path: root/eval/src/tests/ann/remove-bm.cpp

// Copyright 2020 Oath Inc. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.

#include <vespa/vespalib/testkit/test_kit.h>
#include <vespa/vespalib/util/priority_queue.h>
#include <sys/types.h>
#include <sys/stat.h>
#include <fcntl.h>
#include <unistd.h>
#include <stdio.h>
#include <chrono>

#define NUM_DIMS 960
#define NUM_DOCS 250000
#define NUM_DOCS_REMOVE 50000
#define EFFECTIVE_DOCS (NUM_DOCS - NUM_DOCS_REMOVE)
#define NUM_Q 1000

#include "doc_vector_access.h"
#include "nns.h"
#include "for-sift-hit.h"
#include "for-sift-top-k.h"

std::vector<TopK> bruteforceResults;
std::vector<float> tmp_v(NUM_DIMS);

struct PointVector {
    float v[NUM_DIMS];
    using ConstArr = vespalib::ConstArrayRef<float>;
    operator ConstArr() const { return ConstArr(v, NUM_DIMS); }
};

static PointVector *aligned_alloc(size_t num) {
    size_t num_bytes = num * sizeof(PointVector);
    double mega_bytes = num_bytes / (1024.0*1024.0);
    fprintf(stderr, "allocate %.2f MB of vectors\n", mega_bytes);
    char *mem = (char *)malloc(num_bytes + 512);
    mem += 512;
    size_t val = (size_t)mem;
    size_t unalign = val % 512;
    mem -= unalign;
    return (PointVector *)mem;
}

static PointVector *generatedQueries = aligned_alloc(NUM_Q);
static PointVector *generatedDocs = aligned_alloc(NUM_DOCS);

struct DocVectorAdapter : public DocVectorAccess<float>
{
    vespalib::ConstArrayRef<float> get(uint32_t docid) const override {
        ASSERT_TRUE(docid < NUM_DOCS);
        return generatedDocs[docid];
    }
};

double computeDistance(const PointVector &query, uint32_t docid) {
    const PointVector &docvector = generatedDocs[docid];
    return l2distCalc.l2sq_dist(query, docvector, tmp_v);
}

void read_queries(std::string fn) {
    int fd = open(fn.c_str(), O_RDONLY);
    ASSERT_TRUE(fd > 0);
    int d;
    size_t rv;
    fprintf(stderr, "reading %u queries from %s\n", NUM_Q, fn.c_str());
    for (uint32_t qid = 0; qid < NUM_Q; ++qid) {
        rv = read(fd, &d, 4);
        ASSERT_EQUAL(rv, 4u);
        ASSERT_EQUAL(d, NUM_DIMS);
        rv = read(fd, &generatedQueries[qid].v, NUM_DIMS*sizeof(float));
        ASSERT_EQUAL(rv, sizeof(PointVector));
    }
    close(fd);
}

void read_docs(std::string fn) {
    int fd = open(fn.c_str(), O_RDONLY);
    ASSERT_TRUE(fd > 0);
    int d;
    size_t rv;
    fprintf(stderr, "reading %u doc vectors from %s\n", NUM_DOCS, fn.c_str());
    for (uint32_t docid = 0; docid < NUM_DOCS; ++docid) {
        rv = read(fd, &d, 4);
        ASSERT_EQUAL(rv, 4u);
        ASSERT_EQUAL(d, NUM_DIMS);
        rv = read(fd, &generatedDocs[docid].v, NUM_DIMS*sizeof(float));
        ASSERT_EQUAL(rv, sizeof(PointVector));
    }
    close(fd);
}

using TimePoint = std::chrono::steady_clock::time_point;
using Duration = std::chrono::steady_clock::duration;

double to_ms(Duration elapsed) {
    std::chrono::duration<double, std::milli> ms(elapsed);
    return ms.count();
}

void read_data(std::string dir) {
    TimePoint bef = std::chrono::steady_clock::now();
    read_queries(dir + "/gist_query.fvecs");
    TimePoint aft = std::chrono::steady_clock::now();
    fprintf(stderr, "read queries: %.3f ms\n", to_ms(aft - bef));
    bef = std::chrono::steady_clock::now();
    read_docs(dir + "/gist_base.fvecs");
    aft = std::chrono::steady_clock::now();
    fprintf(stderr, "read docs: %.3f ms\n", to_ms(aft - bef));
}


