[i]); + uint8x16_t vec2_u8 = vld1q_u8(&data[offset_to_dimension_start + i * 4] + ); // This 4 is because everytime I read 4 dimensions + uint8x16_t diff_u8 = vabdq_u8(vec1_u8, vec2_u8); + vst1q_u32(&distances_p[i], squared_dot_accumulate(res, diff_u8)); + } + } + for (; i < n_vectors; ++i) { + size_t vector_idx = i; + if constexpr (SKIP_PRUNED) { + vector_idx = pruning_positions[vector_idx]; + } + int to_multiply_a = + query[dimension_idx] - data[offset_to_dimension_start + (vector_idx * 4)]; + int to_multiply_b = query[dimension_idx + 1] - + data[offset_to_dimension_start + (vector_idx * 4) + 1]; + int to_multiply_c = query[dimension_idx + 2] - + data[offset_to_dimension_start + (vector_idx * 4) + 2]; + int to_multiply_d = query[dimension_idx + 3] - + data[offset_to_dimension_start + (vector_idx * 4) + 3]; + distances_p[vector_idx] += + (to_multiply_a * to_multiply_a) + (to_multiply_b * to_multiply_b) + + (to_multiply_c * to_multiply_c) + (to_multiply_d * to_multiply_d); + } + } + if (dim_idx < end_dimension) { + auto remaining = static_cast(end_dimension - dim_idx); + size_t offset = dim_idx * total_vectors; + for (size_t i = 0; i < n_vectors; ++i) { + size_t vector_idx = i; + if constexpr (SKIP_PRUNED) { + vector_idx = pruning_positions[vector_idx]; + } + for (uint32_t k = 0; k < remaining; ++k) { + int diff = query[dim_idx + k] - data[offset + vector_idx * remaining + k]; + distances_p[vector_idx] += diff * diff; + } + } + } + } - static distance_t Horizontal(const query_t *PDX_RESTRICT vector1, const data_t *PDX_RESTRICT vector2, - size_t num_dimensions) { - uint32x4_t sum_vec = vdupq_n_u32(0); - size_t i = 0; - for (; i + 16 <= num_dimensions; i += 16) { - uint8x16_t a_vec = vld1q_u8(vector1 + i); - uint8x16_t b_vec = vld1q_u8(vector2 + i); - uint8x16_t d_vec = vabdq_u8(a_vec, b_vec); - sum_vec = squared_dot_accumulate(sum_vec, d_vec); - } - distance_t distance = vaddvq_u32(sum_vec); - for (; i < num_dimensions; ++i) { - int n = static_cast(vector1[i]) - vector2[i]; - distance += n * n; - } - return distance; - }; + static distance_t Horizontal( + const query_t* PDX_RESTRICT vector1, + const data_t* PDX_RESTRICT vector2, + size_t num_dimensions + ) { + uint32x4_t sum_vec = vdupq_n_u32(0); + size_t i = 0; + for (; i + 16 <= num_dimensions; i += 16) { + uint8x16_t a_vec = vld1q_u8(vector1 + i); + uint8x16_t b_vec = vld1q_u8(vector2 + i); + uint8x16_t d_vec = vabdq_u8(a_vec, b_vec); + sum_vec = squared_dot_accumulate(sum_vec, d_vec); + } + distance_t distance = vaddvq_u32(sum_vec); + for (; i < num_dimensions; ++i) { + int n = static_cast(vector1[i]) - vector2[i]; + distance += n * n; + } + return distance; + }; }; } // namespace PDX diff --git a/include/pdx/distance_computers/scalar_computers.hpp b/include/pdx/distance_computers/scalar_computers.hpp index 371c3bd..33071f6 100644 --- a/include/pdx/distance_computers/scalar_computers.hpp +++ b/include/pdx/distance_computers/scalar_computers.hpp @@ -1,7 +1,7 @@ #pragma once -#include #include "pdx/common.hpp" +#include namespace PDX { @@ -10,93 +10,118 @@ class ScalarComputer {}; template <> class ScalarComputer { -public: - using distance_t = pdx_distance_t; - using query_t = pdx_quantized_embedding_t; - using data_t = pdx_data_t; + public: + using distance_t = pdx_distance_t; + using query_t = pdx_quantized_embedding_t; + using data_t = pdx_data_t; - template - static void Vertical(const query_t *PDX_RESTRICT query, const data_t *PDX_RESTRICT data, size_t n_vectors, - size_t total_vectors, size_t start_dimension, size_t end_dimension, distance_t *distances_p, - const uint32_t *pruning_positions = nullptr) { - size_t dimensions_jump_factor = total_vectors; - for (size_t dimension_idx = start_dimension; dimension_idx < end_dimension; ++dimension_idx) { - size_t offset_to_dimension_start = dimension_idx * dimensions_jump_factor; - for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { - auto true_vector_idx = vector_idx; - if constexpr (SKIP_PRUNED) { - true_vector_idx = pruning_positions[vector_idx]; - } - distance_t to_multiply = query[dimension_idx] - data[offset_to_dimension_start + true_vector_idx]; - distances_p[true_vector_idx] += to_multiply * to_multiply; - } - } - } + template + static void Vertical( + const query_t* PDX_RESTRICT query, + const data_t* PDX_RESTRICT data, + size_t n_vectors, + size_t total_vectors, + size_t start_dimension, + size_t end_dimension, + distance_t* distances_p, + const uint32_t* pruning_positions = nullptr + ) { + size_t dimensions_jump_factor = total_vectors; + for (size_t dimension_idx = start_dimension; dimension_idx < end_dimension; + ++dimension_idx) { + size_t offset_to_dimension_start = dimension_idx * dimensions_jump_factor; + for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { + auto true_vector_idx = vector_idx; + if constexpr (SKIP_PRUNED) { + true_vector_idx = pruning_positions[vector_idx]; + } + distance_t to_multiply = + query[dimension_idx] - data[offset_to_dimension_start + true_vector_idx]; + distances_p[true_vector_idx] += to_multiply * to_multiply; + } + } + } - static distance_t Horizontal(const query_t *PDX_RESTRICT vector1, const data_t *PDX_RESTRICT vector2, - size_t num_dimensions) { - distance_t distance = 0.0; + static distance_t Horizontal( + const query_t* PDX_RESTRICT vector1, + const data_t* PDX_RESTRICT vector2, + size_t num_dimensions + ) { + distance_t distance = 0.0; #pragma clang loop vectorize(enable) - for (size_t dimension_idx = 0; dimension_idx < num_dimensions; ++dimension_idx) { - distance_t to_multiply = vector1[dimension_idx] - vector2[dimension_idx]; - distance += to_multiply * to_multiply; - } - return distance; - }; + for (size_t dimension_idx = 0; dimension_idx < num_dimensions; ++dimension_idx) { + distance_t to_multiply = vector1[dimension_idx] - vector2[dimension_idx]; + distance += to_multiply * to_multiply; + } + return distance; + }; }; template <> class ScalarComputer { -public: - using distance_t = pdx_distance_t; - using query_t = pdx_quantized_embedding_t; - using data_t = pdx_data_t; + public: + using distance_t = pdx_distance_t; + using query_t = pdx_quantized_embedding_t; + using data_t = pdx_data_t; - template - static void Vertical(const query_t *PDX_RESTRICT query, const data_t *PDX_RESTRICT data, size_t n_vectors, - size_t total_vectors, size_t start_dimension, size_t end_dimension, distance_t *distances_p, - const uint32_t *pruning_positions = nullptr) { - size_t dim_idx = start_dimension; - for (; dim_idx + 4 <= end_dimension; dim_idx += 4) { - uint32_t dimension_idx = dim_idx; - size_t offset_to_dimension_start = dimension_idx * total_vectors; - for (size_t i = 0; i < n_vectors; ++i) { - size_t vector_idx = i; - if constexpr (SKIP_PRUNED) { - vector_idx = pruning_positions[vector_idx]; - } - int da = query[dimension_idx] - data[offset_to_dimension_start + (vector_idx * 4)]; - int db = query[dimension_idx + 1] - data[offset_to_dimension_start + (vector_idx * 4) + 1]; - int dc = query[dimension_idx + 2] - data[offset_to_dimension_start + (vector_idx * 4) + 2]; - int dd = query[dimension_idx + 3] - data[offset_to_dimension_start + (vector_idx * 4) + 3]; - distances_p[vector_idx] += (da * da) + (db * db) + (dc * dc) + (dd * dd); - } - } - if (dim_idx < end_dimension) { - auto remaining = static_cast(end_dimension - dim_idx); - size_t offset = dim_idx * total_vectors; - for (size_t i = 0; i < n_vectors; ++i) { - size_t vector_idx = i; - if constexpr (SKIP_PRUNED) { - vector_idx = pruning_positions[vector_idx]; - } - for (uint32_t k = 0; k < remaining; ++k) { - int diff = query[dim_idx + k] - data[offset + vector_idx * remaining + k]; - distances_p[vector_idx] += diff * diff; - } - } - } - } + template + static void Vertical( + const query_t* PDX_RESTRICT query, + const data_t* PDX_RESTRICT data, + size_t n_vectors, + size_t total_vectors, + size_t start_dimension, + size_t end_dimension, + distance_t* distances_p, + const uint32_t* pruning_positions = nullptr + ) { + size_t dim_idx = start_dimension; + for (; dim_idx + 4 <= end_dimension; dim_idx += 4) { + uint32_t dimension_idx = dim_idx; + size_t offset_to_dimension_start = dimension_idx * total_vectors; + for (size_t i = 0; i < n_vectors; ++i) { + size_t vector_idx = i; + if constexpr (SKIP_PRUNED) { + vector_idx = pruning_positions[vector_idx]; + } + int da = query[dimension_idx] - data[offset_to_dimension_start + (vector_idx * 4)]; + int db = query[dimension_idx + 1] - + data[offset_to_dimension_start + (vector_idx * 4) + 1]; + int dc = query[dimension_idx + 2] - + data[offset_to_dimension_start + (vector_idx * 4) + 2]; + int dd = query[dimension_idx + 3] - + data[offset_to_dimension_start + (vector_idx * 4) + 3]; + distances_p[vector_idx] += (da * da) + (db * db) + (dc * dc) + (dd * dd); + } + } + if (dim_idx < end_dimension) { + auto remaining = static_cast(end_dimension - dim_idx); + size_t offset = dim_idx * total_vectors; + for (size_t i = 0; i < n_vectors; ++i) { + size_t vector_idx = i; + if constexpr (SKIP_PRUNED) { + vector_idx = pruning_positions[vector_idx]; + } + for (uint32_t k = 0; k < remaining; ++k) { + int diff = query[dim_idx + k] - data[offset + vector_idx * remaining + k]; + distances_p[vector_idx] += diff * diff; + } + } + } + } - static distance_t Horizontal(const query_t *PDX_RESTRICT vector1, const data_t *PDX_RESTRICT vector2, - size_t num_dimensions) { - distance_t distance = 0; - for (size_t i = 0; i < num_dimensions; ++i) { - int diff = static_cast(vector1[i]) - static_cast(vector2[i]); - distance += diff * diff; - } - return distance; - }; + static distance_t Horizontal( + const query_t* PDX_RESTRICT vector1, + const data_t* PDX_RESTRICT vector2, + size_t num_dimensions + ) { + distance_t distance = 0; + for (size_t i = 0; i < num_dimensions; ++i) { + int diff = static_cast(vector1[i]) - static_cast(vector2[i]); + distance += diff * diff; + } + return distance; + }; }; } // namespace PDX diff --git a/include/pdx/index.hpp b/include/pdx/index.hpp index bcdfa14..6f4460f 100644 --- a/include/pdx/index.hpp +++ b/include/pdx/index.hpp @@ -9,486 +9,547 @@ #include #include +#include "pdx/clustering.hpp" #include "pdx/common.hpp" #include "pdx/ivf_wrapper.hpp" +#include "pdx/layout.hpp" #include "pdx/pruners/adsampling.hpp" +#include "pdx/quantizers/scalar.hpp" #include "pdx/searcher.hpp" -#include "pdx/clustering.hpp" -#include "pdx/layout.hpp" #include "pdx/utils.hpp" -#include "pdx/quantizers/scalar.hpp" namespace PDX { struct PDXIndexConfig { - uint32_t num_dimensions; - DistanceMetric distance_metric = DistanceMetric::L2SQ; - uint32_t seed = 42; - uint32_t num_clusters = 0; // 0 = auto-compute from num_embeddings - uint32_t num_meso_clusters = 0; - bool normalize = false; - float sampling_fraction = 0.0f; // 0 = auto (1.0 if small dataset, 0.3 otherwise) - uint32_t kmeans_iters = 10; + uint32_t num_dimensions; + DistanceMetric distance_metric = DistanceMetric::L2SQ; + uint32_t seed = 42; + uint32_t num_clusters = 0; // 0 = auto-compute from num_embeddings + uint32_t num_meso_clusters = 0; + bool normalize = false; + float sampling_fraction = 0.0f; // 0 = auto (1.0 if small dataset, 0.3 otherwise) + uint32_t kmeans_iters = 10; }; class IPDXIndex { -public: - virtual ~IPDXIndex() = default; - virtual std::vector Search(const float *query_embedding, size_t knn) const = 0; - virtual void BuildIndex(const float *embeddings, size_t num_embeddings) = 0; - virtual void SetNProbe(uint32_t n_probe) const = 0; - virtual void Save(const std::string &path) const = 0; - virtual void Restore(const std::string &path) = 0; - virtual uint32_t GetNumDimensions() const = 0; - virtual uint32_t GetNumClusters() const = 0; - virtual size_t GetInMemorySizeInBytes() const = 0; + public: + virtual ~IPDXIndex() = default; + virtual std::vector Search(const float* query_embedding, size_t knn) const = 0; + virtual void BuildIndex(const float* embeddings, size_t num_embeddings) = 0; + virtual void SetNProbe(uint32_t n_probe) const = 0; + virtual void Save(const std::string& path) const = 0; + virtual void Restore(const std::string& path) = 0; + virtual uint32_t GetNumDimensions() const = 0; + virtual uint32_t GetNumClusters() const = 0; + virtual size_t GetInMemorySizeInBytes() const = 0; }; template class PDXIndex : public IPDXIndex { -public: - using embedding_storage_t = PDX::pdx_data_t; - -private: - PDXIndexConfig config {}; - PDX::IVF index; - std::unique_ptr pruner; - std::unique_ptr> searcher; - - static constexpr PDXIndexType GetIndexType() { - if constexpr (Q == F32) return PDXIndexType::PDX_F32; - else return PDXIndexType::PDX_U8; - } - -public: - PDXIndex() = default; - - explicit PDXIndex(PDXIndexConfig config) : config(config) { - pruner = std::make_unique(config.num_dimensions, config.seed); - } - - void Save(const std::string &path) const override { - std::ofstream out(path, std::ios::binary); - - // Index type flag - uint8_t type_flag = static_cast(GetIndexType()); - out.write(reinterpret_cast(&type_flag), sizeof(uint8_t)); - - // Rotation matrix - const auto &matrix = pruner->GetMatrix(); - uint32_t matrix_rows = static_cast(matrix.rows()); - uint32_t matrix_cols = static_cast(matrix.cols()); - out.write(reinterpret_cast(&matrix_rows), sizeof(uint32_t)); - out.write(reinterpret_cast(&matrix_cols), sizeof(uint32_t)); - out.write(reinterpret_cast(matrix.data()), - sizeof(float) * matrix_rows * matrix_cols); - - // IVF data - index.Save(out); - } - - void Restore(const std::string &path) override { - auto buffer = MmapFile(path); - char *ptr = buffer.get(); - - // Index type flag - ptr += sizeof(uint8_t); - - // Rotation matrix - uint32_t matrix_rows = *reinterpret_cast(ptr); - ptr += sizeof(uint32_t); - uint32_t matrix_cols = *reinterpret_cast(ptr); - ptr += sizeof(uint32_t); - auto *matrix_data = reinterpret_cast(ptr); - ptr += sizeof(float) * matrix_rows * matrix_cols; - - // Load IVF data - index.Load(ptr); - - // Create pruner and searcher - pruner = std::make_unique(index.num_dimensions, matrix_data); - searcher = std::make_unique>(index, *pruner); - } - - std::vector Search(const float *query_embedding, size_t knn) const override { - return searcher->Search(query_embedding, knn); - } - - void SetNProbe(uint32_t n_probe) const override { - searcher->SetNProbe(n_probe); - } - - const PDX::PDXearch &GetSearcher() const { - return *searcher; - } - - uint32_t GetNumDimensions() const override { - return index.num_dimensions; - } - - uint32_t GetNumClusters() const override { - return index.num_clusters; - } - - size_t GetInMemorySizeInBytes() const override { - size_t size = sizeof(*this); - // IVF heap allocations (sizeof(IVF) is inline in sizeof(*this)) - size += index.GetInMemorySizeInBytes() - sizeof(index); - // Pruner: rotation matrix (D x D floats) + ratios vector (D floats) - if (pruner) { - size += sizeof(*pruner); - const auto &m = pruner->GetMatrix(); - size += static_cast(m.rows()) * m.cols() * sizeof(float); - size += pruner->num_dimensions * sizeof(float); - } - // Searcher: cluster_offsets array - if (searcher) { - size += sizeof(*searcher); - size += index.num_clusters * sizeof(size_t); - } - return size; - } - - void BuildIndex(const float *const embeddings, const size_t num_embeddings) override { - std::vector row_ids(num_embeddings); - std::iota(row_ids.begin(), row_ids.end(), 0); - BuildIndex(row_ids.data(), embeddings, num_embeddings); - } - - void BuildIndex(const size_t *const row_ids, const float *const embeddings, const size_t num_embeddings) { - const auto num_dimensions = config.num_dimensions; - auto num_clusters = config.num_clusters; - if (num_clusters == 0) { - num_clusters = ComputeNumberOfClusters(num_embeddings); - } - const bool normalize = config.normalize || DistanceMetricRequiresNormalization(config.distance_metric); - - assert(num_dimensions > 0); - assert(num_embeddings > 0); - assert(pruner); - - // Preprocess: normalize (if needed) + rotate. - const size_t total_floats = num_embeddings * num_dimensions; - std::unique_ptr normalized; - const float *rotation_input = embeddings; - if (normalize) { - normalized.reset(new float[total_floats]); - std::memcpy(normalized.get(), embeddings, total_floats * sizeof(float)); - PDX::Quantizer quantizer(num_dimensions); - for (size_t i = 0; i < num_embeddings; i++) { - quantizer.NormalizeQuery(normalized.get() + i * num_dimensions, - normalized.get() + i * num_dimensions); - } - rotation_input = normalized.get(); - } - std::unique_ptr preprocessed(new float[total_floats]); - pruner->PreprocessEmbeddings(rotation_input, preprocessed.get(), num_embeddings); - - float quantization_base = 0.0f; - float quantization_scale = 1.0f; - if constexpr (Q == PDX::U8) { - const auto params = PDX::ScalarQuantizer::ComputeQuantizationParams( - preprocessed.get(), static_cast(num_embeddings) * num_dimensions); - quantization_base = params.quantization_base; - quantization_scale = params.quantization_scale; - index = PDX::IVF(num_dimensions, num_embeddings, num_clusters, - normalize, quantization_scale, quantization_base); - } else { - index = PDX::IVF(num_dimensions, num_embeddings, num_clusters, normalize); - } - - KMeansResult kmeans_result = ComputeKMeans(preprocessed.get(), num_embeddings, num_dimensions, - num_clusters, config.distance_metric, config.seed, - config.normalize, config.sampling_fraction, config.kmeans_iters); - index.centroids = std::move(kmeans_result.centroids); - - size_t max_cluster_size = 0; - for (size_t i = 0; i < num_clusters; i++) { - max_cluster_size = std::max(max_cluster_size, kmeans_result.assignments[i].size()); - } - std::unique_ptr tmp_cluster_embeddings( - new embedding_storage_t[static_cast(max_cluster_size) * num_dimensions]); - - for (size_t cluster_idx = 0; cluster_idx < num_clusters; cluster_idx++) { - const auto cluster_size = kmeans_result.assignments[cluster_idx].size(); - auto &cluster = index.clusters.emplace_back(cluster_size, num_dimensions); - - for (size_t position_in_cluster = 0; position_in_cluster < cluster_size; position_in_cluster++) { - const auto embedding_idx = kmeans_result.assignments[cluster_idx][position_in_cluster]; - cluster.indices[position_in_cluster] = row_ids[embedding_idx]; - - if constexpr (Q == PDX::U8) { - PDX::ScalarQuantizer quantizer(num_dimensions); - quantizer.QuantizeEmbedding(preprocessed.get() + (embedding_idx * num_dimensions), - quantization_base, quantization_scale, - tmp_cluster_embeddings.get() + (position_in_cluster * num_dimensions)); - } else { - std::memcpy(tmp_cluster_embeddings.get() + (position_in_cluster * num_dimensions), - preprocessed.get() + (embedding_idx * num_dimensions), - num_dimensions * sizeof(float)); - } - } - StoreClusterEmbeddings(cluster, index, tmp_cluster_embeddings.get(), - cluster_size); - } - - searcher = std::make_unique>(index, *pruner); - } - + public: + using embedding_storage_t = PDX::pdx_data_t; + + private: + PDXIndexConfig config{}; + PDX::IVF index; + std::unique_ptr pruner; + std::unique_ptr> searcher; + + static constexpr PDXIndexType GetIndexType() { + if constexpr (Q == F32) + return PDXIndexType::PDX_F32; + else + return PDXIndexType::PDX_U8; + } + + public: + PDXIndex() = default; + + explicit PDXIndex(PDXIndexConfig config) : config(config) { + pruner = std::make_unique(config.num_dimensions, config.seed); + } + + void Save(const std::string& path) const override { + std::ofstream out(path, std::ios::binary); + + // Index type flag + uint8_t type_flag = static_cast(GetIndexType()); + out.write(reinterpret_cast(&type_flag), sizeof(uint8_t)); + + // Rotation matrix + const auto& matrix = pruner->GetMatrix(); + uint32_t matrix_rows = static_cast(matrix.rows()); + uint32_t matrix_cols = static_cast(matrix.cols()); + out.write(reinterpret_cast(&matrix_rows), sizeof(uint32_t)); + out.write(reinterpret_cast(&matrix_cols), sizeof(uint32_t)); + out.write( + reinterpret_cast(matrix.data()), sizeof(float) * matrix_rows * matrix_cols + ); + + // IVF data + index.Save(out); + } + + void Restore(const std::string& path) override { + auto buffer = MmapFile(path); + char* ptr = buffer.get(); + + // Index type flag + ptr += sizeof(uint8_t); + + // Rotation matrix + uint32_t matrix_rows = *reinterpret_cast(ptr); + ptr += sizeof(uint32_t); + uint32_t matrix_cols = *reinterpret_cast(ptr); + ptr += sizeof(uint32_t); + auto* matrix_data = reinterpret_cast(ptr); + ptr += sizeof(float) * matrix_rows * matrix_cols; + + // Load IVF data + index.Load(ptr); + + // Create pruner and searcher + pruner = std::make_unique(index.num_dimensions, matrix_data); + searcher = std::make_unique>(index, *pruner); + } + + std::vector Search(const float* query_embedding, size_t knn) const override { + return searcher->Search(query_embedding, knn); + } + + void SetNProbe(uint32_t n_probe) const override { searcher->SetNProbe(n_probe); } + + const PDX::PDXearch& GetSearcher() const { return *searcher; } + + uint32_t GetNumDimensions() const override { return index.num_dimensions; } + + uint32_t GetNumClusters() const override { return index.num_clusters; } + + size_t GetInMemorySizeInBytes() const override { + size_t size = sizeof(*this); + // IVF heap allocations (sizeof(IVF) is inline in sizeof(*this)) + size += index.GetInMemorySizeInBytes() - sizeof(index); + // Pruner: rotation matrix (D x D floats) + ratios vector (D floats) + if (pruner) { + size += sizeof(*pruner); + const auto& m = pruner->GetMatrix(); + size += static_cast(m.rows()) * m.cols() * sizeof(float); + size += pruner->num_dimensions * sizeof(float); + } + // Searcher: cluster_offsets array + if (searcher) { + size += sizeof(*searcher); + size += index.num_clusters * sizeof(size_t); + } + return size; + } + + void BuildIndex(const float* const embeddings, const size_t num_embeddings) override { + std::vector row_ids(num_embeddings); + std::iota(row_ids.begin(), row_ids.end(), 0); + BuildIndex(row_ids.data(), embeddings, num_embeddings); + } + + void BuildIndex( + const size_t* const row_ids, + const float* const embeddings, + const size_t num_embeddings + ) { + const auto num_dimensions = config.num_dimensions; + auto num_clusters = config.num_clusters; + if (num_clusters == 0) { + num_clusters = ComputeNumberOfClusters(num_embeddings); + } + const bool normalize = + config.normalize || DistanceMetricRequiresNormalization(config.distance_metric); + + assert(num_dimensions > 0); + assert(num_embeddings > 0); + assert(pruner); + + // Preprocess: normalize (if needed) + rotate. + const size_t total_floats = num_embeddings * num_dimensions; + std::unique_ptr normalized; + const float* rotation_input = embeddings; + if (normalize) { + normalized.reset(new float[total_floats]); + std::memcpy(normalized.get(), embeddings, total_floats * sizeof(float)); + PDX::Quantizer quantizer(num_dimensions); + for (size_t i = 0; i < num_embeddings; i++) { + quantizer.NormalizeQuery( + normalized.get() + i * num_dimensions, normalized.get() + i * num_dimensions + ); + } + rotation_input = normalized.get(); + } + std::unique_ptr preprocessed(new float[total_floats]); + pruner->PreprocessEmbeddings(rotation_input, preprocessed.get(), num_embeddings); + + float quantization_base = 0.0f; + float quantization_scale = 1.0f; + if constexpr (Q == PDX::U8) { + const auto params = PDX::ScalarQuantizer::ComputeQuantizationParams( + preprocessed.get(), static_cast(num_embeddings) * num_dimensions + ); + quantization_base = params.quantization_base; + quantization_scale = params.quantization_scale; + index = PDX::IVF( + num_dimensions, + num_embeddings, + num_clusters, + normalize, + quantization_scale, + quantization_base + ); + } else { + index = PDX::IVF(num_dimensions, num_embeddings, num_clusters, normalize); + } + + KMeansResult kmeans_result = ComputeKMeans( + preprocessed.get(), + num_embeddings, + num_dimensions, + num_clusters, + config.distance_metric, + config.seed, + config.normalize, + config.sampling_fraction, + config.kmeans_iters + ); + index.centroids = std::move(kmeans_result.centroids); + + size_t max_cluster_size = 0; + for (size_t i = 0; i < num_clusters; i++) { + max_cluster_size = std::max(max_cluster_size, kmeans_result.assignments[i].size()); + } + std::unique_ptr tmp_cluster_embeddings( + new embedding_storage_t[static_cast(max_cluster_size) * num_dimensions] + ); + + for (size_t cluster_idx = 0; cluster_idx < num_clusters; cluster_idx++) { + const auto cluster_size = kmeans_result.assignments[cluster_idx].size(); + auto& cluster = index.clusters.emplace_back(cluster_size, num_dimensions); + + for (size_t position_in_cluster = 0; position_in_cluster < cluster_size; + position_in_cluster++) { + const auto embedding_idx = + kmeans_result.assignments[cluster_idx][position_in_cluster]; + cluster.indices[position_in_cluster] = row_ids[embedding_idx]; + + if constexpr (Q == PDX::U8) { + PDX::ScalarQuantizer quantizer(num_dimensions); + quantizer.QuantizeEmbedding( + preprocessed.get() + (embedding_idx * num_dimensions), + quantization_base, + quantization_scale, + tmp_cluster_embeddings.get() + (position_in_cluster * num_dimensions) + ); + } else { + std::memcpy( + tmp_cluster_embeddings.get() + (position_in_cluster * num_dimensions), + preprocessed.get() + (embedding_idx * num_dimensions), + num_dimensions * sizeof(float) + ); + } + } + StoreClusterEmbeddings( + cluster, index, tmp_cluster_embeddings.get(), cluster_size + ); + } + + searcher = std::make_unique>(index, *pruner); + } }; template class PDXTreeIndex : public IPDXIndex { -public: - using embedding_storage_t = PDX::pdx_data_t; - -private: - PDXIndexConfig config {}; - PDX::IVFTree index; - std::unique_ptr pruner; - std::unique_ptr> searcher; - std::unique_ptr> top_level_searcher; - - static constexpr PDXIndexType GetIndexType() { - if constexpr (Q == F32) return PDXIndexType::PDX_TREE_F32; - else return PDXIndexType::PDX_TREE_U8; - } - -public: - PDXTreeIndex() = default; - - explicit PDXTreeIndex(PDXIndexConfig config) : config(config) { - pruner = std::make_unique(config.num_dimensions, config.seed); - } - - void Save(const std::string &path) const override { - std::ofstream out(path, std::ios::binary); - - // Index type flag - uint8_t type_flag = static_cast(GetIndexType()); - out.write(reinterpret_cast(&type_flag), sizeof(uint8_t)); - - // Rotation matrix - const auto &matrix = pruner->GetMatrix(); - uint32_t matrix_rows = static_cast(matrix.rows()); - uint32_t matrix_cols = static_cast(matrix.cols()); - out.write(reinterpret_cast(&matrix_rows), sizeof(uint32_t)); - out.write(reinterpret_cast(&matrix_cols), sizeof(uint32_t)); - out.write(reinterpret_cast(matrix.data()), - sizeof(float) * matrix_rows * matrix_cols); - - // IVFTree data - index.Save(out); - } - - void Restore(const std::string &path) override { - auto buffer = MmapFile(path); - char *ptr = buffer.get(); - - // Index type flag - ptr += sizeof(uint8_t); - - // Rotation matrix - uint32_t matrix_rows = *reinterpret_cast(ptr); - ptr += sizeof(uint32_t); - uint32_t matrix_cols = *reinterpret_cast(ptr); - ptr += sizeof(uint32_t); - auto *matrix_data = reinterpret_cast(ptr); - ptr += sizeof(float) * matrix_rows * matrix_cols; - - // Load IVFTree data - index.Load(ptr); - - // Create pruner and searchers - pruner = std::make_unique(index.num_dimensions, matrix_data); - searcher = std::make_unique>(index, *pruner); - top_level_searcher = std::make_unique>(index.l0, *pruner); - } - - std::vector Search(const float *query_embedding, size_t knn) const override { - auto n_probe_top_level = GetTopLevelNumClusters(); - // We confidently prune half of the search space - if (searcher->GetNProbe() < GetNumClusters() / 2) { - n_probe_top_level /= 2; - } - top_level_searcher->SetNProbe(n_probe_top_level); - auto top_level_results = top_level_searcher->Search(query_embedding, searcher->GetNProbe()); - - std::vector top_level_indexes(top_level_results.size()); - for (size_t i = 0; i < top_level_results.size(); i++) { - top_level_indexes[i] = top_level_results[i].index; - } - searcher->SetClusterAccessOrder(top_level_indexes); - - return searcher->Search(query_embedding, knn); - } - - void BuildIndex(const float *const embeddings, const size_t num_embeddings) override { - std::vector row_ids(num_embeddings); - std::iota(row_ids.begin(), row_ids.end(), 0); - BuildIndex(row_ids.data(), embeddings, num_embeddings); - } - - void BuildIndex(const size_t *const row_ids, const float *const embeddings, const size_t num_embeddings) { - const auto num_dimensions = config.num_dimensions; - auto num_clusters = config.num_clusters; - if (num_clusters == 0) { - num_clusters = ComputeNumberOfClusters(num_embeddings); - } - const bool normalize = config.normalize || DistanceMetricRequiresNormalization(config.distance_metric); - - assert(num_dimensions > 0); - assert(num_embeddings > 0); - assert(pruner); - - // Preprocess: normalize (if needed) + rotate. - const size_t total_floats = num_embeddings * num_dimensions; - std::unique_ptr normalized; - const float *rotation_input = embeddings; - if (normalize) { - normalized.reset(new float[total_floats]); - std::memcpy(normalized.get(), embeddings, total_floats * sizeof(float)); - PDX::Quantizer quantizer(num_dimensions); - for (size_t i = 0; i < num_embeddings; i++) { - quantizer.NormalizeQuery(normalized.get() + i * num_dimensions, - normalized.get() + i * num_dimensions); - } - rotation_input = normalized.get(); - } - std::unique_ptr preprocessed(new