milvus/core/unittest/test_indexing.cpp

#include <gtest/gtest.h>

#include <iostream>
#include <random>
#include <string>
#include <thread>
#include <vector>

#include <faiss/utils/distances.h>
#include "dog_segment/ConcurrentVector.h"
#include "dog_segment/SegmentBase.h"
// #include "knowhere/index/vector_index/helpers/IndexParameter.h"

#include "dog_segment/SegmentBase.h"
#include "dog_segment/AckResponder.h"
#include <knowhere/index/vector_index/VecIndex.h>
#include <knowhere/index/vector_index/adapter/VectorAdapter.h>
#include <knowhere/index/vector_index/VecIndexFactory.h>
#include <algorithm>


using std::cin;
using std::cout;
using std::endl;
using namespace milvus::engine;
using namespace milvus::dog_segment;
using std::vector;
using namespace milvus;

namespace {
template<int DIM>
auto generate_data(int N) {
    std::vector<char> raw_data;
    std::vector<uint64_t> timestamps;
    std::vector<int64_t> uids;
    std::default_random_engine er(42);
    std::uniform_real_distribution<> distribution(0.0, 1.0);
    std::default_random_engine ei(42);
    for (int i = 0; i < N; ++i) {
        uids.push_back(10 * N + i);
        timestamps.push_back(0);
        // append vec
        float vec[DIM];
        for (auto &x: vec) {
            x = distribution(er);
        }
        raw_data.insert(raw_data.end(), (const char *) std::begin(vec), (const char *) std::end(vec));
//            int age = ei() % 100;
//            raw_data.insert(raw_data.end(), (const char *) &age, ((const char *) &age) + sizeof(age));
    }
    return std::make_tuple(raw_data, timestamps, uids);
}
}



void
merge_into(int64_t queries, int64_t topk, float *distances, int64_t *uids, const float *new_distances, const int64_t *new_uids) {
    for(int64_t qn = 0; qn < queries; ++qn) {
        auto base = qn * topk;
        auto src2_dis = distances + base;
        auto src2_uids = uids + base;

        auto src1_dis = new_distances + base;
        auto src1_uids = new_uids + base;

        std::vector<float> buf_dis(topk);
        std::vector<int64_t> buf_uids(topk);

        auto it1 = 0;
        auto it2 = 0;

        for(auto buf = 0; buf < topk; ++buf){
            if(src1_dis[it1] <= src2_dis[it2]) {
                buf_dis[buf] = src1_dis[it1];
                buf_uids[buf] = src1_uids[it1];
                ++it1;
            } else {
                buf_dis[buf] = src2_dis[it2];
                buf_uids[buf] = src2_uids[it2];
                ++it2;
            }
        }
        std::copy_n(buf_dis.data(), topk, src2_dis);
        std::copy_n(buf_uids.data(), topk, src2_uids);
   }
}


TEST(TestIndex, SmartBruteForce) {
    // how to ?
    // I'd know
    constexpr int N = 100000;
    constexpr int DIM = 16;
    constexpr int TOPK = 10;

    auto bitmap = std::make_shared<faiss::ConcurrentBitset>(N);
    // exclude the first
    for (int i = 0; i < N / 2; ++i) {
        bitmap->set(i);
    }

    auto[raw_data, timestamps, uids] = generate_data<DIM>(N);
    auto total_count = DIM * TOPK;
    auto raw = (const float *) raw_data.data();


    constexpr int64_t queries = 3;
    auto heap = faiss::float_maxheap_array_t{};

    auto query_data = raw;

    vector<int64_t> final_uids(total_count);
    vector<float> final_dis(total_count, std::numeric_limits<float>::max());



    for (int beg = 0; beg < N; beg += DefaultElementPerChunk) {
        vector<int64_t> buf_uids(total_count, -1);
        vector<float> buf_dis(total_count, std::numeric_limits<float>::max());

        faiss::float_maxheap_array_t buf = {
                queries, TOPK, buf_uids.data(), buf_dis.data()};