struct BfHitComparator {
    bool operator() (const Hit &lhs, const Hit& rhs) const {
        if (lhs.distance < rhs.distance) return false;
        if (lhs.distance > rhs.distance) return true;
        return (lhs.docid > rhs.docid);
    }
};

class BfHitHeap {
private:
    size_t _size;
    vespalib::PriorityQueue<Hit, BfHitComparator> _priQ;
public:
    explicit BfHitHeap(size_t maxSize) : _size(maxSize), _priQ() {
        _priQ.reserve(maxSize);
    }
    ~BfHitHeap() {}
    void maybe_use(const Hit &hit) {
        if (_priQ.size() < _size) {
            _priQ.push(hit);
        } else if (hit.distance < _priQ.front().distance) {
            _priQ.front() = hit;
            _priQ.adjust();
        }
    }
    std::vector<Hit> bestHits() {
        std::vector<Hit> result;
        size_t i = _priQ.size();
        result.resize(i);
        while (i-- > 0) {
            result[i] = _priQ.front();
            _priQ.pop_front();
        }
        return result;
    }
};

TopK bruteforce_nns(const PointVector &query) {
    TopK result;
    BfHitHeap heap(result.K);
    for (uint32_t docid = 0; docid < EFFECTIVE_DOCS; ++docid) {
        const PointVector &docvector = generatedDocs[docid];
        double d = l2distCalc.l2sq_dist(query, docvector, tmp_v);
        Hit h(docid, d);
        heap.maybe_use(h);
    }
    std::vector<Hit> best = heap.bestHits();
    for (size_t i = 0; i < result.K; ++i) {
        result.hits[i] = best[i];
    }
    return result;
}

void verifyBF(uint32_t qid) {
    const PointVector &query = generatedQueries[qid];
    TopK &result = bruteforceResults[qid];
    double min_distance = result.hits[0].distance;
    std::vector<double> all_c2;
    for (uint32_t i = 0; i < EFFECTIVE_DOCS; ++i) {
        double dist = computeDistance(query, i);
        if (dist < min_distance) {
            fprintf(stderr, "WARN dist %.9g < mindist %.9g\n", dist, min_distance);
        }
        EXPECT_FALSE(dist+0.000001 < min_distance);
        if (min_distance > 0.0) all_c2.push_back(dist / min_distance);
    }
    if (all_c2.size() != EFFECTIVE_DOCS) return;
    std::sort(all_c2.begin(), all_c2.end());
    for (uint32_t idx : { 1, 3, 10, 30, 100, 300, 1000, 3000, EFFECTIVE_DOCS/2, EFFECTIVE_DOCS-1}) {
        fprintf(stderr, "c2-factor[%u] = %.3f\n", idx, all_c2[idx]);
    }
}

using NNS_API = NNS<float>;

#if 1
TEST("require that HNSW via NNS api remove all works") {
    DocVectorAdapter adapter;
    std::unique_ptr<NNS_API> nns = make_hnsw_nns(NUM_DIMS, adapter);
    fprintf(stderr, "adding and removing all docs forward...\n");
    for (uint32_t i = 0; i < 1000; ++i) {
        nns->addDoc(i);
    }
    for (uint32_t i = 0; i < 1000; ++i) {
        nns->removeDoc(i);
    }
    fprintf(stderr, "adding and removing all docs reverse...\n");
    for (uint32_t i = 1000; i < 2000; ++i) {
        nns->addDoc(i);
    }
    for (uint32_t i = 2000; i-- > 1000; ) {
        nns->removeDoc(i);
    }
}
#endif

TEST("require that brute force works") {
    TimePoint bef = std::chrono::steady_clock::now();
    fprintf(stderr, "generating %u brute force results\n", NUM_Q);
    bruteforceResults.reserve(NUM_Q);
    for (uint32_t cnt = 0; cnt < NUM_Q; ++cnt) {
        const PointVector &query = generatedQueries[cnt];
        bruteforceResults.emplace_back(bruteforce_nns(query));
    }
    TimePoint aft = std::chrono::steady_clock::now();
    fprintf(stderr, "timing for brute force: %.3f ms = %.3f ms per query\n",
            to_ms(aft - bef), to_ms(aft - bef)/NUM_Q);
    for (int cnt = 0; cnt < NUM_Q; cnt = (cnt+1)*2) {
        verifyBF(cnt);
    }
}