float[total_floats]); - pruner->PreprocessEmbeddings(rotation_input, preprocessed.get(), num_embeddings); - - float quantization_base = 0.0f; - float quantization_scale = 1.0f; - if constexpr (Q == PDX::U8) { - const auto params = PDX::ScalarQuantizer::ComputeQuantizationParams( - preprocessed.get(), static_cast(num_embeddings) * num_dimensions); - quantization_base = params.quantization_base; - quantization_scale = params.quantization_scale; - index = PDX::IVFTree(num_dimensions, num_embeddings, num_clusters, - normalize, quantization_scale, quantization_base); - } else { - index = PDX::IVFTree(num_dimensions, num_embeddings, num_clusters, normalize); - } - - KMeansResult kmeans_result = ComputeKMeans(preprocessed.get(), num_embeddings, num_dimensions, - num_clusters, config.distance_metric, config.seed, - config.normalize, config.sampling_fraction, config.kmeans_iters); - index.centroids = std::move(kmeans_result.centroids); - - size_t max_cluster_size = 0; - for (size_t i = 0; i < num_clusters; i++) { - max_cluster_size = std::max(max_cluster_size, kmeans_result.assignments[i].size()); - } - std::unique_ptr tmp_cluster_embeddings( - new embedding_storage_t[static_cast(max_cluster_size) * num_dimensions]); - - for (size_t cluster_idx = 0; cluster_idx < num_clusters; cluster_idx++) { - const auto cluster_size = kmeans_result.assignments[cluster_idx].size(); - auto &cluster = index.clusters.emplace_back(cluster_size, num_dimensions); - - for (size_t position_in_cluster = 0; position_in_cluster < cluster_size; position_in_cluster++) { - const auto embedding_idx = kmeans_result.assignments[cluster_idx][position_in_cluster]; - cluster.indices[position_in_cluster] = row_ids[embedding_idx]; - - if constexpr (Q == PDX::U8) { - PDX::ScalarQuantizer quantizer(num_dimensions); - quantizer.QuantizeEmbedding(preprocessed.get() + (embedding_idx * num_dimensions), - quantization_base, quantization_scale, - tmp_cluster_embeddings.get() + (position_in_cluster * num_dimensions)); - } else { - std::memcpy(tmp_cluster_embeddings.get() + (position_in_cluster * num_dimensions), - preprocessed.get() + (embedding_idx * num_dimensions), - num_dimensions * sizeof(float)); - } - } - StoreClusterEmbeddings(cluster, index, tmp_cluster_embeddings.get(), - cluster_size); - } - searcher = std::make_unique>(index, *pruner); - - // L0 index - auto l0_num_clusters = config.num_meso_clusters; - if (l0_num_clusters == 0){ - l0_num_clusters = static_cast(std::sqrt(num_clusters)); - } - - index.l0 = PDX::IVF(num_dimensions, num_clusters, l0_num_clusters, normalize); - KMeansResult l0_kmeans_result = ComputeKMeans(index.centroids.data(), num_clusters, num_dimensions, - l0_num_clusters, config.distance_metric, config.seed, - config.normalize, 1.0f, 10); // No sampling for l0 - index.l0.centroids = std::move(l0_kmeans_result.centroids); - - size_t l0_max_cluster_size = 0; - for (size_t i = 0; i < l0_num_clusters; i++) { - l0_max_cluster_size = std::max(l0_max_cluster_size, l0_kmeans_result.assignments[i].size()); - } - std::unique_ptr l0_tmp_cluster_embeddings( - new float[static_cast(l0_max_cluster_size) * num_dimensions]); - - for (size_t cluster_idx = 0; cluster_idx < l0_num_clusters; cluster_idx++) { - const auto cluster_size = l0_kmeans_result.assignments[cluster_idx].size(); - auto &cluster = index.l0.clusters.emplace_back(cluster_size, num_dimensions); - - for (size_t position_in_cluster = 0; position_in_cluster < cluster_size; position_in_cluster++) { - const auto embedding_idx = l0_kmeans_result.assignments[cluster_idx][position_in_cluster]; - cluster.indices[position_in_cluster] = embedding_idx; - - std::memcpy(l0_tmp_cluster_embeddings.get() + (position_in_cluster * num_dimensions), - index.centroids.data() + (embedding_idx * num_dimensions), - num_dimensions * sizeof(float)); - } - StoreClusterEmbeddings(cluster, index.l0, l0_tmp_cluster_embeddings.get(), - cluster_size); - } - - top_level_searcher = std::make_unique>(index.l0, *pruner); - } - - void SetNProbe(uint32_t n_probe) const override { - searcher->SetNProbe(n_probe); - } - - const PDX::PDXearch &GetSearcher() const { - return *searcher; - } - - uint32_t GetNumDimensions() const override { - return index.num_dimensions; - } - - uint32_t GetNumClusters() const override { - return index.num_clusters; - } - - uint32_t GetTopLevelNumClusters() const { - return index.l0.num_clusters; - } - - size_t GetInMemorySizeInBytes() const override { - size_t size = sizeof(*this); - // IVFTree heap allocations (L1 + L0 clusters and centroids) - size += index.GetInMemorySizeInBytes() - sizeof(index); - // Pruner: rotation matrix (D x D floats) + ratios vector (D floats) - if (pruner) { - size += sizeof(*pruner); - const auto &m = pruner->GetMatrix(); - size += static_cast(m.rows()) * m.cols() * sizeof(float); - size += pruner->num_dimensions * sizeof(float); - } - // L1 searcher: cluster_offsets array - if (searcher) { - size += sizeof(*searcher); - size += index.num_clusters * sizeof(size_t); - } - // L0 top-level searcher: cluster_offsets array - if (top_level_searcher) { - size += sizeof(*top_level_searcher); - size += index.l0.num_clusters * sizeof(size_t); - } - return size; - } - + public: + using embedding_storage_t = PDX::pdx_data_t; + + private: + PDXIndexConfig config{}; + PDX::IVFTree index; + std::unique_ptr pruner; + std::unique_ptr> searcher; + std::unique_ptr> top_level_searcher; + + static constexpr PDXIndexType GetIndexType() { + if constexpr (Q == F32) + return PDXIndexType::PDX_TREE_F32; + else + return PDXIndexType::PDX_TREE_U8; + } + + public: + PDXTreeIndex() = default; + + explicit PDXTreeIndex(PDXIndexConfig config) : config(config) { + pruner = std::make_unique(config.num_dimensions, config.seed); + } + + void Save(const std::string& path) const override { + std::ofstream out(path, std::ios::binary); + + // Index type flag + uint8_t type_flag = static_cast(GetIndexType()); + out.write(reinterpret_cast(&type_flag), sizeof(uint8_t)); + + // Rotation matrix + const auto& matrix = pruner->GetMatrix(); + uint32_t matrix_rows = static_cast(matrix.rows()); + uint32_t matrix_cols = static_cast(matrix.cols()); + out.write(reinterpret_cast(&matrix_rows), sizeof(uint32_t)); + out.write(reinterpret_cast(&matrix_cols), sizeof(uint32_t)); + out.write( + reinterpret_cast(matrix.data()), sizeof(float) * matrix_rows * matrix_cols + ); + + // IVFTree data + index.Save(out); + } + + void Restore(const std::string& path) override { + auto buffer = MmapFile(path); + char* ptr = buffer.get(); + + // Index type flag + ptr += sizeof(uint8_t); + + // Rotation matrix + uint32_t matrix_rows = *reinterpret_cast(ptr); + ptr += sizeof(uint32_t); + uint32_t matrix_cols = *reinterpret_cast(ptr); + ptr += sizeof(uint32_t); + auto* matrix_data = reinterpret_cast(ptr); + ptr += sizeof(float) * matrix_rows * matrix_cols; + + // Load IVFTree data + index.Load(ptr); + + // Create pruner and searchers + pruner = std::make_unique(index.num_dimensions, matrix_data); + searcher = std::make_unique>(index, *pruner); + top_level_searcher = std::make_unique>(index.l0, *pruner); + } + + std::vector Search(const float* query_embedding, size_t knn) const override { + auto n_probe_top_level = GetTopLevelNumClusters(); + // We confidently prune half of the search space + if (searcher->GetNProbe() < GetNumClusters() / 2) { + n_probe_top_level /= 2; + } + top_level_searcher->SetNProbe(n_probe_top_level); + auto top_level_results = top_level_searcher->Search(query_embedding, searcher->GetNProbe()); + + std::vector top_level_indexes(top_level_results.size()); + for (size_t i = 0; i < top_level_results.size(); i++) { + top_level_indexes[i] = top_level_results[i].index; + } + searcher->SetClusterAccessOrder(top_level_indexes); + + return searcher->Search(query_embedding, knn); + } + + void BuildIndex(const float* const embeddings, const size_t num_embeddings) override { + std::vector row_ids(num_embeddings); + std::iota(row_ids.begin(), row_ids.end(), 0); + BuildIndex(row_ids.data(), embeddings, num_embeddings); + } + + void BuildIndex( + const size_t* const row_ids, + const float* const embeddings, + const size_t num_embeddings + ) { + const auto num_dimensions = config.num_dimensions; + auto num_clusters = config.num_clusters; + if (num_clusters == 0) { + num_clusters = ComputeNumberOfClusters(num_embeddings); + } + const bool normalize = + config.normalize || DistanceMetricRequiresNormalization(config.distance_metric); + + assert(num_dimensions > 0); + assert(num_embeddings > 0); + assert(pruner); + + // Preprocess: normalize (if needed) + rotate. + const size_t total_floats = num_embeddings * num_dimensions; + std::unique_ptr normalized; + const float* rotation_input = embeddings; + if (normalize) { + normalized.reset(new float[total_floats]); + std::memcpy(normalized.get(), embeddings, total_floats * sizeof(float)); + PDX::Quantizer quantizer(num_dimensions); + for (size_t i = 0; i < num_embeddings; i++) { + quantizer.NormalizeQuery( + normalized.get() + i * num_dimensions, normalized.get() + i * num_dimensions + ); + } + rotation_input = normalized.get(); + } + std::unique_ptr preprocessed(new float[total_floats]); + pruner->PreprocessEmbeddings(rotation_input, preprocessed.get(), num_embeddings); + + float quantization_base = 0.0f; + float quantization_scale = 1.0f; + if constexpr (Q == PDX::U8) { + const auto params = PDX::ScalarQuantizer::ComputeQuantizationParams( + preprocessed.get(), static_cast(num_embeddings) * num_dimensions + ); + quantization_base = params.quantization_base; + quantization_scale = params.quantization_scale; + index = PDX::IVFTree( + num_dimensions, + num_embeddings, + num_clusters, + normalize, + quantization_scale, + quantization_base + ); + } else { + index = PDX::IVFTree(num_dimensions, num_embeddings, num_clusters, normalize); + } + + KMeansResult kmeans_result = ComputeKMeans( + preprocessed.get(), + num_embeddings, + num_dimensions, + num_clusters, + config.distance_metric, + config.seed, + config.normalize, + config.sampling_fraction, + config.kmeans_iters + ); + index.centroids = std::move(kmeans_result.centroids); + + size_t max_cluster_size = 0; + for (size_t i = 0; i < num_clusters; i++) { + max_cluster_size = std::max(max_cluster_size, kmeans_result.assignments[i].size()); + } + std::unique_ptr tmp_cluster_embeddings( + new embedding_storage_t[static_cast(max_cluster_size) * num_dimensions] + ); + + for (size_t cluster_idx = 0; cluster_idx < num_clusters; cluster_idx++) { + const auto cluster_size = kmeans_result.assignments[cluster_idx].size(); + auto& cluster = index.clusters.emplace_back(cluster_size, num_dimensions); + + for (size_t position_in_cluster = 0; position_in_cluster < cluster_size; + position_in_cluster++) { + const auto embedding_idx = + kmeans_result.assignments[cluster_idx][position_in_cluster]; + cluster.indices[position_in_cluster] = row_ids[embedding_idx]; + + if constexpr (Q == PDX::U8) { + PDX::ScalarQuantizer quantizer(num_dimensions); + quantizer.QuantizeEmbedding( + preprocessed.get() + (embedding_idx * num_dimensions), + quantization_base, + quantization_scale, + tmp_cluster_embeddings.get() + (position_in_cluster * num_dimensions) + ); + } else { + std::memcpy( + tmp_cluster_embeddings.get() + (position_in_cluster * num_dimensions), + preprocessed.get() + (embedding_idx * num_dimensions), + num_dimensions * sizeof(float) + ); + } + } + StoreClusterEmbeddings( + cluster, index, tmp_cluster_embeddings.get(), cluster_size + ); + } + searcher = std::make_unique>(index, *pruner); + + // L0 index + auto l0_num_clusters = config.num_meso_clusters; + if (l0_num_clusters == 0) { + l0_num_clusters = static_cast(std::sqrt(num_clusters)); + } + + index.l0 = PDX::IVF(num_dimensions, num_clusters, l0_num_clusters, normalize); + KMeansResult l0_kmeans_result = ComputeKMeans( + index.centroids.data(), + num_clusters, + num_dimensions, + l0_num_clusters, + config.distance_metric, + config.seed, + config.normalize, + 1.0f, + 10 + ); // No sampling for l0 + index.l0.centroids = std::move(l0_kmeans_result.centroids); + + size_t l0_max_cluster_size = 0; + for (size_t i = 0; i < l0_num_clusters; i++) { + l0_max_cluster_size = + std::max(l0_max_cluster_size, l0_kmeans_result.assignments[i].size()); + } + std::unique_ptr l0_tmp_cluster_embeddings( + new float[static_cast(l0_max_cluster_size) * num_dimensions] + ); + + for (size_t cluster_idx = 0; cluster_idx < l0_num_clusters; cluster_idx++) { + const auto cluster_size = l0_kmeans_result.assignments[cluster_idx].size(); + auto& cluster = index.l0.clusters.emplace_back(cluster_size, num_dimensions); + + for (size_t position_in_cluster = 0; position_in_cluster < cluster_size; + position_in_cluster++) { + const auto embedding_idx = + l0_kmeans_result.assignments[cluster_idx][position_in_cluster]; + cluster.indices[position_in_cluster] = embedding_idx; + + std::memcpy( + l0_tmp_cluster_embeddings.get() + (position_in_cluster * num_dimensions), + index.centroids.data() + (embedding_idx * num_dimensions), + num_dimensions * sizeof(float) + ); + } + StoreClusterEmbeddings( + cluster, index.l0, l0_tmp_cluster_embeddings.get(), cluster_size + ); + } + + top_level_searcher = std::make_unique>(index.l0, *pruner); + } + + void SetNProbe(uint32_t n_probe) const override { searcher->SetNProbe(n_probe); } + + const PDX::PDXearch& GetSearcher() const { return *searcher; } + + uint32_t GetNumDimensions() const override { return index.num_dimensions; } + + uint32_t GetNumClusters() const override { return index.num_clusters; } + + uint32_t GetTopLevelNumClusters() const { return index.l0.num_clusters; } + + size_t GetInMemorySizeInBytes() const override { + size_t size = sizeof(*this); + // IVFTree heap allocations (L1 + L0 clusters and centroids) + size += index.GetInMemorySizeInBytes() - sizeof(index); + // Pruner: rotation matrix (D x D floats) + ratios vector (D floats) + if (pruner) { + size += sizeof(*pruner); + const auto& m = pruner->GetMatrix(); + size += static_cast(m.rows()) * m.cols() * sizeof(float); + size += pruner->num_dimensions * sizeof(float); + } + // L1 searcher: cluster_offsets array + if (searcher) { + size += sizeof(*searcher); + size += index.num_clusters * sizeof(size_t); + } + // L0 top-level searcher: cluster_offsets array + if (top_level_searcher) { + size += sizeof(*top_level_searcher); + size += index.l0.num_clusters * sizeof(size_t); + } + return size; + } }; using PDXIndexF32 = PDXIndex; @@ -496,57 +557,69 @@ using PDXIndexU8 = PDXIndex; using PDXTreeIndexF32 = PDXTreeIndex; using PDXTreeIndexU8 = PDXTreeIndex; -inline std::unique_ptr LoadPDXIndex(const std::string &path) { - auto buffer = MmapFile(path); - auto type = static_cast(buffer.get()[0]); - std::unique_ptr idx; - switch (type) { - case PDXIndexType::PDX_F32: - idx = std::make_unique(); - break; - case PDXIndexType::PDX_U8: - idx = std::make_unique(); - break; - case PDXIndexType::PDX_TREE_F32: - idx = std::make_unique(); - break; - case PDXIndexType::PDX_TREE_U8: - idx = std::make_unique(); - break; - default: - throw std::runtime_error("Unknown PDX index type: " + std::to_string(static_cast(type))); - } - idx->Restore(path); - return idx; +inline std::unique_ptr LoadPDXIndex(const std::string& path) { + auto buffer = MmapFile(path); + auto type = static_cast(buffer.get()[0]); + std::unique_ptr idx; + switch (type) { + case PDXIndexType::PDX_F32: + idx = std::make_unique(); + break; + case PDXIndexType::PDX_U8: + idx = std::make_unique(); + break; + case PDXIndexType::PDX_TREE_F32: + idx = std::make_unique(); + break; + case PDXIndexType::PDX_TREE_U8: + idx = std::make_unique(); + break; + default: + throw std::runtime_error( + "Unknown PDX index type: " + std::to_string(static_cast(type)) + ); + } + idx->Restore(path); + return idx; } class EmbeddingPreprocessor { -private: - PDX::ADSamplingPruner pruner; - PDX::Quantizer quantizer; - const size_t num_dimensions; - -public: - explicit EmbeddingPreprocessor(const size_t num_dimensions, const float *const rotation_matrix) - : pruner(num_dimensions, rotation_matrix), quantizer(num_dimensions), num_dimensions(num_dimensions) { - } - - void PreprocessEmbedding(float *const input_embedding, float *const output_embedding, const bool normalize) const { - if (normalize) { - quantizer.NormalizeQuery(input_embedding, input_embedding); - } - pruner.PreprocessQuery(input_embedding, output_embedding); - } - - void PreprocessEmbeddings(float *const input_embeddings, float *const output_embeddings, - const size_t num_embeddings, const bool normalize) const { - if (normalize) { - for (size_t i = 0; i < num_embeddings; i++) { - quantizer.NormalizeQuery(input_embeddings + i * num_dimensions, input_embeddings + i * num_dimensions); - } - } - pruner.PreprocessEmbeddings(input_embeddings, output_embeddings, num_embeddings); - } + private: + PDX::ADSamplingPruner pruner; + PDX::Quantizer quantizer; + const size_t num_dimensions; + + public: + explicit EmbeddingPreprocessor(const size_t num_dimensions, const float* const rotation_matrix) + : pruner(num_dimensions, rotation_matrix), quantizer(num_dimensions), + num_dimensions(num_dimensions) {} + + void PreprocessEmbedding( + float* const input_embedding, + float* const output_embedding, + const bool normalize + ) const { + if (normalize) { + quantizer.NormalizeQuery(input_embedding, input_embedding); + } + pruner.PreprocessQuery(input_embedding, output_embedding); + } + + void PreprocessEmbeddings( + float* const input_embeddings, + float* const output_embeddings, + const size_t num_embeddings, + const bool normalize + ) const { + if (normalize) { + for (size_t i = 0; i < num_embeddings; i++) { + quantizer.NormalizeQuery( + input_embeddings + i * num_dimensions, input_embeddings + i * num_dimensions + ); + } + } + pruner.PreprocessEmbeddings(input_embeddings, output_embeddings, num_embeddings); + } }; } // namespace PDX diff --git a/include/pdx/ivf_wrapper.hpp b/include/pdx/ivf_wrapper.hpp index 22ef6f0..dd20d05 100644 --- a/include/pdx/ivf_wrapper.hpp +++ b/include/pdx/ivf_wrapper.hpp @@ -1,344 +1,404 @@ #pragma once +#include "pdx/common.hpp" +#include "pdx/utils.hpp" +#include #include #include -#include +#include #include #include -#include -#include "pdx/common.hpp" -#include "pdx/utils.hpp" namespace PDX { template struct Cluster { - using data_t = pdx_data_t; - - Cluster(uint32_t num_embeddings, uint32_t num_dimensions) - : num_embeddings(num_embeddings), num_dimensions(num_dimensions), indices(new uint32_t[num_embeddings]), - data(new data_t[static_cast(num_embeddings) * num_dimensions]) { - } - - ~Cluster() { - delete[] data; - delete[] indices; - } - - uint32_t num_embeddings {}; - const uint32_t num_dimensions {}; - uint32_t *indices = nullptr; - data_t *data = nullptr; - - size_t GetInMemorySizeInBytes() const { - return sizeof(*this) + num_embeddings * sizeof(*indices) + - num_embeddings * static_cast(num_dimensions) * sizeof(*data); - } - + using data_t = pdx_data_t; + + Cluster(uint32_t num_embeddings, uint32_t num_dimensions) + : num_embeddings(num_embeddings), num_dimensions(num_dimensions), + indices(new uint32_t[num_embeddings]), + data(new data_t[static_cast(num_embeddings) * num_dimensions]) {} + + ~Cluster() { + delete[] data; + delete[] indices; + } + + uint32_t num_embeddings{}; + const uint32_t num_dimensions{}; + uint32_t* indices = nullptr; + data_t* data = nullptr; + + size_t GetInMemorySizeInBytes() const { + return sizeof(*this) + num_embeddings * sizeof(*indices) + + num_embeddings * static_cast(num_dimensions) * sizeof(*data); + } }; template class IVF { -public: - using cluster_t = Cluster; - using data_t = pdx_data_t; - - uint32_t num_dimensions {}; - uint64_t total_num_embeddings {}; - uint32_t num_clusters {}; - uint32_t num_vertical_dimensions {}; - uint32_t num_horizontal_dimensions {}; - std::vector clusters; - bool is_normalized {}; - std::vector centroids; - - // U8-specific quantization parameters - float quantization_scale = 1.0f; - float quantization_scale_squared = 1.0f; - float inverse_quantization_scale_squared = 1.0f; - float quantization_base = 0.0f; - - IVF() = default; - ~IVF() = default; - IVF(IVF &&) = default; - IVF &operator=(IVF &&) = default; - - IVF(uint32_t num_dimensions, uint64_t total_num_embeddings, uint32_t num_clusters, bool is_normalized) - : num_dimensions(num_dimensions), total_num_embeddings(total_num_embeddings), num_clusters(num_clusters), - num_vertical_dimensions(GetPDXDimensionSplit(num_dimensions).vertical_dimensions), - num_horizontal_dimensions(GetPDXDimensionSplit(num_dimensions).horizontal_dimensions), - is_normalized(is_normalized) { - clusters.reserve(num_clusters); - } - - IVF(uint32_t num_dimensions, uint64_t total_num_embeddings, uint32_t num_clusters, bool is_normalized, - float quantization_scale, float quantization_base) - : num_dimensions(num_dimensions), total_num_embeddings(total_num_embeddings), num_clusters(num_clusters), - num_vertical_dimensions(GetPDXDimensionSplit(num_dimensions).vertical_dimensions), - num_horizontal_dimensions(GetPDXDimensionSplit(num_dimensions).horizontal_dimensions), - is_normalized(is_normalized), quantization_scale(quantization_scale), - quantization_scale_squared(quantization_scale * quantization_scale), - inverse_quantization_scale_squared(1.0f / (quantization_scale * quantization_scale)), - quantization_base(quantization_base) { - clusters.reserve(num_clusters); - } - - void Load(char *input) { - char *next_value = input; - num_dimensions = ((uint32_t *) input)[0]; - num_vertical_dimensions = ((uint32_t *) input)[1]; - num_horizontal_dimensions = ((uint32_t *) input)[2]; - - next_value += sizeof(uint32_t) * 3; - num_clusters = ((uint32_t *) next_value)[0]; - next_value += sizeof(uint32_t); - auto *nums_embeddings = (uint32_t *) next_value; - next_value += num_clusters * sizeof(uint32_t); - clusters.reserve(num_clusters); - for (size_t i = 0; i < num_clusters; ++i) { - clusters.emplace_back(nums_embeddings[i], num_dimensions); - memcpy(clusters[i].data, next_value, sizeof(data_t) * clusters[i].num_embeddings * num_dimensions); - next_value += sizeof(data_t) * clusters[i].num_embeddings * num_dimensions; - } - for (size_t i = 0; i < num_clusters; ++i) { - memcpy(clusters[i].indices, next_value, sizeof(uint32_t) * clusters[i].num_embeddings); - next_value += sizeof(uint32_t) * clusters[i].num_embeddings; - } - - is_normalized = ((char *) next_value)[0]; - next_value += sizeof(char); - - centroids.resize(num_clusters * num_dimensions); - memcpy(centroids.data(), (float *) next_value, sizeof(float) * num_clusters * num_dimensions); - next_value += sizeof(float) * num_clusters * num_dimensions; - - if constexpr (Q == U8) { - quantization_base = ((float *) next_value)[0]; - next_value += sizeof(float); - quantization_scale = ((float *) next_value)[0]; - next_value += sizeof(float); - quantization_scale_squared = quantization_scale * quantization_scale; - inverse_quantization_scale_squared = 1.0f / quantization_scale_squared; - } - } - - void Save(std::ostream &out) const { - out.write(reinterpret_cast(&num_dimensions), sizeof(uint32_t)); - out.write(reinterpret_cast(&num_vertical_dimensions), sizeof(uint32_t)); - out.write(reinterpret_cast(&num_horizontal_dimensions), sizeof(uint32_t)); - out.write(reinterpret_cast(&num_clusters), sizeof(uint32_t)); - - for (size_t i = 0; i < num_clusters; ++i) { - out.write(reinterpret_cast(&clusters[i].num_embeddings), sizeof(uint32_t)); - } - for (size_t i = 0; i < num_clusters; ++i) { - out.write(reinterpret_cast(clusters[i].data), - sizeof(data_t) * clusters[i].num_embeddings * num_dimensions); - } - for (size_t i = 0; i < num_clusters; ++i) { - out.write(reinterpret_cast(clusters[i].indices), - sizeof(uint32_t) * clusters[i].num_embeddings); - } - - char norm = is_normalized; - out.write(&norm, sizeof(char)); - - out.write(reinterpret_cast(centroids.data()), - sizeof(float) * num_clusters * num_dimensions); - - if constexpr (Q == U8) { - out.write(reinterpret_cast(&quantization_base), sizeof(float)); - out.write(reinterpret_cast(&quantization_scale), sizeof(float)); - } - } - - size_t GetInMemorySizeInBytes() const { - size_t in_memory_size_in_bytes = 0; - in_memory_size_in_bytes += sizeof(*this); - for (const auto &cluster : clusters) { - in_memory_size_in_bytes += cluster.GetInMemorySizeInBytes(); - } - in_memory_size_in_bytes += (clusters.capacity() - clusters.size()) * sizeof(*clusters.data()); - in_memory_size_in_bytes += centroids.capacity() * sizeof(*centroids.data()); - return in_memory_size_in_bytes; - } - + public: + using cluster_t = Cluster; + using data_t = pdx_data_t; + + uint32_t num_dimensions{}; + uint64_t total_num_embeddings{}; + uint32_t num_clusters{}; + uint32_t num_vertical_dimensions{}; + uint32_t num_horizontal_dimensions{}; + std::vector clusters; + bool is_normalized{}; + std::vector centroids; + + // U8-specific quantization parameters + float quantization_scale = 1.0f; + float quantization_scale_squared = 1.0f; + float inverse_quantization_scale_squared = 1.0f; + float quantization_base = 0.0f; + + IVF() = default; + ~IVF() = default; + IVF(IVF&&) = default; + IVF& operator=(IVF&&) = default; + + IVF(uint32_t num_dimensions, + uint64_t total_num_embeddings, + uint32_t num_clusters, + bool is_normalized) + : num_dimensions(num_dimensions), total_num_embeddings(total_num_embeddings), + num_clusters(num_clusters), + num_vertical_dimensions(GetPDXDimensionSplit(num_dimensions).vertical_dimensions), + num_horizontal_dimensions(GetPDXDimensionSplit(num_dimensions).horizontal_dimensions), + is_normalized(is_normalized) { + clusters.reserve(num_clusters); + } + + IVF(uint32_t num_dimensions, + uint64_t total_num_embeddings, + uint32_t num_clusters, + bool is_normalized, + float quantization_scale, + float quantization_base) + : num_dimensions(num_dimensions), total_num_embeddings(total_num_embeddings), + num_clusters(num_clusters), + num_vertical_dimensions(GetPDXDimensionSplit(num_dimensions).vertical_dimensions), + num_horizontal_dimensions(GetPDXDimensionSplit(num_dimensions).horizontal_dimensions), + is_normalized(is_normalized), quantization_scale(quantization_scale), + quantization_scale_squared(quantization_scale * quantization_scale), + inverse_quantization_scale_squared(1.0f / (quantization_scale * quantization_scale)), + quantization_base(quantization_base) { + clusters.reserve(num_clusters); + } + + void Load(char* input) { + char* next_value = input; + num_dimensions = ((uint32_t*) input)[0]; + num_vertical_dimensions = ((uint32_t*) input)[1]; + num_horizontal_dimensions = ((uint32_t*) input)[2]; + + next_value += sizeof(uint32_t) * 3; + num_clusters = ((uint32_t*) next_value)[0]; + next_value += sizeof(uint32_t); + auto* nums_embeddings = (uint32_t*) next_value; + next_value += num_clusters * sizeof(uint32_t); + clusters.reserve(num_clusters); + for (size_t i = 0; i < num_clusters; ++i) { + clusters.emplace_back(nums_embeddings[i], num_dimensions); + memcpy( + clusters[i].data, + next_value, + sizeof(data_t) * clusters[i].num_embeddings * num_dimensions + ); + next_value += sizeof(data_t) * clusters[i].num_embeddings * num_dimensions; + } + for (size_t i = 0; i < num_clusters; ++i) { + memcpy(clusters[i].indices, next_value, sizeof(uint32_t) * clusters[i].num_embeddings); + next_value += sizeof(uint32_t) * clusters[i].num_embeddings; + } + + is_normalized = ((char*) next_value)[0]; + next_value += sizeof(char); + + centroids.resize(num_clusters * num_dimensions); + memcpy( + centroids.data(), (float*) next_value, sizeof(float) * num_clusters * num_dimensions + ); + next_value += sizeof(float) * num_clusters * num_dimensions; + + if constexpr (Q == U8) { + quantization_base = ((float*) next_value)[0]; + next_value += sizeof(float); + quantization_scale = ((float*) next_value)[0]; + next_value += sizeof(float); + quantization_scale_squared = quantization_scale * quantization_scale; + inverse_quantization_scale_squared = 1.0f / quantization_scale_squared; + } + } + + void Save(std::ostream& out) const { + out.write(reinterpret_cast(&num_dimensions), sizeof(uint32_t)); + out.write(reinterpret_cast(&num_vertical_dimensions), sizeof(uint32_t)); + out.write(reinterpret_cast(&num_horizontal_dimensions), sizeof(uint32_t)); + out.write(reinterpret_cast(&num_clusters), sizeof(uint32_t)); + + for (size_t i = 0; i < num_clusters; ++i) { + out.write(reinterpret_cast(&clusters[i].num_embeddings), sizeof(uint32_t)); + } + for (size_t i = 0; i < num_clusters; ++i) { + out.write( + reinterpret_cast(clusters[i].data), + sizeof(data_t) * clusters[i].num_embeddings * num_dimensions + ); + } + for (size_t i = 0; i < num_clusters; ++i) { + out.write( + reinterpret_cast(clusters[i].indices), + sizeof(uint32_t) * clusters[i].num_embeddings + ); + } + + char norm = is_normalized; + out.write(&norm, sizeof(char)); + + out.write( + reinterpret_cast(centroids.data()), + sizeof(float) * num_clusters * num_dimensions + ); + + if constexpr (Q == U8) { + out.write(reinterpret_cast(&quantization_base), sizeof(float)); + out.write(reinterpret_cast(&quantization_scale), sizeof(float)); + } + } + + size_t GetInMemorySizeInBytes() const { + size_t in_memory_size_in_bytes = 0; + in_memory_size_in_bytes += sizeof(*this); + for (const auto& cluster : clusters) { + in_memory_size_in_bytes += cluster.GetInMemorySizeInBytes(); + } + in_memory_size_in_bytes += + (clusters.capacity() - clusters.size()) * sizeof(*clusters.data()); + in_memory_size_in_bytes += centroids.capacity() * sizeof(*centroids.data()); + return in_memory_size_in_bytes; + } }; template class IVFTree : public IVF { -public: - using data_t = pdx_data_t; - - IVF l0; // Meso clusters - - IVFTree() = default; - ~IVFTree() = default; - IVFTree(IVFTree &&) = default; - IVFTree &operator=(IVFTree &&) = default; - - IVFTree(uint32_t num_dimensions, uint64_t total_num_embeddings, uint32_t num_clusters, bool is_normalized) - : IVF(num_dimensions, total_num_embeddings, num_clusters, is_normalized) {} - - IVFTree(uint32_t num_dimensions, uint64_t total_num_embeddings, uint32_t num_clusters, bool is_normalized, - float quantization_scale, float quantization_base) - : IVF(num_dimensions, total_num_embeddings, num_clusters, is_normalized, - quantization_scale, quantization_base) {} - - void Load(char *input) { - char *next_value = input; - - // Header - uint32_t dims = ((uint32_t *) input)[0]; - uint32_t v_dims = ((uint32_t *) input)[1]; - uint32_t h_dims = ((uint32_t *) input)[2]; - next_value += sizeof(uint32_t) * 3; - - uint32_t n_clusters_l1 = ((uint32_t *) next_value)[0]; - next_value += sizeof(uint32_t); - uint32_t n_clusters_l0 = ((uint32_t *) next_value)[0]; - next_value += sizeof(uint32_t); - - // === L0 (meso-clusters, always F32) === - l0.num_dimensions = dims; - l0.num_vertical_dimensions = v_dims; - l0.num_horizontal_dimensions = h_dims; - l0.num_clusters = n_clusters_l0; - - auto *nums_embeddings_l0 = (uint32_t *) next_value; - next_value += n_clusters_l0 * sizeof(uint32_t); - - l0.clusters.reserve(n_clusters_l0); - for (size_t i = 0; i < n_clusters_l0; ++i) { - l0.clusters.emplace_back(nums_embeddings_l0[i], dims); - memcpy(l0.clusters[i].data, next_value, - sizeof(float) * l0.clusters[i].num_embeddings * dims); - next_value += sizeof(float) * l0.clusters[i].num_embeddings * dims; - } - for (size_t i = 0; i < n_clusters_l0; ++i) { - memcpy(l0.clusters[i].indices, next_value, - sizeof(uint32_t) * l0.clusters[i].num_embeddings); - next_value += sizeof(uint32_t) * l0.clusters[i].num_embeddings; - } - - // === L1 (data clusters, inherited fields) === - this->num_dimensions = dims; - this->num_vertical_dimensions = v_dims; - this->num_horizontal_dimensions = h_dims; - this->num_clusters = n_clusters_l1; - - auto *nums_embeddings_l1 = (uint32_t *) next_value; - next_value += n_clusters_l1 * sizeof(uint32_t); - - this->clusters.reserve(n_clusters_l1); - for (size_t i = 0; i < n_clusters_l1; ++i) { - this->clusters.emplace_back(nums_embeddings_l1[i], dims); - memcpy(this->clusters[i].data, next_value, - sizeof(data_t) * this->clusters[i].num_embeddings * dims); - next_value += sizeof(data_t) * this->clusters[i].num_embeddings * dims; - } - for (size_t i = 0; i < n_clusters_l1; ++i) { - memcpy(this->clusters[i].indices, next_value, - sizeof(uint32_t) * this->clusters[i].num_embeddings); - next_value += sizeof(uint32_t) * this->clusters[i].num_embeddings; - } - - // === Shared metadata === - bool normalized = ((char *) next_value)[0]; - this->is_normalized = normalized; - l0.is_normalized = normalized; - next_value += sizeof(char); - - // === L0 centroids (centroids_pdx from file) === - l0.centroids.resize(n_clusters_l0 * dims); - memcpy(l0.centroids.data(), (float *) next_value, - sizeof(float) * n_clusters_l0 * dims); - next_value += sizeof(float) * n_clusters_l0 * dims; - - // === U8 quantization params === - if constexpr (Q == U8) { - this->quantization_base = ((float *) next_value)[0]; - next_value += sizeof(float); - this->quantization_scale = ((float *) next_value)[0]; - next_value += sizeof(float); - this->quantization_scale_squared = - this->quantization_scale * this->quantization_scale; - this->inverse_quantization_scale_squared = - 1.0f / this->quantization_scale_squared; - } - } - - void Save(std::ostream &out) const { - // Header: dimensions (shared between L0 and L1) - out.write(reinterpret_cast(&this->num_dimensions), sizeof(uint32_t)); - out.write(reinterpret_cast(&this->num_vertical_dimensions), sizeof(uint32_t)); - out.write(reinterpret_cast(&this->num_horizontal_dimensions), sizeof(uint32_t)); - - // Number of clusters: L1 then L0 - out.write(reinterpret_cast(&this->num_clusters), sizeof(uint32_t)); - uint32_t n_clusters_l0 = l0.num_clusters; - out.write(reinterpret_cast(&n_clusters_l0), sizeof(uint32_t)); - - // === L0 (meso-clusters, always F32) === - for (size_t i = 0; i < n_clusters_l0; ++i) { - out.write(reinterpret_cast(&l0.clusters[i].num_embeddings), sizeof(uint32_t)); - } - for (size_t i = 0; i < n_clusters_l0; ++i) { - out.write(reinterpret_cast(l0.clusters[i].data), - sizeof(float) * l0.clusters[i].num_embeddings * this->num_dimensions); - } - for (size_t i = 0; i < n_clusters_l0; ++i) { - out.write(reinterpret_cast(l0.clusters[i].indices), - sizeof(uint32_t) * l0.clusters[i].num_embeddings); - } - - // === L1 (data clusters) === - for (size_t i = 0; i < this->num_clusters; ++i) { - out.write(reinterpret_cast(&this->clusters[i].num_embeddings), sizeof(uint32_t)); - } - for (size_t i = 0; i < this->num_clusters; ++i) { - out.write(reinterpret_cast(this->clusters[i].data), - sizeof(data_t) * this->clusters[i].num_embeddings * this->num_dimensions); - } - for (size_t i = 0; i < this->num_clusters; ++i) { - out.write(reinterpret_cast(this->clusters[i].indices), - sizeof(uint32_t) * this->clusters[i].num_embeddings); - } - - // === Shared metadata === - char norm = this->is_normalized; - out.write(&norm, sizeof(char)); - - // L0 centroids - out.write(reinterpret_cast(l0.centroids.data()), - sizeof(float) * n_clusters_l0 * this->num_dimensions); - - // === U8 quantization params === - if constexpr (Q == U8) { - out.write(reinterpret_cast(&this->quantization_base), sizeof(float)); - out.write(reinterpret_cast(&this->quantization_scale), sizeof(float)); - } - } - - size_t GetInMemorySizeInBytes() const { - size_t size = sizeof(*this); - - // L1 clusters (inherited from base) - for (const auto &cluster : this->clusters) { - size += cluster.GetInMemorySizeInBytes(); - } - size += (this->clusters.capacity() - this->clusters.size()) * sizeof(Cluster); - size += this->centroids.capacity() * sizeof(float); - - // L0 meso-clusters - for (const auto &cluster : l0.clusters) { - size += cluster.GetInMemorySizeInBytes(); - } - size += (l0.clusters.capacity() - l0.clusters.size()) * sizeof(Cluster); - size += l0.centroids.capacity() * sizeof(float); - - return size; - } + public: + using data_t = pdx_data_t; + + IVF l0; // Meso clusters + + IVFTree() = default; + ~IVFTree() = default; + IVFTree(IVFTree&&) = default; + IVFTree& operator=(IVFTree&&) = default; + + IVFTree( + uint32_t num_dimensions, + uint64_t total_num_embeddings, + uint32_t num_clusters, + bool is_normalized + ) + : IVF(num_dimensions, total_num_embeddings, num_clusters, is_normalized) {} + + IVFTree( + uint32_t num_dimensions, + uint64_t total_num_embeddings, + uint32_t num_clusters, + bool is_normalized, + float quantization_scale, + float quantization_base + ) + : IVF( + num_dimensions, + total_num_embeddings, + num_clusters, + is_normalized, + quantization_scale, + quantization_base + ) {} + + void Load(char* input) { + char* next_value = input; + + // Header + uint32_t dims = ((uint32_t*) input)[0]; + uint32_t v_dims = ((uint32_t*) input)[1]; + uint32_t h_dims = ((uint32_t*) input)[2]; + next_value += sizeof(uint32_t) * 3; + + uint32_t n_clusters_l1 = ((uint32_t*) next_value)[0]; + next_value += sizeof(uint32_t); + uint32_t n_clusters_l0 = ((uint32_t*) next_value)[0]; + next_value += sizeof(uint32_t); + + // === L0 (meso-clusters, always F32) === + l0.num_dimensions = dims; + l0.num_vertical_dimensions = v_dims; + l0.num_horizontal_dimensions = h_dims; + l0.num_clusters = n_clusters_l0; + + auto* nums_embeddings_l0 = (uint32_t*) next_value; + next_value += n_clusters_l0 * sizeof(uint32_t); + + l0.clusters.reserve(n_clusters_l0); + for (size_t i = 0; i < n_clusters_l0; ++i) { + l0.clusters.emplace_back(nums_embeddings_l0[i], dims); + memcpy( + l0.clusters[i].data, + next_value, + sizeof(float) * l0.clusters[i].num_embeddings * dims + ); + next_value += sizeof(float) * l0.clusters[i].num_embeddings * dims; + } + for (size_t i = 0; i < n_clusters_l0; ++i) { + memcpy( + l0.clusters[i].indices, next_value, sizeof(uint32_t) * l0.clusters[i].num_embeddings + ); + next_value += sizeof(uint32_t) * l0.clusters[i].num_embeddings; + } + + // === L1 (data clusters, inherited fields) === + this->num_dimensions = dims; + this->num_vertical_dimensions = v_dims; + this->num_horizontal_dimensions = h_dims; + this->num_clusters = n_clusters_l1; + + auto* nums_embeddings_l1 = (uint32_t*) next_value; + next_value += n_clusters_l1 * sizeof(uint32_t); + + this->clusters.reserve(n_clusters_l1); + for (size_t i = 0; i < n_clusters_l1; ++i) { + this->clusters.emplace_back(nums_embeddings_l1[i], dims); + memcpy( + this->clusters[i].data, + next_value, + sizeof(data_t) * this->clusters[i].num_embeddings * dims + ); + next_value += sizeof(data_t) * this->clusters[i].num_embeddings * dims; + } + for (size_t i = 0; i < n_clusters_l1; ++i) { + memcpy( + this->clusters[i].indices, + next_value, + sizeof(uint32_t) * this->clusters[i].num_embeddings + ); + next_value += sizeof(uint32_t) * this->clusters[i].num_embeddings; + } + + // === Shared metadata === + bool normalized = ((char*) next_value)[0]; + this->is_normalized = normalized; + l0.is_normalized = normalized; + next_value += sizeof(char); + + // === L0 centroids (centroids_pdx from file) === + l0.centroids.resize(n_clusters_l0 * dims); + memcpy(l0.centroids.data(), (float*) next_value, sizeof(float) * n_clusters_l0 * dims); + next_value += sizeof(float) * n_clusters_l0 * dims; + + // === U8 quantization params === + if constexpr (Q == U8) { + this->quantization_base = ((float*) next_value)[0]; + next_value += sizeof(float); + this->quantization_scale = ((float*) next_value)[0]; + next_value += sizeof(float); + this->quantization_scale_squared = this->quantization_scale * this->quantization_scale; + this->inverse_quantization_scale_squared = 1.0f / this->quantization_scale_squared; + } + } + + void Save(std::ostream& out) const { + // Header: dimensions (shared between L0 and L1) + out.write(reinterpret_cast(&this->num_dimensions), sizeof(uint32_t)); + out.write(reinterpret_cast(&this->num_vertical_dimensions), sizeof(uint32_t)); + out.write( + reinterpret_cast(&this->num_horizontal_dimensions), sizeof(uint32_t) + ); + + // Number of clusters: L1 then L0 + out.write(reinterpret_cast(&this->num_clusters), sizeof(uint32_t)); + uint32_t n_clusters_l0 = l0.num_clusters; + out.write(reinterpret_cast(&n_clusters_l0), sizeof(uint32_t)); + + // === L0 (meso-clusters, always F32) === + for (size_t i = 0; i < n_clusters_l0; ++i) { + out.write( + reinterpret_cast(&l0.clusters[i].num_embeddings), sizeof(uint32_t) + ); + } + for (size_t i = 0; i < n_clusters_l0; ++i) { + out.write( + reinterpret_cast(l0.clusters[i].data), + sizeof(float) * l0.clusters[i].num_embeddings * this->num_dimensions + ); + } + for (size_t i = 0; i < n_clusters_l0; ++i) { + out.write( + reinterpret_cast(l0.clusters[i].indices), + sizeof(uint32_t) * l0.clusters[i].num_embeddings + ); + } + + // === L1 (data clusters) === + for (size_t i = 0; i < this->num_clusters; ++i) { + out.write( + reinterpret_cast(&this->clusters[i].num_embeddings), sizeof(uint32_t) + ); + } + for (size_t i = 0; i < this->num_clusters; ++i) { + out.write( + reinterpret_cast(this->clusters[i].data), + sizeof(data_t) * this->clusters[i].num_embeddings * this->num_dimensions + ); + } + for (size_t i = 0; i < this->num_clusters; ++i) { + out.write( + reinterpret_cast(this->clusters[i].indices), + sizeof(uint32_t) * this->clusters[i].num_embeddings + ); + } + + // === Shared metadata === + char norm = this->is_normalized; + out.write(&norm, sizeof(char)); + + // L0 centroids + out.write( + reinterpret_cast(l0.centroids.data()), + sizeof(float) * n_clusters_l0 * this->num_dimensions + ); + + // === U8 quantization params === + if constexpr (Q == U8) { + out.write(reinterpret_cast(&this->quantization_base), sizeof(float)); + out.write(reinterpret_cast(&this->quantization_scale), sizeof(float)); + } + } + + size_t GetInMemorySizeInBytes() const { + size_t size = sizeof(*this); + + // L1 clusters (inherited from base) + for (const auto& cluster : this->clusters) { + size += cluster.GetInMemorySizeInBytes(); + } + size += (this->clusters.capacity() - this->clusters.size()) * sizeof(Cluster); + size += this->centroids.capacity() * sizeof(float); + + // L0 meso-clusters + for (const auto& cluster : l0.clusters) { + size += cluster.GetInMemorySizeInBytes(); + } + size += (l0.clusters.capacity() - l0.clusters.size()) * sizeof(Cluster); + size += l0.centroids.capacity() * sizeof(float); + + return size; + } }; } // namespace PDX diff --git a/include/pdx/layout.hpp b/include/pdx/layout.hpp index 16970e3..16bf727 100644 --- a/include/pdx/layout.hpp +++ b/include/pdx/layout.hpp @@ -10,64 +10,77 @@ namespace PDX { // See the README of the following for a description of the PDX layout: // https://github.com/cwida/pdx template -inline void StoreClusterEmbeddings(typename PDX::IVF::cluster_t &cluster, const PDX::IVF &index, - const T *embeddings, const size_t num_embeddings); +inline void StoreClusterEmbeddings( + typename PDX::IVF::cluster_t& cluster, + const PDX::IVF& index, + const T* embeddings, + const size_t num_embeddings +); template <> -inline void -StoreClusterEmbeddings(PDX::IVF::cluster_t &cluster, - const PDX::IVF &index, - const float *const embeddings, const size_t num_embeddings) { - using matrix_t = PDX::eigen_matrix_t; - using h_matrix_t = Eigen::Matrix; +inline void StoreClusterEmbeddings( + PDX::IVF::cluster_t& cluster, + const PDX::IVF& index, + const float* const embeddings, + const size_t num_embeddings +) { + using matrix_t = PDX::eigen_matrix_t; + using h_matrix_t = Eigen::Matrix; - const auto vertical_d = index.num_vertical_dimensions; - const auto horizontal_d = index.num_horizontal_dimensions; + const auto vertical_d = index.num_vertical_dimensions; + const auto horizontal_d = index.num_horizontal_dimensions; - Eigen::Map in(embeddings, num_embeddings, index.num_dimensions); + Eigen::Map in(embeddings, num_embeddings, index.num_dimensions); - Eigen::Map out(cluster.data, vertical_d, num_embeddings); - out.noalias() = in.leftCols(vertical_d).transpose(); + Eigen::Map out(cluster.data, vertical_d, num_embeddings); + out.noalias() = in.leftCols(vertical_d).transpose(); - float *horizontal_out = cluster.data + num_embeddings * vertical_d; - for (size_t j = 0; j < horizontal_d; j += PDX::H_DIM_SIZE) { - Eigen::Map out_h(horizontal_out, num_embeddings, PDX::H_DIM_SIZE); - out_h.noalias() = in.block(0, vertical_d + j, num_embeddings, PDX::H_DIM_SIZE); - horizontal_out += num_embeddings * PDX::H_DIM_SIZE; - } + float* horizontal_out = cluster.data + num_embeddings * vertical_d; + for (size_t j = 0; j < horizontal_d; j += PDX::H_DIM_SIZE) { + Eigen::Map out_h(horizontal_out, num_embeddings, PDX::H_DIM_SIZE); + out_h.noalias() = in.block(0, vertical_d + j, num_embeddings, PDX::H_DIM_SIZE); + horizontal_out += num_embeddings * PDX::H_DIM_SIZE; + } } template <> -inline void -StoreClusterEmbeddings(PDX::IVF::cluster_t &cluster, - const PDX::IVF &index, - const uint8_t *const embeddings, const size_t num_embeddings) { - using u8_matrix_t = Eigen::Matrix; - using u8_v_matrix_t = Eigen::Matrix; - using u8_h_matrix_t = Eigen::Matrix; +inline void StoreClusterEmbeddings( + PDX::IVF::cluster_t& cluster, + const PDX::IVF& index, + const uint8_t* const embeddings, + const size_t num_embeddings +) { + using u8_matrix_t = Eigen::Matrix; + using u8_v_matrix_t = + Eigen::Matrix; + using u8_h_matrix_t = Eigen::Matrix; - const auto vertical_d = index.num_vertical_dimensions; - const auto horizontal_d = index.num_horizontal_dimensions; + const auto vertical_d = index.num_vertical_dimensions; + const auto horizontal_d = index.num_horizontal_dimensions; - Eigen::Map in(embeddings, num_embeddings, index.num_dimensions); + Eigen::Map in(embeddings, num_embeddings, index.num_dimensions); - size_t dim = 0; - for (; dim + PDX::U8_INTERLEAVE_SIZE <= vertical_d; dim += PDX::U8_INTERLEAVE_SIZE) { - Eigen::Map out_v(cluster.data + dim * num_embeddings, num_embeddings, PDX::U8_INTERLEAVE_SIZE); - out_v.noalias() = in.block(0, dim, num_embeddings, PDX::U8_INTERLEAVE_SIZE); - } - if (dim < vertical_d) { - auto remaining = static_cast(vertical_d - dim); - Eigen::Map out_v(cluster.data + dim * num_embeddings, num_embeddings, remaining); - out_v.noalias() = in.block(0, dim, num_embeddings, remaining); - } + size_t dim = 0; + for (; dim + PDX::U8_INTERLEAVE_SIZE <= vertical_d; dim += PDX::U8_INTERLEAVE_SIZE) { + Eigen::Map out_v( + cluster.data + dim * num_embeddings, num_embeddings, PDX::U8_INTERLEAVE_SIZE + ); + out_v.noalias() = in.block(0, dim, num_embeddings, PDX::U8_INTERLEAVE_SIZE); + } + if (dim < vertical_d) { + auto remaining = static_cast(vertical_d - dim); + Eigen::Map out_v( + cluster.data + dim * num_embeddings, num_embeddings, remaining + ); + out_v.noalias() = in.block(0, dim, num_embeddings, remaining); + } - uint8_t *horizontal_out = cluster.data + num_embeddings * vertical_d; - for (size_t j = 0; j < horizontal_d; j += PDX::H_DIM_SIZE) { - Eigen::Map out_h(horizontal_out, num_embeddings, PDX::H_DIM_SIZE); - out_h.noalias() = in.block(0, vertical_d + j, num_embeddings, PDX::H_DIM_SIZE); - horizontal_out += num_embeddings * PDX::H_DIM_SIZE; - } + uint8_t* horizontal_out = cluster.data + num_embeddings * vertical_d; + for (size_t j = 0; j < horizontal_d; j += PDX::H_DIM_SIZE) { + Eigen::Map out_h(horizontal_out, num_embeddings, PDX::H_DIM_SIZE); + out_h.noalias() = in.block(0, vertical_d + j, num_embeddings, PDX::H_DIM_SIZE); + horizontal_out += num_embeddings * PDX::H_DIM_SIZE; + } } } // namespace PDX diff --git a/include/pdx/lib/lib.hpp b/include/pdx/lib/lib.hpp index 0803ff0..ec5efdf 100644 --- a/include/pdx/lib/lib.hpp +++ b/include/pdx/lib/lib.hpp @@ -1,9 +1,9 @@ #pragma once -#include +#include #include +#include #include -#include #include #include @@ -14,103 +14,108 @@ namespace py = pybind11; namespace PDX { inline DistanceMetric ToDistanceMetric(uint8_t metric) { - switch (metric) { - case 0: return DistanceMetric::L2SQ; - case 1: return DistanceMetric::COSINE; - case 2: return DistanceMetric::IP; - default: throw std::runtime_error("Unknown distance metric: " + std::to_string(metric)); - } + switch (metric) { + case 0: + return DistanceMetric::L2SQ; + case 1: + return DistanceMetric::COSINE; + case 2: + return DistanceMetric::IP; + default: + throw std::runtime_error("Unknown distance metric: " + std::to_string(metric)); + } } class PyPDXIndex { - std::unique_ptr index; - - PyPDXIndex() = default; - -public: - PyPDXIndex(const std::string &index_type, uint32_t num_dimensions, - uint8_t distance_metric, uint32_t seed, uint32_t num_clusters, - uint32_t num_meso_clusters, bool normalize, - float sampling_fraction, uint32_t kmeans_iters) { - PDXIndexConfig config { - .num_dimensions = num_dimensions, - .distance_metric = ToDistanceMetric(distance_metric), - .seed = seed, - .num_clusters = num_clusters, - .num_meso_clusters = num_meso_clusters, - .normalize = normalize, - .sampling_fraction = sampling_fraction, - .kmeans_iters = kmeans_iters, - }; - if (index_type == "pdx_f32") { - index = std::make_unique(config); - } else if (index_type == "pdx_u8") { - index = std::make_unique(config); - } else if (index_type == "pdx_tree_f32") { - index = std::make_unique(config); - } else if (index_type == "pdx_tree_u8") { - index = std::make_unique(config); - } else { - throw std::runtime_error("Unknown index type: " + index_type + - ". Valid types: pdx_f32, pdx_u8, pdx_tree_f32, pdx_tree_u8"); - } - } - - static PyPDXIndex LoadFromFile(const std::string &path) { - PyPDXIndex self; - self.index = PDX::LoadPDXIndex(path); - return self; - } - - void BuildIndex(const py::array_t &data) { - auto buf = data.request(); - if (buf.ndim != 2) { - throw std::runtime_error("data must be a 2D numpy array (n_embeddings x dimensions)"); - } - auto *ptr = static_cast(buf.ptr); - size_t n = static_cast(buf.shape[0]); - index->BuildIndex(ptr, n); - } - - std::pair, py::array_t> - Search(const py::array_t &query, uint32_t knn) const { - auto buf = query.request(); - if (buf.ndim != 1) { - throw std::runtime_error("query must be a 1D numpy array"); - } - auto *ptr = static_cast(buf.ptr); - auto results = index->Search(ptr, knn); - size_t n = results.size(); - py::array_t ids(n); - py::array_t distances(n); - auto ids_ptr = ids.mutable_unchecked<1>(); - auto distances_ptr = distances.mutable_unchecked<1>(); - for (size_t i = 0; i < n; ++i) { - ids_ptr(i) = results[i].index; - distances_ptr(i) = results[i].distance; - } - return {ids, distances}; - } - - void SetNProbe(uint32_t n) const { - index->SetNProbe(n); - } - - void Save(const std::string &path) const { - index->Save(path); - } - - uint32_t GetNumDimensions() const { - return index->GetNumDimensions(); - } - - uint32_t GetNumClusters() const { - return index->GetNumClusters(); - } - - size_t GetInMemorySizeInBytes() const { - return index->GetInMemorySizeInBytes(); - } + std::unique_ptr index; + + PyPDXIndex() = default; + + public: + PyPDXIndex( + const std::string& index_type, + uint32_t num_dimensions, + uint8_t distance_metric, + uint32_t seed, + uint32_t num_clusters, + uint32_t num_meso_clusters, + bool normalize, + float sampling_fraction, + uint32_t kmeans_iters + ) { + PDXIndexConfig config{ + .num_dimensions = num_dimensions, + .distance_metric = ToDistanceMetric(distance_metric), + .seed = seed, + .num_clusters = num_clusters, + .num_meso_clusters = num_meso_clusters, + .normalize = normalize, + .sampling_fraction = sampling_fraction, + .kmeans_iters = kmeans_iters, + }; + if (index_type == "pdx_f32") { + index = std::make_unique(config); + } else if (index_type == "pdx_u8") { + index = std::make_unique(config); + } else if (index_type == "pdx_tree_f32") { + index = std::make_unique(config); + } else if (index_type == "pdx_tree_u8") { + index = std::make_unique(config); + } else { + throw std::runtime_error( + "Unknown index type: " + index_type + + ". Valid types: pdx_f32, pdx_u8, pdx_tree_f32, pdx_tree_u8" + ); + } + } + + static PyPDXIndex LoadFromFile(const std::string& path) { + PyPDXIndex self; + self.index = PDX::LoadPDXIndex(path); + return self; + } + + void BuildIndex(const py::array_t& data) { + auto buf = data.request(); + if (buf.ndim != 2) { + throw std::runtime_error("data must be a 2D numpy array (n_embeddings x dimensions)"); + } + auto* ptr = static_cast(buf.ptr); + size_t n = static_cast(buf.shape[0]); + index->BuildIndex(ptr, n); + } + + std::pair, py::array_t> Search( + const py::array_t& query, + uint32_t knn + ) const { + auto buf = query.request(); + if (buf.ndim != 1) { + throw std::runtime_error("query must be a 1D numpy array"); + } + auto* ptr = static_cast(buf.ptr); + auto results = index->Search(ptr, knn); + size_t n = results.size(); + py::array_t ids(n); + py::array_t distances(n); + auto ids_ptr = ids.mutable_unchecked<1>(); + auto distances_ptr = distances.mutable_unchecked<1>(); + for (size_t i = 0; i < n; ++i) { + ids_ptr(i) = results[i].index; + distances_ptr(i) = results[i].distance; + } + return {ids, distances}; + } + + void SetNProbe(uint32_t n) const { index->SetNProbe(n); } + + void Save(const std::string& path) const { index->Save(path); } + + uint32_t GetNumDimensions() const { return index->GetNumDimensions(); } + + uint32_t GetNumClusters() const { return index->GetNumClusters(); } + + size_t GetInMemorySizeInBytes() const { return index->GetInMemorySizeInBytes(); } }; } // namespace PDX diff --git a/include/pdx/pruners/adsampling.hpp b/include/pdx/pruners/adsampling.hpp index 0f31c98..f83fcab 100644 --- a/include/pdx/pruners/adsampling.hpp +++ b/include/pdx/pruners/adsampling.hpp @@ -1,9 +1,9 @@ #pragma once +#include "pdx/common.hpp" #include #include #include -#include "pdx/common.hpp" #ifdef HAS_FFTW #include @@ -12,111 +12,125 @@ namespace PDX { class ADSamplingPruner { - using matrix_t = eigen_matrix_t; - -public: - const uint32_t num_dimensions; - - ADSamplingPruner(const uint32_t num_dimensions, const int32_t seed) : num_dimensions(num_dimensions) { - ratios.resize(num_dimensions); - for (size_t i = 0; i < num_dimensions; ++i) { - ratios[i] = GetRatio(i); - } - std::mt19937 gen(seed); - std::normal_distribution normal_dist; - Eigen::MatrixXf random_matrix = Eigen::MatrixXf::Zero( - static_cast(num_dimensions), static_cast(num_dimensions)); - for (size_t i = 0; i < num_dimensions; ++i) { - for (size_t j = 0; j < num_dimensions; ++j) { - random_matrix(static_cast(i), static_cast(j)) = normal_dist(gen); - } - } - const Eigen::HouseholderQR qr(random_matrix); - matrix = qr.householderQ(); - } - - ADSamplingPruner(const uint32_t num_dimensions, const float *matrix_p) : num_dimensions(num_dimensions) { - ratios.resize(num_dimensions); - for (size_t i = 0; i < num_dimensions; ++i) { - ratios[i] = GetRatio(i); - } + using matrix_t = eigen_matrix_t; + + public: + const uint32_t num_dimensions; + + ADSamplingPruner(const uint32_t num_dimensions, const int32_t seed) + : num_dimensions(num_dimensions) { + ratios.resize(num_dimensions); + for (size_t i = 0; i < num_dimensions; ++i) { + ratios[i] = GetRatio(i); + } + std::mt19937 gen(seed); + std::normal_distribution normal_dist; + Eigen::MatrixXf random_matrix = Eigen::MatrixXf::Zero( + static_cast(num_dimensions), static_cast(num_dimensions) + ); + for (size_t i = 0; i < num_dimensions; ++i) { + for (size_t j = 0; j < num_dimensions; ++j) { + random_matrix(static_cast(i), static_cast(j)) = + normal_dist(gen); + } + } + const Eigen::HouseholderQR qr(random_matrix); + matrix = qr.householderQ(); + } + + ADSamplingPruner(const uint32_t num_dimensions, const float* matrix_p) + : num_dimensions(num_dimensions) { + ratios.resize(num_dimensions); + for (size_t i = 0; i < num_dimensions; ++i) { + ratios[i] = GetRatio(i); + } #ifdef HAS_FFTW - if (num_dimensions >= D_THRESHOLD_FOR_DCT_ROTATION) { - matrix = Eigen::Map(matrix_p, 1, num_dimensions); - } else { - matrix = Eigen::Map(matrix_p, num_dimensions, num_dimensions); - } + if (num_dimensions >= D_THRESHOLD_FOR_DCT_ROTATION) { + matrix = Eigen::Map(matrix_p, 1, num_dimensions); + } else { + matrix = Eigen::Map(matrix_p, num_dimensions, num_dimensions); + } #else - matrix = Eigen::Map(matrix_p, num_dimensions, num_dimensions); + matrix = Eigen::Map(matrix_p, num_dimensions, num_dimensions); #endif - } - - void SetPruningAggresiveness(const float pruning_aggressiveness) { - ADSamplingPruner::pruning_aggressiveness = pruning_aggressiveness; - for (size_t i = 0; i < num_dimensions; ++i) { - ratios[i] = GetRatio(i); - } - } - - void SetMatrix(const Eigen::MatrixXf &matrix) { - ADSamplingPruner::matrix = matrix; - } - - const matrix_t& GetMatrix() const { return matrix; } - - float GetPruningThreshold(uint32_t, - std::priority_queue, VectorComparator> &heap, - const uint32_t current_dimension_idx) const { - float ratio = current_dimension_idx == num_dimensions ? 1 : ratios[current_dimension_idx]; - return heap.top().distance * ratio; - } - - void PreprocessQuery(const float *PDX_RESTRICT const raw_query_embedding, - float *PDX_RESTRICT const output_query_embedding) const { - PreprocessEmbeddings(raw_query_embedding, output_query_embedding, 1); - } - - void PreprocessEmbeddings(const float *PDX_RESTRICT const input_embeddings, - float *PDX_RESTRICT const output_embeddings, const size_t num_embeddings) const { - Rotate(input_embeddings, output_embeddings, num_embeddings); - } - -private: - float pruning_aggressiveness = ADSAMPLING_PRUNING_AGGRESIVENESS; - matrix_t matrix; - std::vector ratios; - - float GetRatio(const size_t &visited_dimensions) const { - if (visited_dimensions == 0) { - return 1; - } - if (visited_dimensions == num_dimensions) { - return 1.0; - } - return static_cast(visited_dimensions) / num_dimensions * - (1.0 + pruning_aggressiveness / std::sqrt(visited_dimensions)) * - (1.0 + pruning_aggressiveness / std::sqrt(visited_dimensions)); - } - - /** - * @brief Rotates embeddings using the rotation matrix. - * - * Transforms embeddings to a rotated space where dimensions contribute more equally - * to the total distance, enabling effective early termination. - * - * @param embeddings Input embeddings (row-major, n × num_dimensions) - * @param out_buffer Output buffer for rotated embeddings (n × num_dimensions) - * @param n Number of embeddings to rotate - */ - void Rotate(const float *PDX_RESTRICT const embeddings, float *PDX_RESTRICT const out_buffer, - const size_t n) const { + } + + void SetPruningAggresiveness(const float pruning_aggressiveness) { + ADSamplingPruner::pruning_aggressiveness = pruning_aggressiveness; + for (size_t i = 0; i < num_dimensions; ++i) { + ratios[i] = GetRatio(i); + } + } + + void SetMatrix(const Eigen::MatrixXf& matrix) { ADSamplingPruner::matrix = matrix; } + + const matrix_t& GetMatrix() const { return matrix; } + + float GetPruningThreshold( + uint32_t, + std::priority_queue, VectorComparator>& heap, + const uint32_t current_dimension_idx + ) const { + float ratio = current_dimension_idx == num_dimensions ? 