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/*
* Copyright (c) Meta Platforms, Inc. and affiliates.
*
* This source code is licensed under the MIT license found in the
* LICENSE file in the root directory of this source tree.
*/
#include <gflags/gflags.h>
#include <omp.h>
#include <cstdio>
#include <map>
#include <benchmark/benchmark.h>
#include <faiss/impl/ScalarQuantizer.h>
#include <faiss/perf_tests/utils.h>
#include <faiss/utils/random.h>
using namespace faiss;
DEFINE_uint32(d, 128, "dimension");
DEFINE_uint32(n, 2000, "dimension");
DEFINE_uint32(iterations, 20, "iterations");
static void bench_distance(
benchmark::State& state,
ScalarQuantizer::QuantizerType type,
int d,
int n) {
std::vector<float> x(d * n);
float_rand(x.data(), d * n, 12345);
// make sure it's idempotent
ScalarQuantizer sq(d, type);
omp_set_num_threads(1);
sq.train(n, x.data());
size_t code_size = sq.code_size;
state.counters["code_size"] = sq.code_size;
// encode
std::vector<uint8_t> codes(code_size * n);
sq.compute_codes(x.data(), codes.data(), n);
std::unique_ptr<ScalarQuantizer::SQDistanceComputer> dc(
sq.get_distance_computer());
dc->codes = codes.data();
dc->code_size = sq.code_size;
for (auto _ : state) {
float sum_dis = 0;
for (int i = 0; i < n; i++) {
dc->set_query(&x[i * d]);
for (int j = 0; j < n; j++) {
benchmark::DoNotOptimize(sum_dis += (*dc)(j));
}
}
}
}
int main(int argc, char** argv) {
benchmark::Initialize(&argc, argv);
gflags::AllowCommandLineReparsing();
gflags::ParseCommandLineFlags(&argc, &argv, true);
int iterations = FLAGS_iterations;
int d = FLAGS_d;
int n = FLAGS_n;
auto benchs = ::perf_tests::sq_types();
for (auto& [bench_name, quantizer_type] : benchs) {
benchmark::RegisterBenchmark(
bench_name.c_str(), bench_distance, quantizer_type, d, n)
->Iterations(iterations);
}
benchmark::RunSpecifiedBenchmarks();
benchmark::Shutdown();
}
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