bool reach_with_nns_1(NNS_API &nns, uint32_t docid) {
    const PointVector &qv = generatedDocs[docid];
    vespalib::ConstArrayRef<float> query(qv.v, NUM_DIMS);
    auto rv = nns.topK(1, query, 1);
    if (rv.size() != 1) {
        fprintf(stderr, "Result/A from query for %u is %zu hits\n", docid, rv.size());
        return false;
    }
    if (rv[0].docid != docid) {
      if (rv[0].sq.distance != 0.0)
        fprintf(stderr, "Expected/A to find %u but got %u with sq distance %.3f\n",
                docid, rv[0].docid, rv[0].sq.distance);
    }
    return (rv[0].docid == docid || rv[0].sq.distance == 0.0);
}

bool reach_with_nns_100(NNS_API &nns, uint32_t docid) {
    const PointVector &qv = generatedDocs[docid];
    vespalib::ConstArrayRef<float> query(qv.v, NUM_DIMS);
    auto rv = nns.topK(10, query, 100);
    if (rv.size() != 10) {
        fprintf(stderr, "Result/B from query for %u is %zu hits\n", docid, rv.size());
    }
    if (rv[0].docid != docid) {
      if (rv[0].sq.distance != 0.0)
        fprintf(stderr, "Expected/B to find %u but got %u with sq distance %.3f\n",
                docid, rv[0].docid, rv[0].sq.distance);
    }
    return (rv[0].docid == docid || rv[0].sq.distance == 0.0);
}

bool reach_with_nns_1k(NNS_API &nns, uint32_t docid) {
    const PointVector &qv = generatedDocs[docid];
    vespalib::ConstArrayRef<float> query(qv.v, NUM_DIMS);
    auto rv = nns.topK(10, query, 1000);
    if (rv.size() != 10) {
        fprintf(stderr, "Result/C from query for %u is %zu hits\n", docid, rv.size());
    }
    if (rv[0].docid != docid) {
      if (rv[0].sq.distance != 0.0)
        fprintf(stderr, "Expected/C to find %u but got %u with sq distance %.3f\n",
                docid, rv[0].docid, rv[0].sq.distance);
    }
    return (rv[0].docid == docid || rv[0].sq.distance == 0.0);
}

TopK find_with_nns(uint32_t sk, NNS_API &nns, uint32_t qid) {
    TopK result;
    const PointVector &qv = generatedQueries[qid];
    vespalib::ConstArrayRef<float> query(qv.v, NUM_DIMS);
    auto rv = nns.topK(result.K, query, sk);
    for (size_t i = 0; i < result.K; ++i) {
        result.hits[i] = Hit(rv[i].docid, rv[i].sq.distance);
    }
    return result;
}

void verify_nns_quality(uint32_t sk, NNS_API &nns, uint32_t qid) {
    TopK perfect = bruteforceResults[qid];
    TopK result = find_with_nns(sk, nns, qid);
    int recall = perfect.recall(result);
    EXPECT_TRUE(recall > 40);
    double sum_error = 0.0;
    double c_factor = 1.0;
    for (size_t i = 0; i < result.K; ++i) {
        double factor = (result.hits[i].distance / perfect.hits[i].distance);
        if (factor < 0.99 || factor > 25) {
            fprintf(stderr, "hit[%zu] got distance %.3f, expected %.3f\n",
                    i, result.hits[i].distance, perfect.hits[i].distance);
        }
        sum_error += factor;
        c_factor = std::max(c_factor, factor);
    }
    EXPECT_TRUE(c_factor < 1.5);
    fprintf(stderr, "quality sk=%u: query %u: recall %d  c2-factor %.3f  avg c2: %.3f\n",
            sk, qid, recall, c_factor, sum_error / result.K);
}

void timing_nns(const char *name, NNS_API &nns, std::vector<uint32_t> sk_list) {
    for (uint32_t search_k : sk_list) {
        TimePoint bef = std::chrono::steady_clock::now();
        for (int cnt = 0; cnt < NUM_Q; ++cnt) {
            find_with_nns(search_k, nns, cnt);
        }
        TimePoint aft = std::chrono::steady_clock::now();
        fprintf(stderr, "timing for %s search_k=%u: %.3f ms = %.3f ms/q\n",
                name, search_k, to_ms(aft - bef), to_ms(aft - bef)/NUM_Q);
    }
}