1 : ratios[current_dimension_idx]; + return heap.top().distance * ratio; + } + + void PreprocessQuery( + const float* PDX_RESTRICT const raw_query_embedding, + float* PDX_RESTRICT const output_query_embedding + ) const { + PreprocessEmbeddings(raw_query_embedding, output_query_embedding, 1); + } + + void PreprocessEmbeddings( + const float* PDX_RESTRICT const input_embeddings, + float* PDX_RESTRICT const output_embeddings, + const size_t num_embeddings + ) const { + Rotate(input_embeddings, output_embeddings, num_embeddings); + } + + private: + float pruning_aggressiveness = ADSAMPLING_PRUNING_AGGRESIVENESS; + matrix_t matrix; + std::vector ratios; + + float GetRatio(const size_t& visited_dimensions) const { + if (visited_dimensions == 0) { + return 1; + } + if (visited_dimensions == num_dimensions) { + return 1.0; + } + return static_cast(visited_dimensions) / num_dimensions * + (1.0 + pruning_aggressiveness / std::sqrt(visited_dimensions)) * + (1.0 + pruning_aggressiveness / std::sqrt(visited_dimensions)); + } + + /** + * @brief Rotates embeddings using the rotation matrix. + * + * Transforms embeddings to a rotated space where dimensions contribute more equally + * to the total distance, enabling effective early termination. + * + * @param embeddings Input embeddings (row-major, n × num_dimensions) + * @param out_buffer Output buffer for rotated embeddings (n × num_dimensions) + * @param n Number of embeddings to rotate + */ + void Rotate( + const float* PDX_RESTRICT const embeddings, + float* PDX_RESTRICT const out_buffer, + const size_t n + ) const { #ifdef HAS_FFTW Eigen::Map query_matrix(raw_query, num_dimensions); Eigen::Map output(query, num_dimensions); if (num_dimensions >= D_THRESHOLD_FOR_DCT_ROTATION) { Eigen::RowVectorXf first_row = matrix.row(0); Eigen::RowVectorXf pre_output = query_matrix.array() * first_row.array(); - fftwf_plan plan = fftwf_plan_r2r_1d(num_dimensions, pre_output.data(), output.data(), FFTW_REDFT10, FFTW_ESTIMATE); + fftwf_plan plan = fftwf_plan_r2r_1d( + num_dimensions, pre_output.data(), output.data(), FFTW_REDFT10, FFTW_ESTIMATE + ); fftwf_execute(plan); fftwf_destroy_plan(plan); output[0] *= std::sqrt(1.0 / (4 * num_dimensions)); @@ -131,11 +145,22 @@ class ADSamplingPruner { const float beta = 0.0f; int dim = static_cast(num_dimensions); int n_blas = static_cast(n); - sgemm_(&trans_a, &trans_b, &dim, &n_blas, &dim, &alpha, matrix.data(), &dim, embeddings, &dim, &beta, - out_buffer, &dim); + sgemm_( + &trans_a, + &trans_b, + &dim, + &n_blas, + &dim, + &alpha, + matrix.data(), + &dim, + embeddings, + &dim, + &beta, + out_buffer, + &dim + ); } - }; } // namespace PDX - diff --git a/include/pdx/quantizers/scalar.hpp b/include/pdx/quantizers/scalar.hpp index 10f1c8c..73ee622 100644 --- a/include/pdx/quantizers/scalar.hpp +++ b/include/pdx/quantizers/scalar.hpp @@ -1,83 +1,90 @@ #pragma once -#include -#include +#include "pdx/common.hpp" #include +#include +#include #include -#include "pdx/common.hpp" namespace PDX { struct ScalarQuantizationParams { - float quantization_base; - float quantization_scale; + float quantization_base; + float quantization_scale; }; class Quantizer { -public: - explicit Quantizer(size_t num_dimensions) : num_dimensions(num_dimensions) { - } - virtual ~Quantizer() = default; + public: + explicit Quantizer(size_t num_dimensions) : num_dimensions(num_dimensions) {} + virtual ~Quantizer() = default; -public: - void NormalizeQuery(const float *src, float *out) const { - float sum = 0.0f; - for (size_t i = 0; i < num_dimensions; ++i) { - sum += src[i] * src[i]; - } + public: + void NormalizeQuery(const float* src, float* out) const { + float sum = 0.0f; + for (size_t i = 0; i < num_dimensions; ++i) { + sum += src[i] * src[i]; + } - if (sum == 0.0f) { - return; - } + if (sum == 0.0f) { + return; + } - float norm = std::sqrt(sum); - for (size_t i = 0; i < num_dimensions; ++i) { - out[i] = src[i] / norm; - } - } - const size_t num_dimensions; + float norm = std::sqrt(sum); + for (size_t i = 0; i < num_dimensions; ++i) { + out[i] = src[i] / norm; + } + } + const size_t num_dimensions; }; template class ScalarQuantizer : public Quantizer { -public: - using quantized_embedding_t = pdx_quantized_embedding_t; + public: + using quantized_embedding_t = pdx_quantized_embedding_t; - explicit ScalarQuantizer(size_t num_dimensions) : Quantizer(num_dimensions) { - } + explicit ScalarQuantizer(size_t num_dimensions) : Quantizer(num_dimensions) {} #ifdef __AVX512F__ - // We rely on _mm512_dpbusds_epi32 that has asymmetric operands - static constexpr uint8_t MAX_VALUE = 127; + // We rely on _mm512_dpbusds_epi32 that has asymmetric operands + static constexpr uint8_t MAX_VALUE = 127; #else - static constexpr uint8_t MAX_VALUE = 255; + static constexpr uint8_t MAX_VALUE = 255; #endif - static ScalarQuantizationParams ComputeQuantizationParams(const float *embeddings, const size_t total_elements) { - float global_min = std::numeric_limits::max(); - float global_max = std::numeric_limits::lowest(); - for (size_t i = 0; i < total_elements; ++i) { - global_min = std::min(global_min, embeddings[i]); - global_max = std::max(global_max, embeddings[i]); - } - const float range = global_max - global_min; - return {global_min, (range > 0) ? static_cast(MAX_VALUE) / range : 1.0f}; - } + static ScalarQuantizationParams ComputeQuantizationParams( + const float* embeddings, + const size_t total_elements + ) { + float global_min = std::numeric_limits::max(); + float global_max = std::numeric_limits::lowest(); + for (size_t i = 0; i < total_elements; ++i) { + global_min = std::min(global_min, embeddings[i]); + global_max = std::max(global_max, embeddings[i]); + } + const float range = global_max - global_min; + return {global_min, (range > 0) ? static_cast(MAX_VALUE) / range : 1.0f}; + } - void QuantizeEmbedding(const float *embedding, const float quantization_base, const float quantization_scale, - quantized_embedding_t *output_quantized_embedding) { - for (size_t i = 0; i < num_dimensions; ++i) { - const int rounded = static_cast(std::round((embedding[i] - quantization_base) * quantization_scale)); - if (PDX_UNLIKELY(rounded > MAX_VALUE)) { - output_quantized_embedding[i] = MAX_VALUE; - } else if (PDX_UNLIKELY(rounded < 0)) { - output_quantized_embedding[i] = 0; - } else { - output_quantized_embedding[i] = static_cast(rounded); - } - } - } + void QuantizeEmbedding( + const float* embedding, + const float quantization_base, + const float quantization_scale, + quantized_embedding_t* output_quantized_embedding + ) { + for (size_t i = 0; i < num_dimensions; ++i) { + const int rounded = + static_cast(std::round((embedding[i] - quantization_base) * quantization_scale) + ); + if (PDX_UNLIKELY(rounded > MAX_VALUE)) { + output_quantized_embedding[i] = MAX_VALUE; + } else if (PDX_UNLIKELY(rounded < 0)) { + output_quantized_embedding[i] = 0; + } else { + output_quantized_embedding[i] = static_cast(rounded); + } + } + } }; } // namespace PDX diff --git a/include/pdx/searcher.hpp b/include/pdx/searcher.hpp index caf511f..c11aaf1 100644 --- a/include/pdx/searcher.hpp +++ b/include/pdx/searcher.hpp @@ -1,539 +1,811 @@ #pragma once -#include -#include -#include -#include -#include -#include #include "pdx/common.hpp" -#include "pdx/utils.hpp" #include "pdx/db_mock/predicate_evaluator.hpp" #include "pdx/distance_computers/base_computers.hpp" -#include "pdx/quantizers/scalar.hpp" #include "pdx/ivf_wrapper.hpp" #include "pdx/pruners/adsampling.hpp" +#include "pdx/quantizers/scalar.hpp" +#include "pdx/utils.hpp" +#include +#include +#include +#include +#include +#include namespace PDX { -template , class Quantizer = ScalarQuantizer, - DistanceMetric alpha = DistanceMetric::L2SQ, class Pruner = ADSamplingPruner> +template < + Quantization Q = F32, + class Index = IVF, + class Quantizer = ScalarQuantizer, + DistanceMetric alpha = DistanceMetric::L2SQ, + class Pruner = ADSamplingPruner> class PDXearch { -public: - using distance_t = pdx_distance_t; - using data_t = pdx_data_t; - using quantized_embedding_t = pdx_quantized_embedding_t; - using index_t = Index; - using cluster_t = Cluster; - using distance_computer_t = DistanceComputer; - - Quantizer quantizer; - Pruner &pruner; - index_t &pdx_data; - - PDXearch(index_t &data_index, Pruner &pruner) - : quantizer(data_index.num_dimensions), pruner(pruner), pdx_data(data_index), - cluster_offsets(new size_t[data_index.num_clusters]) { - for (size_t i = 0; i < data_index.num_clusters; ++i) { - cluster_offsets[i] = total_embeddings; - total_embeddings += data_index.clusters[i].num_embeddings; - max_cluster_size = std::max(max_cluster_size, static_cast(data_index.clusters[i].num_embeddings)); - } - } - - void SetNProbe(size_t nprobe) { - ivf_nprobe = nprobe; - } - - size_t GetNProbe() const { - return ivf_nprobe; + public: + using distance_t = pdx_distance_t; + using data_t = pdx_data_t; + using quantized_embedding_t = pdx_quantized_embedding_t; + using index_t = Index; + using cluster_t = Cluster; + using distance_computer_t = DistanceComputer; + + Quantizer quantizer; + Pruner& pruner; + index_t& pdx_data; + + PDXearch(index_t& data_index, Pruner& pruner) + : quantizer(data_index.num_dimensions), pruner(pruner), pdx_data(data_index), + cluster_offsets(new size_t[data_index.num_clusters]) { + for (size_t i = 0; i < data_index.num_clusters; ++i) { + cluster_offsets[i] = total_embeddings; + total_embeddings += data_index.clusters[i].num_embeddings; + max_cluster_size = std::max( + max_cluster_size, static_cast(data_index.clusters[i].num_embeddings) + ); + } } - void SetClusterAccessOrder(const std::vector &cluster_indexes) { + void SetNProbe(size_t nprobe) { ivf_nprobe = nprobe; } + + size_t GetNProbe() const { return ivf_nprobe; } + + void SetClusterAccessOrder(const std::vector& cluster_indexes) { cluster_indices_in_access_order.reset(new uint32_t[cluster_indexes.size()]); - std::copy(cluster_indexes.begin(), cluster_indexes.end(), cluster_indices_in_access_order.get()); + std::copy( + cluster_indexes.begin(), cluster_indexes.end(), cluster_indices_in_access_order.get() + ); } std::unique_ptr cluster_offsets; -protected: + protected: float selectivity_threshold = 0.80; size_t ivf_nprobe = 0; - size_t total_embeddings {0}; - size_t max_cluster_size {0}; + size_t total_embeddings{0}; + size_t max_cluster_size{0}; // Prioritized list of indices of the clusters to probe. E.g., [0, 2, 1]. std::unique_ptr cluster_indices_in_access_order; // Start: State for the current filtered search. uint32_t k = 0; - quantized_embedding_t *prepared_query = nullptr; + quantized_embedding_t* prepared_query = nullptr; // Predicate evaluator for this rowgroup. std::unique_ptr predicate_evaluator; - void ResetPruningDistances(size_t n_vectors, distance_t *pruning_distances) { - std::fill(pruning_distances, pruning_distances + n_vectors, distance_t {0}); + void ResetPruningDistances(size_t n_vectors, distance_t* pruning_distances) { + std::fill(pruning_distances, pruning_distances + n_vectors, distance_t{0}); } - // The pruning threshold by default is the top of the heap - void GetPruningThreshold(uint32_t k, - std::priority_queue, VectorComparator> &heap, - distance_t &pruning_threshold, uint32_t current_dimension_idx) { + // The pruning threshold by default is the top of the heap + void GetPruningThreshold( + uint32_t k, + std::priority_queue, VectorComparator>& heap, + distance_t& pruning_threshold, + uint32_t current_dimension_idx + ) { const float float_threshold = pruner.GetPruningThreshold(k, heap, current_dimension_idx); if constexpr (Q == U8) { // We need to avoid undefined behaviour when overflow happens const float scaled = float_threshold * pdx_data.quantization_scale_squared; pruning_threshold = scaled >= static_cast(std::numeric_limits::max()) - ? std::numeric_limits::max() - : static_cast(scaled); + ? std::numeric_limits::max() + : static_cast(scaled); } else { pruning_threshold = float_threshold; } }; - void EvaluatePruningPredicateScalar(uint32_t &n_pruned, size_t n_vectors, distance_t *pruning_distances, - const distance_t pruning_threshold) { - for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { - n_pruned += pruning_distances[vector_idx] >= pruning_threshold; - } - }; + void EvaluatePruningPredicateScalar( + uint32_t& n_pruned, + size_t n_vectors, + distance_t* pruning_distances, + const distance_t pruning_threshold + ) { + for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { + n_pruned += pruning_distances[vector_idx] >= pruning_threshold; + } + }; - void EvaluatePruningPredicateOnPositionsArray(size_t n_vectors, size_t &n_vectors_not_pruned, - uint32_t *pruning_positions, distance_t pruning_threshold, - distance_t *pruning_distances) { + void EvaluatePruningPredicateOnPositionsArray( + size_t n_vectors, + size_t& n_vectors_not_pruned, + uint32_t* pruning_positions, + distance_t pruning_threshold, + distance_t* pruning_distances + ) { n_vectors_not_pruned = 0; for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { pruning_positions[n_vectors_not_pruned] = pruning_positions[vector_idx]; - n_vectors_not_pruned += pruning_distances[pruning_positions[vector_idx]] < pruning_threshold; + n_vectors_not_pruned += + pruning_distances[pruning_positions[vector_idx]] < pruning_threshold; } }; - template - void InitPositionsArray(size_t n_vectors, size_t &n_vectors_not_pruned, uint32_t *pruning_positions, - distance_t pruning_threshold, distance_t *pruning_distances, - const uint8_t *selection_vector = nullptr) { - n_vectors_not_pruned = 0; - if constexpr (IS_FILTERED) { - for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { - pruning_positions[n_vectors_not_pruned] = vector_idx; - n_vectors_not_pruned += - (pruning_distances[vector_idx] < pruning_threshold) && (selection_vector[vector_idx] == 1); - } - } else { - for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { - pruning_positions[n_vectors_not_pruned] = vector_idx; - n_vectors_not_pruned += pruning_distances[vector_idx] < pruning_threshold; - } - } - }; - - void InitPositionsArrayFromSelectionVector(size_t n_vectors, size_t &n_vectors_not_pruned, - uint32_t *pruning_positions, const uint8_t *selection_vector) { - n_vectors_not_pruned = 0; - for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { - pruning_positions[n_vectors_not_pruned] = vector_idx; - n_vectors_not_pruned += selection_vector[vector_idx] == 1; - } - }; - - void MaskDistancesWithSelectionVector(size_t n_vectors, distance_t *pruning_distances, - const uint8_t *selection_vector) { - for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { - if (selection_vector[vector_idx] == 0) { - // Why max()/2? To prevent overflow if distances are still added to these - pruning_distances[vector_idx] = std::numeric_limits::max() / 2; - } - } - }; - - static void GetClustersAccessOrderIVF(const float *PDX_RESTRICT query, const index_t &data, size_t nprobe, - uint32_t *clusters_indices) { - std::unique_ptr distances_to_centroids(new float[data.num_clusters]); - for (size_t cluster_idx = 0; cluster_idx < data.num_clusters; cluster_idx++) { - distances_to_centroids[cluster_idx] = DistanceComputer::Horizontal( - query, data.centroids.data() + cluster_idx * data.num_dimensions, data.num_dimensions); - } - std::iota(clusters_indices, clusters_indices + data.num_clusters, static_cast(0)); - if (nprobe >= data.num_clusters) { - std::sort(clusters_indices, clusters_indices + data.num_clusters, - [&distances_to_centroids](size_t i1, size_t i2) { - return distances_to_centroids[i1] < distances_to_centroids[i2]; - }); - } else { - std::partial_sort(clusters_indices, clusters_indices + static_cast(nprobe), - clusters_indices + data.num_clusters, [&distances_to_centroids](size_t i1, size_t i2) { - return distances_to_centroids[i1] < distances_to_centroids[i2]; - }); - } - } - - // On the first bucket, we do a full scan (we do not prune vectors) - void Start(const quantized_embedding_t *PDX_RESTRICT query, const data_t *data, const size_t n_vectors, uint32_t k, - const uint32_t *vector_indices, uint32_t *pruning_positions, distance_t *pruning_distances, - std::priority_queue, VectorComparator> &heap) { - ResetPruningDistances(n_vectors, pruning_distances); - distance_computer_t::Vertical(query, data, n_vectors, n_vectors, 0, pdx_data.num_vertical_dimensions, - pruning_distances, pruning_positions); - for (size_t horizontal_dimension = 0; horizontal_dimension < pdx_data.num_horizontal_dimensions; - horizontal_dimension += H_DIM_SIZE) { - for (size_t vector_idx = 0; vector_idx < n_vectors; vector_idx++) { - size_t data_pos = (pdx_data.num_vertical_dimensions * n_vectors) + (horizontal_dimension * n_vectors) + - (vector_idx * H_DIM_SIZE); - pruning_distances[vector_idx] += distance_computer_t::Horizontal( - query + pdx_data.num_vertical_dimensions + horizontal_dimension, data + data_pos, H_DIM_SIZE); - } - } - size_t max_possible_k = std::min(static_cast(k) - heap.size(), - n_vectors); // Note: Start() should not be called if heap.size() >= k - std::unique_ptr indices_sorted(new size_t[n_vectors]); - std::iota(indices_sorted.get(), indices_sorted.get() + n_vectors, static_cast(0)); - std::partial_sort(indices_sorted.get(), indices_sorted.get() + static_cast(max_possible_k), - indices_sorted.get() + n_vectors, [pruning_distances](size_t i1, size_t i2) { - return pruning_distances[i1] < pruning_distances[i2]; - }); - // insert first k results into the heap - for (size_t idx = 0; idx < max_possible_k; ++idx) { - auto embedding = KNNCandidate {}; - size_t index = indices_sorted[idx]; - embedding.index = vector_indices[index]; - embedding.distance = static_cast(pruning_distances[index]); - if constexpr (Q == U8) { - embedding.distance *= pdx_data.inverse_quantization_scale_squared; - } - heap.push(embedding); - } - } - - // On the first bucket, we do a full scan (we do not prune vectors) - void FilteredStart(const quantized_embedding_t *PDX_RESTRICT query, const data_t *data, const size_t n_vectors, - uint32_t k, const uint32_t *vector_indices, uint32_t *pruning_positions, - distance_t *pruning_distances, - std::priority_queue, VectorComparator> &heap, - uint8_t *selection_vector, uint32_t passing_tuples) { - ResetPruningDistances(n_vectors, pruning_distances); - size_t n_vectors_not_pruned = 0; - float selection_percentage = (static_cast(passing_tuples) / static_cast(n_vectors)); - InitPositionsArrayFromSelectionVector(n_vectors, n_vectors_not_pruned, pruning_positions, selection_vector); - // Always start with horizontal block, regardless of selectivity - for (size_t horizontal_dimension = 0; horizontal_dimension < pdx_data.num_horizontal_dimensions; - horizontal_dimension += H_DIM_SIZE) { - size_t offset_data = (pdx_data.num_vertical_dimensions * n_vectors) + (horizontal_dimension * n_vectors); - for (size_t vector_idx = 0; vector_idx < n_vectors_not_pruned; vector_idx++) { - size_t v_idx = pruning_positions[vector_idx]; - size_t data_pos = offset_data + (v_idx * H_DIM_SIZE); - pruning_distances[v_idx] += distance_computer_t::Horizontal( - query + pdx_data.num_vertical_dimensions + horizontal_dimension, data + data_pos, H_DIM_SIZE); - } - } - if (selection_percentage > (1 - selectivity_threshold)) { - // It is then faster to do the full scan (thanks to SIMD) - distance_computer_t::Vertical(query, data, n_vectors, n_vectors, 0, pdx_data.num_vertical_dimensions, - pruning_distances, pruning_positions); - } else { - // We access individual values - distance_computer_t::VerticalPruning(query, data, n_vectors_not_pruned, n_vectors, 0, - pdx_data.num_vertical_dimensions, pruning_distances, - pruning_positions); - } - // TODO: Everything down from here is a bottleneck when selection % is ultra low - size_t max_possible_k = std::min(static_cast(k) - heap.size(), static_cast(passing_tuples)); - MaskDistancesWithSelectionVector(n_vectors, pruning_distances, selection_vector); - std::unique_ptr indices_sorted(new size_t[n_vectors]); - std::iota(indices_sorted.get(), indices_sorted.get() + n_vectors, static_cast(0)); - std::partial_sort(indices_sorted.get(), indices_sorted.get() + static_cast(max_possible_k), - indices_sorted.get() + n_vectors, [pruning_distances](size_t i1, size_t i2) { - return pruning_distances[i1] < pruning_distances[i2]; - }); - // insert first k results into the heap - for (size_t idx = 0; idx < max_possible_k; ++idx) { - auto embedding = KNNCandidate {}; - size_t index = indices_sorted[idx]; - embedding.index = vector_indices[index]; - embedding.distance = static_cast(pruning_distances[index]); - if constexpr (Q == U8) { - embedding.distance *= pdx_data.inverse_quantization_scale_squared; - } - heap.push(embedding); - } - } - - // On the warmup phase, we keep scanning dimensions until the amount of not-yet pruned vectors is low - template - void Warmup(const quantized_embedding_t *PDX_RESTRICT query, const data_t *PDX_RESTRICT data, - const size_t n_vectors, uint32_t k, float tuples_threshold, uint32_t *pruning_positions, - distance_t *pruning_distances, distance_t &pruning_threshold, - std::priority_queue, VectorComparator> &heap, - uint32_t ¤t_dimension_idx, size_t &n_vectors_not_pruned, uint32_t passing_tuples = 0, - uint8_t *selection_vector = nullptr) { - current_dimension_idx = 0; - size_t cur_subgrouping_size_idx = 0; - size_t tuples_needed_to_exit = static_cast(std::ceil(tuples_threshold * static_cast(n_vectors))); - ResetPruningDistances(n_vectors, pruning_distances); - uint32_t n_tuples_to_prune = 0; - if constexpr (FILTERED) { - float selection_percentage = (static_cast(passing_tuples) / static_cast(n_vectors)); - MaskDistancesWithSelectionVector(n_vectors, pruning_distances, selection_vector); - if (selection_percentage < (1 - tuples_threshold)) { - // Go directly to the PRUNE phase for direct tuples access in the Horizontal block - return; - } - } - GetPruningThreshold(k, heap, pruning_threshold, current_dimension_idx); - while (n_tuples_to_prune < tuples_needed_to_exit && current_dimension_idx < pdx_data.num_vertical_dimensions) { - size_t last_dimension_to_fetch = - std::min(current_dimension_idx + DIMENSIONS_FETCHING_SIZES[cur_subgrouping_size_idx], - pdx_data.num_vertical_dimensions); - distance_computer_t::Vertical(query, data, n_vectors, n_vectors, current_dimension_idx, - last_dimension_to_fetch, pruning_distances, pruning_positions); - current_dimension_idx = last_dimension_to_fetch; - cur_subgrouping_size_idx += 1; - GetPruningThreshold(k, heap, pruning_threshold, current_dimension_idx); - n_tuples_to_prune = 0; - EvaluatePruningPredicateScalar(n_tuples_to_prune, n_vectors, pruning_distances, pruning_threshold); - } - } - - // We scan only the not-yet pruned vectors - template - void Prune(const quantized_embedding_t *PDX_RESTRICT query, const data_t *PDX_RESTRICT data, const size_t n_vectors, - uint32_t k, uint32_t *pruning_positions, distance_t *pruning_distances, distance_t &pruning_threshold, - std::priority_queue, VectorComparator> &heap, - uint32_t ¤t_dimension_idx, size_t &n_vectors_not_pruned, - const uint8_t *selection_vector = nullptr) { - GetPruningThreshold(k, heap, pruning_threshold, current_dimension_idx); - InitPositionsArray(n_vectors, n_vectors_not_pruned, pruning_positions, pruning_threshold, - pruning_distances, selection_vector); - size_t cur_n_vectors_not_pruned = 0; - size_t current_vertical_dimension = current_dimension_idx; - size_t current_horizontal_dimension = 0; - while (pdx_data.num_horizontal_dimensions && n_vectors_not_pruned && - current_horizontal_dimension < pdx_data.num_horizontal_dimensions) { - cur_n_vectors_not_pruned = n_vectors_not_pruned; - size_t offset_data = - (pdx_data.num_vertical_dimensions * n_vectors) + (current_horizontal_dimension * n_vectors); - for (size_t vector_idx = 0; vector_idx < n_vectors_not_pruned; vector_idx++) { - size_t v_idx = pruning_positions[vector_idx]; - size_t data_pos = offset_data + (v_idx * H_DIM_SIZE); - __builtin_prefetch(data + data_pos, 0, 3); - } - size_t offset_query = pdx_data.num_vertical_dimensions + current_horizontal_dimension; - for (size_t vector_idx = 0; vector_idx < n_vectors_not_pruned; vector_idx++) { - size_t v_idx = pruning_positions[vector_idx]; - size_t data_pos = offset_data + (v_idx * H_DIM_SIZE); - pruning_distances[v_idx] += - distance_computer_t::Horizontal(query + offset_query, data + data_pos, H_DIM_SIZE); - } - // end of clipping - current_horizontal_dimension += H_DIM_SIZE; - current_dimension_idx += H_DIM_SIZE; - GetPruningThreshold(k, heap, pruning_threshold, current_dimension_idx); - assert(current_dimension_idx == current_vertical_dimension + current_horizontal_dimension); - EvaluatePruningPredicateOnPositionsArray(cur_n_vectors_not_pruned, n_vectors_not_pruned, pruning_positions, - pruning_threshold, pruning_distances); - } - // GO THROUGH THE REST IN THE VERTICAL - while (n_vectors_not_pruned && current_vertical_dimension < pdx_data.num_vertical_dimensions) { - cur_n_vectors_not_pruned = n_vectors_not_pruned; - size_t last_dimension_to_test_idx = std::min(current_vertical_dimension + H_DIM_SIZE, - static_cast(pdx_data.num_vertical_dimensions)); - distance_computer_t::VerticalPruning(query, data, cur_n_vectors_not_pruned, n_vectors, - current_vertical_dimension, last_dimension_to_test_idx, - pruning_distances, pruning_positions); - current_dimension_idx = - std::min(current_dimension_idx + H_DIM_SIZE, static_cast(pdx_data.num_dimensions)); - current_vertical_dimension = std::min(static_cast(current_vertical_dimension + H_DIM_SIZE), - pdx_data.num_vertical_dimensions); - assert(current_dimension_idx == current_vertical_dimension + current_horizontal_dimension); - GetPruningThreshold(k, heap, pruning_threshold, current_dimension_idx); - EvaluatePruningPredicateOnPositionsArray(cur_n_vectors_not_pruned, n_vectors_not_pruned, pruning_positions, - pruning_threshold, pruning_distances); - if (current_dimension_idx == pdx_data.num_dimensions) { - break; - } - } - } - - void MergeIntoHeap(const uint32_t *vector_indices, const size_t n_vectors, const uint32_t k, - const uint32_t *pruning_positions, const distance_t *pruning_distances, - std::priority_queue, VectorComparator> &heap) { - for (size_t position_idx = 0; position_idx < n_vectors; ++position_idx) { - const size_t index = pruning_positions[position_idx]; - float current_distance = static_cast(pruning_distances[index]); - if constexpr (Q == U8) { - current_distance *= pdx_data.inverse_quantization_scale_squared; - } - if (heap.size() < k || current_distance < heap.top().distance) { - KNNCandidate embedding {}; - embedding.distance = current_distance; - embedding.index = vector_indices[index]; - if (heap.size() >= k) { - heap.pop(); - } - heap.push(embedding); - } - } - } - - [[nodiscard]] static std::vector - BuildResultSetFromHeap(uint32_t k, - std::priority_queue, VectorComparator> &heap) { - // Pop the initialization element from the heap, as it can't be part of the result. - if (!heap.empty() && heap.top().distance == std::numeric_limits::max()) { - heap.pop(); - } - - size_t result_set_size = std::min(heap.size(), static_cast(k)); - std::vector result; - result.resize(result_set_size); - for (size_t i = result_set_size; i > 0; --i) { - result[i - 1] = heap.top(); - heap.pop(); - } - return result; - } + template + void InitPositionsArray( + size_t n_vectors, + size_t& n_vectors_not_pruned, + uint32_t* pruning_positions, + distance_t pruning_threshold, + distance_t* pruning_distances, + const uint8_t* selection_vector = nullptr + ) { + n_vectors_not_pruned = 0; + if constexpr (IS_FILTERED) { + for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { + pruning_positions[n_vectors_not_pruned] = vector_idx; + n_vectors_not_pruned += (pruning_distances[vector_idx] < pruning_threshold) && + (selection_vector[vector_idx] == 1); + } + } else { + for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { + pruning_positions[n_vectors_not_pruned] = vector_idx; + n_vectors_not_pruned += pruning_distances[vector_idx] < pruning_threshold; + } + } + }; + + void InitPositionsArrayFromSelectionVector( + size_t n_vectors, + size_t& n_vectors_not_pruned, + uint32_t* pruning_positions, + const uint8_t* selection_vector + ) { + n_vectors_not_pruned = 0; + for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { + pruning_positions[n_vectors_not_pruned] = vector_idx; + n_vectors_not_pruned += selection_vector[vector_idx] == 1; + } + }; + + void MaskDistancesWithSelectionVector( + size_t n_vectors, + distance_t* pruning_distances, + const uint8_t* selection_vector + ) { + for (size_t vector_idx = 0; vector_idx < n_vectors; ++vector_idx) { + if (selection_vector[vector_idx] == 0) { + // Why max()/2? To prevent overflow if distances are still added to these + pruning_distances[vector_idx] = std::numeric_limits::max() / 2; + } + } + }; + + static void GetClustersAccessOrderIVF( + const float* PDX_RESTRICT query, + const index_t& data, + size_t nprobe, + uint32_t* clusters_indices + ) { + std::unique_ptr distances_to_centroids(new float[data.num_clusters]); + for (size_t cluster_idx = 0; cluster_idx < data.num_clusters; cluster_idx++) { + distances_to_centroids[cluster_idx] = + DistanceComputer::Horizontal( + query, + data.centroids.data() + cluster_idx * data.num_dimensions, + data.num_dimensions + ); + } + std::iota(clusters_indices, clusters_indices + data.num_clusters, static_cast(0)); + if (nprobe >= data.num_clusters) { + std::sort( + clusters_indices, + clusters_indices + data.num_clusters, + [&distances_to_centroids](size_t