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223 lines
7.4 KiB
C++
223 lines
7.4 KiB
C++
#include <gtest/gtest.h>
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#include <iostream>
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#include <random>
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#include <string>
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#include <thread>
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#include <vector>
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#include <faiss/utils/distances.h>
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#include "segcore/ConcurrentVector.h"
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#include "segcore/SegmentBase.h"
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// #include "knowhere/index/vector_index/helpers/IndexParameter.h"
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#include "segcore/SegmentBase.h"
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#include "segcore/AckResponder.h"
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#include <knowhere/index/vector_index/VecIndex.h>
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#include <knowhere/index/vector_index/adapter/VectorAdapter.h>
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#include <knowhere/index/vector_index/VecIndexFactory.h>
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#include <knowhere/index/vector_index/IndexIVF.h>
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#include <algorithm>
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#include <chrono>
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#include "test_utils/Timer.h"
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#include "segcore/Reduce.h"
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using std::cin;
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using std::cout;
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using std::endl;
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using namespace milvus::engine;
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using namespace milvus::segcore;
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using std::vector;
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using namespace milvus;
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namespace {
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template <int DIM>
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auto
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generate_data(int N) {
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std::vector<float> raw_data;
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std::vector<uint64_t> timestamps;
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std::vector<int64_t> uids;
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std::default_random_engine er(42);
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std::uniform_real_distribution<> distribution(0.0, 1.0);
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std::default_random_engine ei(42);
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for (int i = 0; i < N; ++i) {
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uids.push_back(10 * N + i);
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timestamps.push_back(0);
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// append vec
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float vec[DIM];
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for (auto& x : vec) {
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x = distribution(er);
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}
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raw_data.insert(raw_data.end(), std::begin(vec), std::end(vec));
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}
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return std::make_tuple(raw_data, timestamps, uids);
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}
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} // namespace
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TEST(Indexing, SmartBruteForce) {
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// how to ?
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// I'd know
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constexpr int N = 100000;
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constexpr int DIM = 16;
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constexpr int TOPK = 10;
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auto bitmap = std::make_shared<faiss::ConcurrentBitset>(N);
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// exclude the first
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for (int i = 0; i < N / 2; ++i) {
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bitmap->set(i);
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}
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auto [raw_data, timestamps, uids] = generate_data<DIM>(N);
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auto total_count = DIM * TOPK;
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auto raw = (const float*)raw_data.data();
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constexpr int64_t queries = 3;
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auto heap = faiss::float_maxheap_array_t{};
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auto query_data = raw;
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vector<int64_t> final_uids(total_count, -1);
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vector<float> final_dis(total_count, std::numeric_limits<float>::max());
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for (int beg = 0; beg < N; beg += DefaultElementPerChunk) {
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vector<int64_t> buf_uids(total_count, -1);
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vector<float> buf_dis(total_count, std::numeric_limits<float>::max());
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faiss::float_maxheap_array_t buf = {queries, TOPK, buf_uids.data(), buf_dis.data()};
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auto end = beg + DefaultElementPerChunk;
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if (end > N) {
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end = N;
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}
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auto nsize = end - beg;
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auto src_data = raw + beg * DIM;
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faiss::knn_L2sqr(query_data, src_data, DIM, queries, nsize, &buf, nullptr);
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for (auto& x : buf_uids) {
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x = uids[x + beg];
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}
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merge_into(queries, TOPK, final_dis.data(), final_uids.data(), buf_dis.data(), buf_uids.data());
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}
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for (int qn = 0; qn < queries; ++qn) {
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for (int kn = 0; kn < TOPK; ++kn) {
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auto index = qn * TOPK + kn;
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cout << final_uids[index] << "->" << final_dis[index] << endl;
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}
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cout << endl;
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}
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}
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TEST(Indexing, DISABLED_Naive) {
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constexpr int N = 10000;
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constexpr int DIM = 16;
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constexpr int TOPK = 10;
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auto [raw_data, timestamps, uids] = generate_data<DIM>(N);
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auto index = knowhere::VecIndexFactory::GetInstance().CreateVecIndex(knowhere::IndexEnum::INDEX_FAISS_IVFPQ,