void quality_nns(NNS_API &nns, std::vector<uint32_t> sk_list) {
    for (uint32_t search_k : sk_list) {
        for (int cnt = 0; cnt < NUM_Q; ++cnt) {
            verify_nns_quality(search_k, nns, cnt);
        }
    }
    uint32_t reached = 0;
    for (uint32_t i = 0; i < 20000; ++i) {
        if (reach_with_nns_1(nns, i)) ++reached;
    }
    fprintf(stderr, "Could reach %u of 20000 first documents with k=1\n", reached);
    reached = 0;
    for (uint32_t i = 0; i < 20000; ++i) {
        if (reach_with_nns_100(nns, i)) ++reached;
    }
    fprintf(stderr, "Could reach %u of 20000 first documents with k=100\n", reached);
    reached = 0;
    for (uint32_t i = 0; i < 20000; ++i) {
        if (reach_with_nns_1k(nns, i)) ++reached;
    }
    fprintf(stderr, "Could reach %u of 20000 first documents with k=1000\n", reached);
}

void benchmark_nns(const char *name, NNS_API &nns, std::vector<uint32_t> sk_list) {
    fprintf(stderr, "trying %s indexing...\n", name);

#if 0
    TimePoint bef = std::chrono::steady_clock::now();
    for (uint32_t i = 0; i < NUM_DOCS_REMOVE; ++i) {
        nns.addDoc(EFFECTIVE_DOCS + i);
    }
    for (uint32_t i = 0; i < EFFECTIVE_DOCS - NUM_DOCS_REMOVE; ++i) {
        nns.addDoc(i);
    }
    for (uint32_t i = 0; i < NUM_DOCS_REMOVE; ++i) {
        nns.removeDoc(EFFECTIVE_DOCS + i);
        nns.addDoc(EFFECTIVE_DOCS - NUM_DOCS_REMOVE + i);
    }
    TimePoint aft = std::chrono::steady_clock::now();
    fprintf(stderr, "build %s index with %u docs: %.3f ms\n", name, EFFECTIVE_DOCS, to_ms(aft - bef));

    timing_nns(name, nns, sk_list);
    fprintf(stderr, "Quality for %s realistic build with %u documents:\n", name, EFFECTIVE_DOCS);
    quality_nns(nns, sk_list);
#endif

#if 1
    TimePoint bef = std::chrono::steady_clock::now();
    for (uint32_t i = 0; i < EFFECTIVE_DOCS; ++i) {
        nns.addDoc(i);
    }
    TimePoint aft = std::chrono::steady_clock::now();
    fprintf(stderr, "build %s index with %u docs: %.3f ms\n", name, EFFECTIVE_DOCS, to_ms(aft - bef));

    timing_nns(name, nns, sk_list);
    fprintf(stderr, "Quality for %s clean build with %u documents:\n", name, EFFECTIVE_DOCS);
    quality_nns(nns, sk_list);

    bef = std::chrono::steady_clock::now();
    for (uint32_t i = 0; i < NUM_DOCS_REMOVE; ++i) {
        nns.addDoc(EFFECTIVE_DOCS + i);
    }
    for (uint32_t i = 0; i < NUM_DOCS_REMOVE; ++i) {
        nns.removeDoc(EFFECTIVE_DOCS + i);
    }
    aft = std::chrono::steady_clock::now();
    fprintf(stderr, "build %s index add then remove %u docs: %.3f ms\n",
            name, NUM_DOCS_REMOVE, to_ms(aft - bef));

    timing_nns(name, nns, sk_list);
    fprintf(stderr, "Quality for %s remove-damaged build with %u documents:\n", name, EFFECTIVE_DOCS);
    quality_nns(nns, sk_list);
#endif

#if 0
    TimePoint bef = std::chrono::steady_clock::now();
    for (uint32_t i = 0; i < EFFECTIVE_DOCS; ++i) {
        nns.addDoc(i);
    }
    TimePoint aft = std::chrono::steady_clock::now();
    fprintf(stderr, "build %s index with %u docs: %.3f ms\n", name, EFFECTIVE_DOCS, to_ms(aft - bef));

    timing_nns(name, nns, sk_list);
    fprintf(stderr, "Quality for %s clean build with %u documents:\n", name, EFFECTIVE_DOCS);
    quality_nns(nns, sk_list);

    bef = std::chrono::steady_clock::now();
    for (uint32_t i = 0; i < EFFECTIVE_DOCS; ++i) {
        nns.removeDoc(i);
    }
    aft = std::chrono::steady_clock::now();
    fprintf(stderr, "build %s index removed %u docs: %.3f ms\n", name, EFFECTIVE_DOCS, to_ms(aft - bef));