i1, size_t i2) { + return distances_to_centroids[i1] < distances_to_centroids[i2]; + } + ); + } else { + std::partial_sort( + clusters_indices, + clusters_indices + static_cast(nprobe), + clusters_indices + data.num_clusters, + [&distances_to_centroids](size_t i1, size_t i2) { + return distances_to_centroids[i1] < distances_to_centroids[i2]; + } + ); + } + } + + // On the first bucket, we do a full scan (we do not prune vectors) + void Start( + const quantized_embedding_t* PDX_RESTRICT query, + const data_t* data, + const size_t n_vectors, + uint32_t k, + const uint32_t* vector_indices, + uint32_t* pruning_positions, + distance_t* pruning_distances, + std::priority_queue, VectorComparator>& heap + ) { + ResetPruningDistances(n_vectors, pruning_distances); + distance_computer_t::Vertical( + query, + data, + n_vectors, + n_vectors, + 0, + pdx_data.num_vertical_dimensions, + pruning_distances, + pruning_positions + ); + for (size_t horizontal_dimension = 0; + horizontal_dimension < pdx_data.num_horizontal_dimensions; + horizontal_dimension += H_DIM_SIZE) { + for (size_t vector_idx = 0; vector_idx < n_vectors; vector_idx++) { + size_t data_pos = (pdx_data.num_vertical_dimensions * n_vectors) + + (horizontal_dimension * n_vectors) + (vector_idx * H_DIM_SIZE); + pruning_distances[vector_idx] += distance_computer_t::Horizontal( + query + pdx_data.num_vertical_dimensions + horizontal_dimension, + data + data_pos, + H_DIM_SIZE + ); + } + } + size_t max_possible_k = std::min( + static_cast(k) - heap.size(), + n_vectors + ); // Note: Start() should not be called if heap.size() >= k + std::unique_ptr indices_sorted(new size_t[n_vectors]); + std::iota(indices_sorted.get(), indices_sorted.get() + n_vectors, static_cast(0)); + std::partial_sort( + indices_sorted.get(), + indices_sorted.get() + static_cast(max_possible_k), + indices_sorted.get() + n_vectors, + [pruning_distances](size_t i1, size_t i2) { + return pruning_distances[i1] < pruning_distances[i2]; + } + ); + // insert first k results into the heap + for (size_t idx = 0; idx < max_possible_k; ++idx) { + auto embedding = KNNCandidate{}; + size_t index = indices_sorted[idx]; + embedding.index = vector_indices[index]; + embedding.distance = static_cast(pruning_distances[index]); + if constexpr (Q == U8) { + embedding.distance *= pdx_data.inverse_quantization_scale_squared; + } + heap.push(embedding); + } + } + + // On the first bucket, we do a full scan (we do not prune vectors) + void FilteredStart( + const quantized_embedding_t* PDX_RESTRICT query, + const data_t* data, + const size_t n_vectors, + uint32_t k, + const uint32_t* vector_indices, + uint32_t* pruning_positions, + distance_t* pruning_distances, + std::priority_queue, VectorComparator>& heap, + uint8_t* selection_vector, + uint32_t passing_tuples + ) { + ResetPruningDistances(n_vectors, pruning_distances); + size_t n_vectors_not_pruned = 0; + float selection_percentage = + (static_cast(passing_tuples) / static_cast(n_vectors)); + InitPositionsArrayFromSelectionVector( + n_vectors, n_vectors_not_pruned, pruning_positions, selection_vector + ); + // Always start with horizontal block, regardless of selectivity + for (size_t horizontal_dimension = 0; + horizontal_dimension < pdx_data.num_horizontal_dimensions; + horizontal_dimension += H_DIM_SIZE) { + size_t offset_data = + (pdx_data.num_vertical_dimensions * n_vectors) + (horizontal_dimension * n_vectors); + for (size_t vector_idx = 0; vector_idx < n_vectors_not_pruned; vector_idx++) { + size_t v_idx = pruning_positions[vector_idx]; + size_t data_pos = offset_data + (v_idx * H_DIM_SIZE); + pruning_distances[v_idx] += distance_computer_t::Horizontal( + query + pdx_data.num_vertical_dimensions + horizontal_dimension, + data + data_pos, + H_DIM_SIZE + ); + } + } + if (selection_percentage > (1 - selectivity_threshold)) { + // It is then faster to do the full scan (thanks to SIMD) + distance_computer_t::Vertical( + query, + data, + n_vectors, + n_vectors, + 0, + pdx_data.num_vertical_dimensions, + pruning_distances, + pruning_positions + ); + } else { + // We access individual values + distance_computer_t::VerticalPruning( + query, + data, + n_vectors_not_pruned, + n_vectors, + 0, + pdx_data.num_vertical_dimensions, + pruning_distances, + pruning_positions + ); + } + // TODO: Everything down from here is a bottleneck when selection % is ultra low + size_t max_possible_k = + std::min(static_cast(k) - heap.size(), static_cast(passing_tuples)); + MaskDistancesWithSelectionVector(n_vectors, pruning_distances, selection_vector); + std::unique_ptr indices_sorted(new size_t[n_vectors]); + std::iota(indices_sorted.get(), indices_sorted.get() + n_vectors, static_cast(0)); + std::partial_sort( + indices_sorted.get(), + indices_sorted.get() + static_cast(max_possible_k), + indices_sorted.get() + n_vectors, + [pruning_distances](size_t i1, size_t i2) { + return pruning_distances[i1] < pruning_distances[i2]; + } + ); + // insert first k results into the heap + for (size_t idx = 0; idx < max_possible_k; ++idx) { + auto embedding = KNNCandidate{}; + size_t index = indices_sorted[idx]; + embedding.index = vector_indices[index]; + embedding.distance = static_cast(pruning_distances[index]); + if constexpr (Q == U8) { + embedding.distance *= pdx_data.inverse_quantization_scale_squared; + } + heap.push(embedding); + } + } + + // On the warmup phase, we keep scanning dimensions until the amount of not-yet pruned vectors + // is low + template + void Warmup( + const quantized_embedding_t* PDX_RESTRICT query, + const data_t* PDX_RESTRICT data, + const size_t n_vectors, + uint32_t k, + float tuples_threshold, + uint32_t* pruning_positions, + distance_t* pruning_distances, + distance_t& pruning_threshold, + std::priority_queue, VectorComparator>& heap, + uint32_t& current_dimension_idx, + size_t& n_vectors_not_pruned, + uint32_t passing_tuples = 0, + uint8_t* selection_vector = nullptr + ) { + current_dimension_idx = 0; + size_t cur_subgrouping_size_idx = 0; + size_t tuples_needed_to_exit = + static_cast(std::ceil(tuples_threshold * static_cast(n_vectors))); + ResetPruningDistances(n_vectors, pruning_distances); + uint32_t n_tuples_to_prune = 0; + if constexpr (FILTERED) { + float selection_percentage = + (static_cast(passing_tuples) / static_cast(n_vectors)); + MaskDistancesWithSelectionVector(n_vectors, pruning_distances, selection_vector); + if (selection_percentage < (1 - tuples_threshold)) { + // Go directly to the PRUNE phase for direct tuples access in the Horizontal block + return; + } + } + GetPruningThreshold(k, heap, pruning_threshold, current_dimension_idx); + while (n_tuples_to_prune < tuples_needed_to_exit && + current_dimension_idx < pdx_data.num_vertical_dimensions) { + size_t last_dimension_to_fetch = std::min( + current_dimension_idx + DIMENSIONS_FETCHING_SIZES[cur_subgrouping_size_idx], + pdx_data.num_vertical_dimensions + ); + distance_computer_t::Vertical( + query, + data, + n_vectors, + n_vectors, + current_dimension_idx, + last_dimension_to_fetch, + pruning_distances, + pruning_positions + ); + current_dimension_idx = last_dimension_to_fetch; + cur_subgrouping_size_idx += 1; + GetPruningThreshold(k, heap, pruning_threshold, current_dimension_idx); + n_tuples_to_prune = 0; + EvaluatePruningPredicateScalar( + n_tuples_to_prune, n_vectors, pruning_distances, pruning_threshold + ); + } + } + + // We scan only the not-yet pruned vectors + template + void Prune( + const quantized_embedding_t* PDX_RESTRICT query, + const data_t* PDX_RESTRICT data, + const size_t n_vectors, + uint32_t k, + uint32_t* pruning_positions, + distance_t* pruning_distances, + distance_t& pruning_threshold, + std::priority_queue, VectorComparator>& heap, + uint32_t& current_dimension_idx, + size_t& n_vectors_not_pruned, + const uint8_t* selection_vector = nullptr + ) { + GetPruningThreshold(k, heap, pruning_threshold, current_dimension_idx); + InitPositionsArray( + n_vectors, + n_vectors_not_pruned, + pruning_positions, + pruning_threshold, + pruning_distances, + selection_vector + ); + size_t cur_n_vectors_not_pruned = 0; + size_t current_vertical_dimension = current_dimension_idx; + size_t current_horizontal_dimension = 0; + while (pdx_data.num_horizontal_dimensions && n_vectors_not_pruned && + current_horizontal_dimension < pdx_data.num_horizontal_dimensions) { + cur_n_vectors_not_pruned = n_vectors_not_pruned; + size_t offset_data = (pdx_data.num_vertical_dimensions * n_vectors) + + (current_horizontal_dimension * n_vectors); + for (size_t vector_idx = 0; vector_idx < n_vectors_not_pruned; vector_idx++) { + size_t v_idx = pruning_positions[vector_idx]; + size_t data_pos = offset_data + (v_idx * H_DIM_SIZE); + __builtin_prefetch(data + data_pos, 0, 3); + } + size_t offset_query = pdx_data.num_vertical_dimensions + current_horizontal_dimension; + for (size_t vector_idx = 0; vector_idx < n_vectors_not_pruned; vector_idx++) { + size_t v_idx = pruning_positions[vector_idx]; + size_t data_pos = offset_data + (v_idx * H_DIM_SIZE); + pruning_distances[v_idx] += distance_computer_t::Horizontal( + query + offset_query, data + data_pos, H_DIM_SIZE + ); + } + // end of clipping + current_horizontal_dimension += H_DIM_SIZE; + current_dimension_idx += H_DIM_SIZE; + GetPruningThreshold(k, heap, pruning_threshold, current_dimension_idx); + assert( + current_dimension_idx == current_vertical_dimension + current_horizontal_dimension + ); + EvaluatePruningPredicateOnPositionsArray( + cur_n_vectors_not_pruned, + n_vectors_not_pruned, + pruning_positions, + pruning_threshold, + pruning_distances + ); + } + // GO THROUGH THE REST IN THE VERTICAL + while (n_vectors_not_pruned && current_vertical_dimension < pdx_data.num_vertical_dimensions + ) { + cur_n_vectors_not_pruned = n_vectors_not_pruned; + size_t last_dimension_to_test_idx = std::min( + current_vertical_dimension + H_DIM_SIZE, + static_cast(pdx_data.num_vertical_dimensions) + ); + distance_computer_t::VerticalPruning( + query, + data, + cur_n_vectors_not_pruned, + n_vectors, + current_vertical_dimension, + last_dimension_to_test_idx, + pruning_distances, + pruning_positions + ); + current_dimension_idx = std::min( + current_dimension_idx + H_DIM_SIZE, static_cast(pdx_data.num_dimensions) + ); + current_vertical_dimension = std::min( + static_cast(current_vertical_dimension + H_DIM_SIZE), + pdx_data.num_vertical_dimensions + ); + assert( + current_dimension_idx == current_vertical_dimension + current_horizontal_dimension + ); + GetPruningThreshold(k, heap, pruning_threshold, current_dimension_idx); + EvaluatePruningPredicateOnPositionsArray( + cur_n_vectors_not_pruned, + n_vectors_not_pruned, + pruning_positions, + pruning_threshold, + pruning_distances + ); + if (current_dimension_idx == pdx_data.num_dimensions) { + break; + } + } + } + + void MergeIntoHeap( + const uint32_t* vector_indices, + const size_t n_vectors, + const uint32_t k, + const uint32_t* pruning_positions, + const distance_t* pruning_distances, + std::priority_queue, VectorComparator>& heap + ) { + for (size_t position_idx = 0; position_idx < n_vectors; ++position_idx) { + const size_t index = pruning_positions[position_idx]; + float current_distance = static_cast(pruning_distances[index]); + if constexpr (Q == U8) { + current_distance *= pdx_data.inverse_quantization_scale_squared; + } + if (heap.size() < k || current_distance < heap.top().distance) { + KNNCandidate embedding{}; + embedding.distance = current_distance; + embedding.index = vector_indices[index]; + if (heap.size() >= k) { + heap.pop(); + } + heap.push(embedding); + } + } + } + + [[nodiscard]] static std::vector BuildResultSetFromHeap( + uint32_t k, + std::priority_queue, VectorComparator>& heap + ) { + // Pop the initialization element from the heap, as it can't be part of the result. + if (!heap.empty() && heap.top().distance == std::numeric_limits::max()) { + heap.pop(); + } + + size_t result_set_size = std::min(heap.size(), static_cast(k)); + std::vector result; + result.resize(result_set_size); + for (size_t i = result_set_size; i > 0; --i) { + result[i - 1] = heap.top(); + heap.pop(); + } + return result; + } void GetClustersAccessOrderRandom() { - std::iota(cluster_indices_in_access_order.get(), cluster_indices_in_access_order.get() + pdx_data.num_clusters, 0); + std::iota( + cluster_indices_in_access_order.get(), + cluster_indices_in_access_order.get() + pdx_data.num_clusters, + 0 + ); } -public: + public: /****************************************************************** * Search methods ******************************************************************/ - std::vector Search(const float *PDX_RESTRICT const raw_query, const uint32_t k) { - Heap local_heap {}; - std::unique_ptr query(new float[pdx_data.num_dimensions]); - if (!pdx_data.is_normalized) { - pruner.PreprocessQuery(raw_query, query.get()); - } else { - std::unique_ptr normalized_query(new float[pdx_data.num_dimensions]); - quantizer.NormalizeQuery(raw_query, normalized_query.get()); - pruner.PreprocessQuery(normalized_query.get(), query.get()); - } - size_t clusters_to_visit = - (ivf_nprobe == 0 || ivf_nprobe > pdx_data.num_clusters) ? pdx_data.num_clusters : ivf_nprobe; - std::unique_ptr local_cluster_order(new uint32_t[pdx_data.num_clusters]); - if (cluster_indices_in_access_order) { - std::copy(cluster_indices_in_access_order.get(), - cluster_indices_in_access_order.get() + clusters_to_visit, local_cluster_order.get()); - } else { - GetClustersAccessOrderIVF(query.get(), pdx_data, clusters_to_visit, local_cluster_order.get()); - } - // PDXearch core - std::unique_ptr local_quantized_query( - new quantized_embedding_t[pdx_data.num_dimensions]); - quantized_embedding_t *local_prepared_query; - if constexpr (Q == U8) { - quantizer.QuantizeEmbedding(query.get(), pdx_data.quantization_base, pdx_data.quantization_scale, - local_quantized_query.get()); - local_prepared_query = local_quantized_query.get(); - } else { - local_prepared_query = query.get(); - } - - std::unique_ptr pruning_distances(new distance_t[max_cluster_size]); - std::unique_ptr pruning_positions(new uint32_t[max_cluster_size]); - - for (size_t cluster_idx = 0; cluster_idx < clusters_to_visit; ++cluster_idx) { - distance_t pruning_threshold = std::numeric_limits::max(); - uint32_t current_dimension_idx = 0; - size_t n_vectors_not_pruned = 0; - - const size_t current_cluster_idx = local_cluster_order[cluster_idx]; - cluster_t &cluster = pdx_data.clusters[current_cluster_idx]; - if (cluster.num_embeddings == 0) { - continue; - } - if (local_heap.size() < k) { - // We cannot prune until we fill the heap - Start(local_prepared_query, cluster.data, cluster.num_embeddings, k, cluster.indices, - pruning_positions.get(), pruning_distances.get(), local_heap); - continue; - } - Warmup(local_prepared_query, cluster.data, cluster.num_embeddings, k, selectivity_threshold, - pruning_positions.get(), pruning_distances.get(), pruning_threshold, local_heap, - current_dimension_idx, n_vectors_not_pruned); - Prune(local_prepared_query, cluster.data, cluster.num_embeddings, k, pruning_positions.get(), - pruning_distances.get(), pruning_threshold, local_heap, current_dimension_idx, n_vectors_not_pruned); - if (n_vectors_not_pruned) { - MergeIntoHeap(cluster.indices, n_vectors_not_pruned, k, pruning_positions.get(), - pruning_distances.get(), local_heap); - } - } - std::vector result = BuildResultSetFromHeap(k, local_heap); + std::vector Search(const float* PDX_RESTRICT const raw_query, const uint32_t k) { + Heap local_heap{}; + std::unique_ptr query(new float[pdx_data.num_dimensions]); + if (!pdx_data.is_normalized) { + pruner.PreprocessQuery(raw_query, query.get()); + } else { + std::unique_ptr normalized_query(new float[pdx_data.num_dimensions]); + quantizer.NormalizeQuery(raw_query, normalized_query.get()); + pruner.PreprocessQuery(normalized_query.get(), query.get()); + } + size_t clusters_to_visit = (ivf_nprobe == 0 || ivf_nprobe > pdx_data.num_clusters) + ? pdx_data.num_clusters + : ivf_nprobe; + std::unique_ptr local_cluster_order(new uint32_t[pdx_data.num_clusters]); + if (cluster_indices_in_access_order) { + std::copy( + cluster_indices_in_access_order.get(), + cluster_indices_in_access_order.get() + clusters_to_visit, + local_cluster_order.get() + ); + } else { + GetClustersAccessOrderIVF( + query.get(), pdx_data, clusters_to_visit, local_cluster_order.get() + ); + } + // PDXearch core + std::unique_ptr local_quantized_query( + new quantized_embedding_t[pdx_data.num_dimensions] + ); + quantized_embedding_t* local_prepared_query; + if constexpr (Q == U8) { + quantizer.QuantizeEmbedding( + query.get(), + pdx_data.quantization_base, + pdx_data.quantization_scale, + local_quantized_query.get() + ); + local_prepared_query = local_quantized_query.get(); + } else { + local_prepared_query = query.get(); + } + + std::unique_ptr pruning_distances(new distance_t[max_cluster_size]); + std::unique_ptr pruning_positions(new uint32_t[max_cluster_size]); + + for (size_t cluster_idx = 0; cluster_idx < clusters_to_visit; ++cluster_idx) { + distance_t pruning_threshold = std::numeric_limits::max(); + uint32_t current_dimension_idx = 0; + size_t n_vectors_not_pruned = 0; + + const size_t current_cluster_idx = local_cluster_order[cluster_idx]; + cluster_t& cluster = pdx_data.clusters[current_cluster_idx]; + if (cluster.num_embeddings == 0) { + continue; + } + if (local_heap.size() < k) { + // We cannot prune until we fill the heap + Start( + local_prepared_query, + cluster.data, + cluster.num_embeddings, + k, + cluster.indices, + pruning_positions.get(), + pruning_distances.get(), + local_heap + ); + continue; + } + Warmup( + local_prepared_query, + cluster.data, + cluster.num_embeddings, + k, + selectivity_threshold, + pruning_positions.get(), + pruning_distances.get(), + pruning_threshold, + local_heap, + current_dimension_idx, + n_vectors_not_pruned + ); + Prune( + local_prepared_query, + cluster.data, + cluster.num_embeddings, + k, + pruning_positions.get(), + pruning_distances.get(), + pruning_threshold, + local_heap, + current_dimension_idx, + n_vectors_not_pruned + ); + if (n_vectors_not_pruned) { + MergeIntoHeap( + cluster.indices, + n_vectors_not_pruned, + k, + pruning_positions.get(), + pruning_distances.get(), + local_heap + ); + } + } + std::vector result = BuildResultSetFromHeap(k, local_heap); return result; } - std::vector FilteredSearch(const float *PDX_RESTRICT const raw_query, const uint32_t k, - const PredicateEvaluator &predicate_evaluator) { - Heap local_heap {}; - std::unique_ptr query(new float[pdx_data.num_dimensions]); - if (!pdx_data.is_normalized) { - pruner.PreprocessQuery(raw_query, query.get()); - } else { - std::unique_ptr normalized_query(new float[pdx_data.num_dimensions]); - quantizer.NormalizeQuery(raw_query, normalized_query.get()); - pruner.PreprocessQuery(normalized_query.get(), query.get()); - } - - size_t clusters_to_visit = - (ivf_nprobe == 0 || ivf_nprobe > pdx_data.num_clusters) ? pdx_data.num_clusters : ivf_nprobe; - - std::unique_ptr local_cluster_order(new uint32_t[pdx_data.num_clusters]); - GetClustersAccessOrderIVF(query.get(), pdx_data, clusters_to_visit, local_cluster_order.get()); - // PDXearch core - std::unique_ptr local_quantized_query( - new quantized_embedding_t[pdx_data.num_dimensions]); - quantized_embedding_t *local_prepared_query; - if constexpr (Q == U8) { - quantizer.QuantizeEmbedding(query.get(), pdx_data.quantization_base, pdx_data.quantization_scale, - local_quantized_query.get()); - local_prepared_query = local_quantized_query.get(); - } else { - local_prepared_query = query.get(); - } - - std::unique_ptr pruning_distances(new distance_t[max_cluster_size]); - std::unique_ptr pruning_positions(new uint32_t[max_cluster_size]); - - for (size_t cluster_idx = 0; cluster_idx < clusters_to_visit; ++cluster_idx) { - distance_t pruning_threshold = std::numeric_limits::max(); - uint32_t current_dimension_idx = 0; - size_t n_vectors_not_pruned = 0; - - const size_t current_cluster_idx = local_cluster_order[cluster_idx]; - auto [selection_vector, passing_tuples] = - predicate_evaluator.GetSelectionVector(current_cluster_idx, cluster_offsets[current_cluster_idx]); - if (passing_tuples == 0) { - continue; - } - cluster_t &cluster = pdx_data.clusters[current_cluster_idx]; - if (cluster.num_embeddings == 0) { - continue; - } - if (local_heap.size() < k) { - // We cannot prune until we fill the heap - FilteredStart(local_prepared_query, cluster.data, cluster.num_embeddings, k, cluster.indices, - pruning_positions.get(), pruning_distances.get(), local_heap, selection_vector, - passing_tuples); - continue; - } - Warmup(local_prepared_query, cluster.data, cluster.num_embeddings, k, selectivity_threshold, - pruning_positions.get(), pruning_distances.get(), pruning_threshold, local_heap, - current_dimension_idx, n_vectors_not_pruned, passing_tuples, selection_vector); - Prune(local_prepared_query, cluster.data, cluster.num_embeddings, k, pruning_positions.get(), - pruning_distances.get(), pruning_threshold, local_heap, current_dimension_idx, - n_vectors_not_pruned, selection_vector); - if (n_vectors_not_pruned) { - MergeIntoHeap(cluster.indices, n_vectors_not_pruned, k, pruning_positions.get(), - pruning_distances.get(), local_heap); - } - } - std::vector result = BuildResultSetFromHeap(k, local_heap); + std::vector FilteredSearch( + const float* PDX_RESTRICT const raw_query, + const uint32_t k, + const PredicateEvaluator& predicate_evaluator + ) { + Heap local_heap{}; + std::unique_ptr query(new float[pdx_data.num_dimensions]); + if (!pdx_data.is_normalized) { + pruner.PreprocessQuery(raw_query, query.get()); + } else { + std::unique_ptr normalized_query(new float[pdx_data.num_dimensions]); + quantizer.NormalizeQuery(raw_query, normalized_query.get()); + pruner.PreprocessQuery(normalized_query.get(), query.get()); + } + + size_t clusters_to_visit = (ivf_nprobe == 0 || ivf_nprobe > pdx_data.num_clusters) + ? pdx_data.num_clusters + : ivf_nprobe; + + std::unique_ptr local_cluster_order(new uint32_t[pdx_data.num_clusters]); + GetClustersAccessOrderIVF( + query.get(), pdx_data, clusters_to_visit, local_cluster_order.get() + ); + // PDXearch core + std::unique_ptr local_quantized_query( + new quantized_embedding_t[pdx_data.num_dimensions] + ); + quantized_embedding_t* local_prepared_query; + if constexpr (Q == U8) { + quantizer.QuantizeEmbedding( + query.get(), + pdx_data.quantization_base, + pdx_data.quantization_scale, + local_quantized_query.get() + ); + local_prepared_query = local_quantized_query.get(); + } else { + local_prepared_query = query.get(); + } + + std::unique_ptr pruning_distances(new distance_t[max_cluster_size]); + std::unique_ptr pruning_positions(new uint32_t[max_cluster_size]); + + for (size_t cluster_idx = 0; cluster_idx < clusters_to_visit; ++cluster_idx) { + distance_t pruning_threshold = std::numeric_limits::max(); + uint32_t current_dimension_idx = 0; + size_t n_vectors_not_pruned = 0; + + const size_t current_cluster_idx = local_cluster_order[cluster_idx]; + auto [selection_vector, passing_tuples] = predicate_evaluator.GetSelectionVector( + current_cluster_idx, cluster_offsets[current_cluster_idx] + ); + if (passing_tuples == 0) { + continue; + } + cluster_t& cluster = pdx_data.clusters[current_cluster_idx]; + if (cluster.num_embeddings == 0) { + continue; + } + if (local_heap.size() < k) { + // We cannot prune until we fill the heap + FilteredStart( + local_prepared_query, + cluster.data, + cluster.num_embeddings, + k, + cluster.indices, + pruning_positions.get(), + pruning_distances.get(), + local_heap, + selection_vector, + passing_tuples + ); + continue; + } + Warmup( + local_prepared_query, + cluster.data, + cluster.num_embeddings, + k, + selectivity_threshold, + pruning_positions.get(), + pruning_distances.get(), + pruning_threshold, + local_heap, + current_dimension_idx, + n_vectors_not_pruned, + passing_tuples, + selection_vector + ); + Prune( + local_prepared_query, + cluster.data, + cluster.num_embeddings, + k, + pruning_positions.get(), + pruning_distances.get(), + pruning_threshold, + local_heap, + current_dimension_idx, + n_vectors_not_pruned, + selection_vector + ); + if (n_vectors_not_pruned) { + MergeIntoHeap( + cluster.indices, + n_vectors_not_pruned, + k, + pruning_positions.get(), + pruning_distances.get(), + local_heap + ); + } + } + std::vector result = BuildResultSetFromHeap(k, local_heap); return result; } - }; } // namespace PDX diff --git a/include/pdx/utils.hpp b/include/pdx/utils.hpp index 3987046..292a4bc 100644 --- a/include/pdx/utils.hpp +++ b/include/pdx/utils.hpp @@ -1,9 +1,9 @@ #pragma once #include -#include #include #include +#include #include #include #include @@ -17,7 +17,8 @@ inline std::unique_ptr MmapFile(const std::string& filename) { struct stat file_stats {}; int fd = ::open(filename.c_str(), O_RDONLY); - if (fd == -1) throw std::runtime_error("Failed to open file"); + if (fd == -1) + throw std::runtime_error("Failed to open file"); fstat(fd, &file_stats); size_t file_size = file_stats.st_size; diff --git a/python/lib.cpp b/python/lib.cpp index 255c008..b9162df 100644 --- a/python/lib.cpp +++ b/python/lib.cpp @@ -1,5 +1,5 @@ -#include #include "pdx/lib/lib.hpp" +#include namespace py = pybind11; @@ -7,17 +7,27 @@ PYBIND11_MODULE(compiled, m) { m.doc() = "PDXearch: Faster similarity search with a transposed data layout for vectors"; py::class_(m, "PDXIndex") - .def(py::init(), - py::arg("index_type"), - py::arg("num_dimensions"), - py::arg("distance_metric") = 0, - py::arg("seed") = 42, - py::arg("num_clusters") = 0, - py::arg("num_meso_clusters") = 0, - py::arg("normalize") = false, - py::arg("sampling_fraction") = 0.0f, - py::arg("kmeans_iters") = 10) + .def( + py::init< + const std::string&, + uint32_t, + uint8_t, + uint32_t, + uint32_t, + uint32_t, + bool, + float, + uint32_t>(), + py::arg("index_type"), + py::arg("num_dimensions"), + py::arg("distance_metric") = 0, + py::arg("seed") = 42, + py::arg("num_clusters") = 0, + py::arg("num_meso_clusters") = 0, + py::arg("normalize") = false, + py::arg("sampling_fraction") = 0.0f, + py::arg("kmeans_iters") = 10 + ) .def("build_index", &PDX::PyPDXIndex::BuildIndex, py::arg("data")) .def("search", &PDX::PyPDXIndex::Search, py::arg("query"), py::arg("knn")) .def("set_nprobe", &PDX::PyPDXIndex::SetNProbe, py::arg("nprobe")) @@ -26,6 +36,10 @@ PYBIND11_MODULE(compiled, m) { .def("get_num_clusters", &PDX::PyPDXIndex::GetNumClusters) .def("get_in_memory_size_in_bytes", &PDX::PyPDXIndex::GetInMemorySizeInBytes); - m.def("load_index", &PDX::PyPDXIndex::LoadFromFile, py::arg("path"), - "Load a PDX index from a single file (auto-detects type)."); + m.def( + "load_index", + &PDX::PyPDXIndex::LoadFromFile, + py::arg("path"), + "Load a PDX index from a single file (auto-detects type)." + ); } diff --git a/scripts/format.sh b/scripts/format.sh new file mode 100755 index 0000000..992ead6 --- /dev/null +++ b/scripts/format.sh @@ -0,0 +1,62 @@ +#!/bin/bash + +# Format C++ files using clang-format +# This script formats all .cpp, .h, and .hpp files in the project + +set -e + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +PROJECT_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)" + +echo "Formatting C++ files in SuperKMeans project..." +echo "Project root: $PROJECT_ROOT" + +REQUIRED_VERSION="18.1.8" + +# Check if clang-format is available +if ! command -v clang-format &> /dev/null; then + echo "Error: clang-format not found. Please install it first." + exit 1 +fi + +CURRENT_VERSION=$(clang-format --version | grep -oE '[0-9]+\.[0-9]+\.[0-9]+' | head -1) +if [ "$CURRENT_VERSION" != "$REQUIRED_VERSION" ]; then + echo "Error: clang-format version $REQUIRED_VERSION required, but found $CURRENT_VERSION" + echo "Install the correct version: pip install clang-format==$REQUIRED_VERSION" + exit 1 +fi + +DIRECTORIES=( + "include" + "python" + "tests" + "benchmarks" + "examples" +) + +EXTENSIONS=("cpp" "h" "hpp") + +total_files=0 + +# Format files in each directory +for dir in "${DIRECTORIES[@]}"; do + dir_path="$PROJECT_ROOT/$dir" + + if [ ! -d "$dir_path" ]; then + echo "Warning: Directory $dir does not exist, skipping..." + continue + fi + + echo "Processing directory: $dir" + + for ext in "${EXTENSIONS[@]}"; do + while IFS= read -r -d '' file; do + echo " Formatting: ${file#$PROJECT_ROOT/}" + clang-format -i "$file" + total_files=$((total_files + 1)) + done < <(find "$dir_path" -type f -name "*.$ext" -print0) + done +done + +echo "" +echo "Done! Formatted $total_files file(s)." diff --git a/scripts/format_check.sh b/scripts/format_check.sh new file mode 100755 index 0000000..59354ea --- /dev/null +++ b/scripts/format_check.sh @@ -0,0 +1,80 @@ +#!/bin/bash + +# Check C++ file formatting using clang-format +# This script checks if all .cpp, .h, and .hpp files are properly formatted +# Exits with error code 1 if any files need formatting + +set -e + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +PROJECT_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)" + +echo "Checking C++ formatting in SuperKMeans project..." +echo "Project root: $PROJECT_ROOT" + +REQUIRED_VERSION="18.1.8" + +if ! command -v clang-format &> /dev/null; then + echo "Error: clang-format not found. Please install it first." + exit 1 +fi + +CURRENT_VERSION=$(clang-format --version | grep -oE '[0-9]+\.[0-9]+\.