        auto end = beg + DefaultElementPerChunk;
        if (end > N) {
            end = N;
        }
        auto nsize = end - beg;
        auto src_data = raw + beg * DIM;

        faiss::knn_L2sqr(query_data, src_data, DIM, queries, nsize, &buf, nullptr);
        if(beg == 0) {
            final_uids = buf_uids;
            final_dis = buf_dis;
        } else {
            merge_into(queries, TOPK, final_dis.data(), final_uids.data(), buf_dis.data(), buf_uids.data());
        }
    }

    for (int qn = 0; qn < queries; ++qn) {
        for (int kn = 0; kn < TOPK; ++kn) {
            auto index = qn * TOPK + kn;
            cout << final_uids[index] << "->" << final_dis[index] << endl;
        }
        cout << endl;
    }
}


TEST(TestIndex, Naive) {
    constexpr int N = 100000;
    constexpr int DIM = 16;
    constexpr int TOPK = 10;

    auto[raw_data, timestamps, uids] = generate_data<DIM>(N);
    auto index = knowhere::VecIndexFactory::GetInstance().CreateVecIndex(knowhere::IndexEnum::INDEX_FAISS_IVFPQ,
                                                                         knowhere::IndexMode::MODE_CPU);
    auto conf = milvus::knowhere::Config{
            {milvus::knowhere::meta::DIM,           DIM},
            {milvus::knowhere::meta::TOPK,          TOPK},
            {milvus::knowhere::IndexParams::nlist,  100},
            {milvus::knowhere::IndexParams::nprobe, 4},
            {milvus::knowhere::IndexParams::m,      4},
            {milvus::knowhere::IndexParams::nbits,  8},
            {milvus::knowhere::Metric::TYPE,        milvus::knowhere::Metric::L2},
            {milvus::knowhere::meta::DEVICEID,      0},
    };

//    auto ds = knowhere::GenDataset(N, DIM, raw_data.data());
//    auto ds2 = knowhere::GenDatasetWithIds(N / 2, DIM, raw_data.data() + sizeof(float[DIM]) * N / 2, uids.data() + N / 2);
    // NOTE: you must train first and then add
//    index->Train(ds, conf);
//    index->Train(ds2, conf);
//    index->AddWithoutIds(ds, conf);
//    index->Add(ds2, conf);



    std::vector<knowhere::DatasetPtr> datasets;
    std::vector<std::vector<float>> ftrashs;
    auto raw = (const float *) raw_data.data();
    for (int beg = 0; beg < N; beg += DefaultElementPerChunk) {
        auto end = beg + DefaultElementPerChunk;
        if (end > N) {
            end = N;
        }
        std::vector<float> ft(raw + DIM * beg, raw + DIM * end);

        auto ds = knowhere::GenDataset(end - beg, DIM, ft.data());
        datasets.push_back(ds);
        ftrashs.push_back(std::move(ft));

        // // NOTE: you must train first and then add
        // index->Train(ds, conf);
        // index->Add(ds, conf);
    }

    for (auto &ds: datasets) {
        index->Train(ds, conf);
    }
    for (auto &ds: datasets) {
        index->AddWithoutIds(ds, conf);
    }

    auto bitmap = std::make_shared<faiss::ConcurrentBitset>(N);
    // exclude the first
    for (int i = 0; i < N / 2; ++i) {
        bitmap->set(i);
    }

//    index->SetBlacklist(bitmap);
    auto query_ds = knowhere::GenDataset(1, DIM, raw_data.data());

    auto final = index->Query(query_ds, conf);
    auto ids = final->Get<idx_t *>(knowhere::meta::IDS);
    auto distances = final->Get<float *>(knowhere::meta::DISTANCE);
    for (int i = 0; i < TOPK; ++i) {
        if (ids[i] < N / 2) {
            cout << "WRONG: ";
        }
        cout << ids[i] << "->" << distances[i] << endl;
    }
    int i = 1 + 1;
}