    const uint32_t addFirst = NUM_DOCS - (NUM_DOCS_REMOVE * 3);
    const uint32_t addSecond = NUM_DOCS - (NUM_DOCS_REMOVE * 2);

    bef = std::chrono::steady_clock::now();
    for (uint32_t i = 0; i < addFirst; ++i) {
        nns.addDoc(i);
    }
    aft = std::chrono::steady_clock::now();
    fprintf(stderr, "build %s index with %u docs: %.3f ms\n", name, addFirst, to_ms(aft - bef));

    bef = std::chrono::steady_clock::now();
    for (uint32_t i = 0; i < NUM_DOCS_REMOVE; ++i) {
        nns.addDoc(EFFECTIVE_DOCS + i);
        nns.addDoc(addFirst + i);
    }
    aft = std::chrono::steady_clock::now();
    fprintf(stderr, "build %s index added %u docs: %.3f ms\n",
            name, 2 * NUM_DOCS_REMOVE, to_ms(aft - bef));

    bef = std::chrono::steady_clock::now();
    for (uint32_t i = 0; i < NUM_DOCS_REMOVE; ++i) {
        nns.removeDoc(EFFECTIVE_DOCS + i);
        nns.addDoc(addSecond + i);
    }
    aft = std::chrono::steady_clock::now();
    fprintf(stderr, "build %s index added %u and removed %u docs: %.3f ms\n",
            name, NUM_DOCS_REMOVE, NUM_DOCS_REMOVE, to_ms(aft - bef));

    timing_nns(name, nns, sk_list);
    fprintf(stderr, "Quality for %s with %u documents some churn:\n", name, EFFECTIVE_DOCS);
    quality_nns(nns, sk_list);

#endif

#if 0
    bef = std::chrono::steady_clock::now();
    fprintf(stderr, "removing and adding %u documents...\n", EFFECTIVE_DOCS);
    for (uint32_t i = 0; i < EFFECTIVE_DOCS; ++i) {
        nns.removeDoc(i);
        nns.addDoc(i);
    }
    aft = std::chrono::steady_clock::now();
    fprintf(stderr, "build %s index rem/add %u docs: %.3f ms\n",
            name, EFFECTIVE_DOCS, to_ms(aft - bef));

    timing_nns(name, nns, sk_list);
    fprintf(stderr, "Quality for %s with %u documents full churn:\n", name, EFFECTIVE_DOCS);
    quality_nns(nns, sk_list);
#endif
}

#if 0
TEST("require that Locality Sensitive Hashing mostly works") {
    DocVectorAdapter adapter;
    std::unique_ptr<NNS_API> nns = make_rplsh_nns(NUM_DIMS, adapter);
    benchmark_nns("RPLSH", *nns, { 200, 1000 });
}
#endif

#if 0
TEST("require that Annoy via NNS api mostly works") {
    DocVectorAdapter adapter;
    std::unique_ptr<NNS_API> nns = make_annoy_nns(NUM_DIMS, adapter);
    benchmark_nns("Annoy", *nns, { 8000, 10000 });
}
#endif

#if 1
TEST("require that HNSW via NNS api mostly works") {
    DocVectorAdapter adapter;
    std::unique_ptr<NNS_API> nns = make_hnsw_nns(NUM_DIMS, adapter);
    benchmark_nns("HNSW-like", *nns, { 100, 150, 200 });
}
#endif

#if 0
TEST("require that HNSW wrapped api mostly works") {
    DocVectorAdapter adapter;
    std::unique_ptr<NNS_API> nns = make_hnsw_wrap(NUM_DIMS, adapter);
    benchmark_nns("HNSW-wrap", *nns, { 100, 150, 200 });
}
#endif

/**
 * Before running the benchmark the ANN_GIST1M data set must be downloaded and extracted:
 *   wget ftp://ftp.irisa.fr/local/texmex/corpus/gist.tar.gz
 *   tar -xf gist.tar.gz
 *
 * The benchmark program will load the data set from $HOME/gist if no directory is specified.
 *
 * More information about the dataset is found here: http://corpus-texmex.irisa.fr/.
 */
int main(int argc, char **argv) {
    TEST_MASTER.init(__FILE__);
    std::string gist_dir = ".";
    if (argc > 1) {
        gist_dir = argv[1];
    } else {
        char *home = getenv("HOME");
        if (home) {
            gist_dir = home;
            gist_dir += "/gist";
        }
    }
    read_data(gist_dir);
    TEST_RUN_ALL();
    return (TEST_MASTER.fini() ? 0 : 1);
}