[0-9]+' | head -1) +if [ "$CURRENT_VERSION" != "$REQUIRED_VERSION" ]; then + echo "Error: clang-format version $REQUIRED_VERSION required, but found $CURRENT_VERSION" + echo "Install the correct version: pip install clang-format==$REQUIRED_VERSION" + exit 1 +fi + +DIRECTORIES=( + "include" + "python" + "tests" + "benchmarks" + "examples" +) + +EXTENSIONS=("cpp" "h" "hpp") + +total_files=0 +unformatted_files=() + +for dir in "${DIRECTORIES[@]}"; do + dir_path="$PROJECT_ROOT/$dir" + + if [ ! -d "$dir_path" ]; then + echo "Warning: Directory $dir does not exist, skipping..." + continue + fi + + echo "Checking directory: $dir" + + for ext in "${EXTENSIONS[@]}"; do + while IFS= read -r -d '' file; do + total_files=$((total_files + 1)) + + # Run clang-format and compare with original + if ! diff -q <(clang-format "$file") "$file" > /dev/null 2>&1; then + echo " ✗ ${file#$PROJECT_ROOT/}" + unformatted_files+=("$file") + fi + done < <(find "$dir_path" -type f -name "*.$ext" -print0) + done +done + +echo "" +echo "Checked $total_files file(s)." + +# Report results +if [ ${#unformatted_files[@]} -eq 0 ]; then + echo "✓ All files are properly formatted!" + exit 0 +else + echo "✗ Found ${#unformatted_files[@]} file(s) that need formatting:" + for file in "${unformatted_files[@]}"; do + echo " - ${file#$PROJECT_ROOT/}" + done + echo "" + echo "Run './scripts/format.sh' to fix formatting issues." + exit 1 +fi diff --git a/scripts/tidy_check.sh b/scripts/tidy_check.sh new file mode 100755 index 0000000..b36d119 --- /dev/null +++ b/scripts/tidy_check.sh @@ -0,0 +1,81 @@ +#!/bin/bash + +# Run clang-tidy on all C++ source files in the project +# Requires compile_commands.json (generated by cmake -DCMAKE_EXPORT_COMPILE_COMMANDS=ON) +# Exits with error code 1 if any warnings are found + +set -e + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +PROJECT_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)" + +# Allow overriding the clang-tidy binary, e.g.: +# CLANG_TIDY=/opt/homebrew/opt/llvm@18/bin/clang-tidy ./scripts/tidy_check.sh +CLANG_TIDY="${CLANG_TIDY:-clang-tidy}" + +echo "Running clang-tidy on SuperKMeans project..." +echo "Project root: $PROJECT_ROOT" +echo "Using: $CLANG_TIDY ($($CLANG_TIDY --version 2>&1 | head -1))" + +if ! command -v "$CLANG_TIDY" &> /dev/null; then + echo "Error: $CLANG_TIDY not found. Please install it first." + exit 1 +fi + +# Check for compile_commands.json +COMPILE_DB="$PROJECT_ROOT/compile_commands.json" +if [ ! -f "$COMPILE_DB" ]; then + echo "Error: compile_commands.json not found." + echo "Generate it with: cmake . -DCMAKE_EXPORT_COMPILE_COMMANDS=ON" + exit 1 +fi + +DIRECTORIES=( + "tests" + "examples" + "python" +) + +EXTENSIONS=("cpp") + +total_files=0 +files_with_warnings=0 + +for dir in "${DIRECTORIES[@]}"; do + dir_path="$PROJECT_ROOT/$dir" + + if [ ! -d "$dir_path" ]; then + echo "Warning: Directory $dir does not exist, skipping..." + continue + fi + + echo "Checking directory: $dir" + + for ext in "${EXTENSIONS[@]}"; do + while IFS= read -r -d '' file; do + total_files=$((total_files + 1)) + relative_file="${file#$PROJECT_ROOT/}" + + # Only check warnings from headers in include/superkmeans/ + header_warnings=$($CLANG_TIDY -p "$PROJECT_ROOT" "$file" 2>&1 | grep "warning:" | grep "include/superkmeans/" || true) + if [ -z "$header_warnings" ]; then + echo " ✓ $relative_file" + else + echo " ✗ $relative_file" + echo "$header_warnings" | head -50 + files_with_warnings=$((files_with_warnings + 1)) + fi + done < <(find "$dir_path" -type f -name "*.$ext" -not -name "generate_*" -print0) + done +done + +echo "" +echo "Checked $total_files file(s)." + +if [ "$files_with_warnings" -eq 0 ]; then + echo "✓ No clang-tidy warnings found!" + exit 0 +else + echo "✗ Found warnings in $files_with_warnings file(s)." + exit 1 +fi From dd9c7c193039b72a357b1f5d536beba9bfff5ee3 Mon Sep 17 00:00:00 2001 From: lkuffo Date: Tue, 24 Feb 2026 15:37:21 +0100 Subject: [PATCH 16/39] FFTW working but deactivating for now --- CMakeLists.txt | 6 +- .../DEFAULT/END_TO_END_PDX_ADSAMPLING.csv | 3 + include/pdx/common.hpp | 4 + .../pdx/distance_computers/avx2_computers.hpp | 16 +++ .../distance_computers/avx512_computers.hpp | 23 ++++ .../pdx/distance_computers/base_computers.hpp | 1 + .../pdx/distance_computers/neon_computers.hpp | 15 +++ .../distance_computers/scalar_computers.hpp | 9 ++ include/pdx/index.hpp | 16 ++- include/pdx/pruners/adsampling.hpp | 120 +++++++++++++----- 10 files changed, 174 insertions(+), 39 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index a621b10..348c05f 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -58,7 +58,7 @@ set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "extern/findFFTW") # add_definitions(-DHAS_FFTW) # include_directories(${FFTW_INCLUDE_DIRS}) #else() -# message(WARNING "FFTW (+float capabilities) not found, we recommend you install it") +# message(STATUS "FFTW (+float capabilities) not found, we recommend you install them for increased performance") #endif() add_compile_definitions(CMAKE_SOURCE_DIR="${CMAKE_SOURCE_DIR}") @@ -92,6 +92,10 @@ if(Python_FOUND AND pybind11_FOUND) target_link_libraries(compiled PRIVATE ${BLAS_LINK_LIBRARIES}) endif() + #if(FFTW_FOUND) + # target_link_libraries(compiled PRIVATE ${FFTW_FLOAT_LIB} ${FFTW_FLOAT_OPENMP_LIB}) + #endif() + target_compile_options(compiled PRIVATE -Wall -Wno-unknown-pragmas diff --git a/benchmarks/results/DEFAULT/END_TO_END_PDX_ADSAMPLING.csv b/benchmarks/results/DEFAULT/END_TO_END_PDX_ADSAMPLING.csv index c3b58b8..c761ae7 100644 --- a/benchmarks/results/DEFAULT/END_TO_END_PDX_ADSAMPLING.csv +++ b/benchmarks/results/DEFAULT/END_TO_END_PDX_ADSAMPLING.csv @@ -48,3 +48,6 @@ mxbai,end_to_end_pdx_tree_u8,0.183406,0.271166,0.102541,0.901288,25,0,100,1000,0 mxbai,end_to_end_pdx_tree_f32,0.360179,0.596792,0.133375,0.902719,25,0,100,1000,0,1,0.3602,0.5968,0.1334 mxbai,end_to_end_pdx_f32,0.488553,0.715792,0.268833,0.903179,25,0,100,1000,0,1,0.4888,0.7748,0.2688 mxbai,end_to_end_pdx_u8,0.321055,0.396917,0.254417,0.901748,25,0,100,1000,0,1,0.3217,0.4234,0.2544 +mxbai,end_to_end_pdx_f32,0.462268,0.677333,0.263375,0.901899,25,0,100,1000,0,1,0.4625,0.7097,0.2634 +mxbai,end_to_end_pdx_tree_u8,0.173538,0.253792,0.104583,0.899069,25,0,100,1000,0,1,0.1738,0.2658,0.09 +mxbai,end_to_end_pdx_tree_u8,0.178165,0.26575,0.094291,0.901288,25,0,100,1000,0,1,0.1787,0.2787,0.09429 diff --git a/include/pdx/common.hpp b/include/pdx/common.hpp index b013eb8..26d402d 100644 --- a/include/pdx/common.hpp +++ b/include/pdx/common.hpp @@ -32,6 +32,10 @@ namespace PDX { static constexpr float PROPORTION_HORIZONTAL_DIM = 0.75f; static constexpr size_t D_THRESHOLD_FOR_DCT_ROTATION = 512; + +inline bool IsPowerOf2(const uint32_t x) { + return x > 0 && (x & (x - 1)) == 0; +} static constexpr size_t PDX_MAX_DIMS = 16384; static constexpr size_t H_DIM_SIZE = 64; static constexpr size_t U8_INTERLEAVE_SIZE = 4; diff --git a/include/pdx/distance_computers/avx2_computers.hpp b/include/pdx/distance_computers/avx2_computers.hpp index 382fbe2..5ffad50 100644 --- a/include/pdx/distance_computers/avx2_computers.hpp +++ b/include/pdx/distance_computers/avx2_computers.hpp @@ -90,6 +90,22 @@ class SIMDComputer { return static_cast(d2); }; + + static void FlipSign(const data_t* data, data_t* out, const uint32_t* masks, size_t d) { + size_t j = 0; + for (; j + 8 <= d; j += 8) { + __m256 vec = _mm256_loadu_ps(data + j); + __m256i mask = _mm256_loadu_si256(reinterpret_cast(masks + j)); + __m256i vec_i = _mm256_castps_si256(vec); + vec_i = _mm256_xor_si256(vec_i, mask); + _mm256_storeu_ps(out + j, _mm256_castsi256_ps(vec_i)); + } + auto data_bits = reinterpret_cast(data); + auto out_bits = reinterpret_cast(out); + for (; j < d; ++j) { + out_bits[j] = data_bits[j] ^ masks[j]; + } + } }; template <> diff --git a/include/pdx/distance_computers/avx512_computers.hpp b/include/pdx/distance_computers/avx512_computers.hpp index 2fed70b..7bcb620 100644 --- a/include/pdx/distance_computers/avx512_computers.hpp +++ b/include/pdx/distance_computers/avx512_computers.hpp @@ -77,6 +77,29 @@ class SIMDComputer { r = _mm_hadd_ps(r, r); return _mm_cvtss_f32(_mm_hadd_ps(r, r)); }; + + static void FlipSign(const data_t* data, data_t* out, const uint32_t* masks, size_t d) { + size_t j = 0; + for (; j + 16 <= d; j += 16) { + __m512 vec = _mm512_loadu_ps(data + j); + __m512i mask = _mm512_loadu_si512(reinterpret_cast(masks + j)); + __m512i vec_i = _mm512_castps_si512(vec); + vec_i = _mm512_xor_si512(vec_i, mask); + _mm512_storeu_ps(out + j, _mm512_castsi512_ps(vec_i)); + } + for (; j + 8 <= d; j += 8) { + __m256 vec = _mm256_loadu_ps(data + j); + __m256i mask_avx = _mm256_loadu_si256(reinterpret_cast(masks + j)); + __m256i vec_i = _mm256_castps_si256(vec); + vec_i = _mm256_xor_si256(vec_i, mask_avx); + _mm256_storeu_ps(out + j, _mm256_castsi256_ps(vec_i)); + } + auto data_bits = reinterpret_cast(data); + auto out_bits = reinterpret_cast(out); + for (; j < d; ++j) { + out_bits[j] = data_bits[j] ^ masks[j]; + } + } }; template <> diff --git a/include/pdx/distance_computers/base_computers.hpp b/include/pdx/distance_computers/base_computers.hpp index 4f7a2cc..0c8ae6b 100644 --- a/include/pdx/distance_computers/base_computers.hpp +++ b/include/pdx/distance_computers/base_computers.hpp @@ -39,6 +39,7 @@ class DistanceComputer { constexpr static auto Vertical = computer::Vertical; constexpr static auto Horizontal = computer::Horizontal; + constexpr static auto FlipSign = computer::FlipSign; }; template <> diff --git a/include/pdx/distance_computers/neon_computers.hpp b/include/pdx/distance_computers/neon_computers.hpp index 5dffd1d..acf3e79 100644 --- a/include/pdx/distance_computers/neon_computers.hpp +++ b/include/pdx/distance_computers/neon_computers.hpp @@ -73,6 +73,21 @@ class SIMDComputer { return distance; #endif }; + + static void FlipSign(const data_t* data, data_t* out, const uint32_t* masks, size_t d) { + size_t j = 0; + for (; j + 4 <= d; j += 4) { + float32x4_t vec = vld1q_f32(data + j); + const uint32x4_t mask = vld1q_u32(masks + j); + vec = vreinterpretq_f32_u32(veorq_u32(vreinterpretq_u32_f32(vec), mask)); + vst1q_f32(out + j, vec); + } + auto data_bits = reinterpret_cast(data); + auto out_bits = reinterpret_cast(out); + for (; j < d; ++j) { + out_bits[j] = data_bits[j] ^ masks[j]; + } + } }; // Equivalent of vdotq_u32(acc, a, a) for squared accumulation. diff --git a/include/pdx/distance_computers/scalar_computers.hpp b/include/pdx/distance_computers/scalar_computers.hpp index 33071f6..e0210bf 100644 --- a/include/pdx/distance_computers/scalar_computers.hpp +++ b/include/pdx/distance_computers/scalar_computers.hpp @@ -55,6 +55,15 @@ class ScalarComputer { } return distance; }; + + static void FlipSign(const data_t* data, data_t* out, const uint32_t* masks, size_t d) { + auto data_bits = reinterpret_cast(data); + auto out_bits = reinterpret_cast(out); +#pragma clang loop vectorize(enable) + for (size_t j = 0; j < d; ++j) { + out_bits[j] = data_bits[j] ^ masks[j]; + } + } }; template <> diff --git a/include/pdx/index.hpp b/include/pdx/index.hpp index 6f4460f..eeb192b 100644 --- a/include/pdx/index.hpp +++ b/include/pdx/index.hpp @@ -129,12 +129,16 @@ class PDXIndex : public IPDXIndex { size_t size = sizeof(*this); // IVF heap allocations (sizeof(IVF) is inline in sizeof(*this)) size += index.GetInMemorySizeInBytes() - sizeof(index); - // Pruner: rotation matrix (D x D floats) + ratios vector (D floats) + // Pruner: rotation matrix or flip_masks (DCT mode) + ratios vector if (pruner) { size += sizeof(*pruner); const auto& m = pruner->GetMatrix(); + // matrix heap data (1 x D for DCT sign vector, D x D for full rotation) size += static_cast(m.rows()) * m.cols() * sizeof(float); - size += pruner->num_dimensions * sizeof(float); + size += pruner->num_dimensions * sizeof(float); // ratios + if (m.rows() == 1) { + size += pruner->num_dimensions * sizeof(uint32_t); // flip_masks + } } // Searcher: cluster_offsets array if (searcher) { @@ -531,12 +535,16 @@ class PDXTreeIndex : public IPDXIndex { size_t size = sizeof(*this); // IVFTree heap allocations (L1 + L0 clusters and centroids) size += index.GetInMemorySizeInBytes() - sizeof(index); - // Pruner: rotation matrix (D x D floats) + ratios vector (D floats) + // Pruner: rotation matrix or flip_masks (DCT mode) + ratios vector if (pruner) { size += sizeof(*pruner); const auto& m = pruner->GetMatrix(); + // matrix heap data (1 x D for DCT sign vector, D x D for full rotation) size += static_cast(m.rows()) * m.cols() * sizeof(float); - size += pruner->num_dimensions * sizeof(float); + size += pruner->num_dimensions * sizeof(float); // ratios + if (m.rows() == 1) { + size += pruner->num_dimensions * sizeof(uint32_t); // flip_masks + } } // L1 searcher: cluster_offsets array if (searcher) { diff --git a/include/pdx/pruners/adsampling.hpp b/include/pdx/pruners/adsampling.hpp index f83fcab..7ad1a50 100644 --- a/include/pdx/pruners/adsampling.hpp +++ b/include/pdx/pruners/adsampling.hpp @@ -1,6 +1,7 @@ #pragma once #include "pdx/common.hpp" +#include "pdx/distance_computers/base_computers.hpp" #include #include #include @@ -13,6 +14,7 @@ namespace PDX { class ADSamplingPruner { using matrix_t = eigen_matrix_t; + using flip_sign_fn = DistanceComputer; public: const uint32_t num_dimensions; @@ -24,18 +26,35 @@ class ADSamplingPruner { ratios[i] = GetRatio(i); } std::mt19937 gen(seed); - std::normal_distribution normal_dist; - Eigen::MatrixXf random_matrix = Eigen::MatrixXf::Zero( - static_cast(num_dimensions), static_cast(num_dimensions) - ); - for (size_t i = 0; i < num_dimensions; ++i) { - for (size_t j = 0; j < num_dimensions; ++j) { - random_matrix(static_cast(i), static_cast(j)) = - normal_dist(gen); + bool matrix_created = false; +#ifdef HAS_FFTW + if (UsesDCTRotation()) { + fftwf_init_threads(); + matrix.resize(1, num_dimensions); + std::uniform_int_distribution dist(0, 1); + for (size_t i = 0; i < num_dimensions; ++i) { + matrix(i) = dist(gen) ? 1.0f : -1.0f; + } + BuildFlipMasks(); + matrix_created = true; + } +#endif + if (!matrix_created) { + std::normal_distribution normal_dist; + Eigen::MatrixXf random_matrix = Eigen::MatrixXf::Zero( + static_cast(num_dimensions), + static_cast(num_dimensions) + ); + for (size_t i = 0; i < num_dimensions; ++i) { + for (size_t j = 0; j < num_dimensions; ++j) { + random_matrix(static_cast(i), static_cast(j)) = + normal_dist(gen); + } } + const Eigen::HouseholderQR qr(random_matrix); + matrix = qr.householderQ() * + Eigen::MatrixXf::Identity(num_dimensions, num_dimensions); } - const Eigen::HouseholderQR qr(random_matrix); - matrix = qr.householderQ(); } ADSamplingPruner(const uint32_t num_dimensions, const float* matrix_p) @@ -45,8 +64,10 @@ class ADSamplingPruner { ratios[i] = GetRatio(i); } #ifdef HAS_FFTW - if (num_dimensions >= D_THRESHOLD_FOR_DCT_ROTATION) { + if (UsesDCTRotation()) { + fftwf_init_threads(); matrix = Eigen::Map(matrix_p, 1, num_dimensions); + BuildFlipMasks(); } else { matrix = Eigen::Map(matrix_p, num_dimensions, num_dimensions); } @@ -94,6 +115,19 @@ class ADSamplingPruner { float pruning_aggressiveness = ADSAMPLING_PRUNING_AGGRESIVENESS; matrix_t matrix; std::vector ratios; + std::vector flip_masks; + + bool UsesDCTRotation() const { +#ifdef HAS_FFTW +#ifdef __AVX2__ + return num_dimensions >= D_THRESHOLD_FOR_DCT_ROTATION && IsPowerOf2(num_dimensions); +#else + return num_dimensions >= D_THRESHOLD_FOR_DCT_ROTATION; +#endif +#else + return false; +#endif + } float GetRatio(const size_t& visited_dimensions) const { if (visited_dimensions == 0) { @@ -107,35 +141,53 @@ class ADSamplingPruner { (1.0 + pruning_aggressiveness / std::sqrt(visited_dimensions)); } - /** - * @brief Rotates embeddings using the rotation matrix. - * - * Transforms embeddings to a rotated space where dimensions contribute more equally - * to the total distance, enabling effective early termination. - * - * @param embeddings Input embeddings (row-major, n × num_dimensions) - * @param out_buffer Output buffer for rotated embeddings (n × num_dimensions) - * @param n Number of embeddings to rotate - */ + void BuildFlipMasks() { + flip_masks.resize(num_dimensions); + for (size_t i = 0; i < num_dimensions; ++i) { + flip_masks[i] = (matrix(i) < 0.0f ? 0x80000000u : 0u); + } + } + + void FlipSign(const float* data, float* out, const size_t n) const { + for (size_t i = 0; i < n; ++i) { + const size_t offset = i * num_dimensions; + flip_sign_fn::FlipSign(data + offset, out + offset, flip_masks.data(), num_dimensions); + } + } + void Rotate( const float* PDX_RESTRICT const embeddings, float* PDX_RESTRICT const out_buffer, const size_t n ) const { #ifdef HAS_FFTW - Eigen::Map query_matrix(raw_query, num_dimensions); - Eigen::Map output(query, num_dimensions); - if (num_dimensions >= D_THRESHOLD_FOR_DCT_ROTATION) { - Eigen::RowVectorXf first_row = matrix.row(0); - Eigen::RowVectorXf pre_output = query_matrix.array() * first_row.array(); - fftwf_plan plan = fftwf_plan_r2r_1d( - num_dimensions, pre_output.data(), output.data(), FFTW_REDFT10, FFTW_ESTIMATE - ); - fftwf_execute(plan); - fftwf_destroy_plan(plan); - output[0] *= std::sqrt(1.0 / (4 * num_dimensions)); - for (int i = 1; i < num_dimensions; ++i) - output[i] *= std::sqrt(1.0 / (2 * num_dimensions)); + if (UsesDCTRotation()) { + Eigen::Map out(out_buffer, n, num_dimensions); + FlipSign(embeddings, out_buffer, n); + const float s0 = std::sqrt(1.0f / (4.0f * num_dimensions)); + const float s = std::sqrt(1.0f / (2.0f * num_dimensions)); + if (n == 1) { + fftwf_plan plan = fftwf_plan_r2r_1d( + num_dimensions, out.data(), out.data(), FFTW_REDFT10, FFTW_ESTIMATE + ); + fftwf_execute(plan); + fftwf_destroy_plan(plan); + } else { + int n0 = static_cast(num_dimensions); + int howmany = static_cast(n); + fftw_r2r_kind kind[1] = {FFTW_REDFT10}; + auto flag = FFTW_ESTIMATE; + if (!IsPowerOf2(num_dimensions)) { + flag = FFTW_MEASURE; + } + fftwf_plan plan = fftwf_plan_many_r2r( + 1, &n0, howmany, out.data(), NULL, 1, n0, out.data(), NULL, 1, n0, kind, flag + ); + fftwf_execute(plan); + fftwf_destroy_plan(plan); + } + out.col(0) *= s0; + out.rightCols(num_dimensions - 1) *= s; return; } #endif From 305e52f50a0759df242812896cd2dc38872a83ed Mon Sep 17 00:00:00 2001 From: lkuffo Date: Tue, 24 Feb 2026 17:03:14 +0100 Subject: [PATCH 17/39] Filtered search working --- .gitignore | 12 +- benchmarks/CMakeLists.txt | 10 +- benchmarks/pdx_end_to_end.cpp | 8 +- benchmarks/pdx_filtered.cpp | 166 ++++++++++++++++ benchmarks/pdx_ivf.cpp | 12 +- .../pdx_ivf2_adsampling_u8_filtered.cpp | 133 ------------- benchmarks/pdx_ivf_adsampling_filtered.cpp | 132 ------------ benchmarks/pdx_serialization.cpp | 8 +- benchmarks/pdx_special_filtered.cpp | 169 ++++++++++++++++ .../python_scripts/WrapperBruteForce.py | 42 ++-- benchmarks/python_scripts/requirements.txt | 1 + .../python_scripts/setup_filtered_search.py | 188 ++++++------------ benchmarks/python_scripts/setup_pdx.py | 8 +- .../DEFAULT/END_TO_END_PDX_ADSAMPLING.csv | 3 + .../results/DEFAULT/PDX_F32_ADSAMPLING.csv | 1 + .../DEFAULT/PDX_F32_ADSAMPLING_FILTERED.csv | 5 + include/pdx/index.hpp | 104 ++++++++++ include/pdx/lib/lib.hpp | 42 ++++ python/lib.cpp | 17 ++ python/pdxearch/index_factory.py | 28 +++ 20 files changed, 644 insertions(+), 445 deletions(-) create mode 100644 benchmarks/pdx_filtered.cpp delete mode 100644 benchmarks/pdx_ivf2_adsampling_u8_filtered.cpp delete mode 100644 benchmarks/pdx_ivf_adsampling_filtered.cpp create mode 100644 benchmarks/pdx_special_filtered.cpp create mode 100644 benchmarks/results/DEFAULT/PDX_F32_ADSAMPLING_FILTERED.csv diff --git a/.gitignore b/.gitignore index e765b47..e58b231 100644 --- a/.gitignore +++ b/.gitignore @@ -90,16 +90,8 @@ cmake_install.cmake /benchmarks/milvus/volumes/ /benchmarks/python_scripts/indexes -/benchmarks/BenchmarkPDXADSampling /benchmarks/BenchmarkEndToEnd /benchmarks/BenchmarkSerialization /benchmarks/BenchmarkPDXIVF -/benchmarks/BenchmarkIVF2ADSampling -/benchmarks/FilteredBenchmarkPDXADSampling -/benchmarks/FilteredBenchmarkU8IVF2ADSampling -/benchmarks/BenchmarkU8PDXADSampling -/benchmarks/BenchmarkPDXLinearScan -/benchmarks/BenchmarkU* -/benchmarks/G4* -/benchmarks/BenchmarkU8* -/benchmarks/BenchmarkEndToEnd \ No newline at end of file +/benchmarks/BenchmarkFiltered +/benchmarks/BenchmarkSpecialFilters \ No newline at end of file diff --git a/benchmarks/CMakeLists.txt b/benchmarks/CMakeLists.txt index 5ff1fc6..6afe1a3 100644 --- a/benchmarks/CMakeLists.txt +++ b/benchmarks/CMakeLists.txt @@ -10,13 +10,11 @@ endif() add_executable(BenchmarkPDXIVF pdx_ivf.cpp) add_executable(BenchmarkEndToEnd pdx_end_to_end.cpp) add_executable(BenchmarkSerialization pdx_serialization.cpp) +add_executable(BenchmarkFiltered pdx_filtered.cpp) +add_executable(BenchmarkSpecialFilters pdx_special_filtered.cpp) target_link_libraries(BenchmarkPDXIVF ${BENCH_COMMON_LIBS}) target_link_libraries(BenchmarkEndToEnd ${BENCH_COMMON_LIBS}) target_link_libraries(BenchmarkSerialization ${BENCH_COMMON_LIBS}) - -# Filtered benchmarks (need rewriting for new interface) -# add_executable(FilteredBenchmarkU8IVFTreeADSampling pdx_ivf2_adsampling_u8_filtered.cpp) -# add_executable(FilteredBenchmarkPDXADSampling pdx_ivf_adsampling_filtered.cpp) -# target_link_libraries(FilteredBenchmarkPDXADSampling ${BENCH_COMMON_LIBS}) -# target_link_libraries(FilteredBenchmarkU8IVFTreeADSampling ${BENCH_COMMON_LIBS}) +target_link_libraries(BenchmarkFiltered ${BENCH_COMMON_LIBS}) +target_link_libraries(BenchmarkSpecialFilters ${BENCH_COMMON_LIBS}) diff --git a/benchmarks/pdx_end_to_end.cpp b/benchmarks/pdx_end_to_end.cpp index 305c8ba..e6a7aa0 100644 --- a/benchmarks/pdx_end_to_end.cpp +++ b/benchmarks/pdx_end_to_end.cpp @@ -118,9 +118,9 @@ void RunBenchmark( } int main(int argc, char* argv[]) { - if (argc < 3) { - std::cerr << "Usage: " << argv[0] << " [ivf_nprobe]\n"; - std::cerr << "Index types: pdx_f32, pdx_u8, pdx_tree_f32, pdx_tree_u8\n"; + if (argc < 2) { + std::cerr << "Usage: " << argv[0] << " [index_type] [nprobe]\n"; + std::cerr << "Index types: pdx_f32 (default), pdx_u8, pdx_tree_f32, pdx_tree_u8\n"; std::cerr << "Available datasets:"; for (const auto& [name, _] : RAW_DATASET_PARAMS) { std::cerr << " " << name; @@ -129,7 +129,7 @@ int main(int argc, char* argv[]) { return 1; } std::string dataset = argv[1]; - std::string index_type = argv[2]; + std::string index_type = (argc > 2) ? argv[2] : "pdx_f32"; size_t arg_ivf_nprobe = (argc > 3) ? std::atoi(argv[3]) : 0; auto it = RAW_DATASET_PARAMS.find(dataset); diff --git a/benchmarks/pdx_filtered.cpp b/benchmarks/pdx_filtered.cpp new file mode 100644 index 0000000..9f982b4 --- /dev/null +++ b/benchmarks/pdx_filtered.cpp @@ -0,0 +1,166 @@ +#include "benchmark_utils.hpp" +#include "pdx/index.hpp" +#include "pdx/utils.hpp" +#include +#include +#include +#include +#include + +std::vector LoadPassingRowIds(const std::string& path) { + auto buffer = MmapFile(path); + char* ptr = buffer.get(); + uint32_t num_ids = *reinterpret_cast(ptr); + ptr += sizeof(uint32_t); + auto* ids = reinterpret_cast(ptr); + std::vector result(num_ids); + for (uint32_t i = 0; i < num_ids; i++) { + result[i] = ids[i]; + } + return result; +} + +int main(int argc, char* argv[]) { + if (argc < 2) { + std::cerr << "Usage: " << argv[0] + << " [index_type] [nprobe] [selectivity]\n"; + std::cerr << "Index types: pdx_f32 (default), pdx_u8, pdx_tree_f32, pdx_tree_u8\n"; + std::cerr << "Available datasets:"; + for (const auto& [name, _] : RAW_DATASET_PARAMS) { + std::cerr << " " << name; + } + std::cerr << "\n"; + return 1; + } + + std::string arg_dataset = argv[1]; + std::string index_type = "pdx_f32"; + size_t arg_ivf_nprobe = 0; + std::string arg_selectivity = "0_99"; + + if (argc > 2) { + index_type = argv[2]; + } + if (argc > 3) { + arg_ivf_nprobe = atoi(argv[3]); + } + if (argc > 4) { + arg_selectivity = argv[4]; + } + + std::cout << "==> PDX IVF ADSampling Filtered (" << index_type << ")\n"; + std::cout << "==> Selectivity: " << arg_selectivity << "\n"; + + std::string ALGORITHM = "adsampling_filtered"; + const bool VERIFY_RESULTS = BenchmarkUtils::VERIFY_RESULTS; + uint8_t KNN = BenchmarkUtils::KNN; + size_t NUM_QUERIES; + size_t NUM_MEASURE_RUNS = BenchmarkUtils::NUM_MEASURE_RUNS; + + // Build results file name from index type + std::string index_type_upper = index_type; + for (auto& c : index_type_upper) + c = toupper(c); + std::string RESULTS_PATH = + BENCHMARK_UTILS.RESULTS_DIR_PATH + index_type_upper + "_ADSAMPLING_FILTERED.csv"; + + // Parse selectivity string to float for metadata + float selectivity_value = 0.0f; + try { + std::string sel = arg_selectivity; + std::replace(sel.begin(), sel.end(), '_', '.'); + selectivity_value = std::stof(sel); + } catch (...) { + } + + for (const auto& [dataset, info] : RAW_DATASET_PARAMS) { + if (!arg_dataset.empty() && arg_dataset != dataset) { + continue; + } + + std::string index_path = BenchmarkUtils::PDX_DATA + dataset + "-" + index_type; + std::cout << "Loading " << index_path << "...\n"; + auto pdx_index = PDX::LoadPDXIndex(index_path); + std::cout << "Index in-memory size: " << std::fixed << std::setprecision(2) + << static_cast(pdx_index->GetInMemorySizeInBytes()) / (1024.0 * 1024.0) + << " MB\n"; + + // Load queries + std::unique_ptr query_ptr = + MmapFile(BenchmarkUtils::QUERIES_DATA + info.pdx_dataset_name); + auto* query = reinterpret_cast(query_ptr.get()); + NUM_QUERIES = info.num_queries; + query += 1; // skip number of embeddings header + + // Load filtered ground truth + std::unique_ptr ground_truth = MmapFile( + BenchmarkUtils::FILTERED_GROUND_TRUTH_DATA + info.pdx_dataset_name + "_100_norm_" + + arg_selectivity + ); + auto* int_ground_truth = reinterpret_cast(ground_truth.get()); + + // Load passing row IDs (binary format: [uint32 count][uint32[] ids]) + std::vector passing_row_ids = LoadPassingRowIds( + BenchmarkUtils::SELECTION_VECTOR_DATA + info.pdx_dataset_name + "_" + arg_selectivity + + ".bin" + ); + std::cout << "Passing row IDs: " << passing_row_ids.size() << "\n"; + + std::vector nprobes_to_use; + if (arg_ivf_nprobe > 0) { + nprobes_to_use = {arg_ivf_nprobe}; + } else { + nprobes_to_use.assign( + std::begin(BenchmarkUtils::IVF_PROBES), std::end(BenchmarkUtils::IVF_PROBES) + ); + } + + for (size_t ivf_nprobe : nprobes_to_use) { + if (pdx_index->GetNumClusters() < ivf_nprobe) { + continue; + } + if (arg_ivf_nprobe > 0 && ivf_nprobe != arg_ivf_nprobe) { + continue; + } + std::vector runtimes; + runtimes.resize(NUM_MEASURE_RUNS * NUM_QUERIES); + pdx_index->SetNProbe(ivf_nprobe); + + float recalls = 0; + if (VERIFY_RESULTS) { + for (size_t l = 0; l < NUM_QUERIES; ++l) { + auto result = pdx_index->FilteredSearch( + query + l * pdx_index->GetNumDimensions(), KNN, passing_row_ids + ); + BenchmarkUtils::VerifyResult( + recalls, result, KNN, int_ground_truth, l + ); + } + } + TicToc clock; + for (size_t j = 0; j < NUM_MEASURE_RUNS; ++j) { + for (size_t l = 0; l < NUM_QUERIES; ++l) { + clock.Reset(); + clock.Tic(); + pdx_index->FilteredSearch( + query + l * pdx_index->GetNumDimensions(), KNN, passing_row_ids + ); + clock.Toc(); + runtimes[j + l * NUM_MEASURE_RUNS] = {clock.accum_time}; + } + } + BenchmarkMetadata results_metadata = { + dataset, + ALGORITHM, + NUM_MEASURE_RUNS, + NUM_QUERIES, + ivf_nprobe, + KNN, + recalls, + selectivity_value + }; + BenchmarkUtils::SaveResults(runtimes, RESULTS_PATH, results_metadata); + } + } + return 0; +} diff --git a/benchmarks/pdx_ivf.cpp b/benchmarks/pdx_ivf.cpp index 09e19e4..19162e1 100644 --- a/benchmarks/pdx_ivf.cpp +++ b/benchmarks/pdx_ivf.cpp @@ -7,8 +7,8 @@ int main(int argc, char* argv[]) { if (argc < 2) { - std::cerr << "Usage: " << argv[0] << " [dataset] [nprobe]\n"; - std::cerr << "Index types: pdx_f32, pdx_u8, pdx_tree_f32, pdx_tree_u8\n"; + std::cerr << "Usage: " << argv[0] << " [index_type] [nprobe]\n"; + std::cerr << "Index types: pdx_f32 (default), pdx_u8, pdx_tree_f32, pdx_tree_u8\n"; std::cerr << "Available datasets:"; for (const auto& [name, _] : RAW_DATASET_PARAMS) { std::cerr << " " << name; @@ -17,11 +17,11 @@ int main(int argc, char* argv[]) { return 1; } - std::string index_type = argv[1]; - std::string arg_dataset; + std::string arg_dataset = argv[1]; + std::string index_type = "pdx_f32"; size_t arg_ivf_nprobe = 0; if (argc > 2) { - arg_dataset = argv[2]; + index_type = argv[2]; } if (argc > 3) { arg_ivf_nprobe = atoi(argv[3]); @@ -44,7 +44,7 @@ int main(int argc, char* argv[]) { BENCHMARK_UTILS.RESULTS_DIR_PATH + index_type_upper + "_ADSAMPLING.csv"; for (const auto& [dataset, info] : RAW_DATASET_PARAMS) { - if (arg_dataset.size() > 0 && arg_dataset != dataset) { + if (!arg_dataset.empty() && arg_dataset != dataset) { continue; } diff --git a/benchmarks/pdx_ivf2_adsampling_u8_filtered.cpp b/benchmarks/pdx_ivf2_adsampling_u8_filtered.cpp deleted file mode 100644 index 139d1c7..0000000 --- a/benchmarks/pdx_ivf2_adsampling_u8_filtered.cpp +++ /dev/null @@ -1,133 +0,0 @@ -#ifndef BENCHMARK_TIME -#define BENCHMARK_TIME = true -#endif - -#ifndef PDX_USE_EXPLICIT_SIMD -#define PDX_USE_EXPLICIT_SIMD = true -#endif - -#include "benchmark_utils.hpp" -#include "pdx/db_mock/predicate_evaluator.hpp" -#include "pdx/pruners/adsampling.hpp" -#include "pdx/searcher.hpp" -#include "pdx/utils.hpp" -#include -#include - -int main(int argc, char* argv[]) { - std::string arg_dataset; - std::string arg_selectivity; - size_t arg_ivf_nprobe = 0; - if (argc > 1) { - arg_dataset = argv[1]; - } - if (argc > 2) { - arg_ivf_nprobe = atoi(argv[2]); - } - if (argc > 3) { - arg_selectivity = argv[3]; - } else { - arg_selectivity = "0_99"; - } - std::cout << "==> PDX IVF ADSampling\n"; - - std::string ALGORITHM = "adsampling"; - const bool VERIFY_RESULTS = BenchmarkUtils::VERIFY_RESULTS; - - uint8_t KNN = BenchmarkUtils::KNN; - float EPSILON0 = BenchmarkUtils::EPSILON0; - size_t NUM_QUERIES; - size_t NUM_MEASURE_RUNS = BenchmarkUtils::NUM_MEASURE_RUNS; - - PDX::DimensionsOrder DIMENSION_ORDER = PDX::SEQUENTIAL; - - std::string RESULTS_PATH; - RESULTS_PATH = BENCHMARK_UTILS.RESULTS_DIR_PATH + "U8_IMI_PDX_ADSAMPLING_FILTERED.csv"; - - std::cout << "==> SELECTIVITY: " << arg_selectivity << std::endl; - for (const auto& dataset : BenchmarkUtils::DATASETS) { - if (arg_dataset.size() > 0 && arg_dataset != dataset) { - continue; - } - PDX::IndexPDXIVF2 pdx_data = PDX::IndexPDXIVF2(); - pdx_data.Restore(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf2-u8"); - std::unique_ptr _matrix_ptr = - MmapFile(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf2-u8-matrix"); - auto* _matrix = reinterpret_cast(_matrix_ptr.get()); - - std::unique_ptr query_ptr = MmapFile(BenchmarkUtils::QUERIES_DATA + dataset); - auto* query = reinterpret_cast(query_ptr.get()); - - NUM_QUERIES = 1000; - std::unique_ptr ground_truth = MmapFile( - BenchmarkUtils::FILTERED_GROUND_TRUTH_DATA + dataset + "_100_norm_" + arg_selectivity - ); - auto* int_ground_truth = reinterpret_cast(ground_truth.get()); - query += 1; // skip number of embeddings - - PDX::PredicateEvaluator predicate_evaluator = - PDX::PredicateEvaluator(pdx_data.num_clusters); - predicate_evaluator.LoadSelectionVectorFromFile( - BenchmarkUtils::SELECTION_VECTOR_DATA + dataset + "_" + arg_selectivity + ".bin" - ); - PDX::ADSamplingPruner pruner = - PDX::ADSamplingPruner(pdx_data.num_dimensions, EPSILON0, _matrix); - PDX::PDXearch searcher = PDX::PDXearch>( - pdx_data, pruner, 1, DIMENSION_ORDER - ); - - std::vector nprobes_to_use; - if (arg_ivf_nprobe > 0) { - nprobes_to_use = {arg_ivf_nprobe}; - } else { - nprobes_to_use.assign( - std::begin(BenchmarkUtils::IVF_PROBES), std::end(BenchmarkUtils::IVF_PROBES) - ); - } - - for (size_t ivf_nprobe : nprobes_to_use) { - if (pdx_data.num_clusters < ivf_nprobe) { - continue; - } - if (arg_ivf_nprobe > 0 && ivf_nprobe != arg_ivf_nprobe) { - continue; - } - std::vector runtimes; - runtimes.resize(NUM_MEASURE_RUNS * NUM_QUERIES); - searcher.SetNProbe(ivf_nprobe); - - float recalls = 0; - if (VERIFY_RESULTS) { - for (size_t l = 0; l < NUM_QUERIES; ++l) { - auto result = searcher.FilteredSearch( - query + l * pdx_data.num_dimensions, KNN, predicate_evaluator - ); - BenchmarkUtils::VerifyResult( - recalls, result, KNN, int_ground_truth, l - ); - } - } - for (size_t j = 0; j < NUM_MEASURE_RUNS; ++j) { - for (size_t l = 0; l < NUM_QUERIES; ++l) { - searcher.FilteredSearch( - query + l * pdx_data.num_dimensions, KNN, predicate_evaluator - ); - runtimes[j + l * NUM_MEASURE_RUNS] = {searcher.end_to_end_clock.accum_time}; - } - } - float real_selectivity = 1 - BenchmarkUtils::SELECTIVITY_THRESHOLD; - BenchmarkMetadata results_metadata = { - dataset, - ALGORITHM, - NUM_MEASURE_RUNS, - NUM_QUERIES, - ivf_nprobe, - KNN, - recalls, - real_selectivity - }; - BenchmarkUtils::SaveResults(runtimes, RESULTS_PATH, results_metadata); - } - } - return 0; -} \ No newline at end of file diff --git a/benchmarks/pdx_ivf_adsampling_filtered.cpp b/benchmarks/pdx_ivf_adsampling_filtered.cpp deleted file mode 100644 index c9c2d14..0000000 --- a/benchmarks/pdx_ivf_adsampling_filtered.cpp +++ /dev/null @@ -1,132 +0,0 @@ -#ifndef BENCHMARK_TIME -#define BENCHMARK_TIME = true -#endif - -#ifndef PDX_USE_EXPLICIT_SIMD -#define PDX_USE_EXPLICIT_SIMD = true -#endif - -#include "benchmark_utils.hpp" -#include "pdx/ivf_wrapper.hpp" -#include "pdx/pruners/adsampling.hpp" -#include "pdx/searcher.hpp" -#include "pdx/utils.hpp" -#include -#include - -int main(int argc, char* argv[]) { - std::string arg_dataset; - std::string arg_selectivity; - size_t arg_ivf_nprobe = 0; - if (argc > 1) { - arg_dataset = argv[1]; - } - if (argc > 2) { - arg_ivf_nprobe = atoi(argv[2]); - } - if (argc > 3) { - arg_selectivity = argv[3]; - } else { - arg_selectivity = "0_99"; - } - std::cout << "==> PDX IVF ADSampling\n"; - - std::string ALGORITHM = "adsampling"; - const bool VERIFY_RESULTS = BenchmarkUtils::VERIFY_RESULTS; - - uint8_t KNN = BenchmarkUtils::KNN; - float SELECTIVITY_THRESHOLD = BenchmarkUtils::SELECTIVITY_THRESHOLD; - float EPSILON0 = BenchmarkUtils::EPSILON0; - size_t NUM_QUERIES; - size_t NUM_MEASURE_RUNS = BenchmarkUtils::NUM_MEASURE_RUNS; - - PDX::DimensionsOrder DIMENSION_ORDER = PDX::SEQUENTIAL; - - std::string RESULTS_PATH; - RESULTS_PATH = BENCHMARK_UTILS.RESULTS_DIR_PATH + "IVF_PDX_ADSAMPLING_FILTERED.csv"; - std::cout << "==> SELECTIVITY: " << arg_selectivity << std::endl; - for (const auto& dataset : BenchmarkUtils::DATASETS) { - if (arg_dataset.size() > 0 && arg_dataset != dataset) { - continue; - } - PDX::IndexPDXIVF pdx_data = PDX::IndexPDXIVF(); - pdx_data.Restore(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-ivf"); - - std::unique_ptr _matrix_ptr = - MmapFile(BenchmarkUtils::PDX_ADSAMPLING_DATA + dataset + "-matrix"); - auto* _matrix = reinterpret_cast(_matrix_ptr.get()); - - std::unique_ptr query_ptr = MmapFile(BenchmarkUtils::QUERIES_DATA + dataset); - auto* query = reinterpret_cast(query_ptr.get()); - NUM_QUERIES = 1000; - - std::unique_ptr ground_truth = MmapFile( - BenchmarkUtils::FILTERED_GROUND_TRUTH_DATA + dataset + "_100_norm_" + arg_selectivity - ); - auto* int_ground_truth = reinterpret_cast(ground_truth.get()); - query += 1; // skip number of embeddings - - PDX::PredicateEvaluator predicate_evaluator = - PDX::PredicateEvaluator(pdx_data.num_clusters); - predicate_evaluator.LoadSelectionVectorFromFile( - BenchmarkUtils::SELECTION_VECTOR_DATA + dataset + "_" + arg_selectivity + ".bin" - ); - PDX::ADSamplingPruner pruner = - PDX::ADSamplingPruner(pdx_data.num_dimensions, EPSILON0, _matrix); - PDX::PDXearch searcher = PDX::PDXearch(pdx_data, pruner, 1, DIMENSION_ORDER); - - std::vector nprobes_to_use; - if (arg_ivf_nprobe > 0) { - nprobes_to_use = {arg_ivf_nprobe}; - } else { - nprobes_to_use.assign( - std::begin(BenchmarkUtils::IVF_PROBES), std::end(BenchmarkUtils::IVF_PROBES) - ); - } - - for (size_t ivf_nprobe : nprobes_to_use) { - if (pdx_data.num_clusters < ivf_nprobe) { - continue; - } - if (arg_ivf_nprobe > 0 && ivf_nprobe != arg_ivf_nprobe) { - continue; - } - std::vector runtimes; - runtimes.resize(NUM_MEASURE_RUNS * NUM_QUERIES); - searcher.SetNProbe(ivf_nprobe); - - float recalls = 0; - if (VERIFY_RESULTS) { - for (size_t l = 0; l < NUM_QUERIES; ++l) { - auto result = searcher.FilteredSearch( - query + l * pdx_data.num_dimensions, KNN, predicate_evaluator - ); - BenchmarkUtils::VerifyResult( - recalls, result, KNN, int_ground_truth, l - ); - } - } - for (size_t j = 0; j < NUM_MEASURE_RUNS; ++j) { - for (size_t l = 0; l < NUM_QUERIES; ++l) { - searcher.FilteredSearch( - query + l * pdx_data.num_dimensions, KNN, predicate_evaluator - ); - runtimes[j + l * NUM_MEASURE_RUNS] = {searcher.end_to_end_clock.accum_time}; - } - } - float real_selectivity = 1 - SELECTIVITY_THRESHOLD; - BenchmarkMetadata results_metadata = { - dataset, - ALGORITHM, - NUM_MEASURE_RUNS, - NUM_QUERIES, - ivf_nprobe, - KNN, - recalls, - real_selectivity - }; - BenchmarkUtils::SaveResults(runtimes, RESULTS_PATH, results_metadata); - } - } - return 0; -} \ No newline at end of file diff --git a/benchmarks/pdx_serialization.cpp b/benchmarks/pdx_serialization.cpp index e83ef1e..d9088be 100644 --- a/benchmarks/pdx_serialization.cpp +++ b/benchmarks/pdx_serialization.cpp @@ -46,9 +46,9 @@ void BuildAndSave( } int main(int argc, char* argv[]) { - if (argc < 3) { - std::cerr << "Usage: " << argv[0] << " \n"; - std::cerr << "Index types: pdx_f32, pdx_u8, pdx_tree_f32, pdx_tree_u8\n"; + if (argc < 2) { + std::cerr << "Usage: " << argv[0] << " [index_type]\n"; + std::cerr << "Index types: pdx_f32 (default), pdx_u8, pdx_tree_f32, pdx_tree_u8\n"; std::cerr << "Available datasets:"; for (const auto& [name, _] : RAW_DATASET_PARAMS) { std::cerr << " " << name; @@ -57,7 +57,7 @@ int main(int argc, char* argv[]) { return 1; } std::string dataset = argv[1]; - std::string index_type = argv[2]; + std::string index_type = (argc > 2) ? argv[2] : "pdx_f32"; auto it = RAW_DATASET_PARAMS.find(dataset); if (it == RAW_DATASET_PARAMS.end()) { diff --git a/benchmarks/pdx_special_filtered.cpp b/benchmarks/pdx_special_filtered.cpp new file mode 100644 index 0000000..b16ccb8 --- /dev/null +++ b/benchmarks/pdx_special_filtered.cpp @@ -0,0 +1,169 @@ +#include "benchmark_utils.hpp" +#include "pdx/index.hpp" +#include "pdx/utils.hpp" +#include +#include +#include +#include +#include + +struct SpecialFilter { + std::string name; + uint32_t n_clusters; + bool plus_one_per_remaining; // PART+ mode: add 1 random row from each remaining cluster +}; + +static const SpecialFilter SPECIAL_FILTERS[] = { + {"PART_1", 1, false}, + {"PART_30", 30, false}, + {"PART+_1", 1, true}, +}; + +std::vector BuildSpecialFilterRowIds( + const PDX::IPDXIndex& index, + const SpecialFilter& filter, + uint32_t seed +) { + std::vector passing_row_ids; + uint32_t total_clusters = index.GetNumClusters(); + uint32_t n_full = std::min(filter.n_clusters, total_clusters); + + // All rows from the first n_full clusters + for (uint32_t c = 0; c < n_full; c++) { + auto ids = index.GetClusterRowIds(c); + for (auto id : ids) { + passing_row_ids.push_back(id); + } + } + + // PART+ mode: add 1 random row from each remaining cluster + if (filter.plus_one_per_remaining) { + std::mt19937 gen(seed); + for (uint32_t c = n_full; c < total_clusters; c++) { + uint32_t cluster_size = index.GetClusterSize(c); + if (cluster_size == 0) { + continue; + } + auto ids = index.GetClusterRowIds(c); + std::uniform_int_distribution dist(0, cluster_size - 1); + passing_row_ids.push_back(ids[dist(gen)]); + } + } + + return passing_row_ids; +} + +int main(int argc, char* argv[]) { + if (argc < 2) { + std::cerr << "Usage: " << argv[0] << " [index_type] [nprobe]\n"; + std::cerr << "Index types: pdx_f32 (default), pdx_u8, pdx_tree_f32, pdx_tree_u8\n"; + std::cerr << "Available datasets:"; + for (const auto& [name, _] : RAW_DATASET_PARAMS) { + std::cerr << " " << name; + } + std::cerr << "\n"; + return 1; + } + + std::string arg_dataset = argv[1]; + std::string index_type = "pdx_f32"; + size_t arg_ivf_nprobe = 0; + if (argc > 2) { + index_type = argv[2]; + } + if (argc > 3) { + arg_ivf_nprobe = atoi(argv[3]); + } + + std::cout << "==> PDX Special Filtered Benchmark (" << index_type << ")\n"; + + std::string ALGORITHM = "special_filtered"; + uint8_t KNN = BenchmarkUtils::KNN; + size_t NUM_QUERIES; + size_t NUM_MEASURE_RUNS = BenchmarkUtils::NUM_MEASURE_RUNS; + + std::string index_type_upper = index_type; + for (auto& c : index_type_upper) + c = toupper(c); + std::string RESULTS_PATH = + BENCHMARK_UTILS.RESULTS_DIR_PATH + index_type_upper + "_SPECIAL_FILTERED.csv"; + + for (const auto& [dataset, info] : RAW_DATASET_PARAMS) { + if (!arg_dataset.empty() && arg_dataset != dataset) { + continue; + } + + std::string index_path = BenchmarkUtils::PDX_DATA + dataset + "-" + index_type; + std::cout << "Loading " << index_path << "...\n"; + auto pdx_index = PDX::LoadPDXIndex(index_path); + std::cout << "Index in-memory size: " << std::fixed << std::setprecision(2) + << static_cast(pdx_index->GetInMemorySizeInBytes()) / (1024.0 * 1024.0) + << " MB\n"; + std::cout << "Num clusters: " << pdx_index->GetNumClusters() << "\n"; + + // Load queries + std::unique_ptr query_ptr = + MmapFile(BenchmarkUtils::QUERIES_DATA + info.pdx_dataset_name); + auto* query = reinterpret_cast(query_ptr.get()); + NUM_QUERIES = info.num_queries; + query += 1; // skip number of embeddings header + + for (const auto& filter : SPECIAL_FILTERS) { + if (filter.n_clusters > pdx_index->GetNumClusters()) { + std::cout << "Skipping " << filter.name << " (needs " << filter.n_clusters + << " clusters, index has " << pdx_index->GetNumClusters() << ")\n"; + continue; + } + + auto passing_row_ids = BuildSpecialFilterRowIds(*pdx_index, filter, 42); + std::cout << "\n--- " << filter.name << ": " << passing_row_ids.size() + << " passing row IDs ---\n"; + + std::vector nprobes_to_use; + if (arg_ivf_nprobe > 0) { + nprobes_to_use = {arg_ivf_nprobe}; + } else { + nprobes_to_use.assign( + std::begin(BenchmarkUtils::IVF_PROBES), std::end(BenchmarkUtils::IVF_PROBES) + ); + } + + for (size_t ivf_nprobe : nprobes_to_use) { + if (pdx_index->GetNumClusters() < ivf_nprobe) { + continue; + } + if (arg_ivf_nprobe > 0 && ivf_nprobe != arg_ivf_nprobe) { + continue; + } + std::vector runtimes; + runtimes.resize(NUM_MEASURE_RUNS * NUM_QUERIES); + pdx_index->SetNProbe(ivf_nprobe); + + TicToc clock; + for (size_t j = 0; j < NUM_MEASURE_RUNS; ++j) { + for (size_t l = 0; l < NUM_QUERIES; ++l) { + clock.Reset(); + clock.Tic(); + pdx_index->FilteredSearch( + query + l * pdx_index->GetNumDimensions(), KNN, passing_row_ids + ); + clock.Toc(); + runtimes[j + l * NUM_MEASURE_RUNS] = {clock.accum_time}; + } + } + BenchmarkMetadata results_metadata = { + dataset, + ALGORITHM + "_" + filter.name, + NUM_MEASURE_RUNS, + NUM_QUERIES, + ivf_nprobe, + KNN, + 0.0f, // no recall (timing-only) + 0.0f + }; + BenchmarkUtils::SaveResults(runtimes, RESULTS_PATH, results_metadata); + } + } + } + return 0; +} diff --git a/benchmarks/python_scripts/WrapperBruteForce.py b/benchmarks/python_scripts/WrapperBruteForce.py index 29253a0..8156f30 100644 --- a/benchmarks/python_scripts/WrapperBruteForce.py +++ b/benchmarks/python_scripts/WrapperBruteForce.py @@ -21,36 +21,32 @@ def query(self, data, v, n): class BruteForceFAISS: - def __init__(self, metric, dimension): - if metric not in ("angular", "euclidean", "hamming", "ip"): + def __init__(self, metric, njobs=-1): + if metric not in ("angular", "euclidean", "ip"): raise NotImplementedError("BruteForce doesn't support metric %s" % metric) self.metric = metric - if metric == "euclidean": - self._metric = faiss.METRIC_L2 - elif metric == "ip": - self._metric = faiss.METRIC_INNER_PRODUCT - else: - self._metric = faiss.METRIC_L2 - - self._metric = metric - self.name = "BruteForceSIMD()" - self._nbrs = None - self.dimension = dimension - faiss.omp_set_num_threads(1) + self.name = "BruteForceFAISS()" def fit(self, X): - if self.metric == "euclidean": - self.index = faiss.IndexFlatL2(self.dimension) - elif self.metric == "ip": - self.index = faiss.IndexFlatIP(self.dimension) + X = numpy.ascontiguousarray(X, dtype=numpy.float32) + dimension = X.shape[1] + if self.metric == "ip": + self.index = faiss.IndexFlatIP(dimension) else: - self.index = faiss.IndexFlatL2(self.dimension) + self.index = faiss.IndexFlatL2(dimension) self.index.add(X) - def query(self, v, n, X=None, force_fit=True): - if force_fit and X is not None: self.fit(X) - points, distances = self.index.search(numpy.array([v]), k=n) - return points, distances + def query(self, v, n): + v = numpy.ascontiguousarray(numpy.array([v]), dtype=numpy.float32) + distances, indices = self.index.search(v, k=n) + return distances[0], indices[0] + + def query_batch(self, v, n): + """Batch query. Returns (distances, indices). + Note: for L2, distances are squared L2 (unlike sklearn which returns L2).""" + v = numpy.ascontiguousarray(v, dtype=numpy.float32) + distances, indices = self.index.search(v, k=n) + return distances, indices class BruteForceSKLearn: def __init__(self, metric, njobs=1): diff --git a/benchmarks/python_scripts/requirements.txt b/benchmarks/python_scripts/requirements.txt index d56a057..b6128ed 100644 --- a/benchmarks/python_scripts/requirements.txt +++ b/benchmarks/python_scripts/requirements.txt @@ -2,5 +2,6 @@ numpy~=2.2.2 h5py~=3.12.1 usearch~=2.16.9 scikit-learn~=1.6.1 +faiss-cpu~=1.9.0 gdown==5.1.0 scipy~=1.15.3 \ No newline at end of file diff --git a/benchmarks/python_scripts/setup_filtered_search.py b/benchmarks/python_scripts/setup_filtered_search.py index fc8e863..a6cb8d2 100644 --- a/benchmarks/python_scripts/setup_filtered_search.py +++ b/benchmarks/python_scripts/setup_filtered_search.py @@ -1,145 +1,87 @@ import numpy as np -np.random.seed(42) - import json -import random +import os +from decimal import Decimal from setup_utils import * -from pdxearch.index_base import BaseIndexPDXIVF, BaseIndexPDXIVFTree from sklearn import preprocessing -from WrapperBruteForce import BruteForceSKLearn +from WrapperBruteForce import BruteForceFAISS -from decimal import Decimal +np.random.seed(42) SELECTIVITIES = [ - 0.0001, - 0.000135, - 0.00015, 0.001, 0.01, + 0.0001, 0.000135, 0.00015, 0.001, 0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.75, 0.9, 0.95, 0.99, ] -SPECIAL_SELECTIVITIES = [ - "PART 30", - "PART 1", - "PART+ 1", -] - -def generate_ground_truth(dataset, train, test, filtered_ids, selectivity_str, KNNS=(100,), normalize=True): - print('Generating ground truth for', len(train), 'points', 'at', selectivity_str, 'selectivity') - N_QUERIES = len(test) - algo = BruteForceSKLearn("euclidean", njobs=-1) - algo.fit(train) - for knn in KNNS: - gt_filename = f"{dataset}_{knn}_norm_{selectivity_str}.json" - gt_name = os.path.join(SEMANTIC_FILTERED_GROUND_TRUTH_PATH, gt_filename) - gt = {} - index_data = [] - distance_data = [] - print('Querying for GT...') - dist, index = algo.query_batch(test, n=knn) - for i in range(N_QUERIES): - index_data.append(filtered_ids[index[i]]) - distance_data.append(dist[i] ** 2) - gt[i] = filtered_ids[index[i]].tolist() - with open(os.path.join(FILTERED_GROUND_TRUTH_DATA, gt_filename.replace('.json', '')), "wb") as file: - file.write(np.array(index_data, dtype=np.uint32).tobytes("C")) - file.write(np.array(distance_data, dtype=np.float32).tobytes("C")) - with open(gt_name, 'w') as f: - json.dump(gt, f) - -def generate_selection_vector(dataset_name: str, _type='pdx', normalize=True): - print(dataset_name) - - train, test = read_hdf5_data(dataset_name) +KNN = 100 + +def generate_filtered_ground_truth(hdf5_name, train, test, passing_row_ids, selectivity_str): + """Brute-force kNN on filtered subset; write binary GT + JSON GT.""" + filtered_train = train[passing_row_ids] + actual_knn = min(KNN, len(filtered_train)) + if actual_knn == 0: + print(f' WARNING: 0 passing points for {selectivity_str}, skipping GT') + return + + print(f' GT: {len(filtered_train)} filtered points, {len(test)} queries') + algo = BruteForceFAISS("euclidean") + algo.fit(filtered_train) + + dist, index = algo.query_batch(test, n=actual_knn) + + index_data = [] + distance_data = [] + gt = {} + for i in range(len(test)): + original_ids = passing_row_ids[index[i]] + # Pad to KNN for consistent binary format + padded_ids = np.full(KNN, 0xFFFFFFFF, dtype=np.uint32) + padded_ids[:actual_knn] = original_ids + padded_dists = np.full(KNN, np.finfo(np.float32).max, dtype=np.float32) + padded_dists[:actual_knn] = dist[i] + index_data.append(padded_ids) + distance_data.append(padded_dists) + gt[i] = original_ids.tolist() + + gt_filename = f"{hdf5_name}_{KNN}_norm_{selectivity_str}" + with open(os.path.join(FILTERED_GROUND_TRUTH_DATA, gt_filename), "wb") as f: + f.write(np.array(index_data, dtype=np.uint32).tobytes("C")) + f.write(np.array(distance_data, dtype=np.float32).tobytes("C")) + with open(os.path.join(SEMANTIC_FILTERED_GROUND_TRUTH_PATH, gt_filename + ".json"), 'w') as f: + json.dump(gt, f) + + +def generate_filtered_data(dataset_abbrev, normalize=True): + """For each selectivity, generate passing row IDs + filtered ground truth.""" + hdf5_name, dims = DATASET_INFO[dataset_abbrev] + print(f'\n{dataset_abbrev} -> {hdf5_name}') + + train, test = read_hdf5_data(hdf5_name) if normalize: train = preprocessing.normalize(train, axis=1, norm='l2') test = preprocessing.normalize(test, axis=1, norm='l2') - base_idx = BaseIndexPDXIVF(DIMENSIONALITIES[dataset_name], 'l2sq') - # Core index IVF must exist - index_path = os.path.join(CORE_INDEXES_FAISS, get_core_index_filename(dataset_name, norm=normalize)) - # Reads the core index created by faiss to generate the PDX index - base_idx.core_index.read_index(index_path) - - n_clusters = base_idx.core_index.nbuckets - labels = base_idx.core_index.labels - all_ordered_labels = np.concatenate([np.array(sub) for sub in labels]) - assert(n_clusters == len(labels)) - - # The GT is not correctly calculated - - total_points = sum(len(lst) for lst in labels) - print('Total points', total_points) + total_points = len(train) + print(f'Total points: {total_points}') for selectivity in SELECTIVITIES: - selection_per_cluster = [] - selection_vector = (np.random.rand(total_points) < selectivity).astype(np.uint8) - selected = np.sum(selection_vector) - print(f'For selectivity {selectivity}, {selected} points were chosen ({float(selected) / total_points})') - real_selection_vectors = np.array([]).astype(np.uint8) - for l in labels: - l_np = np.array(l) - real_selection_vector = selection_vector[l_np] - selected_on_this_cluster = np.sum(real_selection_vector) - selection_per_cluster.append(selected_on_this_cluster) - real_selection_vectors = np.concatenate((real_selection_vectors, real_selection_vector)) - selection_per_cluster = np.array(selection_per_cluster).astype(np.uint32) - assert(len(selection_per_cluster) == len(labels)) - assert(np.sum(selection_per_cluster) == selected) - assert(total_points == len(real_selection_vectors)) - assert(np.sum(selection_per_cluster) == np.sum(real_selection_vectors)) + mask = np.random.rand(total_points) < selectivity + passing_row_ids = np.where(mask)[0].astype(np.uint32) + num_passing = len(passing_row_ids) + print(f' Selectivity {selectivity}: {num_passing} points ({num_passing / total_points:.6f})') selectivity_str = format(Decimal(str(selectivity)), 'f').replace('.', '_') - selectivity_filename = f'{dataset_name}_{selectivity_str}.bin' - selectivity_filename_path = os.path.join(FILTER_SELECTION_VECTORS, selectivity_filename) - data = bytearray() - data.extend(selection_per_cluster.tobytes("C")) - data.extend(real_selection_vectors.tobytes("C")) - with open(selectivity_filename_path, "wb") as file: - file.write(bytes(data)) - filtered_ids = all_ordered_labels[real_selection_vectors.astype(bool)] - filtered_train = train[all_ordered_labels[real_selection_vectors.astype(bool)]] - generate_ground_truth(dataset_name, filtered_train, test, filtered_ids, selectivity_str, KNNS=(100,), normalize=normalize) - - - for mode in SPECIAL_SELECTIVITIES: - _type, param = mode.split(' ') - n = int(param) - selection_per_cluster = [] - selected = 0 - real_selection_vectors = np.array([]).astype(np.uint8) - for l in labels: - n_points = len(l) - if n <= 0: - real_selection_vector = np.full((n_points), 0, dtype=np.uint8) - if '+' in _type: - real_selection_vector[random.randint(0, n_points - 1)] = 1 - else: - real_selection_vector = np.full((n_points), 1, dtype=np.uint8) - n -= 1 - selected_on_this_cluster = np.sum(real_selection_vector) - selection_per_cluster.append(selected_on_this_cluster) - real_selection_vectors = np.concatenate((real_selection_vectors, real_selection_vector)) - selected += selected_on_this_cluster - selection_per_cluster = np.array(selection_per_cluster).astype(np.uint32) - print(f'For selectivity {_type} {param}, {selected} points were chosen ({float(selected) / total_points * 100})') - assert(len(selection_per_cluster) == len(labels)) - assert(sum(selection_per_cluster) == selected) - assert(total_points == len(real_selection_vectors)) - - selectivity_str = f'{_type}_{param}' - selectivity_filename = f'{dataset_name}_{selectivity_str}.bin' - selectivity_filename_path = os.path.join(FILTER_SELECTION_VECTORS, selectivity_filename) - data = bytearray() - data.extend(selection_per_cluster.tobytes("C")) - data.extend(real_selection_vectors.tobytes("C")) - with open(selectivity_filename_path, "wb") as file: - file.write(bytes(data)) - filtered_ids = all_ordered_labels[real_selection_vectors.astype(bool)] - filtered_train = train[all_ordered_labels[real_selection_vectors.astype(bool)]] - generate_ground_truth(dataset_name, filtered_train, test, filtered_ids, selectivity_str, KNNS=(100,), normalize=normalize) + + # Write passing row IDs as binary: [uint32 count][uint32[] ids] + filename = f'{hdf5_name}_{selectivity_str}.bin' + filepath = os.path.join(FILTER_SELECTION_VECTORS, filename) + with open(filepath, "wb") as f: + f.write(np.uint32(num_passing).tobytes()) + f.write(passing_row_ids.tobytes("C")) + + generate_filtered_ground_truth(hdf5_name, train, test, passing_row_ids, selectivity_str) if __name__ == "__main__": - # generate_selection_vector('agnews-mxbai-1024-euclidean', normalize=True) - generate_selection_vector('instructorxl-arxiv-768', normalize=True) + generate_filtered_data('mxbai', normalize=True) diff --git a/benchmarks/python_scripts/setup_pdx.py b/benchmarks/python_scripts/setup_pdx.py index 1b8843b..042631b 100644 --- a/benchmarks/python_scripts/setup_pdx.py +++ b/benchmarks/python_scripts/setup_pdx.py @@ -2,7 +2,7 @@ import sys from setup_utils import * from pdxearch import IndexPDXIVF, IndexPDXIVFSQ8, IndexPDXIVFTree, IndexPDXIVFTreeSQ8 -from WrapperBruteForce import BruteForceSKLearn +from WrapperBruteForce import BruteForceFAISS from sklearn import preprocessing INDEX_CLASSES = { @@ -14,7 +14,7 @@ def generate_ground_truth(dataset_abbrev, KNNS=(100,), normalize=True): - """Generate ground truth with SKLearn brute-force search.""" + """Generate ground truth with FAISS brute-force search.""" hdf5_name, dims = DATASET_INFO[dataset_abbrev] print(f'Generating ground truth: {dataset_abbrev} -> {hdf5_name}') @@ -26,7 +26,7 @@ def generate_ground_truth(dataset_abbrev, KNNS=(100,), normalize=True): train = preprocessing.normalize(train, axis=1, norm='l2') test = preprocessing.normalize(test, axis=1, norm='l2') - algo = BruteForceSKLearn("euclidean", njobs=-1) + algo = BruteForceFAISS("euclidean") algo.fit(train) for knn in KNNS: gt_filename = get_ground_truth_filename(hdf5_name, knn, normalize) @@ -38,7 +38,7 @@ def generate_ground_truth(dataset_abbrev, KNNS=(100,), normalize=True): dist, index = algo.query_batch(test, n=knn) for i in range(N_QUERIES): index_data.append(index[i]) - distance_data.append(dist[i] ** 2) + distance_data.append(dist[i]) gt[i] = index[i].tolist() with open(os.path.join(GROUND_TRUTH_DATA, gt_filename.replace('.json', '')), "wb") as file: file.write(np.array(index_data, dtype=np.uint32).tobytes("C")) diff --git a/benchmarks/results/DEFAULT/END_TO_END_PDX_ADSAMPLING.csv b/benchmarks/results/DEFAULT/END_TO_END_PDX_ADSAMPLING.csv index c761ae7..2930e64 100644 --- a/benchmarks/results/DEFAULT/END_TO_END_PDX_ADSAMPLING.csv +++ b/benchmarks/results/DEFAULT/END_TO_END_PDX_ADSAMPLING.csv @@ -51,3 +51,6 @@ mxbai,end_to_end_pdx_u8,0.321055,0.396917,0.254417,0.901748,25,0,100,1000,0,1,0. mxbai,end_to_end_pdx_f32,0.462268,0.677333,0.263375,0.901899,25,0,100,1000,0,1,0.4625,0.7097,0.2634 mxbai,end_to_end_pdx_tree_u8,0.173538,0.253792,0.104583,0.899069,25,0,100,1000,0,1,0.1738,0.2658,0.09 mxbai,end_to_end_pdx_tree_u8,0.178165,0.26575,0.094291,0.901288,25,0,100,1000,0,1,0.1787,0.2787,0.09429 +mxbai,end_to_end_pdx_f32,4.93171,8.31346,1.88596,0.995019,2048,0,100,1000,0,1,5.088,12.93,1.886 +mxbai,end_to_end_pdx_f32,0.480698,0.707334,0.268708,0.903179,25,0,100,1000,0,1,0.4807,0.7073,0.2687 +mxbai,end_to_end_pdx_f32,0.474792,0.701416,0.267583,0.903179,25,0,100,1000,0,1,0.4748,0.7014,0.2676 diff --git a/benchmarks/results/DEFAULT/PDX_F32_ADSAMPLING.csv b/benchmarks/results/DEFAULT/PDX_F32_ADSAMPLING.csv index 44dadfc..13855c0 100644 --- a/benchmarks/results/DEFAULT/PDX_F32_ADSAMPLING.csv +++ b/benchmarks/results/DEFAULT/PDX_F32_ADSAMPLING.csv @@ -2,3 +2,4 @@ dataset,algorithm,avg,max,min,recall,ivf_nprobe,epsilon,knn,n_queries,selectivit openai,adsampling,0.915137,1.33392,0.493292,0.839519,32,0,100,1000,0.2,1,0.9182,1.812,0.4849 arxiv,adsampling,0.524415,0.765917,0.306708,0.906279,32,0,100,1000,0.2,1,0.5256,0.8312,0.3067 arxiv,adsampling,0.520666,0.764041,0.304625,0.906279,32,0,100,1000,0.2,1,0.5223,0.8508,0.3046 +mxbai,adsampling,0.479905,0.710458,0.260292,0.900609,25,0,100,1000,0.2,1,0.4807,0.807,0.2603 diff --git a/benchmarks/results/DEFAULT/PDX_F32_ADSAMPLING_FILTERED.csv b/benchmarks/results/DEFAULT/PDX_F32_ADSAMPLING_FILTERED.csv new file mode 100644 index 0000000..5b70346 --- /dev/null +++ b/benchmarks/results/DEFAULT/PDX_F32_ADSAMPLING_FILTERED.csv @@ -0,0 +1,5 @@ +dataset,algorithm,avg,max,min,recall,ivf_nprobe,epsilon,knn,n_queries,selectivity,num_measure_runs,avg_all,max_all,min_all +mxbai,adsampling_filtered,1.23474,1.47562,1.00887,0.902459,25,0,100,1000,0.99,1,1.235,1.476,1.009 +mxbai,adsampling_filtered,1.24076,1.46942,1.02992,0.902459,25,0,100,1000,0.99,1,1.242,1.508,1.03 +mxbai,adsampling_filtered,0.806941,0.955292,0.660334,0.82982,25,0,100,1000,0.3,1,0.8068,0.9945,0.6092 +mxbai,adsampling_filtered,1.02453,1.30917,0.76325,0.995968,3509,0,100,1000,0.001,1,1.029,1.476,0.7217 diff --git a/include/pdx/index.hpp b/include/pdx/index.hpp index eeb192b..7ee777e 100644 --- a/include/pdx/index.hpp +++ b/include/pdx/index.hpp @@ -8,6 +8,7 @@ #include #include #include +#include #include "pdx/clustering.hpp" #include "pdx/common.hpp" @@ -35,12 +36,19 @@ class IPDXIndex { public: virtual ~IPDXIndex() = default; virtual