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knowhere::IndexMode::MODE_CPU);
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auto conf = milvus::knowhere::Config{
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{knowhere::meta::DIM, DIM},
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{knowhere::meta::TOPK, TOPK},
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{knowhere::IndexParams::nlist, 100},
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{knowhere::IndexParams::nprobe, 4},
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{knowhere::IndexParams::m, 4},
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{knowhere::IndexParams::nbits, 8},
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{knowhere::Metric::TYPE, milvus::knowhere::Metric::L2},
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{knowhere::meta::DEVICEID, 0},
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};
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// auto ds = knowhere::GenDataset(N, DIM, raw_data.data());
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// auto ds2 = knowhere::GenDatasetWithIds(N / 2, DIM, raw_data.data() +
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// sizeof(float[DIM]) * N / 2, uids.data() + N / 2);
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// NOTE: you must train first and then add
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// index->Train(ds, conf);
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// index->Train(ds2, conf);
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// index->AddWithoutIds(ds, conf);
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// index->Add(ds2, conf);
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std::vector<knowhere::DatasetPtr> datasets;
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std::vector<std::vector<float>> ftrashs;
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auto raw = raw_data.data();
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for (int beg = 0; beg < N; beg += DefaultElementPerChunk) {
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auto end = beg + DefaultElementPerChunk;
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if (end > N) {
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end = N;
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}
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std::vector<float> ft(raw + DIM * beg, raw + DIM * end);
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auto ds = knowhere::GenDataset(end - beg, DIM, ft.data());
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datasets.push_back(ds);
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ftrashs.push_back(std::move(ft));
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// // NOTE: you must train first and then add
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// index->Train(ds, conf);
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// index->Add(ds, conf);
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}
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for (auto& ds : datasets) {
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index->Train(ds, conf);
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}
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for (auto& ds : datasets) {
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index->AddWithoutIds(ds, conf);
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}
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auto bitmap = std::make_shared<faiss::ConcurrentBitset>(N);
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// exclude the first
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for (int i = 0; i < N / 2; ++i) {
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bitmap->set(i);
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}
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// index->SetBlacklist(bitmap);
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auto query_ds = knowhere::GenDataset(1, DIM, raw_data.data());
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auto final = index->Query(query_ds, conf, bitmap);
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auto ids = final->Get<idx_t*>(knowhere::meta::IDS);
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auto distances = final->Get<float*>(knowhere::meta::DISTANCE);
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for (int i = 0; i < TOPK; ++i) {
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if (ids[i] < N / 2) {
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cout << "WRONG: ";
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}
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cout << ids[i] << "->" << distances[i] << endl;
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}
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int i = 1 + 1;
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}
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TEST(Indexing, IVFFlatNM) {
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// hello, world
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constexpr auto DIM = 16;
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constexpr auto K = 10;
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auto N = 1024 * 1024 * 10;
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auto num_query = 100;
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Timer timer;
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auto [raw_data, timestamps, uids] = generate_data<DIM>(N);
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std::cout << "generate data: " << timer.get_step_seconds() << " seconds" << endl;
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auto indexing = std::make_shared<knowhere::IVF>();
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auto conf = knowhere::Config{{knowhere::meta::DIM, DIM},
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{knowhere::meta::TOPK, K},
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{knowhere::IndexParams::nlist, 100},
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{knowhere::IndexParams::nprobe, 4},
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{knowhere::Metric::TYPE, milvus::knowhere::Metric::L2},
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{knowhere::meta::DEVICEID, 0}};
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auto database = knowhere::GenDataset(N, DIM, raw_data.data());
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std::cout << "init ivf " << timer.get_step_seconds() << " seconds" << endl;
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indexing->Train(database, conf);
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std::cout << "train ivf " << timer.get_step_seconds() << " seconds" << endl;
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indexing->AddWithoutIds(database, conf);
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std::cout << "insert ivf " << timer.get_step_seconds() << " seconds" << endl;
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EXPECT_EQ(indexing->Count(), N);
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EXPECT_EQ(indexing->Dim(), DIM);
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auto query_dataset = knowhere::GenDataset(num_query, DIM, raw_data.data() + DIM * 4200);
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auto result = indexing->Query(query_dataset, conf, nullptr);
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std::cout << "query ivf " << timer.get_step_seconds() << " seconds" << endl;
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auto ids = result->Get<int64_t*>(milvus::knowhere::meta::IDS);
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auto dis = result->Get<float*>(milvus::knowhere::meta::DISTANCE);
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for (int i = 0; i < std::min(num_query * K, 100); ++i) {
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cout << ids[i] << "->" << dis[i] << endl;
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}
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}
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