1 files changed, 514 insertions, 0 deletions
diff --git a/eval/src/tests/ann/remove-bm.cpp b/eval/src/tests/ann/remove-bm.cpp
new file mode 100644
index 00000000000..2da735f1929
--- /dev/null
+++ b/eval/src/tests/ann/remove-bm.cpp
@@ -0,0 +1,514 @@
+// 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);
+}