std::vector Search(const float* query_embedding, size_t knn) const = 0; + virtual std::vector FilteredSearch( + const float* query_embedding, + size_t knn, + const std::vector& passing_row_ids + ) const = 0; virtual void BuildIndex(const float* embeddings, size_t num_embeddings) = 0; virtual void SetNProbe(uint32_t n_probe) const = 0; virtual void Save(const std::string& path) const = 0; virtual void Restore(const std::string& path) = 0; virtual uint32_t GetNumDimensions() const = 0; virtual uint32_t GetNumClusters() const = 0; + virtual uint32_t GetClusterSize(uint32_t cluster_id) const = 0; + virtual std::vector GetClusterRowIds(uint32_t cluster_id) const = 0; virtual size_t GetInMemorySizeInBytes() const = 0; }; @@ -54,6 +62,7 @@ class PDXIndex : public IPDXIndex { PDX::IVF index; std::unique_ptr pruner; std::unique_ptr> searcher; + std::vector> row_id_cluster_mapping; static constexpr PDXIndexType GetIndexType() { if constexpr (Q == F32) @@ -62,6 +71,31 @@ class PDXIndex : public IPDXIndex { return PDXIndexType::PDX_U8; } + void BuildRowIdClusterMapping() { + size_t total = 0; + for (size_t c = 0; c < index.num_clusters; c++) { + total += index.clusters[c].num_embeddings; + } + row_id_cluster_mapping.resize(total); + for (uint32_t c = 0; c < index.num_clusters; c++) { + for (uint32_t p = 0; p < index.clusters[c].num_embeddings; p++) { + row_id_cluster_mapping[index.clusters[c].indices[p]] = {c, p}; + } + } + } + + PDX::PredicateEvaluator CreatePredicateEvaluator( + const std::vector& passing_row_ids + ) const { + PDX::PredicateEvaluator evaluator(index.num_clusters, row_id_cluster_mapping.size()); + for (const auto row_id : passing_row_ids) { + const auto& [cluster_id, index_in_cluster] = row_id_cluster_mapping[row_id]; + evaluator.n_passing_tuples[cluster_id]++; + evaluator.selection_vector[searcher->cluster_offsets[cluster_id] + index_in_cluster] = 1; + } + return evaluator; + } + public: PDXIndex() = default; @@ -111,12 +145,22 @@ class PDXIndex : public IPDXIndex { // Create pruner and searcher pruner = std::make_unique(index.num_dimensions, matrix_data); searcher = std::make_unique>(index, *pruner); + BuildRowIdClusterMapping(); } std::vector Search(const float* query_embedding, size_t knn) const override { return searcher->Search(query_embedding, knn); } + std::vector FilteredSearch( + const float* query_embedding, + size_t knn, + const std::vector& passing_row_ids + ) const override { + auto evaluator = CreatePredicateEvaluator(passing_row_ids); + return searcher->FilteredSearch(query_embedding, knn, evaluator); + } + void SetNProbe(uint32_t n_probe) const override { searcher->SetNProbe(n_probe); } const PDX::PDXearch& GetSearcher() const { return *searcher; } @@ -125,6 +169,15 @@ class PDXIndex : public IPDXIndex { uint32_t GetNumClusters() const override { return index.num_clusters; } + uint32_t GetClusterSize(uint32_t cluster_id) const override { + return index.clusters[cluster_id].num_embeddings; + } + + std::vector GetClusterRowIds(uint32_t cluster_id) const override { + const auto& cluster = index.clusters[cluster_id]; + return {cluster.indices, cluster.indices + cluster.num_embeddings}; + } + size_t GetInMemorySizeInBytes() const override { size_t size = sizeof(*this); // IVF heap allocations (sizeof(IVF) is inline in sizeof(*this)) @@ -145,6 +198,8 @@ class PDXIndex : public IPDXIndex { size += sizeof(*searcher); size += index.num_clusters * sizeof(size_t); } + // Row ID to cluster mapping + size += row_id_cluster_mapping.capacity() * sizeof(std::pair); return size; } @@ -262,6 +317,7 @@ class PDXIndex : public IPDXIndex { } searcher = std::make_unique>(index, *pruner); + BuildRowIdClusterMapping(); } }; @@ -276,6 +332,7 @@ class PDXTreeIndex : public IPDXIndex { std::unique_ptr pruner; std::unique_ptr> searcher; std::unique_ptr> top_level_searcher; + std::vector> row_id_cluster_mapping; static constexpr PDXIndexType GetIndexType() { if constexpr (Q == F32) @@ -284,6 +341,31 @@ class PDXTreeIndex : public IPDXIndex { return PDXIndexType::PDX_TREE_U8; } + void BuildRowIdClusterMapping() { + size_t total = 0; + for (size_t c = 0; c < index.num_clusters; c++) { + total += index.clusters[c].num_embeddings; + } + row_id_cluster_mapping.resize(total); + for (uint32_t c = 0; c < index.num_clusters; c++) { + for (uint32_t p = 0; p < index.clusters[c].num_embeddings; p++) { + row_id_cluster_mapping[index.clusters[c].indices[p]] = {c, p}; + } + } + } + + PDX::PredicateEvaluator CreatePredicateEvaluator( + const std::vector& passing_row_ids + ) const { + PDX::PredicateEvaluator evaluator(index.num_clusters, row_id_cluster_mapping.size()); + for (const auto row_id : passing_row_ids) { + const auto& [cluster_id, index_in_cluster] = row_id_cluster_mapping[row_id]; + evaluator.n_passing_tuples[cluster_id]++; + evaluator.selection_vector[searcher->cluster_offsets[cluster_id] + index_in_cluster] = 1; + } + return evaluator; + } + public: PDXTreeIndex() = default; @@ -334,6 +416,7 @@ class PDXTreeIndex : public IPDXIndex { pruner = std::make_unique(index.num_dimensions, matrix_data); searcher = std::make_unique>(index, *pruner); top_level_searcher = std::make_unique>(index.l0, *pruner); + BuildRowIdClusterMapping(); } std::vector Search(const float* query_embedding, size_t knn) const override { @@ -354,6 +437,15 @@ class PDXTreeIndex : public IPDXIndex { return searcher->Search(query_embedding, knn); } + std::vector FilteredSearch( + const float* query_embedding, + size_t knn, + const std::vector& passing_row_ids + ) const override { + auto evaluator = CreatePredicateEvaluator(passing_row_ids); + return searcher->FilteredSearch(query_embedding, knn, evaluator); + } + void BuildIndex(const float* const embeddings, const size_t num_embeddings) override { std::vector row_ids(num_embeddings); std::iota(row_ids.begin(), row_ids.end(), 0); @@ -519,6 +611,7 @@ class PDXTreeIndex : public IPDXIndex { } top_level_searcher = std::make_unique>(index.l0, *pruner); + BuildRowIdClusterMapping(); } void SetNProbe(uint32_t n_probe) const override { searcher->SetNProbe(n_probe); } @@ -529,6 +622,15 @@ class PDXTreeIndex : public IPDXIndex { uint32_t GetNumClusters() const override { return index.num_clusters; } + uint32_t GetClusterSize(uint32_t cluster_id) const override { + return index.clusters[cluster_id].num_embeddings; + } + + std::vector GetClusterRowIds(uint32_t cluster_id) const override { + const auto& cluster = index.clusters[cluster_id]; + return {cluster.indices, cluster.indices + cluster.num_embeddings}; + } + uint32_t GetTopLevelNumClusters() const { return index.l0.num_clusters; } size_t GetInMemorySizeInBytes() const override { @@ -556,6 +658,8 @@ class PDXTreeIndex : public IPDXIndex { size += sizeof(*top_level_searcher); size += index.l0.num_clusters * sizeof(size_t); } + // Row ID to cluster mapping + size += row_id_cluster_mapping.capacity() * sizeof(std::pair); return size; } }; diff --git a/include/pdx/lib/lib.hpp b/include/pdx/lib/lib.hpp index ec5efdf..4a43135 100644 --- a/include/pdx/lib/lib.hpp +++ b/include/pdx/lib/lib.hpp @@ -107,6 +107,36 @@ class PyPDXIndex { return {ids, distances}; } + std::pair, py::array_t> FilteredSearch( + const py::array_t& query, + uint32_t knn, + const py::array_t& row_ids + ) const { + auto query_buf = query.request(); + if (query_buf.ndim != 1) { + throw std::runtime_error("query must be a 1D numpy array"); + } + auto row_ids_buf = row_ids.request(); + if (row_ids_buf.ndim != 1) { + throw std::runtime_error("row_ids must be a 1D numpy array"); + } + auto* query_ptr = static_cast(query_buf.ptr); + auto* row_ids_ptr = static_cast(row_ids_buf.ptr); + size_t n_row_ids = static_cast(row_ids_buf.shape[0]); + std::vector passing_row_ids(row_ids_ptr, row_ids_ptr + n_row_ids); + auto results = index->FilteredSearch(query_ptr, knn, passing_row_ids); + size_t n = results.size(); + py::array_t ids(n); + py::array_t distances(n); + auto ids_ptr = ids.mutable_unchecked<1>(); + auto distances_ptr = distances.mutable_unchecked<1>(); + for (size_t i = 0; i < n; ++i) { + ids_ptr(i) = results[i].index; + distances_ptr(i) = results[i].distance; + } + return {ids, distances}; + } + void SetNProbe(uint32_t n) const { index->SetNProbe(n); } void Save(const std::string& path) const { index->Save(path); } @@ -115,6 +145,18 @@ class PyPDXIndex { uint32_t GetNumClusters() const { return index->GetNumClusters(); } + uint32_t GetClusterSize(uint32_t cluster_id) const { return index->GetClusterSize(cluster_id); } + + py::array_t GetClusterRowIds(uint32_t cluster_id) const { + auto ids = index->GetClusterRowIds(cluster_id); + py::array_t result(ids.size()); + auto ptr = result.mutable_unchecked<1>(); + for (size_t i = 0; i < ids.size(); ++i) { + ptr(i) = ids[i]; + } + return result; + } + size_t GetInMemorySizeInBytes() const { return index->GetInMemorySizeInBytes(); } }; diff --git a/python/lib.cpp b/python/lib.cpp index b9162df..5a59219 100644 --- a/python/lib.cpp +++ b/python/lib.cpp @@ -30,10 +30,27 @@ PYBIND11_MODULE(compiled, m) { ) .def("build_index", &PDX::PyPDXIndex::BuildIndex, py::arg("data")) .def("search", &PDX::PyPDXIndex::Search, py::arg("query"), py::arg("knn")) + .def( + "filtered_search", + &PDX::PyPDXIndex::FilteredSearch, + py::arg("query"), + py::arg("knn"), + py::arg("row_ids") + ) .def("set_nprobe", &PDX::PyPDXIndex::SetNProbe, py::arg("nprobe")) .def("save", &PDX::PyPDXIndex::Save, py::arg("path")) .def("get_num_dimensions", &PDX::PyPDXIndex::GetNumDimensions) .def("get_num_clusters", &PDX::PyPDXIndex::GetNumClusters) + .def( + "get_cluster_size", + &PDX::PyPDXIndex::GetClusterSize, + py::arg("cluster_id") + ) + .def( + "get_cluster_row_ids", + &PDX::PyPDXIndex::GetClusterRowIds, + py::arg("cluster_id") + ) .def("get_in_memory_size_in_bytes", &PDX::PyPDXIndex::GetInMemorySizeInBytes); m.def( diff --git a/python/pdxearch/index_factory.py b/python/pdxearch/index_factory.py index 5698d80..5bd4edc 100644 --- a/python/pdxearch/index_factory.py +++ b/python/pdxearch/index_factory.py @@ -33,6 +33,13 @@ def search(self, query: np.ndarray, knn: int, nprobe: int = 16): self._index.set_nprobe(nprobe) return self._index.search(np.ascontiguousarray(query, dtype=np.float32), knn) + def filtered_search(self, query: np.ndarray, knn: int, row_ids: np.ndarray, nprobe: int = 16): + self._index.set_nprobe(nprobe) + return self._index.filtered_search( + np.ascontiguousarray(query, dtype=np.float32), knn, + np.ascontiguousarray(row_ids, dtype=np.uint64), + ) + def save(self, path: str) -> None: self._index.save(path) @@ -88,6 +95,13 @@ def search(self, query: np.ndarray, knn: int, nprobe: int = 16): self._index.set_nprobe(nprobe) return self._index.search(np.ascontiguousarray(query, dtype=np.float32), knn) + def filtered_search(self, query: np.ndarray, knn: int, row_ids: np.ndarray, nprobe: int = 16): + self._index.set_nprobe(nprobe) + return self._index.filtered_search( + np.ascontiguousarray(query, dtype=np.float32), knn, + np.ascontiguousarray(row_ids, dtype=np.uint64), + ) + def save(self, path: str) -> None: self._index.save(path) @@ -145,6 +159,13 @@ def search(self, query: np.ndarray, knn: int, nprobe: int = 16): self._index.set_nprobe(nprobe) return self._index.search(np.ascontiguousarray(query, dtype=np.float32), knn) + def filtered_search(self, query: np.ndarray, knn: int, row_ids: np.ndarray, nprobe: int = 16): + self._index.set_nprobe(nprobe) + return self._index.filtered_search( + np.ascontiguousarray(query, dtype=np.float32), knn, + np.ascontiguousarray(row_ids, dtype=np.uint64), + ) + def save(self, path: str) -> None: self._index.save(path) @@ -202,6 +223,13 @@ def search(self, query: np.ndarray, knn: int, nprobe: int = 16): self._index.set_nprobe(nprobe) return self._index.search(np.ascontiguousarray(query, dtype=np.float32), knn) + def filtered_search(self, query: np.ndarray, knn: int, row_ids: np.ndarray, nprobe: int = 16): + self._index.set_nprobe(nprobe) + return self._index.filtered_search( + np.ascontiguousarray(query, dtype=np.float32), knn, + np.ascontiguousarray(row_ids, dtype=np.uint64), + ) + def save(self, path: str) -> None: self._index.save(path) From 46a289cfe0c498aa7ffeb8a6d98e2193b0999db2 Mon Sep 17 00:00:00 2001 From: lkuffo Date: Tue, 24 Feb 2026 21:25:33 +0100 Subject: [PATCH 18/39] Test suite --- .gitignore | 10 +- CMakeLists.txt | 7 + benchmarks/CMakeLists.txt | 9 + benchmarks/pdx_filtered.cpp | 7 +- .../distance_computers/scalar_computers.hpp | 27 + include/pdx/index.hpp | 55 +- include/pdx/pruners/adsampling.hpp | 6 +- python/lib.cpp | 12 +- tests/CMakeLists.txt | 60 ++ tests/generate_test_data.cpp | 32 + tests/generate_test_ground_truth.cpp | 58 ++ tests/test_data.bin | Bin 0 -> 8448000 bytes tests/test_distance_computers.cpp | 193 ++++++ tests/test_filtered_search.cpp | 202 +++++++ tests/test_index_properties.cpp | 294 +++++++++ tests/test_search.cpp | 559 ++++++++++++++++++ tests/test_serialization.cpp | 110 ++++ tests/test_utils.hpp | 242 ++++++++ 18 files changed, 1855 insertions(+), 28 deletions(-) create mode 100644 tests/CMakeLists.txt create mode 100644 tests/generate_test_data.cpp create mode 100644 tests/generate_test_ground_truth.cpp create mode 100644 tests/test_data.bin create mode 100644 tests/test_distance_computers.cpp create mode 100644 tests/test_filtered_search.cpp create mode 100644 tests/test_index_properties.cpp create mode 100644 tests/test_search.cpp create mode 100644 tests/test_serialization.cpp create mode 100644 tests/test_utils.hpp diff --git a/.gitignore b/.gitignore index e58b231..b94c808 100644 --- a/.gitignore +++ b/.gitignore @@ -42,6 +42,10 @@ venv /cmake-build-debug/ /cmake-build-release/ /cmake-build*/ +/build_debug/ +/build_release/ +/build_relwithdebinfo/ +/tests/cmake_test_discovery_*.json /.idea/ /dummy/ /Testing/ @@ -94,4 +98,8 @@ cmake_install.cmake /benchmarks/BenchmarkSerialization /benchmarks/BenchmarkPDXIVF /benchmarks/BenchmarkFiltered -/benchmarks/BenchmarkSpecialFilters \ No newline at end of file +/benchmarks/BenchmarkSpecialFilters + +# Test binaries (but keep the committed test data) +*.bin +!tests/test_data.bin \ No newline at end of file diff --git a/CMakeLists.txt b/CMakeLists.txt index 348c05f..8ed7e84 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -66,6 +66,13 @@ add_compile_definitions(CMAKE_SOURCE_DIR="${CMAKE_SOURCE_DIR}") include_directories(include extern/Eigen) add_subdirectory(benchmarks) +# Tests: disabled by default, enable with -DPDX_COMPILE_TESTS=ON +set(PDX_COMPILE_TESTS OFF CACHE BOOL "Whether to compile tests") +if(PDX_COMPILE_TESTS) + message(STATUS "Tests enabled") + add_subdirectory(tests) +endif() + # Python bindings: only build if pybind11 is available find_package(Python COMPONENTS Interpreter Development.Module QUIET) set(PYBIND11_FINDPYTHON ON) diff --git a/benchmarks/CMakeLists.txt b/benchmarks/CMakeLists.txt index 6afe1a3..fa95195 100644 --- a/benchmarks/CMakeLists.txt +++ b/benchmarks/CMakeLists.txt @@ -18,3 +18,12 @@ target_link_libraries(BenchmarkEndToEnd ${BENCH_COMMON_LIBS}) target_link_libraries(BenchmarkSerialization ${BENCH_COMMON_LIBS}) target_link_libraries(BenchmarkFiltered ${BENCH_COMMON_LIBS}) target_link_libraries(BenchmarkSpecialFilters ${BENCH_COMMON_LIBS}) + +add_custom_target(benchmarks + DEPENDS + BenchmarkPDXIVF + BenchmarkEndToEnd + BenchmarkSerialization + BenchmarkFiltered + BenchmarkSpecialFilters +) diff --git a/benchmarks/pdx_filtered.cpp b/benchmarks/pdx_filtered.cpp index 9f982b4..255e2c9 100644 --- a/benchmarks/pdx_filtered.cpp +++ b/benchmarks/pdx_filtered.cpp @@ -22,8 +22,7 @@ std::vector LoadPassingRowIds(const std::string& path) { int main(int argc, char* argv[]) { if (argc < 2) { - std::cerr << "Usage: " << argv[0] - << " [index_type] [nprobe] [selectivity]\n"; + std::cerr << "Usage: " << argv[0] << " [index_type] [nprobe] [selectivity]\n"; std::cerr << "Index types: pdx_f32 (default), pdx_u8, pdx_tree_f32, pdx_tree_u8\n"; std::cerr << "Available datasets:"; for (const auto& [name, _] : RAW_DATASET_PARAMS) { @@ -132,9 +131,7 @@ int main(int argc, char* argv[]) { auto result = pdx_index->FilteredSearch( query + l * pdx_index->GetNumDimensions(), KNN, passing_row_ids ); - BenchmarkUtils::VerifyResult( - recalls, result, KNN, int_ground_truth, l - ); + BenchmarkUtils::VerifyResult(recalls, result, KNN, int_ground_truth, l); } } TicToc clock; diff --git a/include/pdx/distance_computers/scalar_computers.hpp b/include/pdx/distance_computers/scalar_computers.hpp index e0210bf..3701536 100644 --- a/include/pdx/distance_computers/scalar_computers.hpp +++ b/include/pdx/distance_computers/scalar_computers.hpp @@ -1,6 +1,7 @@ #pragma once #include "pdx/common.hpp" +#include #include namespace PDX { @@ -133,4 +134,30 @@ class ScalarComputer { }; }; +template <> +class ScalarComputer { + public: + using distance_t = float; + using query_t = float; + using data_t = float; + + static distance_t Horizontal( + const query_t* PDX_RESTRICT vector1, + const data_t* PDX_RESTRICT vector2, + size_t num_dimensions + ) { + float dot = 0.0f, norm1 = 0.0f, norm2 = 0.0f; +#pragma clang loop vectorize(enable) + for (size_t i = 0; i < num_dimensions; ++i) { + dot += vector1[i] * vector2[i]; + norm1 += vector1[i] * vector1[i]; + norm2 += vector2[i] * vector2[i]; + } + float denom = std::sqrt(norm1) * std::sqrt(norm2); + if (denom == 0.0f) + return 1.0f; + return 1.0f - (dot / denom); + } +}; + } // namespace PDX diff --git a/include/pdx/index.hpp b/include/pdx/index.hpp index 7ee777e..bc29d55 100644 --- a/include/pdx/index.hpp +++ b/include/pdx/index.hpp @@ -30,6 +30,41 @@ struct PDXIndexConfig { bool normalize = false; float sampling_fraction = 0.0f; // 0 = auto (1.0 if small dataset, 0.3 otherwise) uint32_t kmeans_iters = 10; + + void Validate() const { + if (num_dimensions == 0 || num_dimensions > PDX_MAX_DIMS) { + throw std::invalid_argument( + "num_dimensions must be between 1 and " + std::to_string(PDX_MAX_DIMS) + ", got " + + std::to_string(num_dimensions) + ); + } + if (sampling_fraction < 0.0f || sampling_fraction > 1.0f) { + throw std::invalid_argument( + "sampling_fraction must be between 0.0 and 1.0, got " + + std::to_string(sampling_fraction) + ); + } + if (num_meso_clusters > 0 && num_clusters > 0 && num_meso_clusters >= num_clusters) { + throw std::invalid_argument( + "num_meso_clusters (" + std::to_string(num_meso_clusters) + + ") must be smaller than num_clusters (" + std::to_string(num_clusters) + ")" + ); + } + if (kmeans_iters == 0 || kmeans_iters >= 100) { + throw std::invalid_argument( + "kmeans_iters must be between 1 and 99, got " + std::to_string(kmeans_iters) + ); + } + } + + void ValidateNumEmbeddings(size_t num_embeddings) const { + if (num_clusters > 0 && num_clusters > num_embeddings) { + throw std::invalid_argument( + "num_clusters (" + std::to_string(num_clusters) + ") exceeds num_embeddings (" + + std::to_string(num_embeddings) + ")" + ); + } + } }; class IPDXIndex { @@ -84,14 +119,14 @@ class PDXIndex : public IPDXIndex { } } - PDX::PredicateEvaluator CreatePredicateEvaluator( - const std::vector& passing_row_ids + PDX::PredicateEvaluator CreatePredicateEvaluator(const std::vector& passing_row_ids ) const { PDX::PredicateEvaluator evaluator(index.num_clusters, row_id_cluster_mapping.size()); for (const auto row_id : passing_row_ids) { const auto& [cluster_id, index_in_cluster] = row_id_cluster_mapping[row_id]; evaluator.n_passing_tuples[cluster_id]++; - evaluator.selection_vector[searcher->cluster_offsets[cluster_id] + index_in_cluster] = 1; + evaluator.selection_vector[searcher->cluster_offsets[cluster_id] + index_in_cluster] = + 1; } return evaluator; } @@ -100,6 +135,7 @@ class PDXIndex : public IPDXIndex { PDXIndex() = default; explicit PDXIndex(PDXIndexConfig config) : config(config) { + config.Validate(); pruner = std::make_unique(config.num_dimensions, config.seed); } @@ -214,6 +250,8 @@ class PDXIndex : public IPDXIndex { const float* const embeddings, const size_t num_embeddings ) { + config.ValidateNumEmbeddings(num_embeddings); + const auto num_dimensions = config.num_dimensions; auto num_clusters = config.num_clusters; if (num_clusters == 0) { @@ -222,7 +260,6 @@ class PDXIndex : public IPDXIndex { const bool normalize = config.normalize || DistanceMetricRequiresNormalization(config.distance_metric); - assert(num_dimensions > 0); assert(num_embeddings > 0); assert(pruner); @@ -354,14 +391,14 @@ class PDXTreeIndex : public IPDXIndex { } } - PDX::PredicateEvaluator CreatePredicateEvaluator( - const std::vector& passing_row_ids + PDX::PredicateEvaluator CreatePredicateEvaluator(const std::vector& passing_row_ids ) const { PDX::PredicateEvaluator evaluator(index.num_clusters, row_id_cluster_mapping.size()); for (const auto row_id : passing_row_ids) { const auto& [cluster_id, index_in_cluster] = row_id_cluster_mapping[row_id]; evaluator.n_passing_tuples[cluster_id]++; - evaluator.selection_vector[searcher->cluster_offsets[cluster_id] + index_in_cluster] = 1; + evaluator.selection_vector[searcher->cluster_offsets[cluster_id] + index_in_cluster] = + 1; } return evaluator; } @@ -370,6 +407,7 @@ class PDXTreeIndex : public IPDXIndex { PDXTreeIndex() = default; explicit PDXTreeIndex(PDXIndexConfig config) : config(config) { + config.Validate(); pruner = std::make_unique(config.num_dimensions, config.seed); } @@ -457,6 +495,8 @@ class PDXTreeIndex : public IPDXIndex { const float* const embeddings, const size_t num_embeddings ) { + config.ValidateNumEmbeddings(num_embeddings); + const auto num_dimensions = config.num_dimensions; auto num_clusters = config.num_clusters; if (num_clusters == 0) { @@ -465,7 +505,6 @@ class PDXTreeIndex : public IPDXIndex { const bool normalize = config.normalize || DistanceMetricRequiresNormalization(config.distance_metric); - assert(num_dimensions > 0); assert(num_embeddings > 0); assert(pruner); diff --git a/include/pdx/pruners/adsampling.hpp b/include/pdx/pruners/adsampling.hpp index 7ad1a50..232d4f0 100644 --- a/include/pdx/pruners/adsampling.hpp +++ b/include/pdx/pruners/adsampling.hpp @@ -42,8 +42,7 @@ class ADSamplingPruner { if (!matrix_created) { std::normal_distribution normal_dist; Eigen::MatrixXf random_matrix = Eigen::MatrixXf::Zero( - static_cast(num_dimensions), - static_cast(num_dimensions) + static_cast(num_dimensions), static_cast(num_dimensions) ); for (size_t i = 0; i < num_dimensions; ++i) { for (size_t j = 0; j < num_dimensions; ++j) { @@ -52,8 +51,7 @@ class ADSamplingPruner { } } const Eigen::HouseholderQR qr(random_matrix); - matrix = qr.householderQ() * - Eigen::MatrixXf::Identity(num_dimensions, num_dimensions); + matrix = qr.householderQ() * Eigen::MatrixXf::Identity(num_dimensions, num_dimensions); } } diff --git a/python/lib.cpp b/python/lib.cpp index 5a59219..78e2c55 100644 --- a/python/lib.cpp +++ b/python/lib.cpp @@ -41,16 +41,8 @@ PYBIND11_MODULE(compiled, m) { .def("save", &PDX::PyPDXIndex::Save, py::arg("path")) .def("get_num_dimensions", &PDX::PyPDXIndex::GetNumDimensions) .def("get_num_clusters", &PDX::PyPDXIndex::GetNumClusters) - .def( - "get_cluster_size", - &PDX::PyPDXIndex::GetClusterSize, - py::arg("cluster_id") - ) - .def( - "get_cluster_row_ids", - &PDX::PyPDXIndex::GetClusterRowIds, - py::arg("cluster_id") - ) + .def("get_cluster_size", &PDX::PyPDXIndex::GetClusterSize, py::arg("cluster_id")) + .def("get_cluster_row_ids", &PDX::PyPDXIndex::GetClusterRowIds, py::arg("cluster_id")) .def("get_in_memory_size_in_bytes", &PDX::PyPDXIndex::GetInMemorySizeInBytes); m.def( diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt new file mode 100644 index 0000000..dc8b128 --- /dev/null +++ b/tests/CMakeLists.txt @@ -0,0 +1,60 @@ +include(FetchContent) +FetchContent_Declare( + googletest + GIT_REPOSITORY https://github.com/google/googletest.git + GIT_TAG v1.14.0 +) +set(gtest_force_shared_crt ON CACHE BOOL "" FORCE) +FetchContent_MakeAvailable(googletest) + +enable_testing() + +set(TEST_COMMON_LIBS + gtest + gtest_main + ${MKL_COMMON_LIBS} + ${BLAS_LINK_LIBRARIES} +) + +if (FFTW_FOUND) + list(APPEND TEST_COMMON_LIBS ${FFTW_FLOAT_LIB} ${FFTW_FLOAT_OPENMP_LIB}) +endif() + +# Utilities (not tests) +add_executable(generate_test_data.out generate_test_data.cpp) +target_link_libraries(generate_test_data.out PRIVATE ${MKL_COMMON_LIBS} ${BLAS_LINK_LIBRARIES}) + +add_executable(generate_test_ground_truth.out generate_test_ground_truth.cpp) +target_link_libraries(generate_test_ground_truth.out PRIVATE ${MKL_COMMON_LIBS} ${BLAS_LINK_LIBRARIES}) + +# Test executables +add_executable(test_distance_computers.out test_distance_computers.cpp) +target_link_libraries(test_distance_computers.out PRIVATE ${TEST_COMMON_LIBS}) + +add_executable(test_search.out test_search.cpp) +target_link_libraries(test_search.out PRIVATE ${TEST_COMMON_LIBS}) + +add_executable(test_serialization.out test_serialization.cpp) +target_link_libraries(test_serialization.out PRIVATE ${TEST_COMMON_LIBS}) + +add_executable(test_filtered_search.out test_filtered_search.cpp) +target_link_libraries(test_filtered_search.out PRIVATE ${TEST_COMMON_LIBS}) + +add_executable(test_index_properties.out test_index_properties.cpp) +target_link_libraries(test_index_properties.out PRIVATE ${TEST_COMMON_LIBS}) + +include(GoogleTest) +gtest_discover_tests(test_distance_computers.out) +gtest_discover_tests(test_search.out) +gtest_discover_tests(test_serialization.out) +gtest_discover_tests(test_filtered_search.out) +gtest_discover_tests(test_index_properties.out) + +add_custom_target(tests + DEPENDS + test_distance_computers.out + test_search.out + test_serialization.out + test_filtered_search.out + test_index_properties.out +) diff --git a/tests/generate_test_data.cpp b/tests/generate_test_data.cpp new file mode 100644 index 0000000..17807e9 --- /dev/null +++ b/tests/generate_test_data.cpp @@ -0,0 +1,32 @@ +#include +#include +#include + +#include "superkmeans/pdx/utils.h" + +int main() { + constexpr size_t N_TOTAL = 5500; + constexpr size_t D = 384; + constexpr size_t N_TRUE_CENTERS = 200; + constexpr float CLUSTER_STD = 5.0f; + constexpr float CENTER_SPREAD = 2.0f; + constexpr unsigned int SEED = 42; + + std::cerr << "Generating test data (" << N_TOTAL << " x " << D << ")...\n"; + auto data = + skmeans::MakeBlobs(N_TOTAL, D, N_TRUE_CENTERS, true, CLUSTER_STD, CENTER_SPREAD, SEED); + + std::string out_path = CMAKE_SOURCE_DIR "/tests/test_data.bin"; + std::ofstream out(out_path, std::ios::binary); + if (!out) { + std::cerr << "Error: Could not open " << out_path << " for writing\n"; + return 1; + } + out.write(reinterpret_cast(data.data()), data.size() * sizeof(float)); + out.close(); + + size_t size_mb = data.size() * sizeof(float) / 1024 / 1024; + std::cerr << "Saved " << out_path << " (" << size_mb << " MB)\n"; + std::cerr << "First 5000 rows = train, last 500 = queries\n"; + return 0; +} diff --git a/tests/generate_test_ground_truth.cpp b/tests/generate_test_ground_truth.cpp new file mode 100644 index 0000000..65de243 --- /dev/null +++ b/tests/generate_test_ground_truth.cpp @@ -0,0 +1,58 @@ +#include +#include +#include + +#include "pdx/index.hpp" +#include "test_utils.hpp" + +int main() { + std::vector index_types = {"pdx_f32", "pdx_u8"}; + // TODO: add "pdx_tree_f32", "pdx_tree_u8" once tree index crash is fixed + std::vector dimensions = {384}; + std::vector nprobes = {1, 2, 4, 8, 16, 32, 64}; + + std::cout << std::fixed << std::setprecision(4); + std::cout << "// Ground truth recall@" << TestUtils::KNN << " values for test_search.cpp\n"; + std::cout << "// Generated by generate_test_ground_truth.out\n"; + std::cout << "// Copy-paste into RECALL_GROUND_TRUTH map:\n\n"; + + for (size_t d : dimensions) { + std::cerr << "Loading test data (d=" << d << ")...\n"; + auto data = TestUtils::LoadTestData(d); + auto gt = TestUtils::ComputeBruteForceKNN( + data.train.data(), + data.queries.data(), + TestUtils::N_TRAIN, + TestUtils::N_QUERIES, + d, + TestUtils::KNN + ); + + for (const auto& index_type : index_types) { + std::cerr << " Building " << index_type << " (d=" << d << ")...\n"; + auto index = + TestUtils::BuildIndex(index_type, data.train.data(), TestUtils::N_TRAIN, d); + uint32_t num_clusters = index->GetNumClusters(); + std::cerr << " Clusters: " << num_clusters << "\n"; + + std::cout << " // " << index_type << ", d=" << d << ", clusters=" << num_clusters + << "\n"; + + auto all_nprobes = nprobes; + all_nprobes.push_back(num_clusters); + for (size_t nprobe : all_nprobes) { + if (nprobe > num_clusters) + continue; + index->SetNProbe(nprobe); + float recall = TestUtils::ComputeAverageRecall( + *index, data.queries.data(), TestUtils::N_QUERIES, d, TestUtils::KNN, gt + ); + std::cout << " {{\"" << index_type << "\", " << d << ", " << nprobe << "}, " + << recall << "f},\n"; + } + } + } + + std::cerr << "Done.\n"; + return 0; +} diff --git a/tests/test_data.bin b/tests/test_data.bin new file mode 100644 index 0000000000000000000000000000000000000000..ab5617c3d329f4155d2cd345a571e426f9a835c2 GIT binary patch literal 8448000 zcmWKXhd-8W6vyqz3dtxlE2N=}=e`b-tY}hiI~7Smp+Z6` z6lqgQTfP2)=l*=2YyHmmd{2Y2A=ZAoMa27e!c)~5pr+3uKkc^S6DxC;WVs+S%b4W} zQG&ki6}am4F?gI7Pm|KZ*jN7ViLKmbnE$7l+$ajidFh?7#z>JBIoeFz@5teu)!I;- zm6fKk3Xw+pHpTu-+E-tX_;I zTP(0lpoK1OdBilE`Jol7Ne{+`RzRux-uqhJ&1J8404r2mix|6$g9fgJShdPXPgMhNR8 zj{dx#xGx=}fcMR1_$RA^b#bMj;w%Q*e-hYgkizb9Ra|po2ShEl!hoUm(BgiTR+2!> z%07phMMvS<=1kc2{TG3d`OxrY57-~kh6MNdSh%Z>j9$LYigmDLtz9Z*wUz@roge0% 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